esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Survey Area and Data  
### Survey Area  
A large-scale residential area, which is located in the suburbs of large cities in developing countries, is the focus of this study. Shanghai, one of the largest cities in China [25], is chosen to be the case area because it is a key instance of large-scale residential area development in the course of megacity suburbanization and redevelopment.  
The definition of a \"large-scale residential area on the megacity periphery\" is: (1) It is large, with a size of 2 km\({}^{2}\) and a population of thirty to fifty thousand; (2) It is primarily affordable residential housing; (3) It is located on the megacity periphery, which in the case of Shanghai, means outside of the Outer Ring Highway [25]. Among various large-scale peripheral residential areas, Jinhexincheng (Golden Crane New Area in Chinese) in western Shanghai was chosen based on two criteria: having affordable residential housing for relocatees and non-relocatees, and having a high occupancy rate.  
Jinhexincheng covers approximately 2 km\({}^{2}\) and has a population of approximately 28,000. It is located to the west of downtown Shanghai. Jinhexincheng has 10 housing projects overall, eight of which are for relocatees, and the others are commercial housing. The inhabitants have moved in since 2005.
# A New Concept of Instantaneous Whirling Speed for Cracked Rotor's Axis Orbit
## 6 Conclusions  
In this paper, a new concept of instantaneous whirling speed of axis orbit is defined and studied by a Jeffcott rotor model. The instantaneous whirling speed is a new attribute of axis orbit and a new perspective for the vibration analysis of cracked rotors. The addition of this new attribute significantly increases the effect of axis orbit for distinguishing normal and cracked rotor systems.  
The characteristic analysis shows that the relative whirling speed (RWS) of a cracked rotor system varies transiently and appears two maxima and two minima in a whirling cycle. The mechanism qualitative analysis shows that the transient variation of the RWS is caused by the influence of the work of the additional stiffness excitation on the normal conversion between kinetic energy and potential energy. In addition, the minimum value of the RWS decreases linearly with the increase of the crack depths, which can be a potential monitoring index for the severity of cracks.  
The explanation of the transient characteristics of axis orbit can increase the understanding of the dynamic characteristics of cracked rotors. The new analysis perspective and the new diagnosis index are potential supplements to the crack diagnosis.  
As we all know, most diagnostic methods and indicators have their applicable conditions or limitations, and so do the methods in this paper. First, the instantaneous whirling speed is a new additional attribute of axis orbit, thus this index is effective for steady-state rotor systems; hence, it is difficult to obtain the approximate circular axis orbit for unsteady rotor systems. Second, when compound faults exist, such as misalignment, oil whirl, rubbing, and other faults, these may lead to the confusion of instantaneous whirl characteristics, resulting in misdiagnosis of crack faults. In view of this, more types of diagnostic indicators should be studied to complement each other and improve the accuracy of the crack diagnosis.  
Conceptualization, J.X.; methodology, J.X.; formal analysis, Y.P.; investigation, J.X.; writing--original draft preparation, J.C.; project administration, Y.Z.; funding acquisition, J.X., Y.P., and Y.Z.  
This work was funded by the National Key Research and Development Program of China (No. 2017 YFC0805701), the National Natural Science Foundation of China (No. 51,905,054), the Scientific Research Starting Foundation of Central South University (No. 202,044,013).  
The authors declare no conflict of interest.
## 4 In-Depth Analysis and Results
### Qualitative Content Analysis
#### 4.1.2 Group 2: Theory of BIM-LPS Integration  
Besides the fundamental theoretical work of BIM-Lean interactions of [PERSON] and his co-authors [10; 85], the second group of the final literature sample regarding the theory of BIM-LPS integrations contains two main aspects (i) conceptual models and frameworks for integrations [1; 52; 64; 68; 76], and (ii) findings of applications in case studies of such [1; 53; 75; 92]. Some of the analyzed papers cover both aspects [1; 52; 82].  
Regarding the first aspect, we found examples for BIM-lean frameworks [68; 83], that propose to \"better use BIM models together with LPS\" [68]. These kinds of frameworks are basically instructions on where and when to take a look at the BIM model during the Last Planner(r) process to improve the process itself. In this very example, an Autodesk Revit(r) BIM model is used, which does not constitute an integrated BIM-LPS IT-system, but requires the use of external, purchasable software.  
