esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
This paper presents a deep-learning based method for fishing gear-type identification which uses both AIS data and marine environmental data. It introduces a preprocessing method for AIS data to handle different messaging intervals, missing messages, and contaminated messages. It uses a sliding window-based data slicing method to capture complicated dynamic patterns over trajectories depending on the fishing gear type. The developed deep-learning based method uses two 1D CNN (one dimensional convolutional neural network) submodules and a fully connected network module for feature extraction and a fully connected network-based prediction module which suggests a promising fishing gear type from the input trajectory data. The trained model classifies the fishing works into six fishing gear types, i.e., purse seine, stow net, longline, drift gill nets, traps, and trawl. The model has been trained and tested using real trajectory data of 1380 fishing ships over the one year. It presents a new performance index, DPI, which stands for total performance of day-wise performance index. In the experiments, the trained model showed a 0.963 DPI value for a 24-h window. The high value of the DPI index indicates that the trained model is good at identifying fishing gear types. The remainder of this paper is organized as follows. Section 2 presents some work related to ship activity identification. Section 3 describes how to preprocess AIS data and label the training and testing data and how to prepare the training data and organize a deep-learning model for activity identification. Section 4 shows some experimental results of the proposed method. Finally, Section 5 draws conclusions from the previously presented information.
## 3 Results
### BET Analysis  
The BET surface areas, average pore diameter, and total pore volume of different samples were obtained by utilizing the nitrogen adsorption/desorption isotherms (Figure 2b), and the corresponding results are summarized in Table 2. The average pore diameter of these samples was distributed in the range of 2-50 nm, suggesting their mesoporous nature according to the IUPAC classification [44]. The surface area of Ca/AC was smaller than that of AC, which indicated that the surface and pores of AC were partially covered after the modification [45]. There were obvious decreases in the surface area and porosity of CaNa/AC as compared to those of Ca/AC. However, this does not detrimentally affect the catalyst performance in the transesterification, as the introduction of sodium generates strong basic sites on the surface of CaNa/AC [46]. The activity of a solid alkali catalyst could be influenced not only by its surface structural properties but also by its alkali strength and amount of alkali, with the latter frequently having a larger effect [29]. The SSA, pore volume, and pore size of Na/AC were all smaller than those of the catalyst sample CaNa/AC, particularly the pore volume, because when NaOH was loaded directly onto AC, the strong base NaOH severely corroded the montmorillonite lamellae in AC, forming a denser structure that reduced the pore size and caused the material's pore volume to drop dramatically. This also verifies the conclusions of XRD and FT-IR analyses.
# Nonlinear Response Prediction of Spar Platform in Deep Water Using an Artificial Neural Network
###### Abstract  
The finite element method (FEM) is an essential method for predicting the response of the spar platform considering all nonlinear variables. Although FEM is an extremely laborious and time-consuming process for predicting platform responses using hydrodynamic loads, artificial neural networks (ANNs) can predict the response quickly, as required for platform management to either linger or stop the production of oil and gas. The application of ANN approaches to estimate the wave height and period from the expected wind forces is investigated in this paper. The ANN model can also predict the nonlinear responses of the spar platform subjected to the structural parameter as well as the wave height and wave period. The backpropagation technique depletes feed-forward neural networks, allowing the network to be trained. Following the formation of the neural network, rapid reactions from a freshly anticipated wind force are obtained. The results are validated via a comparison with results from a conventional finite element analysis. The findings demonstrate that the artificial neural network (ANN) technique is effective and is able to significantly reduce the required time to make predictions when compared to the conventional FEM.  
