esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Model Evaluation Metrics  
In this study, statistical metrics were used to evaluate the performance of ML models and to analyze the relationship between drought indices and crop yield. The metrics employed include Spearman's correlation coefficient (R), root mean square error (RMSE), and Akaike information criterion (AIC). Due to the generally non-normal distribution of the data and the limited number of yield values available at the district level (20 samples, excluding ML models), Spearman's correlation coefficient was preferred over the Pearson correlation coefficient. Spearman's correlation coefficient is nonparametric, robust to outliers, and capable of revealing nonlinear relationships [111]. Additionally, RMSE and AIC were used alongside the correlation coefficient to assess the performance of the ML models. The equations for RMSE, AIC, and Spearman's correlation coefficient are provided below.  
\[RMSE=\sqrt{\sum_{i=1}^{n}\frac{\left(y_{i}-\hat{y}_{i}\right)^{2}}{n}} \tag{7}\]  
\[R=1-\frac{6\sum d_{i}^{2}}{n(n^{2}-1)} \tag{8}\]  
\[AIC=n\times ln\left(\frac{SS_{e}}{n}\right)+2K \tag{9}\]  
where \(d_{i}\) is the difference between the two ranks of each observation, n is the number of observations, \(y_{i}\) is the actual yield, \(\hat{y}_{i}\) is estimated yield, \(SS_{e}\) is sum square of errors, and \(K\) is number of parameters. Its value lies within the range [\(-1\), 1], where \(R<0\) indicates a negative correlation, \(R>0\) indicates a positive correlation, and \(R=0\) indicates no correlation. The larger the absolute value of r is, the stronger the correlation between two variables. The RMSE ranges from 0 to infinity, with models that achieve RMSE closer to 0 being considered superior. Lower AIC values indicate that the model is more useful.
# Singing Voice Detection in Electronic Music with a Long-Term Recurrent Convolutional Network
## 4 Conclusions  
In this paper, an SVD system based on LRCN was presented to detect vocals in a new electronic music dataset and evaluated against previous DL models (CNN, LSTM, LRCN) using existing pop music datasets, exhibiting an F1-measure of 0.828 for the electronic music dataset (Electrobyte); however, this was lower compared to its pop music genre counterpart (Jamendo) with an \(F1\)-measure of 0.942. This provides a benchmark result to further evaluate electronic music in the SVD field with new models and achieve a better performance.  
Future work will expand the research on electronic music attributes to find the root causes of the result in terms of vocal signals. Furthermore, new datasets for different genres as popular as pop music (such as rock or hip-hop) will be created to evaluate the LRCN and perform the same experiments. Finally, datasets with combinations of various music genres will be generated and tested to prove the robustness of SVD systems and find patterns or similarities between genres.  
Conceptualization, R.R.-A. and A.G.-E.; methodology, R.R.-A. and A.G.-E.; software, R.R.-A.; validation, R.R.-A.; formal analysis, R.R.-A. and A.G.-E.; investigation, R.R.-A.; resources, R.R.-A.; data curation, R.R.-A.; writing--original draft preparation, R.R.-A.; writing--review and editing, R.R.-A., A.G.-E. and B.V.-A.; visualization, R.R.-A.; supervision, A.G.-E. and B.V.-A.; project administration, R.R.-A., A.G.-E. and B.V.-A.; funding acquisition, B.V.-A. All authors have read and agreed to the published version of the manuscript.  
The authors would like to acknowledge the financial support of Tecnologico de Monterrey in the production of this work.  
