esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
miRNAs are important small RNAs (sRNAs) that are generated from single-stranded RNA precursors capable of forming hairpin structures. miRNAs can regulate cellular and metabolic processes by binding and cleaving mRNA or suppressing translation [1]. Blueberry is an economically important fruit crop. Its fruit is rich in anthocyanins, which are reportedly beneficial to human health because of their ability to improve night vision, retard tumor growth, and reduce the risk of cardiovascular and neurodegenerative diseases, among other effects [2; 3; 4; 5]. However, our understanding of the regulation of blueberry miRNAs remains very limited.  
Recently, using next-generation sequencing technology, a total of 412 conserved miRNAs and 35 predicted novel miRNAs have been identified from blueberry fruits [6]. The potential target genes of many of these miRNAs were also predicted to be transcription factors and enzymes. They were involved in many important biological processes, such as anthocyanin biosynthesis. Yet, whether these targets are overstated, and the interactions between a given miRNA and its targets are actual are still uncertain. Therefore, it is necessary to further perform a genome-wide experimental validation, which will contribute to a better understanding of the regulation of blueberry miRNAs. More recently, another study contributing to this motivation has been performed [7]. It analyzed fruit ripening-related miRNAs and their targets, and validated 178 targets for 41 known and 7 novel miRNAs based on degradome analyses. However, it is still insufficient for us to perfectly understand the regulation of blueberry miRNAs based on the amount of known miRNAs identified in a relatively sophisticated plant, Arabidopsis [8].  
Transcriptome sequencing has been used to assemble transcript sequences and analyze gene expression [9; 10; 11], and sRNA sequencing has been used for the genome-wide identification of miRNAs, including new, low-abundance miRNAs as well as the conserved, highly abundant miRNAs [6]. Meanwhile, degradome sequencing has been applied to identify miRNA target genes at the genome-wide level [12; 13]. Combining the data derived from transcriptome, sRNA, and degradome sequencing experiments represents an alternative strategy for the systematic analysis of miRNA regulation on a genome-wide level [14].  
In this study, we identified blueberry miRNAs and their targets in flowers, young fruits, and ripe fruits via the bioinformatics-based analysis of transcriptome, sRNA, and degradome sequencing data. We identified 127 known and 101 potentially novel miRNAs. Moreover, 141 miRNA targets were confirmed by degradome sequencing. Of these targets, three encoded the enzymes or transcription factors related to anthocyanin biosynthesis, and one affected cytokinin signal transduction.
## 3 Materials and Methods
### Development of the Geodatabase  
The geodatabase was prepared with ESRI ArcGIS Pro software, licensed to the Wroclaw University of Science and Technology, based on archival cartographic documentation comprising of 67 scanned paper maps representing underground and open-pit workings. The scanned maps were obtained from the State Mining Authority archives, and are drawn at a scale of 1:1000. The preserved and available maps do not cover the entire area of the study (all test fields). However, for the analyzed test field (three), the documentation is complete. The scanned and georeferenced maps were used to create vector-type features representing location and 3D geometry of the former underground workings. An example map is shown in Figure 4, and a visualization of the underground workings in this test field is shown in Figure 3. In addition, the location of geological profiles made prior to the start of the mining operation in the area, obtained from the Polish Geological Institute-National Research Institute archives, was included in the geodatabase. Other data used for this study included DEM with a 1 m resolution from the year 2011, as well as Database of Topographical Objects at a scale 1:10,000, both downloaded from the Head Office of Geodesy and Cartography geoportal  
Figure 3: Area of test field three in relation to topography and documented underground workings; left, topographical background; right, orthophoto mosaic background.
## 3 Rain Attenuation  
The difference between the minimum RSL under clear sky conditions and the minimum RSL under rainy conditions was used to calculate the rain attenuation. The difference between the RSL in the clear sky condition period and the RSL during the first hour after the rainfall has stopped represents the average wet antenna's radomes effect. It was calculated to be 1.5 dB and excluded from rain attenuation [18]. The cumulative distribution functions (CDFs) for the maximum attenuation and maximum rainfall every 15 min in the 1.8-km 73.5- and 83.5-GHz LOS terrestrial link is shown in Figure 3. It shows that the rain attenuations are almost the same for both 73.5-GHz and 83.5-GHz E-Band links at all time of percentages. The maximum rain attenuation for both links is 40.10 dB at 0.03% of time. Our results agree with previous studies showing dominant and constant rain losses for microwave link with operating frequencies at 70 and 80 GHz bands [29; 30]. From the rain-rate curve in this figure, it is worth noticing that for any rain rate above 108 mm/h, both links are down. It means that the two links can support the full-duplex transmission under rainy conditions with the outage probability of \(2.9\times 10^{-4}\).  
