esa-sceva/satcom-chunk-collection huggingface dataset:
_Appl. Sci._**2012**, \(2\), 375-395; doi:10.3390/app2020375  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
Electrically Small Resonators for Planar Metamaterial, Microwave Circuit and Antenna Design: A Comparative Analysis  
**[PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] n ***  
GEMMA/CIMITEC, Departament d'Enginyeria Electronica, Universitat Autonoma de Barcelona,  
08193 BELLATERRA (Barcelona), Spain; E-Mails: [PERSON]D.-S.);  
[PERSON]ESS] (J.N.); [PERSON]] (F.P.); [PERSON]S] (J.B.)  
* Author to whom correspondence should be addressed; E-Mail: [PERSON]S];  
Tel.: +34-93-581-35-22; Fax: +34-93-581-26-00.  
_Received: 15 March 2012 / Accepted: 10 April 2012 / Published: 20 April 2012_  
**Abstract:** Planar metamaterials and many microwave circuits and antennas are designed by means of resonators with dimensions much smaller than the wavelength at their resonance frequency. There are many types of such electrically small resonators, and the main purpose of this paper is to compare them as building blocks for the implementation of microwave components. Aspects such as resonator size, bandwidth, their circuit models when they are coupled to transmission lines (as is usually required), as well as key applications, will be considered.  
**Keywords:** metamaterials; electrically small resonators; split ring resonator (SRR); stepped impedance resonator (SIR); duality
## 1 Introduction  
Programmed death-ligand 1 (PD-L1), which is overexpressed on the surface of tumor cells, is a transmembrane protein that interacts with programmed cell death-1 (PD-1), which is expressed on the surface of T cells [1]. PD-L1 binds to PD-L1, blocks T cell activities, such as migration, proliferation, blocks the secretion of cytotoxic mediators, and consequently limits tumor cell death [1, 2]. As antibodies bind to PD-L1 and block the binding of PD-L1 to PD-1, monoclonal anti-PD-L1 antibodies have been generated to prevent the inhibition of the activation of tumor cells. However, monoclonal antibody production is costly and requires unstable hybridoma cell culture or generation of antibody libraries via phage display technology. Moreover, the size of full-length antibodies can disrupt antibody penetration into dense tumor cells. To overcome these issues, the need for generating recombinant antibody fragments has increased.  
A single chain variable fragment (scFv) is an antibody fragment consisting of a variable heavy chain (VH) and a variable light chain (VL) of one arm of an antibody linked together via a flexible polypeptide linker (Figure 1). ScFv's retains specificity and affinity for their antigens without a constant region of antibody [3]. Moreover, owing to their small size, scFv's have demonstrated higher tissue-penetrating capabilities than full-sized antibodies [4, 5, 6]. Furthermore, the reduced complexity of scFv's allows for rapid and large-scale production in _Escherichia coli_ (_E. coli_), which is beneficial owing to the reduced labor intensity and more rapid growth rate compared with eukaryotic hosts [7, 8]. In addition to scFv's, single domain antibodies (sdAbs), which are unpaired variable domain of antibody (VH and VL), have also been applied as a promising biologic modality. SdAbs are expected to be imaging and therapeutic reagents _in vivo_ because their smaller molecular size comparedwith other fragments, such as antigen-binding fragment (Fab) and scFv, enables high tissue penetration and rapid elimination from the body.  
In this study, we constructed three recombinant DNAs encoding antibody fragments--scFv, VH, and VL--against mouse PD-L1 and expressed them in _E. coli_ in a soluble form. After purification, we compared PD-L1-binding efficiencies of the three antibody fragments to highlight their capabilities as attractive reagents for blocking or detecting PD-L1.
