esa-sceva/satcom-chunk-collection huggingface dataset:
# Defect Detection in Striped Images Using a One-Dimensional Median Filter
## 3 Results and Discussion
\(TPR\) is defined as:  
\[TPR=\frac{TP}{TP+FN} \tag{3}\]  
and \(ACC\) is defined as:  
\[ACC=\frac{TP+TN}{TP+FP+FN+TN}. \tag{4}\]  
The evaluation criteria were based on the bounding box and its center, as shown in Figure 19. The red bounding box is the correct position, and the blue or yellow bounding boxes are the results obtained by the proposed algorithm. If the position of the center was off the correct position, it was considered a failed defect detection. If the found bounding box was not completely consistent with the red bounding box, but the center of both boxes matched well, it was considered correct. There were 126 defects in the 85 images. Positive was defined as a defective region; negative was defined as a defect-free region. For defect detection, the number of defect regions could be counted, but not the number of defect-free regions. To make the subsequent calculation possible, this study assumed that there were as many defect-free regions as defect regions, which was equal to 126. The results achieved using the algorithm are listed in Table 4. The \(TP\) value was 119, which meant that the proposed algorithm correctly detected 119 of 126 defects. The remaining seven defects that could not be detected were the \(FN\) cases. The \(FP\) value was 0, which meant that the proposed algorithm had sufficient robustness for noises that it did not generate any false positives. Using Equations (2) and (3), the \(FPR\) was 0%, and the \(TPR\) was 94.4%. Using Equation (4) yielded an accuracy of 97.2% [17]. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**No. of Defective Regions** & \(TP\) & \(FN\) & \(FP\) & \(TN\) & \(TPR\) & \(FPR\) & \(ACC\) \\ \hline
126 & 119 & 7 & 0 & 126 & 94.4\% & 0\% & 97.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation results of 126 defects in 85 images. Figure 19: Evaluation criteria for regional comparison. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(T_{w}\) & **185** & **190** & **195** & **200** & **205** & **210** & **215** \\ \hline \hline \(RSS\) **of FNR** & 0.135 & 0.109 & 0.112 & 0.122 & 0.129 & 0.167 & 0.429 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Root sum square of \(FNR\) and \(FPR\) when \(T_{w}\) varied from 185 to 215. This study analyzed the seven defects that were not detected by the proposed algorithm. The seven defects occurred in six images. These defects were all scratches. From the enlarged views shown in Figure 20, it can be seen that only the black portions of the scratches were detected, while their white portions were not. This failure was because the grayscale values of the white portions were close to the grayscale values of the background. Therefore, the proposed algorithm may fail to detect scratches whose grayscale values are too close to those of the background.
# Experimental Study on Properties of Ultrasonic Coupling Agent with Graphene in NDT
## 4 Experimental Results
### Microscopic Image of the Glycerol-Graphene Composite Coupling Agent  
As is shown in Figure 5 and Table 2, a few large agglomeration particles appear when the mass fraction is 0.25%. In Figure 5b, the maximum size (6.9 \(\upmu\)m) of the aggregate particles decreases but the number of particles is very large. During the propagation of ultrasonic waves, the interface of different media causes their attenuation. In Figure 5c, graphene is uniformly dispersed, and the average size (2.7 \(\upmu\)m) of the agglomerated particles is moderate. Figure 5d shows that the aggregate particles (maximum: 11.2 \(\upmu\)m) of graphene in glycerol are so large that a stable dispersion is hardly formed. This results in a great deal of ultrasonic attenuation. Excessive graphene cannot form uniform dispersion solution in glycerol, which results in inability of the composite graphene coupling agent prepared with a mass fraction of 1% to effectively promote ultrasonic propagation.  
Figure 5: Micrograph of the glycerol–graphene coupling agent. (**a**) graphene mass fraction is 0.25%, (**b**) graphene mass fraction is 0.5%, (**c**) graphene mass fraction is 0.75%, (**d**) graphene mass fraction is 1%.
## 3 System Model Description and Problem Statement  
### Integration Scenario  
The implemented scenario operates within the O-RAN architecture, facilitating automated and direct control over the disaggregated modules advocated by this framework. This set of modules, referred to as E2 Nodes, comprises the Open Radio Unit (O-RU), Open Distributed Unit (O-DU), and Open Centralized Unit (O-CU), with their functions managed by the xApps housed within the Near-real-time RAN Intelligent Controller (near-RT RIC).  
