esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Simulation of Tooth Movement  
In the present simulation, a tooth was assumed to move in the same direction as its initial displacement, which occurred due to the elastic deformation of the PDL. This assumption was based on clinical observations, which indicate that movement patterns during orthodontic tooth movement resemble those of the initial displacement. Additionally, an in vivo experiment has demonstrated that the initial displacement could serve as a predictor of long-term movement [14].  
The movement pattern of a tooth varies depending on the movement-force (_MF_) ratio acting on it. The canine and premolars were in contact with the expander arm, and no movement acted on these teeth, meaning the _MF_ ratio was zero. Hence, these teeth tipped during expansion. On the other hand, the molars were fixed to the expanders, and a movement that prevented tipping acted on it. Initially, the _MF_ ratio acting on the molar was so small that it tipped. As the molar moved bucally, the _MF_ ratio increased, causing it to become upright. Consequently, the molar moved bodily. This change in the  
\begin{table}
\begin{tabular}{l l l l l l} \hline  & & **Canine** & **1 st Premolar** & **2 nd Premolar** & **Molar** \\ \hline Parallel expansion & Quad-helix & 6.4 & 7.8 & 8.3 & \(-0.01\) \\  & W-arch & 6.3 & 7.6 & 8.1 & \(-0.08\) \\ \hline Fan-shaped expansion & Quad-helix & 9.7 & 12.1 & 7.6 & \(-1.3\) \\  & W-arch & 9.5 & 11.5 & 6.7 & \(-1.1\) \\ \hline \end{tabular}  
\end{table}
Table 1: Buccal tipping angle of teeth at \(N=500\).  
_MF_ ratio was accounted for through iterative calculations, which is a feature of the present simulation method.  
It is difficult to predict long-term movement patterns based on the initial force system alone. Changes in the force system and movement pattern have been observed in other types of treatments, such as those using wire brackets and aligners [15; 16].  
The number of iterative calculations corresponded to the treatment period. In each iteration, the tooth moved by the amount of the initial displacement. However, the exact period required for this process has not yet been clarified in clinical settings, making it impossible to convert the number of iterations, \(N\), into a treatment duration. In the present study, predicting the treatment duration required for arch expansion was not possible. This was a limitation of the present FEM simulation. Despite this limitation, the arch expansion patterns could be predicted, allowing for a comparison of the expansion abilities of the W-arch and the quad-helix.  
Figure 5B shows that the molar tipped distally due to the sliding of the arm on the lingual surface of the canine crown. In the present simulation model, the frictional coefficient between the arm and the crown surface was assumed to be 0.15. If the coefficient of friction were smaller, the arm would slide more easily on the crown surface, which would result in increased tipping of the molar and decreased labial movement of the canine. The effect of the frictional coefficient on arch expansion needs to be investigated in future studies.
# The Duality of Similarity and Metric Spaces
## 2 Metric Space  
The theory of metric space is a well defined mathematical concept. Recall the formal definition of a distance metric.  
**Definition 1** (Metric Space [5]).: _Let \(\mathbf{X}\) be a non-empty set. Then, a function \(d\colon\mathbf{X}\times\mathbf{X}\to\mathbb{R}\) is a distance metric if for all subsets \(x,y,z\in\mathbf{X}\), the following conditions are fulfilled:_  
1. \(d(x,y)=d(y,x)\) (symmetry),
2. \(d(x,z)\leq d(x,y)+d(y,z)\) (triangle inequality),
3. \(d(x,y)=0\Longleftrightarrow x=y\) (identity of indiscernibles).  
_A metric space is an ordered pair \((\mathbf{X},d)\)._  
Given the above three axioms, we also have that \(d(x,y)\geq 0\) (non-negativity) for any \(x,y\in\mathbf{X}\). The definition from [1,4] contains this axiom redundantly, there is a derivation in [9].
