esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Epileptic Seizures Detection Model
### Classification Techniques
#### 4.4.2 Decision Tree (DT)  
Decision Trees are intuitive and easy-to-interpret models that split the data into subsets based on the value of input features. Each node in the tree represents a feature, each branch represents a decision rule, and each leaf node represents an outcome. DTs are chosen for their simplicity and ease of visualization, which makes them an excellent tool for understanding the decision-making process of the model. Additionally, DTs are computationally efficient and can handle both numerical and categorical data, making them versatile for various types of EEG features. The interpretability of DTs is a significant advantage, as it allows for better insights into the factors contributing to seizure detection.
# Internet-of-Things and Information Fusion: Trust Perspective Survey
## 10 Future Directions
### Fog-Based Distributed Trust Models  
IoT smart objects are deployed as low power, memory, and processing data sources to provide smart application domains such as smart homes and smart health care systems. To fully rely on these smart applications, objects need to be trustworthy and honest. Various trust models exist that can effectively cope with trust attacks but are not suitable for IoT as they incur high consumption of resources. One way to address this problem is by offloading the trust-related operations to a more resourceful entity such as a fog-based node. Generally, fog computing enables trust operations to be done directly at the network's edge.  
Hence, new IoT architectures for modeling trust should be proposed. These architectures must be efficient, scalable, and support mobility as well as taking into consideration the constrained capabilities of IoT devices. Furthermore, distributed architectures at the fog layer should be further investigated. Such architectures can avoid the bottleneck created at the cloud layer as well as avoiding the constrained capabilities at the things layer. Fundamental trust functionalities such as trust computation, trust storage, and advertisement can be done by the fog layer. In addition, these fog nodes can interact with each other to avoid visiting the cloud. For example, the fog-based methods for propagating trust levels received insufficient consideration [13; 95]. So in order to address this research gap, fog-based distributed trust models need to be explored further.
# Breast Geometry Characterization of Young American Females Using 3D Image Analysis
## 1 Introduction
The current research proposes a comprehensive analysis of breast geometry by employing all-encompassing measurements. The measurements include angles, volumes, surface areas, and cross-sectional areas, as well as length, width, and depth. Each measurement is evaluated at the quadrantal sides to focus more on shapes and geometries than dimensions. Another methodological improvement is to separate the breast placement aspects from their shapes. Placement specifies the relative location of breasts to upper body structures, which could be independent of the breast shapes. This approach enables spatial assessments with an emphasis on three-dimensional characteristics. Young American females were selected because this population has never been studied for breast geometry characterization. Considering that the anatomical figures and body proportions vary according to demographic characteristics [22], it would be valuable to investigate how this specific group differs from other populations. The target population \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Subjects (Age)** & **Measurements Considered** & **Breast Shape Clusters** & **Prevalence** \\ \hline \multirow{3}{*}{[4]} & \multirow{3}{*}{182 Korean females} & & (1) spheric & 14\% \\  & & breast girth & (2) flat & 41\% \\  & (20–25) & breast volume & (3) drooped & 34\% \\  & & & (4) protruded & 11\% \\ \hline \multirow{3}{*}{[23]} & \multirow{3}{*}{174 Korean females} & 45 types of breast lengths & (1) drooped & 29\% \\  & & 5 types of breast angles & (2) spheric & 28\% \\  & (30–39) & body weight & (3) protruded & 22\% \\  & & & (4) flat & 20\% \\ \hline \multirow{3}{*}{[24]} & \multirow{3}{*}{83 Korean females} & 45 types of breast lengths & (1) drooped & 34\% \\  & (40–49) & 5 types of breast angles & (2) spheric & 31\% \\  & & & (3) protruded & 19\% \\  & & body weight & (4) flat & 16\% \\ \hline \multirow{3}{*}{[25]} & \multirow{3}{*}{208 Chinese females} & 34 types of lengths & (1) dome-shaped & 35\% \\  & (18–24) & 4 types of angles & (2) cone-shaped & 28\% \\  & & height and weight & (3) flat & 28\% \\  & & & (4) protruded & 19\% \\ \hline \multirow{3}{*}{[5]} & \multirow{3}{*}{345 Australian females} & breast volume & (1) extra-large, very potic & 12\% \\  & & breast surface area & (2) large, potic & 28\% \\ \cline{1-1}  & (18–84) & sternal notch-nipple length & (3) medium, mildly potic & 34\% \\ \cline{1-1}  & & underbust girth & (4) small, not potic & 26\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of breast shape clusters reported in literature [4,5,23,24,25]. was selected and screened from the Size USA database, and individual body scan files were three-dimensionally inspected using 3D image analysis tools. Through the statistical analysis, different types of breast shapes and placements were clustered, and characteristic breast anthropometry was identified for each cluster. A meaningful relationship between breast shapes and placements was also investigated.
