esa-sceva/satcom-chunk-collection huggingface dataset:
# Propolis as Natural Product in the Oral Cavity Bacterial Infections Treatment: A Systematic Review
## 4 Results
### Antibacterial Activity of Propolis in Oral Cavity Bacterial Infections
#### 4.2.1 In Vitro Studies
[PERSON] et al. [98] investigated the antimicrobial effect of Spanish ethanolic extract of propolisols against _S. mutans_ and _S. sanguinis_. The obtained results showed that Spanish propolisols is effective against _S. mutans_ and _S. sanguinis_, which play a crucial role in dental plaque formation. The authors determined the MIC and MBC values, which are presented in Table 3.3 The ethanol used in the extract had no impact on microbial viability, since a minimum 12.5% concentration was necessary to inhibit the growth of the analyzed strains. The MIC values for ampicillin sodium (positive control) were 0.16 and 0.31 \(\upmu\)g/mL, while MBC values were 0.08 and 0.31 \(\upmu\)g/mL, respectively, for _S. mutans_ and _S. sanguinis_. The analyzed propolisols extract does not possess as strong antimicrobial activity as ampicillin sodium. _S. sanguinis_ is more susceptible to the propolisols extract in comparison to _S. mutans_[98]. [PERSON] et al. [99] researched the antimicrobial effect of propolisols drops on _S. mutans_. The authors analyzed seven different drops: Umay Herbal Organic Propolis, Bee'o Up (15%), Propoli EVSP, Brazilian Green Liquid Propolis, Egricayir Propolis, Bee'o Up (30%), Biostore Propolis as well as 2% chlorhexidine and 10 \(\upmu\)L of chloramphenicol as a positive control. The obtained minimal area of zones for _S. mutans_ was: 0, 20, 14, 15, 30, 24, 12, 34, and 22 mm, respectively, for Umay Herbal Organic Propolis, Bee'o Up (15%), Propoli EVSP, Brazilian Green Liquid Propolis, Egricayir Propolis, Bee'o Up, Biostore Propolis, 2% chlorhexidine, and chloramphenicol. Interestingly, Egricayir Propolis and Bee'o Up showed stronger activity than chloramphenicol, while chlorhexidine showed the strongest impact against _S. mutans_[99]. [PERSON] et al. [7] studied the antimicrobial activities of propolis solutions against some oral cariogenic (_S. mutans, S. mitis, S. sanguis_, and _L. acidophilus_) and periodontopathic bacteria (_A. odontolyticus, E. corordens_, and _F. nucleatum_). Their results indicate that propolis may help prevent dental caries and other oral infectious diseases. The authors investigated the antimicrobial activity of propolis dissolved in benzene, diethyl ether, and methyl chloride by the agar dilution method. Dilutions for propolis were 50, 25, 12.5, and 6.3 ug/mL of active propolis solutions. The results showed that propolis solutions dissolved in benzene, diethyl ether, and methyl chloride demonstrated equal effectiveness against all investigated oral bacteria. However, propolis solution dissolved in acetone displayed a lower MIC value only for _L. acidophilus_ (Table 3) [7]. Finally, the studies performed by [PERSON] et al. [100] showed that MIC and MBC values of water-based Turkish propolis extract against _L. acidophilus_ were 5000 ug/mL (ppm) [100].
# The Evolution and Optimization Strategies of a PBFT Consensus Algorithm for Consortium Blockchains
## 3 An Introduction to Mainstream Consensus Algorithms
### The Mainstream Consensus Algorithms of Public Blockchains  
The commonly used consensus algorithms in public blockchains include PoW and PoS. PoW relies on computational power competition, ensuring high security but consuming a substantial amount of energy. PoS selects validators based on stake weight, enhancing efficiency while posing a risk of centralization. Different consensus algorithms balance security, efficiency, and decentralization, making them suitable for various blockchain applications.
