esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods for Assessing the Aesthetic Quality of Images
### Deep-Learning-Based Method for Assessing the Aesthetic Quality of Images
#### 3.2.3 Image Aesthetic-Assessment Method Based on Fine-Tuned Convolutional Neural Network  
Image aesthetic-quality assessment has been a very challenging task due to its subjective and conceptual nature. [PERSON] [81] suggested adapting convolutional neural networks to achieve image aesthetic assessment, using pre-trained models and calibrating them to assess image aesthetic quality. AlexNet and VGG were fine-tuned to provide two types of outputs. VGG is a deeper network that offers higher accuracy, and both global and local views are available to train the network [82]. To address the problem of small-scale data and quantifying the aesthetic quality of images, [PERSON] [83] proposed an image content-based convolutional neural network embedding fine-tuning to assess the aesthetic quality of images. The method uses image content to train aesthetic-quality-classification models, but  
Figure 3: Multi-task learning model architecture [77].  
the training samples are becoming smaller, and using fine-tuning only once cannot make full use of small-scale datasets. Therefore, the researchers proposed a method based on two consecutive fine-tunings of aesthetic-quality labels and content labels. In 2022, [PERSON] [84] from the Communication University of China fine-tuned the traditional convolutional neural network VGG16 by treating image aesthetic assessment as an aesthetic-score assignment task, replacing the fully-connected layer with a convolutional layer, and adding an adaptive spatial pooling layer at the end of the network to circumvent the input size limitation of the traditional network.
# A Review of Neurotransmitters Sensing Methods for Neuro-Engineering Research
## 2 Neurotransmitter Detection Methods
### Electrochemistry
#### 2.3.1 Fast-Scan Cyclic Voltammetry (FSCV)
Then, a brain tissue dopamine detection limit of 50 nM was achieved [193]. However, the detection of dopamine and serotonin is problematic because of the reactive species that are generated after oxidation and stick to the electrode surface, thus affecting the system sensitivity [194; 195]. Therefore, when dopamine and serotonin are mixed together, selectivity becomes a challenge due to the similarities of the two molecules. That limitation can be improved by using carbon nanotube-modified microelectrodes [196]. Furthermore, selective dopamine detection in the presence of ascorbic acid was reported through electropolymerization of polyglycine on carbon paste electrodes, which improves the redox peak currents of dopamine [197]. Figure 3: Voltammetric detection of dopamine. Dopamine is oxidized to dopamine-o-quinone when a potential is applied to the electrode. The resulting electrons from this oxidation generate a current that is detected. When the potential is removed from the electrode, the dopamine-o-quinone is reduced back to dopamine, thus producing a current in the reverse direction [179].
# Exploring Dentists' Preferences in Selecting Adhesive Systems: A Survey Analysis
## 2 Materials and Methods  
This observational cross-sectional study was conducted between November 2023 and February 2024 among dentists authorized to practice in Romania. The questionnaire was distributed via email to 700 Romanian dentists. Only 585 answered, and of these, 534 completed the questionnaire correctly. To be included in the study, dentists had to graduate from a Romanian university between 1990 and now, practice clinically in Romania, treat at least 40 patients per month, and perform at least 20 direct restorations using adhesive techniques. The respondents were divided into four groups according to the year of their graduation: Group A (1990-1997), Group B (1998-2005), Group C (2006-2013), and Group D (2014-2022). All participants comprehended the study's objective, consented to participate, and granted permission to publish findings. The participants provided their informed consent and agreed to the handling and utilization of their personal information. Written informed consent has been obtained from the participants to publish this paper. The questionnaire included seven open-ended questions. The first two asked about the respondent's gender and graduation year. The next five questions focused on the number of patients treated monthly, the number of direct coronal restorations performed, the adhesive system used (etch-and-rinse or self-etch), the preferred adhesive system, and the most common challenges faced in direct restorations.  
