esa-sceva/satcom-chunk-collection huggingface dataset:
# Dual-Band Multiple-Element MIMO Antenna System for Next-Generation Smartphones
## 5 Fabrication and Measurement Results  
The proposed MIMO antenna system is fabricated using an LPFK machine and is tested using an in-house facility. Figure 7a shows the fabricated prototype before assembling, while Figure 7b,c shows the final fabricated prototype's front and back sides. Figure 7d depicts the radiation pattern measurement setup, where the MIMO antenna is placed in an anechoic chamber.  
A Precision Network Analyzer (PNA) E8363C by Agilent Technologies was utilized for the measurement of reflection coefficients and port isolation. Due to symmetry, only one side of the MIMO system is tested. Figure 8 shows the measured reflection coefficient and port isolation level among radiating elements. From Figure 8a, it is observed that the antenna elements are resonating well for the bands of interest. The reflection coefficients are slightly shifted, but all the antennas cover the desired bands of interest, and this error can be attributed to measurement setup, cable, and system losses. Furthermore, the minimum measured isolation is found to be \(>\)13 dB between Ant 1 and Ant 2, and other radiating elements.  
Figure 5: Simulated S-parameters of the MIMO antenna system. (**a**) Reflection coefficient; (**b**) ports isolation.  
Figure 6: (**a**) Radiation efficiency and (**b**) total efficiency of the MIMO antenna system.  
The radiation patterns of the proposed MIMO antenna for two principal planes (\(xz\) and \(yz\)) are depicted in Figures 9 and 10. Figure 9a-d shows the radiation patterns of Ant 1 to Ant 4 for the 3.5 GHz frequency band, while Figure 10a-d shows the radiation characteristics for the 5.4 GHz frequency band for Ant 1 to Ant 4. It is observed that the MIMO antenna offers quasi-omnidirectional characteristics for both the planes. From Figures 9 and 10, one can also observe that Ant 1 and Ant 4 offer pattern diversity for both the planes.  
Figure 8: Measured S-parameters of the MIMO antenna system. (**a**) Reflection coefficient; (**b**) ports isolation.  
Figure 7: Fabricated prototype (**a**) before assembly; (**b**) front side; (**c**) back side; (**d**) radiation pattern measurement setup.  
Figure 10: Radiation characteristics of the proposed antenna at 5.4 GHz for (**a**) Ant 1, (**b**) Ant 2, (**c**) Ant 3, and (**d**) Ant 4.  
Figure 9: Radiation characteristics of the proposed antenna at 3.5 GHz for (**a**) Ant 1, (**b**) Ant 2, (**c**) Ant 3, and (**d**) Ant 4.
## 2 Importance of miRNAs in Urinary Tract Carcinoma
### Bladder Carcinoma  
Bladder carcinoma is clinically detected by identifying urinary irritation symptoms such as hematuria, frequency, and urinary urgency. Risk factors include smoking; excessive use of fanacetin; prolonged use of cyclophosphamide; parasites; prolonged placement of urinary catheters; chronic irritation caused by bladder stones; exposure to hydrocarbons, tryptophan metabolites, and industrial chemicals, especially aromatic amines (such as napthylamine, which is used in the dye industry and aniline dyes); and exposure to chemicals used in the rubber, electrical wire, paint, and textile industries (Figure 2) [56; 57; 58; 59]. Most bladder carcinomas are urothelial carcinomas (transitional epithelial carcinomas), but squamous cell carcinomas and adenocarcinomas are also frequently observed and can be