esa-sceva/satcom-chunk-collection huggingface dataset:
_Applied_  
_sciences_  
_Article_  
**Study on the Effect of Contrast Agent on Biofilms and Their Visualization in Porous Substrate Using X-ray \(\upmu\)CT**  
**[PERSON] 1,2,*, [PERSON] 3, [PERSON] 3, [PERSON] 2,4,5**(), [PERSON] 6() and [PERSON] 1,2,*()  
1 Radiation Physics Research Group, Department of Physics and Astronomy, Ghent University,  
Proofetiunstraat 86/N12, B-9000 Gent, Belgium  
2 Centre for X-ray Tomography, Ghent University, Proeftuinstraat 86/N12, B-9000 Gent, Belgium;  
[PERSON]@UGent.  
3 Microbial Communities & Antimicrobialobials (MICA) Laboratory, KU Leuven, Centre of Microbial and Plant Genetics, Kasteelpark Arenberg 20, B-3000 Leuven, Belgium; [PERSON]X.V.); [PERSON](H.S.)  
4 Pore-Scale Processes in Geomaterials Research Group (PProGRess), Department of Geology,  
Ghent University, Krijgslaan 281/S8, B-9000 Gent, Belgium  
5 Environmental Hydrogeology, Department of Earth Sciences, Utrecht University, Princetonlaan 8a,  
3584 CB Utrecht, The Netherlands  
6 The Procter and Gamble Company, Newcastle Innovation Center, Whitley Road, Longbenton,  
Newcastle-Upon-Tyne NE12 9 TS, UK; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS] (A.S.); [PERSON]] (M.N.B.)  
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**Featured Application: The proposed method may be adopted for in-situ visualisation of low virulence gram-negative bacterial strains using contrast-enhanced X-ray \(\upmu\)CT. The use of different techniques in this study enables future researchers to evaluate the toxicity of other contrast agents on biofilms.**  
**Abstract: Investigation of biofilms and visualization using non-destructive imaging techniques like X-ray \(\upmu\)CT has recently gained interest. Biofilms are congregations of microorganisms that attach to surfaces and comprise of microbial cells embedded in extracellular polymeric substances (EPS). They are ubiquitous entities that are commonly found in any non-sterile setting and have direct implications on human health. Methods to visualize them in-situ are highly needed to understand their behaviour (attachment and detachment) inside a substrate. Contrast-enhanced X-ray \(\upmu\)CT is a 3D imaging technique that is capable of visualising objects that have very low attenuation contrast. The use of contrast agents in X-ray \(\upmu\)CT has been an evolving process, however, the possible toxic effect of these chemical compounds against biofilms has not been studied in detail. In this study, we focus on the toxic effect of contrast agents and study the diffusion and drainage of contrast agents in biofilms. We propose using water-soluble potassium bromide (KBr) as a suitable contrast agent for enhancement of the attenuation coefficient of a monoculture of _Pseudomonas fluorescens_ biofilms inside a porous substrate. At the given concentration, KBr proved to be less bactericidal compared to other commonly used contrast agents and at 5% \(w/v\) concentration we were able to clearly distinguish between the biofilm and the porous substrate.**  
**Keywords: X-ray \(\upmu\)CT; biofilms; contrast agents; in-situ; flow cell**
# QoS-Based Pattern Recognition Approach for Web Service Discovery: Ar_WSDS
## 3 Abstract Composition Model  
An abstract composition model (ACM) of WSs is a sequential workflow, as shown in Figure 2, represented through an acyclic graph of size \(n\), composed of the WSs \(I\), \(\ldots\), \(n\). Equation (1) shows \(WS_{T}\), which lists the services (nodes) needed to fulfill the composition. Each \(WS_{i}\) consists of a 3-tuple: <I, O, [QoS]>, where \(I\) is its input parameters, \(O\) is its output interface, and _[QoS]_ is its quality parameter matrix.  
\[WS_{T}=\{WS_{i}\ |\ WS_{i}\in ACM\}. \tag{1}\]  
As such, there exists a matrix, \(QoS\), such that \(QoS_{i,j}\) is the value of a quality attribute \(j\) for a WS at position \(i\) within the ACM; that is, \(WS_{1}\) has a value \(QoS_{1,0}\), which can be, for example, a latency value and so on for each service. Therefore, each WS has an associated QoS parameter matrix. The set of these is indicated by an array of \(QoS_{T}\) matrixes.  
