esa-sceva/satcom-chunk-collection huggingface dataset:
# Resizer Swin Transformer-Based Classification Using sMRI for Alzheimer's Disease
## 5 Conclusions and Future Work  
This paper presents a Resizer Swin Transformer architecture that combines cross-channel learning with a CNN and extracts features from two-dimensional axial-plane slices of brain MRI. The analysis and visualization of the experimental data demonstrated the accuracy with which the RST architecture can accomplish the categorization of AD, as well as its strong adaptability to various datasets. This experiment does still have some flaws, though. The RST model has comparatively high model parameters, and cross-channel learning in conjunction with the CNN enhances the classification performance using the model. Meanwhile, we discovered that the accuracy of the experimental findings is lower than 85% when the model trained on ADNI is directly assessed using AIBL. The model may undergo further development to overcome these problems and provide a lighter and more scalable model. And we hope that in the future, we can better connect image preprocessing with the deep-learning model to construct an end-to-end model that can actually be used in clinical diagnosis.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **Data** & **ACC** \\ \hline RST & No skull stripping & 98.74\% \\ RST & 2.5D skull stripping & 96.36\% \\ RST & Skull stripping & 98.99\% \\ CNN+RST & Skull stripping & 99.98\% \\ ST & No skull stripping & 95.87\% \\ CNN+ST & No skull stripping & 98.87\% \\
**CNN+RST** & **No skull stripping** & **99.62\%** \\ \hline \hline \end{tabular}  
* RST: ReSwin Transformer; ST: Swin Transformer. The input consists of axial, sagittal, and coronal sMRI slices, called 2.5D data. The bold denotes the best results.  
\end{table}
Table 4: Comparison of different data types and network structures.  
Figure 6: Experimental results of RST on different datasets. The left horizontal coordinate represents the experimental results obtained under the corresponding training set and test set.  
**Author Contributions:** Conceptualization, Y.H. and W.L.; methodology, Y.H. and W.L.; software, Y.H.; validation, Y.H. and W.L.; formal analysis, Y.H.; investigation, Y.H.; resources, W.L.; data curation, Y.H.; writing--original draft preparation, Y.H.; writing--review and editing, W.L.; visualization, Y.H.; supervision, W.L.; project administration, W.L.; funding acquisition, W.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was funded by the Research Foundation for Youth Scholars of Beijing Technology and Business University (PXM2020_014213_00017) under Grant QNJJ2020-29.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data used in this paper were derived from open-source datasets. Data sharing is not applicable to this article as no new data were created in this study.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Congestion Prediction Model
### Research Objectives and Scope  
WSN congestion is caused by the fact that packets produced at each node must be forwarded to a sink [34,35]. In other words, because each node cannot send data directly to the sink, transfers are accomplished through multiple nodes by using a variety of routing protocols. Thus, network congestion occurs as a result of attempting to meet the demand to move these data.  
Factors that affect data transfer between the nodes in a WSN are as follows:  
* Location of the Source Node
* Location of the Sink Node
* Packet type (Text/Image/Sound Data...)* Number of transmission paths
* Periodic/Event Data incidence
* Path change in case of the occurrence of Periodic Data/Event Data
* Sink Node change in case of the occurrence of Periodic Data/Event Data
* Transfer method (Protocol)
* Resource of Sensor Node  
The factors that affect data transfer at each node can be represented by a hierarchical system. For example, the type of packet is determined initially according to the sensor node, but the transmission path between the nodes can be determined when network configuration is finished or it can change periodically. In addition, any choice is made based on the cluster unit and district unit, and any other choice is made based on each node. Therefore, the decision can be layered into three steps, as shown in Figure 2:  
1. The entire packet flow can be predicted by dividing it into periodical and event packets and by using the production function from economics after identifying the factors influencing communication between nodes in the WSN.
2. Traffic can be estimated by using transportation demand analysis techniques.
3. The congestion zone is predicted by estimating congestion costs through the BPR function after the optimal parameter values are determined according to the network type.  
Transmission entails time and node energy consumption; these are both considered to be costs. The throughput that occurs under each different cost is network traffic demand, which is considered to be different from network traffic.  