In this environment, the capabilities of the E2 interface are leveraged to gather information from the O-RU, O-DU, and O-CU modules of the Next-Generation Node B (gNB) and the MEC platform. Figure 1 illustrates this scenario, highlighting the aforementioned disaggregated modules following the 7.2x functional split, the interfaces defined for their interconnection, and their integration with the MEC system across both the data and control planes, with the latter being facilitated through the 5G Core Network (CN).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Work** & **Main Focus** & **Performance Gain** & **Limitations** \\ \hline
[10] & Power control, slice & +25\% throughput, \(-\)30\% \\  & allocation & latency & No MEC integration \\
[11] & Task offloading, routing & \(-\)40\% delay, optimized & No slicing control, PRB \\  & & scheduling & allocation, nor \\  & & MEC integration \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of FL–DRL approaches in O-RAN slicing and resource allocation.  
Figure 1: O-RAN integration scenario in 5G and MEC systems.  
This scenario has been implemented to ensure that the near-RT RIC agents running in the xApps utilize the E2 interface with O-CU for the purpose of collecting RAN parameters. Additionally, they will leverage the N2 interface to access the 5G core network through the Access and Mobility Management Function (AMF). This access facilitates communication with the Network Exposure Function (NEF), a crucial component for disseminating information regarding the capabilities and services of 5G CN functions to external entities.  
The MEC host, in contrast, is deployed at the edge of the 5G RAN, providing direct exposure to the O-CU. Its interconnection with the User Plane Function (UPF) is established via the N6 reference point [12]. The MEC orchestrator, functioning as the 5G Application Function (AF), facilitates efficient coordination with other NFs to support monitoring, provisioning, and policy enforcement.
## 2 Experiment
### Electrochemical Measurement  
After drying at 120 \({}^{\circ}\)C for 2 h, active material (AM, pristine and SiO\({}_{2}\)-coated NMC622 powders) and carbon black (CB) were premixed manually in an agate mortar, then a part of 7 wt.% polyvinylidene fluoride (PVDF, SOLEF 5130/1001) \(N\)-Methyl pyrrolidone (NMP) solution was dropped in it and kneaded. After the mixture became a dough-form, it was transferred to a glass beaker, and the rest PVDF-NMP solution was added. The mass ratio of NMC622, CB, and PVDF was 90:5:5, and the solid content of the slurry was 55 wt.%. The slurry was stirred magnetically for 12 h. This final slurry was coated on a 15-m-thick aluminum current collector through doctor blade with a gap thickness of 90 \(\upmu\)m and dried at 60 \({}^{\circ}\)C in the air for 30 min. Subsequently, the dried cathode film was transferred into a vacuum oven at 120 \({}^{\circ}\)C for 2 h to remove the NMP solvent completely. The electrode was calendared at 100 \({}^{\circ}\)C and its thickness was reduced by 10%. Through calculation [14], the film porosity decreased from uncalendared 55% \(\pm\) 2% down to 48% \(\pm\) 2%.  
The calendared electrodes (90 mm * 200 mm) of the pristine and the SiO\({}_{2}\)-coated NMC622 was punched into 40 pieces of 15.0 mm diameter circular electrodes. All the electrodes applied in this work had similar parameters, the areal capacity loading of AM was 1.1 \(\pm\) 0.1 mAh/cm\({}^{2}\), based on a material loading of 7.0 \(\pm\) 0.5 mg/cm\({}^{2}\) and a specific AM capacity of 160 mAh/g. CR2016 button cells were assembled in an argon-filled glove box from MBRAUN, whose water and oxygen content were both <0.1 ppm.  
NMC622 electrode as the cathode, microporous polypropylene membrane (Celgard 2500) as the separator, and Li metal as the anode, together with one spacer and spring, were placed into the CR2016 button cells, where the electrolyte was 1 M LiPF\({}_{6}\) dissolved in ethylene carbonate (EC):ethyl methyl carbonate (EMC) = 3:7 in weight, and 2% vinylene carbonate (VC) as additive (SoulBrain).  
The charge-discharge and rate capability measurements (with discharge rate of 0.1 C, 0.2 C, 0.5 C, 1 C and 2 C) were carried out on Maccor series 4000 battery tester in CCCV mode (constant current constant voltage, with a cutoff current of 10% charge current), cyclic voltammetry (CV), and electrochemical impedance spectroscopy (EIS) were performed on BioLogic VSP electrochemical workstation. After a long-term cycling, the cells were disassembled in the glove box, electrodes were washed with EMC, and studied with XRD and SEM.
