esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermal Buckling and Vibration Analysis of SMA Hybrid Composite Sandwich Beams
## 5 Conclusions  
The present work has focused on the buckling and free vibration response of a simply supported sandwich beam with an FGM core and external layers reinforced with SMA wires, immersed in a thermal environment. The proposed approach, based on a higher order theory and a variational energy formulation, has been verified in its reliability against predictions from literature, with an accurate response. After this preliminary validation, a systematic study followed for different input parameters, with interesting concluding remarks that could serve as theoretical benchmarks for a further computational treatment of the topic. More specifically, critical thermal buckling has been found to increase for both linear and uniform distributions, by increasing the initial strain field and the reinforcement volume fraction. At the same time, the critical thermal buckling for TD properties is always higher than the one associated with TID properties, especially for higher thicknesses of the structural member. An increased value of \(n\) and layer thickness, together with a decreased length-to-thickness ratio, caused an overall enhancement of the critical thermal buckling, which is, in turn, affected by the different thermal distributions assumed in the problem. In detail, the critical thermal buckling, for a linear thermal distribution, always achieves higher values than a uniform distribution. As far as the vibration response is concerned, we have determined the first dimensionless frequency response of the sandwich beam, whose value has been revealed to increase for higher values of \(\varepsilon_{0}\), \(V_{SMA}\), and \(n\). Moreover, TD and TID properties can affect the first dimensionless frequency, so that the first dimensionless frequency of TID seems to be higher than TD.  
Figure 14: Variation of the first dimensionless frequency vs. temperature for both TD and TID properties, different SMA volume fraction powers, and a uniform temperature distribution (\(n=1\), \(h_{c}/h=0.5\) and \(\varepsilon_{0}=2\%\)).  
**Author Contributions:** Conceptualization, R.D. and F.T.; Data curation, M.N. and S.S.J.; Formal analysis, M.N., S.S.J., R.D. and F.T.; Investigation, M.N., S.S.J., R.D. and F.T.; Methodology, M.N., S.S.J., R.D. and F.T.; Supervision, R.D. and F.T.; Validation, R.D. and F.T.; Writing--original draft, M.N. and S.S.J.; Writing--review & editing, R.D. and F.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Proposed Method
### Tracking Frequency Block  
The Tracking Frequency block (Figure 3) tracks a peak in the current spectrum and outputs the frequency of the peak. The block diagram of Tracking Frequency is shown in Figure 6. This scheme is the same as the scheme of a Phase Locked Loop (PLL). There are four elements: the Voltage Controlled Oscillator (VCO), the Multiplier or Phase Detector, the Low Pass Filter, and the Adder. These four elements are the typical elements present in a PLL [32].  
The Tracking Frequency block has three inputs: the current, \(i\), the central frequency, \(f_{0}\), and the _reset_, and one output: the frequency of the peak that has been found, \(f_{m}\).  
When the reset is enabled, the Tracking Frequency block tracks the peak in the spectrum nearest to \(f_{0}\), and when the reset is disabled, it tracks the peak nearest to the one detected in the previous iteration.  
In the Tracking Frequency block, the VCO generates a sinusoidal signal with frequency \(f_{m}\), that is the frequency of the peak detected in the previous iteration. The Multiplier block multiplies the current by the signal generated by the VCO. The Low Pass Filter gets the dc component and this value is related to the frequency of the peak. The Adder adds \(f_{0}\) to the dc component that was obtained by the Low Pass Filter.  
The Low Pass Filter proposed and used in this work has the following transfer function:  
\[F(s)=(1+s\tau_{2})/(s\tau_{1})\ =\tau_{2}/\tau_{1}+(1/\tau_{1})(1/s) \tag{15}\]  
where \(\tau_{1}\) and \(\tau_{2}\) are the time constants of the filter and \(s\) is the variable of the Laplace transform. The second part of the transfer function can be implemented digitally. For this work, the integrator \(1/s\) was implemented with the trapezoidal rule as:  
\[v[n]=v[n-1]+0.5(u[n]+u[n-1])dt \tag{16}\]  
where \(v\) is the output of the integrator, \(u\) is the input, \(n\) is the present discrete time and \(dt\) is the sample time or the inverse of the sampling frequency.  