Figure 1: Ar2p model [8].  
Figure 2: Abstract composition model for the Ar_WSDS approach.  
Every step in the ACM requires the use of one \(WS_{i}\). The composition goal is achieved when all of the \(WS_{i}\) are available and correlated in the model, such that the execution is based on the ordered path of each node in fulfilling the adjacent functionality. Although other types of workflow models may exist, they may be transformed into sequential models [26]. All WSs must match the assembly condition provided by its composition model, which corresponds to the request and invocation of a predecessor WS with its predecessor candidate. Figure 3 shows the transitive relationship between the parameters \(I\) and \(O\), with respect to each other.  
Web services input to the system are denoted as the set \(WS_{g}\), where \(WS_{g_{i}}\) is a general web service to be recognized by the system. A candidate \(WS\), denoted by \(WS_{c}\), is a service that has been discovered and selected from the \(WS_{g}\), and is considered a possible solution for a node of the composition model. Combining the \(WS_{c}\) creates the SP, or different subsets that satisfy the ACM and the QoS conditions provided by the user for composition.  
The following section presents the Ar_WSDS design, specified through the presented ACM model.
## 4 Result Analysis
### Hypothesis Testing
#### 4.4.2 Second Hypothesis  
In this subsection, we test the null hypothesis H20 which states that:  
\(\mu\) (Time taken by T1 to determine VR and CR) = \(\mu\) (Time taken by T2 to determine VR and CR)  
As mentioned above, the time to determine VR and CR by both teams was recorded. We applied a t-test on that data, and we took a 95% confidence interval, therefore, statistical significance was set to \(\alpha\) = 0.05. As a result of our t-test, we generated the following result.  
Table 12 shows upper and lower bounds on the 95% confidence interval, Mean difference, standard-error difference, and other important significant levels of dependent variable efficiency of both teams T1 and T2 in order to determine variable and common requirements. The significant value for the SNS-based proposed approach was 0.001, as compared to identifying the variable and common requirements by the traditional RE approach. Table 12 shows that the significant value (0.001) is less than \(\alpha\) = 0.05, meaning that team T1 has shown more efficiency in determining variable and common requirements than team T2.  
Figure 13 also shows that the median time taken by team T1 was 27 min and the median time for team T2 was 48 min. This means that the time taken by team T2 to determine variable and common requirements was less than team T2. Therefore, we can describe it as:  
\(\mu\) (Time taken by T1 to determine VR and CR) = 27 min  
\(\mu\) (Time taken by T2 to determine VR and CR) = 48 min.  
Hence, \(\mu\) (Time taken by T1 to determine VR and CR) \(<\mu\) (Time taken by T2 to determine VR and CR). Thus, we rejected the null hypothesis H20 because it states that \(\mu\) (Time taken by T1 to determine VR and CR) = \(\mu\) (Time taken by T2 to determine VR and CR) and accepted the alternative hypothesis H2.
# A Survey of Routing Protocols in WBAN for Healthcare Applications
## 3 Classification of Routing Protocols for WBAN
### Cross-Layer Routing
#### 3.3.3 Cross-Layer Retransmit Strategy (CLRS)  
[PERSON] and [PERSON] [27] proposed a cross-layer retransmit protocol CLRS, based on the IEEE 802.15.6 standard, which was different from other cross-layer methods, this protocol was designed to retransmit data packets that failed to transmit. In this study, collision and shadow effects were the two main reasons for data transmission failure, and different waveforms the appeared on the output waveforms of the receiving end. The reason of the failure was judged by the characteristics of the waveforms of the data frame, and then the method of retransmitting in MAC was decided. The superframe structure is shown in Figure 10.  
The B and D modes of the superframe is specially used for low priority data in the MAP1 phase, to avoid high priority data encroaching on transmission resources. The use of TDMA to access channels in the MAP1 phase of the low priority frame, can increase the transmission success rate and reduce the transmission failure rate. For data failure caused by collision, the current time is judged to be in the period of the superframe, and the retransmission resources are allocated according to the superframe model. If the transmission fails in the MAP phase, the data is retransmitted in the subsequent competitive period; if the transmission fails in the EAP or RAP phase, then the data is re-competed and retransmitted, and the random back-off time is determined by the priority of the data. For the communication failure caused by human shadow, the sink node informs the data sending node to suspend data retransmit and wait for the shadow effect to end, before the data retransmission.
