esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Experiments
### Experimental Results with Features Selection
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline  & **RF** & **SVM** & **MLP3** & **MLP2** & **MLP1** & **GBDT** & **kNNU** & **kNND** & **QDA** & **NB** \\ \hline
**Accuracy** & 0.741 & 0.727 & 0.688 & 0.680 & 0.674 & 0.670 & 0.646 & 0.643 & 0.631 & 0.627 \\
**Improvement** & +0.016 & +0.010 & +0.004 & +0.006 & +0.023 & +0.003 & +0.104 & +0.113 & +0.216 & +0.030 \\
**Significance** & \(=\) & \(=\) & \(=\) & \(=\) & \(=\) & \(\blacktriangle\) & \(=\) & \(\blacktriangle\) & \(\blacktriangle\) & \(\blacktriangle\) & \(\blacktriangle\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average accuracy scores for the experiment with features selection. indices--summed up for each category of features--of the RF model. Figure 4b shows that the most impactful features are in the three most selected categories (syntactic, morpho-syntactic, and lexical) and the raw-text features. In particular, let us note that, though only four raw-text features were selected (Figure 4a), they have a very similar Gini importance with respect to the top three categories. Overall, the syntactic features look to have the highest discriminating power. Finally, it is important to highlight that some additional experiments were performed by asking linguist experts to feed the system with: (i) very long B1 texts of about 5000 words, and (ii) very short C2 texts of about 500 words. Interestingly, both types of texts were correctly classified, thus showing that the proposed system is more effective and, in some sense, more intelligent than a silly classification only based on the length of the text in input.
# Joint Learning of Temporal Models to Handle Imbalanced Data for Human Activity Recognition
## 4 Experimental Setup and Evaluation
### Models Parameter  
For all of the models in this study, we use a range of learning rates from 0.0001 to 0.01, a range of batch sizes from 8 to 64, a range of dropout rate from 15% to 50%, and a range of the number of epochs from 5 to 50. We have conducted a series of trial and error experiments over these ranges. We have noticed that 10 epochs at a learning rate of 0.001 and the batch size of 10 with 40% dropout rate are optimal for the models to converge. While a large batch size often can render rapid training, it needs more memory space and it delays the convergence of deep learning models [18]. On the contrary, smaller batch sizes that need less memory space could make the training process slower but could make the convergence of deep learning models faster; therefore, it is mostly a trade-off problem [18; 50]. To prevent the models from overfitting, the 40% dropout rate as a regularization technique is used [51].  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Activity** & **Home C** & **Activity** & **Home B** & **Activity** & **Home A** \\ \hline Eating & 345 & Brush\_teeth & 25 & Idle & 7888 \\ Idle & 5883 & Eat\_brunch & 132 & Brush\_teeth & 21 \\ Brush\_teeth & 75 & Eat\_dinner & 46 & Get\_drink & 21 \\ Get\_dressed & 70 & Get\_a\_drink & 6 & Get\_snack & 24 \\ Get\_drink & 20 & Get\_dressed & 27 & Go\_to\_bed & 11,599 \\ Get\_snack & 8 & Go\_to\_bed & 6050 & Leave\_house & 19,693 \\ Go\_to\_bed & 7395 & Idle & 20,049 & Prepare\_Breakfast & 59 \\ Leave\_house & 11,915 & Leaving\_the\_house & 12,223 & Prepare\_Dinner & 325 \\ Prepare\_Breakfast & 78 & Prepare\_brunch & 82 & Take\_shower & 221 \\ prepare\_Dinner & 300 & Prepare\_dinner & 87 & Use\_toilet & 154 \\ Prepare\_Lunch & 58 & Take\_shower & 109 & - & - \\ Shave & 57 & Use\_toilet & 39 & & \\ Take\_medication & 6 & Wash\_dishes & 25 & - & - \\ Take\_shower & 184 & & - & - & \\ Use\_toilet\_downstairs & 57 & - & - & - & - \\ Use\_toilet\_upstairs & 35 & - & - & - & - \\ Total & 26,486 & Total & 38,900 & Total & 40,005 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Frequency of activities in the Kasteren datasets.  