In order to estimate congestion, the number of node-specific packet generations, the average time per node transmission, and the network type must be considered. However, it is difficult to capture all  
Figure 2: Congestion Prediction Methodology.  
packet information, and it is possible to overestimate the probability when using the average transmission time of the whole network. In addition, if the network was estimated using the arithmetic mean from the average transfer rate without reflecting differences in the type of network, then problems may be underestimated when there is a low-speed network near the sink node and a relatively high-speed network in the outlying areas. The purpose of this study is to identify the factors that determine demand on the WSN network and to predict how these factors impact network traffic.
## 4 Results
### Simulation Results
#### 4.2.2 Effects of the Node Density  
Figure 7a shows results of discovery success ratio versus node density. Compared with AODV and DSAADDV, RFAODV can improve the routing discovery success rate by at least 20%. Because AODV protocol uses flooding strategy in the process of route discovery, when the number of nodes increases, the number of nodes participating in RREQ data increases accordingly. This further increases the conflict and reduces the probability of route discovery. Therefore, this index of AODV decreases as the number of nodes increases. DSAADDV uses forwarding control strategy in the process of route discovery. Compared with AODV, the probability of route establishment is improved when the number of nodes increases. However, when determining the forwarding area, DSAADDV takes more account of the node density at the source node, so that the forwarding area cannot adapt to the change in the global number of nodes. Moreover, when there are few nodes, DSAADDV is vulnerable to routing holes. In the process of route discovery, RFAODV can not only avoid the influence of the number of nodes by setting the forwarding area and delay adaptively, and also find a more reasonable route.  
Figure 7b presents the effects of the node density on the number of received packets. As can be seen, the RFAODV performs better than the AODV and DSAADDV. Especially when the number of nodes increases, RFAODV improves by at least 8% relative to AODV, and at least 5% relative to DSAADDV. This is due to the discovery success ratio of RFAODV that can adapt to changes in node density and is always higher than that of DSAADDV and AODV, as shown in Figure 7a. With the discovery of routing successfully, more data can be transmitted effectively.  
Figure 7: Effects of the node density. (**a**) Discovery success ratio vs node density; (**b**) packet delivery ratio vs node density; (**c**) protocol overhead vs node density; (**d**) consumption energy vs node density.  
Figure 7c,d describe the effect of node density from the perspective of protocol overhead and consumption energy, respectively. Compared with AODV and DSAODV, RFAODV reduces protocol overhead by at least 40% and energy by at least 15% in large-scale and high-density node scenarios. The flood mechanism of AODV routing leads to a large amount of overhead and consumption energy. And with the increase in node density, it is further reflected. DSAODV has more nodes participating in forwarding than RFAODV, so it has more overhead and consumption energy.
## 3 Analysis of Control Strategies  
### Three-Phase Rectification and High-Frequency Isolated DC-DC Stage Control Strategy  
To minimize IGBT switching losses, lower costs, and simplify the system architecture, the three-phase rectification stage utilized a diode-based uncontrolled rectification strategy, resulting in stable DC voltage \(U_{\text{dc1}}\)[35].  
The DC-DC stage with high-frequency isolation utilized an EPWM control strategy. Under this control strategy, the DC voltage \(U_{\text{dc1}}\) generated by the three-phase uncontrolled rectification was modulated into a high-frequency square wave voltage \(U_{\text{h1}}\) with a duty cycle of 50% and a frequency of 20 kHz. The high-frequency square wave voltage \(U_{\text{h1}}\) was coupled to the secondary side of the transformer to form a high-frequency square wave voltage \(U_{\text{h2}}\). This open-loop control had the advantages of a simple structure and good reliability. The specific high-frequency isolated DC-DC stage control process is shown in Figure 4.
## 2 Materials and Methods
### Methods  
The structure and morphology of the samples have been investigated according to the following:1. X-ray diffraction (XRD) patterns of samples have been collected with a PAN analytical PW3050/60 X'Pert PRO MPD diffractometer with a Cu anode and a Ni filter, in Bragg-Brentano configuration. The diffractograms were acquired in an interval equal to \(10^{\circ}\leq 20\leq 80^{\circ}\) with an acquisition step of 0.02\({}^{\circ}\).
