esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Deep Dense Block-Based Model for Detecting Alzheimer's Disease
## 4 Results and Experiments
### Strengths and Limitations  
So far, studies have generally focused on binary classification problems for Alzheimer's diagnosis. This study specifically addresses the multiple classification problem of AD/MCI/CN. This study was conducted using a publicly available dataset. To mitigate overfitting issues and extract disease-specific features, data augmentation was employed due to the limited number of images in the dataset.  
The proposed model utilizes a deep neural network and does not require separate feature extraction. Various databases are utilized in the literature for AD diagnosis. However, collecting data from entire neuroimaging databases can be challenging. Additionally, neuroimages are processed using different methods. In this study, an ROI-based method was implemented to improve the accuracy rate. Furthermore, performance enhancements were attempted via transfer learning.  
Morphometric images may possess higher dimensions compared to normal MRI images, thereby requiring increased memory and processing power. Furthermore, manually labeling such datasets can be arduous. Hence, expert knowledge and time-consuming manual procedures may be necessary for generating labeling data.  
Despite CNN's favorable performance in medical image analysis, there are still lingering issues. Limited data availability is particularly problematic in the field of medical image processing. To overcome this, a large database was preferred for this study.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Biomarker** & **Database** & **Method(s)** & **ACC (AD, MCI, CN)** & **Participant** & **Approach** \\ \hline \multirow{4}{*}{[32]} & \multirow{4}{*}{MRI} & \multirow{4}{*}{ADNI} & \multirow{4}{*}{SNN + CNN} & \multirow{4}{*}{NC/MCI: 83.90\%} & \multirow{4}{*}{450} & \multirow{4}{*}{ROI} \\  & & & & & NC/MCI: 87\% & & \\  & & & & & MCI/AD: 68\% & & \\ \cline{1-1} \cline{5-8}  & \multirow{4}{*}{VBM} & \multirow{4}{*}{ADNI} & SegNet + ResNet-101 & \multirow{4}{*}{AD/CN/MCI: 96\%} & \multirow{4}{*}{240} & \multirow{4}{*}{ROI} \\  & & & LeNet & NC/AD: 93.83\% & & \\ \cline{1-1} \cline{5-8}  & & & AlexNet & NC/AD: 96.22\% & & \\ \cline{1-1} \cline{5-8}  & \multirow{4}{*}{VBM} & \multirow{4}{*}{ADNI} & VGGNet & NC/AD: 96.08\% & \multirow{4}{*}{479} & \multirow{4}{*}{3D subject level} \\  & & & & GoogLeNet & NC/AD: 97.15\% & & \\ \cline{1-1} \cline{5-8}  & & & ResNet & NC/AD: 94.60\% & & \\ \cline{1-1} \cline{5  
Although the transfer-learning method employed in this study boasts numerous advantages, a neural network based on the complex structure of the Xception architecture presents challenges. These complex model structures can affect the method's applicability, including training, hyperparameter tuning, and computational resource requirements. To overcome these limitations, successful adaptive methods such as the Adam optimizer were utilized.
## 4 Results
### Optimization of the \(g\) Value for Prediction of Ambient Noise Levels  
Figure 3 reports the results from the sound levels and occupancy monitoring in the living room and compares them with the prediction model (1) for ambient noise levels, considering a Lombard slope values \(c=0.5\) dB/dB, a sound absorption per person value \(A_{p}=0.2\) m\({}^{2}\), and the group size values \(g\) of 8, 9 and 10, accordingly [24]. Assuming that groups size \(g\) values in the 8-10 persons range were being considered, datapoints related to occupancy lower than eight people in the living room were removed (i.e., since \(g\) represents the ratio between present persons and speaking persons, it does not make sense to have \"less than 1 person\" speaking in the room). From the trend line (linear regression) of the sound levels measured on site, it can be observed that a group size value \(g=9\) gives the best match between the prediction model and the measured noise levels values.  
