esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Bond-Slip on Dynamic Response of FRP-Confined RC Columns with Non-Linear Damping
## 2 Numerical Model of FRP-C RC Columns Considering Bond-Slip Effect  
### Modeling Method for Bond-Slippage  
As stated by [PERSON] et al. [34], the strain penetration effects or the fixed end rotations may be accounted accurately by the zero-length section element available in the OpenSees. A zero-length section element is a fiber discretization of the cross section of a structural member, which is assumed by [PERSON] and [PERSON] [33] to have a unit length such that the section deformation (for example, curvature) is equal to the element deformation (forexample, rotation). The unit length assumption also implies that the material model for the steel fibers in the section element would represent the bar slip instead of strain for a given bar stress. By attaching a zero-length element at the end of a beam-column element instead of a spring element, the bending deformation and sliding deformation of the element can be separated, as shown in Figure 1. A new node j is added at the bottom of the beam-column element and the node i and j (the distance between Node i and j is zero) are connected by a zero-length element which is used to simulate the bond-slip of reinforcement in reinforced concrete members.  
It should be emphasized that (1) the zero-length section element and the adjacent beam-column element have the same fiber division, steel bar, and concrete configuration; (2) the stress-strain relationship of reinforcement is used for the FRP-C RC beam-column element, and the stress-slip relationship of reinforcement is used for the zero-length element; (3) the research object of this paper is the fully wrapped FRP-C RC columns; (4) and the restraining effect of FRP is considered, while the double restraint effect of FRP and stirrup is neglected.
## 2 Materials and Methods
### Microstructural Characterization Preperation  
X-ray diffraction was performed on a Rigaku D/MAX 2500 V/PC diffractometer (Rigaku, Tokyo, Japan) using Cu K\(\alpha\) radiation (\(\uplambda\) = 1.5406 A) at 40 kV and 30 mA. Patterns were collected over 2\(\uptheta\) = 20-110\({}^{\circ}\) with a step size of 0.02\({}^{\circ}\) and 1 s dwell per step. For the critical overlap between \(\uplambda\)-Al (111) and Al\({}_{2}\)Cu (211), we carried out peak deconvolution in the 38.0-39.0\({}^{\circ}\) 2\(\uptheta\) window using OriginPro 2021 and Rigaku SmartLab software ([[https://rigaku.com/products/x-ray-diffraction-and-scattering/xrd/smartlab](https://rigaku.com/products/x-ray-diffraction-and-scattering/xrd/smartlab)]([https://rigaku.com/products/x-ray-diffraction-and-scattering/xrd/smartlab](https://rigaku.com/products/x-ray-diffraction-and-scattering/xrd/smartlab)), accessed on 16 July 2025). Two pseudo-Voigt functions--with a linear baseline and internal Si standard for 2\(\uptheta\) calibration--were simultaneously fitted to the raw data. Fit quality was assessed via R\({}^{2}\) and \(\uplambda^{2}\) metrics, and the extracted peak centroids, full width at half maximum (FWHM), and integrated areas were used for quantitative phase analysis.  
Microstructural analysis was carried out using a combination of scanning electron microscopy (SEM), energy-dispersive spectroscopy (EDS), and electron probe microanalysis (EPMA). Metallographic preparation involved sequential grinding with SiC papers (400 to 2400 grit), followed by polishing using 3 \(\upmu\)m and 1 \(\upmu\)m diamond pastes, and final finishing with 0.03 \(\upmu\)m colloidal silica suspension.  
Field emission scanning electron microscopy (FE-SEM; CLARA, TESCAN, Brno, Czech Republic) was employed at an accelerating voltage of 15.0 kV to investigate phase morphology and distribution. The chemical composition of intermetallic compounds was determined using an integrated EDS system (EDAX, CLARA, TESCAN). For more precise elemental mapping and precipitate analysis, EPMA (JXA-8230, JEOL, Tokyo, Japan) was also utilized.  
To maintain consistency in analysis and discussion, specimens in the as-cast condition were labeled as AC_A and AC_B for Alloys A and B, respectively. After T6 heat treatment, the specimens were designated according to both alloy type and testing temperature: RT_A and RT_B for tests at 25 \({}^{\circ}\)C, 150_A and 150_B at 150 \({}^{\circ}\)C, and 250_A and 250_B at 250 \({}^{\circ}\)C.