The statistical analyses were conducted using the software DATAtab version 2024 [15]. The primary outcome variable was the selection of the adhesive system. Secondary outcomes included the graduation year and the most encountered difficulty in direct restorations. A descriptive analysis was conducted, and the frequency of distribution was calculated in percentages. Chi-square tests were conducted to evaluate the association between the year of graduation and the selected adhesive agent. Differences between groups for variables measured on continuous or ordinal scales were analyzed using Kruskal-Wallis tests, with the Bonferroni correction applied to adjust for multiple comparisons. Statistical significance was established at \(p\) values less than 0.05.
# Efficient FPGA Implementation of an ANN-Based Demapper Using Cross-Layer Analysis
## 7 Conclusions  
We have presented a hardware architecture of an AE-based communication system that is able to fine-tune for channel fluctuations in the form of phase offset variations, by retraining the receiver-ANN during runtime. Our FPGA implementation is ideally suited for low-power and low-latency communication scenarios as it operates on a small batch size and is designed as a pipelined on-chip architecture. Additionally, the design is highly flexible as it supports variable quantization and a DOP that can be configured individually for inference and training. Furthermore, we propose a framework that bridges the gap between the communication system layer and the hardware layer by adjusting the DOP depending on application requirements and hardware constraints. We have demonstrated the advantages of our hardware architecture and FPGA as an implementation platform by comparing it to different general-purpose processors. As a result our FPGA-based AE implementation achieves 2000\(\times\) higher throughput than a high-performance GPU, draws 5\(\times\) less power than an embedded CPU, and is 5800\(\times\) more energy efficient compared to an embedded GPU, for small batch size.
# Numerical Investigation of a Novel Grinding Device
###### Abstract
Such a device offers several advantages in terms of safety, convenience, and environmental friendliness, since it allows NPs to be produced in a one-pot operation, i.e., directly as a suspended phase in the process solvent and directly from a bulky solid such as millimeter-sized metal spheres. Due to the establishment of special operating conditions, it has been observed that this configuration allows the production of NPs with a size reduction factor of up to \(1/10^{5}\) compared to the precursor beads. This makes it possible to overcome the well-known rule of thumb that sets the limit of the reduction factor for this type of processes at \(1/100\)[10]. To expand the application potential of the discussed approach while preserving its advantages, an alternative configuration of the device has also been developed and tested for the production of Ag glycanoparticles with a narrow size distribution in the range 10-20 nm [16]. In this new setup, the stirring is performed by an impeller that is driven by a magnetic bar kept outside the fluid containing the precursor beads. As a result, in the case of ferromagnetic NPs, particle magnetization and consequent aggregation are avoided, in contrast to the original configuration, thus favoring a more stable and homogeneous dispersion. The complete confinement of the impeller shaft inside the vessel enables operation under pressure and/or with volatile solvents. In addition, it is also suitable for one-pot purification processes, where the NPs are used as soon as they are produced, with obvious advantages in terms of safety, cost, and the environment. Considering the same synthesis of silver NPs previously studied in the standard apparatus [23], a first attempt to investigate the new configuration by means of numerical simulations was made in [PERSON] et al. [24]. The aim of the present work is to deepen the analysis by considering additional operating conditions, including different stirring speeds and loading configurations. The process is modeled through the Discrete Element Method (DEM), which can reproduce the movement and collisions of the precursor and grinding beads. Although no direct information about the produced NPs can be obtained from DEM simulations, such an approach provides access to key system features, such as impact frequency, energy, and angle. This makes it possible to understand how changes in the experimental setup affect the dynamics of the system and, ultimately, the efficiency of the abrasion process underlying the generation of NPs.
# Design and Implementation of LoRa Based IoT Scheme for Indonesian Rural Area
## 3 Design and Implementation
### The Design of LoRa Infrastructure  
The design of the Internet of Things (IoT) based on LoRa communications is depicted in Figure 1. The sensor node is equipped by the LoRa transmitter to send the information of sensing measurement. From the sensor node, sensing data is transmitted to the LoRa gateway using half-duplex communication. Therefore, data is acquired by the server, acting as the cloud of our scheme. Eventually, users can monitor the environment of their field and use the record of the data to have an action based on the reality of the field.  