easily differentiated into various types [60; 61]. Bladder carcinomas tend to occur simultaneously or heterochronously. Heterochronous cancers are poorly differentiated carcinomas or small-cell carcinomas that may develop into more aggressive types with an early transition to invasive carcinoma [60; 61; 62; 63; 64]. Carcinoma in situ is noninvasive; however, it is more malignant and often causes multiple lesions. In bladder carcinoma, cells often pass into the urine with the detached urothel, but in the case of low atypical papillary invasive carcinoma, it is difficult to determine whether the carcinoma is benign or malignant. No useful serum tumor markers have been found for the treatment of bladder cancer. Several urinary tumor markers have been used clinically [65; 66; 67; 68]. Urinary NMP22, BTA, cytokeratin 8-cytokeratin 18 (CK8-18), and BFP are considered more sensitive and useful for urothelial carcinoma [69; 70; 71]. However, because they are less sensitive and specific than serum tumor markers such as PSA for prostate cancer, the practical application of tumor markers in urine with more sensitivity and specificity is desirable. Bladder carcinoma has a high recurrence rate, and the early detection of recurrence or progression during treatment and initial diagnosis is important. Since it is ideal to detect bladder carcinoma at the level of specific genes at an early stage, attempts have been made to detect miRNA in urine [14; 72; 73; 74; 75; 76; 77]. Moreover, miRNAs are extracted and analyzed in cells and outside cells in the form of cell-free and endoplasmic reticulum miRNAs [73; 75; 77; 78; 79; 80]. Among the thousands of miRNAs in urine, those specific to bladder cancer have been identified  
Figure 2: **Cancer is triggered by a variety of factors.** Cancer is caused by genetic changes in normal tissues, either by congenital factors or by factors acquired through exogenous stimulation.  
through comparative studies between non-cancer and cancer patients [66; 79; 81]. This will lead to the discovery of cancer-specific miRNAs. We believe that the use of urine, which contains relatively few cellular components, will improve the specificity and sensitivity of urinary-tract-system tumor detection.
## 3 Preliminaries
### LSB Substitution and OPAP  
LSB (Least-Significant-Bit) alternative technology is a method of directly concealing the watermark in the LSB of the pixels constituting the cover image. [PERSON] et al. [34] introduced an optimal LSB substitution and genetic algorithms, and it was found that the Worst-case Mean Squared Error (WMSE) (which is a measurement obtained by comparing the original and marked image) is \(1/2\) of that obtained with simple LSB substitution techniques. Let us look at the DH procedure for the original 8-bit grayscale represented by \(x_{i}\in\{0,1,\ldots,255\}\). \(S\) denotes n-bit hidden values represented as \(S=\{s_{k}|0\leq k<n,s_{k}\in\{0,1\}\}\). The mapping between the \(n\)-bit secret bits \(S=\{s_{k}\}\) and the embedded bits \(S^{\prime}=s^{\prime}_{k}\) can be defined as follows: \(s_{k}^{\prime}=\sum_{j=0}^{\delta-1}s_{k\times k+j}\times 2^{\delta-1-j}\). The pixel value \(x_{i}\) for embedding the \(\delta\)-bit \(s_{k}^{\prime}\) is changed to form the stego-pixel \(x_{i}^{\prime}\) like \(x_{i}^{\prime}=x_{i}-(x_{i}\bmod 2^{\delta})+s_{k}^{\prime}\). The \(\delta\) LSBs of the pixels are extracted by \(s_{k}=x_{i}^{\prime}\bmod 2^{\delta}\).  