## 2 Functioning Principles and General Concepts for Transduction
### Transduction Principles  
Biosensors are detection devices comprising recognition entities of biological origin coupled with a transducer for translating the formation of a biological complex into an electrical signal. By extension, a biosensor can also be made of recognition entities that could be fully synthetic, but for the recognition of biomolecules. Biosensors are then composed of two parts: a (bio)chemical recognition element (the probe) and a transducing element (the transducer). This review focuses on small target molecules; therefore, we do not deal with applications targeting deoxyribonucleic acid (DNA), antibodies, cells or other microorganisms. However, probes having affinity for small target molecules are extremely diverse and can be DNA (as aptamers), antibodies, enzymes or even synthetic ligands or molecularly imprinted polymers.  
The concept of electrochemical transduction historically started at the beginning of the 19 th century and largely developed in the 1970s with the possibility of using ion receptors (e.g., crown ethers, peptides or other chelators) immobilized on an electrode, which can increase the charge concentration or the distribution of charges over an electrode, then be sensed potentiometrically by a change in (electro)chemical potential at the electrode vicinity. However, potentiometry can be considered as a passive transduction in the sense that no external signal is supplied to the system. Consequently, and as predicted by the logarithmic form of the Nernst equation, a relatively large change in concentration results in a limited change in potential, which limits the overall sensitivity of the method. Readers who want to go deeper into theory can read the book of [PERSON] [37] or the review of [PERSON] et al. [38].  
Converse to passive transductions, active transductions rely on the fact that the transducer supplies energy to the system, for example by applying an external electrochemical potential able to inject or withdraw electrons from the system, as all amperometric sensors do (the first amperometric sensor was reported in 1956 by [PERSON] [39]). Consequently, amperometry has a higher sensitivity compared to potentiometry. The very first active device which combined potentiometry with an amplification strategy was the ion-selective field-effect transistor (ISFET) from [PERSON] [15].  
Today, transduction schemes used in OECT-based chemical sensors rely on polarization of the interface where the probe molecule is immobilized, or on electron transfer reactions occurring on these interfaces. The choice of one transduction or the other largely depends on the process which is involved upon recognition of the target by the immobilized probe: does it lead to accumulation of charges at the interface, or does it produce charge transfer (a current)?
## 3 Results  
### Shear Bond Strength and Failure Modes  
Means and standard deviations, as well as the number of adhesive or mixed failures are depicted in Table 2. In general, higher SBS values were obtained for the tested adhesives after 24 h of a storage period compared to those obtained after 6 months (\(t\)-test, \(p\) < 0.05), except for Scotchbond Universal (SU) and Iperbond Max (IPM) (\(p\) > 0.05) (Figure 1).  
After 24 h, no significant difference was found between the SBS values of the five tested adhesives (one-way ANOVA on ranks, \(p\) > 0.05). Conversely, after six months of storage, no significant difference was found between SU, IPM, and FuturaBond M+ (FB) (\(p\) > 0.05). SU and IPM had higher SBS values than Iperbond Ultra (IPU) and Ibond Universal (IB) after prolonged storage (\(p\) < 0.05). Different failure modes were reported (Figure 2 and Table 2).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **FB** & **SU** & **IPM** & **IPU** & **IB** \\ \hline
24 h & \(\begin{subarray}{c}17.6\pm 3.4\\ \text{a,b,1}\end{subarray}\) & 19.2 \(\pm\) 6.4 a,1 & 18.6 \(\pm\) 4 b,1 & 15.9 \(\pm\) 3.7 b,1 & 15.3 \(\pm\) 3.3 b,1 \\ \hline Fracture mode (Adhesive/Mix) & (13/2) & (8/7) & (10/5) & (12/3) & (12/3) \\ \hline
6 months & 14.7 \(\pm\) 4.1 a,2 & 20 \(\pm\) 6.1 b,1 & \(\begin{subarray}{c}18.6\pm 3.9\\ \text{a,b,1}\end{subarray}\) & 12.9 \(\pm\) 3.1 a,2 & 12.9 \(\pm\) 2.3 a,2 \\ \hline Fracture mode (Adhesive/Mix) & (12/3) & (9/6) & (11/4) & (13/2) & (13/2) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Shear bond strength means and standard deviations of the five adhesives at the two storage periods and the number of adhesive or mixed failures. FuturaBond M+—”FB”, Scotchbond Universal—“SU”, Iperbond Max—”IPM”, Iperbond Ultra—”IPU”, Ibond Universal—”IP”. Different superscript letters indicate significant differences in rows, while different superscript numbers indicate significant differences in columns. Significance at 5% significant level (\(p\) < 0.05).  
Figure 1: Evolution of the bond strength for the different universal adhesives at 24 h and 6 months. (* \(p\) < 0.05). FuturaBond M+—”FB”, Scotchbond Universal—”SU”, Iperbond Max—”IPM”, Iperbond Ultra—”IPU”, Ibond Universal—”IB”.  
