esa-sceva/satcom-chunk-collection huggingface dataset:
# Symptom Burden and Dietary Changes Among Older Adults with Cancer: A Cross-Sectional Study
## 1 Introduction  
Over 60% of all individuals diagnosed with cancer are aged 65 and over, and the prevalence of malnutrition amongst cancer patients is estimated to be 20-70% [1; 2; 3]. Guidelines define malnutrition either as a qualitative insufficiency due to the inadequate intake of nutrients and food components and/or as a quantitative (energetic) problem due to an insufficient caloric intake [2]. The underlying genesis of malnutrition is variable, as cancer itself but also treatment-related side effects might promote malnutrition. The transition from malnutrition toward cachexia or sarcopenia is possible and can occur at any stage of the disease, sometimes even before diagnosis [4]. Older people with cancer are a particularly vulnerable group due to being prone to lower caloric intake and following a less varied diet. Especially reduced food intake is often present before the onset of the disease [5]. The variability of the triggers for malnutrition is often underestimated, and guidelines emphasize considering patients' symptom burden, as local impairments (e.g., dysphagia) as well as general symptoms affect nutritional status [6]. Battling malnutrition is essential. Malnourished patients have a worse prognosis [7]. Thus, recognizing these patients and addressing malnutrition directly affects the success of tumor therapy and patient survival. It is therefore of utmost importance to provide help and counseling as early as possible to counteract a negative spiral [8]. A range of screening instruments and assessments is available to uncover nutritional deficiencies. The selection of the appropriatetool depends on the screening setting and the disease entity or patient population [9]. In an inpatient setting, the Nutritional Risk Score-2002 (NRS-2002) [10] is often used as a screening tool, as recommended by guidelines [11]. The NRS-2002 is a tool combining a simple prescreen (yes/no questions) using questions about weight loss, decreased calorie intake, low body mass index (BMI), and severity of disease. If one question is answered with \"yes\", patients undergo more detailed screening. Depending on the severity of disease or decrease in nutritional intake, points are assigned. A score \(\geq\) 3 points considers a patient at risk for malnutrition [10]. However, the tool gives no information on symptoms leading to a changed nutrition intake. Further, a previous study has demonstrated that dietary changes are common in cancer patients and may influence nutritional status [12]. To address this information gap concerning both symptom load and dietary changes, we adapted our screening for patients at risk: based on the NRS-2002 [10] in combination with the G8 questionnaire [13], we extended our screening tool-box by recording symptom burden and dietary changes, as previously proposed [14]. We aimed to identify the older patients (age \(\geq\) 65 years) at risk and the extent of symptom burden as well as dietary changes. Knowledge of patients' diet and symptom load should then enable medical staff to optimize both nutrition and supportive cancer care.
# Waterproof Performance of Sealing Gasket in Shield Tunnel: A Review
## 6 Conclusions  
Throughout this article, it can be seen that the research on the waterproof sealing performance of shield tunnel joints is a worthy and in-depth subject. Scholars have carried out a considerable amount of productive research, from full-scale and reduced-scale specimen waterproofing tests to numerical simulations and other aspects. However, the following problems remain to be further probed:  
1. Current research mainly focuses on the sealing performance at normal temperature, and the deterioration evolution mechanism of sealing performance considering the variation of temperature is worth exploring.
2. The current aging mechanism of rubber is rather vague, and full-scale, long-term aging tests are not commonly conducted. Starting from the actual working conditions, the relationship between the gasket material aging and the waterproof ability deterioration of the shield tunnel can be obtained through the full-scale test, which will aid engineering practice.
3. The continuous-discontinuous numerical method of gasket waterproof capability is limited to the normal temperature scenarios while failing to cover extreme working conditions, such as fire, etc., which needs to be modified to consider the temperature effect.
4. Research on the thermal-mechanical coupling mechanical performance analysis model of shield tunnel lining structure and the joint is more in-depth and systematic, while the research on the thermal-mechanical coupling sealing performance analysis model is relatively rare.  