## 3 Results and Discussions
### Liver Biomarkers and Hepatocellular Injury Indicators
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters Groups** & **Liver Weight (g)** & **ALT(U L\({}^{-1}\))** & **AST(U L\({}^{-1}\))** & **ALP (U L\({}^{-1}\))** \\ \hline
**Control -** & 9.70 \(\pm\) 0.88 \({}^{\text{a}}\) & 63.86 \(\pm\) 4.07 \({}^{\text{e}}\) & 111.40 \(\pm\) 2.7 \({}^{\text{e}}\) & 70.45 \(\pm\) 3.76 \({}^{\text{c}}\) \\
**Control +** & 4.21 \(\pm\) 0.06 \({}^{\text{d}}\) & 92.73 \(\pm\) 2.20 \({}^{\text{b}}\) & 180.2 \(\pm\) 3.6 \({}^{\text{b}}\) & 94.02 \(\pm\) 5.03 \({}^{\text{b}}\) \\
**G3-1** & 4.28 \(\pm\) 0.22 \({}^{\text{d}}\) & 115.43 \(\pm\) 4.8 \({}^{\text{a}}\) & 201.4 \(\pm\) 7.5 \({}^{\text{a}}\) & 108.0 \(\pm\) 13.2 \({}^{\text{a}}\) \\
**G3-2** & 5.51 \(\pm\) 0.33 \({}^{\text{c}}\) & 83.14 \(\pm\) 2.4 \({}^{\text{c}}\) & 144.9 \(\pm\) 5.0 \({}^{\text{c}}\) & 64.34 \(\pm\) 4.7 \({}^{\text{d}}\) \\
**G3-3** & 4.57 \(\pm\) 0.29 \({}^{\text{d}}\) & 73.47 \(\pm\) 2.6 \({}^{\text{d}}\) & 126.21 \(\pm\) 3.35 \({}^{\text{d}}\) & 62.72 \(\pm\) 5.7 \({}^{\text{d}}\) \\
**G3-4** & 5.82 \(\pm\) 0.34 \({}^{\text{c}}\) & 86.64 \(\pm\) 1.4 \({}^{\text{c}}\) & 146.33 \(\pm\) 3.21 \({}^{\text{c}}\) & 78.64 \(\pm\) 5.2 \({}^{\text{cd}}\) \\
**G3-5** & 7.38 \(\pm\) 0.28 \({}^{\text{b}}\) & 63.44 \(\pm\) 3.0 \({}^{\text{e}}\) & 119.0 \(\pm\) 1.0 \({}^{\text{d}}\) & 64.92 \(\pm\) 4.8 \({}^{\text{d}}\) \\ \hline \hline \end{tabular}  
* ALT—alanine aminotransferase; ALP—alkaline phosphatase; AST—aspartate aminotransferase. The values with the different letters in each column showed a significant difference.  
\end{table} 
Table 5: Effect of wheat and barley grass on changes in alanine aminotransferase and aspartate aminotransferase, alkaline phosphates, and liver weight in rats exposed to toxic metals (mean \(\pm\) SD).
## 2 Related Work  
This section discusses the main contributions of the current works. An assessment of the environmental and human health implications of base station and mobile phone radiation is provided [11]. A key invention that has changed people's lifestyles is the cell phone. With the widespread use of mobile phones in everyday life, the standard of living has significantly improved around the world. There have always been concerns about the effects of radio frequency radiation on humans, plants, and animals. Furthermore, it is alleged that the radiation emitted by mobile phones damages human health and jeopardizes the enjoyment and convenience derived from using the devices. The authors in [12] analyzed the changes that these smart phone technologies can bring to human-nature interactions while focusing on the outdoor behaviors of experienced outdoor users.  
GHz [13] presented that the exposure of the human body to electromagnetic fields (EMF) with different frequencies can cause different biophysical effects. Thermal effects are typically minimal with frequencies less than 100 kHz; however, effects appear when increasing the frequencies. Smartphones communicate via high-frequency signals, and extended use of the generated electromagnetic field affects the skull. Additionally, irritability, memory impairment, weariness, anxiety, headaches, and disrupted sleep are primary indicators of changes in the body. It is believed that the changes caused by EMF are able to accumulate in the body under conditions of prolonged exposure.  
