esa-sceva/satcom-chunk-collection huggingface dataset:
# THBase: A Coprocessor-Based Scheme for Big Trajectory Data Management
## 7 Experiment
### Performance of Single-Object Query  
In terms of query performance, we first experiment with the single-object query. In the experiment, we randomly select 100 MOIDs and 50% of the time range as the query conditions, and use the average latency result as the evaluation criteria, as shown in Figure 5.  
The experimental results show that the query latency of the three schemes gradually increases with the growth of the dataset size. This is due to the increase of the query time range. As expected, THBase works more efficiently than the two others. Benefitting from MOID and time attributes contained in rowkey structure, THBase can more accurately locate the query data range. While the rowkey structure of CTDM does not contain the time attribute, so it needs to filter all the data of the queried MO to get the final result. Compared with THBase and CTDM, WPDM supports parallel single-object query, but the data size of a single MO is not large so that the advantages of parallelism are not reflected. On the other hand, WPDM requires a larger deserialization overhead to restore each data cell to one or more trajectory points, so the efficiency of WPDM is lower than the former two.  
Figure 4: Time and storage cost for inserting data: (**a**) time cost, (**b**) database cost, (**c**) index cost.
## 4 Results
### Model Testing on the Combined Dataset (Climate Change and COVID-19 Datasets Combined)  
The combined dataset DS3 was also analyzed with LIWC, n-grams, and their combination and was tested using the four machine learning models (Table 5). By testing the model on the features obtained with the LIWC analysis, the SVM model achieved the highest accuracy of 74.60%. It is followed by the logistic regression and random forest with 74.40% while multinomial Naive Bayes achieved an accuracy of 72.20%. When comparing the performance of the models achieved with the LIWC to the n-gram analysis, virtually all models classify better using the LIWC approach except for the multinomial Naive Bayes model, which achieved an accuracy of 74.00% by analyzing unigrams and bigrams, which is an increase of 1.8% compared to the LIWC analysis. The logistic regression and SVM achieved their maximum accuracy of 72.80% and 71.80% by testing on bigrams while the least favourable results were obtained with classification using the random forest model (69.60%). By combining analysis with n-grams and LIWC, the models achieved the best performance. SVM tested on bigrams with the maximum accuracy of 77.00%, followed by logistic regression with 76.20% and random forest with 75.40%. In this analysis, the multinomial Naive Bayes model (75.20%) achieved slightly worse results by analyzing data using the combined techniques (n-grams and LIWC). By comparing the results by testing the model on different sets of n-grams, it was found that the models mostly perform better using an analysis combining n-grams and LIWC as opposed to an analysis using LIWC only. Likewise, the combined approach, compared to the n-gram analysis, gives better results for each set of n-grams. The highest overall accuracy of 77.00% was achieved by testing the SVM on the data analyzed with bigrams and LIWC, which is a 5.20% better result compared to an analysis with bigrams only. The mentioned model also achieved
# Quadcopters in Smart Agriculture: Applications and Modelling
## 2 Types of Quadcopters
### Commercial Quadcopters
#### 2.1.2 DJI Phantom 3 Standard  
The DJI Phantom 3 Standard, illustrated in Figure 4, is equipped with a three-axis gimbal, enabling it to capture stable images and videos. Many research papers have used this drone in different agricultural applications, such as mapping and monitoring [42; 43; 44; 45], plant health assessment [47], and in chemical spraying [48]. For example, the authors in [44] used the DJI Phantom 3 equipped with a smart vision system to capture multispectral images in various experimental fields, including an apple orchard, an onion field, and a peach orchard in Idaho, United States. This was carried out to monitor crops and improve production.  
Figure 3: Agricultural drone global market size over the years.
## 5 Conclusions  
In the past 20 years, based on the progress of new laser materials, optical technology and the traction of application requirements in many fields, the research of mid-infrared laser has made many breakthroughs and rapid progress. In order to improve the performance of mid-infrared lasers, it is urgent to study and improve the physical and chemical properties of the gain medium for achieving mid-infrared laser output and develop technologies to improve the performance of mid-infrared lasers. In general, the paper briefly introduces the development of mid-infrared optical parametric oscillators, direct-pumped mid-infrared solid-state lasers and direct lasing mid-infrared fiber lasers. Looking forward to the future, the main development trends mainly include: (1) output power increases; in the future, we can continue to improve mid-infrared laser technology and soft glass pretreatment and find new gain media to continuously increase the output power of 3-5 \(\upmu\)m mid-infrared laser and (2) lift the conversion efficiency furthermore; with the low-loss beam-coupling technology development and the successful development of lower loss optical fiber, based on the improvement of passive InF\({}_{3}\) fiber and chalcogenide purification technology, it can be expected that there is still room for improvement in conversion efficiency.  
