esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of University Students' Behavior Based on a Fusion K-Means Clustering Algorithm
## 1 Introduction
[PERSON] proposed a way to optimal cluster centers for detecting images [30]. The clustering by fast search and find of density peaks (CFSFDP) clustering algorithm was proposed in 2014 [31]. The clustering algorithm takes into account the characteristics of clustering centers and data. The algorithm is simple in logic, but the clustering result is much better than the traditional clustering algorithm. However, the efficiency of the algorithm is low. It is unable to meet the requirement of a large amount of data clustering time. There are also some other clustering algorithms [32; 33; 34; 35; 36; 37]. [PERSON] considered the effects of resampling and adaptive methods on the clustering effect [38]. [PERSON] selected categories based on a new cluster stability measure [39]. [PERSON] combined the ant colony algorithm and clustering algorithm [40]. Some studies of clustering algorithms considered the weight [41; 42; 43]. The application of fuzzy clustering is also becoming more and more widespread [44; 45; 46; 47]. The algorithms above can get better clustering results, however, based on the recommendation system to select cluster centers, the efficiency is very low. Aiming to improve the disadvantages of both K-Means and CFSFDP in application, we proposed a new algorithm to describe the students' behavior. The innovations and contributions of this research are as follows:  
1. We applied the relevant theories and knowledge of data mining and machine learning to the analysis of university students' behavior. This is an application innovation in the field of machine learning and education. 2. Compared with the traditional behavior analysis model of college students, this study used data for analysis, which reduces the subjectivity of human judgment and avoids prejudice caused by preconceptions. Therefore, the analysis results of this study are more objective. 3. Most current research used the K-Means algorithm, but the number of student categories and cluster centers are difficult to determine. The K-Means and clustering by fast search and find of density peaks (K-CFSFDP) proposed in this study can automatically determine the number of student behavior types and typical representatives based on data. Therefore, K-CFSFDP has high flexibility and wide applicability, and it also avoids human intervention in the clustering process. 4. The K-CFSFDP proposed in this research did not completely rely on the CFSFDP framework, instead, it improved CFSFDP in some aspects. Therefore, the running time is shorter and the running efficiency is higher, which has application advantages in the environment of campus big data.
# Recent Advances in Optical Detection of Aminoglycosodies
## 3 Colorimetric Assays
### Colorimetric Reactions Used in AG Detection  
Peroxidase-catalyzed chromogenic reactions are commonly used in immunoassays of AGs. Tetramethyl benzidine (TMB), 2,2'-azinobis-(3-ethylbenzthiaazoline-6-sulphonate) (ABTS), and _o_-phenylenediamine (OPD) are chromogenic substrates of peroxididases. The peroxidase-catalyzed redox reaction between these substrates and H\({}_{2}\)O\({}_{2}\) leads to the production of colored products. Afteradding sulfuric acid to terminate the reaction, the color of TMB, ABTS, and OPD reactions are yellow, green, and orange, with maximum absorption wavelengths at 450, 405, and 492 nm, respectively. Horseradish peroxidase (HRP) is one of the most commonly used peroxididases and is commonly used as a label in competitive ELISA for the detection of AGs [45]. In addition to HRP, DNAzyme with a G-quadruplex structure and nanoparticles such as Au NPs, Ag NPs, Fe3O4 NPs, Au NCs, etc., has been reported to have peroxidase-like activity [46]. These mimetic peroxididases have been employed for the development of colorimetric assays for AGs [47]. Typically, DNAzyme-catalyzed colorimetric reactions have been coupled with amplification methods to improve the detection sensitivity, which will be discussed in the section Amplification Strategies.  