# MIDOM--A DICOM-Based Medical Image Communication System
## 4 Evaluation
### Latency Reduction
#### 4.3.1 Angio  
Figure 6 displays latency reduction results for Angio study. As expected, CBPC2 and CBPC compression methods achieve the best results for all three simulated network environments. For LTE, CBPC2, and CBPC results are identical, thus rendering CBPC2 unnecessary, as it lacks any benefits in terms of latency reduction while being more computationally expensive. The best result is achieved in the 3G environment for the CBPC2 method, with latency lowered down to 42% of the latency needed to transfer the raw study, which is particularly important as 3G is the network environment with the relatively smallest throughput used due to its wide applicability. In the context of the 3G network, a couple of percentage points gained by using CBPC2 make a significant difference. At the same time, analogously, that benefit can be disregarded in the context of a Wi-Fi network. Therefore, it can be concluded that, due to its added computational cost, CBPC2 pays off for the 3G network only. ZIP compression yields results close to 70% for all networks. Compared to ZIP, both CBPC2 and CBPC yield significantly better results and quickly pay off for every network environment.
## 9 Conclusions  
The application of new technologies to a system, historically not the subject of much innovation, can transform it into an extremely efficient system allowing energy and money savings if compared with classical systems, as the Smart City paradigm teaches.  
The example shown in this article is a clear demonstration of this thesis. An isle of lamp posts used to light a crossroads and placed far from the city where the Internet signal does not arrive, has been re-designed combining all new technologies available on the market: LEDs for the lamps, PV panels for the power supply and an intelligent management.  
This last foresees an architecture that uses local sensors for intelligent lighting of the lamp, the storage of the functioning data, and their sharing by a local communication wireless mesh realized by ZigBee devices that send information to the coordinator lamp equipped with a RaspBerry-Pi card. The RaspBerry-Pi has been chosen for its low costs and for the possibility to drive also a WiMAX modem/router which allows to make the data system visible by a web site accessible by Internet also for areas very far from the city and not reached neither by the ADSL line nor by 3G signals.  
Some analysis and comparisons testify the validity of the adopted choices. For its reliability, simplicity and low cost, the proposed system can also be used to update existing conventional lamp posts making it a serious candidate to efficiently manage a set of sensors applicable in different fields including monitoring of energy consumption, other Smart City application and smart grids which needs to diffuse sensors and actuators to realize an efficient management of the system under control.  
[PERSON] conceived the idea of the system, all the authors realized the hardware and the software of the system. The Zigbee tests have been designed and performed by [PERSON] and [PERSON], while the test for the WiMAX have been conceived by [PERSON] and performed by all the three authors. [PERSON] wrote the paper.
# Tampered and Computer-Generated Face Images Identification Based on Deep Learning
## 3 Methodology
### Tampered Face Identification Framework (TFID)  
Figure 4 illustrates a complete architecture of the proposed TFID model, which shows the input size, the kernel size, and the output size. Overall, the proposed TFID accepts 256 \(\times\) 256 images as input. Next, input data are passed through six convolutional layers (C1 to C6) and five max-pooling layers (M1 to M5), followed by batch normalization and one dense layer to give the final classification decision.  
Initially, a suitable CNN structure must be figured out, and the selection of each layer, such as the convolutional layers and the max-pooling layers, and the dropout depends strongly on the experiments. By reviewing previous research and the dataset size [1,4], the model is configured to accept 256 \(\times\) 256 images as the input. The TFID model contains six convolution layers that are in charge of extracting abstract features. Each convolutional layer requires a proper kernel size to manage the parameters effectively. Therefore, suitable kernel sizes for each convolutional layer that ranged from 11 to 3 are selected. The rectified linear unit (ReLU) nonlinearity function (f = max(0,x)) is used as the activation function for each convolutional layer. It was proved to prevent the overfitting problem more efficiently than the hyperbolic and sigmoid functions [23]. Next, max-pooling layers are added behind the convolutional layer to decrease the feature maps' spatial size and prevent overfitting issues. The output of a pooling layer is pooled or down-sample feature maps, which significantly reduces original features size. Finally, a dense layer that uses a softmax function is added to decide whether the input is a tampered or real image.  