## 2 System
### Attitude Estimation
#### 2.3.1 Complementary Filter Using Quaternions [23]  
The complementary filter used in this study is based on the values estimated by the angular rate sensor and modified by the accelerometer and the geomagnetic sensor.  
We define \(\mathbf{a}=(a_{x},a_{y},a_{z})\) [m/s\({}^{2}\)] as the value obtained from the acceleration sensor, \(\mathbf{\omega}=(\omega_{x},\omega_{y},\omega_{z})\) [rad/s] as the value obtained from the geomagnetic sensor, \(\mathbf{m}=(m_{x},m_{y},m_{z})\) [T] as the value obtained from the geomagnetic sensor, \(\mathbf{R}(\mathbf{q})\) as the rotation matrix that rotates a vector by a quaternion \(q\), \(\Delta T\) [\(s\)] as the difference between the previous processing time \(t-1\) and the current processing time \(t\), \(\mathbf{g}=9.798\) m/s\({}^{2}\) as the gravitational acceleration, \(\mathbf{q_{t}}=[q_{t,0},q_{t,1},q_{t,2},q_{t,3}]^{T}\) as the quaternion representing the attitude at time \(t\), and \(\mathbf{q_{t-1}}=[q_{t-1,0},q_{t-1,1},q_{t-1,2},q_{t-1,3}]^{T}\) as the quaternion representing the attitude at time \(t-1\). The initial value for a quaternion is \(\mathbf{q}=[1,0,0,0]^{T}\).
## 3 Materials and Methods
### General Method for Optimization of Sandwich Structural Parameters  
To balance the moving mass, material costs, and structural stiffness while considering nonlinear characteristics of the foam-filled sandwich structures, the optimal sandwich structural parameters were obtained by employing the multi-objective optimization method. The general formula for multi-objective optimization is as follows:  
\[\begin{array}{ll}\min_{x}&F(x)=(f_{1}(x),f_{2}(x),f_{3}(x),\ldots,f_{i}(x))^ {T}\\ &x_{i}^{l}\leq x_{i}\leq x_{i}^{u}\quad i=1,\ldots N\end{array} \tag{2}\]  
where \(f_{1},f_{2},f_{3},\)\(\ldots,f_{i}\) are the objective functions; \(x_{i}^{l}\) and \(x_{i}^{u}\) are the lower and upper bounds of the design variables; and \(N\) is the number of variables.  
In general, conflict occurs when several objectives are considered within the optimization, meaning that an increased performance in one objective may lead to decreased performance in others [30]. Therefore, there is a trade-off between objectives. In this study, multi-objective optimization acted to solve the contradiction between the mass, material costs, and structural stiffness.  
For the general model of the sandwich structure (Figure 2), the equivalent bending stiffness (\(EI\)) of the sandwich panel can be expressed as follows [12]:  
\[\left\{\begin{array}{l}EI=E_{s}I_{s}+E_{c}I_{c}\\ E_{s}I_{s}=E_{s}[\frac{bd^{3}}{2}-\frac{bc^{3}}{2}]\\ =E_{s}[\frac{db(a+c)^{2}}{2}+\frac{bx^{3}}{6}]\\ E_{c}I_{c}=E_{c}\frac{bc^{2}}{12}\end{array}\right. \tag{3}\]  
Furthermore, the equivalent mass of the sandwich panel can be expressed as:  
\[\left\{\begin{array}{l}m=m_{s}+m_{c}=V\rho\\ m_{s}=V_{s}\rho_{s}=2 abl\rho_{s}\\ m_{c}=V_{c}\rho_{c}=cbl\rho_{c}\end{array}\right. \tag{4}\]  
Similarly, the material cost (\(C\)) can be expressed as follows:  
\[\left\{\begin{array}{l}C=C_{s}+C_{c}=m\times C_{kg}\\ C_{s}=m_{s}C_{kg}\\ C_{c}=m_{c}C_{kgc}\end{array}\right. \tag{5}\]  
where subscripts \(s\) and \(c\) represent the skin material and core material, respectively; \(E\) represents young's modulus; \(I\) represents moment of inertia; \(m\) represents the mass; \(\rho\) represents the equivalent mass density; \(V\) represents the total volume; and \(C_{kg}\) represents the material cost per kg.  