The waterproof performance of shield tunnel gaskets is affected by materials, structures, and external factors. With the growing number of shield tunnels with large burial depth, high water pressure, and long design life, more attention will be paid to the waterproof performance of the gaskets. The design and inspection of joint gaskets in engineering practice will benefit from research on material durability, structural characteristics, external hazards, and simulation methods.  
Investigation, C.G.; resources, C.G.; data curation, C.G.; writing--original draft preparation, C.G. and Y.W.; writing--review and editing, Y.W.; supervision, W.D., M.L. and C.S.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant number: 51908557, 52090083, 51978669).  
Not applicable.  
The authors declare no conflict of interest.
# Fault Diagnosis of Induction Motor Using Convolutional Neural Network
## 2 Experimental Environment for Fault Diagnosis of Induction Motor  
For fault diagnosis of induction motors, a simulator was constructed by using three three-phase induction motor to produce three states, namely, normal, rotor fault, and bearing fault, as shown in Figure 1. Figure 1-1 shows an induction motor in the normal state, Figure 1-2 an induction motor with rotor fault, and Figure 1-3 an induction motor with bearing fault. We acquired vibration data from these induction motors by using a vibration sensor and NI-9234 (National Instruments, TX, USA). The specifications of the induction motors used herein are shown in Table 1, and all three induction motors have the same specifications.  
As shown in Figure 2, two types of faults occur in induction motors: rotor fault and bearing fault. As shown in Figure 2a, the rotor bar of the induction motor is punctured with a drill, and the bearing is worn by inserting powder into it, as shown in Figure 2b.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model Number** & **HL105 AL202T1** \\ \hline Rated Power & 2.2 Kw (3 HP) \\ \hline Rated Current & 8.6/5.0 A \\ \hline Rated Speed & 1730 rpm \\ \hline Frequency & 60 Hz \\ \hline Voltage & 3 Phase 380 V \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of induction motor used herein.  
Figure 1: Experimental setup.
## 2 Case Study: Energy Harvesting Floor
### Energy Harvesting Circuit  
An electronic circuit was designed to collect and store the energy generated by the piezoelectric platform. An impedance matching interface is necessary to supply the maximum power to a load. The full bridge rectifier increased the efficiency of the energy conservation above 85%, see [15; 26]. [PERSON] et al. [27] improved the synchronized switch harvesting on an inductor (SSHI) interface for piezoelectric energy harvesting. A circuit based on a full bridge was built in this prototype. A resistive load with the same value as the rectifier output impedance was placed at the output to obtain the highest power transfer.  
According to the manufacturer of the PZT disk, it has an internal capacitance of 20 nF and an internal resistance of 370 \(\Omega\). Simulation Program with Integrated Circuits Emphasis (SPICE) was used to determine the value of the output impedance for frequencies from 0.01 to 2 Hz. Table 2 shows that the impedance varies slightly for this range.  
The load resistance that consumes the maximum power has the same value as the output impedance of the circuit. A parameter sweep analysis was carried out with the SPICE software to check the resistance value of the load that produced the maximum power transfer. Figure 2 shows the power in the load resistance for resistance values from 1 \(\Omega\) to 1000 \(\Omega\), where the maximum energy transfer corresponds to 381 \(\Omega\).  
An inline diode bridge B40C5000-3300, with a forward voltage drop of 1.1 V, was used in the platform (Figure 3). An external capacitor with a capacitance of 22 \(\upmu\)F was chosen according to [28].  
Figure 2: Load resistance for resistance values from 1 \(\Omega\) to 1000 \(\Omega\).  
This capacitor had sufficient capacitance to act as a filter at the rectifier output and to store the charge before supplying a load.
## 2 Materials and Methods
### Design Challenges and Future Trends  
There are several challenges in implementing IoMT systems regarding their security and sustainability. One of the main challenges is the privacy and protection of patient data since the information collected by monitoring devices can be susceptible and personal [23].  
Therefore, it is crucial to implement adequate security measures to protect the privacy and confidentiality of the data [20]. Another major challenge is the interoperability of devices and systems as IoMT systems often comprise various devices and platforms from different manufacturers. This can make it challenging to integrate and share data between them, limiting the effectiveness and usefulness of the system as a whole.  