The CDFs for the maximum attenuation and maximum rainfall every 15 min in the 300-m 73.5 and 83.5 GHz LOS terrestrial link is shown in Figure 4. It shows that the rain attenuation is almost the same for both E-band links at all time of percentage. The maximum rain attenuation for both links is 25.00 dB at 0.006% of time. From the rain-rate curve in Figure 4, it is worth noticing that for any rain rate above 193 mm/h, both links are down. It means that the two links can support the full-duplex transmission under rainy conditions with the outage probability of \(6\times 10^{-5}\).  
Figure 2: Cumulative distribution for maximum rain rate every minute for one year.
# A Review of Counter-UAS Technologies for Cooperative Defensive Teams of Drones
## 4 Neutralization Systems  
Neutralization systems are activated by the command-and-control system to respond to the threat posed by the detected malicious drone(s). Multiple neutralization systems can be activated simultaneously in order to cooperate to improve the effectiveness of the neutralization. Furthermore, these systems can be located on one or more distinct platforms according to the CUS physical architecture.  
According to the classification reported in [9], neutralization systems can carry out the following actions: warning, control, interruption, disabling and destruction. These actions are implemented through neutralization techniques, more simply indicated as neutralizers (or mitigators). Neutralizers have been classified in different ways in the literature. In [9], neutralizers were divided into physical and non-physical based on whether there was physical damage to the m-drone. Moreover, in the case of non-physical neutralizers, there is no contact between the neutralizer and the m-drone, but some of them can actually cause damage to the m-drone. A similar subdivision was also shown in [10], even if some neutralizers fell into different classes. However, in [5], the subdivision was made between electronic neutralizers, based substantially on electromagnetic waves that do not cause direct damage to the m-drone (for example jamming), and kinetics neutralizers, which intercept the drone with physical means (for example nets), but the latter ones also include high power lasers and microwaves. Given that each definition described gives rise to some ambiguity, the following classification will be adopted:  
* Electronic neutralizers, based on the use of electromagnetic waves capable of interrupting (operations), disabling or even destroying (at least partially) a drone;
* Kinetic-mechanical neutralizers, based on the use of mechanical means, which involve contact between the neutralizer (or a part of it) and the malicious drone.
# A Blockchain-Assisted Security Protocol for Group Handover of MTC Devices in 5G Wireless Networks
## 4 System Background
### System Model
In a cellular network architecture, to effectively reuse frequency resources, the entire service area is divided into numerous cells shaped as regular polygons, such as hexagons. Consequently, around a cell controlled by a gNB, typically six adjacent cells are designated as neighbors. During a handover, an MTCD can only hand over to one of these six neighboring cells, which is the cell controlled by the t-gNB associated with the current cell controlled by the s-gNB [22].
# YOLOv8 with Post-Processing for Small Object Detection Enhancement
## 3 Proposed Methodology
### Improvement Using CARAFE Module
This process can be expressed using the following Equations (1) and (2):  
\[\omega_{l^{\prime}}=\psi(N(\chi_{l},k_{\text{encoder}})) \tag{1}\]  
Figure 3: CARAFE module architecture with kernel prediction and content-aware reassembly modules. Figure 2: Architecture of the YOLOv8n model with the CARAFE module applied. \[\chi_{\text{f}^{\prime}}^{\prime}=\phi\big{(}\text{N}\big{(}\chi_{\text{l}},k_{ \text{up}}\big{)},\omega_{\text{f}^{\prime}}\big{)} \tag{2}\]  
Each position in the input feature map \(\chi\) corresponds to \(\sigma^{2}\) target positions in the output feature map \(\chi^{\prime}\). Each target position requires a reassembly kernel of size \(k_{\text{up}}\times k_{\text{up}}\). The Content Encoder encodes the content by taking the compressed feature map as input and generates reassembly kernels. Subsequently, the Kernel Normalization Module normalizes each reassembly kernel using a softmax function. The generated reassembly kernel \(\omega_{\text{f}^{\prime}}\) is used in the Content-Aware Reassembly Module to reassemble the features within the corresponding region, achieved through a simple weighted sum function \(\phi\). The reassembly process for target position \(\text{I}^{\prime}\) and its corresponding square region \(\text{N}\big{(}\chi_{\text{l}},k_{\text{up}}\big{)}\) in the input can be represented by Equation (3):  
\[\text{r}=\bigg{\lfloor}\frac{k_{\text{up}}}{2}\bigg{\rfloor},\chi_{\text{f}^ {\prime}}^{\prime}=\sum_{n=-r}^{\text{r}}\sum_{m=-r}^{\text{r}}\omega_{\text{f }^{\prime}}(n,m)\cdot\chi(i+n,j+m) \tag{3}\]  
In the reassembly kernel \(\text{N}\big{(}\chi_{\text{l}},k_{\text{up}}\big{)}\), each pixel in the region contributes differently to the upsampled pixel \(\text{I}^{\prime}\), depending on the content of the corresponding feature rather than merely the spatial distance. This allows the model to focus more on relevant regions, thereby enhancing the semantic representation capability of the reassembled feature map. The incorporation of the CARAFE module is crucial in enhancing the overall detection performance of the YOLO architecture. Specifically, it improves the detection of small objects and provides stability in complex background environments, demonstrating high applicability across various use cases [31; 32].
