esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved CNN Model for Within-Project Software Defect Prediction
## 8 Conclusions and Future Work  
We proposed an improved CNN model which could better learn semantic representations from source-code ASTs for WPDP. On the basis of [PERSON]'s CNN model, we made further improvements by enhancing global pattern capture ability and improving the model for better generalization.  
To verify that the CNN model could outperform state-of-the-art methods, we performed two experiments. The first experiment was performed on the SPSC dataset to demonstrate that our model was comparable to the existing CNN model for WPDP. The results showed that our model improved the existing CNN model by 2.2% in terms of F-measure, and statistical hypothesis tests showed that our method was comparable to the existing DBN and CNN models, and significantly better than the traditional baseline. The second experiment was performed on the PSC dataset, which was designed especially for AST-based features extracted from the source code to validate that our model could outperform the state-of-the-art machine learning models for WPDP. The results showed that our model improved the F-measure by 6%, the G-measure by 5%, and the MCC by 2% as compared with the best-performing machine learning models among DT, RF, LR, NB, and NET. Statistical hypothesis tests show that our method was almost significantly better than other machine learning models in terms of the evaluation metrics above, except for MCC, for which our model was comparable to the RF model.  
On the basis of our experimental results, we also proposed the concept of hyperparameter instability to describe a model whose performance is significantly different from the same model of different hyperparameter settings in terms of each version or project, however, it performs similarly on average. Our improved model responded negatively to hyperparameter instability, which posed a threat to the robustness of the model, however, also suggested that deep ensemble models may enhance defect prediction.  
In the future, we would like to collect more C/C++ open source projects and build new datasets for deep-feature-based defect prediction. In addition, it would be promising to use other kinds of deep models such as RNN to generate features for predicting defects automatically. Determining what types of defects could be predicted in deep learning-based defect prediction is also essential. Lastly, ensemble methods of deep learning models for defect prediction could also be a future research direction.  
Conceptualization C.P. and B.X.; data curation C.P. and H.G.; methodology C.P.; supervision M.L.; writing--original draft C.P.; revise--original draft B.X. and H.G.; writing--review and editing C.P. and M.L.  
This research received no external funding.  
The authors declare no conflict of interest.
## 4 Analysis and Comparison of Results
### Comparison of Acceleration Response Spectra
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Type**} & \multicolumn{2}{c}{**Direction**} & \multicolumn{2}{c}{**EQ1**} & \multicolumn{2}{c}{**EQ2**} & \multicolumn{2}{c}{**EQ3**} \\  & & & **1-10 Hz** & **10-20 Hz** & **20-20 Hz** & **1-10 Hz** & **20-20 Hz** & **50 Hz** & **1-10 Hz** & **20-20 Hz** & **50 Hz** \\ \hline \multirow{4}{*}{A1} & & X & 1.020 & 1.561 & 0.704 & 2.047 & 3.145 & 1.443 & 3.040 & 4.568 & 2.269 \\  & & Y & 1.040 & 1.557 & 0.658 & 2.113 & 3.014 & 1.390 & 3.129 & 4.474 & 2.177 \\  & & Z & 0.722 & 1.259 & 0.640 & 1.501 & 2.758 & 1.589 & 2.305 & 4.571 & 2.635 \\ \hline \multirow{4}{*}{Test} & & X & 1.114 & 2.409 & 0.955 & 2.253 & 5.351 & 2.215 & 3.425 & 9.141 & 5.196 \\  & & Y & 1.139 & 2.575 & 0.943 & 2.309 & 5.582 & 2.117 & 3.465 & 10.580 & 5.240 \\  & & Z & 0.727 & 1.637 & 0.785 & 1.503 & 3.291 & 1.707 & 2.318 & 6.509 & 7.264 \\ \cline{2-11}  & & & X & 1.191 & 3.424 & 1.224 & 2.390 & 8.275 & 3.048 & 3.759 & 14.642 & 7.078 \\  & & Y & 1.234 & 3.620 & 1.267 & 2.504 & 8.459 & 3.248 & 3.890 & 17.640 & 8.846 \\  & & Z & 0.722 & 1.606 & 0.772 & 1.486 & 3.274 & 1.886 & 2.367 & 6.269 & 5.521 \\ \cline{2-11}  & & & X & 1.262 & 4.422 & 1.504 & 2.580 & 11.012 & 4.061 & 4.072 & 20.477 & 10.061 \\  & & A4 & Y & 1.302 & 4.584 & 1.564 & 2.647 & 11.156 & 4.246 & 4.303 & 23.979 & 10.976 \\  & & & Z & 0.716 & 1.617 & 0.794 & 1.503 & 3.441 & 1.907 & 2.355 & 6.369 & 6.726 \\ \cline{2-11}  & & & X & 1.036 & 1.775 & 0.842 & 2.082 & 3.549 & 1.734 & 3.113 & 5.272 & 2.402 \\  & & Z & 0.782 & 1.083 & 2.143 & 0.794 & 2.175 & 4.113 & 1.570 & 3.246 & 6.149 & 2.432 \\  & & Z & 0.728 & 1.419 & 1.115 & 1.599 & 3.087 & 2.240 & 2.312 & 4.731 & 3.764 \\ \cline{2-11}  & & & X & 1.109 & 2.261 & 0.940 & 2.211 & 4.400 & 1.797 & 3.314 & 6.706 & 2.756 \\ \cline{2-11}  & & Y & 1.178 & 3.009 & 1.020 & 2.378 & 5.854 & 1.983 & 3.539 & 9.036 & 3.166 \\  & & Z & 0.720 & 1.289 & 0.857 & 1.518 & 2.872 & 1.703 & 2.258 & 4.512 & 3.248 \\ \cline{2-11}  & & & X & 1.176 & 2.683 & 1.058 & 2.361 & 5.233 & 2.169 & 3.485 & 8.105 & 3.362 \\ \cline{2-11}  & & A4 & Y & 1.257 & 3.770 & 1.259 & 2.534 & 7.540 & 2.409 & 3.791 & 11.668 & 4.007 \\  & & Z & 0.727 & 1.340 & 0.810 & 1.493 & 2.906 & 1.701 & 2.279 & 4.513 & 3.075 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Calculation results for average over the frequency range and R\({}_{\rm{Avg}}\).
## 6 Simulation of Microdroplet Injection Process
### Effects of Velocity Amplitude (\(v_{max}\)) on Jetting Quality  
The vibrational velocity of the piezoelectric actuator is a primary factor determining whether a regular or a spherical droplet will be formed. It also determines the maximum jetting frequency of the printhead. Figure 8 illustrates the effects of vibrational velocity on microdroplet formation and jetting. Table 2 summarizes the results obtained with each value of \(v_{max}\).  
Figure 8 shows that a microdroplet will not be formed if the vibrational velocity of the piezoelectric actuator is too low. At \(v_{max}=1.8\) m/s, the droplet could not detach from the nozzle as the change in volume in the working chamber was too small. If the composite diaphragm deforms by the optimum amount, a droplet will be formed without a satellite droplet, which is indicative of good jetting quality (e.g., \(v_{max}=2.0\) m/s and \(2.2\) m/s; Figure 8b). However, if the deformation of the composite diaphragm is too large, a satellite droplet will be formed with the main droplet (i.e., \(v_{max}=2.5\) m/s). Furthermore, the satellite droplet will not merge with the main droplet. The presence of satellite droplets during actual production processes affect the dimensional accuracy and quality of the final product adversely. Figure 9 demonstrates the relationship between the single-nozzle flow rate and velocity amplitude.  
Figure 7: Droplet deposition process.  
In this simulation, the nozzle radius was varied to investigate how it affects droplet formation and jetting (Figure 10). At \(r=0.006\) mm and \(0.010\) mm, satellite droplets that could not merge with the main droplet were formed. Furthermore, at \(r=0.006\) mm, a long \"tail\" was formed instead of a regularly shaped droplet. At \(r=0.010\) mm, the droplet  
Figure 8: Changes in droplet jetting with different \(v_{max}\) values, (a) droplet states at \(t=60\)\(\upmu\)s with different \(v_{max}\); (b) jetting process at \(v_{max}=2.000\) m/s.  
separated to form many small satellite droplets. Although a satellite droplet was formed at \(r\) = 0.007 mm and 0.008 mm, the satellite droplet was able to catch up to the main droplet and merge to form a new droplet (the relationship between single-nozzle flow rate and nozzle radius is shown in Figure 11). Therefore, prior to the actual production process, the minimum standoff distance should be made larger than the merging distance of the satellite droplets.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Velocity** & & & & & \\
**Amplitude** & & & & & \\
**m/s** & & & & & \\ \hline
1.800 & No & — & — & — & 1.0636 \\ \hline
2.000 & Yes & No & — & — & 3.1662 \\ \hline
2.200 & Yes & No & — & — & 7.3410 \\ \hline
2.500 & Yes & Yes & No & Yes & 12.0763 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Droplet formation and jetting at different vmax.  