The Electrobyte dataset is publicly available at ([[https://zenodo.org/record/6757945](https://zenodo.org/record/6757945)]([https://zenodo.org/record/6757945](https://zenodo.org/record/6757945))) (accessed on 27 June 2022), while the necessary scripts to reproduce these results are available at ([[https://github.com/Raylogic/SVD-LRCN](https://github.com/Raylogic/SVD-LRCN)]([https://github.com/Raylogic/SVD-LRCN](https://github.com/Raylogic/SVD-LRCN))) (accessed on 9 June 2022).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Accuracy** & **Precision** & **Recall** & **F-Measure** & **Deviation** \\ \hline [PERSON] [30] & 0.822 & - & - & 0.831 & - \\ [PERSON] [12] & 0.923 & - & 0.903 & - & - \\ [PERSON] [33] & 0.882 & 0.880 & 0.862 & 0.871 & - \\ [PERSON] [34] & 0.848 & - & - & 0.846 & - \\ [PERSON] [16] & 0.894 & 0.898 & 0.906 & 0.902 & - \\ [PERSON] [17] & 0.915 & 0.895 & 0.926 & 0.910 & - \\ [PERSON] [11] & 0.924 & 0.926 & 0.924 & 0.927 & - \\ LRCN & 0.939 & 0.937 & 0.945 & 0.942 & 0.004 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the SVD system with existing methods on Jamendo.
## 2 Testing Device and Method  
### Testing Device  
#### 2.1.1 X-ray System  
An X-ray is generated in a vacuum tube, called an X-ray tube. There are anode and cathode enveloped in the X-ray tube. The cathode is a filament made of tungsten and emits thermal electrons when heated by the current. When a high voltage is applied to the anode, electron current of the cathode is transferred at a high velocity towards the anode and made to collide with the target made of tungsten and molybdenum, releasing X-rays.  
* Tube voltage: Tube voltage refers to a high voltage applied to the anode to generate X-rays in the X-ray tube, and its size determines the size of photon energy of the X-rays. Therefore, in case the tube voltage is low, it cannot sufficiently transmit an object, deteriorating the image quality. Conversely, a high tube voltage leads to an excessive amount of X-ray penetrating the object, reducing the contrast of the produced image. Therefore, an adequate level of tube voltage should be determined according to the type of object in use.
* Tube current: Tube current means a current flowing from anode to cathode, and its size determines the number of photons. The quantity of tube current is expressed in the unit mAs, which is generated by multiplying the tube current (mA) by the exposure time (s). Depending on the type of object, an appropriate level of tube current should be set to reduce the noise in images.  
This study was able to obtain proper DPF substrate images using GXR-S of DRGEM Co., Ltd. in Gyeonggi-do, South Korea, which is generally used in medical fields. The specifications of the X-ray generator are shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline
**System Model** & **GXR-S** \\ \hline Output rating & 52 kW \\ kV range & 40\(\sim\)150 kV, 1 kV step \\ mA range & 10–640 mA \\ Timer range & 0.001–10 s, 38 steps \\ mAs range & 0.1–500 mAs \\ \hline \end{tabular}
\end{table}
Table 2: Specifications of the X-ray system.  
Figure 3: CT X-ray image of aged DPF.
# Cryptanalysis of Permutation-Diffusion-Based Lightweight Chaotic Image Encryption Scheme Using CPA
###### Abstract  
In order to meet the requirement of secure image communication in a resource-constrained network environment, a novel lightweight chaotic image encryption scheme based on permutation and diffusion has been proposed. It was claimed that this scheme can resist differential attacks, statistical attacks, etc. However, the original encryption scheme is found to be vulnerable and insecure to chosen-plaintext attack (CPA). In this paper, the original encryption scheme is analyzed comprehensively and attacked successfully. Only by choosing a full zero image as the chosen-plaintext of the diffusion phase, the encrypted image can be restored into permutation-only phase, and by applying the other chosen images as the chosen-plaintexts of the permutation phase, the map matrix which is equivalent to the secret key of the permutation phase can be further revealed. Experiments and analysis verify the feasibility of our proposed attack strategy.  