Another piece of research of this aspect deals with the digital transformation on construction sites by means of mobile applications that enable simultaneously BIM practices and lean principles [52]. Here, the focus is on the delivery of BIM data on-site through tablets according to lean production principles. Also, here the framework is leaning towards commercial BIM software products. Other frameworks focus on prefabrication [64], the integration of BIM, Lean and Green concepts providing for the identification of operational and tactical relations [76] or the maturity of BIM adaption from stakeholder's perspective and its role on BIM-Lean interactions [82].  
Findings of BIM-LPS co-applications, on the other hand, indicate among other things that both Lean and BIM approaches are inextricably linked. However, this link is not fully explored yet [92], BIM can support LPS decision-making processes thanks to the availability of the right information at the right time [75] and the level of BIM maturity plays a significant role in enabling Lean principles on construction sites [82].
## 4 Soft-Collision M* (SC-M*)
### Completeness and Cost-Suboptimality  
A MAPP algorithm is complete if it guarantees that it will either return a path, or determine that no path exists in finite time. An algorithm is optimal if it guarantees returning an optimal path if such a solution exists. SC-M* is complete and suboptimal conditioned on the soft-collision constraint (i.e., \(P\left(Col_{j}|D^{j}\right)<T\), for a given collision threshold \(T\)).
# Vibration Signal Classification Using Stochastic Configuration Networks Ensemble
## 4 Performance Evaluation  
### Experimental Setup  
In this section, the vibration data are used for experimental comparison. The original collected bearing data are time-series vibration signals, and they are sampled via a sliding window with a fixed size. In this experiment, the window size is set to 1024 and used to obtain learning samples, then the Fourier transform and STFT are carried out to obtain samples of the frequency domain and time-frequency domain. The size of the frequency spectrum is 512, and that of the time-frequency image is \(33\times 33\). As shown in Figure 4, the samples of different fault modes in multiple domains are displayed. We can find that the signal of the normal sample is low frequency, while the samples of different fault locations have more high-frequency signals, and there are differences among them.  
We first evaluate the classification results with samples from different domains, and the dataset provided by the Bearing Data Center at Case Western Reserve University (CWRU) [36] is used for comparison. The bearing data are collected from the drive end of the electrical machining with 1797 r/min rotating speed and 12 KHz the sampling frequency. There are four modes in the CWRU dataset including normal, inner ring faults, outer ring faults, and rolling element faults. As shown in Table 1, because of the different bearing diameters, the dataset has ten different classes in total, including normal condition and nine different severity faults. In the learning process, the samples are randomly selected, with 80% as the training dataset and the remaining 20% as the testing dataset. For SCN and 2 DSCN, we take constructive sequence \(r=\{0.9,0.99,0.999,0.9999,0.9999\}\), \(\lambda=\{0.5,1,5,30,50,100\}\), training tolerance error \(\epsilon=0.00001\), maximum number of candidate nodes \(T_{max}=100\), and \(g\) is the sigmoid activation function.  
For the CWRU dataset, the samples from the time domain, frequency domain, and time-frequency domain are used to build the SCN models, respectively. The recognition rates of the SCN models in each domain are shown in Figure 5. The results show that the recognition rate increases with the increase in the number of hidden nodes L; in particular, the SCN model built with the frequency-domain samples obtains high testing accuracy with fewer nodes, followed by the SCN model built with the time-frequency domain samples. However, the SCN model built with time-domain samples has a lower recognition rate at the same number of hidden nodes L. The result demonstrates that the SCN model built by  
Figure 4: Samples of different categories in multiple domains on the CWRU dataset.  
using samples in the frequency domain and the time-frequency domain has advantages in the fault data classification task.
# Cooperative SWIPT MIMO-NOMA for Reliable THz 6G Communications
## 6 Simulation Results
### System Validity Analysis  
In contrast, a mathematical analysis and simulation were carried out to compare the system sum throughput and OP, using a transmission power of 20 dBm for system validation, verifying the intended objectives.  
Figures 4 and 5 exhibit the striking match between the mathematical analysis of the derived close forms and simulation results. They confirm the analysis accuracy and system validity, now including the uniqueness of new values. We thoroughly study system metrics throughout the remaining parts of this paper.  