# Surface Defect Detection Methods for Industrial Products: A Review
###### Abstract  
The comprehensive intelligent development of the manufacturing industry puts forward new requirements for the quality inspection of industrial products. This paper summarizes the current research status of machine learning methods in surface defect detection, a key part in the quality inspection of industrial products. First, according to the use of surface features, the application of traditional machine vision surface defect detection methods in industrial product surface defect detection is summarized from three aspects: texture features, color features, and shape features. Secondly, the research status of industrial product surface defect detection based on deep learning technology in recent years is discussed from three aspects: supervised method, unsupervised method, and weak supervised method. Then, the common key problems and their solutions in industrial surface defect detection are systematically summarized; the key problems include real-time problem, small sample problem, small target problem, unbalanced sample problem. Lastly, the commonly used datasets of industrial surface defects in recent years are more comprehensively summarized, and the latest research methods on the MVTec AD dataset are compared, so as to provide some reference for the further research and development of industrial surface defect detection technology.  
industrial products; defect detection; deep learning; unbalanced samples; image dataset +
Footnote †: journal: Applied Sciences
# Detection of Students' Problems in Distance Education Using Topic Modeling and Machine Learning
## 3 Materials and Methods
#### 3.2.1. BTM Implementation  
The following steps are performed to train the BTM and extract the themes.  
* Selecting the training sample: In this step, random sample was extracted from the corpus to train and fit the BTM model. All empty tweets or with less than three words generated from the preprocessing step were filtered. Then, the resulted 5000 tweets were used for training.
* Estimating the number of topics (k): Identifying k is very important step because it can affect the produced topics. A large k could lead to many clusters that can not be interpreted, while a small k could lead to a general cluster in term of semantic contexts. To define the optimal k, different qualitative or quantitative approaches have been used. We used a coherence score to measure the quality of learned topics; a higher score means high correlation of the topics in the cluster. The UMMass coherence score was used to calculate the scores as shown in Equation (2) [30]. \(D(w_{i},w_{j})\) represents the documents count in which words \(w_{i}\) and \(w_{j}\) co-occur, where 1 is added to this term to avoid calculating the logarithm of zero when the two words never appear together. This step was repeated with different values of k, (k = 5, 7, 10, 15) to ensure that the filtered topics are highly relevant to the issues of interest. We found the best value of k was 5. \[SCORE_{\textit{UIMass}}(w_{i},w_{j})=log\frac{D(w_{i},w_{j})+1}{D(w_{i})}\] (2)
* Training the BTM model: In the traning phase, we trained and fitted BTM with k = 5 as estimated in the previous step. Then, we applied the inference phase; in this phase, the algorithm determines how each tweet correlates to the themes discovered in the learning phase. As a result, the themes were produced with five tweets in each.  
A sample of the result of this stage is demonstrated in Table 2. Notably, there is an overlap between the themes, which might be because the tweets represent very connected issues in distance education.  
Figure 3: The graphical representation of the BTM model.
## 3 Materials and Methods
### Technological and Research Areas
#### 3.2.1 Photogrammetrical Mapping  
Photogrammetry is the method of dimensioning and extracting high-precision metering data using photographs. As a method, it requires a systematic image capture of the objects, in order to produce 3D models of them. The systematicity of image capturing lies in the necessity of collecting photos with significant overlapping so that each object appears in more than three images. Modern photogrammetry specifies a minimum overlapping percentage of 75% on each image [62]. However, the accuracy of metering data depends not only on the systematic collection of photographs, but also on other factors, such as (1) internal orientation, which in modern digital cameras is described by (a) the physical size of the sensor; (b) the number of sensor pixels (resolution); (c) the distance of the sensor from the lens's center of focus; (d) the lens's focal length in mm; and (2) the outer orientation of each shot described by the physical location of the camera collecting photographs.  
The photogrammetric processing software used for \"Mergin' Mode\" demonstration purposes is the Swiss-based Pix4 Dmapper Pro. It is one of the most recognized tools of modern photogrammetry and is capable of producing 3D point clouds, 3D textured grids, 2D orthophotomaps, 2D surface models and 2D terrain models.
