esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Rationale
#### 2.1.1 Multidisciplinary Collaboration  
The methodology assumes that a satellite image analyst and an archaeologist work together. Such a collaboration represents a key element of this research, because it serves not only for integrating two different perspectives, but also for seeking possible solutions to bridge the technical skill gap, and the underestimation by archaeologists on the potential of satellite images for the protection of cultural heritage.  
In the literature, this necessity had been already stressed by [PERSON] and [PERSON] [59] and [PERSON] et al. [33], all calling for an increasing interaction among the different disciplines as well as an improvement in the \"transfer of knowledge\", which does not imply a total exchange of skills among experts of different fields, but rather a mutual improvement of the theoretical knowledge and practical expertise.  
This above mentioned approach of \"co-working\" is organized according to a specific operational chain by which tasks like data collection and analysis are clearly designed and assigned according to the individual researcher's skills, although both the professionals are engaged in the process. The subsequent steps, including the discussion of the results and interpretation are conducted jointly. Furthermore, the critical assessment of the outcomes (especially from the archaeologist's point of view) may suggest the need to go back to the data processing and analysis, if not even to data collection, and iteratively refine data selection, parameter setting for processing, etc., to better suit the purpose.  
Thanks to this collaboration, archaeologists are able to learn the variables that may impact on the quality and visibility of satellite acquisitions, as well as the characteristics of the satellites to use. Vice versa, the image analyst is trained in tailoring satellite data acquisition and their processing to address specific user requirements. Such an improved awareness on the potential of satellite imagery will eventually allow archaeologists to master the data collection processes, tuning images into selection for their research scopes, instead of using available datasets randomly found on the web or through open sources.
## 4 Antimicrobial Properties of Honey
tropicalis_ was observed for both heather and Manuka honey at a concentration of 50% (_w/v_). In regard to _P. aeruginosa_, Manuka honey was more effective in decreasing the cell count already at a concentration of 25% (_w/v_) and up to 4 log (CFU/cm\({}^{2}\)) reduction at 50% (_w/v_) in the single biofilm. Nonetheless, a significant cell inhibition compared to control was obtained with heather honey at 50% (_w/v_). When assessed in association with conventional antifungal treatment, the supplementation with honey allowed the administration of a 50% lower dose of funconazole, but higher inhibition was still achieved with honey monotherapy at 50% (_w/v_) [101]. Different honey varieties have been shown to be remarkably effective against various bacteria, and more broadly against pathogens, including fungi, which is responsible for infections non-respondent to conventional drugs. This indicates that honey represents a potential contributor to the new frontier of natural antimicrobial products emerging as promising agents in antibiotic resistance. Ultimately, Apis mellifera, commonly known as honey bee, is not the only insect responsible for honey production. The species of nectar-foraging insects seems to determine the phytochemicals pattern in the ripened honey, thus leading to a different effectiveness against bacterial infections. Malaysian honey have been reported to have a higher bactericidal effect against _S. aureus_ and _E. coli_ in vitro when produced by the stingless bee species _Heterotrigona itama_ rather than _Apis cerana_ and _Genitorigona thoracica_[78]. Of the eight stingless bee honey samples analysed by [PERSON] et al., honey produced by _Homotrigona fimbrata_ has been found to possess the highest inhibitory capacity against five bacteria: _Serratia marcescens_ (_S. marcescens_), _E. coli_, _Bacillus subtilis (B. subtilis)_, _Alcaligenes faecalis_ (_A. faecalis_), and _S. aureus_. The least active honey was produced by _H. erythrogastra_, and it has been shown to be inactive against the testes bacteria at all the evaluated concentrations [83]. In contrast, _H. erythrogastra_ honey from Borneo was shown to be broadly active against all the bacteria strains tested by [PERSON] et al. Honey produced by _G. thoracica_ has been shown to be significantly more effective than the honey samples from other stingless bees against both Gram-positive (_Staphylococcus xylosus_ (_S. xylosus_)) and Gram-negative (_P. aeruginosa_ and _Vibrio parhahaemolyticus_ (_V. parahaemolyticus_)). Interestingly, _H. itama_ honey was both the least active antimicrobial agent against Gram-negative bacteria and the richest in phenolics, while significant Gram-negative bacteria inhibition was obtained with the honey samples with the highest level of flavonoids (_G. thoracica_). This has been hypothesised to be due to flavonoids-mediated disruption of the outer bacterial membrane integrity or impaired DNA synthesis [102].