The dropout technique ignores neurons that are randomly selected during the training process. THe dropout technique temporally disconnects the ignored neurons on the forward pass; hence, in the backward pass their weights will not be updated.
# Impact of Partial Replacement of Cement with a Blend of Marble and Granite Waste Powder on Mortar
###### Abstract  
The purpose of this study is to examine the effects of partially replacing cement with a blend of marble waste powder (MWP) and granite waste powder (GWP) in mortar, with the goal of reducing the environmental harm caused by cement. The investigation included an analysis of the distinctive properties of the two waste powders individually, as well as initial tests with various ratios to determine the optimal combination that yields the highest strength. It was observed that a 50% MWP to 50% GWP blend produced the most substantial strength. Subsequently, the effect of partial replacement of cement with the blend of marble and granite waste powder (MGWP) at various increments of 5%, ranging from 0% to 30%, was evaluated by subjecting the mortar to numerous tests to assess its workability, physical, mechanical, durability, and microstructural properties. The analysis of the employed waste powders confirmed that the GWP can be classified as a natural pozzolan material belonging to Class N. As the proportion of MGWP increased, the workability of the mortar mixes decreased. However, incorporating MGWP up to 15% resulted in enhancements in bulk density, compression strength, and homogeneity, with the best performance observed at a 10% MGWP content. Microstructure analysis confirmed that the addition of MGWP enhanced the bonding of C-S-H and C-H, leading to a denser morphological structure in the mixes, particularly at a 10% MGWP content. The utilization of MGWP not only significantly reduced the carbon footprint associated with cement production but also fostered sustainability.  
g 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 4 Results and Analysis
### Consistency Analysis of the Observed Data  
An example of the original observed wind speed and air temperature data recorded at 10-min intervals on 1 April 2013 is shown in Figure 2a,b respectively, for heights of 1 m, 2 m, 5 m, 10 m, 15 m, and 25 m. Both the wind speed and air temperature display consistent variation trends at different layers over time, thus confirming the reliability of the original experimental instrument and data. At most times within a given day, a higher altitude is correlated with greater wind speeds. The difference in wind speeds among the various layers is relatively apparent. The wind speed itself shows relatively high fluctuations and randomness. At most times within a given day, the difference in air temperature among different layers is relatively apparent, and the variation trends differ as the observation height increases. During the night, the air temperature increases as the height increases, whereas during the day, the air temperature decreases as the height increases. The air temperature itself shows a clear and stable curve with a single peak.  
Figure 2: Original observed wind speed (**a**) and air temperature (**b**) at heights of 1 m, 2 m, 5 m, 10 m, 15 m, and 25 m.
# Bearing Fault Identification Using Machine Learning and Adaptive Cascade Fault Observer
## 2 Experimental Datasets  
To test the effectiveness of the adaptive cascade observer and SVM technique, two datasets are represented:  
1. The Case Western Reserve University (CWRU) vibration bearing dataset to test the single fault diagnosis accuracy. Table 1 shows information related to the vibration CWRU bearing dataset [1; 49]. A 2-hp motor is used to rotate the bearing in the various rotational speeds and a vibration sensor is used to collect the normal and abnormal data in 48 kHz. This dataset has  
Figure 1: Bearing fault identification using machine learning and adaptive cascade observer by fuzzy orthonormal regressive bearing signal approximation, SVM (support vector machine).  
four classes such as normal (NRM), ball fault (BLF), inner fault (IRF), and outer fault (ORF). In addition, the crack sizes in the abnormal conditions are 0.007, 0.014, and 0.021 inches in diameter, respectively.
