esa-sceva/satcom-chunk-collection huggingface dataset:
# Insight on Extraction and Preservation of Biological Activity of Cereal \(\beta\)-D-Glucans
## 4 Methods of Extraction and Purification of Cereal \(\beta\)-D-Glucans
### Dry Technologies
Concentrated \(\beta\)-D-glucan molecules showed a porous/spongy/fluffy appearance, and, in particular, showed no trace of cell wall structures [33]. Hydro-alcoholic Enzymatic Process Extraction  
Cereal flours or bran are kneaded in a hydro-alcoholic solution and then screened to remove other unwanted components, mainly starch and protein, from the filtrate. Fibrous particles rich in \(\beta\)-D-glucans were retained on the screen. This concentrate is kneaded again in alcohol and then treated with enzyme preparations, namely thermostable proteases and \(\alpha\)-amylases, to hydrolyze the proteins and starch bound to the fibrous particles. Subsequently, the particulates are recovered using simple screening techniques. With this technology, it is possible to obtain fiber concentrates containing up to 65% \(\beta\)-D-glucans. Scanning electron micrographs of the concentrated fiber showed the structure of the native \"honeycomb\" cell wall, indicating that \(\beta\)-D-glucans remain intact within the cell wall and are not solubilized [33].
# Grapevine Branch Recognition and Pruning Point Localization Technology Based on Image Processing
## 2 Materials and Methods
### Pruning Point Localization Algorithm
#### 2.9.3 Pruning Point Positioning  
The coordinates of the buds obtained via the target-detection algorithm are processed. (1) Firstly, it is determined whether the pixel coordinates of the buds can be connected by the mask map obtained via the semantic segmentation algorithm and the set of coordinates of the buds on the same branch are determined. (2) For the comparison of the coordinates of buds on the same branch, the coordinate with the largest vertical coordinate is selected as the key point for determining the pruning point. (3) We decrease the vertical coordinate of the pixel coordinate of the key point by \(\alpha\), traverse the horizontal coordinate from left to right within a certain range and when the pixel value of the skeleton thinning image at this coordinate is not 0, we mark this coordinate position as the pruning point and determine its 3D coordinates.  
As shown in Figure 9, the key parameter \(\alpha\) is calculated as follows: (1) We traverse the coordinates of the key point on the mask map obtained via semantic segmentation from left to right in a certain range to obtain a pixel length \(\gamma\), which is combined with the depth information in the depth image to obtain the actual length \(c\). (2) From the information of the field measurements, it can be known the the value of the length, i.e., the branch diameter, is concentrated between 8 and 10 mm. (3) The value of b is then the pruning length, i.e., 10-20 mm. (4) Formula (11) is introduced according to Formula (12). (5) According to the growth characteristics of grapevine branches, a and c can be regarded as a plane and then the value of alpha can be determined via Formula (13).  
\[\sin\theta=\frac{a}{b}=\frac{length}{c} \tag{11}\]  
\[a=\frac{b*length}{c} \tag{12}\]  
\[\frac{a}{\alpha}=\frac{c}{\gamma} \tag{13}\]
# Integrating Dilated Convolution into DenseLSTM for Audio Source Separation
## 3 Proposed Architecture
### Multi-Scale Multi-Band Dilated Time-Frequency DenseLSTM
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \begin{tabular}{c} **Layer** \\ \end{tabular} & \begin{tabular}{c} **Band (\(k\),\(L\),\(\theta\))** \\ \end{tabular} & \begin{tabular}{c} **Low** \\ **(15, 5, 0.095)** \\ \end{tabular} & \begin{tabular}{c} **Middle** \\ **(4, 4, 0.25)** \\ \end{tabular} & \
\begin{tabular}{c} **High** \\ **(2, 1, 0.4)** \\ \end{tabular} &