Table 1 presents detailed configurations and output of each layer in the TFID model. The dropout value is set to 0.2 for the first five dropout regularizations and 0.5 for the last dropout regularization.  
Figure 4: Tampered Face Identification Framework (TFID) architecture with a detailed configuration for each layer.
## 2 Methodology  
A review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The core search concepts for this review were deep learning (DL), prostate volume (PV), and ultrasound (US). From October 2023 to December 2023, a comprehensive search was conducted across three scientific databases: PubMed [17], IEEE Xplore [18], and Scopus [19]. Search queries were specifically adapted to each search engine and based on the search criteria seen in Listing 1.  
Inclusion Criteria
* Studies reporting on PV estimation or prostate segmentation using DL in US, including both transrectal ultrasound (TRUS) and transabdominal ultrasound (TAUS).
* Studies involving the registration of US images where segmentation is part of the registration process.
* Studies written in English and published from 2016 onwards.  
Exclusion Criteria
* Articles focusing on organs other than the prostate.
* Articles unrelated to US imaging.
* Invalid records, review articles, and duplicates.
* Articles where proposed registration pipelines did not include an automatic segmentation step or relied on imaging modalities other than ultrasound.  
During the title and abstract screening, articles were selected if they met the inclusion criteria. The full-text screening included articles where the proposed segmentation architectures were evaluated on performance. Articles were excluded if they lacked an automatic segmentation step in the proposed registration pipeline or used other imaging modalities.  
A total of 280 studies were retrieved from PubMed [17], IEEE Xplore [18], and Scopus [19]. After duplicate removal and screening of titles and abstracts, 48 studies met the inclusion and exclusion criteria. Full-text screening resulted in 25 studies being included in this review. Figure 2 illustrates the flow diagram showing the number of papers identified at each step.  
To evaluate the deep learning (DL) architectures reviewed, several parameters were analyzed to provide context for their application:  
* The purpose of the DL architecture in relation to 2D or 3D prostate segmentation.
* The amount of ultrasound (US) data used for training and testing.
* The number of patients included in the dataset.
* The results concerning the evaluation metrics used.  
Additionally, the key innovations in the DL architectures that contributed to performance improvements were explained and compared. All stages of the screening process were conducted by an independent reviewer.
# Research on the Flight Performance of Biomimetic Moth Based on Flapping Function Control
## 3 Calculation and Simulation
### Simulation of the Impact of Key Parameters on the Performance of Flapping-Wing Aircraft
#### 3.2.5 Impact of Difference in Flapping Amplitude of Left and Right Wings on Turning  
The biomimetic flapping-wing aircraft designed in this paper does not have a tail rudder to control turning, and it is planned to achieve turning function by creating a difference in the flapping amplitude of the left and right wings. To qualitatively explore whether creating a difference in the flapping amplitude of the left and right wings can achieve the turning function of the aircraft, a group of flapping functions is designed, simulated, and calculated. The control parameters of the left and right wings flapping functions are shown in Table 7.  
The simulation time is set to 1 s, including four flapping cycles. The simulation data is imported into the software Origin and the curve of the overall torque of the aircraft around the \(y\)-axis over time is drawn, as shown in Figure 9. The torque is the counterclockwise torque, which causes the aircraft to turn left when it is positive.  