Figure 10: Droplet jetting states with different nozzle radii, (**a**) r = 0.006 mm; (**b**) r = 0.007 mm; (**c**) r = 0.008 mm; (**d**) r = 0.010 mm.  
Figure 9: Relationship between velocity amplitude and single-nozzle flow rate.
# Double Branch Attention Block for Discriminative Representation of Siamese Trackers
## 4 Results
### Algorithm Comparison
#### 4.2.2 VOT Benchmark  
VOT2016 [18] and VOT2019 [19] are widely used visual object tracking benchmarks. Each tracking benchmark contains 60 sequences with various challenging factors, and part of the video sequences is identical across the two benchmarks. Each frame in the video sequences is calibrated with the bounding box of a single target object. VOT benchmark adopts Accuracy (A), Robustness (R), and Expected Average Overlap (EAO) as measurement standards. A evaluates the tracker based on the IoU of the ground truth and the predicted bounding box. The larger parameter indicates the higher accuracy of the tracker. R is used to evaluate the stability of trackers. The larger the R, the worse the stability of the tracker, the easier it is to lose the target. EAO takes the overlap rate of bounding boxes and the re-initialization time after the tracker loses the target into account. EAO evaluates the overall performance of trackers. The larger EAO parameter indicates a stronger integrated strength of the tracker.  
We tested DBA-Siam on the VOT benchmark and compared it with the advanced trackers. As shown in Table 2, the proposed tracker achieves the best performance on A and EAO on VOT2016 and VOT2019. It shows that the proposed tracker has the highest accuracy for target positioning and the strongest overall performance.  
Compared with the CSTNet and SA-Siam, DBA-Siam is inferior on R. It is because DBA-Siam uses the fixed target branch features for more efficient tracking, which leads to relocating less efficiently. Even so, our overall performance is better. Specifically, DBA-Siam outperforms SA-Siam on EAO with gains of 42.6% on VOT2016 and 3.5% on VOT2019 and outperforms CSTNet on EAO with a gain of 18.9% on VOT2016.  
Thanks to the equipped DBA block, DBA-Siam surpasses the baseline SiamRPN, with 20.6% and 16.5% EAO improvement on VOT2016 and VOT2019.
## 3 Requirements for Visual Semi-automatic Alignment Approaches  
When automatic methods cannot fulfil the requirements in a satisfactory manner, it becomes necessary to include humans in the process. The combination of humans' general knowledge and the immense processing power of the human visual apparatus with the enormous storage capacity and computational power of computers has been proposed, and is an active area of research [22]: visual analytics is an emerging interdisciplinary field of research focusing on reasoning facilitated by interactive visual interfaces [23]. It has also been defined as a combination of automated discovery and interactive visualization [24], introducing what is known as the visual analytics mantra: \"analyze first; show the important; zoom, filter, and analyze further; details on demand\".  
Visual analytics has its roots in information visualization, which is an interdisciplinary field dealing with the interactive visual representation of large, abstract data sets. As these data sets do not have a \"natural\" representation in the real physical world, suitable abstract visual representations must be devised. Basic principles of information visualization are summarized in the older, well-known InfoVis mantra: \"overview first, zoom and filter, details on demand\" [25].  
For the design of visually supported alignment systems, both the visual analytics and information visualization principles have to be considered. While information visualization has a stronger focus on the presentation aspect, visual analytics can be considered more process resp. interaction oriented. Therefore, we derive user driven requirements and process driven requirements for alignment systems in the next subsections. Process driven requirements define steps and step sequences to be supported in the alignment process, and are closely related to the algorithms used. Each step utilizes different algorithms and usually requires different visual components to be conducted most effectively. On the other hand, user driven requirements impose constraints on the use of the process for an alignment task. This not only includes visual representations of ontologies and matching results, but also the establishment of collaboration and communication among users involved in the alignment task.