Table 2 depicts the objective functions defined within the size optimization.
## 1 Introduction  
With the advancement of science and technology, people have changed their reading habits [1, 2, 3, 4, 5] and have been more exposed to digital products. As a result, the need for near work has increased, and accommodation has been considered to be involved in the occurrence and progression of myopia [6, 7, 8, 9, 10, 11]. After using digital devices at a close distance over a long period of time, symptoms such as eye fatigue, dry eyes, double vision, blurred vision, and headaches tend to occur [12]. In the early years, the symptoms were collectively known as 'Computer Vision Syndrome (CVS)' [13, 14]. With the popularization of electronic products in recent years, the term has been changed to 'Digital Eye Strain (DES)', which is caused by working at close distances [15, 16].  
Plenty of evidence suggests that the lag in accommodation is related to myopia. At the same stimulus distance, myopic people have a higher accommodation lag than emmetropic people. In addition, compared to emmetropic children, myopic children read at a closer proximity, which may lead to an increase in accommodation lag due to the increased demand for accommodation. When reading at closer proximity, a larger accommodation lag is more likely to cause the progression of myopia. An extended viewing distance can also stabilize the accommodation fluctuation [17; 18].  
Remote accommodation is a training method that induces periodic pupil constriction, thereby improving the eyesight of myopic children. A previous study used LCD screens to simulate changes in two gazing positions (70 cm and 25 cm) in an attempt to improve the eyesight of myopic children [19].  
[PERSON] et al. presented the effects of accommodative relaxation using far point shift stimuli to improve visual function and to reduce eye fatigue [20]. In the present study, we first proposed a simple optical design that consists of a concave mirror and a convex mirror to extend viewing distance from 0.4 m to 2.285 m so that the accommodation can be relaxed. The accommodation fluctuation also presented stably. In this study, the accommodative response could be reduced for subjects aged between 18 to 22 years old, and we expect that accommodative relaxation could be also achieved for school children. Furthermore, it may be applied to slow down the progression of myopia and to mitigate the problems caused by near work.
## 5 Simulations  
We implement the proposed scheme SCR in the Opportunistic Network Environment simulator (ONE) [27] simulator, and evaluate SCR by performance comparison with PRoPHETv2 [7], DRAFT [19] and BUBBLE [15]. In simulation, the real datasets \(Infocom5\), \(Infocom6\), \(Cambridge\) and \(Intel\) are used for node activity driving, which can be downloaded from CRAWDAD [28]. The last updating date of the datasets was in August 2016, and the detailed information is shown in Table 1. In simulation, the node buffer size is set to \(5M\), the message size is \(1K\), and the node number and TTL are different to the four datasets shown in Table 2. In SCR, the parameters are set as \(\omega=0.4\), \(n=500\), and \(\mu\) as \(1.2\) times the average encounters among all nodes in each dataset. In DRAFT, the parameters are set as \(\tau=7\), \(\delta=0.9\) and \(t=3600\) s.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Dataset** & **Infocom5** & **Infocom6** & **Cambridge** & **Intel** \\ \hline Device & iMote & iMote & iMote & iMote \\ Duration(days) & 3.5 & 4 & 11.5 & 4 \\ Number of experimental devices & 41 & 98 & 52 & 9 \\ Number of internal contacts iMote & 22,459 & 170,601 & 10,873 & 1364 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the four experimental data sets.  
We compare the performance of each routing algorithm in the same simulation environment and analyze the impact of parameters on SCR. The following metrics are used in the performance comparison.  
**Packet Delivery Ratio (PDR):** The ratio of the number of data packets that successfully reached the destination and the amount of data packets sent by the source within a certain time.  
**Transmission Delay (TD):** The delay is the average time it takes for a packet to reach the destination after it leaves the source.  
**Routing Overhead Ratio (ROR):** As is shown in Equation (9), the total number of packets to be forwarded (relayed_number) minus the number of packets successfully transferred to the destination node (delivered_number), and then divided by the number of packets successfully transferred to the destination:  
\[Routing\_Overhead\_Ratio=\frac{relayed\_number-delivered\_number}{delivered\_number}. \tag{9}\]
## 4 Graphene Nanoplatelets for Strain Sensors and Stretchable Electronics
### Pressure Sensors  
In addition to strain sensors and SHM, [PERSON] et al. [120], advanced flexible and compressible pressure sensors functionalizing PDMS foams with GnP dispersions. The manufacture of PDMS foam was achieved following the direct template technique, which implicates the replication of the inverse assembly of a preformed leachable prototype (see Figure 13 for details). In particular, they discovered that such device exhibited a linear regime for pressures <10 kPa, while at higher pressures, the electrical conductivity increase with higher steep (maximum 70 kPa, 800% resistance change). The sensitivity was of 0.23 kPa\({}^{-1}\).  