When the _reset_ is enabled, the only element that is affected is the Low Pass Filter which is reset to its initial status. In the proposed filter, this is done by resetting the integrator to \(v[n]=0\).  
This filter creates a PLL of Type II and order 2. Because the Tracking Frequency is like a PLL, full analysis of it can be found in any manual or book on PLLs [32].  
Figure 6: Block diagram of the Tracking Frequency. VCO: Voltage Controlled Oscillator.
# Segmentation of River Scenes Based on Water Surface Reflection Mechanism
## 3 Results and Discussion  
The proposed algorithm was tested on a dataset made up of 500 images taken from different river scenes. These scenes were divided into simple scenes(110) and complex scenes(390) to test the performance of different methods under both general and special conditions. The simple scenes in this study refer to general and common outdoor river scenes that do not contain complex issues such as shadow and intense sunlight reflections, while the complex scenes are the opposite. Moreover, given that the image sensors used in different scenes are likely to be various, the images we used include different resolutions.  
In the experiments, two principal river segmentation methods utilizing image features [8] and edge detection [9], respectively, were compared with our method. It should be noted that, because the proposed algorithm is designed specifically for river scenes, the general image segmentation algorithm using Deep Learning has high requirements on data sets and operational capabilities, so it is beyond the range of comparison. The running environment in this study was--Python3 in MacOS system with 2.9 GHz Intel Core i5 CPU, 16 GB memory. The algorithm's parameters were set to fixed value empirically in advance. Further discussion about the parameters is in this section later.
## 2 PEMFC Power Supply Modeling
### Model of PEMFC Power Supply System  
The circuit diagram of the power supply is shown in Figure 1. It is composed of an inductor \(L\), a capacitor \(C\), an output load \(R\), a MOSFET switch \(S\), and a diode \(D\). In addition, \(v_{fc}\), \(i_{o}\), and \(v_{o}\) represent stack voltage, load current, and load voltage, respectively.  
The average model of PEMFC-fed DC/DC buck converter is described as:  
\[\left\{\begin{array}{l}\frac{di_{l}}{dt}=\frac{1}{L}\Big{(}uv_{fc}-v_{o} \Big{)}\\ \frac{dv_{o}}{dt}=\frac{1}{L}\big{(}i_{L}-\frac{v_{o}}{R}\big{)}\end{array}\right. \tag{4}\]  
where \(i_{L}\) is inductive current and \(v_{o}\) is the output load voltage. \(u\) represents the switch status, ranging from 0 (switch is OFF) to 1 (switch is ON).  
The following assumptions are introduced to simplify the plant for the control-orientation.  
**Assumption 1**.: _The inductance is large enough to provide continuous inductive current to ensure the system is operating under a continuous conduction mode (CCM)._  
Figure 1: Circuit diagram of PEMFC power supply.  
**Assumption 2**.: _When the MOSFET \(S\) is on, the resistance of it is zero while the diode \(D\) has no drop voltage._  
**Assumption 3**.: _All the passive elements are ideal._  
**Assumption 4**.: _The working pressure of PEMFC is presented at \(P_{H_{2}}\) = 20,265 Pa, \(P_{Air}\) = 101,325 Pa. In addition, \(T\) = 333.15 K, a high efficiency operation temperature is used._  
In practice, there may be parametric uncertainties and disturbance injections existing in the average model. A practical model is considered as [39]:  
\[\left\{\begin{array}{l}\frac{d\dot{t}_{L}}{dt}=\frac{1}{t_{0}+\Delta L}(ug( ui_{L})-v_{o})\\ \frac{dv_{o}}{dt}=\frac{1}{C_{0}+\Delta C}\left(\dot{t}_{L}-\frac{v_{o}}{R_{0}+ \Delta R}\right)\end{array}\right. \tag{5}\]  
where \(L_{0}\), \(C_{0}\), and \(R_{0}\) are the nominal values of \(L\), \(C\), and \(R\), respectively. \(\Delta L\), \(\Delta C\), and \(\Delta R\) are variations of \(L\), \(C\), and \(R\), respectively.  
