esa-sceva/satcom-chunk-collection huggingface dataset:
# Emerging Biomedical Applications of Carbon Dot and Polymer Composite Materials
###### Abstract  
Carbon dot-based composite materials have been extensively developed for versatile biomedical applications, such as drug delivery, tissue engineering, bioimaging, biosensors, and photothermal cancer therapy, owing to their excellent mechanical properties, electrical and thermal conductivity, large surface-to-volume ratio, and biocompatibility. For instance, the hydrophobicity and delocalized \(\pi\)-electrons of carbon dots enable insoluble drug loading in carbon composite-based drug delivery carriers. In addition, carbon dot-based materials are suitable for optical and electrochemical biosensor applications owing to their intrinsic properties. Thus, this review briefly focuses on the following: (1) general aspects, (2) various sources, and (3) versatile biomedical applications of carbon dots and composite materials. More importantly, we present the emerging applications of carbon dot-based materials, such as in agricultural systems, COVID-19 theranostics, counterfeit, and security.  
carbon dots; composite materials; polymers; biomedical applications; emerging applications +
Footnote †: journal: Medical Imaging and Biomedicine and  
mixing of polymers and nanomaterials is not a novel synthesis process, developing better CD/polymer composite materials that exhibit favorable biocompatibility and optoelectrical properties is an emerging research topic. Polymers with different architectures, such as homo- and co-polymers, hyperbranched polymers, and various polymeric chains, have been employed to anchor and coat CDs to fabricate biocompatible CD/polymer hybrid composite materials. Recent studies have shown that several methods, including ligand exchange between polymers and CDs, grafting polymers to CDs, grafting polymers from CDs, capping polymers onto CDs, and growing CDs within the polymer template, are generally used for biocompatible CD/polymer composite materials [14; 15]. As shown in Figure 1b, the facile preparations and remarkable properties of CDs and cross-linked polymeric properties have inspired the potential applications (i.e., drug delivery system, photodynamic therapy, bioimaging, etc.) in biomedical sciences [16].  
The objective of this study is to provide a brief overview of the sources, methodologies, characterizations, and biomedical applications of CDs. Special emphasis was placed on the emerging uses of CDs and their future prospects in such disciplines. This review was designed in the most straightforward manner to provide a context for non-chemist researchers interested in the study of CDs, such as biomedical professionals.
# A Machine Learning Predictive Model to Detect Water Quality and Pollution
## 4 Methodologies
### Data Augmentation  
Our labelled dataset is highly imbalanced, with label 'A' accounting for 43%, label 'B' accounting for 51%, and label 'C' accounting for 6%. In an imbalanced dataset, the predicting accuracy (the number of correctly predicted samples/the total number of samples) will become ineffective. This is because the positive samples in an imbalanced dataset occupy a large percentage, and the accuracy score will become high even if none of the negative samples is successfully predicted. When the ratio of two groups of samples exceeds 4:1, the imbalance problem will be severe.  
There are three standard methods for dealing with an imbalanced dataset: (i) resampling, (ii) over-sampling, and (iii) under-sample. Our original data entries are small, so we choose over-sampling, synthesising data entries for the minority label classes. This process is also known as data augmentation. The most straightforward augmentation technique is picking a small number of samples at random, then making a copy and adding it to the whole sample. However, if the feature dimension of the data is small, simple extraction and replication will lead to over-fitting easily. We adopted a new augmentation method called Synthetic Minority Over-sampling Technique (SMOT). SMOT is to find K numbers of neighbours in P dimensions and then multiply each index of the K neighbours by a random number between 0 and 1 to form a new minority class sample. SMOT can introduce some noise to the synthesised samples to avoid the problem of overfitting.
## 3 Results and Discussion
### Quality Evaluation
#### 3.1.2 \(Q^{AB/F}\)  
\(Q^{AB/F}\) reflects the quality of edge information obtained from the fusion of input images and can be used to compare the performance of different image fusion algorithms [23], which is defined as follows:  
\[Q^{\text{AB/F}}=\frac{\sum_{i=1}^{N}\sum_{j=1}^{M}\bigl{(}Q^{AF}(i,j)W^{A}(i,j )+Q^{BF}(i,j)W^{B}(i,j)\bigr{)}}{\sum_{i=1}^{N}\sum_{j=1}^{M}(W^{A}(i,j)+W^{B} (i,j))} \tag{15}\]  
where \(Q^{AF}(i,j)=Q^{AF}_{g}(i,j)Q^{AF}_{o}(i,j)\), \(Q^{AF}_{g}(i,j)\) and \(Q^{AF}_{o}(i,j)\) are the edge strength and orientation preservation value at the location \((i,\ j)\). \(W^{A}\) and \(W^{B}\) are the weight maps which are equal to the edge strength in the source image. N\(\times\)M are the size of the source images. The higher value of \(Q^{\text{AB/F}}\) is, the less edge information of fused image loss.