As a result, pathologies, such as leukemia, brain tumors, and hormonal diseases, can develop. Research has investigated memory loss, Parkinson's and Alzheimer's disease, amyotrophic sclerosis, AIDS, and an increase in suicides in relation to EMF exposure [14]. Another consequence of exposure to EMF in people is the syndrome of premature aging of the body. Despite extensive investigations, there remain a variety of unknown and undiagnosed addictions in people induced by EMF. All of these impacts have been recorded using various research approaches.  
These include the processing of thermal pictures to analyze thermal effects and the processing of EEG signals to assess brain activity [15]. To obtain a unified thorough evaluation of the impact of smart technology on humans, an intelligent approach for assessing various data is presented. To that end, this study proposes a framework for combining disparate data sets in order to assess the impact of smart technology on humans. Additionally, new methods for acquiring and evaluating empirical and experimental data are required to overcome the problem. A proposed paradigm for unification and intelligent solutions is effectively evaluated in this research for addressing difficulties.
## 6 PAT Segmentation Methods
### Traditional Image Processing Methods  
Semi-automatic methods for PAT segmentation rely on a series of heuristics and thresholding based on manually selected limits. For instance, [PERSON] et al. [50] proposes a method where an expert observer first selects the slices which limit the VoI to the heart. The algorithm then uses adaptive thresholding, region growing, and heuristics to segment out the lungs and other irrelevant structures and determine the heart region. From the heart region, fat voxels are calculated by thresholding. Once the limiting slices are manually selected, their algorithm can perform measurements within 20 s, compared to 9 to 15 min for manual segmentation. The authors report an accuracy of 94% validated on 105 patients, in line with inter-observer variability for PAT segmentation. However, in 8% of cases, the algorithm failed to segment PAT.  
These methods can be made fully automatic by using atlas-based segmentation in place of manual initialization, as presented in [PERSON] et al. [51]. Their method works by first segmenting the heart using atlas-based segmentation. Once the heart is segmented, an input image thresholded to the adipose tissue range is run through a graph segmentation algorithm. An overview of graph-based segmentation is presented in Figure 12. The image is represented as a 3D graph, where the weight on each edge of the graph adaptively measures the similarity between two neighboring voxels, using a similarity function that adapts to local features in the image. By finding low-weighted graph edges, the image can be split into segments. Using these segments and the segmented heart region, the method determines PAT on the image. The authors report a Dice coefficient of 0.82 evaluated on 6 patients. While the result is not indicative of real-world performance due to only evaluating 6 patients, it shows that it is possible to automatically segment PAT on MRI images.
## 1 Introduction
The SORT and Deep-SORT methods share the same overall architecture, divided into three main modules, as shown in Figure 1: KF-based estimation, data association, and track management. To detect objects on the images, the YOLOv3 [16] network is used. Both methods use the KF algorithm to predict the position of the objects in the current frame, which are, as well as the object detections provided by the YOLOv3, the inputs of the data association module, which is a linear assignment problem with a cost matrix association. The SORT method associates objects using bounding box detections to match measurements with predicted tracks, using the overlap of bounding boxes. On the other hand, to improve the bounding box association step, the Deep-SORT uses a CNN to extract appearance features from the object bounding box images. For a detailed evaluation of the object tracking methods, a set of different types of data association cost matrices based on bounding boxes intersection over union, Euclidean distances, and bounding boxes ratios is proposed. To evaluate both tracking methods with the proposed cost matrices, considering an assistive robotics context, the ISR Tracking dataset is proposed. The dataset contains the object conditions from an assistive mobile robot's point of view. The dataset contains 329 object sequences of 9 different object classes. To complement the validation of the SORT and Deep-SORT methods with the proposed cost matrices, evaluation was also performed in the MOT17 [27] dataset. The main contributions of this work can be summarized as follows:  
* Eight new object tracking data association cost matrix formulations based on intersection over union, Euclidean distances, and bounding boxes ratio are proposed. * The ISR Tracking dataset, presenting a mission performed by a mobile robot in a lab setting, represents the object conditions under which robotic platforms may navigate. It is a rearrangement of the ISR RGB-D dataset [28] with object tracking labels for multi-object tracking tasks. * An evaluation, having in view navigation tasks for assistive mobile robot platforms, of two multi-object tracking by detection algorithms, SORT and Deep-SORT, is also presented. The proposed new data association cost matrices were integrated and evaluated on both tracking methods.