spar platform; finite element method; artificial neural networks; response prediction; environmental forces +
Footnote †: journal: 2022, 12, 5954. [[https://doi.org/10.3390/app12125954](https://doi.org/10.3390/app12125954)]([https://doi.org/10.3390/app12125954](https://doi.org/10.3390/app12125954))
# Context-Aware Bidirectional Neural Model for Sindhi Named Entity Recognition
## 3 Methodology
### Bidirectional Network  
Inherently, unidirectional LSTM [45] encode the information in one direction \(\overrightarrow{h_{t}}\). The BiLSTM [46] has the ability to encode the sequences from both right \(\overrightarrow{h_{t}}\) as well as left \(\overleftarrow{h_{t}}\) contexts. The basic idea is to present each sequence into forward \(\overrightarrow{h_{t}}\) and backward \(\overleftarrow{h_{t}}\) hidden states \(h_{t}\) to capture the past and future information for context representation. For example, by giving an input sentence \(s=(w_{1},w_{2},w_{3},\dots,w_{n})\), which contains \(n\) words. The BiLSTM network computes the forward \(\overrightarrow{h}\) and then backward \(\overleftarrow{h}\) hidden states. Finally, the context vector is created by concatenating both hidden states, \(\overrightarrow{h_{t}}\) and \(\overleftarrow{h_{t}}\), for  
Figure 1: The architecture of our proposed CaBiLSTM model. The initial step is to map each sentence as a sequence of characters to output a label distribution for each character position. Afterwards, the BiLSTM network transforms the sequence of tokens into the word representations. In this way, word-level features are composed of the character-level BiLSTM network. Then, the concatenated output of contextual representations is fed to the self-attention layer through a hidden layer. Finally, the CRF jointly decodes the best chain of NE tags of the given input instead of decoding each label independently.  
the final output \(h_{t}=[\overrightarrow{h_{t}};\overleftarrow{h_{t}}]\) of the network. However, the LSTM hidden state \(h_{t}\) takes information only from past, knowing nothing about the future. Thus, we use BiLSTM to extract contextual features. The processing flow in the BiLSTM network can be expressed as follows:  
\[\left[\begin{array}{c}\tilde{c}_{t}\\ f_{t}\\ o_{t}\\ i_{t}\end{array}\right]=\left[\begin{array}{c}\sigma\\ \sigma\\ \sigma\\ \tanh\end{array}\right]\left(W^{\top}\left[\begin{array}{c}\mathbf{x}_{t} \\ h_{t-1}\end{array}\right]+b\right) \tag{1}\]  
\[c_{t}=i_{t}\odot\tilde{c}_{t}+f_{t}\odot c_{t-1} \tag{2}\]  
\[h_{t}=o_{t}\odot\tanh(c_{t}) \tag{3}\]  
where \(i_{t},f_{t}\), and \(o_{t}\) indicate the input, forget, and output gates; \(\sigma()\) represents the sigmoid function; \(W^{\top}\) and \(b\) are the trainable parameters; \(x_{t}\) is an input vector of the current time step; \(\odot\) represents the dot product function.
## 2 Feature Engineering
### FFT Analysis of Voltage and Current Signals  
A comprehensive FFT analysis of voltage and current signals was performed to accurately extract the harmonic spectrum. However, FFT remains a simple yet effective method for calculating the Fourier transform, as expressed in (1) [34].  
\[X[n]=F\{x(t)\}=\int_{-\infty}^{+\infty}x(t)\ e^{-j2\pi ft}dt \tag{1}\]  
where \(x(t)\) denotes the captured signals, representing either voltage or current, in the time domain, and \(X[n]\) signifies the transformed signal in the frequency domain. Given that the signal is a discrete sequence of samples, Equation (1) can be expressed in alternative forms, as illustrated in (2) or (3).  
\[X[k]=\sum_{n=0}^{N-1}x_{n}\,e^{-j2\pi(\frac{kn}{N})} \tag{2}\]  
\[X[k]=\sum_{n=0}^{N-1}x_{n}\left[cos\left(2\pi\left(\frac{kn}{N}\right)\right)-j \,sin\left(2\pi\left(\frac{kn}{N}\right)\right)\right]\,\ k=0,1,2\ldots,\ (n-1) \tag{3}\]  
where '\(x\)' symbolizes the sequence of voltage or current samples, while '\(n\)' represents the count of samples in the dataset.  
This study conducted an extensive harmonic analysis, extending the calculations to the 30 th order for each phase, encompassing both voltage and current quantities. However, the features utilized for classifier training were intentionally limited to the first seven harmonic orders per phase, ensuring a focused and manageable dataset. This selection resulted in 42 unique features representing the entire three-phase circuit, optimizing classification performance while maintaining computational efficiency.
## 4 Results
### Photogrammetry and SfM vs. SLS Comparison  
Figure 12a reports the result of the photogrammetric reconstruction of the bas-relief of the Ellesiya chapel. The 3D model's properties are listed in Table 3. The proposed  
Figure 11: The SLS during the acquisition. The default scanning range of the Artec Leo is 0.35 to 1.2 m.  
methodology and the setup employed in this paper have proven to be successful for the digitalization of the Ellesiya chapel. It is worth noticing that all images have been successfully aligned, and the obtained model reports a high-quality photorealistic texture.  