## 6 Conclusions
### Answers to the Research Questions  
Based on the experience with _Ana_ chatbot, more specific research answers have been drawn.  
_RQ1. What is an appropriate structure of the ontology of the chatbot?_  
Firstly, the **proper structure** of the chatbot ontology covers the main information a candidate must know when applying to an university: from the faculties and the study domains, to the taxes applied and in the end to the documents needed to complete the enrolment process. _Ana_ chatbot, can be scaled up to more universities, and to more countries, using different language pack and with a minimum effort the responses can be easily adapted due the fact that the core of the chatbot contains all these general aspects:  
* faculties and the study domains
* taxes to be applied
* documents needed to complete the entire process  
_RQ2. How and to what extent a chatbot can contribute to the candidate support during the admission period?_  
Secondly, a chatbot can cover a huge amount of questions based on the candidate's needs. It is an **innovative support tool** which handles properly repetitive questions came from different areas of the world, that for traditional admission process involved a considerable amount of money to cover all the expenses generated:  
* for universities: salaries, indirect expenses, equipment usage;
* for candidates: the travel expenses, indirect expenses.  
_RQ3. What are good practices in designing such a chatbot?_  
The good practices and the recommendations regarding the use of Ana chatbot are summarized in Section 5.  
_RQ4. What is the innovation and added value of such a chatbot to the admission process?_  
The innovation brought is described further in Section 6.2.  
_RQ5. What is the acceptance of such a chatbot and what is its (perceived) impact?_  
We live in an era where the technology is all over the domains. The chatbot fits perfect to the new generation, they are more accustom of using such tools. Based on our research, the digital native generation has shown good **acceptance** of _Ana_.  
_RQ6. What actions are needed for making the chatbot sustainable over more admission sessions?_Each admission session offers a huge amount of new information that can be converted into new nodes in the ontology. This leads to a better performing chatbot, that is more and more adapted to the needs of the candidates, and that leads to a higher response rate, that reduce to a minimum all the costs with the admission process. Obviously, the **sustainability** can be achieved by continuous maintenance of the chatbot and ontology and that is why the lifecycle has two extra steps Section 3, unlike the classical lifecycles: deployment into production and maintenance.
# Modeling of Tunneling Total Loads Based on Symbolic Regression Algorithm
## 3 Constructing \(\pi\) Quantities Based on Dimensional Analysis  
Dimensions reflect the most essential attributes of physical parameters. Any physical parameter can be traced back to its definition or derivation source through dimensions. The parameters of tunneling loads typically fall into three categories: geological, operational, and those based on the equipment structure. According to the relevant investigation and mechanical analysis of the geological failure mechanism, and the statistical analysis of some geological parameters, it is found that geological parameters have some linear correlation, and the influence of some geological parameters on the tunneling loads can be considered in some basic mechanical parameters. For example, the influence of rock joints on the tunneling load can be reflected by the shear modulus of rock during exploration. Therefore, six basic mechanical parameters are determined for analysis. The specific parameters and their dimensions are presented in Table 1.  
Figure 3: Basic operation process of genetic algorithm.  
The \(\Pi\) theorem is an important theorem about dimensional analysis, which states that every physical problem can be represented by several dimensionless powers of quantities [38], denoted by \(\pi\). Based on the basic idea of the \(\Pi\) theorem, the relationship between the tunneling loads and the parameters is as follows:  
\[\pi_{F,T}=g(\pi_{1},\pi_{2},\cdots,\pi_{k}) \tag{2}\]  
Here, \(k=(n-3)\), \(n\) is the number of influencing parameters, and \(\pi_{1},\pi_{2},\cdots,\pi_{k},\pi_{F,T}\) are the dimensionless \(\pi\) quantities composed of influencing parameters.  
To construct the dimensionless \(\pi\) quantities mentioned in Equation (1), analysis and calculations are carried out through two steps: the selection of the basic quantities and the construction of \(\pi\) quantities.
## 3 Filters for Feature Extraction: LBP, HOG, and Saliency
### Histogram of Oriented Gradients (HOG)
HOG captures global features that do not rely on the exact orientation of the objects [48; 50]. * Efficiency in detecting shapes and structures: HOG is ideal for recognizing objects with well-defined shapes and edges, such as people, cars, or other rigid objects. By focusing on the detection of gradients and shapes, it is highly efficient in identifying complex structures in images [48; 51].
