esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Investigation of Thermal Efficiency of a Solar Cell
## 2 Materials and Methods  
### Experimental Setup  
#### 2.1.1 Description of the Experimental Setup  
An experimental setup was designed and fabricated at 18.7518\({}^{\circ}\) N, 73.5514\({}^{\circ}\) E and positioned at the roof to investigate the effect of water-based cooling on the voltage-current (VI) characteristics of the PV collector (Figure 1). Abundant availability and many applications of water attracted attention as a heat transfer medium. Anodized aluminum of 3 mm thickness and 82% reflectivity was used for the fabrication of the heat exchanger. The heat exchanger of square cross section was preferred because of high thermal conductivity, better surface contact, and its light weight.  
The rectangular flow design improved surface contact time with PV that can be supportive of extracting maximum heat from the PV module. A commercial Si-based multi-crystalline PV module of 50 W capacity and 0.55 m \(\times\) 0.65 m size was under investigation, and the heat exchanger was attached at the back of the PV module. The insulation was provided from the backside to avoid thermal loss by convection. Water flows from the tank through the pipe, and the flow rate was measured by an acrylic rotameter (\(\pm\)3%). The water passed through a heat exchanger and extracts heat of the PV module (Figure 2).  
K-type thermocouples with an accuracy of 0.7 \({}^{\circ}\)C were placed at various locations, such as the tank, the inlet to the heat exchanger, the outlet of the heat exchanger, and the surface of the PV module, and were connected to the temperature acquisition system. The VI characteristics of the PV module are recorded with a digital multi-meter. The MECO-made digital solar meter (\(\pm\)10 W/m\({}^{2}\)) was used to measure solar irradiance falling on the PV module. PV modules with supporting instruments were fixed on the supporting stand at a height in the east-west direction to face the sun with no obstruction. The wind speed over the collector was measured with a vane-type anemometer PCE-VA 11 with \(\pm\)3% uncertainty.  
Figure 1: Experimental setup.
Sensors_**2014**, _14_, 24046-24097; doi:10.3390/s141224046  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Review_  
**A Survey of Middleware for Sensor and Network Virtualization**  
**[PERSON] ***, [PERSON] and [PERSON]. [PERSON]**  
UTM-MIMOs Centre of Excellence in Telecommunication Technology, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Malaysia;  
E-Mails: [EMAIL_ADDRESS] (N.F.); [PERSON]SS] (M.R.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +6-111-629-6102; Fax: +6-07-556-6272.  
External Editor: [PERSON]  
_Received: 2 September 2014; in revised form: 1 December 2014 / Accepted: 2 December 2014 / Published: 12 December 2014_  
**Abstract:** Wireless Sensor Network (WSN) is leading to a new paradigm of Internet of Everything (IoE). WSNs have a wide range of applications but are usually deployed in a particular application. However, the future of WSNs lies in the aggregation and allocation of resources, serving diverse applications. WSN virtualization by the middleware is an emerging concept that enables aggregation of multiple independent heterogeneous devices, networks, radios and software platforms; and enhancing application development. WSN virtualization, middleware can further be categorized into sensor virtualization and network virtualization. Middleware for WSN virtualization poses several challenges like efficient decoupling of networks, devices and software. In this paper efforts have been put forward to bring an overview of the previous and current middleware designs for WSN virtualization, the design goals, software architectures, abstracted services, testbeds and programming techniques. Furthermore, the paper also presents the proposed model, challenges and future opportunities for further research in the middleware designs for WSN virtualization.  