## 2 Materials and Methods
### Taxonomic Characterization of Lentzea sp. JNUCC 0626  
The actinobacterium _Lentzea_ sp. JNUCC 0626 utilized in this study was originally isolated from Hwasun Gotjawal, located on Jeju Island (33\({}^{\circ}\)15\({}^{\prime}\)52.6\({}^{\prime\prime}\) N, 126\({}^{\circ}\)19\({}^{\prime}\)56.4\({}^{\prime\prime}\) E), Republic of Korea, using ISP5 medium. The growth of the strain was observed on various media, including ISP2, ISP4, ISP5, AIA, and Bennett's agar plates. The strain was routinely subcultured in TSB at 28 \({}^{\circ}\)C and preserved in a 20% (\(v/v\)) glycerol suspension at \(-\)70 \({}^{\circ}\)C for long-term storage.  
The morphological and physiological characteristics of _Lentzea_ sp. JNUCC 0626 were primarily investigated on TSB medium, unless otherwise specified. The temperature range for growth was determined by incubating the strain in TSB at temperatures ranging from 4 \({}^{\circ}\)C to 50 \({}^{\circ}\)C. Salt tolerance was tested by growing the cells in modified TSB medium containing various NaCl concentrations (0-10.0%, \(w/v\)) at 1.0% increments. Additionally, the pH range for growth was assessed by incubating the cells at 28 \({}^{\circ}\)C in TSB at pH levels ranging from 4 to 10 (with increments of one pH unit). The pH conditions were adjusted using the following buffer systems: pH 4.0-5.0, 0.1 M citric acid/0.1 M sodium citrate; pH 6.0-8.0, 0.1 M KH\({}_{2}\)PO\({}_{4}\)/0.1 M NaOH; and pH 9.0-10.0, 0.1 M NaHCO\({}_{3}\)/0.1 M Na\({}_{2}\)CO\({}_{3}\)[28].  
To evaluate the biochemical characteristics, the ability of _Lentzea_ sp. JNUCC 0626 to produce acids from various carbon sources was assessed using the API 50 CH system (bioMerieux,Marcy-I'Etoile, France), and enzymatic activity was further tested using API ZYM strips (bioMerieux), following the manufacturer's instructions. Respiratory quinones were extracted and purified from lyophilized cells and analyzed via HPLC, as described by [PERSON] et al. [29]. Polar lipids were examined using two-dimensional thin-layer chromatography (TLC) according to the procedure outlined by [PERSON] et al. [30]. Cellular fatty acids from logarithmically growing cells were analyzed using the Microbial Identification System following standard procedures (Sherlock Version 6.3; MIDI database: TSBA6).
## 6 Results of the Simulations
### Reproducibility of the Results  
The training, validation, and testing datasets were synthetically generated, as is described in Section 5.2, using the system simulator presented in Section 5. Our results can be reproduced through the implementation of the system simulator along with the NN structures presented in the same section.
## 4 Numerical Experiments and Applications
### Application in a Machine Selection Problem  
We now apply our method to study a machine selection problem for a manufacturing company, which is engaged in manufacturing precision machined components required for automotive and general engineering industries, such as automobile industries and textile machine manufacturers.  
To meet the customer demand, it is important to enhance the manufacturing capability of the company. The machine selection problem is to select the best machine from the set of some feasible alternative proposals. To make the best selection, three criteria, flexibility, quality and productivity, will be taken into consideration to evaluate the various alternative machines. In addition, four experts from inside and outside the company are involved in the decision-making process. We suppose that the initial score matrices by all the experts are given in Table 7 (also see [7]).  
We implement Algorithm 2 to solve the above machine selection problem. From Steps 1 and 2 of Algorithm 2, we obtain the overall score matrix of the four experts in Table 8, where \(DM_{i}\), \(i=1,2,3,4\), denote the \(i\)-th decision-maker.  
From Steps 3 and 4 of Algorithm 2, the final rank of the alternatives is obtained (see Table 9), where IDNN represents the improved decision neural network method in [7].  
\begin{table}
\begin{tabular}{c|c c|c c} \hline \hline \multirow{2}{*}{**Alternatives**} & \multicolumn{2}{c|}{**DBM [8]**} & \multicolumn{2}{c}{**Min-Max**} \\ \cline{2-5}  & **Result** & **Rank** & **Result** & **Rank** \\ \hline \(\mathbf{A_{1}}\) & 0.3016 & 5 & 0.3032 & 5 \\ \(\mathbf{A_{2}}\) & 0.3110 & 4 & 0.3148 & 4 \\ \(\mathbf{A_{3}}\) & 0.3188 & 2 & 0.3251 & 2 \\ \(\mathbf{A_{4}}\) & 0.3384 & 1 & 0.3384 & 1 \\ \(\mathbf{A_{5}}\) & 0.3161 & 3 & 0.3180 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Rank of alternatives.