# On the Feasibility of Wireless Multimedia Sensor Networks over IEEE 802.15.5 Mesh Topologies
## 5 Performance Evaluation
### Scalability  
The evolution of the obtained performance metrics according to the size of the networks is shown in Figure 17, ranging from 2 hops for a 2 \(\times\) 2 network to 10 hops for a 6 \(\times\) 6 network. All metrics remain within acceptable values for any network size, proving the scalability of the IEEE 802.15.5 SES mechanism for video transmission. For the largest network sizes, though, there is a small drop in performance. In these cases, increasing the value of the WI parameter will improve the quality of the received video at the expense of throughput.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**PSNR** & **MOS** & **Impairment** \\ \hline \(>\)37 & 5 (Excellent) & Imperceptible \\
31–37 & 4 (Good) & Perceptible but not annoying \\
25–31 & 3 (Fair) & Slightly annoying \\
20–25 & 2 (Poor) & Annoying \\ \(<\)20 & 1 (Bad) & Very annoying \\ \hline \hline \end{tabular}
\end{table}
Table 7: PSNR to MOS mapping [37].  
Figure 17: Performance metrics according to the size of the network, WO–AO = 5-3.
# Superconducting Sub-Terahertz Oscillator with Continuous Frequency Tuning
## 1 Introduction
This boundary is visible in the form of a characteristic compression of the IVC at a third of the gap voltage, which is approximately 0.95 and 1.25 mV for junctions based on the Nb/Al-AlO\({}_{x}\)/Nb and Nb/Al-AlN/NbN structures, respectively; these voltage values according to the Josephson relation correspond to FFO frequencies of 460 and 606 GHz. The figure clearly shows the resonant behavior of the IVC at voltages \(\rm V<V_{g}/3\), due to the presence of Fiske steps. For most practical applications, continuous frequency tuning of the superconducting local oscillator is required at the arbitrary value of the bias current. This requirement is naturally fulfilled in the flux-flow regime, while in operation in the regime of Fiske steps, stable generation is possible only at Fiske steps, the distance between which in frequency is tens of GHz. This article presents the results of a comprehensive study of the FFO; a method for suppressing Fiske steps was proposed and tested to ensure continuous frequency tuning at voltages \(\rm V<V_{g}/3\). It should be noted that spectral measurements by the integrated receiver require not only continuous tuning of the local oscillator frequency, but also the implementation of the FFO phase locking [39]. Considering the delay in the phase locking loop (PLL) between the SIR microcircuit located in the cryostat at helium temperature and the PLL system operating at room temperature, the linewidth of the free-running FFO should not exceed 10-15 MHz to implement synchronization with a sufficiently high spectral quality. The linewidth of the oscillator is proportional to the square of the differential resistance \(\rm R_{d}\) of the FFO at the operating point [40; 41; 42]; this in turn imposes restrictions on the value of  
Figure 1: IVCs of the Nb/Al-AlO\({}_{x}\)/Nb (**a**) and the Nb/Al-AlN/NbN (**b**) FFOs, measured at different magnetic fields. The pumping current of the integrated SIS mixer induced by the FFO radiation is shown by the color. required to implement the PLL regime, therefore special attention should be paid to this problem as \(\mathrm{R_{d}}\) is increased for the geometry with the suppression. The purpose of this paper is the development and systematical study of the continuous frequency tunable FFO with low \(\mathrm{R_{d}}\).