**Keywords:** sensor virtualization; network virtualization; middleware design; WSN
### Power Amplifier  
A schematic diagram of the power amplifier is shown in Figure 2. A class-F [11] condition is employed for high efficiency. The output matching circuit was terminated by short for even harmonics as well as open for odd harmonics at a drain terminal of the device. The device used in this amplifier was GaN-HFET (MACOM MAGE-102425-300S00, Lowell, MA, 01851 USA). It is necessary to terminate up to the 3 rd harmonics by considering the high frequency performances. In Figure 2, a short stub T\({}_{\text{d1}}\) was designed as a quarter-wavelength for the fundamental frequency, which becomes a half-wavelength for the 2 nd harmonics. Transmission lines T\({}_{\text{d2}}\), T\({}_{\text{d3}}\), short stub T\({}_{\text{d4}}\), and shunt capacitance C\({}_{\text{d1}}\) consists of a low-pass filter, which becomes open at the 3 rd harmonics and also achieves a complete impedance matching at the fundamental frequency. Moreover, transmission lines T\({}_{\text{g2}}\), T\({}_{\text{g3}}\), short stub T\({}_{\text{g1}}\), and shunt capacitance C\({}_{\text{g1}}\) consist of a low-pass filter, which plays the same role as the output circuit.  
Figure 1: Schematic diagram of the feedback oscillator.  
The circuit simulation has been done for the schematic diagram of Figure 2 by using ADS (Advanced Design System) and a nonlinear model name of \"EEHEMT\". A supply voltage is +50 V and \(\mathrm{I_{dq}}\) is 800 mA. The simulated power, efficiency and gain were plotted in Figure 3. A small-signal gain of greater than 17 dB, a \(\mathrm{P_{2 dB}}\) of 54.4 dBm (275 W) and a drain efficiency of 62.3% have been successfully accomplished at 2.45 GHz. Moreover, the measured data using LDMOS FETs (Field Effect Transistors) [9] are newly added in Figure 3 for making clear the difference between them. A magnetron oscillator has an output power of 800 W and an efficiency of 66% [12].
## 4 Calculation Results
### Effect of Auxiliary Parameter \(\mathds{h}\) on Convergence  
When \(n=3,\varepsilon=8/\pi^{2}\), the 3 rd-order approximation of \(\theta_{b}\) by IHAM is:  
\[\begin{split}&\theta_{b}=0.5\alpha+\left(0.12014\mathds{h}+0.11 145\mathds{h}^{2}+0.037151\mathds{h}^{3}\right)\alpha^{3}\\ &+\left(0.016031\mathds{h}^{2}+0.010687\mathds{h}^{3}\right) \alpha^{5}+9.5862\times 10^{-4}\mathds{h}^{3}\alpha^{7}\end{split} \tag{87}\]  
From Equation (87), the 3 rd-order approximation of \(\Theta_{b}\) with \(\mathds{h}=-1/2\) is calculated as:  
\[\Theta_{b}=0.31831\alpha-2.3458\times 10^{-2}\alpha^{3}+1.7010\times 10^{-3} \alpha^{5}-7.6284\times 10^{-5}\alpha^{7} \tag{88}\]  
When the relative error \(\Delta\leq 1\%\), the convergent region of the 3 rd-order approximation by IHAM is:  
\[\alpha\in[0,2.0],\ \Theta_{b}\in[0,0.49], \tag{89}\]  
In the case of \(n=3\), \(\varepsilon=8/\pi^{2}\), when \(\mathds{h}=-0.6\), \(\mathds{h}=-0.4\) and \(\mathds{h}=-0.5\) respectively, the 3 rd-order \(\Theta_{b}^{[n-3]}\) by IHAM are compared and with the exact solution in Table 3.  
By IHAM with only three calculation iterations, the convergence region of \(\Theta_{b}\) is derived to be \(0<\alpha\leq 0.6\), \(0<\alpha\leq 2.0\), and \(0<\alpha\leq 1.6\) when \(\mathds{h}=-0.4\), \(\mathds{h}=-0.5\), and \(\mathds{h}=-0.6\), respectively. It is observed from Table 3 that the convergence region expands by 233% when \(\mathds{h}=-0.4\) changes to \(\mathds{h}=-0.5\), whereas it expands by 25% when \(\mathds{h}=-0.6\) changes to \(\mathds{h}=-0.5\).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\(\boldsymbol{\alpha}\)} & \multicolumn{2}{c}{**Exact Solution**} & \multicolumn{2}{c}{\(\mathds{h}=-0.6\)} & \multicolumn{2}{c}{\(\mathds{h}=-0.5\)} & \multicolumn{2}{c}{\(\mathds{h}=-0.4\)} \\ \cline{2-9}  & \(\boldsymbol{\Theta_{b}}\) & \(\boldsymbol{  
In the case of \(n=3\), \(\varepsilon=8/\pi^{2}\), the 3 rd-order solution \(\Theta_{b}^{[n=3]}\) to the rotation angle at the free end by IHAM vs. the point force curve is compared to the exact solutions \(\Theta_{b}\) in Figure 3. It can be stated that the improved homotopy analysis method has the optimal control parameters \(\hbar\) for the convergence rate for an arbitrary \(n\)th-order approximation solution. For example, when \(n=3\), the optimal control parameter is identified to be \(\hbar=-0.5\).