2. The Ulsan Industrial Artificial Intelligence (UIAI) Lab AE bearing dataset is used as a second dataset to test the power of adaptive cascade with SVM technique for multiple fault identification. Table 2 shows the information of UIAI dataset [39]. This dataset has eight classes, such as normal (NRM), ball fault (BLF), inner fault (IRF), outer fault (ORF), inner-ball fault (IRF-BLF), outer-ball fault (ORF-BLF), inner-outer fault (IRF-ORF), and inner-outer-ball fault (IRF-ORF-BLF) conditions. This motor is working under four rotational speeds, such as 300, 400, 450, and 500 RPM. In addition, the crack sizes are 3 and 6 mm in diameter.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datasets** & **Conditions** & **Load (hp)** & **Crack Sizes (in)** \\ \hline \multirow{4}{*}{A} & NRM & 0 & \\  & BLF & 0 & \\  & IRF & 0 & \\  & ORF & 0 & \\ \hline \multirow{4}{*}{B} & NRM & 1 & \\  & BLF & 1 & \\  & IRF & 1 & \\  & ORF & 1 & \\ \hline \multirow{4}{*}{C} & NRM & 2 & \\  & BLF & 2 & \\  & IRF & 2 & \\ \cline{1-1}  & ORF & 2 & \\ \hline \multirow{4}{*}{D} & NRM & 3 & \\ \cline{1-1}  & BLF & 3 & \\ \cline{1-1}  & IRF & 3 & \\ \cline{1-1}  & ORF & 3 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Case Western Reserve University (CWRU) Vibration Datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datasets** & **Faults** & **Motor (RPM)** & **Crack Sizes (mm)** \\ \hline \multirow{4}{*}{A} & NRM & 300 & \\  & IRF & 300 & \\  & ORF & 300 & \\  & BLF & 300 & \\  & IRF-ORF & 300 & \\  & ORF-BLF & 300 & \\  & IRF-ORF-BLF & 300 & \\  & NRM & 400 & \\  & IRF & 400 & \\  & ORF & 400 & \\ \cline{1-1}  & BLF & 400 & \\ \cline{1-1}  & IRF-ORF & 400 & \\ \cline{1-1}  & ORF-BLF & 400 & \\ \cline{1-1}  & ORF-BLF & 400 & \\ \cline{1-1}  & IRF-ORF-BLF & 400 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ulsan Industrial Artificial Intelligence (UIAI) Lab Acoustic Emission Bearing Datasets.
# Passive Microwave Imagers, Their Applications, and Benefits: A Review
## 2 PMWI Applications and Impact Areas
### Applications and Benefits of PMWIs
#### 2.1.1 TCs and Hurricanes  
Different parameters, including waves and currents at the ocean surface, play roles in forecasting TC intensity, and understanding TC intensity changes is an observationally challenging problem [41]. However, in recent years, there have been improvements in  
Figure 1: NASA IMERG precipitation rate from all available microwave sensors over Tropical Storm Helene on 26 September 2024 [source: figure created by authors].  
TC intensity guidance [42; 43; 44; 45; 46; 47]. PMWIs are critical in monitoring and forecasting winds, TCs, and hurricanes. PMWI data are a crucial source of satellite analysis due to their capacity to observe storm rainbands, eyewalls, shear effects, and low-level circulations [14]. These observations enhance the accuracy of forecasting models, leading to improved preparedness, and response strategies. Various TC datasets have been developed using PMWI to further advance the understanding of TC patterns and disaster management. For instance, two datasets, namely a Secondary Eyewall (SE) eye-labeled dataset and an SE progression dataset, were developed using PMWI for SE evolution and progressions observed in numerous TCs [15]. Microwave Imagery from the Naval Research Laboratory Tropical Cyclones (NRL-TC) collection, commonly known as MINT, includes Tb from DMSP SSM/I and SSMIS, TRMM Microwave Imager (TMI), and Aqua AMSR-E. The dataset is developed using SSM/I sensors on the DMSP F8-F15 satellites, which operate at the 37.0 GHz and 85.5 GHz bands, and SSMIS sensors on the DMSP F16-F18 satellites, which operate at 37.0 GHz and 91.7 GHz [48]. These sensors are operated by the DOD-NOAA and provide vital data on Maximum Sustained Wind (MSW) speeds, which are crucial for assessing storm intensity. The data from TRMM TMI, which operates at 37.0 GHz and 85.5 GHz, and Aqua AMSR-E, which operates at 36.5 GHz and 89.0 GHz, are also key to developing this dataset [48]. Furthermore, TMI data from Goddard Space Flight Center (GSFC) and SSM/I data from the Global Hydrology Resource Center (GHRC) at 19 GHz and 37 GHz (for average and maximum concentration of rainfall), 22 GHz (water vapor), and 85 GHz (precipitation ice) are used for hurricane intensity prediction [49]. The Tropical Cyclone Precipitation, Infrared, Microwave, and Environmental Dataset (TC PRIMED) is another dataset that utilizes TMI, GMI, SSM/I, and SSMIS to monitor precipitation from TCs [50]. Moreover, NOAA uses data from AMSR2 to estimate hurricane intensity through the Hurricane Severity Index (HSI) [51]. The Cooperative Institute for Research in the Atmosphere (CIRA) also uses microwave data from instruments such as the AMSR2 to estimate hurricane intensity and predict hurricane paths [52].