Following the work developed by [PERSON] [24], additional values of the Lombard slope and absorption per person were tested, as a function of group size. For this purpose, predicted ambient noise levels were determined according to the model in (1) using actual occupancy levels, under  
Figure 3: Prediction curves of the assumed group size parameter \(g\)[24]: measured (with linear regression) and predicted ambient noise levels as a function of occupancy.  
Figure 2: Occupancy levels stratified by user types and A-weighted equivalent sound levels as a function of time during the monitoring day in the living rooms.  
different configurations of \(c\), \(A_{p}\) and \(g\) values; these were then compared with measured noise levels and average deviations were calculated. Table 1 shows that only for \(c\) = 0.5 dB/dB are the average deviations from measured levels reasonably small (0.8-1.2 dB), with the smallest deviation using \(g\) = 9 (0.3 dB). When looking at the same information (\(g\) = 9) from the point of view of absorption per person, Table 2 shows that a variation in \(A_{p}\) from 0.2 to 0.5 m\({}^{2}\) leads to a rather small effect on the average deviations (0.1-0.5 dB). Therefore, the application of the prediction model to the present case study would return the most accurate results with \(c\) = 0.5 dB/dB, \(A_{p}\) = 0.2 m\({}^{2}\) and \(g\) = 9; this suggests that, in the case of facilities hosting older adults in special care, values higher than \(g\) = 8 [24; 30] might be appropriate.
## 6 Results and Discussion
### Simulation of Cable Disturbing Force
When the cable length is 150 m and the top end of the cable is static, the expressions of the cable terminal tension versus the relative velocity and the spatial position under the typical velocity conditions can be expressed as follows. When the current velocity is (\(-1.5\) m/s, 0 m/s, 0 m/s), the top end of the cable is static with the coordinates (0 m, 0 m, 0 m) and the bottom end of the cable or the ROV moves. The ROV often swims against the current in the water, and the lateral and vertical velocities are relative small. Therefore, the effect of the longitudinal velocity on the cable tension is mainly considered here. The tension components at the bottom end of the cable to the ROV can be expressed as follows:  
\[F_{cablex}=366-1308u_{re}+\left[-0.3381X-0.0164Y^{2}-0.025(Z-100)^{2}-3.1634(Z -100)\right]\times 9\] \[F_{cabley}=-0.9126Y\times 9\] \[F_{cablez}=114-564u_{re}+\left[-0.0076X^{2}+0.5277X-0.0125Y^{2}-0.077(Z-100)^{2}-3.6955(Z-100)\right]\times 9 \tag{57}\]  
where \(u_{re}\) is the relative current velocity component along the \(x\) direction in earth coordinates, \(u_{re}=u_{e}-u_{c}\). Further, \(u_{e}\) is the absolute velocity in earth coordinates and \(u_{c}\) is the current velocity. Equation (57) is mainly used for estimating the disturbance compensation in the observer. The tension components at the end of the cable \(F_{cablex}\), \(F_{cablez}\) are obtained in the earth coordinate system. The cable tension in the body-fixed coordinate system of the ROV can be obtained with a coordinate transformation.
# Topology Optimization Based Parametric Design of Balloon Borne Telescope's Primary Mirror
###### Abstract  
For balloon-borne telescopes, the primary mirror is the most important optical element, but designing a primary mirror with an excellent overall performance is a challenge. To comprehensively consider the contradictory objectives of the root mean square (RMS) surface error under gravity in the X and Z directions, the mass and fundamental frequency of the primary mirror, a parametric primary mirror design using the compromise programming method based on topology optimization is proposed. The parametric design of the compromise programming method based on topology optimization is used to find the optimal solution for X-direction RMS (RMSx), \(Z\)-direction RMS (RMSz), mass, and fundamental frequency. Compared with the initial primary mirror structure designed according to traditional experience, the overall performance is improved. Results show that the respective mass of the primary mirror, the RMSx and the RMSz decreased by 8.5%, 14.3% and 10.5% compared to those before optimization. Comprehensive consideration can prove the effectiveness of parametric design based on the topology optimization of the primary mirror. This method provides a reference for the design of other primary mirrors for balloon-borne telescope and space cameras.  