## 3 Discussion
### Effects of QG on Fatty Acid Content  
Fatty acids are the main components of vegetable oils, including saturated, monounsaturated and polyunsaturated fatty acids, and polyunsaturated fatty acids are generally believed to be beneficial to human health [55]. The fatty acids in soybean oil mainly consist of oleic acid, linoleic acid, linoleic acid, palmitic acid and stearic acid, with linoleic acid having the highest content [56]. The results of our study showed that after the accelerated oxidation of soybean oil, the proportion of unsaturated fatty acids among all fatty acids was 69.06% \(\pm\) 0.52%, while the unsaturated fatty acids in the QG group constituted 82.94% \(\pm\) 0.65% of total fatty acids, indicating that QG significantly inhibited the oxidation of unsaturated fatty acids. More importantly, the polyunsaturated fatty acid (9E,11E)-9,11-octadecadienoic acid (C18:2) is related to linoleic acid, which exhibits the highest content in soybean oil, and the content in the QG group was approximately five times that of the control group. At the same time, the monounsaturated fatty acid elaidic acid (C18:1) belongs to oleic acid, and its content in soybean oil was second only to that of (9E,11E)-9,11-octadecadienoic acid. The content in the QG group was significantly higher than that of the control group. Both oleic acid and linoleic acid are 18-carbon unsaturated fatty acids, which have good nutritional and health care effects [57]. Oleic acid is a monounsaturated fatty acid with strong thermal stability; the carbon chain of linoleic acid is a polyunsaturated fatty acid containing two double bonds, and its thermal stability and antioxidant capacity are not as high as those of oleic acid [58]. Our results showed that linoleic acid accounted for the highest proportion among unsaturated fatty acids, the oxidation of linoleic acid was significantly inhibited by the addition of antioxidants, and the oxidation of oleic acid was also inhibited. It is speculated that the quercetin in marigolds delays oxidative rancidity of soybean oil by inhibiting the oxidation of the linoleic acid (9E,11E)-9,11-octadecadienoic acid methyl ester (C18:2) and the oleic acid elaidic acid methyl ester (C18:1).
## 5 Results
### Fuzzy Multimodel Control with 4 Models  
This approach involves obtaining a controller that provides a global control signal starting from the local 30%, 50%, 70%, and 90% PWM models. When the speed reference is below 30% of PWM according to the values in Table 2, the local controller obtained for the 30% model is used; when the speed reference is above 90 %, the local controller developed for the 90% model is used. However, when the speed reference is at intermediate values between 30%, 50%, 70%, and 90%, the fuzzy logic calculates the output based on the weights of the membership functions and fuzzy rules. Figure 23 presents the result of the dynamics of the right and left wheels of the robot and the control signal for changes in the reference considering the positive direction, and Figure 24 shows changes in the reference considering the negative direction.  
Figure 23: Fuzzy multimodel control with 4 models for positive direction. Tracking signal in black and system response in blue for (**a**) left wheel and (**c**) right wheel. Control signal applied to the motors for (**b**) left wheel and (**d**) right wheel.  
Figure 24: Fuzzy multimodel control with 4 models for negative direction. Tracking signal in black and system response in blue for (**a**) left wheel and (**c**) right wheel. Control signal applied to the motors for (**b**) left wheel and (**d**) right wheel.  
Similarly, from Figures 23 and 24, it can be seen that the tracking of the input reference was maintained for both the right wheel motors and the left wheel. Furthermore, it is noted that the control signal applied to the motors is within the acceptable PWM percentage range based on the drive electronics developed for the robot wheel motors.
###### Abstract  
Ambient Assisted Living (AAL) has become an attractive research topic due to growing interest in remote monitoring of older people. Development in sensor technologies and advances in wireless communications allows to remotely offer smart assistance and monitor those people at their own home, increasing their quality of life. In this context, Wireless Acoustic Sensor Networks (WASN) provide a suitable way for implementing AAL systems which can be used to infer hazardous situations via environmental sounds identification. Nevertheless, satisfying sensor solutions have not been found with the considerations of both low cost and high performance. In this paper, we report the design and implementation of a wireless acoustic sensor to be located at the edge of a WASN for recording and processing environmental sounds which can be applied to AAL systems for personal healthcare because it has the following significant advantages: low cost, small size, audio sampling and computation capabilities for audio processing. The proposed wireless acoustic sensor is able to record audio samples at least to 10 kHz sampling frequency and 12-bit resolution. Also, it is capable of doing audio signal processing without compromising the sample rate and the energy consumption by using a new microcontroller released at the last quarter of 2016. The proposed low cost wireless acoustic sensor has been verified using four randomness tests for doing statistical analysis and a classification system of the recorded sounds based on audio fingerprints.  