Figure 2: Representative images obtained with optical microscope and scanning electron microscopy (SEM). (**a**) Adhesive failure (\(\times\)50 magnification); (**b**) mixed failure (\(\times\)50 magnification). Ad: adhesive; Co: Composite.
## 3 Important Factors in Efficacy of Amphiphiles
### Role of Amphiphiles Concentration  
In order for the amphiphiles to be economically viable in biomass conversion process, the quantity of the additive used should be minimized as much as possible. An important question is whether the application of amphiphiles beyond a certain concentration would be un-necessary in improving the hydrolysis reaction yield. [PERSON] _et al._[36, 37] found that at a fixed optimum temperature of 51 \({}^{\circ}\)C, a relative increase of 7.0% (from 58.7% to 63.2%) was achieved in glucose yield by increasing the PEG concentration up to 0.5 g/g glucan when 14.2 mg/g glucan of CellicTMtec was applied. The increase in xylose yield was 5.1% (from 29.8% to 31.3%), when the PEG concentration was increased up to 0.6 g/g glucan. Similar to these results, [PERSON] _et al._[18] reported an increase in xylan conversion from dilute acid pretreated wheat straw, as the concentration of PEG was incrementally increased. According to [PERSON] _et al._[22], the activity of xylanase was improved slightly as the concentration of nonionic surfactants was increased, except when olinoor 1100 was used. In agreement with this [PERSON] and [PERSON], [53] stated that a higher concentration of PEG was more effective at improving enzymatic hydrolysis.  
Although the results of an study from [PERSON] and [PERSON], [26] on AFEX, ARP, dilute acid, controlled pH, and lime pretreated corn stover demonstrated that the increase of PEG concentration beyond 0.3 g/g glucan did not have a significant effect on sugar yield, their study showed that higher PEG concentrations did result in significant increases in sugar yield for lime and SO2 pretreated CS. Perhaps the increase in concentration of surfactants is contributing to the increase of the hydrolysis yield by the increase in the unit number of surfactant micelles. This is because it was shown that at a concentration lower than 0.1 M, the increase in surfactant concentration was reported to impact only the micelle numbers [82]. It is important to note that according to [PERSON] _et al._[83], it is not the increasing total concentration or the micellar concentration of amphiphils that result in an increase in the binding ratio (bovine serum albumin, chymotrypsinogen), but an increase in the equilibrium monomer concentration leads to a larger numbers of g SDS/g protein.  
According to a model suggested by [PERSON] _et al._[84], the micelles act like micro carrier supports for the enzyme, which improve the catalytic reaction that occurs at the interface of the micelle-aqueous phase when compared to that of pure aqueous phase. In addition, increasing the concentration of surfactants up to a certain extent could have the effect of increasing the number of surfactants adsorbed to the hydrophobic surfaces of biomass that would otherwise cause an irreversible adsorption of proteins and deactivation.
# Research on the Coverage of Ultraviolet Communication Network in the Arbitrary Polygon Area
## 3 k-Connectivity  
The analysis of the UV k-connectivity network is placed in a two-dimensional arbitrary convex polygon area \(E\), and the area of \(E\) is \(A\). The UV terminal nodes are evenly distributed in \(E\), and the number of nodes is \(n\). Therefore the probability density function is  
\[H(x,y)=\left\{\begin{array}{cc}\frac{n}{A}&(x,y)\in E\\ 0&\text{else}\end{array}\right. \tag{7}\]  
For the arbitrary node \(i(l_{i},\alpha_{i})\) in area \(E\), its communication coverage \(\text{B}_{r}(l_{i},\alpha_{i})\) is a circle area with \((l_{i},\alpha_{i})\) as its center and \(r\) as its radius. In the polar coordinate system, it can be expressed as  
\[\left(l\cos\alpha-l_{i}\cos\alpha_{i}\right)^{2}+\left(l\sin\alpha-l_{i}\sin \alpha_{i}\right)^{2}=r^{2} \tag{8}\]  
When the distance between \(i(l_{i},\alpha_{i})\) and the pole O is greater than or equal to the communication radius \(r\), \(l\geq r\) as Figure 5a.  