## 3 Proposed Method
### Embedding Example  
We take a cover image with 12 pixels as shown in Figure 6 to illustrate the data embedding procedure. The cover image is split into two images, \(I_{\mathit{HSB}}\) and \(I_{\mathit{LSB}}\), through Equation (6). The quotient pixels in the \(I_{\mathit{HSB}}\) image are divided into gray pixels and white pixels in a chessboard manner. Let us suppose there is a secret bitstream \(S=s_{1}s_{2}s_{3}s_{4}s_{5}s_{6}s_{7}s_{8}=\) \"110 1110\" and the predictor method 1 is used in this example.  
\begin{table}
\begin{tabular}{l l} \hline
**Extra Information Items** & **Required Bit Sizes (Bits)** \\ \hline \(n\) & 2 \\ \(t_{g}\) & 2 \\ \(t_{w}\) & 2 \\ The length of the \(LM_{i,j}^{(t)}\) where \(\mathrm{t}\in\{t_{g},\ t_{w}\}\) & \(\lceil log_{2}(H\times W)\rceil\times(t_{g}+t_{w})\) \\ Compressed location maps & \(\mathit{l_{clm}}^{k}\) where \(k=1,2,\ldots,\ \langle t_{g}+t_{w}\rangle\) \\ Index value \(S_{\mathit{end}}\) & \(\lceil log_{2}(H\times W)\rceil\) \\ \hline \end{tabular}
\end{table}
Table 2: The extra information items and required bit sizes.  
Figure 6: The example of secret data embedding process.  
First, the recurrent round-trip embedding strategy (double R-TES) for the gray pixel is used to calculate the prediction error \(e^{out}=x_{2,2}-p_{1}=20-19=1\). According to Equation (9), the first secret bit \(s_{1}=1\) is taken out from the secret message \(S\), and then \(x_{2,2}^{(out)}=20+1=21\) is calculated. This completes the embedding of the outbound value \(x_{1}^{(out)}=21\) and then calculates the backbound value \(e^{back}=x_{2,2}^{(out)}-p_{2}=21-22=-1\). According to Equation (10), the secret bit \(s_{2}=1\) is embedded to obtain the backbound value \(x_{2,2}^{(back)}=21-1=20\). It is observed that \(x_{2,2}^{(back)}=x_{2,2}\). According to the double R-TES embedding strategy, the gray pixel \(x_{2,2}\) can continue embedding more secret data bits. At this time, the recursive parameter \(t_{g}=2\). According to Equation (6), the prediction error \(e^{out}=x_{2,2}^{(back)}-p_{1}=20-19=1\), and \(s_{3}=1\) is taken from the message \(S\) again to make \(x_{2,2}^{(out)}=20+1=21\). According to Equation (7) again, \(e^{back}=x_{2,2}^{(out)}-p_{2}=21-22=-1\). After \(s_{4}=0\) is embedded into \(x_{2,2}^{(out)}\), the result \(x_{2,2}^{(back)}=21-0=21\) is obtained, and the embedding of the backbound is completed.  