Using the simulation data, the average torque within a period (0.25 s-0.5 s) is calculated to be 0.017 N-m; that is, increasing the flapping amplitude of the left-wing and decreasing the flapping amplitude of the right wing can achieve the left turn of the aircraft. This  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Parameter**} & **Flapping Motion with a 30\({}^{\circ}\)** & **Flapping Motion with a 40\({}^{\circ}\)** \\  & **Flapping Amplitude** & **Flapping Amplitude** \\ \hline \(\phi_{max}\) & \(-5\pi/18\) & \(\pi/6\) \\ \(T_{1}\) & 0.15 & 0.15 \\ \(T_{2}\) & 0.1 & 0.1 \\ \(\phi_{1}\) & \(\pi/2\) & \(\pi/2\) \\ \(\phi_{2}\) & 0 & 0 \\ \(\phi_{0}\) & \(-\pi/18\) & \(\pi/18\) \\ \(T\) & 0.25 & 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Control parameters of flapping motion with amplitude difference for turning.  
Figure 9: Torque change curve generated by amplitude difference.  
conclusion is contrary to subjective cognition, and whether this conclusion is correct needs to be verified by the flight test of the aircraft.
## 3 Experimental Setups and Methodologies
### Noise Measurement  
The sound pressure level meter AS-6224 and FFT (fast Fourier transform) dual-frequency spectrum analyzer AD-3651 from A&D Inc. (Tokyo, Japan) are used to measure noise in this work. The sound pressure measurement range is valid from 28 to 10 dB and the frequency measurement range is from 20 Hz to 12.5 kHz, respectively. Besides, the FFT dual frequency analyzer can analyze any signal variation at different time domains and frequency ranges by applying FFT to transform and store the analog and digital signals. The entire measurement procedure is set and performed based on the international standards. To ensure the reliability and accuracy of noise data, the experimental results are attained in the semi-anechoic chamber in accordance to the ISO 3745 code [17]. Hence, the reflected sound between microphone and wall can be eliminated to less than 0.5%. Also, the background noise in the test chamber is 19 \(\pm\) 2 dB during the measurement. Also, three measuring points are taken at a distance of 1 meter away from the stand fan. One of the measurement points is located at the fan inlet and the other two are placed at the fan outlet with a 45 degrees angle inclined to the center axis.
# Brain-Like Navigation Scheme based on MEMS-INS and Place Recognition
## 3 Algorithm  
To achieve the effect of the proposed brain-like navigation scheme, the UAV needs to have the functions of memory and recognition to the scene. The memory process can be simulated by storing the coordinates and images of key locations in advance, including intersections, buildings, etc. Recognition refers to the UAV recognizing the template image corresponding to the scene and returning its real coordinate value when passing close to the location of the previous memory, to make the cell reset firing and eliminate the accumulated error. Traditional image matching algorithms generally consist of three categories, which are respectively based on grayscale [27], feature [28] and relation [29]. It is necessary for image matching algorithms to take both efficiency and precision into consideration. [PERSON] proposed place recognition algorithms RatSLAM and SeqSLAM based on continuous multi-frame image information in 2004 and 2012 respectively [17; 22], and the algorithm of comparing the similarity of gray information between two images in a certain area was employed to determine whether the place cell node was encountered. This algorithm does not require large calculation and has the ideal real-time performance, but the algorithm tends to be affected by external environmental factors including weather and traffic conditions. This algorithm calculates the similarity value between the two graphs and comparing it with the threshold, thus knowing that whether the location of the image is in one place. If the threshold value is set too low, it may have omissions in judgment, but if too large, it may generate errors in judgment. Grid-based motion statistics (GMS) [20; 21; 22] is proposed for fast and ultra-robust feature correspondence. Compared with the traditional SIFT feature matching algorithm, GMS eliminates the uncorrected matching points more effectively with an execution speed of 30 frames per second on the PC, which meets the real-time requirements, but the speed is getting slower on the lower computer and not suitable for real-time application. To solve the above problems, a novel place recognition algorithm for place cell correction is proposed by integrating these two algorithms with complementary advantages.