It has been mathematically proven that OPAP can improve the quality of marked images by reducing WMSE by using LSB replacement based on the minimization rule. Let \(x_{i}\) be the pixel of the cover image, \(x_{i}^{\prime}\) be the obtained pixel from pixel \(x_{i}\) using the LSB replacement, and \(x_{i}^{\prime\prime}\) is the optimized pixel derived from \(x_{i}^{\prime}\) by the OPAP method. The value of \(\Delta_{i}\) may be segmented into three intervals. The OPAP modifies \(x^{\prime}\) to form the stego-pixel \(x^{\prime\prime}\) as the following rules:  
1. Rule 1 (\(2^{\delta-1}<\Delta_{i}<2^{\delta}\)): if \(x_{i}^{\prime}\geq 2^{\delta}\), then \(x_{i}^{\prime\prime}=x_{i}^{\prime}-2^{\delta}\); otherwise \(x_{i}^{\prime\prime}=x_{i}^{\prime}\);
2. Rule 2 (\(-2^{\delta-1}\leq\Delta_{i}\leq 2^{\delta-1}\)): \(x_{i}^{\prime\prime}=x_{i}^{\prime}\);
3. Rule 3 (\(-2^{\delta}<\Delta_{i}<-2^{\delta-1}\)): if \(x_{i}^{\prime}<256-2^{\delta}\), then \(x_{i}^{\prime\prime}=x_{i}^{\prime}+2^{\delta}\); otherwise \(x_{i}^{\prime\prime}=x_{i}^{\prime}\);
## 5 Conclusions  
In this paper, we proposed an energy-efficient data collection method for large scale WSNs, where Sink-based Centralized transmission scheduling (SC-Sched) is realized by integrating asymmetric communication and wake-up radio. In SC-Sched, sensor nodes do not update routing information. Instead, a sink node collects node position information and on this basis determines a tree-based routing and a transmission order. Each sensor node is equipped with a wake-up receiver. At the time that a packet is to be transmitted between two sensor nodes, the sink node transmits a Wake-up Call (WuC) using a transmission power larger than that used for data transmission, directly activating both transmitting and receiving sensor nodes simultaneously to start the communication. WuC includes routing and communication possible time determined by the sink node. In this way, the sink node directly controls each sensor node to reduce the sensor node's active time.  
Simulation results of SC-Sched show that (i) the algorithm for adaptively determining the number of WuC transmissions and data retransmission slots per hop helps achieve a high frame delivery ratio while suppressing the total energy consumption and data collection time, and (ii) it is possible to shorten the data collection time by more than 31% by using different channels for WuC and data transmission.  
We compared SC-Sched with WuR-TDMA (where the sink node broadcasts the routing information and transmission timing information to all nodes at once) and ContikiMAC (a conventional duty-cycling method). Simulation results show that SC-Sched reduces the total energy consumption by 62.5% compared with WuR-TDMA and by 96.1% compared with ContikiMAC when the number of sensor nodes is 86. This is because SC-Sched reduces each sensor node's active time to the minimum: it is active only for data collection, which avoids the overhead of updating the routing information and duty cycling. Moreover, SC-Sched prolongs network lifetime by 7.47 times compared with ContikiMAC when the number of sensor nodes is 300 and the number of data collection points is 4.  
In the future, we will further reduce the data collection time. We will also try to extend SC-Sched to cope with an increase in network scale and node failure and improve the routing construction algorithm by considering better traffic load distribution and the residual energy of each sensor node.  
**Author Contributions:** [PERSON] and [PERSON] conceived the basic idea. [PERSON] designed the detailed protocol and performed the evaluations. [PERSON] wrote the article. [PERSON] and [PERSON] gave many suggestions to refine the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction  
Gout is a common form of inflammatory arthritis that affects the joints and musculoskeletal system. It is induced by monosodium urate crystals deposition within the synovial fluid and other body tissues [1]. Hyperuricemia is a condition of the overproduction or under-excretion of uric acid. This condition is caused by high dietary intake of foods that are rich in nucleic acids, and is defined as a serum uric acid level greater than 7.0 mg/mL [2]. Xanthine oxidase (XO) is the key enzyme that catalyses the oxidation of hypoxanthine and xanthine to uric acid. During purine metabolism, XO produces reactive oxygen species (ROS) by transferring electrons to oxygen from hypoxanthine and xanthine [3]. Superoxide anion radicals generated by XO catalysing reactions are involved in various pathological conditions such as inflammation, hepatitis, and carcinogenesis.  
Albopurinol is being used clinically as an XO inhibitor for gout treatment by blocking the terminal step of uric acid biosynthesis. However, it results in many side effects, such as hepatitis, nephropathy, and allergic reactions [4]. Therefore, it is necessary to search for new alternatives which confer higher therapeutic activity and lesser side effects. Natural products are excellent sources of lead compounds in the search of new medication for clinical disorders with fewer side effects. Botanical extracts have been used to treat diseases, and play a major role in primary health care in many developing countries.  