Since \(x_{2,2}^{(back)}\
eq x_{2,2}\), in this example, only two pixels \(x_{2,2}\) and \(x_{2,3}\) are embeddable, so Step 8 is executed to start embedding the message in another embeddable white pixel \(x_{2,3}\). Table 3 shows the embedding process in which the proposed triple-RDH is applied to embed the remainder data \"\(s_{5}s_{6}s_{7}s_{8}\)\" into the white pixels.
# Dataset Transformation System for Sign Language Recognition Based on Image Classification Network
## 2 Related Work
### Motion Recognition
#### 2.1.1 CNN-Based Motion Recognition  
CNN is a model that classifies images by extracting features of a single image. Several approaches are being attempted to use CNN for motion recognition. The first approach extracts the features of each frame through CNN and then fuses time information [14]. Ref. [14] tried to learn features using a 2D CNN model and input multiple frames using various fusion methods for temporal information. Using the 2D CNN structure is advantageous for learning because it is possible to use excellent models pretrained with the ImageNet dataset.  
Figure 1 shows the fusion of temporal data in a 2D CNN in several approaches. The bottom of Figure 1 shows the video sequence data of the input layer, and the red, green, and blue boxes represent the convolution, normalization, and pooling layers, respectively. The top two layers are the fully connected layers. The data fusion method is divided into late fusion, early fusion, and slow fusion depending on the time of fusion. According to [14], the late fusion performed better than the other methods. The second approach extends the convolution operation to the time domain [15]. To overcome the limitations of 2D CNNs and learn temporal features, many studies have been conducted on 3D CNN structures [16]. By adding time to a 2D image, it is used as 3D data, and convolution is performed with a 3D filter to learn temporal features. However, by using a 3D filter, the number of parameters to learn is much larger than that of a 2D CNN.  
The third approach is to encode the video into a dynamic image containing spatial and temporal information and then apply a CNN for image-based recognition [17]. Ref. [17] uses the rank pooling concept to encode video frames into dynamic images with spatial-temporal features. The generated image is trained on a CNN-based network to perform motion recognition. The fourth approach decouples the two elements and adopts a multi-stream network [18]. This approach trains the temporal and spatial parts, respectively, and passes the SoftMax layer at the end. A spatial stream receives each frame of a video as an input like a general image classification model. The temporal stream receives an optical flow as an input. Optical flow expresses the change of motion between frames.
## 2 Methods  
### Ideal OTA-Based Gyrator  
Ideal OTA [23] (Figure 1) is a differential voltage-controlled current source with output current given by the following relation  
\[i_{\text{OUT}}=g_{\text{m}}(v_{+}-v_{-}), \tag{3}\]  
where \(g_{\text{m}}\) is transconductance gain, which is usually electronically adjustable by an external DC current \(I_{\text{SET}}\) or DC voltage \(V_{\text{SET}}\). Input and output terminals of an ideal OTA have infinite internal impedance. The gyrator circuit employing two OTAs is depicted in Figure 2[23].  
This gyrator provides inversion of the loading admittance \(Y\) as apparent from the following relation for its input admittance.  
\[Y_{\text{IN}}(s)=\frac{g_{\text{m1}}g_{\text{m2}}}{Y(s)}. \tag{4}\]  
Figure 1: OTA schematic symbol.  
Figure 2: Gyrator with two OTAs.  