Figure 12: Stretchable and conductive yarns realized through simple layer by layer assembly on different elastomeric textiles. (**a,c,e**) Scanning electron microscope image of the GnPs strain sensors realized with rubber, nylon-rubber and wool fibers, respectively; (**b,d,f**) the same textile stretched (100%, 100% and 50% respectively). Reprinted with permission from ACS Appl. Mater. Interfaces 7, 11, 6317–6324. Copyright 2015 American Chemical Society.  
[PERSON] and collaborators [113] screen printed GnPs and carbon nanotubes dispersed in PMMA or PVDF on flexible substrates. Such conductive composites were employed as the active layer in pressure sensors. The sensitivity of the sensor was proportional to the sheet resistance of the material.
# Power Train System Control of Electric Loader Based on Positive Flow System
## 4 Control Strategy
### Control Strategy  
(1) Positive flow system for the electric loader scheme  
The positive flow system of the loader is mainly composed of a control system, a power train system, and actuators. The control system consists of a vehicle control unit (VCU), an electric motor controller, a pilot handle, etc. The power train system is composed of an electric motor and a hydraulic pump. The working devices are mainly hydraulic cylinders. The basic framework of the positive flow system is shown in Figure 7. The pilot handle controls the opening area of the multi-way valve. The relationship curve between the pilot pressure and the opening of the main valve core is shown in Figure 8. Meanwhile, by establishing a positive correlation between the pilot pressure and the electric motor speed, the larger the opening of the multi-channel valve, the higher the electric motor speed.  
Figure 6: Efficiency curve of the internal gear pump.  
Figure 7: Framework of the positive flow system.  
Control strategy of the positive flow system  
The opening area of the main valve core is directly proportional to the demand flow of the system. According to the opening area of the valve core, the demand flow of the system can be deduced as  
\[Q=a\cdot k \tag{11}\]  
The maximum speed in the high-efficiency area of the electric motor was chosen for the calculation basis of system maximum flow. Combined with the displacement of the pump, the system maximum flow can be calculated according to Equation (10).  
The opening area of the valve core depends on the pilot pressure of the system. The open area of the main valve core can be expressed as  
\[k=b\cdot P_{\mathrm{P}} \tag{12}\]  
The working speed of electric motor can be calculated according to the demand flow of the system and expressed as  
\[n=\frac{Q}{V\cdot\eta_{\mathrm{r}}}=\frac{a\cdot b\cdot P_{\mathrm{P}}}{V\cdot \eta_{\mathrm{r}}} \tag{13}\]  
According to the above analysis, the basic control flow chart of the positive flow system is given in Figure 9. Firstly, the pilot pressure is generated through the action of the pilot handle, which is used to push the main spool of the multi-way valve. Secondly, the demand flow of the system is calculated according to the valve core opening. Then, the target speed of the motor is calculated through the demand flow. Finally, the VCU sends a request speed command to the electric motor controller by detecting the pilot pressure, and updates the request value in real time according to the change of pilot pressure so as to control the output flow of the main pump. The output flow of the pump depends entirely on the action amplitude of the pilot handle so as to realize flow matching.  
Figure 8: Relationship between the pilot pressure and spool displacement.
###### Abstract  
In order to overcome the poor robustness of traditional image registration algorithms in illuminating and solving the problem of low accuracy of a learning-based image homography matrix estimation algorithm, an image registration algorithm based on convolutional neural network (CNN) and local homography transformation is proposed. Firstly, to ensure the diversity of samples, a sample and label generation method based on moving direct linear transformation (MDLT) is designed. The generated samples and labels can effectively reflect the local characteristics of images and are suitable for training the CNN model with which multiple pairs of local matching points between two images to be registered can be calculated. Then, the local homography matrices between the two images are estimated by using the MDLT and finally the image registration can be realized. The experimental results show that the proposed image registration algorithm achieves higher accuracy than other commonly used algorithms such as the SIFT, ORB, ECC, and APAP algorithms, as well as another two learning-based algorithms, and it has good robustness for different types of illumination imaging.  