_Chrysanthemum morifolium_ is an edible flower that is commonly used in traditional Chinese medicine, particularly for its liver balancing properties. The flower portion of _C. morifolium_ has been used as an edible and medicinal material for hundreds of years as a sedative due to its cooling abilities [5]. It contains essential oils, sesquiterpenes, and phenolic compounds, and has been reported to demonstrate various biological activities, such as anti-oxidative, anti-inflammatory, anti-mutagenicity, and anti-HIV [1]. Numerous in vivo studies on the anti-hyperuricemic activity of other plant extracts have been conducted [4; 6], but deeper investigation into _C. molifolium_ has not been performed. Therefore, the present study investigated the anti-hyperuricemic potential of _C. morifolium_ flower ethyl acetate sub-fractions in rat models, followed by an XO mRNA gene expression study.  
The objectives of the present study were to investigate the XO inhibitory activity, cytotoxicity, and cellular antioxidant capacity of ethyl acetate sub-fractions of _C. morifolium_ Ramat. dried flower and its mode of XO inhibition. The effects of _C. morifolium_ flower ethyl acetate sub-fraction on rats' uric acid levels and XO gene expression were also determined in this study. The compounds present in the bioactive sub-fractions were identified using HPLC-Q-TOF-MS/MS.
# A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques
## 7 Endoscopy
### Classification  
Different researchers combined deep learning techniques such as CNN and LSTM to extract spatial and temporal features from endoscopy videos. The proposed model consists of ResNet to extract temporal features, LSTM to extract spatial features, and a stack of fully connected and softmax layers to classify the image into one of the 37 classes. The proposed classification model is also used to design a retrieval system for similar frames. To classify ulcer images from endoscopy videos, a pretrained CNN-based architecture is proposed, which uses principal component analysis for dimension reduction and Xception for classification. Another architecture is developed to classify frames from colonoscopy videos into informative and noninformative categories using handcrafted features and a pretrained Inception V3. Additionally, a novel method utilizing a CNN, channel attention mechanism, and a classifier achieves the best accuracy by using ResNet with a proposed blockwise channel squeeze and excitation attention module.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Research Paper** & **Purpose** & **Method** & **Dataset** & **Performance** & **Limitations** & **Advantages** \\ \hline \multirow{8}{*}{[PERSON] et al., 2018 [44]} & \multirow{8}{*}{Detection of aortic wall} & \multirow{8}{*}{\begin{tabular}{c} SSD with faster \\ RCNN only \\ spatial features \\ are extracted \\ \end{tabular} } & 30 videos, 55 & \multirow{8}{*}{\begin{tabular}{c} Accuracy = 94\% \\ resolution, not \\ publicly \\ available \\ \end{tabular} } & \multirow{8}{*}{\
\begin{tabular}{c} Small dataset \\ \end{tabular} } & \multirow{8}{*}{
\begin{tabular}{c} Automatic \\ detection \\ system for \\ aortic wall \\ \end{tabular} } \\ \cline{3-5}  & & & 3087 echo & & \\  & & & videos, DICOM & & \\  & & & format, average & & \\  & & & of 42 sequential & Error & Only & \\  & & & frames in each & measurement & high-quality & Improved loss \\  & & and ED frames & features are & video, & for ED = 0.49, & echo reports & function \\  & & extracted & 120 \(\times\) 120 & ES = 1.33 & were used & \\  & & & resolution, not & & \\  & & & publicly & & \\  & & & available & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of the contributions where the detection task is applied to echocardiography videos.  
The availability of large and fully annotated databases, such as ImageNet, is crucial for facilitating the development of deep classification models for endoscopy imaging. This poses a primary challenge in terms of training and testing the network. The active participation of endoscopists is also critical for establishing a large medical image dataset for training and thorough clinical validation.