## 4 Analyses of the Applications of Digital Twin Models
### Medicine
#### 4.2.1 Application and Adoption of Digital Twin in Medicine
In another study on the application of Digital Twin in ophthalmology, [PERSON] et al. [88] illustrated how this technology aids in the early diagnosis of glaucoma, incorporatingsystems medicine and predictive medicine concepts. They showed that the Digital Twin not only facilitates the early detection of glaucoma but also identifies correlations among various parameters such as age, gender, and race. Based on these correlations, personalized treatment plans can be developed, enhancing the patient's quality of life. However, one limitation identified in the process of monitoring glaucoma progression [88] is that it requires the assistance of medical staff during periodic ophthalmological consultations. This limitation is due to the lack of sensors or medical devices capable of real-time intraocular pressure measurement, a crucial parameter for tracking this condition. In ref. [89], [PERSON] et al., proposed the initial development of a static model of the eye. This model aims to illustrate the structural and functional behavior of the human eye, enabling medical staff to identify various pathologies, provide personalized treatment, and simulate surgical interventions. For the construction of a comprehensive eye model, data will be collected from ophthalmology consultations and medical imaging techniques such as OCT (Optical Coherence Tomography), confocal microscopy, gonioscopy, and ultrasound. The integration of data from ophthalmological consultations, medical imaging, specific biomarkers of various eye diseases, and the application of prediction algorithms presents a significant challenge due to the difficulty in ensuring model accuracy. Additionally, the functionality of visual acuity testing using Digital Twins could lead to legal issues if mispredictions occur, particularly in situations where visual ability is a determining factor, such as in disability claims or the granting or revocation of driving privileges.
# Efficacy of High-Dose Polyclonal Intravenous Immunoglobulin in COVID-19: A Systematic Review
## 2 Material and Methods
### Search Methods  
For this systematic review we analyzed the medical literature for published articles on the use of IVIG in COVID-19 patients. A literature search of the MEDLINE (through PUBMED), EMBASE, SCOPUS, OVID and Cochrane Library electronic databases was carried out from 1 January 2020 to 31 August 2021, using English language as a restriction. Only articles published following a peer-reviewing process were included in the final analysis. The medical subject heading (MeSH) and key words used were: (\"COVID-19\" OR \"SARS-CoV-2\" OR \"coronavirus disease 2019\") AND (\"IVIG\" OR \"immunoglobulin\"). We also screened the reference lists of the most relevant review articles for additional studies not captured in our initial literature search.
## 3 IEEE 802.15.6: The Key-Enabling Standard for WBANs
### IEEE 802.15.6 MAC Layer  
There are three different types of nodes in typical WBANs, including one or more coordinators (which is normally more powerful in terms of energy and processing capabilities), then a sensor node (power-limited) and finally a relaying node (where a coordinator can be a relaying node as well). MAC layer is the core of the standard as it provides greater flexibility to the users to enable hybrid _Time-Division Multiple Access_ (TDMA), _Carrier Sense Multiple Access/Collision Avoidance_ (CSMA/CA) and the combination of both) medium access mechanism to meet the required applications requirements. Below we will explore detailed features and access mechanisms of the MAC protocol.
## 4 Designs of the Small-Scale Tower from Numerical Experiments
### Comparisons between Four-Tray and Empty Tower (Case A and Case C)
As a result, the radius of the liquid column of Case A is reduced more rapidly than that of Case C in Figure 12a, which is further illustrated in Figure 14 in the \(y\)-direction. The size of the liquid column is apparently larger for Case C without sieve tray. The region outside the liquid column is the uprising gas flow. It is basically located around the wall of tower with poor two-phase mixing and corresponds to a region of high SO\({}_{2}\) mass fraction. Therefore, the narrower liquid column for Case A incurs a larger area of uprising gas flow with high SO\({}_{2}\) mass fraction than that in Case C, as illustrated in Figures 12c and 13. Moreover, one can see that the distributions of high SO\({}_{2}\) regions are not symmetric. As the inlet of gas flow is at the right side of the tower as shown in Figure 3a, a portion of the gas flow has already encountered the liquid column at the region of \(y<0\) in Figure 3a before it enters the left side of the tower as y \(>0\). Consequently, the SO\({}_{2}\) mass fraction is higher on the right side than on the left side of the tower, as shown in Figure 13.  
To sum up, although the two-phase mixing within the liquid column for Case A is better than that of Case C in Figure 12c, the narrower liquid column in Figure 14 produces a larger area with high SO\({}_{2}\) mass fraction when the four sieve trays are implemented. These two competing effects end up with very comparable SO\({}_{2}\) removal efficiencies at outlet, as listed in Table 6 (57.1% and 56.9%).
## 3 Results  
### SSM Estimation from Sentinel-1 Images  
The established semi-empirical model was used to estimate the SSM values from the S1 SAR images combining the S2 vegetation indices. A simulated data set was established based on the WCM, and SSM was estimated by six modes of different polarization (VV and VH) and different vegetation indices (NDVI, NDWI, and MCARI). Figure 5 shows SSM mopping in a sample area. Nonagricultural land, such as buildings and roads, has a good removal effect (Figure 5). The variation trend of SSM obtained by the six modes was consistent in the same space. Figure 5d shows the highest SSM values, while Figure 5c shows the lowest. For the same vegetation index, the SSM values obtained by VH polarization were generally higher than those of VV polarization. Under the same polarization mode, the trend of SSM values obtained by the different vegetation indices is basically the same, which can be clearly seen in the circled part in Figure 5. Under VH polarization, the SSM values obtained by the different indices showed NDVI > NDWI > MCARI. However, under VV polarization, the SSM values obtained by the different indices showed little difference.  