Figure 12b reports the result of the reconstruction of the Ellesiya bas-relief by the SLS, which was employed as ground-truth for comparison.  
The comparison performed between the SfM and SLS models highlighted the difference in the 3D data quality generated by the two technologies. Indeed, photogrammetry led to a 3D model with a minor level of detail than SLS in terms of polygon density: the SfM model is composed of 41 mln of polygons, while the SLS model is composed of 50 mln polygons.  
In addition, the deviation map calculated by CloudCompare between the SfM and SLS models is reported in Figure 13a. The map is reported alongside the corresponding colored histogram of the distances in mm. The positive values are assigned to the points of the 3D SfM model that lie above the reference surface (SLS model). Negative values are assigned to points that are below the surface of the reference model. The areas with points close to 0 (green color) represent the points with the smallest discrepancies and higher accuracy.  
The values represented in the histogram are included within the 99.999% confidence interval. All distances fall in the range between \(-\)57.42 mm and 89.76 mm. It is worth  
Figure 12: Textured 3D model of the Ellesiya bas-relief by (**a**) photogrammetry and (**b**) Structured Light Scanner. Capture views from Metashape (**a**), and Cloud Compare (**b**).  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{2}{c}{**3D Model of the Ellesiya Bas-Relief**} \\ \hline Number of images & 179 \\ Ground resolution & 0.179 mm/pix \\ RMS reprojection error & 0.22 pix \\ Error on scale bars & 4.81 \(\times\) 10\({}^{-7}\) \\ The points & 48 k \\ Dense cloud & 46 min \\ Mesh & 41 mln \\ Texture & 8192 \(\times\) 8192 pix \\ \hline \hline \end{tabular}
\end{table}
Table 3: The properties of the 3D model of the Ellesiya chapel obtained by photogrammetry.  
noticing that, as reported in Figure 13b, 95% of the points present distance values in the range between \(-3.09\) mm and \(36.00\) mm.  
Furthermore, Figure 14 shows the confidence map generated by Metashape. The map is reported alongside the corresponding color scale, with values included in a range of 0-100 confidence. The map provides insights into areas of the model where the reconstruction is less accurate (values close to 0) or more reliable (values close to 100).
# Towards Machine Learning for Error Compensation in Additive Manufacturing
## 5 Deep Learning for Error Compensation in Additive Manufacturing
### Learning-Based Framework with 3D Deep Learning  
The finished shape of a 3D printed object can be acquired by technologies, such as scanning. In this method proposed by [PERSON] et al., '3D deep learning' method is used to train a deep neural network [122]. This method is demonstrated for a specific task which is dental crown printing, where the network can learn the function of distortion when a large amount of data is used for training. According to the authors, it is the first application of the deep neural network to the error compensation in 3D printing. The \"inverse function network\" to compensate for the error is also adapted in the overall framework. Four deformations of the dental crowns are used to verify the performance of the neural network:  
1. Translation.
2. Up-scaling.
3. Down-scaling.
4. Rotation.  
Based on a standard encoding method in [123], it represents a 3D model as a probability distribution of binary variables on a 3D voxel grid, with '1' indicating the voxel is inside the mesh and '0' is not. This way, the model is digitised. The 3D convolutional operation is rather similar to a 2D convolutional operation. However, the input, output, and filters are 3D matrix with width, height, and depth. Kernels move at a certain step in the 3D space, and the overlap of the kernel and the input executes a convolution operation to form a voxel in the output. Post convolution with the input, the value of the voxel in output is summed up. According to the authors, the network follows the idea of an auto-encoder with _Unet_ architecture [124], where the network contains an encoder and a decoder. The encoder and the decoder are concatenated by the structure of \"U-net\" and the fully connected layers are not used. A latent representation of the input volume is extracted by the encoder, and the decoder outputs a prediction according to spatial knowledge gathered by the encoder.  
The cross-entropy loss function is common. However, an improved cross-entropy loss function with \(\alpha\) as the weighting penalty parameter is applied. It is set at 0.85 to punish more false negative case. This idea is inspired from [125], due to the sparsity of voxel data, it may be more probable that the neural network recognizes 1 as 0. The value of \(\alpha\) is gained by experiments. The y is the target value in {0, 1} and the y' is the output of the neural network in \((0,1)\) for each voxel. From the results presented, the inverse deformation approximate performance is proven to be able to compensate the changes from the nominal model used in the tests solely from the voxel representation of STL form of the 3D build.