# M-ary Phase Position Shift Keying Demodulation Using Stacked Denoising Sparse Autoencoders
## 5 Simulation and Analysis  
### Database  
The database used in this paper contains the training set and the test set. When the database is used to detect the 2 PPSK modulation signal, the training set contains 2 classes of 60,000 sampled waveforms, and the test set contains two classes of 10,000 sampled noised waveforms at several SNR values. For example, as shown in Figure 4, there are symbol \"\(1\)\", symbol \"\(2\)\", and \(-3\)dB noised symbol \"\(2\)\", separately. The main parameters of the improved MPPSK system are \(M=2\), \(K=30\), \(N=105\), \(f_{c}=21\times 10^{6}\) Hz, and a sampling rate of \(f_{\rm s}=210\times 10^{6}\) Hz. When the database was used to detect the improved 4 PPSK modulation signal, the training set contained four classes of 60,000 sampled waveforms, and the test set contained four classes of 100,000 sampled noised waveforms at several SNR values.
## 4 Discussion
For example, to create the preclinical endodontics system, the combination of surface scanners and Dental Align software [44] is useful. Three-dimensional visualization, namely MR, was also applied in the case of mesiodens extraction [45]. The holograms of the jaw, which are based on CT data, were shared with three jaw surgeons before the operation. Prior to starting endodontic treatment, it is necessary to conduct a thorough diagnosis to assess the anatomy of the tooth and the condition of the periapical tissues. In addition to the intraoral examination and history taking, an X-ray examination is necessary to make a proper diagnosis and to plan treatment. A two-dimensional X-ray is not always sufficient. Especially in the case of re-treatment, cone-beam computed tomography has a greater diagnostic value. Thus, this paper evaluated the application of mixed reality for root canal treatments due to its complex nature. The created 3D model, which is shown in Figure 8, is helpful at the first stage of treatment when locating canal orifices. Thanks to the visible curvature of the roots, dentists can choose tools that will be able to bend at the right angle around the root tip. In the case of hand and nickel-titanium rotary files, it is possible to bend the tip of the tool at an appropriate angle. Thanks to the possibility of the simultaneous assessment of canal curvatures in all planes, it is also possible to choose the right file insertion path for each canal. The results obtained show that the analysis of 3D holograms takes about 52% less time than the analysis of computed tomography scans. The significant advantage of the proposed approach is the fact that in one image, the entire tooth and the anatomy of its canals are visible, while the CBCT is not able to provide the entire internal structure of the tooth at the same time. Moreover, the application of immersive technologies has one real disadvantage, which is a large financial outlay [46]. At the moment, the limitation is also caused by small datasets in the case of VR/AR/MR applications for teaching future medical staff and young doctors, especially in dentistry [47]. Other limitations may be connected with the fact that dentists must have at least basic knowledge of the VR/AR/MR tools and their software. Staying in virtual reality for a long time can also have health consequences, including loss of concentration, neck pain, and headaches or dizziness [48]. In turn, the case of virtual reality applications, as a tool to help reduce the fear of dental procedures, has limitations in behavioral management [38; 49]. The immersive technology does not prevent the users from hearing sounds associated with dental treatment, which can also cause fear and anxiety [50].
## 3 Elastographic Methods
### Transient Elastography (TE)
#### 3.1.3 Correlation with Fibrosis Stage and Different Etiologies
The technique is feasible and it has been reported that pediatric Fontan patients have markedly elevated LSMs (18.6 versus 4.7 kPa) [18]. There was no association between TE values and patient age, time since Fontan surgery, or median Fontan circuit pressure. [18]. The liver stiffness score of biliary atresia patients was significantly higher than that of normal controls (27.37 \(\pm\) 22.48 and 4.69 \(\pm\) 1.03 kPa; \(p\) < 0.001). The sensitivity (and specificity) of TE (using a cut-off value of 12.7 kPa) and APRI (using a cut-off value of 1.92) in predicting esophageal/ gastric varices were 84% (77%) and 84% (83%), respectively [57].