Therefore, to have a long-distance capability for transferring the data, we follow the LoRa standard protocol. According to the fundamental formula of LoRa based transferring data scheme, longer distances can be achieved by setting a higher spreading factor, as illustrated in Equation (1). To measure the effectiveness of every parameter, we conduct the experiment to find the best combination we may need.  
Figure 1: Internet of Things (IoT) based on Long Range Wide Area (LoRa) communications.
## 3 Estimation of Motion Parameters Based on the CCAE Method
### Proposed CCAE Algorithm
#### 3.2.1 Estimation of Velocity  
Assuming that only the velocity is taken into consideration and that the velocities of different scatterers within a target are the same, the motion of the target can be written as:\[R_{p}(t_{m})=R_{0,p}+vt_{m}, \tag{18}\]  
where \(R_{p}(t_{m})\) denotes the distance between the \(p\)-th scatterer and the radar at \(t_{m}\) instant, \(R_{0,p}\) the initial distance between the \(p\)-th scatterer and the radar and \(v\) the velocity of the target. The acceleration of the target \(a\) is set to zero. Thus, the estimated frequency \(\hat{f}_{m}\) can be rewritten as:  
\[\hat{f}_{m} = \frac{2\gamma(R_{p}(t_{m+1})-R_{p}(t_{m}))}{c}\] \[= \frac{2\gamma vT_{pr}}{c}. \tag{19}\]  
Consequently, using two echo signals, we can estimate the velocity by the following equation:  
\[\hat{v}=\frac{c\hat{f}_{m}}{2\gamma T_{pr}}. \tag{20}\]
## 2 Materials and Methods  
### Study Design, Study Setting, and Sample Selection  
A cross-sectional study was conducted to assess the proportions of oral candidal lesions in patients with type 2 DM and to explore the relationship between demographic factors, risk factors, and the development of such lesions. This study was conducted at the Diabetic Clinic of Teaching Hospital (T/H) Peradeniya and the Department of Oral Pathology, Faculty of Dental Sciences, University of Peradeniya. The study sample comprised of type 2 DM patients, both with and without clinically visible oral candidal lesions, attending the Diabetic Clinic at Teaching Hospital (T/H) Peradeniya. These patients were subjected to the following inclusion and exclusion criteria:  
Inclusion criteria--Patients aged 18 years or older attending the Diabetic Clinic at T/H Peradeniya.  
Exclusion criteria--Patients diagnosed with type 1 DM, type 2 DM patients who had undergone organ transplantation, and type 2 DM patients who were immunocompromised due to other conditions, including malignancies and autoimmune diseases such as systemic lupus erythematosusus and rheumatoid arthritis.  
Due to lack of data regarding the prevalence of oral mucosal lesions caused by _Candida_, a proportion of 64%, which is the prevalence of oral candidiasis in DM patients reported by [PERSON] et al., 2013 [5], was used as the reference value for sample size calculation. The sample size was determined to be 355 using [PERSON]'s formula, as seen below.  
\[n=\frac{p(1-p)\times Z^{2}}{l^{2}};\ n=\frac{0.64(1-0.64)\times 1.962^{2}}{0.05^{ 2}}\]  
\(p=0.64\), which is a proportion of candidiasis obtained from the study by [PERSON] et al., 2013 [5], \(Z^{2}\)= 1.96, which is a standard normal deviate value used to avoid an \(\alpha\) error in two-sided tests.  
\(l=0.05\), which is a level of precision.  
An interviewer-administered data collection form was used to record the following: demographic details, medical history (duration of DM and the presence of other non-communicable diseases (NCDs)), risk factors (denture wearing, smoking, betel chewing, and alcohol consumption), and glycemic levels. For clinical confirmation, photographs of identified oral lesions were taken and documented.