Phenanthroline (Phen) and its derivatives, with bidentate heterocyclic ligands, form metal complexes with stable transition metal ions in lower oxidation states, such as Ru(II), Cu(I), Co(II), and Fe (II). The Fe(II)-Phen complex shows an orange-red color in a pH 7.4 solution. The oxidation of Fe (II) to Fe (III) changes the color of the Fe(II)-Phen complex from orange-red to light yellow. The Fe(II)-Phen reaction can be coupled with a glucose oxidase-glucose reaction, in which glucose reacts with glucose oxidase to produce H2O2. The produced H2O2 oxidizes Fe (II) to Fe (III), leading to the color change of the complex ion [Fe(phen)3]2+ from orange-red to light yellow in pH 7.4 solution [48]. The application of the Fe(II)-Phen reaction will be discussed in the section Amplification Strategies.  
Noble metal NPs, including Au NPs, Ag NPs, Pt NPs, etc., possess unique optical properties. For example, spherical Au NPs with a size of 15 nm has a wine-red color, and the characteristic SPR absorption band is located around 520 nm. The larger the Au NPs' size, the longer the SPR absorption wavelength is. Au NPs have the property of distance-dependent color change. The color of Au NPs changes from red to blue since Au NPs approach each other at a distance shorter than a diameter of one nanoparticle. In other words, the well-dispersed Au NPs have a red color, while the aggregated Au NPs have a blue color [49]. Based on the aggregation-induced color change of Au NPs, various colorimetric assays of aminoglycosides have been developed. The following section discusses colorimetric strategies based on metal NPs.
## 5 Deep Learning
### One-Stage and Two-Stage Approaches  
The models presented may be implemented to solve different problems. As for the detection and classification of defects, two approaches are distinguished:  
* One-stage approaches or classification-based methods. Detection and classification (for example, of defects) is carried out simultaneously in a single stage. The main objective of the approach and its greatest advantage is the detection and classification in real-time. The disadvantage of this approach, compared to the two-stage approach, is that its accuracy is significantly lower. Therefore, it is focused on tasks that must be agile or fast and that do not require high accuracy. An example is the YOLO (You Only Look Once) architecture. YOLO can use different CNN models as a backbone such as VGGNet and GoogleNet.
* Two-stage approaches or region proposal-based methods. The detection and classification tasks are carried out separately. First, a network generates proposals of regions for object detection, and then a different network is fed with those region proposals to definitively locate and classify the object (in our particular case, the defect). Since the detection and classification task is executed in two stages, the time required to perform them is greater than in the single-stage approach. Despite this increase in time, the approach is very popular for tasks that require high accuracy. This approach can be considered for future works if, for instance, the location of the defect is not sufficiently clear. Along the first stage, the defects would be located, while in the second one they would be classified. Further information can be found in [71]. An example of this approach is region-based CNN (R-CNN) and its variants, considered one of the best architectures in terms of accuracy.
## 4 Analysis of On-Site Monitoring Results
### On-Site Monitoring
#### 4.1.2 Control Parameters  
In line with the \"Code for Monitoring Measurement of Urban Rail Transit Engineering\" (GB 50911--2013) [48], the \"Management Measures for Urban Rail Transit Construction Projects (Implementation) by Hohhot Urban Rail Transit Construction Management Co., Ltd.\", and the monitoring plan for this project, the cumulative settlement and deformation value for the rectangular pipe jacking was established at 20 mm.
# Grid Anchor Lane Detection Based on Attribute Correlation
## 4 Experimental Results  
### Experimental Setting  
#### 4.1.1 Datasets and Evaluation Metrics  
In this study, the proposed lane detection algorithm is trained and tested on the Tusimple [21] and CULane [13] lane detection benchmark datasets. Table 3 presents the specific information of the two datasets. The Tusimple dataset primarily includes urban roads and highways, while the CULane dataset presents diverse and challenging scenarios such as shadow occlusion, nighttime, and rainy weather, allowing for the evaluation of the model's ability to recognize difficult situations and its generalization capability.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Train** & **Test** & **Image Size** & **Lanes** \\ \hline CULane & 88,880 & 34,680 & 1640 \(\times\) 590 & \(\leq\)4 \\ Tusimple & 3626 & 2782 & 1280 \(\times\) 720 & \(\leq\)5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Outline of the specific parameters of each dataset.  