wireless acoustic sensor; ambient assisted living; internet of things; edge computing; low cost; ESP32 +
Footnote †: journal: Applied Soft
## 4 Simulation Results and Security Evaluation
### Brute Force Attack  
#### 4.7.1 Key Sensitivity Analysis  
[PERSON] et al. [33] and [PERSON] et al. [11] have demonstrated that the key sensitivity of a system becomes evident when even a minor alteration in parameters leads to a significant and distinct outcome. This can be conducted during encryption and decryption processes. Table 8 shows the correction coefficient when there is a slight change in parameter values during the encryption process: Round 1, \(x_{0}=0.189+10^{-10}\), \(a=1.4\), \(b=0.3\); Round 2, \(x_{0}=0.189-10^{-10}\), \(a=1.4\),\(b=0.3\); Round 3, \(x_{0}=0.189\), \(a=1.4+10^{-10}\), \(b=0.3\); and Round 4, \(x_{0}=0.189\), \(a=1.4\), \(b=0.3+10^{-10}\). While the correlations between the encrypted and original images are close to zero, the significant difference between their averages in all rounds indicates that the cipher images are indeed different. Figure 14 visually demonstrates the magnitude of difference in the resulting outcome when even slight changes occur in the parameters.
# Modeling and Analysis of Contactless Solar Evaporation for Scalable Application
## 3 Result and Discussion
### Heat Loss Suppression for Scalable SCE
#### 3.4.3 Heat Loss Suppression on the Air Side  
An air-filled convection cover that matches the size of the absorber-emitter is used on the absorber surface to minimize heat loss through the air side. Straight holes are drilled on the convection cover to make sure the vapor escape from the absorber-emitter. The thermal conductivity of convection cover is set to be the same as that of air, and the radiative transmittivity is set to 100% in the simulation. Figure 13a shows the design of the scalable SCE, which includes a convection cover, as well as the cross-sectional temperature distribution of the absorber-emitter, vapor diffusion hole, and air layer. The thickness of the convection cover varies between 0 mm, 2 mm, 4 mm, and 6 mm, as shown in the figure. When the thickness of the convective cover increases, the overall temperature of the absorber-emitter, vapor diffusion hole, air layer and the 5 mm thick water layer rise significantly, indicating that the convection cover can effectively reduce the absorber-emitter heat loss. Given the fact that high convective cover thickness will improve vapor diffusion resistance and cause low transmittance, this study only explores the influence of convective cover plate thickness from 0 mm to 6 mm on the evaporation rate. The variation trends of the energy distribution ratio and evaporation rate with the increase in convection cover thickness are shown in Figure 13b. It can be seen that the evaporation rate increases from 0.499 kg.m\({}^{-2}\).h\({}^{-1}\) to 0.531 kg.m\({}^{-2}\).h\({}^{-1}\) with the increase in the convection cover thicknesses from 0 mm to 6 mm. Although the convective cover can effectively reduce the heat loss in air side, at the same time, the rise of water surface temperature will make the  
Figure 12: Potential of foam insulation to reduce conduction heat loss. (**a**) The structure of scalable SCE adding foam insulation and the cross-sectional temperature distribution of the absorber–emitter, vapor diffusion hole, air layer and 5 mm thick water layer under different insulation layer thicknesses of 0 mm, 10 mm, 35 mm and 45 mm. (**b**) The variation trends of energy distribution ratio and evaporation rate under different insulation layer thicknesses.  
bulk water heat loss greatly increases, which leads to the proportion of latent heat used for evaporation increase slightly. Based on the above analysis, it can be concluded that, for the scalable SCE, adding only a convection cover will inevitably cause an increase in heat loss though the bulk water. Therefore, to achieve a significantly improved evaporation rate in the scalable SCE, it is necessary to use both a convection cover and a foam insulation layer simultaneously. The highest evaporation rate reaches 0.797 kg\(\cdot\)m\({}^{-2}\cdot\)h\({}^{-1}\) (\(\eta\) = 50.3%) when the thickness of insulation layer is 35 mm, and the thickness of the convection cover is 6 mm.