# The Immobilization of Lipases on Porous Support by Adsorption and Hydrophobic Interaction Method
## 4 Types of Immobilization Methods on the Support/Carrier-Binding Method  
Lipases can be immobilized using different methods such as covalent attachment, adsorption, encapsulation, entrapment, and crosslinking on numerous supports (Figure 1). The carrier-binding method is the most commonly used in large-scale immobilization processes [76,77]. The carrier-binding method is conducted based on a weak binding force, such as physical adsorption, ionic bonding, and covalent bonding of enzymes on the surface of water-insoluble supports [78]. Thus, this method causes less or no change in enzyme conformation, especially on the active site of an enzyme [6,14]. The physical adsorption method with a proper support, such as synthetic polymers, exhibits excellent mechanical rigidity, high specific areas, and reusable capability for several cycles of reactions [2]. The second method, known as entrapment, is much more stable than physically adsorbed lipase [79]. Next is the crosslinking technique by reagent glutaraldehyde, which forms chemical bonds like covalent bonding without the use of a water-insoluble carrier [13,29]. Glutaraldehyde is the most common reagent used for crosslinking of lipase on porus polypropylene carriers, and the yield achieves 98%, which is higher than that of free lipase [79]. Various immobilization methods are shown in Figure 1.  
The selection of suitable immobilization techniques is essential to ensure the practical usage of enzymes without leakage or detachment from supports [79,80]. Initially, porous glass beads, diatomaceous earth, silica, and alumina are standard supports used for lipase immobilization, but many new supports were used in recent studies [80,81]. The combination of a proper support and immobilization methods will increase the binding force between the enzymes and the support, thus reducing the leakage of the enzymes from the support (Table 2).
## 3 Experiments
### Sample Preparation  
Epitaxial Eu\({}_{1-x}\)Gd\({}_{x}\)O(001) films (\(x=0\)-19.5%) with a thickness of \(\sim\)35 nm were grown under adsorption-controlled conditions on double-side-polished YAlO\({}_{3}\)(110) single-crystal substrates by  
Figure 4: **(a)** Schematic representation of pump-probe nonlinear magneto-optical spectroscopy used in this study. The sample is excited by the pump pulse and the pump-induced changes in magnetic properties are detected by the probe pulse by using magnetization-induced second harmonic generation (MSHG). **(b)** Temperature dependence of the square root of the static MSHG intensity \(\sqrt{\text{TSHG}}\) for undoped and 2.65% Gd-doped EuO (without pump irradiation). **(c)** The upper panel shows magnetic-field dependence of the MSHG intensity at 10 K in undoped EuO. Data represented by closed and open symbols were taken with decreasing and increasing fields, respectively. The lower panel shows magnetization curve extracted from the data in the upper panel.  
Figure 3: Changes in static magnetic properties of ferromagnetic semiconductor Eu\({}_{1-x}\)Gd\({}_{x}\)O films by carrier doping [31]. **(a)** Temperature dependence of the magnetization in Eu\({}_{1-x}\)Gd\({}_{x}\)O for some characteristic Gd concentrations. **(b)** Carrier density \(n_{\text{c}}\) dependence of the Curie temperature \(T_{\text{C}}\). The inset shows the relation between Gd concentration and carrier density \(n_{\text{c}}\).  
molecular-beam epitaxy. The films were protected against air by an amorphous silicon (a-Si) capping layer with 10-20 nm in thickness and found to be free of oxygen vacancies within the resolution limit of X-ray absorption spectroscopy. Uniform growth without secondary phases was confirmed by X-ray diffraction, and the Gd concentration \(x\) was determined by prompt-gamma activation analysis and X-ray absorption spectroscopy. The carrier density \(n_{\mathrm{c}}\) was estimated by Hall measurements and a sharp rise in \(T_{\mathrm{C}}\) was observed at \(n_{\mathrm{c}}\sim 1\times 10^{19}\) cm\({}^{-3}\) (Figure 3b). All Eu\({}_{1-x}\)Gd\({}_{x}\)O films possess an in-plane magnetic easy axis. Their physical properties were reported in detail in Ref. [31].