esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Coarse-Grained Methods
### The DCA Algorithm  
Thus, the algorithm for the DCA is the same as that of the CPA/DMFT, but with coarse-grained propagators and interactions which are now functions of \(\mathbf{K}\): (i) An initial guess for \(\Sigma(\mathbf{K},z)\) is chosen (usually from perturbation theory). (ii) \(\Sigma(\mathbf{K},z)\) is used to calculate the corresponding cluster Green's function  
\[\tilde{G}(\mathbf{K},z)=\frac{N_{c}}{N}\sum_{\tilde{\mathbf{K}}}G(\mathbf{K}+ \tilde{\mathbf{K}},z) \tag{44}\]  
(iii) Starting from \(\tilde{G}(\mathbf{K},z)\) and \(\Sigma(\mathbf{K},z)\) used in the second step, the host Green's function \(\mathcal{G}(\mathbf{K},z)^{-1}=G(\mathbf{K},z)^{-1}+\Sigma(\mathbf{K},z)\) is calculated which serves as bare Green's function of the cluster model. (iv) Starting with \(\mathcal{G}(\mathbf{K},z)\), the cluster Green's function \(G^{c}(\mathbf{K},z)\) is obtained using the Quantum Monte Carlo method (or another technique). (v) Using the QMC output for the cluster Green's function \(G^{c}(\mathbf{K},z)\) and the host Green's function \(\mathcal{G}(\mathbf{K},z)\) from the third step, a new \(\Sigma(\mathbf{K},z)=\mathcal{G}(\mathbf{K},z)^{-1}-G^{c}(\mathbf{K},z)^{-1}\) is calculated, which is then used in step (ii) to reinitialize the process. Steps (ii)-(v) are repeated until convergence is reached. In step (iv) various QMC algorithms, exact enumeration of disorder, etc. may be used to compute the cluster Green's function \(G^{c}(\mathbf{K},z)\) or other physical quantities in imaginary Matsubara frequency \(z=i\omega_{n}\). Local dynamical quantities are then calculated by analytically continuing the corresponding imaginary-time quantities using the Maximum-Entropy Method (MEM) [162].  
This generating-functional-based derivation of the DCA is appealing, since it requires the least initial assumptions. Quantum cluster theories are defined by the maps between the lattice and cluster. The map from the lattice to the cluster is obtained from a coarse-graining approximation for the generating functional \(\Phi^{l}\approx\Phi^{c}\). The map from the cluster back to the lattice is obtained by optimizing the free energy. One may derive the same algorithm for a disordered system following the same prescription as described above [163]. However, the treatment of a system with both disorder and interactions requires Keldysh [164; 165], or Wagner formalism [166] via the replica trick [8; 167] which is beyond the scope of this review.
## 3 Results
Comprehensive results of the subjective qualitative analysis of VNC and TNC images performed by the two blinded observers were illustrated in Figure 5. All TNC images were rated as good in 10 (25%) cases and excellent in 30 (75%) cases (scores 3 and 4, respectively) while VNC images were rated as moderate in 4 (10%) cases, good in 20 (50%) cases, and excellent in 16 (40%) cases (scores 2, 3 and 4, respectively). All VNC images were judged sufficient for diagnosis. No cases of insufficiently diagnostic images were encountered by the two observers. The calculated ICC for the assessment of VNC images was good (0.6) while the ICC of TNC images resulted was excellent (0.9). No cases of significative artefacts related to the permanence of iodine traces or due to the removal of stents or other devices were reported after the assessment of VNC images. Furthermore, in VNC reconstructions, highly calcified plaques were never improperly removed but, at most, were slightly attenuated. The mean Dose Length Product (mGycm) for TNC acquisition was 546 mGycm while the mean DLP related to the DE-CTA arterial phase without the TNC acquisition was 463.5 mGycm. CT dose index volume (CTDIvol) measured values were 3.7 (2.4-9.4) for TNC acquisitions and 3.5 (1.1-6.6) for DE-CTA examinations without the TNC phase. The effective dose value of TNC acquisition was 3.25 (1.72-5.39) mSv while the effective dose value of contrast-enhanced acquisition alone was 2.76 (0.89-5.19) mSv. The cumulative dosimetric data of TNC and arterial phases acquisitions of all DE-CTA examinations have been summarized in Table 4. