esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction. Pathogenic Biofilms: Characteristics and Developmental Stages
In the transition from the planktonic to biofilm growth, c-di-GMP downregulates bacterial motility, switching it from flagellar movement to type IV pili twitching motility [8]. At the same time, c-di-GMP promotes surface attachment and biofilm development by the upregulation of encoding genes responsible for the production of adhesins and the EPS matrix [9]. One can distinguish three stages in the process of surface-associated biofilms formation: attachment, maturation, and dispersion [10]. The attachment step is governed by many forces, some of them attractive and others repulsive, which develop when bacterial cells approach a surface. The attractive forces receive support for overcoming the short distance (10-20 nm) repulsive forces from bacterial flagella, which eventually accomplish mechanical surface attachment. One can further describe the attachment step as a two-stage process: (_i_)--non-specific primary adhesion, which is a reversible process mediated by weak non-covalent intermolecular forces such as hydrophobic and van der Waal's interactions and (_ii_)--specific, irreversible adhesion characterised by the production of specific proteins having the ability of covalent binding to the surface. The surface of an implanted medical device is chemically modified due to a biofouling process that starts soon after implantation resulting in surface deposition of a host-derived conditioning film. The conditioning film consists of small organic compounds and macromolecules, including proteins and polysaccharides. This is very important since the chemical nature of the surface of the conditioning film strongly influences subsequent bacterial cell adhesion [11]. Specific proteins produced in stage (_ii_) such as adhesins, autolysin/adhesins AtIE and Aae, the fibrinogen-binding protein Fbe/Sdrg, the fibronectin-binding protein Embry, and the lipase GehD attach to the human host proteins coating the implanted prosthesis or device. Once cells became irreversibly attached to the surface, biofilm maturation begins. The initial thin layer of cells grows thicker and thicker, developing into a mushroom or tower-shaped structure in the mature biofilm. In this developmental stage, complex biomolecular processes resulting in the upregulation of virulence factors and extracellular matrix constituents secretion and assembly occur. The final dispersion step is also critical for the biofilm life cycle, since it allows for the recolonisation of other available host sites. Several factors such as a lack of nutrients and intense competition might cause cells detachment, resulting in total or partial biofilm dispersal.
## 5 Conclusion  
This paper considers a transceiver design for the downlink of a multiuser non-regenerative MIMO full-duplex (FD) relay system with residual loop interference (LI). The source and relay transceiver matrices and destination combining matrix are optimized to minimize the sum mean squared error (SMSE). The original nonconvex problem is converted to three convex subproblems, and an iterative algorithm is developed to optimize the three matrices. Results are presented which demonstrate that the proposed iterative method outperforms the corresponding half-duplex (HD) relay system in terms of the SMSE and achievable rate. The effectiveness of the proposed solution was illustrated via simulation. Future research can consider interference cancellation techniques, integrating machine learning for dynamic system optimization, and next-generation wireless technologies. Internet of things (IoT) and mobile communication applications can also be examined considering the adaptability and scalability of the proposed approach and to further confirm its impact.  
Conceptualization, Y.S. and T.A.G.; methodology, Y.S. and T.A.G.; validation, Y.S.; writing--original draft preparation, Y.S.; writing--review and editing, Y.S. and T.A.G.; supervision, T.A.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{c c} MIMO & Multiple-Input Multiple-Output; \\ FD & Full-Duplex; \\ HD & Half-Duplex; \\ LI & Loop Interference. \\ \end{tabular}
# A Case Study on Distresses of Concrete Pavements Supported on a Retaining Wall
## 3 Evaluation of the Performance of the Subject Section
### Evaluation of Concrete Pavement Structure
#### 3.1.3 Concrete Material Quality  
Based on the visual survey, no major distresses attributed to concrete material properties were observed. Typically, pavement distresses related to concrete materials are rarely reported in Texas [46; 47]. Observed distresses related to concrete materials in the subject section were limited to minor spalling and alkali-silica reaction.  
To verify the quality of concrete materials, concrete cores samples were taken in areas where significant slab deflections were observed via FWD tests. A total of nine cores were extracted from the subject section, and none of them exhibited any signs of poor concrete material quality. Out of the nine core samples, one of them (see Figure 8) was extracted from the outside lane near the DMI 40 ft (12.2 m), where exceedingly high slab deflections were observed and the worst slab support conditions were expected. As shown in Figure 8, there was a layer of polyurethane foam beneath the concrete, suggesting that the base materials were washed out, creating a significant void, and the polyurethane foam was used to fill the void in the past. Nevertheless, the concrete quality has remained in excellent condition.  