## 6 Pricing Problems
### No Power Control  
In the case when the network nodes transmit with a fixed and predefined transmission power level, the appropriate PP is as follows:  
\[\text{PP}(\lambda^{*})\text{: max }P=\] \[-f+\sum_{\delta\in\mathcal{S}}\sum_{w\in\mathcal{V}}\sum_{w\in \delta^{+}(w)}\lambda^{*}_{s(w,v)}U_{swv} \tag{7a}\] \[X_{w}\geq Y_{wu},\quad w\in\mathcal{V},u\in\delta^{+}(w)\] (7b) \[X_{w}\leq\sum_{u\in\delta^{+}(w)}Y_{wu},\quad w\in\mathcal{V}\] (7c) \[X_{w}+\sum_{u\in\delta^{-}(w)}Y_{uw}\leq 1,\quad w\in\mathcal{V}\] (7d) \[\sum_{m\in\mathcal{M}}Z^{m}_{w}=X_{w},\quad w\in\mathcal{V}\] (7e) \[p(w,u)+M(w,u,m)(1-Y_{wu})\geq\] \[\gamma(m)z^{m}_{w}\eta+\gamma(m)\sum_{w\in\mathcal{V}\setminus\{ w,u\}}p(v,u)Z^{m}_{wv}),w\in\mathcal{V},u\in\delta^{+}(w),m\in\mathcal{M}\] (7f) \[Z^{m}_{wv}\leq z^{m}_{w},\;\;Z^{m}_{wv}\leq X_{v},\;\;Z^{m}_{wv} \geq z^{m}_{w}+X_{v}-1,m\in\mathcal{M},w\in\mathcal{V},v\in\mathcal{V}\setminus\{w\}\] (7g) \[U_{swv}\leq g_{sw},U_{swv}\leq Y_{wv},\;\;U_{swv}\geq g_{sw}+Y_{ wv}-1,w\in\mathcal{V},s\in\mathcal{S},v\in\delta^{+}(w)\] (7h) \[\sum_{\delta\in\mathcal{S}}g_{sw}\leq\sum_{m\in\mathcal{M}}B(m)F ^{m}_{w},\quad w\in\mathcal{V};\;g_{sw}\leq 1,\quad s\in\mathcal{S},w\in\mathcal{V}\] (7i) \[F^{m}_{w}\leq|\mathcal{S}|z^{m}_{w},\;\;F^{m}_{w}\leq f,\;\;F^{m} _{w}\geq f-|\mathcal{S}|(1-z^{m}_{w}),m\in\mathcal{M},w\in\mathcal{V}.\] (7j) \[Y_{yu}\in\mathbb{B},\,w\in\mathcal{V},u\in\delta^{+}(w);\,X_{w} \in\mathbb{B},\,w\in\mathcal{V} \tag{7k}\]\[z_{w}^{m}\in\mathbb{B},\ w\in\mathcal{V},m\in\mathcal{M};\ Z_{wv}^{m} \in\mathbb{B},\ w\in\mathcal{V},V\in\mathcal{V}\setminus\{w\},m\in\mathcal{M} \tag{71}\] \[f\in\mathbb{R};\ F_{w}^{m}\in\mathbb{R},\ m\in\mathcal{M},w\in \mathcal{V};\ g_{sw}\in\mathbb{R}_{+},s\in\mathcal{S},w\in\mathcal{V}. \tag{72}\]  
Note that inequalities (71)-(72) define a valid c-set. Constraints (71) and (73) force the node to broadcast (\(X_{w}=1\)) if its broadcast signal is to be received by any of its neighbors (\(Y_{wu}=1\)); otherwise, \(X_{w}\) is forced to be zero. Next, constraint (72) ensures that, if a node is transmitting, then it cannot receive, and if a node is not transmitting, then it can be receiving, but from, at most, only one neighboring node. Constraint (73) assures that only one MCS can be used (\(z_{w}^{m}=1\)) by each broadcasting node. Finally, constraint (71) expresses the signal-to-interference-to-noise ratio (SINR) condition. The second term of the left-hand side of (72) is added to cancel the constraint whenever \(Y_{wu}=0\) using a \"big M\" constant i.e., the upper bound on the right-hand side of (72).