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **TNC** & **VNC** \\ \hline CTDI vol & 3.7 (2.4–9.4) & 3.5 (1.1–6.6) \\ DLP (mGyxcm) & 546 (289–906) & 463.5 (150–871) \\ E (mSv) & 3.25 (1.72–5.39) & 2.76 (0.89–5.19) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Mean CT dose index volume (CTDIvol), mean Dose Length Product (DLP) and effective dose \(\xi\) values in true non-contrast and virtual non-contrast acquisitions. Figure 5: Cumulative results of qualitative analysis of virtual non-contrast images: 40% rated with a score of 4 (excellent); 50% with a score of 3 (good) and 10% with a score of 2 (moderate but sufficient for diagnosis). No virtual non-contrast reconstructions were considered non-diagnostic: 0% rated with a score of 1 in qualitative analysis. Therefore, a single DE-CTA scan with the lack of the TNC phase and completed by VNC reconstructed images may have resulted in a reduction of the administered mean effective dose.
# Machine Learning-Based Methods for Code Smell Detection: A Survey
## 4 Findings and Discussion
### Rationalize the Research Questions
#### 4.2.4 RQ3.1: Which Metrics Are Found More Useful in Code Smell Detection?  
Our research delved into the use of various software metrics in code smell detection, a crucial aspect of software quality analysis. These metrics, including object-oriented, coupling, complexity, size, cohesion, quality, GQM, LCOM5, NMD, and NAD metrics, have been extensively used in research articles. Among them, LOC, NOM, LCOM, WMC, CC, DIT, etc., have emerged as the most frequently applied metrics, proving their effectiveness in code smell detection. This comprehensive understanding of software metrics in code smell detection significantly contributes to the field.
# Evolution towards Smart and Software-Defined Internet of Things
## 2 IoT Classification and Applications
### Industrial IoT  
IoT has connected everything, including vehicles, to be able communicate with one another, enhancing the concept of smart cities and Vehicular Ad hoc Networks (VANET) [20, 21]. Many areas are investigating the use of IoT in transportation systems to create more efficient and intelligent vehicular networks. [PERSON] et al. in [22] propose a scalable and long-term hybrid IoT framework that addresses efficient multiple access and computational offloading at the physical and MAC layers to support the efficient transmission, computation, and caching of IoT big data generated by the network's large number of vehicles. In another attempt, [PERSON] et al. in [23] use the IoT to investigate the best resource allocation for pushing compute and storage resources to edge devices in  
Figure 1: Taxonomy of the paper.  
order to reduce average service response time. Similar attempts are being made to extend automotive-based IoT applications to the cloud to further enrich and enhance the VANET services [24].  
IoT has impacted industrial standards as well, as it has other aspects of life circles [25]. IoT has applications in all industries, including agriculture, education, telecommunications, manufacturing, and medicine. [PERSON] et al. investigate the importance of IoT in the healthcare system and propose a lightweight authentication scheme for a cloud-based Radio-Frequency Identification (RFID) healthcare environment to address the privacy and security needs of the environment while maintaining cost-effectiveness [26]. [PERSON] et al. examine the disconnect between actual IoT implementation in the manufacturing process and academic and theory [27]. Similarly, [PERSON] et al. examine the various applications of IoT in the agriculture industry, leading to smart farming to improve productivity, efficiency, and global market while reducing human intervention, cost, and time in their research [28].
