esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Paradigms of AI for Physical Layer Optimization and System Design
### AI for Signal Processing Design  
One important study on PLS system design was issued to explore optimization techniques and signal processing design fundamentals. The signal processing methods in  
Figure 11: An illustration of downlink resource allocation for a multi-service network.  
5G networks, such as secure precoding and beamforming, have been utilized to enhance various performance requirements in secure transmission systems. By optimizing these techniques, the appropriate performance metrics of PLS can be achieved while the desired QoS can also be ensured at the same time [170; 171; 172; 173; 174; 175]. However, due to the complexity of the hardware and mathematical models in these performance metrics, the optimization problems of existing signal processing schemes become more and more complicated for future network environments [64; 90; 114; 115; 116], which means that: (1) it is hard to solve these problems using numerical methods because they are too sophisticated and have many imperfections in practical applications; (2) in order to obtain optimal design schemes, the hurdles between various signal processing barriers need to be reduced using optimal solutions; (3) the vastly increasing complexity of the systems and hardware installation that are needed to mitigate computational cost requires robust and intelligent algorithms to make system design more practical.  
To cope with these challenges, the assistance of modern computing technologies has been used as a performance metric. On the other hand, ML and AI techniques can benefit from the optimization of traditional signal processing techniques. Some of the application examples include secure physical layer modules [176; 177], channel estimation and equalization [178], obstacle detection and localization [179; 180], modulation recognition [159] and channel coding and estimation [181; 182]. The advantages of employing AI and ML technologies in 5G networks compared to traditional signal processing methods are two-fold. First, AI and ML approaches are designed to achieve optimal end-to-end performance metrics, while conventional schemes are performed by logically discrete blocks that are separately optimized. Second, artificial neural networks (ANNs) are known to be highly reliable and self-adaptive and have also been shown to have computationally universal functions [183]. ANNs are used to learn inverse mapping from received interference signals to the original signals without the need for explicit channel estimation and equalization. The joint optimization of transmitters and receivers within specific channel environments can produce non-ideal effects in the wireless channel and consequently, transmission performance is enhanced. By applying ANNs in signal processing schemes, we can improve optimal performance metrics as long as a reliable hardware implementation model exists. In summary, ML- and AI-based algorithms are more efficient and powerful for hardware implementation and have extensive applications that are not limited to signal processing schemes.
## 2 Materials and Methods: Ten-Koh Mission Description
### Satellite Platform
#### 2.2.4 Eps  
The main power source of Ten-Koh consists of 12 solar panels composed of five triple-junction solar cells each, with two SPV1040 MPPT (maximum power point tracking) circuits per solar panel: one for managing two cells and one for managing the remaining three cells. All 12 solar panels are connected in parallel in an unregulated power bus that is used to charge two hot-redundant batteries. Each of the two batteries is composed of four individual Li-ion cells connected in parallel. The complete outputs of both batteries are also connected in parallel in order to form a battery-regulated bus.  
The battery-regulated power bus is an input into the LT1370 voltage regulators, which generate 5 and 12 V regulated buses. These buses then feed over-current protection (OCP) circuits implemented with LTC4361 and LT4363 ICs, which in turn deliver power at regulated voltages to the different subsystems. All subsystems were designed to use these voltages to reduce the complexity of the entire system. In certain cases, however, 3.3 V levels are required. These are obtained from the 5 V lines with LD1117 linear regulators within the subsystems.  
The different power lines per subsystem are:  
* Two independent 5 V lines for the payloads--one for the experiment control unit, ECU and one for the double Langmuir probe, DLP.
* One 12 V line for the charged particle detector (CPD) instrument.
* One 12 V line for the magnetometer instrument.
* Two 5 V lines, one for each of the redundant communications subsystems.
* One 5 V line for the attitude determination subsystem (ADS) and all solar panels telemetry acquisition circuits.
* One 5 V line for the entire EPS subsystem.
* One 5 V line for the OBC subsystem.  
Each power line can be controlled (switched on and off) independently from the EPS controller PIC. The default state of the power lines is encoded in hardware to make sure that the OBC and COMM subsystems are on even in case of failure of the EPS controller. This PIC also gathers voltage, current and temperature telemetries from the entire subsystem.  