c 28 December 2018; Accepted: 27 January 2019; Published: 31 January 2019
# Swin Transformer Assisted Prior Attention Network for Medical Image Segmentation
## 2 State of the Art and Related Work  
### CNN-Based Methods  
Early researches on medical image segmentation predominantly focus on contour-based algorithms and traditional machine learning algorithms [7, 8]. Subsequently, after the development of convolution neural networks, U-shaped [9, 10, 11] structure is proposed where the encoder is applied to extract features with the receptive fields of convolution operations, and the decoder up-samples the features extracted by the encoder to generate prediction map with pixel-level scores. [PERSON] [9] proposed a novel network called U-Net for medical image segmentation which was utilized to connect the semantic information gap in encoder-decoder, and had proven its effectiveness in recovering detail features of the target [12, 13]. Due to the simplicity of encoder-decoder U-shaped structure and outstanding performance in various computer vision tasks, many novel UNet-like models are constantly proposed such as UNet++ [14], Res-UNet [15], Attention U-Net [16], nnUNet [17], and UNet 3+ [18], which all achieve superior performance on medical image segmentation. Specially, Unet-like models are also applied into the medical image segmentation of three-dimensional (3D) datasets, such as V-Net [19] and 3D-Unet [20]. Currently, CNN-based methods have gained great popularity and made eminent contributions in medical image segmentation due to their remarkable ability of feature extraction and representation. However, since the inherent locality of the convolution operation exactly caused by the limited receptive fields, it is difficult for existing CNN-based methods to capture global contextual information by the inherent limitation [2]. The difficulties in some medical image tasks are also hard to address and CNN-based methods cannot roundly meet the requirements of these tasks for segmentation accuracy. A great deal of effort has been devoted to address this problem by using atrous convolution layers [21, 22], self-attention mechanisms [23, 24], and image pyramids [25].
## 2 Concepts and Terminologies  
This study adopts a multidisciplinary approach that incorporates education with information and communication technology (ICT). To facilitate a comprehensive understanding of both fields, this section is divided into two subsections: (i) educational terminology and (ii) smart system terminology. These subsections clarify the essential concepts and terminologies used throughout this article.
## 4 Prediction Model of the Leakage Current and Example Analysis
### Verification Analysis and Result
#### 4.2.4 Performance Comparison  
This study divides the accumulated data from the test station into training and validation sets to evaluate the performance of various artificial intelligence methods. The LSTM model achieved a precision of a _MAE_ of 0.0780 and a _MSE_ of 0.0065 for validating data in case study 1; a _MAE_ of 0.0857 and a _MSE_ of 0.0276 for case study 2; and a _MAE_ of 0.0897 and a _MSE_ of 0.011 for case study 3. However, both SVR and GBR could not achieve the optimum evaluation metrics compared with LSTM, indicating the limitations of SVR and GBR in predicting leakage current. Compared to SVM and GBR, the LSTM model exhibits superior overall performance. LSTM incorporates gating mechanisms that control the flow of information in memory units, enabling it to better capture and retain long-term patterns in leakage current and weather parameters, significantly enhancing performance in processing time series data. In contrast to the prediction methods SVM and GBR, the proposed LSTM model is computationally more efficient and provides more accurate predictions. Throughout the study, LSTM attains the highest accuracy in learning operations, as evident in the case studies, where the evaluation metrics reveal lower _MSE_ and _MAE_ values while evaluating performance. The LSTM model exhibits very small prediction errors. Despite progress through validation analysis, some improvements are still needed. The infrequent occurrence of peak leakage current values results in the underprediction of these peaks. The LSTM model, along with other models, cannot achieve high accuracy in the validation data, mainly due to imbalanced factors. The accuracy of multiclass problems is influenced by the dominant class in the imbalanced dataset. While the results of _MSE_ and _MAE_ suggest good predictive performance, the _EVS_, however, is relatively low. This indicates that the model has limited ability to explain the variance in the target variable, suggesting that there might be some variability that the model cannot capture. However, LSTM demonstrates higher capability and precision in handling the imbalanced frequency of leakage current data. Hence, continuous data collection over an extended period may be required to improve the predictive model.  
Figure 19: Case 3: Predicted and measured results of the SVR algorithms.