# A Semi-Analytical Approach for the Linearized Vibration of
## 4 Numerical Results and Discussions
### Results and Discussion
#### 4.2.2 The Effect of Thermal Load  
The effect of thermal load on the structural response without static load was investigated. Table 6 displays the set of thermal loads applied, while Table 7 presents the first five-mode frequencies. The thermal effect was observed to cause a decrease in the structural stiffness, which led to a significant reduction in the structural mode frequencies. However, the mode shapes remained relatively constant despite the thermal load and can be approximated by harmonic functions.  
Figure 6: PSD curves of acceleration in the z-direction under different static loads at various locations: (**a**) PSD curves for a point located at \(x=150\) mm (**b**) PSD curves for a point located at \(x=100\) mm; (**c**) PSD curves for a point located at \(x=75\) mm.  
Figure 7: RMS of acceleration in the z-direction under different static loads.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **Ref** & \(b1\) & \(b2\) \\ \hline \(P_{0}\) (N/mm) & & 0 & \\ Temperature (\({}^{\circ}\)C) & 20 & 50 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Different cases set of thermal loads.  
Figure 8: PSD contour of acceleration in the z-direction under different thermal loads (g\({}^{2}\)/Hz): (a) case \(b1\); (b) case \(b2\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Case** & **1** & **2** & **3** & **4** & **5** \\ \hline ref & 293.9 & 810.6 & 1589.0 & 2626.9 & 3924.1 \\ \(b1\) & 246.0 & 744.4 & 1511.0 & 2536.1 & 3819.1 \\ \(b2\) & 136.4 & 624.0 & 1377.0 & 2383.3 & 3643.7 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The first five-mode frequencies under different thermal loads (Hz).  
The RMS of the acceleration response of the beam considering the influence of thermal load is shown in Figure 10. Analysis reveals that with an increase in thermal load, the structural response slightly decreases. Influenced by the reduction of the acceleration response value corresponding to the first-order mode shape, the central region experiences a greater decrease in amplitude than the side regions.
# Advantage of Steerable Catheter and Haptic Feedback for a 5-DOF Vascular Intervention Robot System
## 2 Robotic System for Vascular Intervention  
### 5-DOF Slave Robot  
This section briefly introduces the 5-DOF slave robot shown in Figure 1. The previous robot for vascular intervention [20] was designed to have 4 DOFs, including insertion and rotation of both the guidewire and catheter. The second design [21] having 5 DOFs included a catheter steering function. The modified version shown in Figure 1 has the same 5 DOFs, but an FT (Force/Torque) sensor is newly installed to measure the interaction force between the catheter and the vascular lines.  
The main reason for adding the steerable function to the catheter is to facilitate access to and branching in the blood vessel. The main reason for employing the haptic function is to prevent perforation as the catheter passes through largely curved vascular lines. To measure the force, an FT sensor was used (ROBOTUS, RFT60-HA01 force load capacity (based on \(z\)-axis): 200 N; force resolution (based on \(z\)-axis): 200 mN; torque load capacity: 4 Nm, torque resolution 5 mNm). The operation principle of FT sensor is capacitance sensing. When the electrode (\(-\)) is moved by an external force, it reacts with the electrode (\(+\)) fixed on the substrate. Therefore, the FT sensor can measure the magnitude of the external force. The operation principle is well described in [22]. The FT sensor is installed at the location where the root of the catheter is attached to the body of the slave robot, as shown in Figure 1, and it provides a ground-truth data for the repulsive force measured during the insertion of the catheter.  
Conventional catheters used in the operating room are operated by two modes: catheter rotation and catheter insertion. However, it is sometimes difficult to make branching in complex vascular lines with movements of the catheter. For this reason, a steerable-catheter module was employed not only for successful branching but also to reduce the possibility of perforation of the patient's vessel during vascular intervention surgery. The detailed results are discussed in Section 5.