# Evaluation of Shear Effect on Deflection of RC Beams
## 2 Experimental Study
### Test Specimens  
In this study, as shown in Table 2, a total of nine specimens were designed. The cross-section of the specimens was 200 \(\times\) 400 mm, and the clear span was planned to be 2250 and 3300 mm. The distance between loading points was 500 mm. The shear span-to-depth ratios were planned to be 2.5 and 4.0. The spacing of shear reinforcement was such that the ratios of the shear strength to the flexural strength of the specimens were 1.1, 1.4, and 1.7. The shear and flexural strengths of specimens were calculated according to the provisions recommended by the ACI building code [19].  
Figure 2 shows the details of the specimens. Strain gauges with 5 mm length and 5% measuring range for steel were attached to the reinforcing bars to measure the strains of the tension, compression, and shear reinforcing bars, as shown in Figure 2 with red remarks. As shown in Table 2, the test variables were planned to be the shear span-to-depth ratio,  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Specimens**} & \multirow{2}{*}{\(\rho-\rho^{{}^{\prime}}\)} & \multirow{2}{*}{\(a_{v}\) (mm)} & \multirow{2}{*}{\(a_{v}\)/\(d\)} & \multicolumn{4}{c}{**Reinforcing Bars**} & \multirow{2}{*}{\(\frac{V_{slope}}{V_{frame}}\)} \\ \cline{5-6}  & & & & & **Tension** & & **Compression** & **\(s\) (mm)** & \(\rho_{w}\) (\%)** \\ \hline B3-2.5a & & & & & & D10@160 & 0.45 & 1.1 \\ B3-2.5b & 0.3\(\rho_{b}\) & 875 & 2.5 & 2-D22 & (\(\rho=0.0111\)) & D10@115 & 0.62 & 1.4 \\ B3-2.5c & & & & & D10@90 & 0.79 & 1.6 \\ \hline B6-2.5a & & & & & & D10@95 & 0.75 & 1.1 \\ B6-2.5b & & 875 & 2.5 & 2-D16 & (\(\rho=0.0057\)) & D10@70 & 1.02 & 1.4 \\ B6-2.5c & & & & 3-D22 & (\(\rho=0.0166\)) & D10@55 & 1.30 & 1.7 \\ \hline B6-4.0a & & & & & (\(\rho=0.0166\)) & D10@180 & 0.40 & 1.1 \\ B6-4.0b & & 1400 & 4.0 & & & D10@130 & 0.55 & 1.4 \\ B6-4.0c & & & & & D10@100 & 0.71 & 1.7 \\ \hline \hline \end{tabular} Note: \(\rho\) and \(\rho^{\prime}\) = tension and compression reinforcement ratios, respectively; \(\rho_{b}\) = balanced steel ratio for singly reinforced section; \(\rho_{w}\) = shear reinforcement ratio; \(a_{v}\) and \(a_{v}/d\) = shear span and shear span-to-depth ratio, respectively; \(s\) = spacing of shear reinforcement; \(V_{shear}\) and \(V_{flcaxer}\) = shear forces reached at \(V_{n}\) and \(M_{n}\) calculated using ACI building code; and \(V_{n}\) and \(M_{n}\) = nominal shear and flexural strengths of specimens, respectively.  
\end{table}
Table 2: Details of tested specimens.  
Figure 1: Stress vs. strain relationships of used materials: (**a**) concrete; (**b**) steel bars.  
shear capacity ratio, \(V_{shear}/V_{flexure}\) in Table 2, and tension reinforcement ratio to evaluate the effect of shear on the deflection of RC beams subjected to combined bending and shear.  
