esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results
### Antibiotic film Assays
#### 2.5.2 Minimum Biofilm Eradication Concentration (MBEC)  
The HRZN-15 peptide, which demonstrated antibiofilm activity in the MBIC assay, was further tested for the eradication of pre-formed (24 h) biofilms through the MBEC assay. A minimum biofilm eradication concentration (MBEC) assay is defined as the lowest drug concentration that eradicates previously established biofilm. Using an MBEC assay plate (Innovotech), we were able to establish biofilms of AB5075 on the pegs for 24 h and stain them with crystal violet for biomass quantification, as previously shown [32]. HRZN-15 eradicated pre-formed biofilms of AB5075 with treatment at MBEC\({}_{100}\) 16 \(\mathrm{\SIUnitSymbolMicro g}\)/mL. (Figure S8A, Table 4) with an MBEC\({}_{50}\) of 8 \(\mathrm{\SIUnitSymbolMicro g}\)/mL. LL-37 acted in a concentration-dependent manner against established biofilm, achieving 80% biofilm eradication at 32 \(\mathrm{\SIUnitSymbolMicro g}\)/mL. (Figure S8B) and MBEC\({}_{50}\) at 4 \(\mathrm{\SIUnitSymbolMicro g}\)/mL, which was consistent with published results [32]. At the highest IDR-1018 concentration tested (32 \(\mathrm{\SIUnitSymbolMicro g}\)/mL), only 20% of biofilm was eradicated (Figure S8C), which was consistent with what was previously shown [66], demonstrating that this peptide can eradicate pre-formed biofilms in _A. baumannii_.  
Notably, biofilms treated with polymyxin B (Figure S8D) were persistent, maintaining up to 40% of crystal violet-stained biofilm mass when treated with between 2 and 16 \(\mathrm{\SIUnitSymbolMicro g}\)/mL polymyxin B, although the MBIC\({}_{50}\) was 2 \(\mathrm{\SIUnitSymbolMicro g}\)/mL (Table 4). Due to this high level (40%) of biofilm staining in the crystal violet MBEC assay, we wanted to investigate if the stained biofilm mass contained bacteria or was just a residual stained extracellular matrix (ECM). A reduction in CFU was determined on polymyxin B-treated pre-formed biofilms, corresponding to Figure S8D. We demonstrated the complete eradication of bacteria at 2 \(\mathrm{\SIUnitSymbolMicro g}\)/mL polymyxin B, illustrating a limitation of the crystal violet-based MBEC assay to detect residual ECM in the absence of viable bacteria.  
\begin{table}
\begin{tabular}{c c c} \hline
**Peptide** & **MBIC\({}_{100}\) (\(\mathrm{\SIUnitSymbolMicro g}\)/mL)** & **MBEC (\(\mathrm{\SIUnitSymbolMicro g}\)/mL)** \\ \hline HRZN-15 & 8 & 16 \\ \hline LL-37 & 64 & \(\sim\)32 \\ \hline IDR-1018 & \(>\)64 * & \(>\)32 \\ \hline Polymyxin B & 1 & * 2 \\ \hline \end{tabular}
* See the discussion of polymyxin and IDR1018 results in the text.  
\end{table}
Table 4: MBIC\({}_{100}\) of _A. baumannii_ BAA-1800 and MBEC of AB5075 against tested peptides.
# Water Extraction in PolSAR Image Based on Superpixel and Graph Convolutional Network
## 2 Materials and Methods
### Superpixel Segmentation and Feature Extraction
#### 2.1.2 Feature Extraction
Land to Water pixel Ratio (LWR)  
With no regard to various defects, TS methods are widely used in water extraction due to their convenience. Thus, their pre-segmentation results can be expected as an important input for a GNN network, and especially in some complex flood areas, they can facilitate the distinction between high-water soil and water bodies. However, almost all TS methods are concentrated on the pixel level, which cannot be used directly at the object level. Therefore, this study proposes an indicator called LWR based on superpixel and TS methods as follows:The standard Ostu method [38] is first applied to all pixels to obtain a binary image. It is a self-adaptive maximum inter-Class variance method and is commonly used in gray image segmentation. Pixels with intensity values lower than the threshold are marked as 0, and others are marked as 1. Then, for each superpixel, the ratio of the land pixel number to water (1 to 0) is counted. The superpixel is more likely to be water as the LWR becomes smaller.