## 2 Materials and Methods
### Methodology
#### 2.3.4 Flood Risk in Future Scenarios  
In this study, flood risk projection focuses on two representative coupled SSP-RCP scenarios (SSP2-RCP4.5 and SSP5-RCP8.5) for three periods, i.e., the 2030s, 2040s and 2050s, covering the near- and mid-term future over a 30-year time frame. These two scenarios are widely analyzed in climate risk assessment due to their representativeness [78; 79]. The result for the 2030s was calculated with the mean value of 21 models from 2028 to 2032 to reduce dataset uncertainty, and the same was conducted for the 2040s and 2050s. The impact of climate change on our baseline model was mainly related to hazardous and vulnerability, as sensitivity factors generally remain stable.  
For hazardousness, we only considered the future RMAX3, since estimating future inundation areas poses great challenges. Specific estimations, such as that of RMAX3 in the 2030s in the RCP4.5 scenario, are exampled in the Supplementary Materials and detailed code in Part D of Appendix A. Regarding vulnerability, similarly to the estimation of RMAX3, we also extrapolated future population density and GDP from the change ratios reflected in the available projection datasets. Additionally, future land-use patterns were projected by using the future land-use simulation (FLUS) model developed by [PERSON] et al. [80]. We collected multiple driving factors for anthropogenic activities and natural effects to simulate land-use evolution.
# Collaborative Filtering Recommendation Algorithm Based on TF-IDF and User Characteristics
## 2 Related Work  
The basic idea of the collaborative filtering algorithm can be simply summarized as recommending items of interest to target users who have similar interests [20; 21]. As shown in Figure 1, the collaborative filtering algorithm is mainly divided into three steps, namely establishing the user-item rating matrix, finding other users with similar interests to the target users, and finally making recommendations by rating and predicting based on similar users. Traditional collaborative filtering (CF) algorithms are mainly divided into user-based collaborative filtering (UCF) and item-based collaborative filtering (ICF) (see Figure 2). There are many improvements in the collaborative filtering recommendation algorithms, to solve the data sparsity and cold start problem. These existing methods give a good research basis for the recommendation system.  
In this paper, the user-based collaborative filtering algorithm is focused on, which is more suitable for responding to the favorite items for groups with similar interests, and the recommendation results are more social. The proposed collaborative recommendation algorithm is similar to those existing TF-IDF-based methods. However, there are many differences between the proposed methods with those existing methods. In the proposed method, the TF-IDF method is applied to rating data, and the user characteristics are fused to optimize the user similarity and improve the accuracy of rating prediction. It is different from those methods that use a time-dependent similarity measure to compute the user similarity without considering user characteristics [22]. It is also different from those methods that directly calculate the user similarity through the TF-IDF method [23].  
The user-based collaborative filtering algorithm first needs to calculate the similarity between the target user and other users. Then, some users with high similarity are selected as the nearest neighbor set. Finally, aim at items in the neighbor set and predict all ratings of the target user. The main process of the traditional user-based collaborative filtering algorithm will be described as follows.