Figure 2: _Cont._
# The State-of-the-Art of Sensors and Environmental Monitoring Technologies in Buildings
## 3 Communication Technologies and Protocols  
Modern-day buildings can be equipped with a wide range of state-of-the-art wired and wireless communication technologies and protocols for the interconnection of various building systems and processes, such as the monitoring and storage of sensor information, data communication and processing in building management systems, and the functioning of equipment and utility appliances etc., (see Figure 1). Wired technologies such as Ethernet, Serial Communication (RS232/RS422/RS485), etc., tend to be more secure (as these connections are typically housed behind a Local Area Network (LAN) firewall meaning there is no transmitting data susceptible to interference), have fast data transfer speeds (as they are less affected by physical hindrances (walls, ceilings, etc.) and signal interference/distortion from other electronic devices), and tend to be more reliable in most cases (as they are less prone to disconnections, and do not require constant debugging and troubleshooting). On the other hand, wireless technologies such as Zigbee, WiFi, Bluetooth, EnOcean, etc., are more scalable and mobile (as there is no need for extensive hardware installations especially, inside walls, ceilings, etc.) and generally, cost less to the end user (as there are no additional cable installation/repair and labor costs). Although wired technologies have been more commonly used over the years, recent literature suggests that wireless technologies are now becoming increasingly ubiquitous due to vast improvements in speed, security, reliability, and an ever-increasing variety of technologies and standards to choose from [77; 78; 79; 80; 81]. Although both sets of technologies have a place in the market, it is vital to assess the viability and current status of each technology to ensure that building designers, owners and occupants can choose the most suitable technology candidate to meet their respective requirements. The following sub-sections present an overview of the most prominent and commercially available wired and wireless communication technologies and protocols used in buildings (shown in Figure 3) with their key characteristics compared in Table 3.  
Figure 3: Prominent communication technologies and protocols for building applications. Ethernet, Power Line Carrier Communication (PLCC) and Serial Communications are widely adopted wired technologies whereas Zigbee, Bluetooth and Wifi are the most widely adopted wireless technologies. On the other hand, relatively new technologies and protocols, such as EnOcean, BACnet, 6 LoWPAN, Z-Wave and LoRaWAN are still in their infancy in terms of market adoption.
## 4 Discussion
The characterization of atrazine-degrading bacterial strains revealed eight strains linked to the existence of atz_A_,\(B\),\(C\),\(E\),\(F\) and _trzN_,\(D\) genes, which encode the enzymes responsible for the mineralization of this herbicide. Five of the eight newly isolated strains possessed the _trzN_ gene. This high proportion of bacteria possessing the _trzN_ gene, which encodes an atrazine chlorohydrolase, shows its widespread occurrence throughout the potential atrazine-degrading bacterial community [60] and its prevalence over the atz_A_ gene. Seven strains out of eight harbored the _trzN_/atz_A_-atz_B_,\(C\) gene combination. This atrazine-degrading gene combination leads to the transformation of atrazine to its intermediate cyanuric acid. The results, therefore, suggest that the _trzN_/atz_A_-atz_B_,\(C\) gene composition may be widespread and highly conserved in different microbial genera. Moreover, such gene combination redundancy suggests an established atrazine-degrading consortium with a cooperative catabolic pathway [61]. By contrast, three of the eight isolated strains showed positive results for the _trzD_-atz_E_,\(F\) gene combination. The detection of _trzN_, atzC, and _trzD_ might be an indicator of the fact that atrazine is being degraded via the hydroxyapatite pathway (_trzN_) to cyanuric acid (_atzC_), then to biuret, NH3, and CO2 (_trzD_). However, the absence of certain genes in the isolated strains, such as atz_D_, does not mean that they are not present in the whole groundwater environment. Furthermore, other similar genes involved in the missing steps (or unknown genes with the potential to perform the missing steps) were not detected by the primers used in this study, but they could be present. Therefore, atrazine catabolic pathways appear to be the result of the recruitment of different genes in broad microbial communities rather than a genetic element encoding the entire pathway [32]. The presence of bacteria such as _Arthrobacter_ sp., _Pseudomonas_ sp., or _Nocardioides_ sp., that have been reported to contain atrazine degradation genes [18; 31; 60; 61], suggest the potential bioremediation adaptation of the groundwater microbial community. This possible atrazine-degrading capacity and microbial community adaptation must be assessed in future experiments. This study highlights the importance of the use of molecular tools to find possible candidates with the genetic potential to perform natural bioremediation processes, although the details of the expression of these genes, and the real degradative capacity should be verified with further research.
