esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis of Results and Discussion
### Comparison of Various ML Models  
To assess the performance of the Blending-CRKL fusion model in predicting the failure modes of STRC shear walls, KNN, BPNN, RF, LightGBM, and Catboost were used for comparison. The input features for all six models included the 18 features described in Section 2.3 of this paper. The training sets were augmented using GAN, while the initial partitioned test sets were used for evaluation. The models were implemented using codes based on the open-source Python-3.6.5 software package Scikit-learn, with default parameter settings.  
The confusion matrix is a commonly used method for evaluating classification tasks, as it provides a detailed overview of the predictive performance of each ML model. In this paper, the diagonal line of the confusion matrix represents the number of correct predictions of STRC shear wall failure modes by the ML models in the test set, while the positions beyond the diagonal line represent the number of incorrect predictions. The confusion matrices for the prediction of failure modes of STRC shear walls by the six models in the test set are shown in Figure 9. From Figure 9, it can be observed that overall, the six models exhibit good predictive accuracy for the failure modes of STRC shear walls. Except for the BPNN model, the accuracy of the other models exceeds 90%, with the Blending-CRKL model achieving the highest accuracy of 97%, followed by the Catboost model. Compared to the KNN and Catboost models, the Blending-CRKL fusion model has the lowest F1-score for the flexural-shear failure mode (FS), indicating poorer predictive performance for this failure mode of STRC shear walls. However, overall, the Stacking-CRK fusion model still demonstrates significant advantages over individual models in predicting the failure modes of STRC shear walls.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline  & **Accuracy** & **Improved** & \multicolumn{2}{c}{**Precision**} & **Improved** & \multicolumn{2}{c}{**Recall**} & **Improved** & \multicolumn{2}{c}{**F1-Score**} & **Improved** \\ \hline  & **Initial** & **GAN** & **Rate** & **Initial** & **GAN** & **Rate** & **Initial** & **GAN** & **Rate** & **Initial** & **GAN** & **Rate** \\ \hline RF & 0.85 & 0.93 & 9\% & 0.50 & 0.85 & 70\% & 0.66 & 0.97 & 47\% & 0.56 & 0.90 & 61\% \\ Catboost & 0.85 & 0.95 & 12\% & 0.56 & 0.98 & 75\% & 0.66 & 0.87 & 32\% & 0.61 & 0.91 & 49\% \\ LightGBM & 0.82 & 0.93 & 13\% & 0.46 & 0.92 & 100\% & 0.59 & 0.86 & 46\% & 0.50 & 0.87 & 74\% \\ BPNN & 0.75 & 0.88 & 17\% & 0.42 & 0.84 & 100\% & 0.56 & 0.89 & 59\% & 0.45 & 0.84 & 87\% \\ KNN & 0.78 & 0.9 & 15\% & 0.45 & 0.85 & 89\% & 0.62 & 0.96 & 55\% & 0.48 & 0.88 & 83\% \\ Blending-CRKL & 0.88 & 0.97 & 10\% & 0.62 & 0.94 & 94\% & 0.67 & 0.99 & 48\% & 0.64 & 0.96 & 50\% \\ \hline Average & & & 13\% & & & 81\% & & & 48\% & & 67\% \\ \hline \end{tabular}
\end{table}
Table 2: Metrics improvement of STRC shear wall failure modes prediction models before and after GAN data augmentation.
## 7 Conclusions  
A new crack identification method is proposed for rotors with a breathing crack based on the Kriging surrogate model using the crack induced super-harmonic responses from two measurement points. Numerical experiments were carried out to investigate the performance of the proposed method based on a validated finite element rotor model with a breathing crack. The work in this paper validates the feasibility of the proposed method for crack identification in rotors. The results clearly show its effectiveness and accuracy. In addition, the presented method is robust to noise. Only a small number of samples are needed to construct the Kriging model, which reduces the cost of experiments or simulations. Furthermore, only two measurement points are required and this will be very useful for real applications. The two measurement points can be arranged at the bearing pedestals, which is quite convenient. However, only \(2\times\) components are considered to establish the surrogate model. To improve the reliability of the method for real rotors, more crack features should be applied. In addition, only one transverse crack with two crack parameters is considered. Multiple cracks with complex crack parameters may appear in real applications. Though the identification process is almost the same, much more effort is required for multi-crack situations, which will be studied in the near future combined with other efficient optimization methods.  
