esa-sceva/satcom-chunk-collection huggingface dataset:
# Development and Control of Biofilms in Diabetic Foot Infections: A Narrative Review
## 5 Detection of Biofilms in DFU  
MolecuLight(tm), (Toronto, ON, Canada) a point-of-care autofluorescence device, is the latest technology used to assess bacterial load in diabetic foot ulcers by employing the principle of autofluorescence. This non-invasive method enables the detection of biofilm formation, as bacterial load is proportional to biofilm presence. The device has a specificity and sensitivity of approximately 78% when guided by autofluorescence imaging. MolecuLight(tm) features a slide-based mechanism to switch between white light and autofluorescence modes, utilizing a 405 nm LED light along with dual-band fluorescence emission filters (500 nm-545 nm and 600 nm-665 nm) during autofluorescence mode. This method functionality aids in accurately identifying the wound area and sampling bacterial load [29].  
In contrast, traditional biopsy methods are less frequently used due to the risk of contamination, although they may be warranted in cases of moderate to severe wound infections. In such cases, tissue from the debrided wound base can be collected and analyzed using techniques such as Fluorescent In-Situ Hybridization (FISH), Confocal Laser Scanning Microscopy, and Scanning Electron Microscopy [30]. Additionally, a microtiter plate assay combined with FISH can provide insights into biofilm thickness and composition [31]. The simplest method for assessing biofilm samples involves staining with Crystal Violet and measuring optical density to estimate bacterial load [32]. Furthermore, combining microtiter assays with ELISA, Scanning Electron Microscopy, and XTT Formazan assays allows for the detection of non-Candida albicans species in biofilms [33].  
Figure 2: Pathophysiological mechanisms of diabetic foot ulcer progression: The role of biofilm formation in chronic infection and amputation risk.
###### Abstract  
The objective of this research was to evaluate the fate of aromatic compounds and organic matter in pilot constructed wetlands (CW) treating _Pinus radiata_ and _Eucalyptus globulus_ sawmill industry leachate. Six lab-scale surface flow CW were built and fed in batches. Three CW were fed with _P. radiata_ leachate, while the other three CW were fed with _E. globulus_ leachate. Each group of three CW included two CW planted with _Pragmites australis_ and one unplanted CW as control unit. A stable hydraulic retention time of seven days was maintained in each CW. The organic loading rate was gradually increased in three phases in the CW fed with _P. radiata_ leachate (i.e., from 12 to 19 g COD/m\({}^{2}\)/day) and with _E. globulus_ leachate (i.e., from 14 to 40 g COD/m\({}^{2}\)/day). The operation of the six CWs lasted 98 days. The CW treating _P. radiata_ and _E. globulus_ leachate had a similar performance. The highest performance was obtained by the unplanted CW (approximately 10-20% higher than the planted CW), without significant differences observed between the _P. radiata_ and _E. globulus_ leachate treatment, regarding the removal efficiencies of organic matter and total phenolic compounds. The planted systems were probably affected by the high concentrations of these compounds applied, which probably created a toxic environment hindering the microbial community growth.  
woodwaste leachate; _Eucalyptus globulus; Pinus radiata_; constructed wetlands; sawmill industry; agro-industrial wastewater; aromatic compounds; phenolic compounds
## 3 Results
### Effect of Nanoemulsions on the Quality Characteristics of Liquid-Smoked Salmon Fillets
#### 3.5.2 Lipid Oxidation and Protein Degradation  
The TBARS values of the salmon fillets under different NE treatments are displayed in Figure 5a. The TBARS index of all groups increased with increasing storage time. The initial TBARS value was 1.18 mg MDA/kg and reached maximum values of 4.38, 3.83, 3.78, and 2.51 mg MDA/kg in the control, ENT-NE, CEO-NE, and CO-NE group on the 49 th day, respectively. Compared with the control, the TBARS of the ENT-NE, CEO-NE, and CO-NE groups decreased significantly (\(p<0.05\)) by 22.43%, 25.82%, and 55.89% on the 42 nd day, respectively. The TBARS index of the control, ENT-NE, and CEO-NE exceeded 3 mg MDA/kg on the 35 th, 42 nd, and 42 nd days, respectively. However, the TBARS of CO-NE was not beyond the acceptable limit throughout the 49 days. The results indicated that CO-NE exhibited the best ability to inhibit lipid oxidation, which may be attributed to the synergistic antioxidant activity of ENT and CEO. Noticeably, there was no significant difference between the TBARS of the ENT-NE and CEO-NE groups.  