# Area-Efficient Early-Termination Technique for Belief-Propagation Polar Decoders
## 1 Introduction  
Polar codes are the first error-correction codes proved to achieve a channel capacity with affordable encoding and decoding complexities [1; 2; 3]. The polar codes employ channel polarization, which implies that bit channels are polarized into completely error-free and noisy channels as the code length increases. Based on channel polarization, (\(N\), \(K\)) polar codes transmit useful information over the most reliable \(K\) bit channels and fixed data over the least reliable \(N\)-\(K\) bit channels. The index set of the most reliable \(K\) bit channels is called information set \(A\), and the index set of the least reliable \(N\)-\(K\) bit channels is called frozen set \(A^{c}\). The classical methods to decode polar codes are the successive-cancellation (SC) [4; 5; 6; 7] and belief-propagation (BP) [8; 9; 10; 11; 12; 13; 14] decoding methods. SC [4; 5; 6; 7] is widely employed for area-stringent applications owing to its serial nature, and BP is widely employed for throughput-demanding applications owing to its parallel nature [8].  
The conventional BP decoder passes reliable information over graphical models for performing inference until the maximum iteration number (_max-iteration_) [8]. In general, an early-termination technique [15; 16; 17; 18] is widely adopted in BP decoders [8] to improve the average throughput. Although previous early terminations including threshold-based [15], information-BER-based [16], and frozen-BER-based [17] criteria succeeded in reducing decoding latency, they suffered from high hardware complexities to check the termination condition. Therefore, this letter presents an area-efficient early-termination method by simplifying the previous early-termination criterion. The proposed method achieves area-efficiency by using only part of the log-likelihood ratio (LLR) values on information bit channels rather than entire bit channels in [15], and introduces no performance degradation incurred in References [16; 17] by prohibiting the use of the windowing method. Experimental results show that the proposed method always provides the smallest hardware complexity, while maintaining error-correction capability and the average number of decoding iterations.
## 2 Fundamental Models and Literature Search
### System Model
#### 2.1.2 Collection with Fixed Path  
The second approach involves the AMV following a predetermined trajectory at a consistent velocity, during which it gathers data from the sensor nodes, see Figure 4. The AMV's task includes completing this route within a user-defined timeframe, after which it returns to a central control station to transmit the accumulated data. In contradistinction to the strategy involving a distributed network, the fixed-path collection method exhibits greater simplicity and efficiency. This characterization is due to the confinement of the AMV's locomotion to a predefined path, executed within the confines of prescribed power parameters. Nevertheless, the effectiveness of these approaches hinges significantly on the selection of the AMV's path. A fixed AMV path greatly limits the flexibility of the mission, as it may not be adjustable to dynamic or adaptive environmental conditions. This is particularly important in scenarios where the underwater terrain is subject to variability, necessitating real-time adjustments to the data collection trajectory for optimized coverage and efficient resource utilization.
## 6 Result and Discussion
### IO Optimization  
The experimental data show that when we run WRF (Case A) in parallel IO mode with a 200-core computing process, the total time is reduced from 1078.80 s for serial IO to 840.89 s, a 22.05% performance improvement, and the IO speed is reduced from 37.27 s to 13.35 s, a significant speed improvement.  