We can expect that, in the near future, with the continuous improvement of various technologies, the high-power, large-energy mid-infrared laser of 3-5 \(\upmu\)m will move from experimental research to practical applications which will play a unique role in scientific research and production.  
Writing--original draft preparation and writing--review and editing, T.R.; methodology, C.W.; funding acquisition, Y.Y.; supervision, F.C., T.D. and Q.P. All authors have read and agreed to the published version of the manuscript.  
Science and Technology Department of Jilin Province in China (Grant No. 202002041 JC).  
We thank the Key Laboratory of Jilin Province Solid-State Laser Technology and Application for the use of the equipment.  
The authors declare no conflict of interest.  
References
# Predicting Algorithm of Tissue Cell Ratio Based on Deep Learning Using Single-Cell RNA Sequencing
###### Abstract  
Background: Understanding the proportion of cell types in heterogeneous tissue samples is important in bioinformatics. It is a challenge to infer the proportion of tissues using bulk RNA sequencing data in bioinformatics because most traditional algorithms for predicting tissue cell ratios heavily rely on standardized specific cell-type gene expression profiles, and do not consider tissue heterogeneity. The prediction accuracy of algorithms is limited, and robustness is lacking. This means that new approaches are needed urgently. Methods: In this study, we introduced an algorithm that automatically predicts tissue cell ratios named Autoptrc. The algorithm uses the data simulated by single-cell RNA sequencing (ScRNA-Seq) for model training, using convolutional neural networks (CNNs) to extract intrinsic relationships between genes and predict the cell proportions of tissues. Results: We trained the algorithm using simulated bulk samples and made predictions using real bulk PBMC data. Comparing Autoptrc with existing advanced algorithms, the Pearson correlation coefficient between the actual value of Autoptrc and the predicted value was the highest, reaching 0.903. Tested on a bulk sample, the correlation coefficient of Lin was 41% higher than that of CSx. The algorithm can infer tissue cell proportions directly from tissue gene expression data. Conclusions: The Autoptrc algorithm uses simulated scRNA-Seq data for training to solve the problem of specific cell-type gene expression profiles. It also has high prediction accuracy and strong noise resistance for the tissue cell ratio. This work is expected to provide new research ideas for the prediction of tissue cell proportions.  
s 19 April 2022  
Accepted: 4 June 2022  
Published: 7 June 2022  
**Publisher's Note**: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
# HP-LSTM: Hawkes Process-LSTM-Based Detection of DDoS Attack for In-Vehicle Network
###### Abstract
Our primary contributions are as follows:  
* We are the first to utilize the Hawkes process for modeling and analysis in the field of DDoS intrusion detection in the context of the detection of in-vehicle DDoS attacks, where the Hawkes process parameters are capable of capturing the dynamic and self-exciting properties inherent in the sequence of attack events. * We propose a novel network structure, the HP-LSTM block, which simulates the Hawkes process by introducing Hawkes parameters to capture more temporal features compared to the original LSTM structure. Moreover, our model combines this structure with residual self-attention blocks, enhancing efficiency in detecting DDoS attacks in the in-vehicle Ethernet SOME/IP. * Based on our extensive experiments, where various HP-LSTM structures were compared, we identified the optimal HP-LSTM design, achieving an average F1-score of 99.3% in detecting DDoS attacks. Furthermore, HP-LSTM demonstrates its capability to meet the stringent traffic and real-time constraints outlined in IEEE 802.1 DG [14] (<1 ms), thereby showcasing its proficiency in achieving real-time detection within real-world vehicular environments. The structure of the remaining sections of this paper is as follows. Section 2 reviews the current detection models for in-vehicle DDoS attacks and the state-of-the-art detection methods based on the Hawkes process in cyber attacks. Section 3 introduces the structure and communication modes of the SOME/IP, as well as the intensity function of the Hawkes process. Section 4 provides a detailed description of our detection model, while Section 5 offers an evaluation of the model's performance. Finally, in Section 7, we present conclusions and the future works.