## 4 Non-Destructive Investigation
### Geological and Geotechnical Investigation
#### 4.1.2 Remote Sensing Analysis: Persistent Scatterer Interferometry (PSI)  
The DInSAR technique is a widely used and consolidated geodetic technique based on satellite Synthetic Aperture Radar images. It allows for measuring displacements with millimetric precision. It has applications in different fields, such as geohazard mapping [14] and monitoring, mining, civil engineering or civil protection [15].  
The study consisted of a historical analysis of the satellite images of the Gjirokastra castle area. To this aim, two independent Sentinel-1 datasets were processed--in one, 180 images acquired in descending geometry and covering the period between November 2014 and January 2020; in the second one, 140 images acquired in ascending geometry and covering the period between November 2015 and March 2020. The use of both datasets allows one to decompose the measured displacements in the west-east direction and vertical displacements in those areas with intersection. This information enables an easier interpretation of the results compared with the usual single geometry DInSAR results. The results of the DInSAR deformation measurement of the Gjirokastra castle and surrounding area indicated that there have been no significant displacements inside the castle in the last 5 years. More specifically, no displacement was observed at the location of the tower (Figure 5).
# An Approach Based on VR to Design Industrial Human-Robot Collaborative Workstations
## 2 The Proposed Approach
### VR-Based Rapid Prototyping of the Workstation Layout
#### 2.1.1 Creation of a Structured Description of the Scene  
We assume that the starting point to the proposed approach is an unstructured and unlabeled description of the workstation to prototype. As such, the first step consists in the extraction of a structured description of the scene so that it can be automatically processed by the proposed system. Such a description contains the elements required in the scene, their position and size. As elements, we refer to architectural items, such as floor, walls or lamps, furniture, which, for example, may include tables, shelves, and any other working tools that may be needed, such as robots or conveyors. In principle, any element can be added to build the desired work environment, provided that its 3D CAD model is available. Moreover, any situational factor that can be relevant in a reliable replica of the environment revshould be considered. This is the case, for example, of specific environmental conditions, such as noise or dust, or the presence of human operators, which might be useful to assess space occupancy or specific interaction dynamics (as explained in Section 2.2.1, we refers to where interactions happen, which they are, what are their direction and when they happen). Table 2 reports a reference list of possible elements to include in the scene. This list has been derived following an analysis of the most recurrent categories of elements in shopfloors and workstations. Nevertheless, it can be extended depending on specific applications. To organize such information (elements and their features), it is necessary to define a structured format, which can subsequently be processed by the system in a (semi)automated manner. For this purpose, several standard formats exist: among the most popular ones, we considered the use of JavaScript Object Notation (JSON, [[https://www.json.org/json-en.html](https://www.json.org/json-en.html)]([https://www.json.org/json-en.html](https://www.json.org/json-en.html)), accessed on 24 June 2021) and Extensible Markup Language (XML, [[https://www.w3](https://www.w3) schools.com/xml/xml_whatis.asp]([https://www.w3](https://www.w3) schools.com/xml/xml_whatis.asp), accessed on 24 June 2021) data-interchange formats. JSON is an open standard file format based on key-value pairs and ordered lists; XML organizes data with a start-tag, its content and an end-tag. Following a comparison between these two formats, JSON has been selected as the most suited for the proposed approach, since it is characterized by low verbosity and concise syntax. Indeed, these features are of particular importance with reference to early and fast prototyping and rapid visualization of changes. As a result, the output of this step is a labeled text, structured according to the JSON format, where, for each element in Table 2, the following information is reported: number
## 4 Results and Discussion
### Distribution of Local Mass Transfer Coefficients in a Mixed Electrolyte System  
Using the concentration profile shown in Figure 5 and the model in Figure 2, the local mass transfer coefficient when the electrolyte diffuses was calculated, and the results are shown in Figure 8. These results provide two pieces of information. First, the mass transfer coefficient of Na\({}^{+}\) is greater than that of Mg\({}^{2+}\), although the actual difference between the two is not very big. Second, the mass transfer coefficient is attenuated as diffusion continues, and the amount of variation for the two electrolytes is also different. These two phenomena are consistent with the changes in concentration shown in Figures 3 and 5 and reflect the inherent reason for the difference between the rates of diffusion for the different salt components. Because Mg\({}^{2+}\) has a strong hydration ability in aqueous solution, it is a structure-making ion of water [30], and this can aggravate the internal cross-linking of the salt-water system. Thus, it can significantly increase the viscosity and decrease the mass transfer coefficient. In this case, the mass transfer coefficient of Mg\({}^{2+}\) is smaller than that of Na\({}^{+}\). Figures 5 and 8 shows that the mass transfer coefficient and the saturation degree are negatively related. For the mass transfer coefficient, when the salt-water interface is farther away, the diffusion coefficient is greater--this is because the solute concentration and viscosity are small, and this causes the mass transfer resistance to be smaller. Such a phenomenon is also consistent with the general law in the diffusion of salt solutes in aqueous solutions [23; 31].  