## 3 Literature Review
### Efficient Surveying and Mapping  
Drones have been employed as effective tools for precisely surveying natural landscapes and mapping, providing crucial information before the execution process to facilitate informed planning [9]. The emphasis on precision implies that drones can contribute to the required accuracy for successful construction projects, supporting decision makers with informative data [10; 11].
# Dynamics and Stability on a Family of Optimal Fourth-Order Iterative Methods
## 2 The Proposed Family
#### Special Cases  
In this section, we present some particular classes of iterative schemes obtained from expression (1) for different weight functions \(H(u)\) that satisfy the conditions of Theorem 1.  
1. Firstly, we consider the well-known Ostrowski method, which is a particular case given by the following weight function: \[H(u)=\dfrac{1-u}{1-2u}.\] (4)This weight function guarantees the fourth-order convergence of [PERSON]'s scheme, whose iterative expression is: \[\left\{\begin{array}{l}y_{n}=x_{n}-\dfrac{f(x_{n})}{f^{\prime}(x_{n})},\\ x_{n+1}=x_{n}-\dfrac{f(x_{n})}{f^{\prime}(x_{n})}\bigg{[}\dfrac{f(x_{n})-f(y_{n })}{f(x_{n})-2f(y_{n})}\bigg{]}.\end{array}\right.\] (5)
2. The following weight function is now considered: \[H(u)=\dfrac{1+u^{2}-u^{3}}{1-u},\] (6) which satisfies all of the conditions of Theorem 1. This scheme was proposed by [PERSON] in [10], and its iterative expression is: \[\left\{\begin{array}{l}y_{n}=x_{n}-\dfrac{f(x_{n})}{f^{\prime}(x_{n})},\\ x_{n+1}=x_{n}-\dfrac{f(x_{n})}{f^{\prime}(x_{n})}\bigg{[}\dfrac{f(x_{n})}{f(x_ {n})-f(y_{n})}+\bigg{(}\dfrac{f(y_{n})}{f(x_{n})}\bigg{)}^{2}\bigg{]}.\end{array}\right.\] (7)
3. The following uniparametric family satisfies the conditions of Theorem 1: \[H(u)=\dfrac{\alpha+u(1+\alpha+u+2\alpha u)}{\alpha+u},\] (8) where \(\alpha\) is a real parameter, and it provides a new iterative class of iterative schemes: \[\left\{\begin{array}{l}y_{n}=x_{n}-\dfrac{f(x_{n})}{f^{\prime}(x_{n})},\\ x_{n+1}=x_{n}-\dfrac{f(x_{n})}{f^{\prime}(x_{n})}\bigg{[}\dfrac{\alpha f(x_{n} )^{2}+f(x_{n})f(y_{n})(1+\alpha)+(1+2\alpha)f(y_{n})^{2}}{f(x_{n})[f(y_{n})+ \alpha f(x_{n})]}\bigg{]}.\end{array}\right.\] (9)
4. Finally, using the convergence conditions to define the Taylor polynomial associated with the generic function \(H(u)\), we obtain the last weight function presented here, thus satisfying Theorem 1. \[H(u)=\dfrac{H^{\prime\prime\prime}(0)}{6}u^{3}+2u^{2}+u+1.\] (10) We know that \(|H^{\prime\prime\prime}(0)|<\infty\), so if we substitute \(H^{\prime\prime\prime}(0)\) with \(\beta\), we have a parametric family of iterative schemes of fourth-order convergence: \[\left\{\begin{array}{l}y_{n}=x_{n}-\dfrac{f(x_{n})}{f^{\prime}(x_{n})},\\ x_{n+1}=x_{n}-\dfrac{f(x_{n})}{f^{\prime}(x_{n})}\bigg{[}\dfrac{\beta}{6} \bigg{(}\dfrac{f(y_{n})}{f(x_{n})}\bigg{)}^{3}+2\bigg{(}\dfrac{f(y_{n})}{f(x_ {n})}\bigg{)}^{2}+\bigg{(}\dfrac{f(y_{n})}{f(x_{n})}\bigg{)}+1\bigg{]}.\end{array}\right.\] (11) Now, we will study the stability of the family given by Equation (11).