# TAWC: Text Augmentation with Word Contributions for Imbalance Aspect-Based Sentiment Classification
## 5 Results and Discussion
### Comparison of Performances with Baselines  
Table 4 compares the performances, including the average, standard deviation, and best accuracy, of the training set without data augmentation (No-DA) and the data augmentation methods for the three classifiers. The results demonstrate that text augmentation methods are valuable for sentiment classification as they improve the accuracy of the two benchmark datasets. Text augmentation methods not only expand the number of features for training models but also consider balancing the class. Consequently, the model tends to fit and increase the performance compared to the No-DA case. Among the baselines, EDA-w2v and IG-w2v are quite competitive in terms of performance. Both methods provide better results than the original method for all classifiers. In addition, IG-w2v achieves outstanding average accuracy for the DistilBERT model on the SemEval 2015 dataset. In other words, the word2 vec model is an appropriate alternative synonym resource for word replacement augmentation. Table 4 also shows that the proposed TAWC method can elevate robust transformer classifiers that achieve outstanding performance in terms of best and average accuracy in most cases. These results demonstrate that the proposed word selection for augmentation is suitable for different versions of the BERT-series model classification. Compared to the No-DA case, the proposed TAWC significantly outperforms the RoBERTa method by over 2% and 4% on the SemEval 2015 and SemEval 2016 datasets, respectively. Furthermore, compared to IG for selective augmentation, the proposed TAWC demonstrates improvement for the two datasets with all classifiers in terms of best and average accuracy. We also averaged the value of each method over the classifier according to the standard deviation. The results imply that our proposed method has better generalization than IG-w2v. On the SemEval 2015 dataset, using the DistilBERT model, the proposed selective augmentation strategy outperformed EDA as a non-selective method by over 2%. Although the average accuracy of TAWC was less than that of IG-w2v, it obtained the highest score in terms of best accuracy. On the SemEval 2016 dataset, among the three word2 vec-based augmentation approaches, TAWC outperformed the other methods by nearly 3% when applying DistilBERT and nearly 2% when using RoBERTa. For the best accuracy, the TAWC method increased the BERT and RoBERTa models to a superior score by over 90%. Generally, text augmentation based on word contributions enhances the accuracy of the sentiment classification model. We believe that the performance improvement of word contribution-based augmentation is due to its advantage of selecting augmented words considering the roles and contributions of sentiment prediction. Furthermore, the robustness of the word2 vec model
## 2 Methods  
### Seasonal-Trend Decomposition Procedure Based on Loess  
The STL is a versatile and robust method of time series decomposition, it assumes that the time series is an additive model composed of trends, seasons, and remaining components [27]. The STL is usually used for time series with great volatility and instability due to it is convenience to calculate and not being sensitive to abnormal signals in the data. The components with independent characterization characteristics can be decomposed through this model to facilitate subsequent analysis [28]. Using this model, the dam deformation time series (\(Y_{t}\)) results from the summation of three components:  
\[Y_{t}=S_{t}+T_{t}+R_{t}(t=1,\ldots,T) \tag{1}\]  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Method Performance** & **Model Analysis Method** & **Process Line Analysis Method** & **Intelligent Analysis Method** & **Intelligent Combinatorial Models** \\ \hline \hline \multirow{4}{*}{Advantage} & Clear physical and mechanical relationship and dynamic criterion & Simple prediction principle and easy operation & Avoid physical and mechanical relationship analysis, intelligent prediction & Provide good prediction results for complex situations \\ \hline \multirow{4}{*}{Disadvantage} & Model performance is sensitive and practicality is limited & Huge subjective influence and application analysis & Insensitive to the noise and heterogeneous static of time series data & \multirow{2}{*}{High calculation cost of time series data} \\ \cline{1-1} \cline{3-3} \cline{5-6}  & The existing methods for dam deformation prediction still exist and actual engineering problems need to be solved urgently, such as sensitive to monitoring data size, insensitive to the noise and heteroscedastic structure of time series data, and low efficiency in calculation and so on. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of advantages and disadvantages of dam deformation prediction methods.  
where \(S_{t}\) is the seasonality, \(T_{t}\) is the trend, and \(R_{t}\) is the residual.  
The STL decomposition method needs to define decomposition frequency parameters for the seasonal component. It was set at 365 due to the dam deformation having annual periodicity.