Figure 4: Changes of total viable counts (TVC) (**a**), total _Pseudomonas_ bacteria counts (TPC) (**b**), psychrotrophic bacteria counts (PBC) (**c**) and hydrogen sulfide-producing bacteria counts (HBC) (**d**) of liquid-smoked salmon fillets treated with nanoemulsions containing entercin Gr17 alone (ENT-NE), cinnamaldehyde alone (CEO-NE), entercin Gr17 and cinnamaldehyde (CO-NE) and no entercin Gr17/cinnamaldehyde (Control) during storage at 4 \({}^{\circ}\)C for 49 days. The red dotted line means the minimal quantification limit.  
The TVB-N values of the salmon fillets under different NE treatments are shown in Figure 5b. A higher TVB-N value means a higher degree in the breakdown of proteins and other nonprotein nitrogen-containing compounds, such as nucleic acids, indicating the worse quality of the product. The TVB-N of the fillets in the control increased from 6.45 \(\pm\) 0.41 mg/100 g to 30.11 \(\pm\) 0.71 mg/100 g across 0-49 days. The TVB-N index of the control exceeded 30 mg/100 g on the 49 th day. However, the TVB-N of ENT-NE, CEO-NE, and CO-NE did not exceed the acceptable limit throughout the 49 days. Compared with the control group, the increase in TVB-N was more gradual in the experimental groups, with 22.03 \(\pm\) 1.22, 22.15 \(\pm\) 1.60, and 15.38 \(\pm\) 0.98 mg/100 g observed on the 49 th day for ENT-NE, CEO-NE, and CO-NE, respectively. Compared with the control, the TVB-N of the ENT-NE, CEO-NE, and CO-NE groups decreased significantly (\(p\) < 0.05) by 34.27%,33.69%, and 62.17% on the 49 th day, respectively. The results showed that the inhibitory effect of CO-NE was more efficient than ENT-NE and CEO-NE in the proteolysis of smoked salmon.
# Antimicrobial Effect of Titanium Hydroxapatite in Denture Base Resin
## 3 Results  
### SEM and EDX Evaluation of TiHA-Containing Resin Specimens  
When present on the specimen surface, TiHA exhibits antimicrobial effect by adsorbing various organic materials and decomposing them under irradiation [19]. Therefore, the surface properties of TiHA-containing specimens were examined using SEM and EDX (Figure 1). SEM analysis revealed that the specimen surface mainly contained two different electron ray-contrast areas (Figure 1a). The bright areas contained more elements with higher atomic weight and appeared to contain more TiHA than the dark areas. To evaluate TiHA content of the specimens (Figure 1b), five bright and five dark areas (5 \(\times\) 5 \(\upmu\)m each) were randomly selected from the SEM images and analyzed using EDX (Figure 1b). The weight percentage of C and O, which are present in both denture base resin and TiHA, to the total weight of the elements detected in the analyzed areas was almost 100% in the dark areas. By contrast, Ca, P, and Ti, which are the constituent elements of TiHA, were detected only in the bright areas. When wider regions of the SEM images, containing both dark and bright areas (500 \(\times\) 500 \(\upmu\)m), were analyzed using EDX, the weight ratio of P and Ca to the total element weight increased in a concentration-dependent manner with the TiHA content (\(p\) < 0.05) (Figure 1c).  
Figure 1: Characterization of the TiHA-containing specimen surface. (**a**) SEM images of the surface morphologies of specimens containing the following concentrations of TiHA: 0% (control), 3%, 6%, or 9%. The fields were scanned at 100\(\times\) magnification. Bar, 100 \(\upmu\)m. Arrows indicate dark and bright areas; (**b**) Element weight percentages in the dark and bright areas on the specimens. The weights of C,O, Ca, P, and Ti relative to the sum of weights of all detected elements were determined using EDX and are shown as percentages for the dark and bright areas (5 \(\times\) 5 \(\upmu\)m). Each area was randomly selected from the SEM image of a specimen containing 9% TiHA (in A). Data are presented as the mean \(\pm\) standard deviation for five areas; (**c**) Element weight percentages in specimens containing different concentrations of TiHA. The wide regions containing both dark and bright areas (500 \(\times\) 500 \(\upmu\)m) were analyzed using EDX, and the weights of Ca and P relative to the total weights of all detected elements were determined and are shown as percentages. Data are presented as the mean \(\pm\) standard deviation for five areas, which were randomly selected from the SEM images of specimens containing 0% (control), 3%, 6%, or 9% TiHA. The asterisk indicates a significant difference between the samples (\(p\) < 0.05).