Therefore, designing IoMT systems with open standards and common protocols is essential to ensure interoperability. Additionally, sustainability is another critical challenge for IoMT systems [12]. Monitoring devices and data analytics platforms requires power, which can be challenging in resource-constrained environments. Therefore, it is essential to design IoMT systems with efficient power management and to use sustainable power sources whenever possible to ensure the system's long-term sustainability.  
The challenges of implementing secure and sustainable IoMT systems are presented in Figure 1, where one of the main challenges is ensuring patient data security. IoMT systems collect sensitive medical information, so protecting this information from hacking and unauthorized access is crucial. Another major challenge is ensuring that different IoMT devices are compatible and can work together seamlessly [24]. Interoperability is also essential for integrating devices from other manufacturers and providing proper data transmission. IoMT systems generate a large number of data, which presents a challenge to manage; it is essential to ensure that data are stored securely and worked efficiently to ensure their availability and reliability [10]. Integrating IoMT systems with existing healthcare systems is challenging due to different architectures and technologies. It is essential to ensure that IoMT systems integrate seamlessly with existing systems to provide high-quality and seamless healthcare. The implementation also presents challenges in terms of maintenance and updates. It is essential to ensure that systems are kept up to date with the latest security and software updates to ensure their safe and effective operation.
# Design of Hybrid Phase Sliding Mode Control Scheme for Lower Extremity Exoskeleton
## 4 Conclusions  
In this article, a hybrid phase sliding mode control scheme is proposed to control double joint lower extremity exoskeleton, which is driven by artificial pneumatic muscle. First, the lower extremity exoskeleton phase models are established by the Euler-Lagrange method, respectively. The extended state observer and sliding mode controller in hybrid phase are designed together. This robust control method can adapt to the nonlinear and strong coupling characteristics of the model and provide excellent observation ability for internal or external disturbances. Secondly, the phase detector designed by the Extreme Learning Machine has strong gait-classification and phase-detection capability through offline learning, and then it helps the system adjust the corresponding controller by online identification. According to the phase detector, the controller and the plant switch at the same time. Finally, the smoothing method plays a role in avoiding the sudden-jump of the controller during the pre-conversion stage, which has practical significance for the exoskeleton. The simulation experiment proves that this method provides excellent control performance for the exoskeleton. Moreover, the contraction rate curve of the artificial pneumatic muscles verifies that the installation position of the pneumatic muscle 1-3 is reasonable.  
The major contribution of this study has great practical significance for lower extremity exoskeleton driven by pneumatic muscles, so this mechanism will be applied to the lower extremity exoskeleton to verify safety and comfort in the future.  
Resources, C.W.; Supervision, L.C. and J.W.; Validation, L.C., C.W., J.W. and X.S.; Writing--original draft, C.W.; Writing--review & editing, L.C., C.W. and J.W.  
This research was funded by National Natural Science Foundation of China (61703135, 61703134), Natural Science Foundation of Hebei Province (F2017202119), and 2019 Graduate Innovation Funding Project of Hebei Province (220056).  
The authors declare no conflict of interest.  
Figure 16: The trajectory of three pneumatic muscles during the assisting process.
# Vibration Analysis of a Piezoelectric Ultrasonic Atomizer to Control Atomization Rate
## 2 Ultrasonic Atomizer Fundamentals
### Piezoelectricity Theory  
Piezoelectric materials are a type of dielectric materials that can be polarized, and they respond in the presence of an electric field or mechanical stress. The piezoelectric effect is the generation of an electric charge due to an external force. Initially, the molecules of negative and positive charges are positioned so that the overall material is electrically neutral. However, when an external mechanical stress is applied, the internal structure can be deformed, thus causing displacement of the positive and negative centers of the molecule. As a result, small electrical dipoles are generated.  