# Urban Traffic State Sensing and Analysis Based on ETC Data: A Survey
## 2 ETC Systems: Historical Development and Current Applications  
### ETC Technology Principles and System Architecture  
Electronic toll collection (ETC) represents an intelligent transportation subsystem that leverages Dedicated Short-Range Communication (DSRC) technology to automate toll collection processes. The system enables wireless data communication between vehicles and toll stations, facilitating automatic vehicle identification and toll data exchange. Through computer network processing, ETC achieves fully automated, non-stop toll collection capabilities.  
The ETC system primarily consists of three components: (1) On-Board Unit (OBU): an electronic tag installed on the vehicle's windshield that stores vehicle information and user account data; (2) Road-Side Unit (RSU): antenna equipment installed at toll stations or roadside locations responsible for communicating with OBUs; (3) Data Center: responsible for data processing, billing settlement, and system management. From the perspective of technological architecture evolution, early ETC systems were primarily based on a single DSRC technology. With technological advancement, modern ETC systems have evolved into comprehensive platforms integrating multiple technologies. This evolution of system architecture not only enhances communication reliability but also provides a technical foundation for subsequent multi-source data fusion and intelligent analysis [5].
## 3 Preliminaries of IMU and Extended Kalman Filter Used in UWB Positioning
### Extended Kalman Filter
#### 3.2.2 Linearization  
We linearized the system mathematically at the point \((\hat{x}_{k},\hat{x}_{k-1})\) as  
\[\left\{\begin{array}{l}f(x_{k-1},u_{k-1},w_{k-1})\approx f(\hat{x}_{k-1},u_ {k},0)+\frac{\partial f}{\partial x_{k-1}}(x_{k-1}-\hat{x}_{k-1})+\frac{ \partial f}{\partial w_{k}}w_{k}\\ h(x_{k},v_{k})=h(x_{k},0)+\frac{\partial f}{\partial v_{k}}v_{k}\end{array}\right. \tag{9}\]  
Under the linear system approximation, both the noise terms and the states are regarded as Gaussian distributions. Therefore, states can be described by estimating their mean and covariance matrices.
## 7 Conclusions
Emphasis on cybersecurity: The paper emphasized the significance of cybersecurity in MGs due to their increasing reliance on digital technologies. The outcomes emphasize the implementation of robust cybersecurity strategies, such as encryption of communication channels, access control, device updates and patches, threat detection and response mechanisms, network security, identification and protection of weak points, personnel training, password management, emergency planning, and physical security. These measures ensure the resilience and protection of MGs against cyberthreats. Need for further research and development: The paper highlights that the digitalization of MGs is a rapidly evolving field that necessitates ongoing research and development efforts. The outcomes stress the importance of continuously integrating and advancing digital technologies while simultaneously addressing the challenges and barriers. The paper encourages future studies to explore innovative solutions, strategies, and frameworks to propel the digital transformation of MGs. The outcomes of this paper have significant implications for the future digitalization of MGs, highlighting the potential benefits of adopting digital technologies in MG design, operation, and management. Emphasizing the crucial role of these technologies in ensuring reliable, secure, and sustainable energy supply to urban and rural communities, the paper underscores their importance in the digitalization process. By demonstrating how digital technologies such as DERMSs, MEMs, IoT, big data analytics, blockchain technology, AI, digital twin technology, cloud computing, and augmented reality can revolutionize MG operations, the paper provides valuable insights for policymakers, energy planners, and industry stakeholders. Furthermore, the paper makes substantial contributions by offering a comprehensive overview of the future digitalization of MGs. It synthesizes and consolidates diverse research and developments in this rapidly evolving field, and addresses the challenges and barriers hindering MG digitalization, including technical complexities, regulatory constraints, and cybersecurity concerns. Additionally, it identifies gaps and areas for future investigation, providing valuable insights for further research in the field. Overall, these contributions and implications contribute to the advancement of knowledge and understanding in the digitalization of MGs, guiding future endeavors and promoting the implementation of effective strategies. This research received no external funding.