Figure 8: Core sample extracted from a location where exceedingly high slab deflections were observed.  
Figure 7: Two types of tie-bar failure modes observed in the subject section: (**a**) tensile failure and (**b**) shear failure.
# Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System
## 5 Results and Discussion
### Results of Proposed Model for K-Fold Cross-Validation  
To validate the proposed model, 10-fold cross-validation was applied. The heart failure dataset is used to validate the model. The CNN-based proposed model classifies the patient data with an average accuracy of 0.9462, while the precision, recall, and F1 scores are 0.9398, 0.9565, and 0.9481, respectively. Table 11 shows 10-fold cross-validation results of the CNN.
## 3 Assessment of Spire Data Characteristics  
This section assesses the Spire data characteristics regarding penetration, precision, and stability. We summarized the penetration height (cut-off height) of Spire and COSMIC-2 in Section 3.1. We used the SRO method to collect the Spire-Spire and COSMIC2-COSMIC-2 pairs and calculate their precision in Section 3.2. We also use the SRO approach to collect Spire-other-RO-mission pairs and quantify the Spire stability in Section 3.3.
## 3 Proposed Protocol
### Data Forwarding Algorithm
#### 3.3.3 Forwarding the Data Packet
```
Input\(P\): the set of base paths, \(s\): source, \(d\): destination, \(\epsilon\): stretch factor Output\(r\): the Euclidean routing path \(\mathcal{B}(s)\leftarrow\) a base path probabilistically chosen from \(P\); \(s\_core\leftarrow\) the core polygon containing \(s\); \(s\_hole\leftarrow\) the hole whose core polygon is \(s\_core\) ; \(d\_core\leftarrow\) the core polygon containing \(d\); \(d\_hole\leftarrow\) the hole whose core polygon is \(d\_core\) ; \(C\leftarrow\) all core polygons excepting \(s\_core\) and \(d\_core\); \(\mathbb{A}\gets C\); if\(s\_hole\)!= nullthen \(\mathbb{A}\)_add(\(s\_hole\)); end if ifd_hole!= null!d_code==nullthen \(\mathbb{A}\)_add(\(d\_hole\)); \(l_{\mathbb{C}}(s,t)\leftarrow\) the shortest length of \(P\) ; \(\theta_{1}=\frac{|\mathcal{B}(s)|}{|l_{\mathbb{C}}(s\mathcal{A})|}\); \(\alpha_{1}=\frac{1+\alpha_{1}}{\theta_{1}}\sin\frac{(n-2)\pi}{2n}\);  end if elseifd_code!=nullthen \(\mathbb{A}\)_add(\(d\_core\)); \(l_{\mathbb{C}}(s,\mathcal{C}(t))\leftarrow\) the shortest length of \(P\) ; \(e_{\mathcal{C}(t)}\leftarrow\) the length of the longest edge of \(d\_code\); \(p_{\mathcal{C}(t)}\leftarrow\) the perimeter of \(d\_code\); \(\theta_{1}=\frac{|\mathcal{B}(s)|}{|l_{\mathbb{C}}(s\mathcal{C}(t))|}\); \(L_{0}=Max\left\{\sin\frac{(n-2)\pi}{2n}\left|l_{\mathbb{C}}(s,\mathcal{C}(t)) \right|-\frac{e_{\mathcal{C}(t)}}{2},\sin\frac{(n-2)\pi}{2n}\left|l_{\mathbb{C }}(s,\mathcal{C}(t))\right|+\left|l_{\mathbb{C}}\right|-e_{\mathcal{C}(t)} \right\}\) ; \(\alpha_{1}=Max\left\{1,\frac{\sin\frac{(n-2)\pi}{2n}\left\{\frac{(1+\alpha_{1} )\theta_{1}}{\theta_{1}}-\frac{P_{\mathcal{C}(t)}}{2}\right\}}{\theta_{1}\left( L_{0}+\frac{\mathcal{C}(t)}{2}\right)}\right\}\)  end if \(r\gets F(\mathcal{B}(s),\mathbb{A},\alpha_{1})\); return\(r\)