# Multi-Scale Safety Helmet Detection Based on SAS-YOLOv3-Tiny
## 4 Experiments and Results Analysis
### Experimental Progress and Result Analysis
#### 4.2.4 Detection Results under Application Scenarios  
To prove that the improved algorithm is more suitable for natural complex scenes in terms of accuracy, we show the detection effect of some test images, which are shown in Figure 7. For small-scale objects, occluded objects and dense objects, SAS-YOLOv3-tiny is superior to the YOLOv3-tiny algorithm. As can be seen from the first and second set of images, SAS-YOLOv3-tiny and the latest YOLOv4-tiny can detect all objects, but YOLOv3-tiny leaves out an ordinary object. As can be seen from the third set of images, SAS-YOLOv3-tiny can detect all objects while YOLOv4-tiny neglects a helmet object, and YOLOv3-tiny detects some of the objects incorrectly. For detecting small objects at long distances, SAS-YOLOv3-tiny has better performance than YOLOv3-tiny and YOLOv4-tiny. In the last set of images, SAS-YOLO-v3-tiny and YOLOv4-tiny can detect some standard objects, but they will miss objects to be detected when a man deliberately lowers his head. As can be seen from the above test images, the improved algorithm is superior to the original algorithm and sometimes even has a better detection effect than the latest YOLOv4-tiny.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**Helmet**} & \multirow{2}{*}{**AP/**} & \multirow{2}{*}{**\%**} & \multirow{2}{*}{**Safey-**} & \multirow{2}{*}{**P/\%**} & \multirow{2}{*}{**R/\%**} & \multirow{2}{*}{**mAP/\%**} & \multirow{2}{*}{**FI/\%**} & \multirow{2}{*}{**Weight/MB**} & \multirow{2}{*}{**Pameters/10\({}^{7}\)**} & \multirow{2}{*}{**Detection**} \\  & & & & & & & & & & & & \\ \hline YOLOv3-tiny & 70.9 & 74.0 & 76.2 & 77.5 & 71.0 & 75.4 & 74.6 & 72.9 & 69.5 & 0.86768 & 2.5 \\ YOLOv4-tiny & 80.4 & 74.7 & 80.8 & 79.8 & 72.6 & 80.0 & 78.9 & 76.0 & 47.2 & 0.58779 & 1.8 \\ YOLOv3 & 84.2 & 81.2 & 92.5 & 86.6 & 75.8 & 85.6 & 86.1 & 80.3 & 492.8 & 6.15399 & 8.6 \\ YOLOv4 & 86.7 & 80.9 & 92.7 & 89.3 & 79.4 & 88.6 & 87.4 & 83.7 & 420.7 & 5.24798 & 7.4 \\ SAS-YOLOv3-tiny & 78.2 & 73.3 & 87.8 & 81.9 & 71.6 & 80.9 & 80.3 & 75.2 & 46.9 & 0.58277 & 3.2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of different algorithms for object detection on the test set.  
Figure 7: _Cont_.
# Optic Disc Detection from Fundus Photography via Best-Buddies Similarity
###### Abstract  
Robust and effective optic disc (OD) detection is a necessary processing step in the research work of the automatic analysis of fundus images. In this paper, we propose a novel and robust method for the automated detection of ODs from fundus photographs. It is essentially carried out by performing template matching using the Best-Buddies Similarity (BBS) measure between the hand-marked OD region and the small parts of target images. For well characterizing the local spatial information of fundus images, a gradient constraint term was introduced for computing the BBS measurement. The performance of the proposed method is validated with Digital Retinal Images for Vessel Extraction (DRIVE) and Standard Diabetic Retinopathy Database Calibration Level 1 (DIARETDB1) databases, and quantitative results were obtained. Success rates/error distances of 100%/10.4 pixel and of 97.7%/12.9 pixel, respectively, were achieved. The algorithm has been tested and compared with other commonly used methods, and the results show that the proposed method shows superior performance.  
optic disc; detection; BBS; template matching +
Footnote †: journal: Applied Sciences
# Finite Element Approaches to Model Electromechanical, Periodic Beams
## 2 Numerical Model
In the classical FEM approach, the unit cell has been divided into three finite elements while in the case of the spectral approach the unit cell has been represented by one finite element, as shown in Figure 2.  