Sum Throughput Versus Transmit Power:  
Users' OP versus Transmit Power:  
\begin{table}
\begin{tabular}{c c c} \hline
**Notation** & **Parameters** & **Value** \\ \hline f & Frequency & 311.04 GHz \\ BW & Bandwidth & 12.96 GHz \\ P & Transmission power & 30 dBm \\ d & Transmission distance & Phase 1 = 20 m \\ \(\alpha\)n & NU power coefficient & 0.25 of total power \\ \(\alpha\)f & FU power coefficient & 0.75 of total power \\ G & Antenna gain & 25 dB \\ eta & Path loss exponent & 4 \\  & Target data rate & 3, 1 Gbps for NU, FU \\ \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 4: Throughput vs. transmit power.
# Development and Assessment of a 4D Printing Technique for Space Applications
## 4 Discussion
### Forced Cool-Down  
While these experiments aimed to expedite the cooling process, the observed outcomes warrant careful consideration. The forced cool-down experiments serve as supplementary investigations, providing additional depth to our understanding of shape memory effects in the context of our experiment setup. These experiments, although not central to the primary objectives, contribute nuanced information about the responsiveness of the 4D printed hinges to rapid cooling conditions.  
Unfortunately, the results presented in Figure 13 reveal a close-to-negligible or only a slight negative influence of the forced cool-down method on both the recovery ratio (\(R_{r}\)) and folding ratio (\(R_{f}\)). Despite the expectation that the forced cool-down method could expedite the cooling process, the hinted worsened performance casts a shadow on its practical benefits. The slight negative impact observed in the 4D printing performance following forced cool-down experiments could be attributed to several factors. Rapid cooling rates may induce thermal stress, potentially leading to incomplete shape fixation. Additionally, variability in cooling conditions introduced by the forced cool-down method might contribute to the observed performance variation.  
The observed negative impact prompts consideration for adjustments in the forced cool-down method. Exploring variations in the cooling rate and duration could be avenues for mitigation, allowing for a more refined understanding of the optimal conditions for shape memory activation. These potential refinements not only address the observed limitations, but also open avenues for future experiments aimed at optimizing the forced cool-down process.  
Forced cool-down experiments, while revealing a nuanced interplay between cooling dynamics and 4D printing performance, set the stage for further methodological refinement and exploration.
# Empirical Study of Test Case and Test Framework Presence in Public Projects on GitHub
## 2 Method
### Number of Test Cases and Test Framework Usage  
To answer the next three research questions, we have used the automatic test case detection script described in our previous paper [15]. The analysis was performed on the downloaded source code archives of all 6.3 M GitHub project with Java as the primary language. The analysis process had the following steps:  
1. Get project name from GHTorrent.
2. Download ZIP with the source code of the project from GitHub.
3. Run analysis using the proposed script ([[https://github.com/madeja/test-overview-in-java/blob/master/ProjectAnalyzer.php](https://github.com/madeja/test-overview-in-java/blob/master/ProjectAnalyzer.php)]([https://github.com/madeja/test-overview-in-java/blob/master/ProjectAnalyzer.php](https://github.com/madeja/test-overview-in-java/blob/master/ProjectAnalyzer.php)); accessed on 20 July 2021).  
The process of source code download and analysis takes substantial time, so to process all the projects we have used 34 parallel processes and we ran them from 22 January to 24 February 2021.  
As a result, the script provided us the number of actual test cases present in each file of a project, and also the presence of import statements for testing frameworks and libraries. The presence of test cases and their number were used to answer RQ 3. To answer RQ 4, we compared the data to the number of occurrences of the word \"test\" in all files content of a project and used the Pearson's correlation coefficient.  
To answer RQ 5, we searched for import statements of the 29 testing frameworks and libraries. The list of frameworks and corresponding import paths were based on our previous work [15], where we analyzed 50 Java testing frameworks and their properties. For this study, we excluded frameworks without identified import (mostly archived ones or test generators) or without the obligation to use the word \"test\", due to searching only in files with \"test\" presence in the content.