\begin{tabular}{c} **Full** \\ **(7, - 0.2)** \\ \end{tabular} \\ \hline \(3\times 3\) Conv & \(1,32\) & \(1,32\) & \(1,32\) & \(1,32\) \\ \hline DDB and CP1 & \(1,13\) & \(1,15\) & \(1,16\) & \(1,14\) \\ \hline DS1 & \(1/2,13\) & \(1/2,15\) & \(1/2,16\) & \(1/2,14\) \\ \hline DDB and CP2 & \(1/2,11\) & \(1/2,10\) & \(1/2,9\) & \(1/2,11\) \\ \hline DS2 & \(1/4,11\) & \(1/4,10\) & \(1/4,9\) & \(1/4,11\) \\ \hline DDB and CP3 & \(1/4,11\) & \(1/4,9\) & \(1/4,7\) (8) & \(1/4,12\) \\ \hline DS3 & \(1/8,11\) & \(1/8,9\) & - & \(1/8,12\) \\ \hline DDB and CP4 & \(1/8,12\,(128)\) & \(1/8,10\,(32)\) & - & \(1/8,13\) \\ \hline DS4 & - & - & - & \(1/16,13\) \\ \hline DDB and CP5 & - & - & - & \(1/16,14\,(128)\) \\ \hline US4 and CC4 & - & - & - & \(1/8,27\) \\ \hline DDB and CP6 & - & - & - & \(1/8,16\) \\ \hline US3 and CC3 & \(1/4,23\) & \(1/4,19\) & - & \(1/4,28\) \\ \hline DDB and CP7 & \(1/4,12\) & \(1/4,11\) & - & \(1/4,15\) \\ \hline US2 and CC2 & \(1/2,23\) & \(1/2,21\) & \(1/2,16\) & \(1/2,26\) \\ \hline DDB and CP8 & \(1/2,13\,(128)\) & \(1/2,12\) & \(1/2,9\) & \(1/2,14\,(128)\) \\ \hline US1 and CC1 & \(1,26\) & \(1,27\) & \(1,25\) & \(1,28\) \\ \hline DDB and CP9 & \(1,13\) & \(1,13\) & \(1,13\) & \(1,14\) \\ \hline Concatenation & & \(1,27\) & & \\ \hline DDB and CP10 & & (Parameter: \(k=12\), \(L=3\), \(\theta=0.2\)) \(1,19\) & & \\ \hline \(2\times 1\) Conv & & \(1,1\) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Details of the proposed architecture (scale, the number of output feature map). If the bidirectional long short-term memory (BLSTM) layer was applied, the number of nodes was indicated in parentheses. The “\(L\)” of full band is shown in Figure 2. The lack of values indicates no architecture. Therefore, DDB and CP4 of the low band is connected to US3 and CC3. \(k\): growth rate, \(L\): the number of the composite function, \(\theta\): compression rate, DDB: dilated dense block, CP: compression block, DS: down-sampling, US: up-sampling, CC: concatenation. To learn the proposed architecture, the following loss function was used. \[L(X,Y)=\|Y-X\bigodot\hat{M}\|_{1} \tag{2}\]  
where \(X\) is the magnitude spectrogram of the input data, \(Y\) is the magnitude spectrogram of the reference data, \(\bigodot\) is element-wise multiplication, \(\hat{M}\) is a mask estimated by neural network, and \(\|.\|_{1}\) is 1-norm. The estimated \(\hat{M}\) is multiplied by the input spectrogram \(X\) to estimate clean speech data, and the difference from \(Y\) is obtained by 1-norm.
# Explainable AI Assisted IoMT Security in Future 6G Networks
## 5 Experiments and Results
### Implementation of Explainable AI Techniques: SHAP, LIME, and DiCE
#### 5.4.3. LIME
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Features** & **Full Form** & **Interpretation** & \begin{tabular}{c} **Reason for ’Normal’** \\ **Prediction** \\ \end{tabular} \\ \hline \multirow{4}{*}{IMEI} & \multirow{4}{*}{\begin{tabular}{c} International \\ Mobile \\ Equipment \\ Identity \\ \end{tabular} } & A valid IMEI typically & Combined with normal \\  & & suggests legitimate & packet sizes and disk \\  & & device activity. No & usage, it reinforces \\  & & signs of spoofed or & legitimate network \\  & & unauthorized & behavior. \\  & & device access. & \\ \hline \multirow{4}{*}{dMaxPktSz} & \multirow{4}{*}{\begin{tabular}{c} Maximum \\ Packet Size in \\ Flow \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} Network packet sizes \\ are within the \\ standard limits (74.00). \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} Along with low total \\ packets and stable CPU \\ usage, it confirms an \\ absence of data tampering, \\ data exfiltration, and \\ packet flooding. \\ \end{tabular} } \\  & & & \
\begin{tabular}{c} Normal disk usage, \\ with no indications of \\ disk exhaustion or \\ malicious activities \\ like disk space \\ manipulation \\ or attacks. \\ \end{tabular} &
\begin{tabular}{c} The disk usage at 49\% is \\ moderate, indicating no \\ signs of resource \\ exhaustion, which is \\ consistent with normal \\ system operation. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: LIME-based feature contribution analysis in normal prediction.