To quantify the model's performance on the two datasets, both CULane and Tusimple use the \(F1\) score for evaluation, calculated as follows:  
\[F1=\frac{2\times Precision\times Recall}{Precision+Recall}, \tag{26}\]  
where \(Precision=\frac{T_{pred}}{T_{pred}+F_{pred}}\), \(Recall=\frac{T_{pred}}{T_{pred}+M_{pred}}\), \(T_{pred}\) represents the number of correctly predicted lane lines, \(F_{pred}\) represents the number of incorrectly predicted lane lines, and \(M_{pred}\) represents the number of undetected lane lines. However, the criteria for determining correct predictions differ between the two datasets. In the CULane dataset, the width of each lane line is 30 pixels, and a predicted lane line is considered correct if the Intersection over Union (IoU) with the ground truth exceeds 0.5. Otherwise, it is considered an incorrect prediction. In the Tusimple dataset, the number of correctly predicted points within a lane line is first calculated. A predicted point is considered correct if its deviation from the ground truth is within 20 pixels. If the ratio of correctly predicted points within a lane line exceeds 0.85, the predicted lane line is considered correct.  
Additionally, in the Tusimple dataset [21], the proportion of correctly predicted points, the False Positive Rate (\(FP\)), and the False Negative Rate (\(FN\)) are also evaluation metrics. Their calculation formulas are as follows:  
\[Accuracy=\frac{\sum_{clip}C_{clip}}{\sum_{clip}S_{clip}},\,FP=\frac{F_{pred}} {F_{pred}+T_{pred}},\,FN=\frac{M_{pred}}{T_{pred}+M_{pred}}, \tag{27}\]  
where \(C_{clip}\) and \(S_{clip}\) represent the number of correctly predicted points and ground truth points in a \(clip\), respectively.
# Motion Trajectory Prediction in Warehouse Management Systems: A Systematic Literature Review
## 6 Discussion
### Practical Implications  
The transformative potential of trajectory prediction in Warehouse Management Systems (WMS) is undeniable, with the promise of enhanced efficiency and safety. However,its practical realization is tethered to a series of essential considerations. A primary concern in the field is the lack of a standardized dataset for trajectory prediction. While the SDD and ETH/UCY datasets offer in-depth information, their vast size makes them challenging for less powerful machines. This lack of uniformity in dataset usage often impedes the consistent evaluation of different methods, stressing the need for a move towards a more standardized approach.  
Moreover, many trajectory prediction methods, while showcasing stellar performances in controlled benchmarks, falter in real-world settings. For instance, the PECNet's excellence on standard benchmarks is compromised when introduced to even slight noise in real-world scenarios. This divergence between controlled tests and real-world applicability underscores the need to rigorously test these methods in genuine environments.  
Open-source practices have the potential to revolutionize the domain. Especially within deep learning trajectory prediction methods, the increasing trend of providing access to code repositories is a step in the right direction. Such transparency not only ensures method reproducibility but also alleviates the need for manual implementations. This openness can significantly expedite the evaluation and comparison of various methods, fostering collaboration and innovation.  
In the bustling arena of WMS, the inclination leans towards simpler, more efficient models over complex ones. The rationale is grounded in real-world demands. Simpler models typically provide faster inference, which is crucial for on-the-spot decision-making in warehouse settings. Moreover, with the rise of IoT devices in modern WMS, data intake has exponentially increased. In the face of this data deluge, complex models might be overwhelmed, potentially delaying real-time responses.  
An enlightening revelation from the study of trajectory predictions is the significance of human target positions. By incorporating an understanding of a person's intended destination, prediction algorithms can vastly improve their precision. Especially in structured settings such as warehouses, human movement is often driven by a clear end goal. Recognizing and integrating this intent can greatly simplify and enhance the trajectory prediction process.  
In essence, for trajectory prediction in WMS to transition from theoretical propositions to pragmatic, actionable tools, these considerations need to be at the forefront of research and implementation endeavours.