# An Analytical Solution for Block Toppling Failure of Rock Slopes during an Earthquake
## 2 Mechanical Model and Analytical Solution to Toppling Slope Failure
In summary, \(P_{n-1}\) can also be simply obtained according to Equation (14): \[P_{n-1}=\max(P_{n-1,s},P_{n-1,t},0)\] (14) If \(P_{n-1}=0\), the \(n\)th block is stable; If \(P_{n-1}=P_{n-1,t}\), the \(n\)th block is toppling; If \(P_{n-1}=P_{n-1,s}\), the \(n\)th block is sliding. \(P_{n-1}\) and the deformation status can also be determined according to \[\sigma_{tM}=\frac{M_{b}\times\frac{1}{2}\xi\Delta x}{\frac{1}{\Omega}\big{(} \xi\Delta x\big{)}^{3}}=\frac{M_{b}}{\frac{1}{6}\big{(}\xi\Delta x\big{)}^{2}} \tag{7}\]0
5. Estimating the stability of the slope. If the deformation status of the lowest block (\(n=1\)) is stable (or \(P_{0}=0\)), the slope is stable. If the deformation status of the lowest block (\(n=1\)) is unstable (or \(P_{0}>0\)), the slope is unstable. 6. Determining the FOS. The FOS of the slope can be determined using the methods as follows: A ratio \(R_{a}\) is adopted to obtain the parameters in calculation based on the actual parameters, which are shown as Equations (15)-(19). \[\sigma_{tM}=\frac{M_{b}\times\frac{1}{2}\xi\Delta x}{\frac{1}{\Omega}\big{(} \xi\Delta x\big{)}^{3}}=\frac{M_{b}}{\frac{1}{6}\big{(}\xi\Delta x\big{)}^{2}} \tag{7}\]1 \[\sigma_{tM}=\frac{M_{b}\times\frac{1}{2}\xi\Delta x}{\frac{1}{\Omega}\big{(} \xi\Delta x\big{)}^{3}}=\frac{M_{b}}{\frac{1}{6}\big{(}\xi\Delta x\big{)}^{2}} \tag{7}\]2 (16) \[\sigma_{tM}=\frac{M_{b}\times\frac{1}{2}\xi\Delta x}{\frac{1}{\Omega}\big{(} \xi\Delta x\big{)}^{3}}=\frac{M_{b}}{\frac{1}{6}\big{(}\xi\Delta x\big{)}^{2}} \tag{7}\]3 (17) \[\sigma_{tM}=\frac{M_{b}\times\frac{1}{2}\xi\Delta x}{\frac{1}{\Omega}\big{(} \xi\Delta x\big{)}^{3}}=\frac{M_{b}}{\frac{1}{6}\big{(}\xi\Delta x\big{)}^{2}} \tag{7}\]4 (18) \[\sigma_{tM}=\frac{M_{b}\times\frac{1}{2}\xi\Delta x}{\frac{1}{\Omega}\big{(} \xi\Delta x\big{)}^{3}}=\frac{M_{b}}{\frac{1}{6}\big{(}\xi\Delta x\big{)}^{2}} \tag{7}\]5  
If the lowest block with the actual mechanical parameters is stable, calculate the slope stability with these various \(R_{a}<1\), and FOS is the \(R_{a}\) that makes \(P_{0}\approx 0\). Similarly, if the lowest block with the actual mechanical parameters is unstable, the FOS is \(R_{a}\) (\(R_{a}>1\)) which makes \(P_{0}\approx 0\). The method can be easily carried out in spreadsheets such as Excel. The flow chart is shown in Figure 5, in which the number in front of the colon is the corresponding column in the calculation sheet, e.g., the column number in \[\sigma_{tM}=\frac{M_{b}\times\frac{1}{2}\xi\Delta x}{\frac{1}{\Omega}\big{(} \xi\Delta x\big{)}^{3}}=\frac{M_{b}}{\frac{1}{6}\big{(}\xi\Delta x\big{)}^{2}} \tag{7}\]6 The procedure will be described in more detail with a case study in the next section.
# Orthogonal Subblock Division Multiple Access for OFDM-IM-Based Multi-User VLC Systems
## 3 Results and Discussions  
In this section, we conduct numerical simulations and hardware experiments to evaluate the performance of the proposed OSDMA scheme with DFT spreading and interleaving for OFDM-IM-based MU-VLC systems. Moreover, the following four schemes are considered, including OFDM-SM-IM, DFT-S-OFDM-SM-IM, OFDM-DM-IM and DFT-S-OFDM-DM-IM. In both simulations and experiments, the length of IFFT/FFT is set to 256 and a total of 108 (i.e., 2 nd to 109 th) subcarriers are utilized to transmit useful signals. Moreover, we consider each subblock with length \(N=2\) and \(k=1\) subcarrier is selected via index selection. For all four schemes, a target spectral efficiency of 2.5 bits/s/Hz is considered. Hence, according to (2) and (4), we set \(M=16\) for OFDM-SM-IM/DFT-S-OFDM-SM-IM and \(M_{\text{A}}=M_{\text{B}}=4\) for OFDM-DM-IM/DFT-S-OFDM-DM-IM. Specifically, square 16-QAM constellation is adopted for OFDM-SM-IM/DFT-S-OFDM-SM-IM, while the specially-designed circular (7,1)-QAM constellation is considered for OFDM-DM-IM/DFT-S-OFDM-DM-IM [20].