## 4 Simulation Results and Discussion
### Case 2: Sensitivity Analysis with Criterion Variation Within \(\pm 10\%\)
#### 4.3.1 Scenario 1: Variation in Upload Speed  
In this scenario, we randomly vary the initial upload speed values while keeping the values of other criteria unchanged. Table 14 shows the random variation rates applied to the initial upload speed values of each satellite. The resulting decision matrix is shown in Table 15.
## 3 Results
### Fit Assessment Outcomes
In studies that made a computerized measurement (eight papers), the evaluation was done using a digital camera (two papers) or microscope (two papers), a superimposition technique by CAE (two papers), a scanning electron microscope (one paper), and a micro-computed tomography equipment (one paper). Two of the eleven studies that used the digital superimposition method for quantitative evaluation applied a similar methodology using the color difference map CAE technique. The other nine differ in the CAE technique, equipment, data analysis software, or locations measured (Table 2). When protocols were compared in qualitative assessments, protocol 6 (direct additive manufacturing using conventional impression) emerged with more Goods (eleven) and a high number of Excellents (two), except for one study that was considered Bad. Considering quantitative outcomes, protocol 6 was the most analyzed (nineteen papers), with discrepancy values between 14 and 365 \(\upmu\)m. The lowest value of discrepancy presented was 5 \(\upmu\)m, set by protocol 2 (conventional production using a physical cast digitally created), and the highest value was 513 \(\upmu\)m, set by protocol 4 (conventional production with CAD analysis and indirect additive manufacturing) (Table 2). The assessment methods presenting discrepancy values that were reported in more than one study were the following: the stereo microscope analysis of silicone specimens focusing on occlusal rest seats (two papers); the digital microscope evaluation of silicone specimens focusing occlusal rests (two papers); the digital superimposition of silicone specimens (two papers) and the digital superimposition of the entire framework to the STL design using a color difference map by CAE technique (two papers). In those studies, protocol 6 always presented better outcomes than protocol 4. Two studies evaluated quantitatively, focusing on occlusal rests, by measuring silicone specimens using a micro-computed tomography technique or scanning electron microscope. Additionally, the other eight studies that evaluate the discrepancy of entire frameworks by digital superimposition to the STL design differ in the CAE technique (color difference map, distance measurement, or contrastive analysis), in the combination of scanner and software, or the framework locations evaluated. Nineteen manuscripts compared protocols and used conventional protocol as a control, and eleven compared digital protocols. Twenty-two studies compared different production protocols and reported fit accuracy outcomes, fifteen by quantitative evaluation. Regarding the number of fit accuracy comparisons between protocols, protocol 6 (conventional impression followed by CAD analysis and direct additive manufacturing) was the most compared (twenty-five comparisons), followed by protocol 4 (indirect additive manufacturing; fifteen comparisons) and protocol 3 (indirect milling; seven comparisons).
## 2 Hybrid VOF-Lagrangian Approach
### VOF Method  
To approximate the transient air-water interface, while tracking it on the FV mesh, the classical VOF method is utilized. This technique was demonstrated to be very efficient when dealing with the complex interface between two immiscible fluids [18] and has been widely used to investigate the droplet aerobreakup [6; 13]. The additional field variable \(\alpha\) that represents the volume fraction of liquid is introduced, which changes discontinuously across the interface. Following this approach, the compressible Navier-Stokes equations are solved for an effective mixture fluid, whose (averaged) properties are determined by the actual volume fractions. For instance, the mixture density and molecular viscosity are defined as:  
\[\rho=\alpha\rho_{\mathrm{I}}+(1-\alpha)\rho_{\mathrm{g}}, \tag{1}\]  
and  
\[\mu=\alpha\mu_{\mathrm{I}}+(1-\alpha)\mu_{\mathrm{g}}, \tag{2}\]  
respectively, where the subscripts denote liquid and gas phases.  
The volume fraction \(\alpha\) is calculated throughout the computational domain by solving a simple advection equation, that is:  
\[\frac{\partial(\alpha\rho_{\mathrm{I}})}{\partial t}+\
abla\cdot(\alpha\rho_ {\mathrm{I}}\mathbf{u})=0. \tag{3}\]  
Therefore, the main advantage of the VOF method is that the liquid mass is correctly conserved for any given mesh resolution, which is particularly important for the present URANS approach. As the non-physical smearing of the interface due to numerical diffusion leads to a loss of accuracy in the definition of the interface location, the sharp interface modeling type, along with interfacial antidiffusion, was used [16]. Another interface capturing method that is frequently employed when simulating the droplet aerobreakup is the level-set (LS) method [25]. The advantage of the LS approach is that it provides a sharp definition of the interface, but it is not mass conservative and thus requires higher-order numerical schemes [26]. To more accurately predict the air-water interface, the application of the coupled LS and VOF method, which combines the advantages of both methods, is currently under study.  