```  
**Algorithm 2**Euclidean routing path determination algorithm.
## 6 Discussion of the Results
### Classification
The relative share of correct classifications (true positives) is shown on the diagonal, whereas class-specific misclassifications are represented by the upper and lower column values (false positives) as well as the the left and right row values (false negatives). Thus, the classification performance appears quite remarkable for most of the 15 classes, especially considering the top 2 confusion matrices. Hence, all transformer-based models achieved a test accuracy of mostly 100% when distinguishing different precipitation types like hail, snow, drizzle, and rain, and, therefore, performed comparably to or better than ML models in previous works, but solely based on noise. However, the models still have difficulties in distinguishing closely related classes, like 4, 5, 6--meaning slight, medium, and heavy rain--or hail shower intensity (13,14). In these classes, all models lack discriminatory power (see also the normalized confusion matrices for the ViViT model Figure 22 and the CCT model Figure 22, as well as the baseline models Figures 22 and 23 in Appendix B). This might occur due to a strong similarity of the corresponding noise characteristics, especially in edge cases between two intensity classes. Moreover, the precipitation intensity can fluctuate quite strongly in short time periods. Since the measurement data are acquired over \(t_{\text{int}}=60\,\text{s}\) intervals, while the targeted time window for prediction is comparatively small with \(t_{\text{int,LiD}}=8\,\text{s}\), misclassification can likely occur close to the decision boundaries. Furthermore, synchronization issues between PSU and WSU, and therefore sporadic faulty labels, cannot always be avoided. In addition to that, CCT-PREC and ViViT-PREC appear to show difficulties in distinguishing slight drizzle from clear weather (0,1). The same applies to the baseline models, with additional difficulties in discriminating slight rain and drizzle (3,4) as well as slight and moderate snowfall (9,10). Figure 13: Test data classification results of the ViT-S-PREC model in the form of normalized confusion matrices, evaluating the predicted precipitation classes (Class ID pred) with respect to the actual, measured value (Class ID meas). In summary, the transformer-based architectures in particular proved to be highly accurate in precipitation classification. The ViT-B-PREC and ViT-S-PREC models especially stand out by solely showing a lack of discriminatory power for 3 out of 15 classes, while being comparatively small in size.
# Impulsive Noise Removal with an Adaptive Weighted Arithmetic Mean Operator for Any Noise Density
To solve it, and to get better results than the ones obtained by the previous filters, in [14] a refinement in the fuzzy mathematical morphology open-close filter is fully described which does not use any corrupted pixel into the operations. In this study, a new filter to restore images with salt-and-pepper impulsive noise is explored. The algorithm consists of two phases: detection and filtering. While fuzzy mathematical morphology is the basis of the detection step, in the filtering step, the value of each corrupted pixel is substituted by a Weighted Arithmetic Mean (WAM) of the values of those noncorrupted neighbors of the corrupted pixel, in which the weights of the WAM depend on the distance of each neighbor to the corrupted pixel. The number of neighbors needed to do this aggregation is a crucial parameter of the algorithm which is deeply analysed. Thus, the algorithm proposed in this paper consists of an adaptive filtering stage, which produces high performance results in comparison with the previously mentioned salt-and-pepper noise filters. This paper extends a preliminary study presented in [15], where the filter was introduced and its potential was briefly analysed. In this paper, the filter is formally defined, more configurations of the filter are considered and a deep statistical study is performed to ensure the superiority of this filter when compared with the other existing ones. The structure of the paper is as follows. In Section 2 some definitions of the noise models, as well as the operators used in the stages of the algorithm are introduced. In Section 3 the algorithm is described in depth. Then, after some experiments are performed to determine the best parameters of the algorithm, the comparison study with the other considered filters is developed in Section 4. Finally, Section 5 contains some conclusions and future work.
