esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrating Interactive Detection of Code Smells into Scrum: Feasibility, Benefits, and Challenges
###### Abstract
By examining the feasibility of this integration and exploring the perceptions of software professionals, we aim to provide valuable insights into the potential advantages, benefits, and limitations of adopting this approach. Additionally, this research seeks to identify the organizational and technical factors that can facilitate or hinder the successful implementation of the ID technique within Scrum teams. To achieve these objectives, we employ a mixed-method approach. Firstly, we conduct a comprehensive literature review to gather the existing knowledge on code smell detection, Scrum, and related integration approaches. Based on this review, we propose integrating the ID into Scrum by adapting roles, artifacts, and events within the framework. Subsequently, we conduct a focus group consisting of software professionals. The participants discuss the proposed integration, sharing their perceptions and providing insights into adopting this approach's advantages, benefits, and limitations. Finally, we conduct a controlled experiment involving 24 software developers to validate and evaluate the proposed approach. This experiment aims to apply the integrated ID technique within Scrum and assess its effectiveness in real-world software development scenarios. This study contributes to the software engineering community by proposing an approach to integrating the ID technique into the Scrum framework, enabling continuous code smell detection and fostering proactive quality management. The findings of this study shed light on the potential benefits and challenges associated with adopting this integration, offering valuable insights for organizations seeking to enhance software quality within their Scrum teams. Furthermore, this research highlights the importance of considering continuous code smell detection as an integral part of the agile development process and paves the way for future research in code quality management within agile methodologies. The remainder of this paper is organized as follows. Section 2 provides background information on basic concepts necessary to understand the study. Section 3 explains the main related work. Section 4 describes the proposed approach to integrating smell detection techniques within the context of agile software development, with a particular focus on Scrum. Section 5 presents the offline validation of the approach, including the results obtained from the focus group conducted with eight software development professionals. Section 6 exposes the online validation of the approach, including the results obtained from the controlled experiment conducted with 24 software developers. Section 7 discusses the main implications of the research from both researchers' and practitioners' perspectives, while Section 8 concludes the paper and provides directions for future research in this area.
## 2 Regional Geological Setting  
The Pearl River Mouth Basin is located on the northern continental margin of the South China Sea, extending subparallel to the shelf-slope system along the coastline of mainland South China (refer to Figure 1). As a submerged extension of the South China continent, this basin spans approximately 17.7 \(\times\) 10\({}^{4}\) km\({}^{2}\) and has been shaped by compressional collision between the Indian and Eurasian plates, combined with subduction-related compression from the Pacific Plate, resulting in a distinctive tectonic stress regime and a complex evolutionary history [14; 15]. Structurally, the basin comprises five first-order tectonic units stretching from north to south: the Northern Uplift Belt, Northern Depression Belt, Central Uplift Belt, Southern Uplift Belt, and Southern Depression Belt [16].  
The Enping Depression, located at the western terminus of the Pearl River Mouth Basin, covers an area of approximately 5000 km\({}^{2}\) with a NE-SW trending geometry. It is bounded by the Northern Fault Terrace Belt to the north, adjacent to the Xijiang Depression to the east, flanked by the Panyu Low Uplift to the south, and connected to the Yangjiang Depression and Yangjiang Low Uplift to the west (Figure 1). This region represents a critical depocenter for the paleo-Pearl River Delta's marine progradation. Its structural architecture is governed by NE-NEE- and NWW-trending fault systems, forming a characteristic fault-controlled basin with a 'northern faulting and southern onlap' configuration [17]. The tectonic evolution of the Enping Depression can be categorized into three distinct phases: Early Eocene syn-rift phase, dominated by extensional faulting with intense NE-striking fault activity, leading to the formation of initial rift basins; Late Eocene detachment-dominated phase, marked by waning fault slip and transition to  
Figure 1: Geological background of the Pearl River Mouth Basin area.  
crustal detachment, triggering southward migration of depocenters; and Oligocene post-rift sag phase, characterized by tectonic quiescence and regional subsidence, accommodating deltaic-shallow marine sedimentation under transgressive conditions [18,19].  