2. High-resolution transmission electron microscopy (HRTEM) images have been obtained with a JEOL 3010-UHR HRTEM microscope operating at 300 kV with a point-to-point resolution of 0.12 nm, equipped with a 2 k \(\times\) 2 k pixels Gatan US1000 CCD camera.
3. Raman spectra were acquired in backscattering mode using a Renishaw In Via Raman spectrophotometer, equipped with an Ar\({}^{+}\) laser emitting at 514.5 nm. The backscattered light was analyzed by a grid with 1200 lines/mm and detected by a CCD detector. The effects of the radiation damage on the samples were reduced by limiting the output power to 0.5%.
4. FTIR spectra were acquired by means of a Bruker IFS 66 FTIR spectrometer equipped with a cryogenic MCT detector with 2 cm\({}^{-1}\) resolution. Each titania sample was pressed in the form of a self-supporting pellet with \"optical thickness\" of ca. 10 mg-cm\({}^{-2}\). To investigate the surface properties, the CO probe molecule was dosed on samples by means of a gas manifold connected to the IR cell, thus allowing us to perform thermal treatments under vacuum and gas dosage. The spectra were collected after CO dosage (70 Torr) at 77 K in an IR cell designed for liquid N\({}_{2}\) flow conditions.
5. The optical properties of the samples dispersed in solution have been investigated by means of transmittance mode using quartz cuvettes with an optical path of 1 cm, while the properties of the powder samples have been studied by means of diffuse reflectance (DR) mode. A Varian Cary UV 5000 spectrophotometer, equipped with a diffuse reflectance sphere, was used in the 2500-190 nm wavelength range.
## 3 Mathematical Modelling of Ship Power Positioning Systems  
### Ship Power Positioning Systems  
The ship's dynamic positioning system (DP system) is a technology that enables the ship to maintain a specific position and direction at sea by utilizing the propulsion system and steering gear on the ship, as well as the corresponding sensors and computer control systems. The main purpose of the ship's dynamic positioning system is to ensure that the ship can hover stably at designated geographical coordinates in harsh sea conditions, deep-sea operations, or situations where precise position control is required.  
In terms of control technology, dynamic positioning systems began to rise in the 1960s. In the 1960s, the first generation of dynamic positioning ships appeared. These usually used conventional control methods and also adopted some auxiliary signal processingmethods, using filters to eliminate high-frequency components in the deviation signal to avoid responding to high-frequency movements.  
In the 1970s, researchers such as [PERSON] combined optimal control with the Kalman filter theory. A new control technology was rapidly applied in ship dynamic positioning systems and began to become the mainstream method of dynamic positioning control. Then, the second modern dynamic positioning system developed rapidly and became the most widely used dynamic positioning system. It is a control technology based on modern control theory and is also the symbol of the second-generation ship dynamic positioning system. The second-generation dynamic positioning control method is currently the most mature system used internationally [2; 15; 16; 17; 18; 19].  
In recent years, research on newer and more advanced control technologies has further developed. Robust control, fuzzy control, neural network control technology, etc., have become increasingly mature. The third-generation dynamic positioning system has begun to adopt intelligent control theories and methods based on intelligent control methods and real-time measurements. Using technology to improve the accuracy of positioning systems is a trend in various countries researching dynamic positioning control, and countries are stepping up research and development. Intelligent control methods are mainly reflected in robust control, fuzzy control, nonlinear model predictive control, etc., [20].
# Leveling Control of Hillside Tractor Body Based on Fuzzy Sliding Mode Variable Structure
## 3 Leveling System Model and Controller Design
### Design of Fuzzy Sliding Mode Variable Structure Controller  
#### 3.2.1 Sliding Mode Controller Design  
Sliding mode variable structure control has the advantages of high reliability and robustness. However, chattering occurs under the influence of uncertainties, such as interference, parameter changes and unmodeled dynamics. Scholars have proposed various weakening methods for the chattering phenomenon of the sliding mode variable structure [14; 15]. The novelty of this paper lies in combining fuzzy control theory with the sliding mode variable structure control algorithm, and by applying it to the leveling control of tractors in hilly and mountainous areas, it can reduce the chattering phenomenon and show a good control effect on tractor leveling in hilly and mountainous areas.  