## 3 Results and Discussion
### Evaluation of SPMESH Sheet-DART-MS vs. SPME-GC-MS
#### 3.3.2 Linalool  
Juice samples (\(n=18\)) were prepared from Muscat-type grapes collected over several weeks of harvest. Linalool concentrations ranged from 135 to 415 \(\mu\)g/L by SPME-GC-MS and 120 to 497 \(\mu\)g/L by SPMESH sheet-DART-MS. These values are within the range for linalool found in Muscat grapes during ripening: approximately 60 to over 1000 \(\mu\)g/L from veraison to ripeness. A moderate correlation (\(R^{2}=0.66\)) was observed between SPME-GC-MS and SPMESH sheet-DART-MS measurements (Figure 5). This correlation was comparable to our previous work using the \"one-shot\" SPMESH card and DART-MS [20]. Variation among detectable samples was 18% CV. As with the IBMP, SPMESH sheet-DART-MS measured higher concentrations than SPME-GC-MS for high linalool samples (slope = 0.52). We observed a similar phenomenon in our earlier SPMESH work [20] which at the time we credited to isobaric interferences from other monoterenes. To evaluate this hypothesis, we re-evaluated the \(m/z=137\) traces from all SPME-GC-MS analyses. Although linalool was the major peak in all traces, we identified several other minor peaks corresponding to other well-known monoterenes (e.g., linalool oxide, geraniol, \(\alpha\)-terpeniol). We then summed the areas of all \(m/z=137\) peaks, or of the four major peaks (linalool, linalool oxide, geraniol, \(\alpha\)-terpeniol) for each SPME-GC-MS analysis and plotted these values against SPMESH-DART-MS data. However, this resulted in worse correlation (\(R^{2}=0.4\) for all peaks, \(R^{2}=0.46\) for four major peaks; See Supplementary Figure S1). Thus, the correlation between SPMESH-DART-MS and SPME-GC-MS for linalool does not appear to be limited by interferences, although an alternative explanation is lacking at this time.  
Figure 4: DART-Orbitrap-MS(-MS) chronograms of extracted ions for **(left)** IBMP 167 - \(>\) 124.0632 m/z and \(d_{3}\)-IBMP 170 - \(>\) 127.0818 \(m/z\) of a Cabernet Sauvignon homogenate (average concentration = 20 ng/L by SPMESH sheet-DART-MS-MS; 24 ng/L by SPME-GC-MS) and **(right)** linalool 137.1326 \(m/z\) and \(d_{3}\)-linalo 140.1512 \(m/z\) in Muscat of Alexandria juice (386 \(\mu\)g/L by SPMESH sheet-DART-MS; 353 \(\mu\)g/L by SPME-GC-MS). Samples were run in triplicate wells, with blank wells in between.
# Linking Phubbing Behavior to Self-Reported Attentional Failures and Media Multitasking
## 1 Introduction
### Past Research
For instance, individuals who frequently phub may also exhibit less attentional control and experience persistent instances of attentional lapses, mind wandering, and cognitive errors as a result of these failures of attention. By evaluating phubbing with a cognitive framework focused on attention and multitasking in particular, we can build more comprehensive profiles of those who are more likely to engage in this behavior. Phubbing also seems very similar to another technology use and multitasking behavior: media multitasking, which involves concurrently using or switching between multiple media activities (e.g., texting while watching TV) [25; 26]. Both phubbing and media multitasking involve paying attention to a media activity either during a social interaction or alongside another media activity. In addition to being conceptually related, these two behaviors also share the same directional relationships across multiple self-reported psychosocial, psychological, and personality constructs, including sensation seeking [14; 27; 28; 29], fear of missing out [6; 7; 13; 29], general anxiety [13; 30], and neuroticism [13; 31]. Moreover, media multitasking has been extensively investigated through a cognitive framework. Frequent media multitaskers report impaired abilities to monitor and switch tasks [32; 33; 34], though some studies have found no association between media multitasking, perceived distractibility, and difficulties in shifting attention [35]. Frequent media multitaskers have also reported more frequent occurrences of mind wandering [35; 36], attentional lapses [35], and cognitive failures as a result of such attentional lapses [35; 37], as well as less overall mindful attentional awareness [36; 37]. Because phubbing and media multitasking appear to be very similar, it is plausible to use the results from media multitasking to infer what self-reported attentional abilities may be implicated in phubbing behavior. If phubbing were to involve the same attentional abilities as media multitasking, this could suggest a common or shared underlying mechanism among the two behaviors. This would also be supported if the same people who frequently engage in phubbing behavior are also likely to engage in media multitasking behavior, implying that these two behaviors are directly related. Investigating the indirect and direct links between phubbing and media multitasking can help us better characterize both behaviors and can contribute to the primary aim of our study.