# Optimization of Slotted Steel Plate Shear Walls Based on Adaptive Genetic Algorithm
## 4 Performance Analysis of SSPSWs  
### Indicator Calibration  
The ductility coefficient \(\upmu\), a critical metric for assessing the seismic deformation capacity of an SSPSW frame, is defined by Equation (5), where \(u_{m}\) and \(u_{y}\) denote the ultimate and yield displacements, respectively. The yield displacement determination follows the graphical method in Figure 8a: (1) construct a tangent through the origin intersecting the peak-load tangent at point A; (2) project vertically from A to intersect the load-displacement curve at B; (3) connect B to the origin, intersecting the peak-load tangent at C; (4) project vertically from C to the curve, defining yield point D. The displacement at D (\(u_{y}\)) and the corresponding load (\(F_{y}\)) establish the yield state, while the peak load displacement (\(u_{m}\)) marks the ultimate deformation capacity.  
\[\mu=\frac{u_{m}}{u_{y}} \tag{5}\]  
The hysteresis energy dissipation capacity is quantified by the enclosed area of the hysteresis loop. The equivalent viscous damping ratio \(\zeta\), serving as the critical metric for assessing the energy dissipation performance, is calculated via Equation (6):  
\[\zeta=\frac{S_{ABC+ADC}}{2\pi S_{\triangle EBO+\triangle ODF}} \tag{6}\]  
Figure 8: Determination of (a) yield displacement and (b) equivalent viscous damping ratio.  
where \(S_{ABC+ADC}\) represents the hysteresis loop area and \(S_{\triangle EBO+\triangle ODF}\) denotes the combined triangular areas defined in Figure 8b.
## 1 Introduction
At this time, it was possible to obtain sharp cut-off frequency characteristics and broadband characteristics due to the transmission-zero characteristics. However, this filter is relatively large in size and has poor insertion and return loss characteristics. Therefore, various methods are being studied to solve problems such as bandwidth, frequency response characteristics, device size, and sharp blocking characteristics [2; 9]. Recently, a tri\(-\)band BPF using a stub-loaded resonator\(-\)based dual\(-\)mode resonator [9] has been studied. The characteristic of the filter is that it consists of a single resonator in which the internal stub and the external stub are integrated. Therefore, the coupling of the internal resonator creates the third passband. In addition, the BPF integrated with T\(-\)type SLR and SIR controls the impedance ratio and loaded open stub length to obtain high-frequency response characteristics, bandwidth, and sharp frequency cutoff characteristics. At this time, the transmission-zero could be adjusted by adjusting the length of the stub, and in the end, a sharp cut\(-\)off frequency characteristic was obtained. It is also analyzed that the size of the device was reduced by integrating SLR and SIR [20]. At this time, if SLR and SIR are used, the size of the circuit can be reduced, sharp cut-off frequency characteristics can be obtained, and it is analyzed that bandwidth can be ad justed [8; 9; 21; 22; 23]. In particular, the BPF using the coupling structure between the hairpin resonator and the coupled SL\(-\)SIR\(-\)based elliptic function makes transmission-zero to obtain excellent frequency cut-off characteristics [23; 24]. In addition, dual\(-\)band BPF with quad-mode using SLR constituted a series transmission\(-\)line between stubs, and a surface was designed in the middle of the transmission line to obtain a transmission-zero effect [25; 26; 27; 28]. Therefore, a sharp frequency cut-off characteristic was obtained through the transmission\(-\)zero characteristic. In this article, a compact wide-bandwidth dual\(-\)band BPF with SIR short stubs is proposed. The size of the proposed filter can be reduced due to its folded structure, and the feature of the filter is that SLR, SIR, and T\(-\)shaped band\(-\)stop filter (BSR) is integrated at the same time. Therefore, the filter has a dual\(-\)band, a low loss and is a small size, and the filter has a sharp frequency cut-off characteristic. Section 1 discusses the background and purpose of the study, Section 2 analyzes the methods to solve the problems of existing research cases. Section 3 proposes the design and manufacturing methods and discusses simulation and measurement results. Sections 4 and 5 discuss the characteristics and advantages of the proposed filter and the characteristics and advantages of the research.