## Nomenclature  
\begin{tabular}{l l} \(a\) & Baffle angle (deg) \\ \(A\) & Surface area (m\({}^{2}\)) \\ \(B_{r}\) & Baffle blockage ratio \\ \(c_{p}\) & Specific heat at a constant pressure (J kg\({}^{-1}\) K\({}^{-1}\)) \\ \(D_{h}\) & Hydraulic diameter (m) \\ \(f\) & Friction factor \\ \(H\) & Duct height (m) \\ \(h\) & Heat transfer coefficient (W m\({}^{-2}\) K\({}^{-1}\)) \\ \(k\) & Thermal conductivity (W m\({}^{-1}\) K\({}^{-1}\)) \\ \(L\) & Duct length (m) \\ \(\dot{m}\) & Air mass flow rate (kg s\({}^{-1}\)) \\ \(Nu\) & Nusselt number \\ \(P\) & Baffle pitch (m) \\ \(p\) & Pressure (Pa) \\ \(P_{r}\) & Baffle pitch ratio \\ \(\mathrm{P_{r}}\) & Prandtl number \\ \(\dot{Q}\) & Heat transfer rate (W) \\ \(Re\) & Reynolds number \\ \(T\) & Temperature (K) \\ U & Air velocity (m s\({}^{-1}\)) \\ \(W\) & Duct width (m) \\ Greek symbols \\ \(\eta\) & Thermohydraulic performance parameter \\ \(\mu\) & Dynamic viscosity (kg m\({}^{-1}\) s\({}^{-1}\)) \\ \(\rho\) & Density (kg m\({}^{-3}\)) \\ Subscripts \\ \(ap\) & Absorber plate \\ \(i\) & Inlet \\ \(o\) & Outlet \\ \(s\) & Smooth \\ \end{tabular}
## 1 Introduction
However, since LSTMs compress all the input information into the network, this will lead to the incorporation of noise from the sensor data acquisition when extracting features, which will affect the effectiveness of action recognition. Based on this, there are some works to solve this problem by introducing the attention mechanism [25, 26, 27, 28, 29]. The attention mechanism enables the model to focus more on the parts that are relevant to the current recognition to improve accuracy. Also, some works optimize the action recognition and window segmentation problems by multi-task  
Figure 1: Example of a window of “Sitting” (**a**) and “Running” (**b**) actions on the PAMAP2 dataset, timestep = 1 s.  
learning for HAR [30]. Although these models have achieved significant results on HAR, they do not adequately consider fine-grained features, which may lead to some confusion in action classification. To address these issues, we proposed a novel hierarchical multi-scale adaptive Conv-LSTM network structure called HMA Conv-LSTM, where we attentively weight sensor signals by sensor feature selection, extract finer-grained spatial features using hierarchical multi-scale convolution, and extract temporal contextual information by a dynamic channel-selection-LSTM network. Meanwhile, we employ adaptive channel feature fusion to process multi-channel feature maps. The main contributions of this paper are as follows:  
1. We propose a novel HMA Conv-LSTM network, which realizes HAR that can well distinguish confusing actions of subtle processes. Extensive experiments on four public datasets of Opportunity, PAMAP2, USC-HAD, and Skoda show the effectiveness of our proposed model. 2. We propose the hierarchical multi-scale convolution module, which performs finer-grained feature extraction by hierarchical architecture and multi-scale convolution on spatial information of feature vectors. 3. In addition, we propose the adaptive channel feature fusion module is capable of fusing features at different scales, which improve the efficiency of the model and remove redundant information. 4. For the multi-channel feature maps extracted by adaptive channel feature fusion, we propose the dynamic channel-selection-LSTM module based on the attention mechanism to extract the temporal context information. The rest of the paper is organized as follows: Section 2 reviews previous work related to us. Section 3 details the methodology proposed in this paper. Section 4 describes the experimental setup and the four HAR benchmark datasets and compares the proposed model with state-of-the-art methods. Section 5 explores the selection of model parameters and ablation experiments, discusses the results, and analyzes the confusion matrix as well as visualizes the attention weights. Finally, Section 6 concludes the paper.