# Synthesis of Nitrogen-Doped Graphene Quantum Dots from Sucrose Carbonization
## 2 Materials and Methods
### Structural Characterizations  
The atomic force microscopy (AFM) images were obtained by intermittent contact mode in Lens AFM Nanosurf equipment with a C3000 controller. To obtain the images, the samples were prepared by depositing about 2 \(\mu\)L of the suspension of GQDs with ethanol onto a previously cleaved mica surface (Ted Pella Inc., Redding, CA, USA), and after evaporation of all the solvent, the analysis by AFM was performed. Transmission Electron Microscopy (TEM) analysis was performed with a JEOL JEM 2100 (200 kV). X-ray diffraction was performed with Rigaku Ultima III equipment, operated with characteristic Cu-K\(\alpha\) radiation (\(\lambda\) 1.54 \({}^{\circ}\)A), a voltage of 40 kV, and a current of 30 mA. Raman spectroscopy was performed with a confocal Raman microscope Alpha 300 R, brand WITEC, using a CW laser of 532 nm with a maximum power of 45 mW and an objective of 50x/NA 0.7 as the excitation source. FTIR analysis was performed with a Perkin Elmer Spectrum 100 spectrometer in attenuated total reflection (ATR) mode. To perform the XRD, Raman, and FTIR analyses, the powders resulting from the drying of the colloidal GQDs and N-GQDs after treatment in DMSO were used.
# Image-Based Structural Health Monitoring: A Systematic Review
## 4 Discussion
### Guidelines and Prerequisites
#### 4.6.1 Creation of Threshold  
Deep learning-based structural system condition assessment methods, which mostly use CNN, have significantly increased in the SHM community [155]. Convolutional neural networks are trained on a wide range of datasets for damage and anomaly assessment as well as post-disaster monitoring. Improved non-contact capability sensors are utilized in the development of self-driving SHM systems by using trained networks to review updated data to identify the kind and extent of the damage. The capabilities of non-contact sensors are then enhanced in the creation of autonomous SHM systems by analyzing more recent data using the trained networks to identify the kind and extent of the damage. In recent years, researchers have developed a broad variety of CNN designs to control the intensity of lighting and weather, picture quality, the amount of background and foreground noise, and multiclass damage in structures.
## 3 Mathematical Formulation
### Sample Average Approximation Methodology
}\ \left(U(V_{s}^{m})=f(x_{s}^{m})+\frac{1}{N^{ \prime}}\sum_{n=1}^{N^{\prime}}Q(y_{n},\xi_{n})\right) \tag{30}\]  
If the optimality gap \(\left(\widehat{U}-\hat{L}\right)/\hat{U}\times 100\%\) is smaller than a given threshold \(\varepsilon\), the algorithm terminates, and the first-stage optimal solution \(\hat{x}\) which corresponds to the upper bound \(\widehat{U}\) will be the optimal solution to the original problem. ```
1. For \(s=1,\;2,\;\ldots,\;S\) do (a)  For \(m=1,\;2,\;\ldots,\;M\) do (a)  Generate a sample set of size \(N\). (b)  Divide \(N\) samples into \(N_{L}\) clusters by the K-means clustering approach. Determine \(N_{L}\) centroids and their probability. (c)  Solve the SAA model in Equation (27) to obtain the solution \(x_{s}^{m}\) and the optimal value \(V_{s}^{m}\). (d)  Generate a large sample set of size \(N^{\prime}\) and evaluate the upper bound of the chance constraint \(U(x_{s}^{m})\) by Equation (29). If \(U(x_{s}^{m})\leq\varepsilon\), go to (v); else, skip (v) and go to the next iteration. (e)  Estimate the upper bound of the optimal value \(U(V_{s}^{m})\). (e)  Determine the \(L\)th smallest optimal values \(V_{s}^{L}\) based on the method presented in [29]. Determine the upper bound \(\widehat{U}\) of the true optimal by Equation (30) and the corresponding solution \(\hat{x}\). (e)  Calculate the lower bound \(L\) as the average of all \(V_{s}^{L}\). (e)  Calculate the optimality gap \(g=\left(\widehat{U}-\hat{L}\right)/\widehat{U}\times 100\%\) and compare it to the threshold \(\varepsilon\). If \(g\leq\varepsilon\), \(\hat{x}\) is the final result; else, adjust \(N_{L}\) and go back to step 1. ```  
**Algorithm 1** SAA method combined K-means clustering technique
## 3 Wireless Sensor Networks  
A WSN is a system comprised of radio frequency (RF) transceivers, sensors, microcontrollers and power sources [10]. Recent advances in wireless sensor networking technology have led to the development of low cost, low power, multifunctional sensor nodes. Sensor nodes enable environment sensing together with data processing. Instrumented with a variety of sensors, such as temperature, humidity and volatile compound detection, allow monitoring of different environments. They are able to network with other sensor systems and exchange data with external users [27].  