# Bioavailability of Liposomal Vitamin C in Powder Form: A Randomized, Double-Blind, Cross-Over Trial
## 2 Materials and Methods
### In Vitro Bioavailability Study
#### 2.4.3 Evaluation of Vitamin C Concentration  
Preparation of Buffer and Wash Solutions  
Pure HPLC-grade formic acid (Sigma Aldrich, St. Louis, MO, USA) was dissolved in ultrapure Millipore water (pH 2.0) or methanol (0.1%) (St. Louis, MO, USA). The mobile phase was filtered through a 0.45 \(\upmu\)m filter each time before use. Three wash solvents, strong wash solution (acetonitrile/isopropanol/water) (5:1:1) (\(v/v/v\)), weak wash solution (water/methanol/acetonitrile) (3:1:1) (\(v/v/v\)), and seal wash solution (acetonitrile/water) (2:8) (\(v/v\)), were developed to minimize the peak disturbance. HPLC-grade acetonitrile and isopropanol were purchased from Sigma-Aldrich (St. Louis, MO, USA). A degasser was arranged to obtain high-quality results of the liquid chromatography (LC). All the chromatographic columns and samples were maintained at a temperature of 30 \({}^{\circ}\)C and 5 \({}^{\circ}\)C, respectively. UV detection was set at 270 nm.  
Method optimization was performed using a Waters Acquity(tm) (Waters Corp., Milford, MA, USA) system equipped with a binary solvent delivery pump, Acquity(tm) Sampler Manager, Acquity(tm) TUV Detector, and Acquity(tm) Column Heater/Cooler. The peak purity was evaluated on a Waters I-Class Plus UPLC with a PDA eA Detector combined with an Acquity(tm) SM-FTN-I (Sample Manager with Flow-Through Needle) and Acquity(tm) BSM (Binary Solvent Manager). Ascorbic acid Certified Reference Material (CRM, PHR1008) was purchased from Supelco(r) by Sigma-Aldrich (St. Louis, MO, USA).  
UPLC Analysis  
The UPLC was carried out on 2.1 mm i.d. \(\times\) 100 mm length, 1.7 \(\upmu\)m columns (Kinetex(r) C18 100 A; Phenomenex Inc., Torrance, CA, USAand Acquity UPLC(r) BEH C18 130 A; Waters Corp., Milford, MA, USA) with several batches. Two mobile phases, (A) 0.1% formic acid in water and (B) 0.1% formic acid in methanol, were used for the separations. The experiments were performed using Empower3 software for instrument control, acquisition, and data analysis. The samples were programmed for isocratic elution at a flow rate of 0.05 mL/min with 1% organic modifier (B) and a 0.5 min re-equilibration time.
# Secondary Primary Carcinoma Arising on the Flap Skin in the Oral Cavity--Case Series
## 1 Introduction  
Oral squamous cell carcinoma (OSCC) is the sixth most widespread malignancy in the world and the most frequent malignancy among head and neck cancers [1]. Due to its sensitive location as well as considerable morbidity and mortality rates with which it is associated, OSCC is a major global health burden. While there are many etiological causes that contribute to the development of OSCC, tobacco and alcohol remain the most important risk factors [2, 3].  
In recent decades there were certain advances regarding the management of OSCC, especially regarding the surgical reconstruction of the post-excisional defect, but still the primary gold standard for the treatment of OSCC is the radical resection along with the dissection of the cervical lymph nodes [4]. Depending on the staging of the disease, usually an adjuvant chemoradiotherapy follows after the surgical management. According to the literature, the 5-year survival rate of OSCC is moving from 40% to 70%, depending on the author [5, 6, 7, 8].  
The goal of post-excisional defect reconstruction using regional or, more recently, free flaps is to recover the integrity and functionality of the oral cavity. Even though these flaps have skin, which is not in its normal surrounding in the oral cavity, some studies are suggesting that this skin undergoes \"mucosalization\", a process in which it gets thicker, loses its stratum corneum, and reduces its pilosebaceous units [9, 10, 11]. However, this topic is still controversial, and further studies are needed to address this issue.  