## Materials and Methods
### Soil Sampling and Laboratory Analysis  
Field sampling was conducted in October 2022 taking into account the uniformity of spatial distribution and the representativeness of sample points. We selected plots in 1.5 km \(\times\) 1.5 km grids and collected soil samples from a depth of 0-20 cm at five points per plot. The samples from the same plot were thoroughly mixed by the quartering method, and 1 kg samples were retained for the analysis of the soil properties. All samples were air-dried upon arrival in the laboratory. After removing large roots and stones, the samples were ground and sieved (2 mm). To determine the SOC, the samples were heated in an oil bath with potassium dichromate (K\({}_{2}\)Cr\({}_{2}\)O\({}_{7}\)). The STP and SAP were determined by the molybdenum antimony antimony colorimetric method after an alkali fusion of the samples with NaOH and extraction with NaHCO\({}_{3}\), respectively [27].  
The longitudinal and latitudinal positions of each sample point were recorded using a portable global positioning system (GPS) (Garmin Ltd., Olathe, KS, USA). The land use type and soil type around the sample points were also recorded in detail. A total of 180 samples were collected in this study (Figure 1). The outliers were detected by three standard deviations, and the data after removing the outliers were used in this study.  
Figure 1: Location of soil sampling points in the study area.
## 3 Results
### Metrics for Experimental Evaluation  
The evaluation of the model's effectiveness was evaluated using the following quantitative indicators in this experiment: mean average precision (mAP), which provides an overall assessment of the detection model's performance; precision (P), which evaluates the correctness of the model's outputs, providing insight into how closely the anticipated results align with the ground truth; recall (R), which indicates whether the model is comprehensive in identifying relevant data; and F1-score (F1), which reflects the harmonic mean of precision and recall, providing a balanced assessment of both the model's accuracy and its capacity to correctly identify relevant instances.  
The formulas used to compute these evaluation metrics are outlined below:  
\[\text{mAP}=\frac{\sum_{i=1}^{n}AP_{i}}{n} \tag{6}\]  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{7}\]  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{8}\]  
\[F_{1}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}} \tag{9}\]  
In the equations above, TP refers to the frequency of instances where the model accurately identifies the class being predicted, FP denotes the count of instances in scenarios where the model incorrectly labels the negative class as positive, and FN represents the quantity of examples where the classifier mistakenly classifies the positive class as negative.
# Weak-Edge Extraction of Nuclear Plate Fuel Neutron Images at Low Lining Degree
###### Abstract  
Neutron imaging is an effective nondestructive testing (NDT) technique widely applied to detect structural defects and the enrichment of nuclear fuel elements due to its high penetration and nuclide-sensitive properties. Since the fuel element pellet is sealed in the cladding, the transmission imaging result is a superposition of the two parts. Therefore, the attenuation of neutrons by the cladding is interference that must be considered in the enrichment analysis. It is necessary to extract and separate cladding and pellets using an edge extraction method. However, the low neutron cross-section of the cladding material (e.g., aluminum and zirconium) leads to poor grayscale contrast at the cladding edge in the imaging result, and the intensity of the cladding edge is significantly lower than that of the pellet edge. In addition, affected by the noise from the imaging environment, the boundaries of targets are further blurred, making edge detection more challenging. Traditional detection algorithms extract the weak edges of cladding incompletely, and the results are discontinuous, with obvious edge breaks and missing areas. This paper proposes a method to extract edges in neutron images based on phase congruency (PC). This study utilized the classical perceptual field model to improve contrast at weak edges. The enriched edge map was generated using our PC model from six directions, allowing more weak edges to be detected accurately. The non-maximum suppression ensured precise localization and avoided edge breaks. Furthermore, the edge results were optimized by eliminating noise through morphological operations. The experimental results demonstrate that the proposed method effectively detects the weak edges of the cladding, is superior in accuracy and integrity to traditional detection, and is able to obtain stable and reliable results with different materials of neutron images. The edge integrity improved by 64.1%, and the edge localization accuracy reached 94.3%. The extracted edge information is useful in the next stage of the high-precision enrichment analysis.  