# Continuous-Wave THz Imaging for Biomedical Samples
## 5 The Application of CW Imaging with Biological Tissues
### Bones
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Imaging Target** & **THz System** & **Results** & **Reference** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Basal cell carcinoma (BCC) \\ \end{tabular} } & \begin{tabular}{c} CW THz transmission imaging \\ mode at two frequencies of 1.39 \\ and 1.63 THz \\ \end{tabular} & \begin{tabular}{c} Good contrast between \\ cancerous and normal tissue \\ was found with spatial \\ resolution of 390 μm at 1.4 THz \\ and 490 μm at 1.6 THz \\ \end{tabular} & [PERSON] et al., 2011 [82] \\ \cline{3-4}  & & \begin{tabular}{c} The location and the different \\ morphological features were \\ presented by cross-polarised \\ and polarisation optical images \\ with resolution of 670 μm \\ \end{tabular} & [PERSON] et al., 2014 [40] \\ \hline \multirow{4}{*}{\begin{tabular}{c} Fresh human tissue of NMSC \\ with either LP or CP radiation at \\ 584 GHz \\ \end{tabular} } & \begin{tabular}{c} Reflective CW THz imaging \\ system working at 584 GHz \\ and normal tissues were found \\ with a resolution of 150 μm \\ \end{tabular} & Contrast between cancerous & [PERSON] et al., 2016 [42] \\ \cline{3-4}  & \begin{tabular}{c} CW THz fibre-scanning \\ near-field microscopy \\ transmission imaging at \\ 320 GHz \\ \end{tabular} & \begin{tabular}{c} Breast tumour tissues could be \\ clearly distinguished from \\ normal tissues without H\&E \\ staining with a resolution of \\ 240 μm \\ \end{tabular} & [PERSON] et al., 2011 [83] \\ \cline{3-4}  & \
\begin{tabular}{c} Reflectivity CW THz imaging \\ mode working at 1.89 THz \\ \end{tabular} &
\begin{tabular}{c} Obtaining the absolute \\ refractive index values of the \\ samples \\ \end{tabular} & [PERSON] et al., 2013 [84] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Current CW THz imaging studies with biomedical samples.
# The Loop-Mediated Isothermal Amplification for the Rapid Detection of Porphromonas Gingivalis
## 2 Materials and Methods
### Detection of Tested Periodontal Pathogens
#### 2.5.2 PCR (Polymerase Chain Reaction)  
PCR determinations were performed against template sequences characteristic of the tested periodontitis, such as in LAMP assay. The designing of the primers used in the PCR reactions was entrusted to the Novazym Poland Laboratory ([[http://novazym.pl/](http://novazym.pl/)]([http://novazym.pl/](http://novazym.pl/))). The primers were designed based on reference sequences publicly available in the bioinformatics databases of NCBI (www.ncbi.nlm.nih.gov/) and GENATLAS ([[http://genatlas.medecine.univ-paris5.fr/](http://genatlas.medecine.univ-paris5.fr/)]([http://genatlas.medecine.univ-paris5.fr/](http://genatlas.medecine.univ-paris5.fr/)))--Table S2. The following software was used for this purpose: Primer 3 ([[http://bioinfo.ut.ee/primer3-0.4.0/](http://bioinfo.ut.ee/primer3-0.4.0/)]([http://bioinfo.ut.ee/primer3-0.4.0/](http://bioinfo.ut.ee/primer3-0.4.0/))), NetPrimer ([[http://www.premierbiosoft.com/netprimer/](http://www.premierbiosoft.com/netprimer/)]([http://www.premierbiosoft.com/netprimer/](http://www.premierbiosoft.com/netprimer/))), BLAST ([[https://blast.ncbi.nlm.nih.gov/Blast.cgi](https://blast.ncbi.nlm.nih.gov/Blast.cgi)]([https://blast.ncbi.nlm.nih.gov/Blast.cgi](https://blast.ncbi.nlm.nih.gov/Blast.cgi))), and Oligo 7.0 ([[https://www.oligo.net/](https://www.oligo.net/)]([https://www.oligo.net/](https://www.oligo.net/))).  