In the VOF framework, the effect of surface tension forces at the air-water interface can be simply simulated by employing the continuum surface force model proposed in [27], where the momentum equation is supplied with an additional source term due to surface tension [28]. The present VOF method for simulating the mixture fluid dynamics is supplemented with a Lagrangian approach for particle tracking as discussed in the following.
## Appendix A Nomenclature  
(1) Indices and Sets:  
t,T  
Index and set of time periods  
\(\Delta t\)  
Time interval  
\(D_{\xi}\)  
Sample set  
\(K\)  
Number of subsets  
\(C_{k}\)  
The kth disjoint subset  
(2) Parameters:  
\(P_{CHP,min(max)}^{E}\)  
Minimum (maximum) of electricity generated by CHP (WM)  
\(\alpha_{CHP}\)  
Electrical and heat conversion efficiency  
\(\beta_{CHP}\)  
Gas consumption efficiency of CHP (m\({}^{3}\)/MWh)  
\(\gamma_{CO_{2}}\)  
Efficiency of CO\({}_{2}\) generation from the combustion of CH\({}_{4}\) (t/m\({}^{3}\))  
\(P_{CT,min(max)}^{E}\)  
Minimum (maximum) electricity output of GT (WM)  
\(\chi_{GT}\)  
Efficiency of electricity generated from gas (MWh/m\({}^{3}\))  
\(\omega_{CCS}\)  
Variable electricity consumed per unit of CO\({}_{2}\) captured (MW)  
\(\eta_{CCS}\)  
Carbon capture efficiency  
\(\phi_{P2G}\)  
Efficiency of electricity related to carbon captured by CCS (MWh/t)  
\(\psi_{P2G}\)  
Efficiency of heat related to carbon captured by CCS (MWh/t)  
\(\xi_{P2G}\)  
Efficiency of gas related to carbon captured by CCS (m\({}^{3}\)/t)  
\(\eta_{ES}\)  
Charging and discharging efficiency of ES  
\(\lambda_{t}^{E}\)  
Price of electricity market (CNY)  
\(\lambda_{t}^{G}\)  
Price of gas market (CNY)  
\(\mu^{C}\)  
Operation cost related to the total amount of carbon (CNY/t)  
\(\lambda_{t}^{C}\)  
Price of carbon market (CNY)  
\(\sigma\)  
Average marginal emission factor (t/MWh)  
\(\mu_{pen}\)  
Penalty factor to curtailment of wind and photovoltaic power (CNY)  
\(\mu_{ES}\)  
Variable cost parameter of ES (CNY/MWh)  
\(\Delta P^{H}\)  
Constant referring to the comfortable range of occupants (MW)  
\(\rho\)  
Constraint violation probability  
\(\omega_{k}\)  
Cluster weight  
\(\epsilon\)  
Radius of uncertainty set  
(3) Variables:  
\(P_{CHP,t}^{E}\)  
Electricity generated by CHP at time t (MW)  
\(P_{CHP,t}^{H}\)  
Heat generated by CHP at time t (MW)  
\(G_{CHP,t}\)  
Gas consumed by CHP at time t (m\({}^{3}\))  
\(C_{CHP,t}\)  
Carbon generated by CHP at time t (t)  
\(P_{GT,t}^{E}\)  
Electricity generated by GT at time t (MW)  
\(G_{GT,t}\)  
Gas consumed by GT at time t (m\({}^{3}\))  
\(C_{GT,t}\)  
Carbon generated by GT at time t (t)  
\(P_{CCS,t}^{E}\)  
Electricity consumed by CCS at time t (MW)  
\(C_{cap,t}\)  
CO\({}_{2}\) captured by CCS at time t (t)  
\(C_{emi,t}\)  
CO\({}_{2}\) emitted by CCS at time t (t)
## 2 Materials and Methods
### Tests and Datasets Creation
#### 2.3.3 Water Depth Datasets  
Two different tests were performed to evaluate the accuracy and precision of the Deeper Smart Sonar CHIRP+ and Pro+ 2 in measuring water depth.  