The total power needed to run all the platform subsystems (OBC, EPS, and COMMS) is 3.3 W. When the payloads are operated, this value increases to at most 9.5 W, which is the case of running the platform plus the CPD and DLP (worst case allowable for payload operation). Depending on the experiment being conducted, the power consumption can be reduced to:  
* Material mission (MM): 4.8 W.
* Charged Particle Detector (CPD): 8.8 W.
* Double Langmuir Probe (DLP): 5.5 W.
* Attitude Determination Subsystem (ADS): 5.5 W.
* Thermal Switch (TSW): 5.5 W.
* Ultracapacitor (UCP): 5 W.  
From the power generation perspective, the minimum available power is 11 W, which leaves sufficient margin to recharge the battery during sunlit parts of the orbit. A detailed description of the solar panels designed for Ten-Koh is shown in [18].
# A Comparison of Electrical Breakdown Models for Polyethylene Nanocomposites
## 3 Results
### Simulation Results of EBMD Model  
Figure 6 depicts the variation in the distributions of space charges, electric fields, molecular chain displacements, and electron energies with time in pure LDPE and LDPE/Al2O3 nanodielectrics. With the increase in nanofiller content, under the same voltage, the accumulation of space charges in the nanocomposites first decreases and then increases, while the electric field concentration first decreases and then increases. It can be obtained from the dynamic equation of molecular chain motion that, with the increase in nanofiller content, the displacement of molecular chains within nanocomposites first decreases and then increases under the same voltage. This causes the energy accumulated by the electrons to decrease first and then increase. At the same time, it is considered that the trapping ability of deep traps in nanocomposites first increases and then decreases. The energy accumulation of charge carriers in the dynamic free volume and the trapping ability of deep traps together determine the breakdown electric field of nanodelectrics as a function of nanofiller content. With the increase in the mass percentage of nanofillers, the breakdown electric field of the nanodielectrics increased first and then decreased.  
Figure 5: Distributions of space charges (**a1**–**a5**), electric fields (**b1**–**b5**), and electron energy gains (**c1**–**c5**) in pure LDPE and LDPE/Al2O3 nanocomposites with nanofiller contents of 0.1 wt%, 0.5 wt%, 2.0 wt%, and 5.0 wt%.
# Trajectory Tracking Outer Loop Regressor-Based Adaptive Controller for a Quadrotor
### Organization and Notation  
The organization of the document is the following. The dynamic model and the control objective are described in Section 2. In Section 3, the design and stability analysis of the proposed regressor-based adaptive controller are presented. The results of the experimental comparative study are shown in Section 4 and the conclusions are given in Section 5.  
Notation: The notation adopted along this document is the following. \(\|\mathbf{x}\|=\sqrt{\mathbf{x}^{\top}\mathbf{x}}\) is the Euclidean norm of the vector \(\mathbf{x}\in\mathbb{R}^{n}\), given a symmetric positive definite matrix \(A\in\mathbb{R}^{n\times n}\); its minimum and maximum eigenvalues are denoted as \(\lambda_{\min}\{A\}\) and \(\lambda_{\max}\{A\}\). \(\text{diag}_{n}\{x\}\in\mathbb{R}^{n\times n}\) stands for diagonal matrices with \(x\) in each diagonal element.
## 3 Spatial-Temporal Spectral Hole-Sensing Model
 \tag{6}\]  
where \(O_{A}^{0}\) denotes the availability of spatial-temporal spectral holes during HD sensing slots either because of \(H_{00}\), or \(H_{01}\) and FD-SU TX is present in \(R2\) (\(S_{A}\)). Similarly, \(O_{A}^{1}\) denotes the availability of spatial-temporal spectral holes during FD sensing slots either because of \(H_{10}\), or \(H_{11}\) and FD-SU TX is present in \(R2\) (\(S_{A}\)). Considering Equations (1), (2), and (4), we now model the non-availability of spatial-temporal spectral holes for FD-SU TX as the following composite hypothesis testing problems  
\[O_{B}^{0}:S(i)=\left\{h_{1}s_{1}(i)+n(i)\quad\left\{H_{01}\right\},\qquad S_{ B},\right. \tag{7}\]  
\[O_{B}^{1}:S(i)=\left\{h_{1}s_{1}(i)+g_{1}s_{2}(i)+n(i)\quad\left\{H_{11} \right\},\qquad S_{B},\right. \tag{8}\]  
where \(O_{B}^{0}\) denotes the non-availability of spatial-temporal spectral holes during HD sensing slots because of \(H_{01}\) and FD-SU TX is present in \(R1\) (\(S_{B}\)). Similarly, \(O_{B}^{1}\) denotes the non-availability of spatial-temporal spectral holes during FD sensing slots because of \(H_{11}\) and FD-SU TX is present in \(R1\) (\(S_{B}\)). It can be observed that the availability/non-availability of spatial-temporal spectral holes (during HD and FD sensing slots) in Equations (5)-(8) is a combination of availability/non-availability of pure temporal and spatial spectral holes in Equations (1)-(4). However, spatial-temporal spectral hole-sensing model incorporates the traffic variations of the PU in both domains (time and space) for the FD-SU TXs present at different spatial positions.