The target interval of this study is situated within the Upper Member of the Zhujiang Formation (Lower Miocene), exhibiting a stratigraphic thickness ranging from 600 to 1200 m. This succession developed in a transitional marine-terrestrial setting during the South China Sea spreading phase, dominated by shallow-water deltaic to neritic depositional systems [13,20]. The provenance system is jointly controlled by the Northern Uplift Belt and the paleo-Pearl River Delta, with primary detrital components derived from Yanshanian granitic rocks and metamorphic suites of the South China Fold Belt. Sedimentary architectures include subaqueous distributary channel sands, mouth bars, and wave-reworked coastal sand ridges [12,21], characterized by thin-bedded sandstone-mudstone alternations that provide optimal lithological cyclicity for high-frequency astronomical cycle identification.
###### Abstract  
In this study, Fe\({}_{3}\)O\({}_{4}\)/ZnO core-shell nanocomposites were synthesized through a chemical method of coating the magnetic core (Fe\({}_{3}\)O\({}_{4}\)) with ZnO by co-precipitation of Fe\({}_{3}\)O\({}_{4}\) with zinc acetate in a basic medium of ammonium hydroxide. The phase structure, morphology and electromagnetic parameters of the Fe\({}_{3}\)O\({}_{4}\)/ZnO core-shell nanocomposites were investigated. The results indicated that the concentration of the solvent was responsible for controlling the morphology of the composites, which further influenced their impedance matching and microwave absorption properties. Moreover, Fe\({}_{3}\)O\({}_{4}\)/ZnO nanocomposites exhibited an enhanced absorption capacity in comparison with the naked Fe\({}_{3}\)O\({}_{4}\) nanospheres. Specifically, the minimum reflection loss value reached \(-50.79\) dB at \(4.38\) GHz when the thickness was \(4.5\) mm. It is expected that the Fe\({}_{3}\)O\({}_{4}\)/ZnO core-shell structured nanocomposites could be a promising candidate as high-performance microwave absorbers.  
Keywords:core-shell structure; electromagnetic absorption; interfacial polarization; Fe\({}_{3}\)O\({}_{4}\); ZnO +
Footnote †: journal: materials
## 2 Literature Overview
### Results of Literature Overview  
Our literature overview was based on 43 papers and studies. We focused on the current works dealing with the key topics of our paper--Industry 4.0, Big Data, CRISP-DM, and predictive maintenance. A summary of the important aspects of the reviewed studies is presented, as follows (a study can belong to more than one category).  
Year of publication:  
* 2021: 28 studies;
* 2019/2020: 7 studies;
* 2018 and older: 8 studies.  
Main topic:  
* Big Data: 20 studies;
* Industry 4.0: 14 studies;
* Predictive maintenance: 8 studies;
* CRISP-DM: 5 studies.  
Most recurring additional topics:  
* Quality;
* Sustainability;
* Smart factory;
* Internet of things;
* Management.  
Type of work:  
* Case study: 14 studies;
* Literature review: 14 studies;
* Method(s) forming/comparing: 11 studies;
* Improving/extending methodology: 4 studies;
* Theoretical work: 4 studies.  
Most of the authors deal with practical problems and applications of existing methods and methodologies, or with compiling and comparing previous works in the field. Part of these two types of researchers also apply their practical/theoretical knowledge to construct new methods, and significantly less focuses on improving the existing methodologies or describing complex best practices. Purely theoretical works are also rare.
# Scene Text Detection Based on Multi-Headed Self-Attention Using Shifted Windows
## 4 Experiments
### Implementation Details  
In this paper, the proposed scene text detection network based on a shifting window multi-headed self-attention mechanism was implemented using the Paddle deep learning framework. The hardware environment used for training was a 16 GB Tesla V100 graphics card.  
For the actual training, a pre-training model was obtained by pre-training on the SynthText dataset for two iterations. Then, 1000 iterations were trained on the corresponding dataset for targeted fine-tuning.  
To consider the training efficiency, all image resolutions were adjusted to 640 \(\times\) 640 during training, and data augmentation operations such as random rotation in the range of \(\pm\)10\({}^{\circ}\), random cropping, and random flipping were performed. The hyperparametric training batch size was set to 8, the window size M was set to 8, and the number of multi-heads H was also set to 8.  
The specific process was as follows: Using the pre-trained model obtained on the SynthText dataset, we trained on the training set of ICDAR2015. Then, we evaluated based on the test set of ICDAR2015 at every 2000 iterations, and we saved the model with the best evaluation after the training was completed. The training and testing curves of the ICDAR2015 dataset are shown in Figure 5.  
Using the pre-trained model obtained on the SynthText dataset and considering that the MSRA-TD500 training set was small, the training sets from MSRA-TD500 and HUST-TR400 were combined as a new training set. Then, we evaluated based on the test set of MSRA-TD500 at every 2000 iterations, and the best evaluated model was saved after the training was completed. The training and testing curves of the MSRA-TD500 dataset are shown in Figure 6.  