**Assumption 5**.: _The variations of \(L\), \(C\), and \(R\) are not measurable and unknown. \(\Delta L\), \(\Delta C\), and \(\Delta R\) are bounded by positive constants \(L_{m}\), \(C_{m}\), and \(R_{m}\), respectively. That is:_  
\[|\Delta L|\leq L_{m},\quad|\Delta C|\leq C_{m},\quad|\Delta R|\leq R_{m} \tag{6}\]  
The parameters of DC/DC buck converter and PEMFC system are listed in Table 1.
## 1 Introduction
To further minimize the damage caused by water supply restriction through efficient dam operation, a new standard for the operation of multi-purpose dams is also required. Multi-purpose dams have been previously studied in South Korea; previous studies have addressed dam operation activities via simulated operation or optimal operation [7; 8; 9; 10; 11]. Other studies have evaluated the water supply capacity of reservoirs [12; 13; 14]. However, in terms of water supply, a standardized method for assessing water supply safety does not exist. Moreover, drought assessment currently requires only the calculation of the total amount of water shortage, while the duration of drought has not been considered [15]. Fundamentally, drought plans are developed to reduce the risk of water shortages that threaten the stable water supply in the future. In this process, drought indices for the quantification of the drought severity are used to express the spatio-temporal state of drought [16; 17; 18]. Moreover, previous studies have considered the supply and demand of water for dam management [19], while indices that consider other hydrological conditions have also been developed and applied to dam operation [20; 21]. In this study, climate change scenarios of the future were applied to the termination criteria at each drought response stage. The analysis was conducted for the watershed of the area in the Nakdong River Basin of South Korea. The analysis considered the maintenance period of the real-time Standard Flow Index (SFI), the maintenance period of reservoir storage volume, and the indicators for assessing water supply capacity. The applicability of the termination criteria by drought response stage was also evaluated for the selected watershed.
# Adaptive BBU Migration Based on Deep Q-Learning for Cloud Radio Access Network
## 1 Introduction
It dynamically learns from past migrations, avoids unnecessary movement, and optimizes VM placement in real-time, making it the optimal strategy for Cloud-RAN dynamic migration strategies. The remainder of the paper is organized as follows: Section 2 presents literature reviews of works on migration. Section 3 provides a detailed explanation of the system model with parameter definitions. Section 4 illustrates the proposed migration model. The evaluation results using iCanCloud are shown in Section 5, followed by a conclusion in Section 6.
# Shape Memory Polyurethane and its Composites for Various Applications
## 2 Shape Memory Polyurethane in Different Applications
### Self-Healing  
Designing polymeric materials mimicking the characteristics of living organisms which constitute the prevention from outer attack, self-degradation, etc., has attracted the focus of the scientific community. Polymeric materials with self-repairing capability can enhance safety, reliability, and the quality of humans. Inspired from nature, scientific groups have tried to develop materials which have a self-healing or self-repairing property. A detailed discussion on the principle of healing and crack-closing in polymers and their composites can be found elsewhere [52]. It is known that most polymeric materials are based on covalent bonds and are naturally limited not to regain initial strength after damage due to random reason [53] or they may have a non-covalent bond or act as host guest polymer [54] or an external stimuli driven dynamic covalent bond may characterize the polymer with self-healing ability [55; 56; 57; 58]. Several research works are listed in Table 1 along with self-healing, shape memory, and mechanical properties. The self-healing capability along with shape memory properties can be an additional feature for any polymer for several applications. However, as the shape memory property is a physical phenomenon and does not involve any chemical bond brakeage and formation, it may not be fully possible to recover 100% of damage. [PERSON] et al. [59] have utilized microcapsules in bulk technique to produce SMP comprising self-healing ability called cooperative self-healing. They produced the microcapsules of a healing agent (Alodine 5200, mainly containing hexafluorotitanic acid (H\({}_{2}\)TiF\({}_{6}\)) and hexafluorozirconic acid (H\({}_{2}\)ZrF\({}_{6}\))) by coating a layer of polyurethane (toluene diisocyanate and 1,4-butanediol based polyurethane) over healing agent as core. The SMP coating on the aluminum alloy (substrate) with self-healing ability was developed by adding polyurethane coated Alodine capsules into PEG based SMP using a bar coating at an elevated temperature. The capsules present in the bulk of the polyurethane break due to generation of defects and react with the substrate and prevent the substrate to be corroded. Cooperative self-healing is completed upon heating by triggering shape recovery (79.6%). The entire cooperative process of self-healing with shape memory property is illustrated in Figure 9.  