# An Improved Dictionary-Based Method for Gas Identification with Electronic Nose
## 2 Materials and Methods
### Pattern Recognition Based on SRC  
At present, SRC has been proven to be robust and has a high identification rate in the presence of noise, even when part of the data is missing.  
As a result, the algorithm works well in the application of electronic noses. The basic principle of the sparse representation aims to discover a set of bases that is sufficiently sparse and to express the original sample with it. Figure 4 shows the basic principle of SRC. The non-white part represents a non-zero element, and the white part represents that element is zero. Meanwhile, different colors express different values. Therefore, an input signal can be expressed as [39]:  
\[Y=SX \tag{2}\]  
where \(X\) is the sparse representation coefficient; \(Y\) is an input signal; \(S\) is a dictionary. To solve the problem better, Equation (2) can be transformed into:  
\[\hat{X}=argmin\|X\|_{0}\;\;s.t.\;\;\;Y=SX \tag{3}\]  
where \(X_{0}\) is the number of non-zero entries in matrix \(X\). Equation (3) expresses how to minimize the non-zero term of the matrix \(X\) and satisfy the conditions \(Y=SX\). By calculating the reconstruction error between the reconstructed sample and the original sample, it can determine the class label. The reconstruction error can be expressed as [40]:  
\[E=\|Y-SX\|_{F}^{2} \tag{4}\]  
where \(E\) is the reconstruction residual. The reconstruction error can reflect the characteristics of the input signal. It is straightforward from Equation (3) that the sparse representation classification algorithm usually uses the l0-norm or l1-norm minimization approach to process data and accomplishes the pattern recognition job. However, solving the norm minimization issue is an NP-hard task that takes a considerable calculation time. Many researchers have presented various strategies to handle this problem such as gradient projection, proximal gradient, iterative shrinkage threshold, and so on. As a result, overcoming this disadvantage is a critical issue.  
Figure 3: The original sample and the sample with 60 times resampling. (**a**) The original sample; (**b**) The sample with 60 times resampling.
# Progress on the Studies of the Key Enzymes of Ginsnoside Biosynthesis
## 5 Squalene Epoxidase  
Squalene epoxidase (SQE) catalyzes the oxygenation of the double bond of squalene to produce 2,3-oxidosqualene, the first oxidation step in phytosterol and triterpenoid biosynthesis. This enzyme is another rate-limiting enzyme in this pathway. _P. ginseng_ was found to have two copies of SQE genes and their sequences have low identity at the N-terminal regions. The transcripts of PgSQE1 exist abundantly in all organs, and PgSQE2 is only weakly expressed except in petioles and flower buds. [PERSON] et al. [25] cloned PgSQE1 and PgSQE2 from the leaves and adventitious roots cDNA libraries of _P. ginseng_. RNA interference of PgSQE1 in transgenic _P. ginseng_ completely suppressed PgSQE1 transcription and resulted in reduction of ginseng production. Interestingly, silencing of PgSQE1 in RNA interference roots strongly upregulated the expression of PgSQE2 and cycloartenol synthase and resulted in enhanced phytosterol accumulation. These results indicate that PgSQE1 and PgSQE2 are regulated in different manner, and that PgSQE1 only regulates ginseng biosynthesis but not that of phytosterol in _P. ginseng_. [PERSON] et al. [26] investigated the accumulation of total ginseng and their monomers, and determined their relationships with the expression of SS and SQE genes in different organs of _P. quinquefulus_. They found that the expression levels of both SS and SQE were significantly different among the 14 organs and were positively correlated with accumulation of total ginseng and monomers. [PERSON] et al. [27] cloned SQE from the root of _P. notoginseng_ by PCR. The gene has 98% identity with that of _P. ginseng_. SQE of _P. notoginseng_ has a FAD function domain, NAD(P)-binding Rossmann-fold domains, hydrophobicity and four transmembrane helices. This SQE may be a microsomal membrane-associated enzyme. Real time quantitative PCR analysis showed that its cDNA had different expression pattern and is highly expressed in root, especially in three-year-old root. These results reveal that SQE play an important role in the ginseng biosynthesis.