## 2 Materials and Methods
### Methodology  
#### 2.2.1 Predictor Selection  
A time-lagged (3-12 months backward) correlation analysis between the monthly climatic indices and reservoir inflow at Boryeong Dam was performed to select the best monthly candidate predictors associated with teleconnection impact on the monthly reservoir inflow. Correlation analysis of less than a 3 months lagged period was not considered in this study because 3-months lead time forecasts were needed for the study area to prepare for a drought, allowing for about a month for multi-stakeholders to reach a decision and 1 or 2 months to take managerial actions [1]. Significance tests were then performed based on a two-tailed \(t\)-test for correlation coefficients between predictors (observed climate indices) and predictand (observed reservoir inflow) values. A multiple linear regression analysis was used to select predictors using the stepwise regression method, which is an iterative process of adding or removing candidate predictors based on the F-statistics of their estimated coefficients and their \(p\)-values, which are significant for a value of less than 5% [19; 29]. Multicollinearity check was also considered for the regression modeling. Multicollinearity results in difficulty in coming up with reliable estimates of parameters in regression models. Multicollinearity checks were run for the models, calculating variance inflation factor (VIF) as described in Equation (1):  
\[\mathit{VIF}_{i}=\frac{1}{\left(1-R_{i}^{2}\right)} \tag{1}\]  
where \(R_{i}^{2}\) is the coefficient of determination of the regression of predictor \(i\). A VIF above 5 indicates high correlation [30].
# Oral and Dental Spectral Image Database--ODSI-DB
## 3 Results and Discussion
### Reflections on Specular Reflections  
As can be seen in Figures 7 and 8, specular reflections may be significant and reduce the analyzable areas of the spectral images. In Figure 7, the lip and oral mucosa of the area under the tongue are shown with many specular reflection markings that should be excluded from lip and oral mucosal analyses. Glossiness of the dental enamel can also cause notable specular reflections: in Figure 8, a front tooth has the majority of its surface segmented as specularly reflecting.  
Our imaging setups did not contain technical solutions for controlling specular reflections besides using a ring illuminator instead of a spotlight from a liquid light guide. This illumination choice provided a more uniform lighting overall, but the specular reflections became circular loops. These loops can be seen in Figure 6c on uvula, in Figure 7 on the lip, and in Figure 8 on a front tooth. It may be possible to reduce specular reflections in case they are polarized by utilizing a linear polarizer in front of the ring illuminator and the spectral camera. This, however, would also reduce the intensity of the illumination and of the reflected light reaching the imaging sensor, leading to unwanted longer image acquisition times. Any longer acquisition times--especially in the case of Nuance EX--would have been unacceptable. It is also important to remember that the spectral cameras themselves may have polarizers. The Nuance EX spectral camera is based on a liquid crystal tunable filter (LCTF). These filter devices rely on parallel linear polarizers enveloping a liquid crystal stack [22]. A linear polarizer attached to the imaging setup would need to be aligned with these internal linear polarizers, as the illumination would still need to be polarized. This would, in turn, mean that finding an optimal angle for the polarizers to minimize specular reflections would require rotating the linear polarizer and the spectral camera together simultaneously. We did not have the tools to implement this, and therefore did not entertain this idea of using polarizers any further.  
Figure 8: Soft-tissue labeling example: tongue (purple), lips (orange), marginal gingiva (yellow green), attached gingiva (spring green) oral mucosa (red), inflammation (purple), ulcer (pink), skin (off-white). Specular reflections (off-white) on teeth and oral mucosa are also shown.
## 3 Results and Discussion
### \(\sum\)16 PAHs Concentration at Different Agricultural Soils  
Figure 3 shows the concentrations of \(\sum\)16 PAHs across various areas. The results revealed that the PAH concentration in different areas was statistically significant. The highest concentration was recorded from the industrial areas, with the following sequences: industrial areas > coastal areas > local market areas > rural areas > research areas. In the industrial area, the concentration is the highest at 1928 \(\mathrm{\SIUnitSymbolMicro g}\)/kg, reflecting the significant impact of industrial activities on PAH pollution. The coastal area follows with a concentration of 1187 \(\mathrm{\SIUnitSymbolMicro g}\)/kg, likely influenced by marine traffic and coastal industries. The local market area has a concentration of 459 \(\mathrm{\SIUnitSymbolMicro g}\)/kg, which can be attributed to vehicular emissions and market activities. The rural area, with 399 \(\mathrm{\SIUnitSymbolMicro g}\)/kg, shows relatively lower PAH levels, possibly due to agricultural practices and distant sources of pollution. The research area has the lowest concentration at 299 \(\mathrm{\SIUnitSymbolMicro g}\)/kg, likely due to minimal anthropogenic influence (restricted human activities). The mean concentration of \(\sum\)16 PAHs (435 \(\mathrm{\SIUnitSymbolMicro g}\)/kg) was determined in the studied soils of Poland [33]. A similar level was also determined in agricultural soils in other European countries [40; 41; 42].