# YOLO-SE: Improved YOLOv8 for Remote Sensing Object Detection and Recognition
## 4 Experimental and Results Analysis
### Experimental Setup and Evaluation Metrics
#### 4.1.2 Evaluation Metrics  
In target-detection tasks, metrics such as recall (R), precision (P), and average precision (AP) are commonly used for evaluation. Recall represents the proportion of correctly detected targets out of the total number of targets, calculated using the formula shown in Equation (4).  
Precision represents the proportion of correctly detected targets out of the total predicted targets, as shown in Equation (5).  
\[R=\frac{TP}{TP+FN}\times 100\% \tag{4}\]  
\[P=\frac{TP}{TP+FP}\times 100\% \tag{5}\]  
where _TP_ represents the count of correctly identified targets, _FP_ is the count of erroneously detected targets, and _FN_ is the count of targets that remain undetected. The Average Precision (AP) is the measure of the area under the Precision-Recall (_P-R_) curve, where recall is plotted on the x-axis and precision is plotted on the y-axis. The calculation formula for Average Precision (AP) is given by Equation (6).  
\[\text{AP}=\int_{0}^{1}\text{P}_{\text{i}}(\text{R}_{\text{i}})\text{dR}_{ \text{i}} \tag{6}\]  
Figure 8: The distribution of targets in the SIMD dataset: (**a**) the distribution of the number of categories; (**b**) the distribution of target width and height; the color gradient from white to blue (from light to dark) signifies a more concentrated distribution.  
To obtain the mean Average Precision (mAP) for multiple classes, the AP values for each class are averaged. The formula for calculating mAP is as shown in Equation (7).  
\[mAP=\frac{\sum_{i=1}^{C}AP_{i}}{C}\times 100\% \tag{7}\]  
In this paper, [EMAIL_ADDRESS] is used as the primary evaluation metric for precision. For convenience, throughout the rest of this paper, [EMAIL_ADDRESS] will be referred to as AP0.5.
# Research Progress on Machine Learning Prediction of Compressive Strength of Nano-Modified Concrete
## 3 Common Algorithms for Machine Learning
### Decision Tree (DT)  
A decision tree (Figure 7) maps input features to outcomes through step-by-step splits. It is simple to understand, needs little data preparation, and works well for rule extraction. In nano-concrete studies (Figure 6), it has been used to identify patterns, but tends to overfit complex interactions (R\({}^{2}\) gap > 0.25 in many cases) and performs poorly with high-dimensional inputs. Random forests [42], which combine hundreds of decision trees with bootstrapping and random feature selection, offer better stability and prediction accuracy.  
Figure 6: KNN schematic diagram.  
Figure 7: Decision tree diagram.  
To prevent overfitting in decision trees, the following strategies are considered:  
1. Limit the depth of the tree (e.g., using max_depth) to avoid overly complex models;
2. Set minimum samples for splitting and leaf nodes (e.g., min_samples_split and min_samples_leaf) to prevent overly fine splits;
3. Control the maximum number of leaf nodes (max_leaf_nodes) to restrict tree size;
4. Apply pruning techniques (such as setting ccp_alpha for cost complexity pruning) to remove redundant branches;
5. Use ensemble methods (like Random Forest and Gradient Boosting Trees) to reduce variance and improve generalization;
6. Clean the data and select relevant features to eliminate noise and reduce the risk of overfitting;
7. Increase the amount of training data or use cross-validation to enhance model robustness and generalization ability.  
However, decision trees can be sensitive to irrelevant or noisy features in high-dimensional spaces, which may lead to unstable model structures and unreliable predictions. To address this issue, we propose the following solutions: (1) performing feature selection or dimensionality reduction to retain the most informative features; (2) using ensemble methods such as Random Forest or Gradient Boosting Trees to reduce the instability of individual trees; and (3) incorporating regularization techniques, such as limiting the tree depth or setting minimum samples for splits, to control model complexity.