# An Approach to Multi-Objective Path Planning Optimization for Underwater Gliders
## 4 Results
#### 4.1.1 Scenario 1 (NS Overlapped)  
Configuration 17, corresponding to PS 100, CP 0.9, and MP 0.15, produces the best result in this scenario, according to Figure 7.  
From a post-hoc procedure [60], Table 6 gives the Bergmann-Hommel test adjusted \(p\)-values compared to case 17. Considering a critical value of \(\alpha=0.05\), the first 21 configurations perform significantly worse than case 17.  
The more conservative Bonferroni test produces the adjusted \(p\)-values presented in Table 7. According to the critical value \(\alpha=0.05/27\), the first 12 configurations perform significantly worse than case 17.  
Figure 12 shows the evolution of the median run hypervolume for configuration 17 and also for configurations 9 and 26, that have same probability values for population sizes 40 and 100.  
Figure 11: Examples of optimized trajectories for overlapping (**left**) and separated (**right**) obstacles scenarios.  
Figure 12: Hypervolume evolution for configurations 17, 9, and 26 (median run) in scenario 1.  
Figure 13 presents the non dominated front for median run for the same 17, 9, and 26 cases. The horizontal axis presents the objective function distance to target (\(f(1)\)) and the vertical axis presents the distance to obstacle safety radius (\(f(2)-safRad\)); negative values indicate that the glider trajectory crossed the safety radius of the obstacles.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Rank** & **Case** & **P Bergmann–Hommel** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 6: Adjusted \(p\)-values Bergmann–Hommel procedure for scenario 1 (ref. c17).  
Figure 13: Non dominated fronts for configurations 17, 9, and 26 (median run) in scenario 1.  
Table 8 details the optimized variable values (incremental headings) for the individuals and trajectories previously illustrated for scenario 1.
# Detecting Lithium (Li) Mineralizations from Space: Current Research and Future Perspectives
## 2 Li Exploration Using Remote Sensing
### Advancements in Satellite-Based Remote Sensing
This deposit would have been formed by a pervasive metasomatism that resulted in a target area of approximately \(250\times 500\) m [75] which means that the spatial resolution of the sensors would no longer be a problem as in pegmatic deposits. Additionally, the metasomatic formation of white mica in other mineral deposits is usually accompanied by wavelength changes of the 2200 nm absorption feature that can be used to target mineralizations using hyperspectral sensors [4]. A similar approach could be applied to detect Li micas in metasedimentary rocks.
## 1 Introduction  
The Global Navigation Satellite System (GNSS), which is a crucial means for navigation and positioning for both civil and military applications, is playing an increasingly significant role in modern society. However, the GNSS signals are extremely fragile and vulnerable, and can easily be influenced by interferences [1]. Therefore, interference monitoring techniques are exceedingly essential for improving the anti-interference performance of GNSS receivers. GNSS interference monitoring must be accurate and real-time. There are plenty of common methods for interference monitoring, such as the hypothesis testing method, method based on characteristic parameter threshold, integrated navigation method, and smart antenna technology.
# Numerical Simulation of Heat Transfer Performance of a Dimpled Tubular Heat Exchanger
## 4 Calculation and Analysis
### Heat Transfer Performance of Dimpled Tubes with Different Dimple Sizes
#### 4.2.1 Heat Transfer Performance of Different Dimpled Tubes  
The heat transfer performance of the cell tube was evaluated by the Nusselt number \(Nu\). Obviously, as shown in Figures 6 and 7, the dimpled structure significantly improves the heat transfer performance. \(Nu\) of dimpled tubes with different structures increases more than twice as much as that of smooth tubes at different Reynolds numbers, regardless of tube side or shell side. In the studied range, the heat transfer performance increases linearly with the Reynolds number. Under the same Reynolds number, the larger the cell size, the stronger the heat transfer performance. Compared with other sizes, the heat transfer performance of the 14 mm dimple structure is significantly increased. Compared with the smooth tube, the protrusion dimple enhances the heat transfer by 1.94-2.74 times in the tube side, and the concave dimple enhances the heat transfer by 1.50-1.87 times in the shell side. The enhancement effect is increasing with Reynolds number.  