## 3 Case Study
### Building Information  
This study utilizes energy consumption data and relevant features from a large public building in Shaanxi Province, China. The energy consumption data employed in this research mainly includes data from the lighting power supply system and the air conditioning power supply system. The building consists of a total of seven floors, including two underground floors and five above-ground floors, with a height of 32.5 m and a footprint of 22,515 square meters. The air-conditioned area constitutes approximately 65% of the total building area, occupying around 14,635 square meters. The building is predominantly composed of commercial and office areas, with a high demand for thermal comfort indoors, resulting in a significant proportion of energy consumption attributed to air conditioning. The building operates throughout the year from 8:00 a.m. to 10:00 p.m., with the air conditioning systems in the commercial and office areas typically running continuously during the open hours to ensure indoor comfort. During the operation time of the building, data sampling was conducted every hour, and a total of 1300 energy consumption data points were collected. As shown in Figure 7, the x-axis represents the sampling points of energy consumption data, and the y-axis represents the specific values of energy consumption data. The building energy consumption data show certain periodicity and volatility.
###### Abstract  
Breast cancer has a 14.8% incidence rate and an 8.5% fatality rate in Saudi Arabia. Mammography is useful for the early detection of breast cancer. Researchers have been developing artificial intelligence (AI) algorithms for early breast cancer diagnosis and reducing false-positive mammography results. The aim of this study was to examine the performance and accuracy of an AI system in breast cancer screening among Saudi women. **Materials and Methods:** This is a retrospective cross-sectional study that included 378 mammograms collected from 2017 to 2021 from government hospitals in Jeddah, Saudi Arabia. The patients' demographic and clinical information were collected from files and electronic medical records. The radiologists' assessments of the mammograms were based on Breast Imaging Reporting and Data System (BIRADS) scores. Follow-up or biopsy reports verified the radiologists' findings. The MammoScreen system was the AI tool used in this study. Data were analyzed using SPSS Version 25. **Results:** The patients' mean age was 50.31 years. Most patients had breast density B (42.3%) followed by A (27.2%) and C (25.9%). Most malignant cases were invasive ductal carcinomas (37.3%). Of the 181 cancer cases, 36.9% were BIRADS category V. The area under the curve for the AI detection (0.923; 95% confidence interval [CI], 0.893-0.954) was greater than that for the radiologists' interpretation (0.838; 95% CI, 0.796-0.881). The AI detection agreed with the histopathological result in 167 positive (91.3%) and 182 negative cases (93.3%). The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of the AI system were 92.8%, 91.9%, 91.3%, 93.3%, and 92.3%, respectively. The radiologist's interpretation agreed with the pathology report in 180 positive (73.8%) and 134 negative cases (100%). Its sensitivity, specificity, PPV, NPV, and accuracy were 100%, 67.7%, 73.8%, 100%, and 83.1%, respectively. **Conclusions**: The AI system tested in this study had better accuracy and diagnostic performance than the radiologists and thus could be used as a support diagnostic tool for breast cancer detection in clinical practice and to reduce false-positive recalls.  
artificial intelligence; breast cancer; mammography; radiology
## 2 Methods and Background
### Model Descriptions and Grid Sensitivity Analysis  
#### 2.2.1 Descriptions of EFDC  
The hydrodynamic module of the EFDC includes quasi-3D flow analysis and solute transport models. The flow analysis model adopts the RANS equation as a governing equation that considers hydrostatic pressure instead of solving the vertical momentum equation [45]. Although this hydrostatic pressure assumption is limited in terms of reproducing vertical flow, this model, with its quasi-three-dimensional characteristics, was determined to be appropriate for this type of research, even though confluences have vertical flow. This flow analysis model adopts the Smagorinsky model [46] and the Mellor-Yamada 2.5 model [47] as turbulence closure methods for estimating the horizontal and vertical eddy viscosities, respectively. The model solves the governing equations using a combination of finite volume and finite difference scheme on an orthogonal curvilinear coordinate system in the horizontal direction. In the vertical direction, the equations are transformed using the sigma coordinate system. The model employs the mode splitting method, which separates the computation into external and internal mode. In the external mode, free surface elevation is obtained from the depth-averaged governing equations. In the internal mode, velocity shear is computed using the results by the external mode. More details about the numerical formulations can be found in Hamrick [45].  