## 2 Materials and Experimental Techniques  
The Inconel 718 alloy, plastically deformed, used for thermal shock testing, has the following composition: 52.16% Ni, 17.05% Fe, 16.76% Cr, 2.96% Mo, 5.36% Nb, 0.23% Mn, 0.17% Si, 1.12% Ti, and 0.42% Co. The characteristics of the Inconel 718 alloy are given by ASTM B637 N07718 \"Standard Specification for Nickel Alloy Bars, Forgings, and Forging Stock for Moderate or High Temperature Service\" [38]. The microstructure revealed intergranular lamellar precipitates distributed relatively uniformly at the grain boundaries (Figure 1b,f), the presence of large polyhedral precipitates/particles inside the grains (Figure 1a,c,d), and the presence of some structures with rectangular geometric development (Figure 1e).  
Cyclic overheating at high temperatures was simulated by thermal shocks using solar energy. The specimens for thermal shock testing have a parallelepipedal shape with a square base of 7 mm sides and 10 mm height.  
The height of the sample is oriented according to the deformation direction of the alloy. The surface of the samples was mechanically polished using 800-gri abrasive paper. The shocks were applied to the upper base of the parallelepipedal sample positioned vertically. Thermal shock testing was carried out in the Solar Furnace at the PROMES laboratory, Font Romeu-Odeillo, France (Figure 2). Cyclic thermal shocks were used in the temperature range of 700-1000 \({}^{\circ}\)C with a constant temperature duration of each cycle of 30 s.  
Figure 1: The SEM microstructures for the alloy as delivered, containing large polyhedral precipitates/particles inside the grains (**a,c,d**), intergranular lamellar precipitates at the grain boundaries (**b,f**) and structures with rectangular geometric development (**e**).  
Figure 2: _Cont._  
The thermal transport properties were investigated using a Laser Flash Analyzer model Netzsch LFA 457 Microflash (NETZSCH-Gerstebau GmbH, Munchen, Germany) from room temperature up to 1050 \({}^{\circ}\)C. The microhardness measurements were carried out with FALCON equipment, series 500 (INNOVATEST Europe, Maastricht, The Netherlands). For microscopic analysis, samples were prepared by mechanical grinding and etched with Adler's reagent. The characterization by SEM-EDS was carried out using a HITACHI SU5000 electron microscope (Hitachi High-Tech Corporation, Tokyo, Japan) equipped with a backscattered electron detector and an energy-dispersive fluorescence spectroscopy module for elemental analysis.
## 3 Smart Feasant Farming System and Data-Mining Process
### Cloud-Computing Platform
#### 3.4.3 IoT Data Analytics Service  
Regarding the data-mining process, all data stored in the NoSQL cloud database, which are raw data, must first be preprocessed to be transformed into data that can be used by machine-learning algorithms for the purpose of discovering knowledge. In particular, within the proposed architecture, this task is carried out by the IoT data analytics service.  
Moreover, data preprocessing commonly involves data cleaning and data transformation itself. On the one hand, data cleaning means fixing or removing anomalies in data, and, in its simplest form, it is reduced to dealing with missing values, removing irrelevant values and removing duplicated values. This is crucial for an IoT sensor-based smart farming system because data captured from the physical world through sensors (in our case, climate data) tend to be noisy and unreliable.  
On the other hand, data transformation typically involves data scaling and data normalization. Data scaling means fitting data within specific scales, whereas data normalization implies scaling data with the intention of transforming them to be normally distributed.  
In this context, it is worth noting that popular cloud platforms (Platform as a Service, PaaS) such as Microsoft Azure and Amazon AWS include services that allow for automatizing common data preprocessing tasks, from data cleaning tasks through to data transformation tasks. On these cloud platforms, data can also be automatically preprocessed before being stored.  
Figure 3 shows a flowchart representation of the data-mining process pipeline proposed in this work.  
Figure 3: Data-Mining Process Pipeline.
## 2 Methodology and System Architecture
### Process Control  
Sequential actuation of the mechanism was carried out using an Arduino microcontroller, which communicates with the PLC (the main controller). Three sensors are connected to the PLC; two proximity sensors were used, the first one detects the arrival of the workpiece to initiate the process, and the second sensor detects the arrival of the workpiece to the drilling unit. The third sensor was a switch-activated sensor that is triggered by the linear vertical motion of the drill. The second proximity sensor signal (checks the arrival of the workpiece to the drilling table) is fed to the Arduino through the PLC.  
The PLC outputs only 24/0 volts as digital outputs, whereas the Arduino operates on a 5/0 logic level. Therefore, an industrial optocoupler is used to convert the 24/0 digital logic to 5/0 digital logic. This signal initiates the drilling process where the Arduino controls a stepper motor that moves a table vertically to the designated position. The workpiece is also clamped by the pneumatic cylinder to hold it in place. The mechanical drill and the pneumatic cylinder are actuated by relays withdrawing signals from the Arduino microcontroller.