near-space; primary mirror; topology optimization; parametric design; compromise programming method +
Footnote †: journal: Applied Sciences  
**Keywords:**
# A Truly Second-Order and Unconditionally Stable Thermal Lattice Boltzmann Method
## 3 Unconditionally Stable Thermal Lattice Boltzmann Method (USTLBM)
### Stability Analysis
Subsequently, Equation (61) is simplified as  
\[\overline{\mathrm{d}S}=\overline{\mathrm{d}y_{2}^{*}} \tag{62}\]  
Differentiating Equation (59) and substituting Equations (54) and (62) into it eventually gives  
\[\overline{\mathrm{d}y_{2}^{n+1}}=\overline{\mathrm{d}y_{2}^{n}} \tag{63}\]  
Other variables in the corrector step can be analyzed through similar procedures to derive the following relationship:  
\[\left\{ \begin{array}{ll}\overline{\mathrm{d}y_{1}^{n+1}}=\overline{\mathrm{ d}y_{1}^{n}}\\ \overline{\mathrm{d}y_{2}^{n+1}}=\overline{\mathrm{d}y_{2}^{n}}\\ \overline{\mathrm{d}y_{3}^{n+1}}=g\beta T_{m}\delta_{t}\overline{\mathrm{d}y_ {1}^{n}}+\overline{\mathrm{d}y_{3}^{n}}-g\beta\delta_{t}\overline{\mathrm{d}y_ {4}^{n}}\\ \overline{\mathrm{d}y_{4}^{n+1}}=\overline{\mathrm{d}y_{4}^{n}}\end{array} \right. \tag{64}\]  
Thus, the overall stability analysis of USTLBM can be written in the matrix form of  
\[\overline{\mathrm{d}\mathbf{Y}}^{n+1}=\left[ \begin{array}{cccc}1&0&0&0\\ 0&1&0&0\\ g\beta T_{m}\delta_{t}&0&1&-g\beta\delta_{t}\\ 0&0&0&1\end{array} \right]\cdot\overline{\mathrm{d}\mathbf{Y}}^{n} \tag{65}\]  
Apparently, all eigenvalues in the characteristic matrix are 1. Therefore, it is proven that USTLBM proposed in this paper is theoretically unconditionally stable. Through the above von Neumann stability analysis, it turns out that USTLBM maintains the merit of STLBM in numerical stability; therefore, both of them are advantageous over the standard LBM. Meanwhile, considering its advantage in temporal accuracy, USTLBM is more competitive than STLBM in the perspective of numerical accuracy.
## 4 WBANs: The Key M2M Area Network Technology for mHealth Applications
### WBAN Channel Modeling
#### 4.2.2 In-Body Radio Propagation Model  
As mentioned in the previous section, communication of medical implantable devices is restricted to the MICS frequency band of 402-405 MHz. In this frequency range, the human body can be considered as an inhomogeneous medium consisting of multiple tissue layers with different electrical characteristics. Therefore, it behaves as a communication channel where losses are mainly due to the power absorption by the different tissue layers.  
Radio propagation through human tissue has been extensively studied in the literature ([42; 43; 44; 45; 46]) and depends on many parameters, including, among others, sex, age, location of the sensor in the human body and general posture of the body. The derivation of an exact expression for the in-body propagation model is extremely difficult. Instead, the SAR level is employed as a standard measure of how much power is absorbed by the tissue. Compared to the free space propagation, an additional 30-35 dB path loss is noticed at small distances. Measurements in [44; 45; 46] demonstrated that direct communication between implanted nodes suffers from a lower path loss with respect to the link establishment between an implant and a node in body surface. Finally, it has been shown that the shape and the position of the patient's body has a significant impact on the radiation pattern of an implanted radio transmitter [47].
# Telerehabilitation--A Viable Option for the Recovery of Post-Stroke Patients
## 4 How to Organize TR at Patient's Home?  
Telerehabilitation can use virtual reality technology, and regarding post-stroke patients, a rigorous assessment is needed before therapy onset. The evaluation is essential for therapy settings and outcome establishment. It requires at least a few sessions of exergames to be performed by the patient before home training, so they can be familiarized with virtual reality technology. It is essential for patients to learn from the physiotherapist how to correctly perform the exercises as a complement to the virtual assistance provided by the technology used [34; 35].  