Parallel IO can significantly reduce the time required for IO operations because, compared to serial IO, in the process of gathering data from other processes using gather_v, the data collection workload that was originally borne by the master process is distributed among other \"master processes\". As a result, the amount of data that the master process needs to collect is reduced, thereby enhancing the speed of IO operations.  
Figure 6: Comparison of WRF’s performance optimisation results at each stage.
## 4 Experimental Investigations
### Confirm the Stiffness and Damping Coefficients  
In real-world practice, a large difference between the measured vibration signal and the computed one (by the finite element method) is inevitable. Therefore, in order to check on the closeness between simulation signals and experimental vibration signals and to ensure the final stiffness and damping coefficients, the finite element model updating technique called TIC [37] is used. The value of TIC serves as the objective function as:  
\[\rho(x,y)=\frac{\sqrt{\sum\limits_{n=1}^{N}\left[x\left(n\right)-y_{m}\left(n \right)\right]^{2}}}{\sqrt{\sum\limits_{n=1}^{N}\left[x\left(n\right)\right]^{ 2}}+\sqrt{\sum\limits_{t=1}^{n}\left[y_{m}\left(n\right)\right]^{2}}} \tag{6}\]  
where \(\rho(x,y)\) is the coefficient between the measured signal and the average of the simulated signal, \(x(n)\) is the measured signal and \(y_{m}\left(n\right)\) is the mean of the simulation signal.  
As a figure-of-merit, the value of TIC \(\rho(x,y)\) has a numerical value between zero and one. If \(\rho(x,y)\) is near zero, this corresponds to accurate predictions by the model. Generally, in the engineering field, \(\rho(x,y)<0.4\) will lead to a satisfactory result [37].  
The classic linear model with four spring and four damping coefficients (shown in Figure 4b) is employed to model bearings in the present work. In the present work, the experimental setup is the geometry symmetry, and the two bearing can be treated as the same model. Nine combinations are carefully selected and listed in Table 4. For the present experimental setup, the test signal is simple, and \(\rho(x,y)<0.4\) can be easily satisfied. Therefore, we just present nine combinations to fulfill the alternative optimization.  
Figure 9: The experimental setup of a rotor-bearing system under normal load conditions.  
The first 400 data points from the experimental and the simulation signals are collected to validate the nine combinations of stiffness and damping coefficients. The values of TIC \(\rho(x,y)\) are shown in the last column of Table 4, and the least value \(\rho(x,y)=0.1821\) indicates the eight coefficients in the combination number; D is the relatively best choice to update the finite element model. The four stiffness coefficients and the four damping coefficients of the rotor-bearing system in the \(x\)+, \(x-\) direction and the \(y+\), \(y-\) direction are: \(K_{x+}=16.3\times 10^{7}\) N/m, \(C_{x+}=1500\) N\(\cdot\)S/m, \(K_{x-}=26.5\times 10^{7}\) N/m, \(C_{x-}=1100\) N\(\cdot\)S/m and \(K_{y+}=15.3\times 10^{7}\) N/m, \(C_{y+}=1600\) N\(\cdot\)S/m, \(K_{y-}=25.5\times 10^{7}\) N/m and \(C_{y-}=1000\) N\(\cdot\)S/m, respectively.