Figure 4: Some samples of the datasets. (**a**) From the ICDAR2015 training set; (**b**) from the ICDAR2015 test set; (**c**) from the MSRA-TD500 training set; (**d**) from the MSRA-TD500 test set.  
The learning rate (\(LR\)) in the training process adopted the Poly strategy of exponential transformation, and the specific formula is given in Equation (25):  
\[lr=base\_lr\times\left(1-\frac{iter}{max\_iter}\right)^{power}, \tag{25}\]  
where \(lr\) is the learning rate of the current iteration, \(base\_lr\) is the initial learning rate, \(iter\) is the number of current iterations, \(max\_iter\) is the maximum number of iterations, and power is the exponent.  
In this experiment, \(base\_lr\) was set to 0.007, power was set to 0.9, and the weight decay and momentum used were 0.0001 and 0.9, respectively.  
In the inference stage, the batch size was set to 1. In addition, considering that the minimum feature map extracted in the feature extraction network was \(1/32\) of the original image, the image resolution of the evaluation set was adjusted to \(736\times 1280\) while maintaining the aspect ratio of the test image to ensure that the aspect was a multiple of 32.
## 4 Systematic Review of the Research Pillars
### Digital Twins in the Built Environment
#### 4.1.2 Sustainability and Smart Cities with Digital Twins  
In smart cities, digital twins (DTs) are essential for improving sustainability and resource allocation since they offer predictive analytics and real-time data integration. Digital Twins (DTs) empower decision-makers and urban planners to maximize resource utilization, enhance productivity, and promote sustainable practices across various industries. For instance, in predictive maintenance, DTs anticipate infrastructure needs, enabling proactive interventions that reduce waste and extend asset life [45]. Digital Twins (DTs) enable dynamic resource allocation by modeling multiple scenarios to ensure the efficient use of energy, water, and other resources [46]. Additionally, DTs support sustainable urban planning by facilitating real-time analysis of environmental data, enabling the identification of suitable locations for renewable energy implementation and reducing carbon footprints [46].  
Moreover, in Smart Agriculture, by using movable plant cultivation devices that can adjust to local conditions, digital twins can maximize urban agriculture and improve sustainability and food security [47]. Digital Twins (DTs) support data-driven decision-making by integrating diverse datasets, such as traffic and emissions statistics, to guide policy and urban planning decisions, ultimately fostering more sustainable outcomes [48]. By using scenario modeling, urban managers can investigate \"what-if\" scenarios and evaluate the possible effects of certain policies or advancements on sustainability [49].
## 4 Results and Discussion
### Influence of Model Parameters  
We investigated the performance under different settings of model parameters, including network depth D and the number of clusters C. A single GNN module contained a GraphSAGE layer, a batch normalization layer [21] and a LeakyReLU layer. One GNN module learned the representation of nodes in the graph; the other GNN module achieved the automatic cluster of nodes. A single layer of DIFFPOOL was added to integrate the nodes into the same cluster. Two GNN modules and a DIFFPOOL layer could be viewed as one unit as a whole. The network depth could be defined as the number of units stacked. Due to the low-pass filtering property of graph convolution, the stack of multiple graph convolution layers will lead to an over-smooth problem. D was chosen from [1; 2; 3; 4]. The clustering number of the next unit was selected as 25% of the previous unit. C was defined as the number of clusters in the last unit of the model. C was chosen from {2, 4, 6, 8, 10}.  
Figure 5 shows that the classification performance tends to be saturated and slightly decreased with the increase in network depth D and number of clusters C. In the experiment, with the increase in D, the training time also presented an approximate linear growth.
## 3 Silicon Photonics
### Grating Coupler  
Our grating couplers were created by a two-step silicon etching process. The process steps are depicted in Figure 11: the first lithographic step defines the grating as well as the rib waveguides on the surface of the silicon dioxide hard mask. The etching includes a sequence of organic bottom antireflection coating (BARC), SiO\({}_{2}\) hard mask and time-controlled 70-nm of Si etching (Figure 11a). Then the lithography for a second (complete) silicon etching is applied, and the photoresist protects the grating coupler area (Figure 11b) as well as the rib waveguides (not shown in these micrographs). The resulting two-step silicon structure on the bonding SiO\({}_{2}\) surface can be seen in the inset of Figure 11c.  