An important aspect and challenge of the implementation of TR is the spatial planning of a patient's home where the training will take place. The risk of fall or injury during exercise has to be reduced to a minimum and redressing support in case of balance loss or standing up after a fall has to be assured [36]. Probably due to safety procedures, some studies found balance to be one of the outcomes where TR did not prove its efficacy [11]; however, there are some research results that suggest the opposite [33; 37]. Therefore, when the exercises performed at home target walking and balance, it is of importance to ensure the safety of therapy sessions with the presence of a family member or another informal caregiver who can supervise, support and help the patient in case of loss of balance (Figure 1). Moreover, the family member can help in establishing the communication with  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Studies** & **Exercises** & **Results** \\ \hline Hemiparesis/upper limb & most studies, diverse methodology [21; 22; 23]; reviews [5; 7; 14; 23; 24] & Constraint—induced movement therapy (CIMT) [25; 26; 27]; heterogeneous exercises [21; 22; 23]; biofeedback [17] & comparable with face-to-face therapy \\ \hline Hemiparesis/lower limb & few studies; not represented primary objective; reviews [5; 23] & heterogeneous exercises & favorable telerehabilitation results; low costs \\ \hline Aphasia & comparative studies [28]; randomized controlled trials [29; 30; 31; 32] & various interventions & controversial results \\ \hline Gait disorder & few studies and reviews [5; 24; 33] & gait exercises; exercises for improving balance & favorable results assessment: Berg balance scale, time up and go test, modified Falls efficacy scale, montricity Index \\ \hline Psycho-emotional and cognitive disorders & secondary objectives; few studies and reviews [5; 22; 24; 32] & cognitive therapy & improvements in depression and cognition [22]; cognitive function [24]; functional communication [5] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Patients with various disabilities who can benefit from TR.  
the rehabilitation team. The exercising room has to be spacious enough in order to maintain a minimum of 2 m between the patient and the surrounding furniture during the entire time of walking and balance training.
## 2 Algorithm Principle
### INS+LEO 3-Satellite Alternate Switching Ranging Integrated Navigation Algorithm  
#### 2.1.1 Algorithm Principle  
The idea behind the algorithm is as follows: According to the satellite selection algorithm of geometric dilution of precision (GDOP) [24], select three satellites that are always visible during the flight of the carrier aircraft. At any time, the aircraft can obtain the real ranging value of one satellite, which changes with the switching of satellites. The remaining two satellites provide virtual ranging values, which also change with the switching of satellites. Then, the ranging values of three LEO satellites cooperate with INS to perform unscented Kalman filter (UKF)-integrated navigation filtering by switching alternately at any time of aircraft flight. The true ranging value is defined as the distance between the current LEO satellite position calculated by LEO satellite ephemeris and the aircraft's true position. In contrast, the virtual ranging value is defined as the distance between the current LEO satellite position calculated by LEO satellite ephemeris and the aircraft position measured by INS.  
The 3-satellite alternate switching ranging algorithm can be divided into three scenarios:  
1. The three satellites participating in the alternation are all on the same orbital plane. This algorithm positioning scene is shown in Figure 1a.  
2. Two satellites are in the same orbit and the other satellite is in a different orbit (it can be adjacent). This algorithm positioning scene is shown in Figure 1b.  
3. All three satellites are in different orbits (may be adjacent to each other). This algorithm positioning scene is shown in Figure 1c.  
We give a specific algorithm diagram of scenario 1, as shown in Figure 2. Scenario 3 is similar to scenario 1, so it is not given here because of space limitations.  
In scenario 1, at any time during the flight of the aircraft, we select three LEO satellites that are visible at all positions and name them PRN1, PRN2 and PRN3. Among these three ranging values, we use the true ranging value of one LEO satellite and the other two LEO satellites use virtual ranging values. Subsequently, the real ranging value and the virtual ranging value are alternately switched with a specific alternate time as the period to realize INS+LEO 3-satellite alternate switching ranging/INS-integrated navigation positioning.  