# Jamming and Spoofing Techniques for Drone Neutralization: An Experimental Study
## 3 System Design and Methodology
### Hardware and Software
#### 3.1.2 Software  
This work uses several software tools optimized for the LINUX operating system, specifically Ubuntu 24.04.1 LTS. GNU Radio (version 3.10.1.1), an open-source platform for signal processing in software-defined radios, was employed. Additionally, Visual Studio Code (VS Code) was used to further enhance Python scripts generated by GNU Radio.
###### Abstract  
This paper proposes a novel code for optical code division multiple access (OCDMA) systems, called the three-dimensional (3D) spectral/temporal/spatial single weight zero cross-correlation (3D-SWZCC) code. The proposed code could potentially be used in the next generation of passive optical networks (NG-PONs) to provide a 3D-SWZCC-OCDMA-NG-PON system. The developed code has a high capacity and a zero cross-correlation property that completely suppresses the multiple access interference (MAI) effects that are a main drawback for OCDMA systems. Previously, a two-dimensional (2D) SWZCC code was proposed for two-dimensional OCDMA (2D-OCDMA) systems. It works by devoting the first and second components to spectral and spatial encodings, respectively. However, the proposed code aims to carry out encoding domains in spectral, time, and spatial aspects for the first, second, and third components, respectively. One-dimensional, 2D, and 3D systems can support up to 68, 157, and 454 active users with total code lengths equal to 68, 171, and 273, respectively. Numerical results reveal that the 3D-SWZCC code outperforms codes from previous studies, including 3D codes such as perfect difference (PD), PD/multi-diagonal (PD/MD), dynamic cyclic shift/MD (DCS/MD), and [PERSON]'s triangle zero cross-correlation (PTZCC), according to various metrics. The system function is provided by exhibiting the architecture of the transmitter and receiver in the PON context, where the proposed code demonstrates its effectiveness in meeting optical communication requirements based on 3D-OCDMA-PON by producing a high quality factor (Q) of 18.8 and low bit error rate (BER) of 3.48 \(\times\) 10\({}^{-29}\) over a long distance that can reach 30 Km for a data rate of 0.622 Gbps.  
three-dimensional; single weight zero cross correlation (SWZCC); OCDMA; passive optical network (PON); next generation (NG)
# Exploring the Evolution of Drought Characteristics in Balochistan, Pakistan
## 2 Materials and Methods
### Drought Regionalization  
Regionalization is a popular technique used in hydrology to group stations into clusters showcasing similar behavior when comparing a certain attribute. The regionalization of droughts has been carried out in this study using a Spatial \"K\"luster Analysis using Tree Edge Removal (SKATER) algorithm [18; 26; 38] to divide Balochistan into six regions having a similar behavior of SPEI. A 24-month SPEI was used to cluster the 124 grid cells in Balochistan into six spatially contiguous regions, as this timescale was found to reflect the climate of Balochistan better than other timescales of SPEI.  
The SKATER method \"combines the use of a minimum spanning tree with combinational optimization techniques\" [38; 39]. It is based on graph theory and it represents objects as vertices, whereas the relationships among the objects are represented by the edges. The relationships are based upon the attribute of interest, which in this case is the time series of SPEI. The relationships among the vertices are quantified by assigning a certain weight to each edge, which is proportional to the dissimilarity measure between the vertices it joins. SKATER uses [PERSON]'s algorithm [40] to construct a Minimum Spanning Tree (MST), which is a connected graph with no circuits where the vertices are connected by an exclusive path and the edges have the minimum weight.  
After Balochistan was divided into six spatially contiguous zones, 3-month and 12-month SPEI were calculated for each grid cell. All the cells falling in a certain zone were averaged to obtain the SPEI for that zone.