Elucidating Equation (8), we derive  
\[\left\{\begin{array}{l}\alpha_{1}=\arcsin\Bigl{(}\frac{l_{i}\sin\alpha_{i}} {l_{i}}\Bigr{)}-\arcsin\Bigl{(}\frac{r}{l_{i}}\Bigr{)}\\ \alpha_{2}=\arcsin\Bigl{(}\frac{l_{i}\sin\alpha_{i}}{l_{i}}\Bigr{)}+\arcsin \Bigl{(}\frac{r}{l_{i}}\Bigr{)}\\ l_{1}=l_{i}\cos\alpha_{i}\cos\alpha+l_{i}\sin\alpha_{i}\sin\alpha-\\ \sqrt{\left(l_{i}\cos\alpha_{i}\cos\alpha+l_{i}\sin\alpha_{i}\sin\alpha\right) ^{2}-\left(l_{i}^{2}-r^{2}\right)}\\ l_{2}=l_{i}\cos\alpha_{i}\cos\alpha+l_{i}\sin\alpha_{i}\sin\alpha+\\ \sqrt{\left(l_{i}\cos\alpha_{i}\cos\alpha+l_{i}\sin\alpha_{i}\sin\alpha\right) ^{2}-\left(l_{i}^{2}-r^{2}\right)}\end{array}\right. \tag{9}\]  
Figure 5: Polygonal network model.
# Investigation of 2D-WH/TS OCDMA Code Stability in Systems with SOA-Based Device
###### Abstract  
This paper investigates for the first time how the implementation of semiconductor optical amplifier (SOA)-based devices in photonic networks can negatively impact the integrity of two-dimensional wavelength-hopping time-spreading (2D-WH/TS) optical code-division multiple access (OCDMA) codes based on multi-wavelength picosecond code carriers. It is demonstrated and confirmed by simulations that the influence of an SOA under driving currents of 50 mA to 250 mA causes a 0.08 to 0.8 nm multi-wavelength picosecond code carriers' wavelength redshift. The results obtained are then used to calculate the degradation of OCDMA system performance in terms of the probability of error _Pe_ and the decrease in the number of simultaneous users. It is shown that, when the SOA-induced 0.8 nm code carriers redshift becomes equal to the code carries wavelength channel spacing, the (8,53)-OCDMA system performs only as a (7,53)-OCDMA system, and the number of simultaneous users drops from 14 to 10 or 84 to 74 with the forward error correction (FEC) _Pe_ of \(10^{-9}\), respectively. The impact of the 0.8 nm redshift is then shown on a (4,53)-OCDMA system, where it causes a drop in the number of simultaneous users from 4 to 3 or 37 to 24 with the FEC _Pe_ of \(10^{-9}\), respectively.  
optical code division multiple access; semiconductor optical amplifier; gain recovery time; code carriers redshift; bit error rate +
Footnote †: journal: Applied Sciences  
_Article_
# Progress in Remote Sensing of Photosynthetic Activity over the Amazon Basin
## 3 Remote Sensing of Sun-Induced Fluorescence  
A measure of Sun-Induced Fluorescence (SIF) provides an alternative to PRI to assess photosynthetic activity [20; 21; 22; 23] by measuring the emittance of photons from leaf chlorophyll as fluorescence. The probability of an absorbed photon being re-emitted as fluorescence depends on both photochemical and non-photochemical quenching. Fluorescence is an indicator of the amount of energy present temporarily within the photosynthetic apparatus. In the past, the relatively weak fluorescence signal from chloroplasts has restricted the use of this approach. Recently, improvements in the detection of narrow absorption features (Fraunhofer lines) permit the determination of SIF from space through the comparison of fluorescing with non-fluorescing vegetation [20; 22; 24; 25]. [PERSON] and collaborators [26] were among the first to investigate the link between solar-induced chlorophyll fluorescence with net carbon exchange across forested areas in southern Amazonia. They estimated seasonal changes in atmospheric CO\({}_{2}\) by acquiring measurements from the NASA Atmospheric CO\({}_{2}\) Observations from Space Build (ACOSb2.9) and compared these with solar-induced chlorophyll fluorescence data collected from the Greenhouse Gases Observing Satellite (GOSAT). The CO\({}_{2}\) content of the atmosphere decreased during the wet season and rose during the dry. Atmospheric concentrations of CO\({}_{2}\), however, were only weakly and inversely correlated with their measurements of SIF (r = \(-\)0.53).  
[PERSON] and collaborators [27] expanded sampling to include the entire Amazon Basin using SIF measurements from GOSAT. The results of their analysis corresponded with that expected, i.e., less stress and higher values of SIF during the wet season (December, January and February) and lower ones during the dry (June, July and August) with up to a 15% difference between years. A still more refined analysis by [PERSON] et al. [28] included acquiring the Enhanced Vegetation Index (EVI) from MODIS and SIF from Global Ozone Monitoring Experiment-2 (GOME-2), changes in total water storage from the Gravity Recovery and Climate Experiment (GRACE) and rainfall from the Tropical Rainfall Measuring Mission (TRMM). These authors established an annual threshold of 2000 mm per year, below which evergreen canopies were not maintained.