# Proteomics in Forensic Analysis: Applications for Human Samples
## 2 Proteomics Methods: Sample Preparation Techniques, Data Acquisition, and Data Analysis
### Sample Preparation
#### 2.1.3 Proteolytic Digestion
Proteolytic enzymes (e.g., trypsin in ammonium carbonate) are added for protein digestion. The resultant peptides are collected after centrifugation [90]. Compared with in-solution digestion, FASP can be applied to samples containing detergents (e.g., sodium dodecyl sulfate) [91]. S-Trap is an alternative to FASP and in-solution digestion. This allows for high concentrations of sodium dodecyl sulfate. Another advantage of S-Trap is its short processing time [92]. In this method, the samples are mixed with sodium dodecyl sulfate and a reduction agent. After alkylation with iodoacetamide or chloroacetamide, phosphoric acid is added to a final concentration of 1.2%, and binding buffer (90% methanol, 10% 100 mM TEAB, pH 7.1) is added, as well. Due to the presence of methanol, proteins are precipitated into fine suspensions that are stabilized by sodium dodecyl sulfate. The suspension is loaded onto an S-Trap filter and centrifuged to remove the flow-through. Trypsin is added to digest the proteins. The resultant peptides are eluted with three stepwise buffers (50 mM TEAB, 0.2% formic acid in water, and 50% acetonitrile + 0.2% formic acid in water) [93; 94]. Several enzymes can be used for protein digestion. Among these, trypsin is the most commonly used. Trypsin cuts the polypeptide backbone at the carboxy-terminal side of lysine and arginine with high specificity, which favors the database searching step [95]. Lysine and arginine residues are frequently present in proteins; thus, digestion with trypsin results in peptides with an average length that is convenient for LC-MS/MS analysis. Since lysine and arginine are basic residues, the resultant tryp peptides have a positive charge, usually +2 and +3. They are suitable for producing positively charged peptides with an ideal _m/z_ in LC-MS/MS analysis [1].
## 6 Thematic Taxonomy of UWSNs
### Applications
#### Industrial  
Industrial applications in UWSNs lead to a significant impact in facilitating commercial activities. UWSN has potential to monitor underwater oil and gas pipeline monitoring applications. The authors in [106] have designed a prototype for monitoring oil and gas in pipeline underwater. The system was developed to provide statistics reporting regarding pipeline health that connected over large areas. In [107; 108], the authors also designed an underwater oil and gas pipeline monitoring system that require control of an actuation component.  
Fish farming is one of the most demanding production that contribute to a good economic resources. However, it requires a strict monitoring system to monitor the habitat conditions of the fish. The authors of [109] developed a Zigbee-based UWSN monitoring for large fish farms and can be accessed remotely by interested user. Besides, the system has the ability to monitor the fish farms based on dissolved oxygen, pHvalues, temperature, the water level, and humidity parameters. Additionally, wireless cameras are used and integrated with the system and the Internet for wireless monitoring anytime and anywhere in the world. In addition, authors of [110; 111] also developed similar commercial fishery monitoring system by using acoustic waves as the communication media.
# A Systematic Review of Virtual Reality Interfaces for Controlling and Interacting with Robots
## 3 Results and Discussion
### Visualization
However, when the operator moves their head, there is a delay between the operator movement and the updated image being returned by the robot, which is caused by a combination of latency in the system and robot hardware speed. This delay can cause intersensory conflict and inflict motion sickness. Additionally, ref. [28] ran a user study and found that their decoupling method, that instantly returns an updated image upon head movement, on average was less nauseating and more visually comfortable to users. In relation, ref. [30] aims to reduce motion sickness by identifying the best way to display stereovision cameras inside a VR headset. In a 15 person user study, individuals, on average, reported feeling less motion sick when the stereo cameras were rendered on a plane inside the VR headset, rather than directly rendering the cameras to each eye,which creates a more immersive view. Ref. [31] studies how using human perception-optimized trajectory planning in mobile telepresence robots can improve motion sickness. Their results overall show that minimizing the amount of turns a robot takes decreases a user's motion sickness.