Conceptualization, Y.L.; Funding acquisition, Y.L.; Investigation, Z.L.; Methodology, Z.L. and H.O.; Validation, Z.L.; Writing--original draft, Z.L.; Writing--review & editing, Y.L. and H.O.  
This study was supported by the National Natural Science Foundation of China (51875416), the China Postdoctoral Science Foundation (2018M632928), and the Open Fund of Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering (2018A08).  
The authors declare no conflict of interest.
# DDL R-CNN: Dynamic Direction Learning R-CNN for Rotated Object Detection
## 4 Experiment
### Dataset  
For a thorough evaluation of the capabilities of our proposed method in detecting oriented targets, it was subjected to testing on two highly regarded standard datasets: UCAS-AOD [22] and HRSC2016 [23].  
The UCAS-AOD dataset, issued by the University of Chinese Academy of Sciences, is dedicated to the detection tasks of aircraft and vehicles in RS imagery. It consists of 1000 images of aircraft and 510 images of vehicles, with a total of 7482 aircraft and 7114 vehicles annotated. The images, derived from Google Earth satellite imagery, ensure the authenticity and diversity of the scenes. All images are uniformly sized at 1280 \(\times\) 659 pixels, stored in.png format, with the entire dataset weighing in at approximately 3.48 GB. This dataset mainly focuses on medium- and small-sized targets in RS images, such as aircraft and vehicles. The targets in these images are typically multi-directional and densely packed, which presents challenges for detection algorithms and also makes it an ideal dataset for assessing the performance of oriented target detection algorithms. In our experiments, since UCAS-AOD emphasizes improving the detection accuracy of multi-directional targets, we performed some preprocessing on the dataset's annotation files using a rotated bounding box format of \((x,y,w,h,\theta)\). And the dataset was divided as follows: 60% training set, 20% validation set, and 20% test set.  
The HRSC2016 dataset is a collection focused on ship detection in high-resolution RS imagery. It comprises 1061 images of varying sizes, with resolutions ranging from \(300\times 300\) to \(1500\times 900\) pixels, encompassing ship instances of various sizes and orientations. Known for their challenging detection tasks, ships in the images may appear slender and densely packed side by side, and there are numerous ship-like distractions such as waves, rectangular warehouses, and slender piers. The dataset offers ample training and testing material with its image count and instance count, making it a vital resource for evaluating and enhancing the performance of ship detection algorithms. In this experiment, the dataset was divided into 60% as the training set, 20% as the validation set, and 20% as the test set.
# Prediction of Cotton Yarn Quality Based on Attention-GRU
###### Abstract  
With the diversification of spinning order varieties and process parameters, the conventional method of determining production plans through trial spinning no longer satisfies the processing requirements of enterprises. Currently, deficiencies exist in predicting spinning quality relying on manual experience and traditional methods. The back propagation (BP) neural network within the realm of deep learning theory faces challenges in handling time series data, while the long short-term memory (LSTM) neural network, despite its intricate mechanism, exhibits an overall lower predictive accuracy. Consequently, a more precise predictive methodology is imperative to assist production personnel in efficiently ascertaining cotton-blending schemes and processing parameters, thereby elevating the production efficiency of the enterprise. In response to this challenge, we propose an attention-GRU-based cotton yarn quality prediction model. By employing the attention mechanism, the model is directed towards the input features most significantly impacting yarn quality. Real-world performance indicators of raw cotton and process parameters are utilized to predict yarn tensile strength. A comparative analysis is conducted against prediction results of BP, LSTM, and gated recurrent unit (GRU) neural networks that do not incorporate the attention mechanism. The outcomes reveal that the GRU model enhanced with the attention mechanism demonstrates reductions of 56.3%, 38.5%, and 36.4% in root mean square error (RMSE), along with 0.367%, 0.158%, and 0.190% in mean absolute percentage error (MAPE), respectively. The model attains a coefficient of determination R-squared of 0.954, indicating a high degree of fitness. This study underscores the potential of the proposed attention-GRU model in refining cotton yarn quality prediction and its consequential implications for process optimization and enhanced production efficiency within textile enterprises.  