The main difference of the TDSFEM in comparison to the FEM is that in the TDSFEM approach the element nodes are not equally distributed. Coordinates of the nodes are defined as roots of a certain orthogonal polynomial:  
\[T_{p}^{c}=(1-\xi^{2})U_{p-2}(\xi), \tag{5}\]  
which in the analysed case has been \(U_{p-2}(\xi)\)--the second order Chebyshev polynomial. The element nodes in the element coordinate system may be calculated as follows:  
\[\xi_{i}=-cos\frac{\pi(j-1)}{p}\quad j=1,...,p+1. \tag{6}\]  
Such a definition of node distribution allows one to use higher order shape functions without the risk of causing the Runge effect. The node distribution used in the calculations performed for this paper has been shown in Figure 3.  
Figure 3: (**a**) A unit cell in the global coordinate system (**b**) A node distribution in element coordinate system. Figure 2: Modelling a unit cell of an electromechanical periodic structure: (**a**) by the finite element method (FEM), (**b**) by the spectral finite element method (SFEM). The stiffness and inertia matrices corresponding to the piezoelectric element within the respective integration limits have been joined with the stiffness and inertia matrices of the aluminium element respectively, as shown in Figure 4. The procedure has been precisely described in [14] for the case of passive periodic structures being a beam and rod with a sequence of drilled holes. The aforementioned mathematical operations ensures that the stiffness matrix of the piezoelectric element is dependent on the frequency, therefore it is possible to actively control the mechanical responses of the analysed element by frequency variation.
# Application of Machine Learning Techniques to
## 3 Methodology
### Validation of Selected GCMs
#### 3.3.1 Seasonal Data Sampling  
The daily precipitation data of the selected GCM, corresponding to each homogeneous climate zone, were seasonally resampled for every grid station (i.e., the grid station within each grid cell). The multi-model ensemble mean data were generated by resampling the data as the average of the GCM daily precipitation data at every grid station.
# Rendering Strategy to Counter Mutual Masking Effect in Multiple Tactile Feedback
## 4 Discussion
### Limitations and Future Work  
The current study focused on proving a newly proposed concept by conducting a small set of essential experiments. Therefore, several aspects should still be further investigated. First, we need to find out the intensity range limits that can be rendered by the proposed algorithm as dictated by the maximum (or rated) output of the device. As shown in Figure 5, a much stronger input intensity is required to produce the desired perceived intensity, especially for the MV. Thus, depending on the rated output of the device, the possible range of perceived intensities could probably be significantly reduced. Using the device with high output intensity could be one possible solution; however, too high of an input (e.g., \(m_{i}\)) intensity might be uncomfortable or painful to the users, although they would perceive the presented stimulus intensity at the sensation level (e.g., \(m_{p}=m_{i}-f(e_{i})\)). To address this, intensive experiments are required to suggest an adequate range of input and perceived (or target) intensities.  
Another drawback is related to the determined efforts to conduct a set of experiments aimed to find the masking functions. As described in the previous sections, the proposed algorithm requires a mutual masking function of a target stimulus set. That is, if we intend to use a stimulus set that was not investigated before (e.g., a combination of EV of 270 Hz and MV of 150 Hz), experiments I and II should be repeated for such a stimulus set. Performing experiments I and II for all candidate stimulus sets would be a huge burden and might not be an effective way in this context. Although several research groups have suggested a method to predict vibrotactile masking functions [23], predicting all possible stimulus combinations might be complex. These issues will be investigated in future studies.  
In the current study, we decided to use EV to render geometry as many studies have verified that lateral force (or frictional force) plays an important role in the perception of object geometry [29; 30; 31]. However, the combination of EV and MV might not be the best way to present both geometry and texture information. For example, the role of EV and MV may have changed, or the texture and geometry could have been rendered by EV alone or by the combination of other tactile feedback methods. It is important to find the optimal combination of stimuli to represent both texture and geometry, and this should be further studied in the future. Note that, in the current study, we focused on investigating and overcoming the mutual masking effect when two types of tactile information are provided at the same time, rather than on showing that the combination of EV and MV is effective for texture and geometry representation. We believe our approach can be extended to a variety of applications and not limited to the representation of geometry and texture or the combination of EV and MV.