Keywords:plate fuel element; neutron imaging; edge detection; phase congruency +
Footnote †: journal:
## 8 Conclusions  
A new miniature single-stage low speed axial compressor was built to investigate a special type of prestall instability and its potential for a stall warning algorithm. In addition, numerical simulations contribute to better understand the physics behind discrete flow disturbances representing a special feature of the prestall phenomenon. The following conclusions can be drawn:  
1. Discrete propagating prestall disturbances can be measured over the tips of the rotor blades. They represent a kind of flow non-uniformity and appear in a broad operating range before the compressor reaches its stalling limit.
2. The disturbances can be clearly identified and quantified by analysing the blade passing signature irregularity. As the mass flow is reduced, the intensity of flow disturbances increases. Furthermore, they become amplified disproportionately with increasing rotor speed.  
Figure 12: Model of prestall instability.  
Based on a spectral analysis, it could be proven that these particular disturbances can be assigned to the subject of Rotating Instabilities. The number of disturbances coincides with the mode order in the spectral signature of RI.
* It could be demonstrated that RI and rotating stall cells can indeed coexist in an operating range where the flow coefficient is below the stalling flow coefficient.
* A new stall indicator has been found which indicates the last stable operating point before stall. For this purpose, the third statistical momentum was applied to the unsteady pressure data measured upstream of the leading edge plane.
* Numerical simulations revealed that the flow disturbances appear as small vortex tubes where the casing ends of the tubes induce low pressure spots while propagating around the circumference. The highly deflected vortices behave like a characteristic feature of a Kelvin-Helmholtz-Instability which is triggered when the entire tip region of the rotor is affected by blockage. It is assumed that the prestall vortices arise if the critical rotor incidence is not exceeded in this flow regime.  
The following are available online at www.mdpi.com/2076-3417/7/3/285/s1, Video S1: Rotating Instability.  
Grateful thanks are extended to all our collegues who supported us in the preparation of this paper. The authors would also like to thank the German Research Foundation (DFG) for funding parts of the investigations. In particular, the extremely fruitful discussions with [PERSON] and [PERSON] are also gratefully acknowledged.  
[PERSON] is the leading author of this paper, performed the measurements and the data reduction of experimentally and numerically acquired data. [PERSON] performed to the simulations and the post processing. [PERSON] contributed to the measurements and the simulations. All three authors contributed to writing the paper.  
The authors declare no conflict of interest.
## 5 The Measurement Methods for Microscale Mechanical Parameters and Deformation Quantities in Shale
### Measurement of Microscale Mechanical Parameters in Shale  
Shale, as a heterogeneous material with complex microstructures and mineral compositions, exhibits deformation and fracture characteristics closely related to mineral distribution and structure. Therefore, obtaining microscale mechanical parameters is fundamental for studying microfracture propagation in shale. Currently, nanoindentation technology and atomic force microscopy (AFM) are widely employed for testing the microscale mechanical properties of shale (Figure 6) [74; 75]. Nanoindentation technology involves real-time monitoring of the penetration of an indenter into the material, allowing for the calculation of parameters such as elastic modulus, hardness, fracture toughness, and creep coefficient from the displacement-load curves. AFM, on the other hand, scans the surface of the test sample using a probe fixed on a microcantilever and measures the position changes of the microcantilever through optical detection or tunneling current detection. This provides information about the surface morphology and mechanical properties of the sample. A comparison between nanoindentation technology and AFM testing results in shale revealed that AFM yielded superior results. This is due to the large contact area in nanoindentation technology, while AFM offers nanoscale measurement precision and captures more comprehensive information [76].  
The mechanical mode of atomic force microscopy (AFM) provides a means to map the modulus distribution of interfacial phases by generating numerous force-displacement curves, which characterize the variation in modulus across interfaces and phases. By connecting AFM probes to predetermined molecular structures and employing corresponding test substrates, the adhesion forces between molecules and nanoscale particles can be quantified. This technique has been successfully applied in various fields including nanocomposites, biological cell materials, and protein enzymes, for characterizing the adhesive forces between two media [77]. In the field of geotechnical engineering, some studies have utilized AFM to measure the interaction forces between coal particles and the elastic modulus field at micro and nanoscale levels [78; 79]. The subsequent application of this method to characterize the adhesive forces between different mineral crystals and between grains with different crystal orientations will advance the theoretical understanding of shale mineral interface phases.