Figure 9 shows that the mass transfer coefficients of the mixed-electrolyte system are smaller than those of the single-electrolyte system. It is speculated that this reflects the interactive influence that the difference in transferability between Na\({}^{+}\) and Mg\({}^{2+}\) has on the diffusion rates. Because the diffusion of ions is mutually restricted, the presence of multiple ions must have an influence on the kinetics of the diffusion process [14]. Therefore, compared to the single-electrolyte system, the mass transfer coefficients in the mixed-salt system decrease.
## 3 Results and Discussion
### Bioactivity
Another factor is that the ionic radius of cobalt (152 pm) is smaller than that of calcium ions (194 pm), which makes it easier for cobalt to combine with phosphorus ions [49]. This may also have resulted in samples irradiated with the CoHA solution being able to induce greater HA deposition. The effect of surface treatment on HA deposition showed that the pure titanium surface was smoother, which made it more difficult for HA to adhere. This study used various methods to make surface junctions with different pores, and results showed that smaller pores \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Atomic\%**} & \multicolumn{1}{c}{**Ti**} & \multicolumn{1}{c}{**Ca**} & \multicolumn{1}{c}{**P**} & \multicolumn{1}{c}{**Ca/P**} & \multicolumn{1}{c}{**Co**} & \multicolumn{1}{c}{**Ca + Co/P**} \\ \hline \multirow{4}{*}{Original} & Ti & 28.71 & 0.82 & 0.77 & 1.06 & \multirow{4}{*}{N.D} \\  & TNT & 28.02 & 0.49 & 0.41 & 1.20 & & \\  & NaOH & 14.88 & 6.88 & 6.11 & 1.13 & & \\  & HCl & 32.24 & 2.14 & 1.76 & 1.22 & & \\ \hline \multirow{4}{*}{LAB} & Ti & 23.45 & 1.17 & 1.02 & 1.15 & \multirow{4}{*}{N.D} \\  & TNT & 1.00 & 8.67 & 6.03 & 1.44 & & \\ \cline{1-1}  & NaOH & 3.48 & 9.50 & 7.80 & 1.22 & & \\ \cline{1-1}  & HCl & 3.23 & 8.90 & 6.80 & 1.31 & & \\ \hline \multirow{4}{*}{LAB} & Ti & 26.73 & 2.97 & 3.68 & 0.81 & 0.55 & 0.96 \\ \cline{1-1}  & TNT & 15.92 & 4.22 & 3.21 & 1.31 & 1.15 & 1.67 \\ \cline{1-1} CoHA solution & NaOH & 23.03 & 2.18 & 1.75 & 1.24 & 0.78 & 1.69 \\ \cline{1-1}  & HCl & 12.10 & 14.20 & 11.76 & 1.21 & 3.09 & 1.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Surface element analysis of the LAB-treated samples after soaking in SBF for seven days by EDX. Figure 7: The surface topography of the LAB-treated samples after soaking in simulated body fluid (SBF) for seven days by FE-SEM. (of about 18-196 nm) had better deposition effects. In addition, the pH value of the solution and the substitution of ions also affected the type of HA. Further, [PERSON] et al. reported that the CaP coating on the metal implants surface is still a challenge in clinical applications, especially in controlling the coating structure and degradation rate [50]. In addition, [PERSON] et al. improved the integration of the implant with the surrounding bone tissue by forming a titanium dioxide layer on the surface of the titanium alloy (Ti6 Al4V) [51]. Therefore, our study will evaluate the degradation and biological behavior of the coating and provide more novel clinical methods and applications. Figure 8: The characteristics of the LAB-treated samples after soaking in SBF for seven days. (**A**–**C**) FTIR spectra obtained from original, LAB CaP solution, and LAB CoHA solution. (**D**–**F**) XRD patterns of the original, LAB CaP solution, and LAB CoHA solution.