Figure 9: (**a**) Waveguide with a vertically modulated DBR and (**b**) optical micrograph of an Echelle grating. Inset—SEM of vertically modulated DBR in the selected square. (**c,d**) Waveguide with a lateral DFB grating. Top—layout and bottom—SEM micrographs show that OPC improves the corners of the lateral grid.
## 3 Results
It was able to solve to optimality all 10 small-sized instances (\(n<50\)), and for medium-size instances (\(50\leq n\leq 101\)), the gaps are within 1.5% of the optimal solution. The computation time does not exceed 1 h.  
Table 3 compares the KS-CVRP2 and the EIM-CVRP in terms of the time consumption to reach the optimal solution. The EIM-CVRP ran under the same conditions as KS-CVRP2, i.e., with a time limit of 1800 s and a stopping criterion of three iterations without any improvement. Additionally, the initial solution and procedure parameter \(\delta\) for particular test instances were identical for both methods. As expected, the KS-CVRP2 is quicker than \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Instance**} & \multicolumn{4}{c}{**KS-O**} & \multicolumn{4}{c}{**KS-CVRP1**} & \multicolumn{2}{c}{**KS-CVRP2**} \\ \hline _id_ & \(n\) & \(p\) & \(\tau\) & \(Opt\) & \(N_{b}\) & \(z^{*}\) & \(Time\) (s) & \(z^{*}\) & \(Time\) (s) & \(z^{*}\) & \(Time\) (s) \\ \hline
1. & 16 & 8 & 0.88 & 450 & 2 & 450 & 0.560 & 450 & 0.254 & 450 & 0.460 \\
2. & 19 & 2 & 0.97 & 212 & 2 & 215 & 5.067 & 212 & 2.310 & 212 & 1.056 \\
3. & 20 & 2 & 0.97 & 216 & 2 & 216 & 2.361 & 216 & 1.930 & 216 & 1.022 \\
4. & 21 & 2 & 0.93 & 211 & 3 & 211 & 1.471 & 211 & 0.519 & 211 & 0.514 \\
5. & 22 & 2 & 0.96 & 216 & 3 & 216 & 1.106 & 216 & 0.730 & 216 & 0.467 \\
6. & 22 & 8 & 0.94 & 603 & 3 & 603 & 31.667 & 603 & 4.057 & 603 & 1.044 \\
7. & 23 & 8 & 0.93 & 529 & 2 & 538 & 1039.707 & 547 & 823.919 & 547 & 310.329 \\
8. & 40 & 5 & 0.88 & 458 & 11 & 458 & 4772.603 & 458 & 327.663 & 458 & 3.336 \\
9. & 45 & 5 & 0.92 & 510 & 12 & 584 & 17,098.917 & 520 & 1803.467 & 510 & 5.205 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of computational times. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Instance**} & \multicolumn{4}{c}{**CWS**} & \multicolumn{4}{c}{**KS-CVRP2**} \\ \hline _id_ & \(n\) & \(p\) & \(\tau\) & \(Opt\) & \(\lambda\) & \(\mu\) & \(\
u\) & \(z^{cus}\) & \(N_{b}\) & \(\delta\) & \(z^{*}\) & \(Gap\) (\%) & \(Time\) (s) \\ \hline
1. & 16 & 8 & 0.88 & 450 & 0.4 & 1.2 & 0  
EIM-CVRP. It is because, unlike the KS-CVRP2, the EIM-CVRP explores the whole solution space at each iteration. Figure 4 shows an example of the solution improvement over time by both methods for test instance No. 8.
###### Abstract  
Structural health monitoring is of great importance for the application of composites in aircrafts. Fiber Bragg grating (FBG) sensors are very suitable for structure strain measurement. However, the strain measured by FBG sensors is different from the original strain in host materials. The relationship between them is defined as strain transfer. As composites are anisotropic, the traditional strain transfer model, which regards the elasticity modulus of host materials as a constant, is inadaptable. In this paper, a new strain transfer model is proposed for FBG sensors bonded to the surface of carbon fiber reinforced polymer (CFRP) laminates. Based on the measurement structure, the model is established and the transfer function is derived. The characteristics influencing the strain transfer are analyzed. The stacking directions, stacking numbers, and stacking sequences of CFRP laminates have a distinct effect on the transfer efficiency, which is different from the isotropy host materials. The accuracy of the proposed model was verified by experiments on a nondestructive tensile system, and the maximum model error is less than 0.5%. Moreover, the model was applied to the strain measurement of CFRP wing skin, which indicates that measurement errors decrease by 11.6% to 19.8% after the compensation according to the model.  