## 4 Test Results and Analysis
### Sensibility Analysis of Factors
#### 4.2.2 Porosity  
Although there is a close relationship between material density and porosity, there were some differences in the sensitivity of different levels of indicators to density and porosity in the orthogonal test. In this study, the porosity sensitivity analysis of materials was to ensure the compactness of similar materials with low strength and a high similarity ratio, which will affect the construction of physical similarity simulation model in the later stage. Through the sensitivity analysis of density and porosity, the effects of different levels and factors of similar materials on the differences of rock density and porosity were compared, so as to provide more scientific guidance for determining the effective material ratio.  
The data processing method of the porosity test results was the same as that in Section 4.2.1. As shown in Table 6, the range of filling material/bonding material was the largest, followed by Portland cement/gypsum, and the range of silicone oil content was the smallest. The range analysis showed that the filling material/bonding material was the main factor to control the porosity. The higher the content of bonding material, the smaller the porosity of the sample. The analysis was because the fineness of the bonding material was larger than that of the filling material, which can fill the gap between standard sand, resulting in the reduction of porosity. Meanwhile, the higher the content of Portland cement in the same amount of bonding material, the smaller the porosity of the sample. This was because the cementation strength of Portland cement was stronger than that of gypsum, which can effectively gel all kinds of raw materials and form small porosity.
## 4 Methodology
### Evaluation Framework
#### 4.5.2 Human Evaluation  
A subset of 200 translations undergoes evaluation by qualified native speakers using 5-point Likert scales for adequacy and fluency assessment. Comprehensive evaluation guidelines, criteria, and evaluator qualifications are specified in Appendix A.2. Inter-annotator agreement is measured using Krippendorff's alpha to ensure evaluation reliability.
# Accurate Extraction of Cableways Based on the LS-PCA Combination Analysis Method
## 3 Experiment and Results
### Experimental Results and Analysis
However, it should be noted that when selecting the elevation threshold, it must be classified according to the intended use. Otherwise, over-segmentation or under-segmentation may occur.
# The Attachment of Carbon Nanotubes to Atomic Force Microscopy Tips Using the Pick-Up Method
## 2 Materials and Methods
### Single AFM Tip CNT Attachment Using the Pick-Up Method  
To attach CNTs to a single AFM probe involves scanning a CNT covered substrate in standard tapping mode. The CNT substrate used was prepared as described in Section 2.1. Standard tapping mode images were acquired with the amplitude set-point typically 70-80% of the free cantilever amplitude. The target amplitude was typically set between 350 to 1500 mV and corresponds to ~10-30 nm. Once in feedback and imaging, the amplitude set-point was then lowered in 10 mV increments until it was approximately 20-50% of the free cantilever amplitude. This high force imaging typically produced unstable scan lines and sections of AFM images. When a CNT became attached the image would stabilize and the amplitude set-point was then promptly returned to standard operating values (70-80% free amplitude). Subsequent AFM images were acquired to ensure stable AFM imaging and CNT attachment. Typically, the high force imaging would comprise 10-20% of the AFM image before CNT attachment occurred. In some cases it was found that relocating the AFM probe to a different area of the CNT substrate produced more stable images as CNTs on the area where pick-up occurred could be displaced and make imaging difficult. This process was successful approximately 80% of the time and could be repeated multiple times for the same probe.  
To further demonstrate CNT attachment amplitude versus distance (AVD) curves were acquired. AVD curves are similar to static force distance curves except it is the cantilever oscillation amplitude that is measured as the AFM probe is pressed into the sample surface. AVD curves can be used to verify CNT attachment by the appearance of a buckling signature which occurs when the CNT attached to the AFM probe is pushed into a hard surface such as silicon. There are a number of research groups that have used AVD distance curves to verify CNT attachment to AFM probes [68; 69; 70; 71] but they are also used to study experimentally and theoretically the compression, bending and buckling behaviour of CNTs [69; 70]. This method was applied to AFM probes with nominal spring constants ranging from 0.24 to 16 N/m which covers AFM probes used for contact, force modulation and tapping mode imaging.