According to the linear theory of piezoelectricity [13], the linear constitutive relationship to identify the coupling between mechanical stress, mechanical strain, electric field, and electric displacement is given as:  
\[\mathbf{\sigma}=\left[C^{E}\right]\cdot\mathbf{\varepsilon}-[\mathbf{\varepsilon}]\cdot \mathbf{E}, \tag{1}\]  
\[\mathbf{D}=[\mathbf{\varepsilon}]^{T}\cdot\mathbf{\varepsilon}+[\mathbf{\varepsilon}^{s}] \cdot\mathbf{E} \tag{2}\]  
where the superscript \(S\) indicates that the values are measured at a constant strain, and the superscript \(E\) means that they are measured at a constant electric field. In addition, \(\mathbf{\sigma}\) is the stress tensor, \(\mathbf{D}\) is the electric displacement vector, \(\mathbf{\varepsilon}\) is the strain tensor, \(\mathbf{E}\) is the electric field, \(\left[C^{E}\right]\) is the elastic constant at a constant electric field, \([\mathbf{\varepsilon}]\) is the piezoelectric stress coefficients, and \(\left[\mathbf{\varepsilon}^{S}\right]\) is the dielectric tensor at a constant mechanical strain. The conventional polarized ferroelectric ceramics used in ultrasonic transducers are governed by the constitutive expressions given in Equations (1) and (2) and by the equations of mechanical and electrical balance, respectively, such as:  
\[\rho\ddot{\mathbf{u}}=\
abla\cdot\mathbf{\sigma}, \tag{3}\]  
\[\
abla\cdot\mathbf{D}=0 \tag{4}\]  
To complete the description of the problem, the equations mentioned above are complemented by the appropriate boundary conditions. The behavior of the PZT controls the vibration modes of the thin plate, and these deformations give rise to the atomization process [14]. There are three different vibration modes in a piezoelectric hollow disc (ring) polarized in the thickness direction, which are thicknesses where the displacements of the upper and lower surfaces are in the opposite phase, radial where the inner and outer walls of the ring vibrate in the phase, and wall thickness direction where the inner and outer walls of the ring vibrate in the opposite phase. Since the outer radius is larger than the thickness in a thin ring, radial motion is induced due to Poisson's ratio. Therefore, the first modes are radial modes governed by radial boundary conditions. In this vibration mode, the inner and outer surfaces of the ring vibrate in the phase, and this movement is transmitted to the thin plate [15; 16].
# Comparative Analysis of Viscous Damping Model and Hysteretic Damping Model
## 3 Comparative Analysis of Steady Responses Based on Different Damping Models
### The Steady Response of Hysteretic Damped System  
Due to the harmonic wave, the time-domain equation for an SDOF system based on hysteretic damping model is expressed as [21]  
\[m\ddot{x}(t)-\eta kx^{\mathrm{H}}(t)+kx(t)=-mp\sin\theta t \tag{25}\]  
Due to the harmonic wave, the steady response of an SDOF system is assumed as  
\[x(t)=E\sin\theta t+F\cos\theta t \tag{26}\]  
where \(E\) and \(F\) are undetermined coefficients.  
Based on Equation (26), \(x^{\mathrm{H}}(t)\) can be obtained as  
\[x^{\mathrm{H}}(t)=-E\cos\theta t+F\sin\theta t \tag{27}\]  
The resisting force of the hysteretic damping model includes the elastic force and damping force. The hysteresis loop of the resisting force is shown in Figure 4a, and the hysteresis loop of the damping force is shown in Figure 4b. The area of the ellipse is the dissipated energy in Figure 4b. Based on the hysteretic damping model, the dissipated energy in each cycle is  
\[W_{\mathrm{h}}=-\int_{0}^{\frac{2\pi}{\theta}}\eta kx^{\mathrm{H}}(t)\mathrm{d }x(t)=\pi\eta kA^{2} \tag{28}\]  
According to Equation (27), the dissipated energy in each period is not related to the excitation frequency based on the hysteretic damping model. The characteristic of the hysteretic damping model is different from that of the viscous damping model.  
Figure 3: The hysteresis loop of damping force based on viscous damping model.  