## 3 Methodology
### Model Construction
#### 3.2.2 Student Model  
The student model selected for DR detection is made of the Xception [57] backbone. Xception is a high-performing CNN architecture that replaces the inception module of the Inception network with a modified depthwise separable convolution (DWSC). DWSC is a lightweight convolution module that is composed of depthwise convolution, followed by pointwise convolution. DWSC has been widely used in MobileNets due to its efficiency in reducing computational complexity, without compromising performance [58]. In the modified DWSC, integrated into Xception, however, pointwise convolution is applied first, which then follows depthwise convolution. Due to the lightweight nature and high  
Figure 5: The proposed teacher model.  
performance of Xception, it is an ideal fit for the student model. Furthermore, many studies have found that the model performs exceptionally well in DR classification [59; 60]. As shown in Figure 6, the Xception backbone is fine-tuned with a modified CBAM block.  
CBAM is a widely used attention module that encompasses both channel attention and spatial attention [28]. Therefore, integrating CBAM into the architecture boosts the performance. As presented in Figure 7, the spatial attention module of CBAM usually consists of a convolutional layer with a kernel size of 7. Due to the involvement of a large kernel, CBAM requires large computational power. Therefore, the modified CBAM integrates a depthwise separable convolution (DWSC) to reduce the future size by a factor of 2, leading to fewer computations. Moreover, the final convolutional layer leverages dilated convolution with a dilation rate of 2. The dilated convolution can capture large-scale features without consuming high resources.
## 5 Results and Discussion
### RQ3: What Are the Parameters That Are Optimized to Enhance the Performance of LoRaWAN Networks?
#### 5.4.3 Adaptability to Environmental and Operational Variabilities  
The adaptability of LoRaWAN to diverse environmental conditions is another gap identified in [30; 42; 44]. These studies have highlighted the importance of developing adaptive algorithms that automatically adjust to changing environmental conditions, such as weather variations and urban topographical changes, which affect signal propagation and network performance. There is also a need to enhance indoor localization accuracy [35]. Current methods have limitations in differentiating signals accurately across closely spaced indoor environments. Traditional path loss and shadow fading models do not account for various environmental effects, which can significantly impact LoRaWAN's performance [52]. The author [41] stated a gap that existed in adapting to dynamically changing underground environments. The main challenges included the difficulty in managing massive data collisions and the constraints imposed by the underground setting on EE. Mobile IoT applications, such as asset tracking, introduce challenges related to rapid changes in signal strength because of their mobility and environmental conditions [34]. The author [28] specifically suggested integrating more diverse environmental data into predictive models to enhance network reliability and efficiency under different climatic conditions.  
Figure 9 shows the frequently identified gap across the literature. Despite the advancements, managing interference and congestion, particularly in dense network deployments, remains a significant challenge. There is a need for more sophisticated machine learning models that can dynamically adjust network parameters to mitigate interference impacts effectively.
## 3 Results
### Data Preprocessing  
In this study, data preprocessing was used to improve prediction performance and speed. As a result, the most effective data preprocessing methods used were ascertained to be the standardization and PCA methods. Figure 10 shows a boxplot of the data imputed by multivariate imputation using the Bayesian ridge, the missing imputation method used in the best prediction model, and a boxplot with standardization applied to the data. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Rank**} & \multirow{2}{*}{**R\({}^{2}\)**} & \multirow{2}{*}{**MAE**} & \multicolumn{3}{c}{**Method**} \\ \cline{4-5}  & & & **Imputation** & **Data Preprocessing** & **Prediction Method** \\ \hline
1 & 0.376 & 729.06 & Multivariate imputation using Bayesian ridge & Standardization & Extra Tree \\
2 & 0.283 & 849.32 & KNN imputation & PCA & XGBoost \\
3 & 0.281 & 822.88 & KNN imputation & PCA & Random forest \\
4 & 0.267 & 783.74 & Multivariate imputation using Extra Tree & Standardization & Extra Tree \\
5 & 0.238 & 811.28 & KNN imputation & Standardization & Extra Tree \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Top 5 proposed results based on R\({}^{2}\) and MAE when missing data is imputed. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Rank**} & \multirow{2}{*}{**R\({}^{2}\)**} & \multirow{2}{*}{**MAE**} & \multicolumn{3}{c}{**Method**} \\ \cline{3-5}  & & & **Imputation** & **Data Preprocessing** & **Prediction Method** \\ \hline
1 & 0.315 & 939.80 & Simple imputation using median & Standardization & Extra Tree \\