The solute transport model is coupled with the flow analysis model, and the governing equation of the solute transport model is given as  
\[\begin{array}{rl}\frac{\partial}{\partial t}\left(m_{x}m_{y}Hc\right)&+\frac {\partial}{\partial x}\left(m_{y}Huc\right)+\frac{\partial}{\partial y}\left(m_ {x}Hvc\right)+\frac{\partial}{\partial x}\left(m_{x}m_{y}wc\right)\\ &=\frac{\partial}{\partial x}\left(\frac{m_{y}}{m_{x}}H\varepsilon_{h}\frac{ \partial x}{\partial x}\right)+\frac{\partial}{\partial y}\left(\frac{m_{x}} {m_{y}}H\varepsilon_{h}\frac{\partial x}{\partial y}\right)+\frac{\partial}{ \partial z}\left(m_{x}m_{y}\frac{\varepsilon_{h}}{\partial z}\right)\end{array} \tag{14}\]  
where \(c\) is the time-averaged concentration, \(m_{x}\) and \(m_{y}\) are the metric coefficients, \(H=\zeta+h\), \(\zeta\) is the bottom elevation, and \(\varepsilon_{h}\) and \(\varepsilon_{z}\) are the horizontal and vertical turbulent diffusion coefficient, respectively. The turbulent diffusion coefficients in Equation (14) are considered as the eddy viscosity according to the Reynolds analogy [40], which has been adopted for simulations without density differences between the main and tributary channels [48; 49]. However, it has been reported that simulation results applying the Reynolds analogy may lead to overestimation of the transverse mixing [50].
## 3 Experiment
### Classification Features Extraction
#### 3.3.3 Higher-Order Crossings (HOC)-Based Features  
The higher-order crossings (HOC) method is also often called zero-crossing and level-crossing method [26]. It counts the number of axis-crossing, i.e., the symbol changes in the dataset. In our dataset, we set the zero mean signal data from each sensor collected from each stop of each participant as a series \(\{\bar{Z}\}=\{Z_{t},t=1,2,\ldots,N\}\). The number of crossing of the horizontal axis, is denoted \(D_{1}\), and it is the same as the number of sign changes in \(\{\bar{Z}\}\)[26].  
The higher-order crossings are defined by using the difference operator \(\triangledown\), and \(\triangledown Z_{t}\) is defined (see Equation (12) [27]):  
\[\triangledown Z_{t}=Z_{t}-Z_{t-1} \tag{12}\]  
For the second order of the difference, it is:  
\[\triangledown^{2}Z_{t} =\triangledown(\triangledown Z_{t}) \tag{13}\] \[=\triangledown(Z_{t}-Z_{t-1})\] \[=Z_{t}-2Z_{t-1}+Z_{t-2}\]  
Higher orders can be computed in the same manner as above. In general, the \(k^{th}\) order difference is (see Equation (14) [28]):  
\[\triangledown^{k-1}Z_{t}=\sum_{i=1}^{k}C_{i-1}^{k-1}(-1)^{i-1}Z_ {t+1-i} \tag{14}\] \[with\ \ \ \ \ C_{i-1}^{k-1}=\frac{(k-1)!}{(i-1)!(k-i)!}\]  
where \(k=1,2,\ldots\), and \(\triangledown^{0}\) is the identity.  