Figure 8: Schematic representation of self-fitting shape memory polymer foam as scaffold for bone regeneration and growth after surgery [48].
# Applications of Capacitive Imaging in Human Skin Texture and Hair Analysis
## 1 Introduction
In a different scientific field, [PERSON] et al. [14] employed ATR FT-IR spectroscopy to differentiate between dyed and undyed hair. Such information can be used in forensic hair analysis, given their static classification was found to be \(98.1\pm 3.0\%\) accurate in detecting whether a sample is dyed or not, but also in identifying the brand and the color of the dye. In the field of cosmetic science, [PERSON] et al. [15] used thermogravimetric methodology to measure the water content of hair and to assess hair damage from bleaching and straightening. They found that bleached hair shown 3.9% reduced water holding capabilities while the straighten hair 9.5%. In this work, a capacitive imaging system is used to extract information from skin and hair samples. To the best of our knowledge, [PERSON] and [PERSON] [16] first used this technology for human skin characterization and surface hydration mapping in 2003. They used a fingerprint system to measure the distribution of skin surface capacitance in different body sites, to detect main microrelief  
Figure 1: Human skin [5] and hair anatomy [6]. orientation and density as well as to support the system's usability in the field of skin research by performing side-by-side experiments with Corneometer CM812. Later on, [PERSON] et al. [17] demonstrated advantages of such technology over other skin hydration apparatuses by pointing out the importance of visually observing the contact of a capacitive sensor with the skin during lips moisturization and volar forearm inflammation experiments. Since then, capacitive imaging systems have been used in order to examine various skin conditions e.g., mapping of psoriasis and acne lesions, assessment of sun exposure, skin aging, damage or irritation [18; 19; 20; 21; 22]. Furthermore, it is worth mentioning successful attempts to improve the capacitive imaging technology. [PERSON] and [PERSON] [23] achieved depth profiling up to 50 \(\upmu\)m by fusing the image with pressure information monitored by a subsystem attached on the back of the measurement apparatus. Also, [PERSON] et al. developed a wearable capacitive imaging system using an \"ultrathin, stretchable sheets with arrays of embedded impedance sensors for precise measurement and spatially multiplexed mapping\" [24]. In the following sections, the measurement principle of capacitive imagining for non-invasive skin and hair measurements is analyzed. Then, image processing algorithms are suggested to extract quantitative values from skin and hair samples. Finally, three experiments are conducted to evaluate both the imaging system but also the integrity of the selected algorithms.
###### Abstract  
Pulmonary arterial hypertension is a severe, progressive disease in children, that causes right ventricular dysfunction over time. Tissue motion annular displacement is a novel speckle-tracking derived echocardiographic parameter used in assessing ventricular function. The aim of our study was to determine the prognostic value of this echocardiographic parameter in children with pulmonary arterial hypertension. We conducted a case-control study by assessing twenty children with pulmonary arterial hypertension (idiopathic or secondary) and twenty age- and sex-matched controls, using clinical (WHO functional class, 6-min walking test), laboratory (brain natriuretic peptide level) and echocardiographic parameters (conventional and speckle-tracking derived tissue motion annular displacement) at enrolment and after one year of follow-up. According to their WHO functional class altering after one year, the pulmonary arterial hypertension patients were divided into two groups: non-worsening (eleven) and worsening (nine). The conventional echocardiographic parameters and all measured tricuspid tissue motion annular displacement indices (lateral, septal, midpoint and midpoint fractional displacement--TMADm%) were significantly lower in both pulmonary arterial hypertension groups (non-worsening and worsening) compared to controls. Comparing the worsening and non-worsening groups, only the TMADm% and brain natriuretic peptide level was significantly lower in worsening in comparison with non-worsening pulmonary arterial hypertension children (\(p\) = 0.010 and \(p\) = 0.018, respectively). In receiver-operating characteristic curve analysis, we found a cut-off value of 16.15% for TMADm% and a cut-off value of 34.35 pg/mL for the brain natriuretic peptide level that can predict worsening in pulmonary arterial hypertension children. In conclusion, tricuspid annulus midpoint fractional displacement, an angle-dependent speckle-tracking derived parameter, could be a good additional parameter in the assessment of the longitudinal right ventricular systolic function and in prediction of clinical worsening in children with pulmonary arterial hypertension.  