# Impact of Partial Replacement of Cement with a Blend of Marble and Granite Waste Powder on Mortar
## 2 Materials and Methods
### Characterization of MGWP  
#### 2.3.1 Fourier Transform Infrared (FTIR) Analysis  
The Fourier Transform Infrared (FTIR) technique was employed to assess the infrared radiation absorbance capacity of MGWP samples. The analyzed MGWP consists of a 1:1 ratio of MWP and GWP. The absorbance spectra of MGWP are plotted on a graph alongside OPC for the purpose of comparison. The absorbance band ranges from 4500 cm\({}^{-1}\)-400 cm\({}^{-1}\). The formation band of alite is expected to be present at 980 cm\({}^{-1}\)[26]. As seen in Figure 3, MGWP contains a higher amount of alite, which is advantageous for achieving high early strength. Bands corresponding to interstitial phases are observed in the range of 800 to 850 cm\({}^{-1}\). Sulfate minerals exhibit distinctive bands within the ranges of 3620 to 3200 cm\({}^{-1}\), 1600 to 1580 cm\({}^{-1}\), and 1300 to 1060 cm\({}^{-1}\). Additionally, calcium carbonate shows characteristic bands between 1580 and 1320 cm\({}^{-1}\), while calcium hydroxide displays a distinctive band at 3645 cm\({}^{-1}\)[27]. The absorbance of tricalcium silicate (C\({}_{3}\)S) is typically around 1000 cm\({}^{-1}\). In the case of MGWP, a broad bandwidth of C\({}_{3}\)S absorbance can be seen at 1241 cm\({}^{-1}\)-932 cm\({}^{-1}\). Cement, on the other hand, displays a C\({}_{3}\)S absorbance broad bandwidth of 1196 cm\({}^{-1}\)-1050 cm\({}^{-1}\). In terms of the formation of dicalcium silicate (C\({}_{2}\)S), it is typically observed around 800 cm\({}^{-1}\). In this context, cement demonstrates a bandwidth of 795 cm\({}^{-1}\)-1050 cm\({}^{-1}\), while a 10% replacement shows a belite formation band ranging from 813-668 cm\({}^{-1}\)[28].  
Figure 3: FTIR of the utilized MGWP and OPC.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**No.** & **Mix Code** & **Cement [\%]** & **Marble [\%]** & **Granite [\%]** \\ \hline
1 & MGWP-0 & 100 & 0 & 0 \\
2 & MGWP-5 & 95 & 2.5 & 2.5 \\
3 & MGWP-10 & 90 & 5 & 5 \\
4 & MGWP-15 & 85 & 7.5 & 7.5 \\
5 & MGWP-20 & 80 & 10 & 10 \\
6 & MGWP-25 & 75 & 12.5 & 12.5 \\
7 & MGWP-30 & 70 & 15 & 15 \\ \hline \end{tabular}
\end{table}
Table 3: Proportions of OPC, MWP, and GWP used in the tested mortar mixes.
# Multimodal Power Management Based on Decision Tree for Internet of Wearable Things Systems
## 2 Circuit Design of the PPG-Based Wearable System
### Microcontroller Unit with BLE Wireless Communication  
The NINA-B302 (U-Blox, Thalwil, CH) module integrates a small, stand-alone Bluetooth low energy (BLE)-5 circuit with the nRF52840 MCU (Nordic Semiconductors, Trondheim, NO, Norway) [30]. It also encloses a planar inverted-F antenna, that is successfully tuned with a passive network of inductor and capacitor components, producing a well-matched transceiver system. The nRF52840 MCU encloses an Arm Cortex-M4, 1 MB flash, and 256 kB RAM. It also contains a CryptoCell CC310 (Nordic Semiconductors, Trondheim, NO, Norway) cryptographic security unit and a high-speed SPI interface at 32 MHz. This chip has a 1.7 to 5.5 V supply voltage range and current consumption of 0.6 \(\upmu\)A in sleep mode and 4.8 mA TX at 0 dBm. The ML-DPMS algorithm is implemented in the MCU and dynamically runs after data acquisition, analyzing the healthcare signs and the supercapacitor level, and adjusting the next IoWT's sleep period.  
BLE is a wireless communication technology designed for low power consumption and a data rate of up to 2 Mbps. The BLE protocol data unit (PDU) is configured with a packet structure that contains a header and a payload. The PDU is assembled with a preamble, the access address, and the cyclic redundant code (CRC). Figure 3 illustrates the used packet structure of 20 bytes size for BLE transmission that is able to transmit 10 bytes of data per packet, with the possibility to transmit multiple packets in succession.