2 & 0.311 & 964.68 & Simple imputation using mean & Standardization & Extra Tree \\
3 & 0.311 & 982.80 & Multivariate imputation using Bayesian ridge & Standardization & Extra Tree \\
4 & 0.305 & 961.72 & KNN imputation & Standardization & Extra Tree \\
5 & 0.268 & 959.85 & Multivariate imputation using Bayesian ridge & PCA & Extra Tree \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Top 5 proposed results based on R\({}^{2}\) and MAE when missing data is not imputed. The results show that there is a difference in the range of each variable before standardization is applied. As this can hinder the interpretation of the analysis results, standardization was used to match the ranges of each variable. Next, in the PCA method, the number of principal components was selected as a number that could explain 85% of the total variation, and as a result, four components were used. In Table 3, standardization was used in the best model, but PCA was used in the second and third best models. Therefore, it is sufficient to use only four principal components from the 14 types of odor substances. It is considered that when there is a large number of data samples, using PCA will be useful in terms of cost. Figure 11 shows a plot of PC1 and PC2 among the four principal components. The explanatory power of PC3 (7.5%) and PC4 (7.3%) is rather low, so only PC1 and PC2 are described here.
## 2 Related Work
### Blockchain and IPFS for Data Storage  
Blockchain technology is receiving a lot of attention due to its potential for secure and decentralized data storage. The studies on blockchain mainly explored the blockchain's immutable ledger system for data storage; however, they frequently emphasized the limits associated with the high costs and inefficiencies of keeping large volumes of raw data directly in the blockchain. In [11], the authors propose an in-depth analysis of these challenges, emphasizing the need for more effective data storage solutions within the blockchain framework. In this regard, IPFS is becoming increasingly common as a large data storage solution, thanks to its decentralized and effective design. In [12], the authors employ blockchain technology to transmit data in a peer-to-peer network and IPFS as data-sharing infrastructure to share pre-trained deep learning models to stakeholders. Meanwhile, in [13], the authors propose a blockchain and cloud-based decentralized secure storage for data availability, privacy, and efficient resource utilization. The work in [14] proposes the integration of blockchain and IPFS, showing the potential for secure data retrieval and storage. The proposed approach leverages different privacy modes for data privacy and security by storing nonsensitive data on IPFS without encryption and sensitive data with two-layered encryption. This approach mitigates the risk of a single point of failure and data tampering. However, data retrieval and query require the decryption of data stored on IPFS, which may introduce additional security and privacy risks associated with the exposure of private keys.
## 4 Results and Analysis
### Comparison of Execution Times of Classification Models  
Figure 8 compares the execution times of all methods for the generated dataset and the validation dataset.  
From the graph, it is clearly visible that K-NN has lowest execution time for both the generated and validation dataset. Since K-NN is an instance-based approach, it does not require a training phase in which a model is learned from training data. Rather, it just stores the training data and uses it to generate predictions during testing. K-NN can predict results quickly because it simply has to calculate the distance between the test instance and the saved training instances.  
Additionally, among the five methods, SVM takes the longest to execute. Due to the complexity of the training process, SVMs require a training phase in order to identify the optimum decision boundary that separates the various classes, which increases the execution time of SVMs.  
Once again, CART is identified as the best algorithm for real-time traffic classification when execution time and the classification report are taken into account. Logistic regression is an other algorithm to be considered in mind. OPTICS is not included in the graph since its execution took longer than a minute for the generated dataset and caused a \"runtime timeout\" for the validation dataset.
## 4 Localization Algorithm  
The several important stages of vehicle positioning are presented in this section. In order to improve the position estimation accuracy, the map-matching result can be updated with vehicle sensor data. The ICP-based map-matching results are evaluated and selected to update the estimation filter. To avoid the problem of false matching, a validation gate was designed.
# Fire Detection and Geo-Localization Using UAV's Aerial Images and Yolo-Based Models
## 6 Geo-Localization  
After identifying YOLO-NAS as the best performing model for fire identification, we now describe the material related to the second stage of the proposed framework where YOLO-NAS was used as the object detector. We first explain how the camera calibration and depth estimation were performed.