citation yarn quality prediction; BP; LSTM; GRU; attention mechanism +
Footnote †: journal: Applied Sciences
## 5 Discussion  
By comparing with existing methods, the approach proposed in this paper constructs spatial adjacency relationships using the convex hull Voronoi diagram of area objects, while incorporating visual distance instead of the Voronoi diagram area in the calculation. The method effectively addresses two issues that affect the measurement and application of spatial information:  
1. The spatial information content in a map is only related to the scale of the spatial distribution structure and is not limited by the map extent;
2. There is no need to balance the Voronoi diagram area of objects in the margin of the map by manually extending the map extent, thus reducing the uncertainty of measurement results.  
In addition, the method proposed in this paper has four advantages: the comparability of eigenvalues, the consideration of the diversity of spatial distances and adjacency relationships among area objects in area maps, the measurement of information with regular distribution and arrangement, and the coupling of geometric information and spatial distribution information.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Simplify Level** & **Li’s Geo (I\({}_{\text{I}}\))** & **Liu’s Geo (I\({}_{\text{Z}}\))** & **Ours** \\ \hline
0 & 0.976202 & 1 & 1 \\
1 & 0.988288 & 0.877214 & 0.9897602 \\
2 & 0.993083 & 0.780028 & 0.9875365 \\
3 & 1 & 0.715667 & 0.9755749 \\
4 & 0.999374 & 0.650745 & 0.9560748 \\
5 & 0.994224 & 0.610627 & 0.9438812 \\
6 & 0.993655 & 0.568757 & 0.9295262 \\
7 & 0.993347 & 0.529545 & 0.9127985 \\
8 & 0.992956 & 0.493074 & 0.8964363 \\
9 & 0.992365 & 0.463701 & 0.8873033 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Normalized measurement values of geometric information content under different levels of simplification.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Simplify Level** & **Li’s Total** & **Liu’s Total** & **Ours** \\ \hline
0 & 0.987649 & 1 & 1 \\
1 & 0.983546 & 0.987044 & 0.9897602 \\
2 & 0.990797 & 0.97865 & 0.9875365 \\
3 & 0.995425 & 0.973677 & 0.9755749 \\
4 & 0.994864 & 0.964935 & 0.9560748
## 1 Introduction
### Scope  
Some other researchers have reviewed authentication schemes in the IoT context. These studies have partially addressed some aspects of authentication in IoMT systems. Table 1 illustrates a brief comparison of these studies, published between 2016 and 2021. [PERSON] et al. [5] provided a systematic literature review of access control schemes, including authentication, authorization, and identity management in the IoT context. Another survey, developed by [PERSON] et al. [6], introduced authentication classification to map their review of authentication schemes. They compared the reviewed schemes according to pre-specified criteria; the criteria are the degree of lightweight, multi-factor use, encryption use, and efficiency. [PERSON] et al. [7] followed the same research method as [PERSON] et al. [6] and depended on the existing classifications of IoT authentication to conduct their survey. [PERSON] et al. [8] aimed to review authentication in IoT from the threats and attacks perspective. Their paper mainly targeted smart home applications and IoT-layered architecture as a base for mapping their review. However, the studies mentioned above did not present a taxonomy of authentication schemes in the IoT context. Moreover, they did not discuss the evaluation techniques and open issues regarding IoT authentication.  