The first test was conducted in a swimming pool to evaluate the performance of the sonar in fresh water and under controlled conditions (purified and transparent water, obstacle-free bottom and no waves). The second test was conducted in the sea to evaluate depth measurements in salt water and under operating conditions (the sites were the same as those indicated in Section 2.3.2). During the two tests, the depths of two points were measured with sonar, one at 0.9 m and one at 1.52 m corresponding to the minimum and maximum depths of the pool. For the comparison between sonar measures and ground-truths, the points in the pool and in the sea were also measured using classical methods,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Path Length [m]** & **Device** & **Positioning** &
\begin{tabular}{c} **Data** \\ **Sample (no.)** \\ \end{tabular} & **Main Data** \\ \hline \multirow{4}{*}{\(\sim\)11.50 linear} & Deeper GNSS & SPP & 150 & Latitude, longitude \\  & CHIRP+ & SPP & 150 & Latitude, longitude \\ \cline{2-5}  & Deeper GNSS Pro+ 2 & SPP & 150 & Latitude, longitude \\ \cline{2-5}  & \(\text{G}>\text{S}>\text{P}\text{Sys}\) & PPP-kinematic & 150 & Latitude, longitude \\  & PPK & 150 & Latitude, longitude \\ \hline \hline \end{tabular}
\end{table}
Table 4: Dynamic datasets composition of the sea test.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Path Length [m]** & **Device** & **Positioning** &
\begin{tabular}{c} **Data** \\ **Sample (no.)** \\ \end{tabular} & **Main Data** \\ \hline \multirow{4}{*}{\(\sim\)26.84 rectangular} & \multirow{4}{*}{G \(>\) S \(>\) P Sys} & SPP & 1000 & Latitude, longitude, ellipsoidal height \\  & & PPP-kinematic & 1000 & Latitude, longitude, ellipsoidal height \\ \cline{1-1}  & & PPK & 1000 & Latitude, longitude, ellipsoidal height \\ \hline \hline \end{tabular}
\end{table}
Table 5: Dynamic datasets composition of the pool test.  
Figure 2: (**a**) Pool test; (**b**) instrumentation mounted on the floating platform.  
such as a rigid metre, a graduated pole and a weighted metric roll [7,8]. The tests were performed with a water temperature as constant as possible between them. The mean salinity of the Mar Grande in Taranto, the test site, can be considered to be ~37.8% [37,38]. In the tests, the \"flexible arm mount 2.0\" by Deeper UAB was used to ensure constant immersion of the sonar in the water. This accessory allows the sonar to be fixed to boats, kayaks and, as in this case, to the edge of the pool and Stand Up Paddle board (SUP).  
Table 6 shows the composition of the water depth datasets.
## 3 Methodology
### Proposed Work Flow Model
#### 3.3.1 Traffic Classification Model
The traffic received in real-time is classified into pre-defined categories, which are already labeled as \"video\", \"voice\", and \"data\", as shown in Figure 5.  
Following the completion of traffic classification by the main controller, the output of the classifier provided by the ML algorithm is used to produce the flow label of the traffic based on its type and is updated in the flow table of the OpenFlow switch. On the basis of the match provided by the OpenFlow switch, the groups are designed to carry out various specific actions on different types of received traffic. The OpenFlow switch manages the additional forwarding of traffic across various switches from source to destination if there is a match in the flow table. If there is no match, the Ryu controller is activated to extract the parameter and classify the traffic into the appropriate label that the controller has pre-defined during training. After being classified, a packet is then delivered to a switch to follow the routing procedure.
## 3 Nanosized-Lipid-Based Drug Delivery Systems in Wound Healing
### Vesicular Systems
#### 3.1.2 Ultra-Deformable Liposomes or Transfersomes in Wound Healing
Deferoxamine-loaded transfersomal gel represents a potential type of therapy for treating human diabetic ulcers because it increases the rate of neovascularization, collagen fiber production and the overall healing process of ulcers [118]. Transfersomes can increase the transdermal flux, extend drug release and enhance the site specificity of bioactive molecules [110]. However, transfersomes also have limitations, such as their instability and tendency for oxidative degradation. Another disadvantage is the expensive equipment required for their production [111].
# Structural Damage Features Extracted by Convolutional Neural Networks from Mode Shapes
## 2 Method
### Finite Element Simulations  
This paper uses a steel truss as the structural model (Figure 1); its length, width and height are 9.912 m, 0.354 m, and 0.354 m. Its left and right ends are simply supported. The truss consists of 355 rods; each rod has a hollow circular cross section with an external radius of 0.005 m and a thickness of 0.002 m. The [PERSON]'s modulus, [PERSON]'s ratio, density, and modal damping of the model are 211 GPa, 0.288, 7800, and 0.02 kg/m\({}^{3}\), respectively.  