strain transfer; fiber Bragg grating (FBG); carbon fiber reinforced polymer (CFRP); wing skin +
Footnote †: journal: Applied Physics Letters B
## 3 Results and Discussion
### Ftir  
Figure 10a,b show the FTIR spectra of all cement paste samples at 1 day and 28 days of age, respectively. The absorption peaks around 3640 cm\({}^{-1}\) and 1650 cm\({}^{-1}\) in the high wavenumber region are caused by the asymmetric stretching vibration of the H-O bond in free water [50]. The absorption peak caused by the asymmetric stretching vibration of the C-O bond (CO\({}_{3}\)\({}^{2-}\)) is observed around 1420 cm\({}^{-1}\). The absorption peaks at 873 and 713 cm\({}^{-1}\) are attributed to the bending vibration of CO\({}_{3}\)\({}^{2-}\)[51]. The absorption peak in the wavenumber range of 997-937 cm\({}^{-1}\) corresponds to the stretching vibration of the Si-O chemical bond in C-S-H [52]. The absorption peak at around 770 cm\({}^{-1}\) corresponds to the bending vibration of the Al-OH chemical bond [53]. The absorption peaks around 620 cm\({}^{-1}\) and 470 cm\({}^{-1}\) correspond to the Si-O asymmetric stretching vibration of QZ [52]. The absorption peak at around 420 cm\({}^{-1}\) corresponds to the asymmetric stretching vibration of the Ca-O chemical bond [52].  
Study Figure 10a found that as the QZ and FA content increased, the intensity of the C-O absorption peak decreased. This is because the absorption peak of C-O in the sample mainly originates from the limestone added to the cement, while QZ and FA do not contain limestone. Additionally, by comparing Figure 10a,b, it is observed that the intensity of the C-O bond absorption peak decreases significantly as the curing time increases from 1 day to 28 days. This is attributed to the reaction of calcium carbonate in the cement mixture with aluminate, which consumes calcium carbonate. This trend is similar to that observed in the XRD pattern.  
For samples with a curing time of 1 day, the intensity of the Si-O bond absorption peak in C-S-H at 969 cm\({}^{-1}\) decreases with the increase in QZ and FA substitution. This is because the cement is replaced by QZ and FA, which reduces the content of early hydration products and decreases the absorption peak intensity of the Si-O chemical bond in hydrated calcium silicate. As the curing age increases from 1 day to 28 days, the intensity of the asymmetric stretching vibration peak of Si-O in the FA group rises, indicating that as the secondary hydration of FA proceeds, additional hydration products such as C-S-H are produced. Moreover, the bending vibration of the Al-OH chemical bond was observed at around 770 cm\({}^{-1}\). When the hydration process lasted for 1 day, the crystalline hydration products of C\({}_{3}\)A in the sample reacted with gypsum to generate AFt; hence, a large amount of Al-OH chemical bonds was detected. The peak intensity of the group with QZ added here is more significant. This is because QZ can provide additional nucleation points for AFt, promoting the generation of AFt. This is also reflected in the hydration heat flow curve. As the QZ content increases, the exothermic peak of AFt generation also increases.  
Figure 10: FTIR spectra. (**a**) 1 day and (**b**) 28 days.
## 2 Mathematical Models
### Standard Optimization  
Consider a set \(X\subseteq\mathbb{R}^{n}\) within the \(n\)-dimensional space that contains all the possible decision options, represented by vectors \(\xi\). This set of valid options, \(X\), is usually defined by constraints related to geometry, physical laws, and real-world limitations. We use a function \(f:X\rightarrow\mathbb{R}^{n}\) to evaluate each of these decision options, assigning a real number that indicates its desirability. For our purposes here, we will focus on finding the option that minimizesthe value of \(f\), without losing generality, since maximizing \(f\) is equivalent to minimizing \(f\). With this in mind, the standard optimization problem can be expressed as follows:  
\[\begin{array}{l}\eta=\min_{\xi}f(\xi)\\ s.t\quad\xi\in X\subseteq\mathbb{R}^{n}.\end{array} \tag{3}\]  
The goal is to identify a specific solution, represented as \(\eta\), which corresponds to the minimum value of the objective function \(f(\xi)\), where the possible choices for \(\eta\) are within the feasible set \(X\). Typically, mathematical optimization methods are employed to locate the extreme points (either local or global) of the function \(f\).