## 4 Experiment and Analysis  
### Experimental Environment and Datasets  
The TSMASAM designed in this paper is implemented based on Python3, Pytorch1.2 and Numpy. The four CPU models are Intel(R), Xeon(R), CPU E5-2620 v2 @ and 2.10 GHz, the graphics card model is Matrox G20 oeR2, and the PyTorch version is the 1.2.0 server environment. The InSDN dataset is generated from environment simulations under four virtual machines; the first virtual machine is Kali Linux, which represents the attacker server; the second virtual machine is Ubuntu 16.4, which acts as the ONOS controller; the third Ubuntu 16.4 machine, as a Mininet and OVS switch; the fourth virtual machine is a Metasploitable 2-based Linux machine that serves as an exploit service to demonstrate the exploit. The controller of SDN is implemented using the open source tool ONOS. This dataset contains anomalous traffic from inside and outside attackers targeting the controller.  
This paper evaluates the performance of TSMASAM through two experiments. This paper applies the network traffic simulation dataset [31] under the SDN architecture. It is derived from the SDN virtual environment and is constructed by multiple virtual machines using the SDN network architecture. Since the abnormal flow in [31] is much more than normal, the research team randomly selects the flow data to simulate the sample imbalance phenomenon in the real environment. The processed dataset contains 76,825 network traffic, of which 8401 are abnormal, accounting for approximately 10.94% of the overall sample. The research team used the hierarchical set-out method to divide the dataset into the training set and test set, each of which contains a total of 84 traffic eigenvalues. This dataset includes seven network attack types (Probe, DDoS, DoS, BFA, Web-Attack, BOTNET, and U2R). The distribution of each network attack is shown in Figure 4. In this experiment, seven network attack types (Probe, DDoS, DoS, BFA, Web-Attack, BOTNET, and U2R) are regarded as abnormal, and the rest are regarded as normal traffic data.  
To verify the generalization ability, not only the InSDN dataset [31] but also the KDD99 and UNSW-NB15 datasets are used in the experiments.
# Application of an Electronic Nose as a New Technology for Rapid Detection of Adulteration in Honey
## 3 Results and Discussion  
### Data Acquisition  
The samples were analyzed using the sensor array, ionogel doped with different concentrations of bare Fe\({}_{3}\)O\({}_{4}\) nanoparticles. As an example, the conductance data obtained for a given sample (authentic honey 5) by the sensors is shown in Figure 2.  
Comparing the curves in Figure 2, it is possible to note that in general, increasing the concentration of Fe\({}_{3}\)O\({}_{4}\) led to a decrease in the signal amplitude, which is consistent with our previous work [25]. The peak height and baseline signal (inset Figure 2) were used to calculate the relative response (RR; Equation (1)) of the sensors in each of the cycles. The data of RR obtained for the sensors during each cycle are presented in Table S1.  
\[\text{RR}=\text{(peak height -- baseline)/baseline} \tag{1}\]
# Error Resilience for Block Compressed Sensing with Multiple-Channel Transmission
## 5 Conclusions  
In this paper, we presented the error resilient transmission scheme for block compressed sensing. For error-free transmission, we found that adaptive sampling enhanced the reconstructed image quality under both equality and the quadratic constraints. For lossy transmission, we observed the vulnerability of compressively sensed information for transmission over lossy channels, and noted the need to provide protection schemes to alleviate the effects of data loss. We proposed our algorithm to transmit compressed information over multiple independent and lossy channels, and to work with multiple description coding for protection and reconstruction. For grey-level images, multiple description coding demonstrated effective protection. For color images, high correlations between the color planes can further aid better quality of reconstruction. The simulation results presented enhanced performance with multiple description coding and adaptive sampling. A wide range of lossy probabilities were simulated to verify the effectiveness of multiple description coding for protecting block compressed sensing. In future, we intend to look for other effective means and the ways to choose the parameter values to ensure error resilient transmission for compressed sensing of images.  
Conceptualization, H.-C.H.; formal analysis, P.-L.C. and F.-C.C.; investigation, P.-L.C.; methodology, H.-C.H. and F.-C.C.; software, P.-L.C.; writing-original draft, H.-C.H.; writing-review and editing, H.-C.H. and F.-C.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology of Taiwan, R.O.C., grant number MOST 107-2221-E-390-018-MY2.  
The authors declare no conflict of interest.