right ventricle; speckle-tracking; tricuspid tissue motion annular displacement; pulmonary arterial hypertension; children +
Footnote †: journal: Applied Sciences
## 3 Effects of F-T Cycles on the Mechanical Properties and Microscopic Parameters  
### Effect of F-T Cycles on Shear Strength and Its Parameters  
By analyzing the shear test results of samples after different confining pressures during F-T cycles on the basis of the Moore criterion, the effect of F-T cycles on the shear strength parameters and the relationship between shear strength and F-T cycles are shown in Figures 2 and 3, respectively.  
Figure 1: Magnification 100\(\times\) microscopic images (**a**) S0 and (**b**) S2.  
Figure 2a shows that the cohesion of S0 and S2 decreased with the number of freezing and thawing. The cohesion dropped sharply after the first F-T cycle and gradually became steady after the fourth F-T cycle. The cohesion of S2 was at least 3.55 times greater than that of S0, indicating that the incorporation of cement allowed the soil to maintain high cohesion after F-T and enhanced the resistance to F-T cycles of the soil. From Figure 2b, the internal friction angles of S0 and S2 had no obvious regularity with the number of F-T; however, the internal friction angle of S2 was larger than that of S0, which was contributed by the improvement of soil gradation due to the hydration reaction of cement.  
From Figure 3, the shear strength of S0 and S2 decreased as the F-T cycles continued, and they both gradually stabilized after the fourth freezing and thawing. The shear strength increased with the increase of confining pressure. Comparing Figure 3a with Figure 3b, the shear strength of S2 was at least 2.79 times greater than that of S0 under F-T. This manifested that the addition of cement, which generated the skeleton structure through the hydration reaction, enhanced the cementation ability and the agglomeration force of the soil particles and could greatly improve the shear strength and enhance the frost resistance of soil.  
Figure 3: The relationship curve of F-T cycles and shear strength: (**a**) S0 and (**b**) S2.  
Figure 2: The effect of freeze-thaw (F-T) cycles on (**a**) cohesion and (**b**) internal friction angle.
# Centralized MPPT Control Architecture for Photovoltaic Systems Using LoRa Technology
## 3 Experimental Setup
### Control System and Wireless Implementation
#### 3.3.2 P&O Controller  
The P&O controller pseudo-code is shown in Algorithm 1. For the implementation of the P&O, some of the values had to be defined, including the initial value \(d_{\text{init}}\), which assumes a value of 0.5, or the step size \(\Delta d\), which was tuned to a value of 0.01. In tandem, the duty cycle was constrained within an upper limit of \(d_{\text{max}}\) and a lower limit of \(d_{\text{min}}\), with predefined values of 0.9 and 0.1, respectively. These limits ensure that the converter is always in continuous conduction mode.  
```
Define \([d_{\text{init}},\,d_{\text{min}},\,d_{\text{max}},\,\Delta d]\)  Initialize \([P_{\text{prev}},\,V_{\text{prev}}]\) \([I_{\text{pv}},\,V_{\text{pv}},\,P_{\text{pv}},\,d]\leftarrow\text{read PV panel information}\) \(P_{\text{pv}}\leftarrow\)\(V_{\text{pv}}*I_{\text{pv}}\) \(\Delta P\leftarrow\)\(P_{\text{pv}}-P_{\text{prev}}\) \(\Delta V\leftarrow\)\(V_{\text{pv}}-V_{\text{prev}}\)  Verify the operating equations: if\(\Delta P<0\)then if\(\Delta V<0\)then \(d\gets d-\Delta d\) elseif\(\Delta V>0\)then \(d\gets d+\Delta d\) endif endif  Check if\(d\) is in the range of \(d_{\text{min}}\) and \(d_{\text{max}}\)  Update \(P_{\text{prev}}\) and \(V_{\text{prev}}\) with the values of \(P_{\text{pv}}\) and \(V_{\text{pv}}\), respectively
```  
**Algorithm 1** P&O controller