image registration; homography matrix; local homography transformation; convolutional neural network; moving direct linear transformation +
Footnote †: journal: Applied sciences
## 1 Introduction
### Related Work
References [24; 25] investigated the performance including latency, reliability, and stability in the platooning. A hybrid security protocol was proposed for platoon communication aiming at ensuring platoon stability in Reference [26]. A resource allocation scheme in Reference [27] was proposed to reduce the transmission delay, the number of transmission hops and the transmission powers for a multi-platooning in a cellular network, although the guarantee for services and the re-allocation were not considered in this scheme. As a promising mathematical tool, stochastic geometry is beneficial to analyze the average performance of vehicular networks, since it can be evaluated quickly and it can be used to explore the performance optimization of the most relevant metrics [28; 29; 30]. One study [28] proposed a stochastic geometry approach to model the temporal and spatial behavior of vehicular networks. Based on an interference analysis using stochastic geometry, a Geo-Location access scheme was presented in Reference [29]. Another study [30] analyzed the capture probability, average throughput, and mean density using stochastic geometry. Therefore, to analyze the interference in a network consisting of platoons of vehicles, stochastic geometry is used in this paper.
# CFD-Simulink Modeling of the Inflatable Solar Dryer for Drying Paddy Rice
Based on recent developments in computer simulation developments, this study applied the first and the second methods for modeling the ISD. Therefore, this study aims: (i) to graphically portray the spatial distribution of airflow across the ISD by applying a computational fluid dynamic (CFD) approach and (ii) to ascertain the heat transfer in the heating area and coupled heat and mass transfer within the drying area using transient thermal modeling. The collaborative models were validated with field experiments and were used to simulate the ISD's performance when drying paddy rice drying under various weather conditions. Furthermore, this computational approach will allow specific development of the ISD design to dry different agricultural commodities with a reduced number of experiments.
## 3 Field Experiment Method  
The in-field applicability of BBUNPROWS was evaluated via experiments performed inside the Yeonhwa underground tunnel (35\({}^{\circ}\)12\({}^{\prime}\)55\({}^{\prime\prime}\) N, 129\({}^{\circ}\)13\({}^{\prime}\)2\({}^{\prime\prime}\) E) located in Gijang-gun, Busan, Korea. The Yeonhwa underground tunnel measures 168 m, 20 m, and 4.8 m, in length, width, and height, respectively. Nine Bluetooth navigation beacons were installed on tunnel walls at 20-m intervals, starting from the point of entry into the underground tunnel (0 m) up to 160 m. In addition, Bluetooth PWS beacons were attached to the safety helmets of pedestrian workers as well as to the rear of vehicles, as depicted in Figure 3.  
Table 1 lists the specifications of RECO Bluetooth beacons (Perples Inc.; Seoul, Korea) employed during field experiments. The said RECO beacon can be operated for up to 24 months on a CR2450 lithium-ion battery (3 V, 620 mAh; Panasonic; Osaka, Japan) without the need for a separate power-supply system. The beacons have been certified by iBeacon (Apple; Santa Clara, CA, USA) and Eddystone (Google; Menlo Park, CA, USA), which adhere to global beacon standards, and have received major radio certifications in Korea as well as in the United States, Europe, and Japan. For the purpose of this study, the intensity of signals transmitted from the Bluetooth beacons was set to 4 dBm, with the cycle lasting 100 ms.  
A Samsung Galaxy S9 smartphone (Table 2) was mounted on each vehicle to receive signals transmitted by the Bluetooth beacons. To configure the application so that it would issue corresponding alerts when the caution and warning zones were located approximately 20 m and 10 m away from transmitting beacons, respectively, signals were received for five minutes from locations 10 m and 20 m  
Figure 4: User interface and database of the BBUNPROWS smartphone application, including information pertaining to navigation and proximity warning system (PWS) functions.  
away from Bluetooth beacons, and the corresponding RSSI value distributions were analyzed (Table 3). The observed mean RSSI values at locations 10 m and 20 m away from the beacons equaled \(-83\) dBm and \(-96\) dBm, respectively. Because the purpose of BBUNPROWS was to prevent collisions inside underground tunnels, the RSSI thresholds for the caution and warning zones were set conservatively, with the set values representing a 5% cumulative percentage. Thus, the set threshold values equaled \(-99\) dBm and \(-91\) dBm for the caution and warning zones, respectively.