The appearance of a secondary primary OSCC on the skin from previous regional/free flaps is a very rare but possible occurrence [12; 13]. There are several proposed explanations for this occurrence: (1) metaplasia of the skin due to chronic inflammation in the oral cavity; (2) colonization of the skin by the cells of adjacent mucosa that have previously been genetically altered and predisposed to the development of cancer; (3) continuation of previous bad habits, such as tobacco and alcohol consumption, which leads to a new primary cancer [14].  
In this report, we present three cases of OSCC which occurred on the skin of two radial forearm free flaps and two regional pectoralis major flaps.
# RSIMS: Large-Scale Heterogeneous Remote Sensing Images Management System
## 2 Materials and Methods
### RSIMeta: Metadata Management of Large-Scale Images with Heterogeneous Structures
#### 2.2.2 Remote Sensing Images Index and Retrieval Based on Hilbert Curve
Steps of divide-and-merge Hilbert girds are described as below:  
1. Divide all the sub-regions into basic Hilbert grids and encode these grids. 2. Sort these Hilbert codes into an ordered list and then split the list into successive segments. 3. Iterate over these segments, and merge grids of each segment into a bigger Hilbert grid. Figure 4.1 Grids generated from Algorithm 2 are not consistent with standard Hilbert grids. Figure 4.2 Relationship of S, B and G.  
Note that only four adjacent grids connected by a basic Hilbert curve (one of Figure 6) can be merged. The process can be illustrated by Figure 4.3  
Pseudo codes are show as Algorithm 3:  
```
\(LEVEL=k\) functionHilbertGrids(regions) { \(hcodes=[\) 1 for reg in regions:  grids = HilbertSplit(reg)  for grid in grids:  x, y = GridCenter(grid)  code = HilbertCode(x, y, \(LEVEL\))  hcodes.push(code) segments = HilbertSplit(hcodes) results = [ ] for seg in segments:  codes = HilbertMerge(seg) results.merge(codes) return results
```  
**Algorithm 3** Hilbert grid split-and-merge  
In summary, the whole process can be illustrated as Figure 4.4  
As shown in Figure 12, the query region is subdivided first into multi-level Hilbert grids based on the Algorithms 2, and then be sent to different database servers simultaneously for parallel geospatial query. Finally, all the results from the different database servers are collected and merged, and then return to the client. Note that the purpose of Hilbert grids used in geospatial intersection query is to reduce the number of candidates drastically. Results retrieved with only Hilbert grids are inaccurate, which are illustrated by Figure 4.5 The green area in the right part of Figure 13  
Figure 4.6 Process of grids merges according to Hilbert curve. Figure 4.7 Remote sensing images parallel query based on Hilbert grids. intersects with the red grid but not the left remote sensing image. However, the image covered by the red grids will be regarded as intersection with the green area, which is wrong obviously.
## 4 Wavelet Transform
### Wavelet Transform Method  
In the process of establishing motor fault models, the wavelet transform (WT) method [7] is effective in extracting various fault features from motor operation, enabling accurate fault diagnosis and model construction by transforming these features into both frequency and time domain information. The fundamental theory of wavelet analysis involves using a prototype function, called the mother wavelet, to generate a set of basis functions that allow for the multi-resolution analysis of signals. WTs are primarily categorized into continuous wavelet transform (CWT) and discrete wavelet transform (DWT). The main difference between them is that the CWT operates over all possible scales and translations, while the DWT uses only a specific subset of scales and translation values. Wavelets find applications in a wide range of fields, including discontinuity detection, compression, filtering, and frequency analysis. A continuous function \(\psi\) (t) is considered a mother wavelet if it has finite energy and zero mean. A zero integral indicates that the function is oscillatory, meaning its graph must exhibit an up-and-down oscillating wave pattern. Finite energy implies that the function's energy is mostly confined within a finite time interval. Consequently, the area enclosed by \(\psi\) (t) must be relatively small, and the amplitude must decay rapidly to zero in both the positive and negative directions. Compared to sinusoidal waves, these functions can be considered as smaller wave shapes, hence the term \"wavelet\".