# A Survey of Machine Learning Methods for Time Series Prediction
## 2 Methodology  
A rigorous and systematic methodology is employed to identify the most relevant research papers for this survey. Web of Science is selected as the primary database because it is a trusted publisher and independent source of data with comprehensive coverage of the peer-reviewed scientific literature. Given the objective of comparing TBML methods with DL approaches in time series prediction, this paper establishes specific inclusion criteria for article selection:  
1. Focus on Time Series Applications: the research must address problems involving time series data;
2. Utilization of Advanced TBML Methods: studies must implement advanced TBML architectures, particularly gradient-boosted decision trees or similar structures (e.g., XG-Boost 2.1.4, LightGBM, or CatBoost 1.2.7);
3. Utilization of Advanced Neural Network (NN) Architectures: papers must explore sophisticated NN architectures, including but not limited to recurrent neural networks (RNN), feedforward neural networks (FFNN), convolutional neural networks (CNN), long short-term memory networks (LSTM), gated recurrent units (GRU), or Transformers;
4. Direct Comparisons Using Identical Datasets: the research must present comparative evaluations of at least one TBML and one DL architecture under identical experimental setups, ensuring consistent datasets and conditions.  
To identify the relevant literature, a comprehensive search query is developed as follows: (ALL = (XGBoost) OR ALL = (LightGBM) OR ALL = (CatBoost) OR ALL = (\"gradient boost\")) AND (ALL = (\"time series\") OR ALL = (\"time sequence\") OR ALL = (\"temporal series\") OR ALL = (\"temporal sequence\") OR ALL = (\"time forecast\")) AND (ALL = (\"LSTM\") OR ALL = (\"ANN\") OR ALL = (\"CNN\") OR ALL = (\"RNN\") OR ALL = (\"transformer\") OR ALL = (\"GRU\") OR ALL = (\"deep neur\") OR ALL = (\"deep lear\")).  
This query yields 589 papers published between 2017 and 2024. To ensure focus on the most influential research, papers are initially selected based on the citation count. From the top 100 most-cited papers, 65 articles [3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67] satisfy the inclusion criteria. To maintain contemporary relevance and capture recent developments in the field, additional temporal criteria are implemented: a minimum of 10 papers per year from 2020 to 2024 must be included. Consequently, four highly cited papers from 2023 [68; 69; 70; 71] and the ten most-cited articles from 2024 [72; 73; 74; 75; 76; 77; 78; 79; 80; 81] are incorporated. In total, this survey encompasses 79 influential papers investigating the comparative performance of TBML and DL approaches in time series analysis.
# Forward and Inverse Studies on Scattering
## 4 Conclusions  
In this paper we present the studies of forward and inverse analysis on Rayleigh wave scattering by surface-breaking canyon-shaped flaws.  
For the forward problem, we show that the modified boundary element method is an effective numerical technique to calculate the scattered wave field. By introduction of modification items concerning far-field displacement patterns in the BEM matrix, one can effectively suppress artificial reflected waves induced by model truncation. In our improved modified BEM version, the far-field reflection and transmission coefficients are solved simultaneously with the whole displacement field in the least-square sense. The improved version corrects [PERSON] original formulation, in which reflection coefficients obtained from \(x_{1}\) and \(x_{2}\) components might not coincidence with each other.  
For the inverse problem, we present an inversion procedure to reconstruct the geometric properties of canyon-shaped flaw by usage of reflection coefficients of Rayleigh wave at a series of frequencies. From scattering theory for elastic waves, the scattered Rayleigh wave field is expressed as a boundary integral equation over the flawed area. By introduction of far-field form of half-space Green's function and the Born approximation, it can be proven that the objective function of flaw depth is reconstructed by inverse Fourier transform of reflection coefficients. This method gives a direct and simple inversion approach for reconstructing locations and shapes of surface flaws, which does not require baseline data and iteration.  
For numerical verifications, the reflection coefficients obtained from forward analysis are used as input data in the inversion procedure. It is shown that the inversion method is effective for both single and multiple flaw cases. The method can reconstruct images especially accurately for shallow and flat notches; while for deeper flaws, the method can still represent their geometric properties (e.g., the location, depth and width), and can produce reliable \"initial images\" for further usage.  
This work was supported by the National Natural Science Foundation of China (Nos. 51405225, 11502108), the Program for New Century Excellent Talents in Universities (No. NCET-12-0625), the Natural Science Foundation of Jiangsu Province (Nos. BK20140808, BK20140037), the Fundamental Research Funds for Central Universities (No. NE2013101), and a project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). [PERSON] and [PERSON] conducted theoretical analysis; [PERSON] performed numerical verifications; [PERSON] wrote the paper.  
The authors declare no conflict of interest.