Sensor networks are used for a variety of applications, including wireless data acquisition, machine monitoring and maintenance, smart buildings and highways, environmental monitoring, site security, automated on-site tracking of expensive materials, safety management, and in many other areas [10].  
A general WSN protocol consists of the application layer, transport layer, network layer, data link layer, physical layer, power management plane, mobility management plane and the task management plane [10].  
Currently two there standard technologies are available for WSN: ZigBee and Bluetooth. Both operate within the Industrial Scientific and Medical (ISM) band of 2.4 GHz, which provides license-free operations, huge spectrum allocation and worldwide compatibility. In general, as frequency increases, bandwidth increases allowing for higher data rates but power requirements are also higher and transmission distance is considerably shorter [27,28]. Multi-hop communication over the ISM band might well be possible in WSN since it consumes less power than traditional single hop communication [28].  
It is also possible to create a WSN using Wi-Fi (IEEE 802.11), but this protocol is usually utilized in PC-based systems because it was developed to extend or substitute for a wired LAN [29]. Its power consumption is rather high, and the short autonomy of a battery power supply still remains an important disadvantage [30].
# Bidirectional Atmospheric Channel Reciprocity-Based Adaptive Power Transmission
###### Abstract  
In atmosphere free-space optical communication (FSO) systems, the scintillation effect produced by turbulence effects increases the bit error rate (BER) of the communication system and reduces the system's performance. However, a high correlation of turbulent noise occurs in the two transmission channels when a signal transmitted in the bidirectional atmospheric channel with channel reciprocity. The performance of the FSO system can be increased by extracting channel state information (CSI) in forward transmission and using adaptive power technology to reduce turbulence in inverse transmission. In this research, we propose a bidirectional atmospheric channel reciprocity-based adaptive power transmission (CR-APT) technique that lowers the bit error rate of the transmitted signal by using the CSI of the relevant channel. To verify the effectiveness of the technique, a bidirectional atmospheric channel with various turbulence intensities is built in the simulation program, along with various background sounds to vary the channel reciprocity, and the impact of reciprocity on signal transmission is examined. The simulation findings demonstrate that adaptive power transmission with high reciprocity is excellent under the weak turbulence condition, and its future development is promising.  