Figure 1: Schematic diagram of the INS+LEO 3-satellite. (**a**) Three satellites are in the same orbit. (**b**) Two satellites are in the same orbit. (**c**) Three satellites are in different orbits.  
Figure 2: 3-satellite alternate switching ranging scene integrated navigation algorithm.
# A Mathematical Model of Universal Basic Income and Its Numerical Simulations
## 2 The Model
### Properties of the Model  
We point out here that independently of some differences between the expressions of certain terms in the present version of the model and those appearing in versions discussed in previous works, the following properties hold true.  
The existence for any \(x_{0}=(x_{01},\ldots,x_{0n})\) with \(x_{0k}\geq 0\) for all \(k=1,2,\ldots,n\) and \(\sum_{k=1}^{n}x_{0k}=1\), of a unique solution \(x(t)=(x_{1}(t),\ldots,x_{n}(t))\) of (1), satisfying \(x(0)=x_{0}\) has been proved in [6]. In particular, it has been proved there that such a solution is defined for all \(t\geq 0\) and satisfies \(x_{k}(t)\geq 0\) for \(k=1,2,\ldots,n\), as well as  
\[\sum_{k=1}^{n}x_{k}(t)=1\qquad\text{for all $t\geq 0$}\,. \tag{3}\]  
Additionally, it has been proved in [6] that the function \(\mu:\{x\in\mathbb{R}^{n}:\sum_{k=1}^{n}x_{k}=1\}\to\mathbb{R}\), defined as \(\mu(x)=\sum_{k=1}^{n}r_{k}x_{k}\) and which represents the global income is a first integral for (1):  
\[\sum_{k=1}^{n}r_{k}x_{k}(t)=\mu\in\mathbb{R}\qquad\text{for all $t\geq 0$}\,. \tag{4}\]  
Lastly, an extensive number of numerical simulations, also carried out with different parameter values, provides evidence that for fixed model parameters, in correspondence to any conceivable value of the global income \(\mu\), a unique stationary solution of (1) exists to which all solutions \(x(t)=(x_{1}(t),\ldots,x_{n}(t))\) satisfying \(x(0)=x_{0}\) with \(\mu(x_{0})=\mu\) tend as \(t\to\infty\).
## 5 System Implementation
### System Presentation  
The running environment of the virtual teacher-learning system in this paper includes two main components: PyCharm and Unreal Engine 5 (UE5). In PyCharm, we implemented the design of the virtual teacher controller. PyCharm serves as a powerful integrated development environment (IDE) for writing and debugging code for controllers that are responsible for managing the behavior of the virtual teacher, responding to user input, and enabling interaction with other systems. The core functions of the controller include the natural language processing, situation recognition, and dynamic generation of teaching content.  
On the other hand, Unreal Engine 5 (UE5) is used to complete the binding and rendering of the virtual teacher model. As an advanced game engine, UE5 provides powerful 3D modeling and real-time rendering functions, which makes the visual effect of  
Figure 8: Model training Effect.  
virtual teachers more realistic. With UE5, we are able to seamlessly interface the 3D model of the virtual teacher with the controller designed in PyCharm, ensuring that the virtual teacher can interact with the user in a natural and fluent way in the virtual environment. Ultimately, this integration allows the virtual teacher system to provide an immersive teaching experience while maintaining a high degree of interactivity and flexibility.  
Turn on the controller and connect the virtual teacher character model. As shown in Figure 9, users can directly communicate with the virtual teacher by voice or text, ask questions, or request explanations. The virtual teacher can understand and analyze the voice input, and can then provide detailed answers or explanations in the form of speech or text to achieve a more intuitive and natural learning interactive experience. During the interaction, the virtual teacher will make some facial micro-expressions when explaining, use hand gestures to emphasize teaching points, and nod or smile when appropriate to encourage students, making the teaching process more vivid and interactive.