Figure 5: Temperature distribution.  
Figure 6: Variation of Nusselt number in tube with Re.
# Clinical Applications of Optical Coherence Angiography Imaging in Ocular Vascular Diseases
###### Abstract  
Optical coherence tomography angiography (OCTA) provides us with a non-invasive and efficient means of imaging anterior and posterior segment vasculature in the eye. OCTA has been shown to be effective in imaging diseases such as diabetic retinopathy; retinal vein occlusions; retinal artery occlusions; ocular ischemic syndrome; and neovascularization of the iris. It is especially useful with depth-resolved imaging of the superficial, intermediate, and deep capillary plexi in the retina, which enables us to study and closely monitor disease progression and response to treatment. With further advances in technology, OCTA has the potential to become a more widely used tool in the clinical setting and may even supersede ocular angiography in some areas.  
Keywords:optical coherence tomography angiography; diabetic retinopathy; retinal vein occlusion; retinal artery occlusion; ocular ischemic syndrome; neovascularization; iris neovascularization +
Footnote †: journal:  
ophthalmology for decades. Current commercially available angiography platforms have the potential to provide a wide field of view and dynamic visualization of blood flow. Leakage of fluid or blood can be observed as hyperfluorescence and may be useful to help monitor disease progression and response to therapy associated with various vascular pathologies [3]. ICG can also be used to assess anterior segment vasculature and is effective where there is inadequate vessel delineation on slit lamp photography [11]. However, both of these methods are invasive, expensive, and time-consuming. The dye used also poses risk of allergy and ICG is contraindicated in pregnancy and kidney disease. Two types of dye are also required to image both retina and choroidal vessels. As the image obtained is two-dimensional, lesions observed are not depth-resolved. Also, while leakage provides useful information, in some chronic, extensive retinal and choroidal diseases, the leakage may not be able to be distinguished clearly or be obscured by blood making the diagnosis ambiguous [3; 4; 7; 12]. Furthermore, leakage of vascular lesions in conventional angiography may differ depending on the exact stage when the lesion is imaged. This makes it challenging to quantify the actual vascular pathology during disease progression [7].  
Chronic systemic vascular diseases such as diabetes mellitus, hypertension, and hyperlipidemia are an increasing burden on healthcare worldwide [13]. These are risk factors for developing ocular vascular diseases, which are often sight-threatening [14; 15; 16]. This review will focus on the clinical applications of anterior and posterior segment OCTA in the diagnosis and disease monitoring of diabetic retinopathy (DR), retinal vein occlusion (RVO), retinal artery occlusion (RAO), and ocular ischemic syndrome (OIS).
## 3 Using IC-BEVS for AAV Production
### Disadvantages of rAAV Production Using Bevs  
The IC-BEVS provides an efficient and economical AAV production method, but there are still some limitations compared with the mammalian cell system in some aspects. The first is post-translational modification. Mammalian cells can undergo complex glycosylation, and the glycosylation pattern of insect cells is significantly different from that of mammalian cells. This difference may affect the stability and functionality of AAV particles [83; 84]. For example, the surface glycoprotein of AAV may lack the necessary glycosylation in insect cells, thus affecting its transduction efficiency to target cells [27; 85]. The second is particle composition and functionality. AAV particles produced by mammalian cells usually have uniform structure and high purity due to their complex assembly and modification mechanisms, while AAV particles in insect cells may have different protein components, with structural and functional differences, which may affect their performance and long-term stability in vivo [35]. Finally, in terms of protein folding and function, although the insect cell system can effectively express recombinant proteins, in some cases, the resulting proteins may be different from proteins in mammalian cell systems in terms of folding and function. This may become a problem for AAV vectors that require highly precise folding and function.  
The differences between Sf9 cell- and HEK293 cell-produced rAAVs have been intensively investigated [35; 83; 85; 86]. It is hard to judge which rAAV production system is absolutely better in all cases. Given that the IC-BEVS system is currently undergoing iterative technological advancements [32; 87], it will play more important role for the production of rAAV.