The sliding mode function is initially designed according to the displacement tracking error, following which, the fuzzy regulator is designed to debounce the function, to replace \((e,e)\) with \((s,s)\) as the input signal, and to effectively evaluate the switching gain according to the sliding mode attainment condition and eliminate interference with the switching gain, thereby removing the chattering [16; 17]. The principle of the controller operation is shown in Figure 4.  
The switching function of the sliding mode surface is defined as [18]  
\[s=c_{1}e_{1}+c_{2}e_{2}+e_{3}=c_{1}e_{1}+c_{2}\dot{e}_{1}+\ddot{e}_{1} \tag{10}\]  
where \(e_{1}=r-y,r\) is the desired output signal and \(y\) is the actual output signal. By selecting the desired pole as \(-34.4\pm 168.52i\) to determine \(c_{1}\) and \(c_{2}\) through the pole configuration method, the parameters \(c_{1}\) = 29,582, \(c_{2}\) = 68.8 are obtained.  
To ensure that \(s\dot{s}<0\), the law of the sliding mode controller is set as [19]  
\[u=\frac{1}{b_{0}}[(c_{1}-a_{2})e_{2}+(c_{2}-a_{3})\,e_{3}+\,K(t)\text{sgn}(s)] \tag{11}\]  
where \(a_{2}=w_{h}^{2}\), \(a_{3}=2\zeta_{h}w_{h}\), \(b_{0}=K_{h}w_{h}^{2}\).  
\(K(t)=max|F(t)|+\eta\), \(F(t)=\ddot{r}+2\zeta_{h}w_{h}\ddot{r}+w_{h}\dot{r}\) and \(\eta>0\).  
The Lyapunov stability analysis is carried out. If the function is V = s\({}^{2}\)/2, then,  
\[\dot{V}=s\dot{s}=s(c_{1}e_{2}+c_{2}e_{3}+(-a_{3}e_{3}-a_{2}e_{2}-b_{0}u(t)+F(t))) \tag{12}\]  
The control law (11) is introduced in (12) as follows,  
\[\dot{V}=s(-K(t)\text{sgn}(s)+F(t))\leq-\eta|s|\leq 0 \tag{13}\]  
In the sliding mode control law, the switching gain value \(K(t)\) is the cause of chattering. It is used to compensate the uncertainty \(F(t)\) to ensure that the sliding mode existence  
Figure 4: Controller principle.  
condition is satisfied. If \(F(t)\) is time-varying, \(K(t)\) should also be time-varying in order to reduce chattering. Fuzzy rules can be used to achieve changes of \(K(t)\) based on experience.
# Advancing Phantom Fabrication: Exploring 3D-Printed Solutions for Abdominal Imaging Research
## 1 Introduction
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Source/Comparison** & **Key Findings** & **Description** \\ \hline [PERSON] et al. [12] & Three-dimensional printing offers precision and customization. & Three-dimensional printing enables the production of phantoms that closely mimic human tissue properties, which is crucial for accurate radiation dose measurements. \\ \hline [PERSON] et al. [13] & Potential for dose reduction through personalized phantom design. & This study highlights the comparison of different materials and printing techniques used to achieve tissue-equivalent properties, emphasizing dose reduction. \\ \hline [PERSON] et al. [14] & Clinical applications in surgery and radiation therapy. & This article discusses the importance of phantoms in training and preoperative planning, in which accurate tissue simulation is critical for clinical applications. \\ \hline [PERSON] et al. [15] & Comparison of commercial and 3D-printed phantoms. & This research compares the pros and cons of commercial and 3D-printed phantoms in terms of cost, accuracy, and clinical utility, providing insights into their relative effectiveness. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of key studies focused on the development and application of 3D-printed anthropomorphic phantoms in medical imaging. A crucial aspect of this study involves the comprehensive evaluation of the phantom's performance. This evaluation encompasses three key areas: anatomical accuracy, mechanical properties, and X-ray attenuation characteristics. The obtained data is compared to reference values for human abdominal tissues, allowing for a comprehensive assessment of the phantom's ability to mimic real tissue behavior under X-ray irradiation.