In order to optimise the thermal conditions of the reaction, temperature gradient PCR was performed. Annealing temperatures for individual primers were optimised using a Mastercycler Eppendorf Gradient thermal cycler (Eppendorf, Hamburg, Germany), which is equipped with a heating block to generate an appropriate temperature distribution. The gradient was made in the temperature range from 62 to 70 \({}^{\circ}\)C. As a control template, a mixture of the genomic DNA of bacteria from gingival fluids collected from patients with a previously confirmed presence of the tested pathogens was used.  
DNA amplification reactions by PCR were carried out in a volume of 20 \(\upmu\)L using the Allegro TaqMan Polymerase reagent kit (Novazym, Poznan, Poland). The process was initiated by maintaining the samples at 95 \({}^{\circ}\)C for 5 min, the second stage consisted of 3 cycles, which was repeated 40 times. Cycle 1-95 \({}^{\circ}\)C for 30 s, cycle 2-60 \({}^{\circ}\)C for 30 s, and cycle 3-72 \({}^{\circ}\)C for 30 s. The reaction products were evaluated by electrophoresis in a 2% agarose gel containing ethidium bromide at a concentration of 0.5 \(\upmu\)g/mL. The electrophoresis process was carried out at constant voltage (200 V) and current (500 mA) for about 20 min in a 1\(\times\)TBE (10%) buffer. Control and verification of the products was carried out after the end of electrophoresis on a transilluminator in UV light against the mass marker Nova 100 bp DNA Ladder (Novazym, Poznan, Poland), and the obtained images were archived using an integrated image acquisition system
# CL-YOLOv8: Crack Detection Algorithm for Fair-Faced Walls Based on Deep Learning
## 6 Conclusions  
This paper presents an improved YOLOv8 model tailored to the detection of cracks in brick walls. Several key contributions and findings emerged from this research, as follows:  
1. Backbone Network Optimization: By replacing the original YOLOv8 backbone network with the more efficient ConvNeXt V2 model, the feature extraction capability was significantly enhanced. This modification improved the model's ability to detect subtle crack features, leading to a better performance in diverse environmental conditions.
2. Enhanced Attention Mechanism: The introduction of the Large Separable Kernel Attention (LSKA) mechanism into the SPPF structure further strengthened feature correlation and expanded the receptive field. This resulted in more accurate crack localization and an improved detection efficiency.
3. Superior Detection Performance: The proposed CL-YOLOv8 model demonstrated a superior performance compared to existing models, such as YOLOv5s, YOLOv7-tiny, and other YOLOv8 variants. It achieved higher accuracy, recall, and mAP values, which are critical for real-time crack detection on fair-faced brick walls. This capability will provide technical support for the early detection and timely repair of wall cracks, reducing potential safety risks and extending the lifespan of buildings.
4. Future Work on Crack Segmentation: While the current focus was on crack detection, future work will explore the task of crack segmentation. By segmenting detected cracks and extracting their skeletons, we will quantify geometric properties such as crack length, width, and area. Integrating a morphological analysis will enable a more comprehensive evaluation of crack damage, providing a deeper understanding of structural risks.  
In summary, the improvements made to YOLOv8 enhanced its feature extraction and detection capabilities, offering a more robust solution for wall crack detection. These contributions lay a strong foundation for future research on crack segmentation and damage assessment in building structures.  
Conceptualization, Q.L.; Methodology, Q.L.; Software, G.Z.; Validation, G.Z.; Investigation, G.Z.; Resources, P.Y.; Data curation, P.Y.; Writing--original draft, G.Z.; Writing--review & editing, P.Y.; Supervision, P.Y.; Project administration, Q.L.; Funding acquisition, Q.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (61871260).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy.  
**Conflicts of Interest:** The authors declare no conflict of interest.