# Financial Market Correlation Analysis and Stock Selection Application Based on TCN-Deep Clustering
## 2 Financial Time-Series Deep Clustering Network: TCN-Deep Clustering
### Autoencoder Network of TCN  
To mine the features of financial time series, this paper designs an autoencoder based on a TCN network. The TCN network adds two operations compared to a 1D CNN: casual and dilated convolutions and a connection at the network level with a residual network, as shown in Figure 2. Each residual block contains two layers of convolutional and nonlinear operations (with ReLU as the activation function), and weightnorm and dropout layers are also added to regularize the network in each layer to avoid gradient disappearance caused by the training process. Unlike the fully connected 1D CNN shown in Figure 3a, causal convolution takes into account that the time series should only be affected by the current state or the past state, as shown in Figure 3b. To solve the problem of the limited receptive field of causal convolutional networks, a TCN network puts forward a dilated convolution. For a one-dimensional input time series \(x=\left[x_{1},x_{2},\ldots,x_{t}\right],f:\left\{0,1,2,\ldots k-1\right\}\) represents a convolution kernel. The dilated convolution at \(x_{t}\) is calculated with Equation (1):  
\[F(x_{t})=\left(x*_{d}f\right)(x_{t})=\sum_{i=0}^{k-1}i\cdot x_{t-d\cdot i} \tag{1}\]  
where \(k\) represents the convolution kernel size, \(d\) represents the dilation factor, and \(t-d\cdot i\) represents the past direction. For example, when \(d=1\), \(k=2\), \(x_{t}\) performs the convolution operation with \(x_{t-1}\) only. Figure 4 presents the TCN network structure with a convolution kernel of \(k=2\) and a dilation factor of \(d=[1,2,4,8]\). As shown in Figure 4, the third layer of the rightmost element can receive eight input elements. Without a dilated convolution, receiving an input of the same length causes the number of layers in the network to rise to eight, which leads to a dramatic increase in the number of parameters to be trained.  
Figure 1: Architecture of TCN-Deep Clustering.
###### Abstract  
The possibility of the differentiation of glioblastoma from traumatic brain injury through blood serum analysis by terahertz time-domain spectroscopy and machine learning was studied using a small animal model. Samples of a culture medium and a U87 human glioblastoma cell suspension in the culture medium were injected into the subcortical brain structures of groups of mice referred to as the culture medium injection groups and glioblastoma groups, accordingly. Blood serum samples were collected in the first, second, and third weeks after the injection, and their terahertz transmission spectra were measured. The injection caused acute inflammation in the brain during the first week, so the culture medium injection group in the first week of the experiment corresponded to a traumatic brain injury state. In the third week of the experiment, acute inflammation practically disappeared in the culture medium injection groups. At the same time, the glioblastoma group subjected to a U87 human glioblastoma cell injection had the largest tumor size. The THz spectra were analyzed using two dimensionality reduction algorithms (principal component analysis and t-distributed Stochastic Neighbor Embedding) and three classification algorithms (Support Vector Machine, Random Forest, and Extreme Gradient Boosting Machine). Constructed prediction data models were verified using 10-fold cross-validation, the receiver operational characteristic curve, and a corresponding area under the curve analysis. The proposed machine learning pipeline allowed for distinguishing the traumatic brain injury group from the glioblastoma group with 95% sensitivity, 100% specificity, and 97% accuracy with the Extreme Gradient Boosting Machine. The most informative features for these groups' differentiation were 0.37, 0.40, 0.55, 0.60, 0.70, and 0.90 THz. Thus, an analysis of mouse blood serum using terahertz time-domain spectroscopy and machine learning makes it possible to differentiate glioblastoma from traumatic brain injury.  
gioloblastoma; traumatic brain injury; liquid optical biopsy; blood serum; terahertz time-domain spectroscopy; machine learning; informative features extraction
# Real-Time Communication Support for Underwater Acoustic Sensor Networks +
## 6 Computing the Minimum Frame with a Genetic Algorithm
### Genetic Algorithms
#### 6.1.5 Complexity  
There are several items that contribute to the GA complexity. In what follows, the complexity (also known as cost) per generation or iteration of the GA is considered. The procedure has two distinct parts. In the first one, the fitness function has to be evaluated for each member of the population. The complexity of this part is given by the product of the population size (\(p\)) and the cube of the number of nodes in the network (\(n\)), i.e., \(O(pn^{3})\). The selection process of GA includes sorting the members of the population according to the fitness value, but this process cost is \(O(p\log(p))\) in the worst case. The complexity per iteration is the combination of both terms, and depending on the relative values of \(p\) and \(n\), this complexity is \(O(p(\log(p)+n^{3}))\). The amount of iterations needed (\(g\)) may be important. The final complexity of the GA method proposed is given by \(O(gp(\log(p)+n^{3}))\).