# NOx Formation Mechanism and Emission Prediction in Turbulent Combustion: A Review
## 3 NOx Formation-Kinetics Approach
### Detailed Mechanisms  
The other approach is the detailed mechanisms, which combine all reactions that are related to the NOx generation. Usually, the chemical document would combine the fuel oxidization process and the detailed process of NOx generation. The NOx generation process would consist of thermal pathways and prompt pathways with specific elementary reactions. If the time steps are short enough, the calculation would be accurate compared with the real situation. Even though the calculation of this method is quite huge, it is still worth performing for significant compounds, such as evaluating the NOx production. The direct numerical calculation and the chemical reaction networks are all applied this way.  
The common reactions assembled from detailed reactions are GRI-Mech 2.11 or 3.0 version, which is relative to the natural gas oxidization process. [PERSON] and [PERSON] [38] investigated the hydrogen/air turbulent non-premixed flame on the model burner and applied the GRI-Mech 3.0 mechanism and partial Mevel mechanism to predict the NO emission. The results showed a better NO prediction of the GRI-Mech 3.0 because of the more elaborative nitrogen reactions. [PERSON] and [PERSON] [39] applied GRI-Mech 3.0 chemical kinetics integrated into the eddy dissipation-concept combustion model to predict the temperature and NOx profile emission of coke oven gas under a colorless distributed combustion technique. The consistency was achieved almost the same for NOx profiles between the measured and predicted ones.
## 4 Result and Discussion
### Falling Weight Deflectometer Test  
The FWD test was conducted to evaluate the pavement deflections in response to a temporally exerted load such as a driving wheel load. The measurements were performed by following the test method of ASTM D 4694-09 and were obtained at 0, 0.4 and 1.97 million ESALs of the APT. As plotted in Figure 4(b), there were sudden changes of vertical deflection in concrete block and bound stone pavement A at 0.4 million ESALs. In the case of concrete block, the response of surface deflection rapidly declined and gradually stabilised in the early stage and end of the APT respectively. This circumstance was initially caused by the compaction of laying sand occurred by the driving wheel loads. Comparing the response of both stone pavements, there was a slight increase in the bound stone pavement A. This would be influenced by the material difference of base courses which were asphalt and concrete.
# Optimal Control Approach to [PERSON]'s Problem and Gibbs' Method
## 4 Optimal Control Approach to Gibbs' Method  
### Gibbs' Method  
The energy-specified Lambert's problem can be used in orbit design, which connects two positions with elliptical, parabolic, and hyperbolic orbits with various energy levels. Gibbs' method calculates the velocity at the second observed position using three observed position vectors \(\overrightarrow{r}_{1}\), \(\overrightarrow{r}_{2}\) and \(\overrightarrow{r}_{3}\) at three successive times \(t_{1},t_{2}\) and \(t_{3}\) (\(t_{3}>t_{2}>t_{1}\)), assuming that the object is in a two-body orbit. Note that the three position vectors are supposed to be coplanar. Gibbs' solutions are rarely found in the literature and all of them are purely geometric-based approaches [6,7].  
In this section, a novel Gibbs' solution is presented, using the proposed energy-specified [PERSON]'s problem. [PERSON] method is formulated as follows:  
\[\frac{d^{2}\overrightarrow{r}}{dt^{2}}+\frac{\mu}{\left(\overrightarrow{r \cdot\overrightarrow{r}}\right)^{3}}\overrightarrow{r}=\overrightarrow{0} \tag{23}\]  
\[\overrightarrow{r}(t_{1})=\overrightarrow{r}_{1},\quad\overrightarrow{r}(t_ {2})=\overrightarrow{r}_{2},\quad\overrightarrow{r}(t_{3})=\overrightarrow{r} _{3}\]  
\[\frac{p}{r_{3}}=1+\left(\frac{p}{r_{1}}-1\right)\text{cos }\Delta\theta_{13}-e\sin\theta_{1}sin\ \Delta\theta_{13} \tag{31}\]  
Using these equations, \(p\) can be found as:  
\[p=\frac{sin\ \Delta\theta_{13}-sin\ \Delta\theta_{12}-sin\ \Delta\theta_{23}}{\left(\frac{sin\ \Delta\theta_{13}}{r_{2}}-\frac{sin\ \Delta\theta_{12}}{r_{3}}-\frac{sin\ \Delta\theta_{23}}{r_{1}}\right)} \tag{32}\]  
From Equations (26), (30), and (32), the true anomaly can be calculated as follows:  
\[\theta_{1}=tan^{-1}\left(\frac{e\ sin\ \theta_{1}}{e\ cos\ \theta_{1}}\right) \tag{33}\]  
Now, with the knowledge of \(p\) and \(\theta_{1}\), the eccentricity becomes:  
\[e=\frac{\frac{p}{r_{1}}-1}{cos\ \theta_{1}} \tag{34}\]  
Finally, the orbital energy can be computed by the following equation:  
\[\mathcal{E}_{set}=-\frac{\mu\left(1-e^{2}\right)}{2p}. \tag{35}\]  
The proposed orbital energy computation algorithm for the given three position vectors is summarized in Table 1.