[PERSON] et al. [9] established a taxonomy of IoT authentication schemes. [PERSON] et al. [9] relied on their previous taxonomy to conduct a systematic literature review of techniques related to IoT authentication. [PERSON] et al. [10] established a comprehensive review of attacks targeting IoT authentication. The paper classified IoT authentication protocols into Internet-of-Sensors, Internet-of-Energy, Internet-of-Vehicles, and Machine-to-Machine communication. They mapped the authentication attacks and schemes according to such a classification.  
[PERSON] et al. [11] conducted an exhaustive systematic review of techniques for IoT authentication. The paper considered the review from multiple aspects, such as authentication phases, threats, evaluation of performance, and adopted technologies. [PERSON] et al. [1] discussed important issues, including security analysis techniques, evaluation platforms, and future directions related to IoMT authentication.  
We considered the global aspects mentioned in Table 1. Our work establishes a taxonomy of authentication in the IoMT context. It then relies on the taxonomy developed to discuss the reviewed articles. Moreover, this work discusses authentication attacks, security analysis and evaluation techniques, open issues, and future directions related to IoMT authentication.
## 3 Results
### Approach Evaluation  
To evaluate our approach, we trained our model on the datasets discussed in Section 2.1. We mainly evaluated the model prediction accuracy in scenarios where we leveraged the available  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Base Model** & **Top-1 Accuracy** \\ \hline InceptionV3 & 0.783 \\ ResNet50 & 0.802 \\ VGG16 & 0.752 \\ VGG19 & 0.775 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance of base image classifiers for the single-input model.  
Figure 10: Training and validation accuracy evolution of the multimodal network training on the >300 dataset.  
information concerning the asset along with its image (visual features + textual features). We also compared the performance of the multimodal classification with other scenarios where only the 2D capture is fed to the model as input while tasked with predicting the same labels. Although we used two outputs in our implemented models (artist and creation year), for our experiments, we mainly focused on predicting the artist class, as it is the most relevant and widely used classification metric for painting categorization. The tests were performed on the four datasets mentioned in Section 2.1 by comparing the multimodal network performance against the single input network. This resulted in four pairs of networks, a pair for each dataset (multimodal, single input).  
In the following, we outline the classification results of the multimodal and the single-input classifications. As previously mentioned in the data collection and pre-processing section, we used the selected data to produce four sub-datasets to compare the impact of varying the number of samples per class on the classification. To more clearly see and interpret the results, we present the evaluation details such as the confusion matrix only for the dataset with 19 classes (300 or more paintings per artist). The limited number of classes enables us to better perceive and discuss these results.  
We evaluate the performance of our classification by using the following metrics: precision, recall, F1 score and Top-1 Accuracy. Following our test protocol, we performed our evaluations on the four different sub-datasets. For each dataset, we created two networks (single-input and multimodal input) and split the data into 80% training and 20% testing. To feed the data to the multimodal network, we wrote a custom batch generation function that yields more than one input. We performed five-fold cross-validation on the eight models, mainly due to the limited number of samples. Table 5 outlines the average results for the classification metrics of the trained networks on the validation sets (Top-1 accuracy, precision, recall and F1 score).  
Figure 11 outlines the confusion matrices of the single-input and multimodal input networks for the >300 dataset. For the other datasets, the confusion matrices would not be clear due to the high number of classes. The results shown are for the best performing networks on the validation set.