## 1 Introduction  
Organic light-emitting diodes (OLEDs) possess distinctive advantages. Therefore, OLEDs presented remarkable potential in application in displays and lighting sources. Thus far, the characteristics for the device still have room to improve. Therefore, in order to optimize the efficiency of the devices, a variety of methods have been presented. The following are some examples: doping phosphorescence material into the emitting layer to enhance the performance of the device [1; 2; 3], improving the balance between holes and electrons within the recombination zone of the excitons [4; 5; 6; 7], or increasing the efficiency of light extraction by decreasing the waveguide effect on the edge of the glass [8; 9]. These methods can enhance the performances of OLEDs, but these methods also increased the production complexity of OLEDs.  
Recently, the application of metal nanoparticles to OLEDs and inorganic light-emitting diodes has attracted considerable interest because of their unusual surface plasmon resonance effect (SPRE). It is well known that the SPRE was induced by the interaction between the surface charge of the metal and the electromagnetic field of the incident light [10; 11; 12; 13; 14; 15; 16]. In addition, when the metal nanoparticles are close to the fluorescent molecule, the luminous efficiency of the fluorescent molecules can be greatly increased by a decrease in the decay time of the excitons for the devices [17]. Nevertheless, when the distance is much smaller between the metal nanoparticles and fluorescent molecules, the nonradiativequenching of the excitons is generated. Furthermore, the nonradiative quenching of the excitons will result in the reduction of the efficiency for devices. Therefore, two competitive courses will take place in devices of SPRE: (I) a decrease in the efficiency of the device by nonradiative quenching of the excitons; and (II) an increase in the efficiency of the device by a reduction of the decay time of the excitons. In short, the distances between the metal nanoparticles and the fluorescent molecules are extremely important parameters in devices of SPRE.  
The GNPs were doped into hole-transport layer (HTL), resulting in an increase in the efficiency of the device [18], but the causes of enhancement and quenching for fluorescence at different distances between the GNPs and the emitting layer were not described in detail. In this paper, the distance between the GNPs and the emitting layer has been optimized for enhancement of the maximum luminescence. In addition, how the distances between the GNPs and the emitting layer influenced the efficiency of the devices was also observed. Furthermore, mechanisms of increase in the radiative intensity and nonradiative quenching have been described in detail.
## 5 Conclusions  
Pyroelectric sensors are finding application in an increasing range of fields nowadays. However, the effects of the substrate thickness on the voltage response of these sensors have thus far attracted relatively little attention in the literature. Consequently, the present study has fabricated ZnO-based pyroelectric sensors with various substrate thicknesses in the range of 1-500 \(\upmu\)m and has characterized the performance of these sensors using both simulation and experimental methods. In general, the results have shown that the temperature variation within the substrate increases with a reducing thickness and gives rise to a higher output voltage as a result. The maximum voltage responsivity is equal to 3800 mV/mW and is obtained using a substrate thickness of 1 \(\upmu\)m and a cutoff frequency of 400 Hz. For all of the sensors, the output voltage reduces to zero as the sensing distance increases beyond 100 cm. In general, the results presented in this study provide a useful source of reference for the further development of pyroelectric sensors with an enhanced performance in various fields.  
Conceptualization and design, L.-M.F. and C.-Y.L.; methodology and simulation, C.-X.Y. and K.-Y.L.; fabrication and experiments, C.-X.Y. and K.-Y.L.; data analysis, L.-M.F. and C.-Y.L., writing, C.-X.Y., L.-M.F. and C.-Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology in Taiwan, grant number MOST 110-2622-E-020-001; MOST 109-2622-E-006-009-CC2, and MOST 109-2221-E-006-043-MY3.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
Figure 12: Comparison of experimental voltage response and simulated temperature variation for ZnO sensors with different substrate thicknesses. (Note that the cutoff frequency is 400 Hz in every case.)