# Comparison of Disturbance Compensators for a Discrete-Time System with Parameter Uncertainty
## 3 Overview of Disturbance Compensators
### Discrete-Time Disturbance Observer  
The discrete-time disturbance observer Equation (21) was proposed in [15] by [PERSON] for discrete-time systems with either matched or mismatched disturbances:  
\[\begin{array}{ll}u_{d6}(k)&=\Lambda u_{d6}(k-1)-(\Lambda-I_{n})f(k-1)\\ &=\Lambda u_{d6}(k-1)-(\Lambda-I_{n})[x(k)-\Phi x(k-1)-\Gamma u(k-1)]\end{array} \tag{21}\]where \(\Lambda=\text{diag}\{\lambda_{1},\,\lambda_{2},\,\ldots,\,\lambda_{n}\}\) with \(|\lambda_{i}|<1,\,i=1,\,2,\,\ldots,\,n\).  
Similar to the previous five methods, the boundedness assumption of the lumped disturbance also is required in the discrete-time disturbance observer (DTDO). It was proved in [15] by [PERSON] that the compensation error of DTDO \(\left|\overline{u_{46}}\right|\) will converge to a bounded region whose width is greater than the upper bound of the lumped disturbance. Also, the upper bound of \(\left|\overline{u_{46}}\right|\) is influenced by \(\Lambda\), which can be adjusted to enhance the compensation accuracy. Additionally, it is observed from Equation (21) that the delay estimation method also is employed in DTDO to obtain the unmeasurable disturbance term \(f(k-1)\).
## 3 Results
### Cortical Activation
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**V1**} & \multicolumn{2}{c}{**V4**} & \multicolumn{2}{c}{**V5**} & \multicolumn{2}{c}{**V1 vs. V4**} & \multicolumn{2}{c}{**V4**\(\star\) vs. V8**} & \multicolumn{2}{c}{**V1 vs. V8**} \\ \cline{3-11}  & & **Mean \(\pm\) SD (mM)** & **Mean \(\pm\) SD (mM)** & **Mean \(\pm\) SD (mM)** & **Mean \(\pm\) SD (mM)** & **t-Value** & **\(P\)-Value** & **\(t\)-Value** & **\(P\)-Value** & **\(t\)-Value** & **\(P\)-Value** \\ \hline \multirow{4}{*}{**CG**} & **M1** & 0.498 \(\pm\) 0.00008 & 0.433 \(\pm\) 0.00004 & 0.658 \(\pm\) 0.00011 & 0.0005 & 1.00 & \(-\)1.224 & 0.23 & \(-\)0.827 & 0.415 \\ \cline{2-11}  & **SMA** & 0.498 \(\pm\) 0.00007 & 0.423 \(\pm\) 0.00004 & 0.453 \(\pm\) 0.00005 & 0.1910 & 0.849 & \(-\)1.408 & 0.163 & \(-\)1.344 & 0.183 \\ \cline{2-11}  & **PFC** & 0.569 \(\pm\) 0.00008 & 0.489 \(\pm\) 0.00004 & 0.482 \(\pm\) 0.00006 & 1.9556 & 0.0542 & \(-\)0.667 & 0.507 & 1.842 & 0.0694 \\ \hline \multirow{4}{*}{**IG**} & **M1** & 0.58 \(\pm\) 0.00007 & 0.419 \(\pm\) 0.00003 & 0.477 \(\pm\) 0.00004 & \(-\)1.708 & 0.0958 & \(-\)3.940 & \(3.37\times 10^{-4\,\star\,\star\,\star}\) & \(-\)5.724 & \(1.36\times 10^{-6\,\star\,\star\,\star}\) \\ \cline{2-11}  & **SMA** & 0.705 \(\pm\) 0.00008 & 0.587 \(\pm\) 0.00004 & 0.568 \(\pm\) 0.00005 & \(-\)4.684 & \(9.94\times 10^{-4\,\star\,\star\,\star}\) & \(-\)3.905 & \(1.82\times 10^{-4\,\star\,\star\,\star}\) & \(-\)6.836 & \(9.44\times 10^{-10\,\star\,\star\,\star}\) \\ \cline{2-11}  & **PFC** & 1.02 \(\pm\) 0.0001 & 0.773 \(\pm\) 0.00005 & 0.667 \(\pm\) 0.00005 & \(-\)1.474 & 0.144 & \(-\)1.508 & 0.135 & \(-\)3.413 & \(9.65\times 10^{-4\,\star\,\star\,\star}\) \\ \hline \hline \end{tabular} Values are mean \(\pm\) standard deviation; \({}^{\star\star\star}\)\(p\)\(<\) 0.001.  