A capacitor is most often connected as the loading element, which yields the inductive character of the input admittance. When connecting a FO capacitor with admittance \(Y(s)=s^{\alpha}F\) and \(0<\alpha<1\) as the gyrator's load, the resulting input admittance is  
\[Y_{\text{IN}}(s)=\frac{F_{\text{IN}}}{s^{\alpha}}=\frac{g_{\text{m1}}g_{\text{ m2}}}{s^{\alpha}F}, \tag{5}\]  
which represents FO inductor admittance with the magnitude and phase  
\[Y_{\text{IN}}(j\omega)=\frac{F_{\text{IN}}}{\omega^{\alpha}}\angle-\frac{ \alpha\pi}{2}=\frac{g_{\text{m1}}g_{\text{m2}}}{\omega^{\alpha}F}\angle-\frac{ \alpha\pi}{2}. \tag{6}\]
## 2 Materials and Methods
### Methods
#### 2.3.4 Model Validation and Evaluation  
The coefficient of determination (R\({}^{2}\)), mean absolute error (MAE), and root mean square error (RMSE) were used to evaluate the performance of the PC concentration retrieval model, and the formulas for each evaluation metric are as follows:  
\[\text{R}^{2}=1-\frac{\sum_{i=1}^{\text{n}}\left(\text{y}_{\text{i}}-\text{y}_{ \text{i,pre}}\right)^{2}}{\sum_{i=1}^{\text{n}}\left(\text{y}_{\text{i}}-\text{ y}_{\text{mean}}\right)^{2}} \tag{9}\]  
\[\text{MAE}=\frac{1}{\text{n}}\cdot\sum_{i=1}^{\text{n}}\left|\text{y}_{\text{i }}\ -\text{y}_{\text{i,pre}}\right| \tag{10}\]  
\[\text{RMSE}=\sqrt{\frac{\sum_{i=1}^{\text{n}}\left(\text{y}_{\text{i}}-\text{ y}_{\text{i,pre}}\right)^{2}}{\text{n}}} \tag{11}\]  
where y\({}_{\text{i}}\) is the measured PC concentration; y\({}_{\text{i,pre}}\) is the predicted PC concentration; and y\({}_{\text{mean}}\) is the average of the measured PC concentrations.
## 4 Incorporation of Proposed Model in Urban Landscape Fumigation Service Robot
### Limitations and Future Scope  
Although there are significant advantages and the ease of design generation with Gen-AI, there are a few limitations that need to be addressed:  
* Interpretability challenges: understanding or interaction conflict between the human and Gen-AI.
* Lack of contextual understanding: this method can provide inaccurate outcomes without proper training.
* Biased output: output is generated based only on the training database.
* Overreliance on AI: this method lacks human-centric considerations and creativity.
* Generalization challenges: requires diverse datasets for task generalization and design adaptability.
* User inputs: continuous inputs are essential to achieving accurate and desirable results.
* Human irreplaceable: the designer's role in defining the problem, results interpretation, and solution selection remains irreplaceable.  
Additionally, to overcome these challenges and for potential future research in design generation via Gen-AI, the following directions need to be investigated:  
* Hybrid architecture: incorporating additional DGM models, GANs, or transformers improves diversity and quality while effectively managing complex multimodal data.
* Design validation metrics: integrating automated evaluation and validation metrics ensures that the generated design satisfies the required functionality, aesthetics, and core requirements.
* Interpretability and explainability: this can increase interpretation by improving transparency for effective validation.
* Human collaboration: develop a framework for enhanced collaboration in design generation between AI and human designer creativity.
* Energy and resource optimization: investigate approaches to integrating energy consumption and resource allocation metrics into the generative process, guiding designs to be more cost- and power-efficient.  
Figure 9: Developed fumigation robot and its major components.  
* Cross-domain transfer learning: leverage insights from adjacent fields (e.g., automated manufacturing and construction robotics) to enrich the dataset and adapt the model's learned representations for broader applications.
* Simulation-driven evaluation: employ large-scale simulation environments (e.g., Gazebo and Webots) to rapidly test generated designs in virtual conditions before physical deployment, reducing overall development cycles.
* Multi-agent collaboration: extend the MMF-VAE framework to design swarms or collaborative robot teams, focusing on communication protocols, task allocation, and synchronized operation.
* Explainable generative models: develop more interpretable architectures, such as feature-attribution or attention mechanisms, to clarify how specific design elements are selected and refined.