### 1.1.1 Article
YOLOv8 is a step up in accuracy compared with YOLOv5 but a slight step down in speed. Compared with previous YOLO-series algorithms, the YOLOv5 algorithm offers improvements in model size. The input terminal uses adaptive anchor frame calculation and adaptive picture scaling methods, considerably improving detection performance. Experimental proof indicates that YOLOv5 is the more classical algorithm in the current YOLO series and is more suitable for defect detection in nitrile medical gloves. In 2022, [PERSON] improved the YOLO algorithm by using pre-trained network classification and multi-scale detection training and changing the screening rules of candidate boxes, which can be effectively used for multi-scale target detection [18]. In 2022, [PERSON] used the YOLO model for training in Google Colab and successfully distinguished torn gloves from normal ones [19] but failed to detect other defects. In the same year, [PERSON] proposed the glove defect detection algorithm YOLO-G [20], which used Ghostnet to replace part of the structure of YOLOv5 but did not realize the defect detection of scratches and tears. Since the YOLO algorithm was proposed, various network structures have been continuously integrated into it to improve detection performance. However, only a few studies have been conducted on defect detection in nitrile medical gloves. Most of the defect detection methods involve physical detection and object detection realized through a simple convolutional neural network. However, problems such as tedious processing,high requirements for hardware and the environment, inconvenient deployment, false detection, and leakage detection still exist. To solve these problems, herein, CCA-YOLO is proposed; this model can meet the performance requirements of glove defect detection. The achievements of this study can be briefly summarized as follows. 1. The receipt of nitrile medical gloves was created using 39,941 images after data enhancement, effectively solving the problem of class imbalance with fewer defective samples. 2. The CCA-YOLO algorithm was used for defect detection, and a small-target detection layer was added to the YOLOv5 network backbone [21]. 3. On the basis of the spatial squeeze and channel excitation block [22] and coordinate attention mechanism, an innovative channel coordinate attention (CCA) mechanism was proposed to improve the detection of tears and scratch defects. This mechanism can focus on the channel relationship in addition to the spatial relationship of the network. 4. On the basis of the different characteristics of tears and scratches, EIoU was introduced to detect glove tear defects and \(\alpha\)-IoU was introduced to detect glove scratch defects, thus improving defect detection accuracy in one step.
## 3 Results and Discussion
### Filament Fabrication and 3D Printing of Green Bodies  
All highly filled polymer compositions were extruded into filaments. Figure 4 shows the obtained filaments. The color of the filaments changed depending on the increase in the graphene oxide content from dark gray to almost black (Figure 4A).  
It is also worth noting that filaments with a graphene content of 0.10-0.25% turned out to be more flexible than filaments without graphene. They could be twisted and even tied into a knot without breaking (Figure 4C), while the filament with 0% GO turned out to be very fragile. Due to the uncontrolled pressure change in the extruder, the diameter of the filaments was not constant and amounted to 1.63 \(\pm\) 0.07 cm.  
To assess the distribution of ceramics in the obtained filaments, they were subjected to microstructural studies, and EDS maps of the distribution of elements were obtained (Figure 5).  
Figure 4: Images of all (**A**) and 0.10%GO/Al\({}_{2}\)O\({}_{3}\) (**B**,**C**) extruded filaments.  
Figure 5: The microstructure of the 0.10% GO/Al\({}_{2}\)O\({}_{3}\) filament at various magnifications (**A**–**C**) and distribution maps of aluminum (**D**), oxygen (**E**) and carbon (**F**) obtained from Figure 5B.  
In the presented images of microstructures and distribution maps, it can be seen that the filament is uniformly filled with a large amount of fine aluminum oxide powder. No obvious defects or porosity were found that could have negatively affected their 3D printing.