# Towards an Indoor Gunshot Detection and Notification System Using Deep Learning
## 3 Materials and Methods
### Prototype System Architecture
#### 3.2.1 Gunshot Detector Device  
Hardware  
The block diagram of the hardware unit of the gunshot detector device is shown in Figure 4. The primary processing unit employed is the NVIDIA(r) Jetson Nano(tm) Developer Kit [49], a single-board computer known for its compact size and energy efficiency. This embedded platform excels in running neural network models effectively, such as image classification, object detection, segmentation, and more. The Jetson Nano(tm) Developer Kit is equipped with a powerful Quad-core ARM A57 microprocessor running at 1.43 GHz, and 4 GB of RAM. Additionally, it has a 128-core Maxwell graphics processing unit (GPU), a micro SD card slot, USB ports, general purpose input/output (GPIO), and various integrated hardware peripherals. An omnidirectional microphone with a built-in sound card [50] is interfaced with the Jetson Nano using a USB. To connect with a smartphone using Bluetooth and to access the Internet wirelessly, a wireless Network Interface Card (NIC) supporting both Bluetooth and Wi-Fi [51] is connected to the M.2 socket of the Jetson Nano. An LED to indicate the program is running--referred to as the heartbeat LED--is interfaced with a GPIO pin of the Jetson Nano. For the power supply, a 110 V AC to 5 V 4A DC adapter is used. To keep the microprocessor cool, a cooling fan is employed with pulse width modulation (PVM)-based speed control, positioned above the microprocessor. The photograph of the gunshot detector device prototype is shown in Section 4.
## 4 Experimental Results
### Performance Indicators  
The effectiveness of the proposed model is evaluated by using accuracy, precision, recall and F1-score. Evaluation of these performance metrics was calculated after the outcome of confusion matrix where four different outcomes were obtained by confusion matrix. In this case true positive (TP) indicates the number of X-rays which are predicted as COVID-19 positive, true negative (TN) denotes the number of X-rays predicted as normal, false positive (FP) represents the number of X-rays that are in fact normal, but predicted as COVID-19 positive and false negative (FN) gives the number of X-rays which are actually COVID-19 positive but predicted as normal.  
\[\text{Accuracy}:\,=\frac{T_{P}+T_{N}}{T_{P}+T_{N}+F_{P}+F_{N}\alpha} \tag{2}\]  
\[\text{Precision}\,=\frac{T_{P}}{T_{P}+F_{P}} \tag{3}\]  
\[\text{Recall}\,=\frac{T_{P}}{T_{P}+F_{N}} \tag{4}\]  
Figure 8: Accuracy evolution: (**a**) picture; (**b**) loss evolution.  
Figure 9: Graph with the execution time of each epoch.  
\[\text{F1}-\text{Score}\ =2\times\frac{\text{Percision}\ \times\ \text{Recall}}{ \text{Precision}\ +\ \text{Recall}} \tag{5}\]  
The accuracy is a metric for evaluation of classification models as shown in Equation (2). Accuracy shows the number of correct predictions made by the model. In the case of binary classification, accuracy is calculated in terms of positive and negative. Precision is calculated to find the ratio between numbers of cases that are correctly predicted as positive as mentioned in Equation (3). Another important factor in CNN evaluation is recall, also known as sensitivity, which is evaluated to find the ratio between COVID-19 positive cases that are predicted positive as indicated in Equation (4). F1- score measurement is also considered as an important factor in the evaluation which is used to identify a balance between precision and recall as shown in Equation (5). As represented in Table 4, the accuracy reached by the model is 98.5%. The precision, recall and F1-score reached by the model was 98%. Confusion matrix shows that the customized CNN model predicted 98 TP and TN cases as shown in Figure 10. This confusion matrix states that the proposed customized CNN model performed very well in predicting the TN cases.  
Some random image samples were also considered to cross check the output generated by the model. The examples of the output produced by the model while testing is mentioned in Figures 11 and 12. Normal X-ray samples and the corresponding predictions are shown in Figure 12 where output X-ray size is also mentioned in the prediction column. COVID-19 X-ray samples and the corresponding predictions are portrayed in Figure 11 along with the output image size. The model classified the images efficiently even though the images used were not preprocessed in this cross-check procedure.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline
98.5\% & 98\% & 98\% & 98\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance Table.  
Figure 10: Confusion matrix.