Substituting Equation (26) into Equation (25), the undetermined coefficients are expressed as  
\[\left\{\begin{array}{l}E=\frac{-(\omega^{2}-\theta^{2})p}{(\omega^{2}-\theta^{2} )^{2}+\eta^{2}\omega^{4}}\\ F=\frac{\eta\omega^{2}p}{(\omega^{2}-\theta^{2})^{2}+\eta^{2}\omega^{4}}\end{array}\right. \tag{29}\]  
The vibration amplitude can be expressed as  
\[Q_{\text{h}}=\sqrt{E^{2}+F^{2}}=\sqrt{\frac{P}{\left(\omega^{2}-\theta^{2} \right)^{2}+\eta^{2}\omega^{4}}} \tag{30}\]
# Stochastic Extreme Wind Speed Modeling and Bayes Estimation under the Inverse Rayleigh Distribution
## 2 A Stochastic Risk Analysis for Extreme Wind Speed Data under the IRD Model
In Figure 1, three typical IRD PDF and CDF curves corresponding to the analytical expressions of (1) and (2) are shown, characterized by three different values of the parameter \(\alpha\) (15, 45, and 90) (Such values of \(\alpha\) correspond, as can be shown afterwards, to the following values of the median (50 th percentile) of the wind speed RV: 5, 8, and 11 m/s respectively). The values on the \(x\)-axis are WS values in m/s. The expression of the RI assuming the IRD as the reference model becomes the following:  
\[R(t,z)=1-u(t,z)=1-e^{-\varphi t[1-e^{-(\frac{\alpha}{2})}]} \tag{11}\]  
A set of possible curves of the RI in time are depicted in Figure 2, assuming a value \(\alpha=90\) (corresponding to a median value of 11 m/s for the EWS); a mean frequency of the process N(\(t\)): \(\phi=10\)/year; and three different threshold values \(z\) of (22, 33, and 44) m/s, i.e., two, three, and four times the median value. From the three curves, it is apparent--as simply deducible from the meaning of the RI as well as from the general expression of (10)--that the RI always increases with time (given the scale parameter value of \(\alpha\) and the threshold value \(z\)), while it decreases for a given time interval if \(z\) increases (allowing for a greater maximum value \(z\) implies decreasing the probability that such a value is exceeded). Figure 1: Various inverse Rayleigh PDFs (**left**) and cumulative distribution functions (CDFs) (**right**) with three different values of the parameter \(\alpha\). Figure 2: The risk index (RI), r (\(t\),\(z\)), vs. time under an inverse Rayleigh distribution (IRD) model for three different threshold value \(z\) in m/s. Finally, as some figures will confirm in a following section--holding fixed the values of time t and threshold value \(z\)--the RI always increases with increasing values of scale parameter \(\alpha\), since both median and mean values (as also apparent from Figure 1) increase with \(\alpha\). Therefore, the PDF curve shifts to the right for increasing values of the parameter \(\alpha\), thus yielding a higher probability that a given threshold value \(z\) is exceeded, which means a higher value of the risk index in any given time interval.