Table 3 shows the estimation equation and accuracy of the model under the different polarization modes (VV and VH) and different indices (NDVI, NDWI, and MCARI). The number of training sets of the model is 91, and the fitting equations in the table are obtained by the least-squares method. As shown in Table 3, compared with the performances of S1 VV and VH polarization on SSM estimation, VV polarization obtained higher accuracy, and the accuracies obtained by NDVI, NDWI, and MCARI were 0.565, 0.576, and 0.588, respectively. Compared with S1 VV polarization, the accuracy of VH polarization was 0.2 lower. The accuracies of NDVI, NDWI, and MCARI were 0.336, 0.329, and 0.385, respectively. According to the fitting effect, it is preliminarily judged that the combination of the VV polarization mode and MCARI is most consistent with the real situation.
# Recent Progress Trend on Abrasive Waterjet Cutting of Metallic Materials: A Review
## 1 Introduction  
Cutting is the process of applying a force to penetrate or separate a workpiece [1]. With current advances in technologies, there has been a rapidly increasing demand for quality cut parts with complex geometries in the fabrication industry. Abrasive waterjet machining (AWJM) is an advanced technology that can be used for cutting processes. AWJM was developed from plain waterjet machines, wherein in 1980 abrasives were first added to plain waterjets to cut industrial material [2]. AWJM allows for versatility in machining an extensive range of materials, from the easiest to the most strenuous to cut. AWJM is recognized as an implicit solution for machining metallic and heat-sensitive materials without leaving a heat-affected zone (HAZ) or any residual stresses during the machining process [3]. Among the various advanced machining technologies, AWJM has exhibited significant emergence in manufacturing industries due to its extensive operations and exceptional quality of cut of intricate profiles with a minimum cutting force on the workpiece and yield of better dimensional accuracy due to insignificant distortion [4]. However, AWJM with proper process parameter settings remains a challenging procedure for manufacturers. Therefore, an enhanced understanding of AWJM mechanisms and modelling is needed to ensure more effective applications.  
This review aims to address gaps in existing studies by foregrounding the leading features of the abrasive waterjet (AWJ) cutting of metallic materials and providing significant, up-to-date research from the theoretical and experimental analysis. This paper is structured to present AWJM mechanism advantages and applications, and identifies limitations and current challenges faced by AWJ cutting, as well its process parameter functions and influences. Accordingly, recent process parameter improvements and optimizations are reviewed.
## 2 Related Works
The comparison between SURF and SIFT local feature is discussed further in Section 3. In recent years, researchers have proposed feature descriptors based on intensity such as binary robust independent elementary features (BRIEF) [24] and SYhnetic BAsis (SYBA) [25]. The BRIEF and SYBA descriptors are binary descriptors that consist of a binary string including the results of intensity comparison at random pre-determined pixel locations. These two descriptor methods employ faster feature detectors and provide lowering descriptor size than SIFT and SURF [26; 27]. For object recognition, BRIEF and SYBA outperformed both SIFT and SURF for high performances value. However, BRIEF and SYBA did not perform well when there is a large viewpoint change, invariance to rotation, and illumination changes [26; 27]. In other words, as the descriptor is mostly responsible for improving the feature detector by extracting rotation and illumination invariant descriptors, descriptors such as BRIEF and SYBA that are truly disassociated with any detector would be unable to enhance the capabilities of the detectors, thereby SURF outperformed both BRIEF and SYBA algorithms for high recall values used with orientation and illumination changes. For this reason, our work uses the SURF algorithm. In our work, to compare the similarity of two images \(Img_{1}\) and \(Img_{2}\), their corresponding SURF descriptor vectors \(V_{1}\) and \(V_{2}\) will be normalized. Then, by utilizing the principal component analysis (PCA) transform [22], lowering the dimensionality of vectors \(V_{1}\) and \(V_{2}\) can be minimized with a loss that has no effect on discriminative power. We will analyse the relationship between lowering dimension and search precision in Section 6. Finally, the similarity of \(Img_{1}\) and \(Img_{2}\) is evaluated by the secure cosine similarity.