\end{table} 
Table 3: Average activation in the regions of interest (ROIs) during treadmill walking in patients with Parkinson’s disease (PD).
## 2 Materials and Methods
### Data Preprocessing
#### 2.4.2 Sentinel-1  
Sentinel-1 (S1) in the band C imagery contains a 12- or 6-day revisit cycle depending on the availability of images 1A and 1B with 10 m spatial resolution. The analysis presented here selected the interferometric wide swath mode, which is the main operational mode and produces data in single (HH or VV) or double (HH + HV or VV + VH) polarisation in both ascending and descending modes. Each scene available at GEE was preprocessed using the Sentinel-1 Toolbox to (1) remove low-intensity noise and invalid data from scene edges and (2) remove thermal noise [19]. The edge of S1 was omitted using the 'connected components' method in GEE, which hides sets of contiguous pixels processed with values less than \(-25\) dB in the VV polarisation [52; 53]. The VV and VH noises were filtered using the refined Lee filter [54]. We collected S1 data on 13 and 18 July 2021. In this study, the mean of both images was combined with the Pleiades image. In addition, Sentinel-1 data helped obtain better results. SAR is able to reflect the soil's roughness, texture, and the different physical conditions of the ground. Another benefit of using SAR is that it has a certain amount of soil penetration in dry sand, which makes it particularly suitable for this specific area.
## 4 Design of RBMM
### Buffer Replacement Strategy Based on Flow Priority Scoring
Subsequently, the packet with the minimum sequence number from the flow with the highest score is forwarded to the terminal based on the probability as follows:  
\[p=\frac{\text{Score}_{k}-\text{Score}_{\text{min}}}{\text{Score}_{\text{max}} -\text{Score}_{\text{min}}} \tag{10}\]  
Compared to the probability calculation that solely relies on packet size, the score-based probability calculation comprehensively reflects various factors influencing the reordering process, allowing for a more accurate representation of the priority differences among flows and improving the fairness of buffer resource allocation.
###### Abstract
However, the UST does not consider the influence of hydrostatic pressure on the deformation of surrounding rock and its complex form of expression is not convenient for application within engineering. The D-P criterion introduces the influence of intermediate principal stress, thereby providing a more practical solution to this kind of problem [22]. Therefore, the present study analyzed the effect of intermediate principal stress on the rock surrounding a tunnel using the D-P criterion. The dilative angle \(\psi\) is generally used in elastoplastic analysis of deeply buried circular tunnels to express capacity expansion after unloading. Most current research on the shear dilative model has concentrated on the rock mass. In contrast, studies analyzing stresses of circular tunnels have mostly taken the dilative angle into account as a constant parameter or linear variable [5; 23; 24; 25; 26]. However, the dilative angle of rock mass cannot be considered a constant or linearly changing within practical engineering due to surrounding pressure and plastic shear strain. [PERSON] [3] proposed an attenuation model of the dilatancy coefficient for the surrounding rock of a circular tunnel. Subsequently, [PERSON] [13] proposed a nonlinear attenuation relationship between the dilatancy angle \(\psi\) and confining pressure based on the peak dilatancy function of the fractured rock mass. [PERSON] [26] found that the dilative angle of the rock mass presents a nonlinear relationship with the surrounding pressure and plastic shear strain. The present study aimed to consider the softening properties of the rock mass and the effect of intermediate principal stress on the strength of the rock mass. The D-P criterion and non-correlated flow rule were applied to derive a finite difference numerical method as representative of the stress field, displacement field, and plastic radius of the surrounding rock under uniform stress. The influences of different factors on the stress, strain, deformation, and plastic range of surrounding rock were studied and compared with the solutions of the M-C and UST criterion. The results of the present study can provide a significant theoretical foundation for the assessment of stability and structural design of a deeply buried tunnel.