## 3 Results and Discussion
### The Catalytic Activity of PEK-C-ILs Powders (5)
#### 3.2.2 Hydrolysis Mechanism of PEK-C-ILs Catalyst  
Based on the previous study [26], two catalytic mechanism of inulin polysaccharide over PEK-C-ILs catalyst were proposed in this work and shown in Figure 3. As displayed in Figure 3a, when the glycosidic oxygen was being protonated by the acid catalyst, anion \(\text{HSO}_{4}\)\({}^{-}\) as nucleophile was first attacked the anomeric carbon of the fructose molecule, leading to breaking of the glycosyl bond and the -SO\({}_{3}\)H groups of cation acts as proton donor for glycosidic oxygen. Then, the oxygen atom of a water molecule made a nucleophilic attack at the anomeric carbon concomitantly with deprotonation by the -SO\({}_{3}\)H group, completing the catalytic cycle of polysaccharide hydrolysis. The another path is synergistic hydrolysis process of cations. As displayed in Figure 3b, the oxygen atom of a water molecule was attacked by the deprotonation of the -SO\({}_{3}\)H groups in the cation. Then the glycosidic oxygen atom was being protonated under acidic condition, at the same time, the hydroxyl group of the water molecule attacked the anomeric carbon, resulting in cleavage of the glycosyl bond. The -SO\({}_{3}\)H groups in another cation acted as a proton acceptor for the other cyclic catalytic process. It should be noted that maybe there is a competitive relationship between them. And eventually the hydrolysis reaction was dominated by synergistic effect of anion and cation (Figure 3a) due to its low activation energy [27]. However, for sulfuric acid, its hydrolysis effect as a catalyst is poorer than PEK-C-ILs because the hydrolysis process only relied on the released hydrogen ion [25]. Therefore, this chemical and macroporous structures of PEK-C-ILs catalyst made it better hydrolysis active.  
Figure 3: A plausible mechanism for the hydrolysis of inulin using PEK-C-ILs as catalyst. (**a**) synergistic effect of cations and anions (**b**) synergistic effect of cations.  
Figure 2: Catalytic performance of PEK-C-ILs powder and sulfuric acid. Hydrolysis conditions: inulin concentration 20%, concentration of PEK-C-ILs 4.0 wt % (ILs 0.628 mmol, 5.11 mol %), sulfuric acid 0.634 mmol, DCM 1.67, temperature 65 \({}^{\circ}\)C.
# Fast Deployment of a UWB-Based IPS for Emergency Response Operations
## 2 State of the Art
### Parameter-Based Positioning  
Regardless of the underlying RF technology, the estimation of the position of an object in a given time relies on the physical parameters of the signal acquired using the IPS. These parameters can be classified into [27]:  
* related to the observed distance (_ranging_) between a reference device and the object to locate.
* signal properties, such as the received signal strength indicator (RSSI) and/or the channel state information (CSI), may be used in distance estimation.
* the propagation time of a wireless signal between a transmitter and a receiver allows one to compute the existing distance between both devices. Well-known parameters to finally obtain this distance are the time of arrival (ToA), the time of flight (ToF), the round trip time (RTT), the two-way ranging (TWR), and the time difference of arrival (TDoA).
* both the phase of arrival (PoA) and the phase difference of arrival (PDoA) estimate the distance by measuring the phase of the carrier signal.
* **Direction-based:** related to the direction of transmitted radio waves. Typical parameters on this matter are the angle of arrival (AoA), the angle difference of arrival (ADoA), and the direction of arrival (DoA).  
Once the IPS has collected enough values of the localization parameters among the different network devices, they are introduced into a positioning technique that will produce as an output a new estimated position. Again, the existing techniques can be classified into different groups [28]:  
* **Lateration techniques:** they use the multiple obtained rangings between the reference devices and the target object as well as the well-known positions of reference devices.
* **Angulation techniques:** in this case, the obtained angles between the reference devices and the target object are employed in combination with the well-known positions of reference devices.
* **Scene analysis/Fingerprinting:** a pre-recorded mapping, consisting of training data, is compared to a new _fingerprint_ by using similarity measurement, statistical, or machine learning methods to identify the best match as the location of the target object [29].
* **Proximity:** the target position is assumed to be that of the closest reference device.