f 2023 10.1067.10.3390/photonics10101067
## 2 Materials and Methods
### Gas Chromatography-Mass Spectrometry (GC-MS) Analysis  
The seed's oil fatty acid composition was analyzed using GCMS-QP2010 Ultra by [PERSON], Kyoto, Japan, which was equipped with a capillary column (30 m by 0.25 mm ID; with 0.25 \(\upmu\)m film thickness) (Table 1). The fatty acid methyl ester (FAME) was prepared by heating the sample with methanolic NaOH first and then with BF3 methanol [56]. A total of 5 mL of \(n\)-heptane was used to recover methyl esters in the organic phase. Finally, a saturated NaCl solution was added to the mixture, and the two layers were separated using a separating funnel. A total of 1 \(\upmu\)L of the \(n\)-heptane phase was injected onto the GC in split mode. Helium gas was used as a carrier with a flow rate of 1.7 mL/min. The oven temperature was initially set at 40 \({}^{\circ}\)C for 2 min, then increased to 200 \({}^{\circ}\)C at a rate of 30 \({}^{\circ}\)C/min, and finally to 280 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min for another 2 min. The mass selective detector was set on scan mode and had a mass range of 50 to 800 \(m/z\). The fatty acids were identified using the National Institute of Standards and Technology 20 (NIST 20) mass database.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{\#} & \multirow{2}{*}{**Methods Used in the Study**} & \multicolumn{3}{c}{**Other Research Methods**} \\ \cline{3-4}  & & **Advantages** & **Disadvantages** \\ \hline \multirow{7}{*}{1} & & - & Soxhlet extraction is faster and & - & Soxhlet extraction in compare to \\  & & - & Soxhlet extraction is faster and & super critical fluid extraction (SFC), \\  & & less solvent-intensive than & and microwave assisted extraction \\  & & - & maceration [57]. & (MAE) requires a lot of solvents and \\  & & - & The plant content is extracted & a prolonged extraction time [57]. \\  & & continuously using a fresh & - & Not suitable for thermolabile \\  & & solvent [57]. & metabolites [59]. \\ \multirow{2}{*}{1} & & seed oil using \(n\)-hexane and & - & The solvents have a low boiling \\  & & - & point, which in favor of & - \\  & & thermolabile compounds [58]. & - & n-hexane poses risks to human \\  & & - & using n-hexane as solvent & health, safety, and the environment, \\  & & results in high Yield\% of oil & prompting the search for superior \\  & & compared to mechanical & alternatives. Due to its similarity to \\  & & extractions [58]. & n-hexane and safer handling, ethyl \\  & & - & acetate is a possible choice [60], or \\  & & - & using mechanical press extraction \\ \hline \multirow{2}{*}{2} & \multirow{2}{*}{GC-MS} & - & Gas chromatography (GC) can & - & The degradation of thermolabile \\  & & separate volatile substances & compounds at elevated operating \\ \multirow{2}{*}{2} & & efficiently and quickly [61]. & temperatures [57]. \\ \hline \end{tabular}
\end{table}
Table 1: Some advantages and disadvantages of the methods used in the study and their comparison with other methods available for the same purpose.
# A Wideband Non-Stationary 3D GBSM for HAP-MIMO Communication Systems at Millimeter-Wave Bands
## 3 GBSM Non-Stationary Channel Statistical Properties
### Temporal ACF  
Considering the birth-death process, the survival probability of a cluster in the range of \(t\) to \(t+\Delta t\) is \(e^{-\lambda_{R}P_{c}(v_{a}\Delta t+v_{b}\Delta t)}\). The proposed GBSM normalized ACF between \(q-m\) link and \(p-n\) link can be defined as [41]  
\[R_{qm,pn}(t,\Delta t)=R_{qm,pn}^{\text{LoS}}(t,\Delta t)+R_{qm,pn}^{\text{NLoS }}(t,\Delta t), \tag{26}\]  
Figure 3: Scatterers in the cylinder distribution-map.  
\[\begin{array}{l}R_{qm,pn}^{\text{LoS}}(t,\Delta t)=e^{-\lambda_{R}P_{\text{(}}(u_{ \Delta}t+v_{b}\Delta t)\times}\\ \frac{K}{K+1}e^{-2\pi(d(p,l)(t)-d(q,m)(t+\Delta t))}\times e^{-2\pi\Delta t(f_{ Tmax}cos(\gamma_{T})+f_{Rmax}cos(\pi-\gamma_{R}))}\end{array}, \tag{27}\]  
\[\begin{array}{l}R_{qm,pn}^{\text{NLoS}}(t,\Delta t)=e^{-\lambda_{R}(v_{T \text{MS}}At+v_{BS\text{MS}}At)}\times\frac{1}{K+1}\int_{R_{\min}}^{R_{\max}} \int_{\beta_{\min}}^{\beta_{\max}}f_{1}(\varphi_{2})\times f_{2}(\theta_{2})\\ \times e^{-\frac{2\pi}{\lambda}(d(p,s)(t)-d(q,s)(t-\Delta t)-d(s,m)(t-\Delta t ))},\\ \times e^{-\frac{2\pi}{\lambda}(v_{2}+v_{b})\Delta t\cos(\alpha_{R}(t)-\gamma_ {R})\cos(\beta_{R}(t))dad\beta}\end{array}, \tag{28}\]  
where \(R_{\min}\) and \(R_{\max}\) denote the minimum and maximum horizontal distance between the scatterer \(s\) and \(O_{R}\), and the symbols \(\beta_{\min}\) and \(\beta_{\max}\) denote the minimum and maximum EAoD, respectively.