From \(\{\bar{Z}\}\), a binary process, \(\{\bar{X}\}\) is defined by (see Equation (15) [27; 28; 29]):  
\[X_{t}=\begin{cases}1,&Z_{t}\geq 0\\ 0,&Z_{t}<0\end{cases} \tag{15}\]  
Let  
\[d_{t}=\begin{cases}1,&X_{t}\
eq X_{t-1}\\ 0,&otherwise,\end{cases} \tag{16}\]  
where \(d_{t}\) is the indicator. It indicates that there is a symbol change in \(\{\bar{X}\}\) when it is \(1\). The number of crossing of the horizontal axis, \(D_{1}\), in \(\{\bar{Z}\}\) is defined as [27; 28]:  
\[D_{1} =d_{2}+d_{3}+\ldots+d_{N}\] \[=\sum_{t=2}^{N}[X_{t}-X_{t-1}]^{2}\]where \(N\) is the length of the data.  
For the \(k^{th}\) order, the count of the symbol changes is:  
\[D_{k} =d_{2}^{(k)}+d_{3}^{(k)}+\ldots+d_{N}^{(k)}\] \[=\sum_{t=2}^{N}[X_{t}(k)-X_{t-1}(k)]^{2}\]  
Above all, for the signal channel for each sample, the HOC-based feature vector, \(FV_{HOC}\), is formed as follows (see Equation (17) [28]):  
\[FV_{HOC}=[D_{1},D_{2},\ldots,D_{L}],\ \ \ \ 1<L\leq J \tag{17}\]  
where \(J\) is the maximum order of the estimated HOC and L is the HOC order chosen in this study.  
For our dataset, the \(FV_{HOC}\) was extracted from the four signals within a range of order \(K=1,\ldots,50(=J)\).
## 1 Introduction
In [12], some micro-optical sensors were integrated into soft robot arms to form their pose sensing system; this system couples the optical fibers with some passive cables mechanically in a basal unit that has sensing function, and a distance modulation array is integrated with the basal sensing unit. The pose of the soft robot arms can be calculated by the mechanical deformation to the voltage variation of the sensing signal. This sensing system can be used to measure the length and bending angle of soft robot arms, their 3D shape reconstruction and monitoring are not attained. [PERSON] et al. [15] designed a hardware and control method for a soft orthosis, the initial quantification of its force augmenting capabilities was performed, and its closed-loop control was implemented through the feedback from the optical-fiber sensors which was embedded into the actuator. However, few studies have been conducted in real-time; 3D shape reconstruction and visualization monitoring of soft surgical actuator which are embedded a in single optical fiber with Fiber Bragg Grating (FBG) sensors. In this paper, to understand real-time shape monitoring of the soft actuator, a 3D shape reconstruction method based on two FBG sensors is proposed. First, an optical fiber with two FBG sensors is embedded into the soft actuator; then the calibration of FBG sensors is conducted, the spectrum and wavelength shift caused by the bending of the soft actuator is also measured; and finally, the 3D shape of the soft actuator is reconstructed based on the interpolation and curve fitting functions. The remainder of this paper is organized as follows. In Section 2, we describe the mechanical design and modeling of the soft silicone actuator. In Section 3, we introduce the sensing theory and shape measurement method of FBG sensors. The experimental setup and testing of the soft actuator are elaborate in Section 4. Section 5 concludes the paper.
_applied sciences_  
_Article_  
**Verification of 3D AE Source Location Technique in Triaxial Compression Tests Using Pencil Lead Break Sources on a Cylindrical Metal Specim**  
[PERSON] 1,*(c), [PERSON] 2, [PERSON] 3(c) and [PERSON] 1(c)  
1 Department of Civil Engineering, The University of Tokyo, Tokyo 113-8656, Japan; [EMAIL_ADDRESS]  
2 Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan; [EMAIL_ADDRESS]  
3 School of Civil and Environmental Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan; [EMAIL_ADDRESS]  
* Correspondence: hd1431 bg.ecc.u-tokyo.ac.jp  
**Abstract:** Recently, the acoustic emission (AE) technique has been widely applied in the field of geotechnical engineering. One of the main applications of this technique is to locate damage sources, which is known as the AE source location technique. In this research, the 3D AE source location technique based on the time difference of arrival is verified by conducting pencil lead break (PLB) tests on a cylindrical metal specimen. From the results of this study, the existing method is proven to have the least error in terms of distance from the coordinate origin, but with some errors along \(x,y\), and \(z\) axes. When the PLB sources originate in the middle part of the specimen, the calculated result has a higher accuracy, as compared to other positions. It seems that the calculated AE sources tend to be concentrated on the central part with some errors. Moreover, outside noises induced by a hammer hit have virtually no effect on this AE source location technique.  
**Keywords:** acoustic emission; source location; pencil lead break; cylindrical metal specimen