The finite element software package, ABAQUS (SIMULIA Inc., Providence, RI, USA), is used to obtain the modal parameters of the steel truss, while PYTHON scripts are used to prepare the input files of the finite element analyses for a variety of designed damage scenarios and submit the analyses to ABAQUS solver. The first order mode shapes and the mode curvature differences are used respectively to train the CNN.  
There are 16 damage degrees and 355 damage locations used in this study. The structural damage is defined as changes to the elastic modulus of the rod concerned. It is assumed that the damage degree of a rod has a linear relation with the reduction of its elastic modulus, so that this paper uses 16 damage degrees, the elastic modulus of which is respectively reduced by 0%, 2%, 4%, 5%, 6%, 8%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% from the intact state, while for each damage degree, there are 355 damage locations to represent the damage in each of the 355 rods respectively, hence, there are a total of 5326 damage scenarios.  
This paper only considers single damage scenarios, i.e., each scenario has only one rod damaged.
# Complete Full Arch Supported by Short Implant (<8 mm) in Edetulous Jaw: A Systematic Review
## 2 Materials and Methods  
This study followed the Preferred Reporting Items for Systematic Review (Figure 1) and Meta-Analyses (PRISMA) statement. Therefore, the aim of this systematic review was to answer the focused question: in patients with edentulous jaws, what is the performance of fixed and removable full-arch restorations supported by short (\(\leq\)6 mm) implants with regard to marginal bone loss, the implant failure rate, and the prevalence of biological and prosthetic complications? The primary research question was captured in the PICO (population, intervention, comparison, outcomes) format: do short implant as full-arch support (I) report the same clinical results (O) in randomized controlled trials in total edentulism of the mandible (P) as a substitute for long implants (C)? The inclusion and exclusion criteria were defined by the authors before the start of the study. Inclusion criteria were all human-based randomized controlled trials (RCTs) with at least 10 patients and a 1 year of follow-up after prosthesis delivery and which were published in English. All studies were designed to evaluate the clinical efficacy of short implants to rehabilitate severe posterior mandibular atrophy. All the studies analyzed were published between 1 January 2010 and 28 February 2023. The exclusion criteria were as follows: studies reporting the same data as subsequent publications by the same authors, systematic reviews, comments and letters to the editor, case reports, in vitro studies, animal model studies, and case series. Relevant systematic review papers, as well as reference lists of all included articles, were searched by hand to identify additional publications. Full-text screening, study selection, and data extraction were performed in duplicate, and disagreements were resolved by consensus.
## 4 Discussion
### Effect of Vibratory Stimulation Conditions on EMG Activation  
Under all stimulation conditions, including non-stimulus, two specific patterns of muscle activity were confirmed during stair climbing. For the TA, the maximum activity appeared in the WA and FC phases. During the WA phase, the body weight is transferred to the leading leg to help with the swing of the trailing leg. At this time, the TA serves to pull the body forward to prepare for weight acceptance. In the FC phase, the TA serves by preventing a foot drop for ensuring foot clearance. For the triceps surea (i.e., GL, GM and Sol), the maximum activity appeared in the FCN phase. In the FCN phase, the body moves forward and the toe-off of the leading leg occurs in the last part of this phase. The maximal activity of the triceps surea is caused by the plantar flexion for performing the toe-off and supporting the body.  
When vibratory stimulation was applied, a change in the EMG activation was observed according to the local vibratory conditions (Figures 2 and 3).  
TA did not significantly change in the WA phase, whereas it showed decreased activity in the PU phase and reached a peak in the FC phase. The vibratory conditions that showed decreased activation in the PU phase were Th-Min, Sub-Max, and Sub-min (Figures 2a and 3a). In the PU phase, the ankle joint is approaching the standing posture, with decreased dorsiflexion and extension of the knee and hip joint [33; 34]. Decreased TA activity is thought to facilitate coordination with other joint movements during the pull-up. Sub-Max was the vibratory condition that showed the highest peak during the FC phase. This ensures toe-clearance and a stable swing by maintaining dorsiflexion of the ankle joint.  
GL, GM, and SOL show increased muscle activity between the PU and FCN phases at sub-threshold intensity conditions (Figure 3b-d). This phase is when the trailing leg begins to swing after the toe-off, and the leading leg performs single-limb support. While the ankle joint is in dorsiflexion, the trailing leg moves upwards to the next step and the body begins to move forward. Thus, the increased muscle activity under vibratory conditions will help in preventing the body from collapsing forward and in maintaining a stable foot support. In addition, the maximum peak appears in the FCN phase. The local vibratory stimulation will help to push upward and support the body by facilitating the plantar flexion of the leading leg.