## 3 Methodology
### Fuzzy EDAS
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Alternatives**} & \multicolumn{6}{c}{**pda**} \\ \cline{2-7}  & \multicolumn{2}{c}{**C\({}_{1}\)**} & \multicolumn{2}{c}{**C\({}_{2}\)**} & \multicolumn{2}{c}{**C\({}_{3}\)**} & \multicolumn{2}{c}{**C\({}_{4}\)**} & \multicolumn{2}{c}{**C\({}_{5}\)**} \\ \hline Brick facade & A1 & \(-\)0.19, 0.21, & \(-\)0.78, \(-\)0.55, & 0.69, 0.92, & \(-\)0.82, \(-\)0.72, & \(-\)0.37, \(-\)0.07, \\  & \(\phantom{-}\)0.49 & \(-\)0.38 & 1.01 & \(-\)0.58 & 0.24 \\ \hline Stone facade & A2 & \(\phantom{-}\)1.08, 1.36, & \(-\)0.67, \(-\)0.44, & 0.34, 0.56, & \(-\)0.71, \(-\)0.42, & \(-\)0.66, \(-\)0.40, \\  & \(\phantom{-}\)1.61 & \(-\)0.21 & 0.75 & \(-\)0.20 & \(-\)0.08 \\ \hline Coatings cement facade & A3 & \(-\)0.65, \(-\)0.29, & 1.22, 1.39, & \(-\)0.21, \(-\)0.02, & 1.32, 1.47, & 1.44, 1.68, \\  & \(\phantom{-}\)0.02 & 1.59 & 0.30 & 1.56 & 1.78 \\ \hline Composite facade & A4 & \(-\)0.82, \(-\)0.56, & 0.03, 0.31, & \(-\)0.90, \(-\)0.69, & 0.08, 0.29, & \(-\)0.84, \(-\)0.69, \\  & \(\phantom{-}\)0.14 & 0.51 & \(-\)0.47 & 0.54 & \(-\)0.50 \\ \hline Curtain wall & A5 & \(\phantom{-}\)0.87, \(-\)0.72, & \(-\)0.83, \(-\)0.72, & \(-\)0.90, \(-\)0.77, & \(-\)0.79, \(-\)0.61, & \(-\)0.84, \(-\)0.52, \\  & \(\phantom{-}\)0.53 & \(-\)0.49 & \(-\)0.62 & \(-\)0.39 & \(-\)0.17 \\ \hline \hline \multicolumn{7}{c}{**nda**} \\ \cline{2-7}  & \multicolumn{2}{c}{**C\({}_{1}\)**} & \multicolumn{2}{c}{**C\({}_{2}\)**} & \multicolumn{2}{c}{**C\({}_{3}\)**} & \multicolumn{2}{c}{**C\({}_{4}\)**} & \multicolumn{2}{c}{**C\({}_{5}\)**} \\ \hline Brick facade & A1 & \(\phantom{-}\)0.49, \(-\)0.21, & 0.38, 0.55, & \(-\)1.01, \(-\)0.92, & 0.58, 0.72, & \(-\)0.24, 0.07, \\  & \(\phantom{-}\)0.19 & 0.78 & \(-\)0.69 & 0.82 & 0.37 \\ \hline Stone facade & A2 & \(\phantom{-}\)1.61, \(-\)1.36, & 0.21, 0.44, & \(-\)0.75, \(-\)0.56, & 0.20, 0.42, & 0.08, 0.40, \\  & \(\phantom{-}\)1.08 & 0.67 & \(-\)0.34 & 0.71 & 0.66 \\ \hline Coatings cement facade & A3 & \(-\)0.02, 0.29, & \(-\)1.59, \(-\)1.39, & \(-\)0.30, 0.02, & \(-\)1.56, \(-\)1.47, & \(-\)1.78, \(-\)1.68, \\  & \(\phantom{-}\)0.65 & \(-\)1.22 & 0.21 & \(-\)1.32 & \(-\)1.44 \\ \hline Composite facade & A4 & \(\phantom{-}\)0.14, 0.56, & \(-\)0.51, \(-\)0.31, & 0.47, 0.69, & \(-\)0.54, \(-\)0.29, & 0.50, 0.69, \\  & \(\phantom{-}\)0.82 & \(-\)0.03 & 0.90 & \(-\)0.08 & 0.84 \\ \hline Curtain wall & A5 & \(\phantom{-}\)0.53, 0.72, & 0.49, 0.72, & 0.62, 0.77, & 0.39, 0.61, & 0.17, 0.52, \\  & \(\phantom{-}\)0.87 & 0.83 & 0.90 & 0.79 & 0.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Negative distance from average (nda) and positive distance from average (pda). The defuzzification method that was used for a triangular fuzzy number -\(\widetilde{A}=(a_{1},a_{2},a_{3})\)- in this case study was as follows:  
\[\kappa(\widetilde{\mathbf{x}})=\frac{a_{1}+2\times a_{2}+a_{3}}{4} \tag{16}\]