## 2 Materials and Methods
### Soil Sample Preparation and Washing Experiments  
The details of soil sample preparation were described in our previous publications [1,2]. Briefly, a soil screening standard of the American Society for Testing and Materials [25] was employed for soil sample preparation. The contaminated soil first taken from a hazardous waste site in Tainan City, South of Taiwan, was manually screened through six different sieving sizes (2, 1, 0.5, 0.25, 0.125, 0.053 mm) with water (wet screening) prior to being sun-dried. The soil chosen for the washing experiments in this study was in the range 0.125-0.250 mm (fine sand) as this accounts for around 70% of the total mass. It should be noted that the other particle sizes, even the finest ones, could be economically treated by the current treatment method employed at the contaminated site (incineration) considering that the amount of these particles is somewhat small. The dioxin TEQs of the other particle sizes can be viewed from our previous publication [1].  
The contaminated soil (100 g) and the liquid wine solvents (250 g) were combined in a Teflon-lined 600 mL Pyrex vessel at a ratio of 1:2.5 (mass basis). The soil and solvent were stirred mechanically by a double-blade propeller powered by a motor (rpm 700) (Figure 1). The vessel was placed in a plate-type ultrasonic cleaner (200 Watt, 40 KHz, model DC200H, Delta New Instrument Co., Ltd, Taipei, Taiwan). Each washing cycle took sixty minutes under an ultrasonic set temperature of 30 \({}^{\circ}\)C (to represent the ambient temperature in the contaminated site in southern Taiwan). Our washing experiments were performed under the conditions (ambient temperatures and a relatively low agitation--700 rpm) that should be energy-advantageous according to [PERSON], et al. [26], [PERSON], et al. [27] and [PERSON], [PERSON], [PERSON], [PERSON] [6]. After treatment, the particles were allowed to settlebefore the liquid phase was vacuum-filtered through a 110 nm hydrophilic membrane filter (Millipore, Billerica, MA, USA). A portion of washed soil (10 g-12 g) was used for PCDD/F analysis.
## 2 Sample Preparation and Testing Techniques
Processing of the diffraction spectra (smoothing, background subtraction, determining the positions and widths of half maxima, breaking the composite maxima into separate components) was carried out using the DHN_PDS Program Package for Powder Diffraction Version 2.0 suite. The obtained carbon types were ground in a ceramic bowl and sieved on screens. A fraction of 40-63 \(\upmu\)m was used to make supercapacitor electrodes. The supercapacitor electrodes were made from both AC from corn stalks and beet pulp, as well as from AC material types dried after modification. Polyvinylidene fluoride (PVDF) was used as the binding agent. The active electrode mass was obtained by mixing carbon material with PVDF, which was previously dissolved in dimethyl sulfoxide. The mass ratio of AC to PVDF was 19:1. The active mass was applied to the nickel mesh and pressed using a hydraulic press with a force of 500 N/cm\({}^{2}\). The electrode dimensions were 0.5 \(\times\) 1.5 cm\({}^{2}\). The finished electrodes were dried in an oven at 100 \({}^{\circ}\)C. The electrolyte was a 30% aqueous solution of potassium hydroxide KOH. Impedance spectra and cyclic voltammograms of the fabricated supercapacitors were measured using an AUTOLAB PGSTAT-100 (Metrohm AG, Herisau, Switzerland) impedance spectrometer. Impedance spectra were measured in the frequency range from 10\({}^{-2}\) to 10\({}^{5}\) Hz. Modelling of the obtained Nyquist diagrams was carried out with electrical equivalent circuits using Zview 3.4 software. The parameters of the elements in the electrical equivalent circuits were determined with an accuracy of not higher than 10%.