esa-sceva/satcom-chunk-collection huggingface dataset:
We previously designed and manufactured a matrix transducer by fabricating an array of piezoelectric elements directly on top of an Application Specific Integrated Circuit (ASIC) [34]. A single tile of \(12\times 40\) (rows \(\times\) columns) elements was developed as a building block for a larger transducer. The ASIC provides element-level switches and control logic which allow us to connect each individual element in a row (elevation direction) to a corresponding row-level transmit/receive bus. In this study, we use a \(4\times 2\) tiles configuration to form a large transducer consisting of \(48\times 80\) (azimuth \(\times\) elevation) elements. Based on the transducer architecture, 80 transmit/receive shots are required to fully read out the element data for a single plane wave imaging. Considering a pulse repetition frequency (PRF) of 20 kHz (for a depth of 37.5 mm deep enough for carotid PWI (CPWI) [8]), a volume rate of 250 per second can be achieved. However, for CPWI, 1000 V/s are needed [8; 35; 36]. Therefore, we are obligated to limit the number of receive/transmit shots to 20. To do so, in this paper, we propose to interconnect elements into non-delayed apertures which can be shifted in the elevation direction. We investigate the effects of receive and transmit aperture sizes and aperture shifting step in numerical simulations and in an in vitro experimental setup. An angular weighting (AW) method (based on the directivity pattern of the receive apertures) is used in the image reconstruction procedure to reduce the effects of grating lobes (caused by the increased effective pitch in elevation direction). The goal is to find a trade-off to form good-quality B-mode images, providing suitable inputs for motion estimation methods used in wall velocity measurement [8; 13].
# No-Reference Image Quality Assessment with Convolutional Neural Networks and Decision Fusion
## 1 Introduction  
The aim of image quality assessment (IQA) is predicting digital images' perceived quality that is consistent with the human visual system's (HVS) perception [1]. The most obvious way to evaluate the quality of digital images is a subjective user study where quality scores are obtained from human observers in a laboratory environment [2] or a crowdsourcing experiment [3]. However, such experiments are expensive, laborious, and time consuming. Thus, they cannot be applied in a real-time system. On the other hand, they result in publicly available benchmark IQA databases, such as KonIQ-10k [4] or SPAQ [5], which consist of images with their quality scores. In most cases, quality scores are given as mean opinion scores (MOS), which are arithmetic averages of all quality opinions given by individual observers. In contrast, the aim of objective IQA is constructing computational, mathematical models that can estimate digital images' human perceptual quality.  
Traditionally, objective IQA is divided into three branches in the literature based on the availability of the distortion-free reference images. The first branch is no-reference image quality assessment (NR-IQA) where no information is available about the reference images. Contrarily, full-reference image quality assessment (FR-IQA) methods have full access to the reference images, while reduced-reference image quality assessment (RR-IQA) models have only partial information about the reference images.  
Recently, deep learning has dominated the field of image processing and computer vision. The field of objective IQA has not been an exception to this trend. The goal of this study is to introduce a novel deep learning-based NR-IQA method for authentic distortions that relies on decision fusion of multiple quality scores coming from different types of convolutional neural network architectures. The proposed decision fusion-based model allows us to better characterize authentic image distortions due to the diverse set of different types of networks.  
The rest of this section is organized as follows. First, related and previous work is reviewed in Section 1.1. Second, the main contributions of this study are enumerated in Section 1.2 in light of previous work. Third, the structure of the paper is defined in Section 1.3.
_Algorithms_**2014**, \(7\), 429-443; doi:10.3390/a7030429  
_algorithms_  
**ISSN 1999-4893**  
www.mdpi.com/journal/algorithms  
_Article_  
\(\ell_{1}\) **Major Component Detection and Analysis (\(\ell_{1}\) MCDA) in Three and Higher Dimensional Spaces**  
**[PERSON] \({}^{1,2}\), [PERSON] \({}^{2,3}\), [PERSON] \({}^{2}\) and [PERSON] \({}^{2,*}\)**  
\({}^{1}\) School of Management, University of Chinese Academy of Sciences, Beijing 100190, China;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) Department of Industrial and Systems Engineering, North Carolina State University, Raleigh,  
NC 27695-7906, USA; E-Mails: [EMAIL_ADDRESS] (J.E.L.); [EMAIL_ADDRESS] (S.-C.F.)  
\({}^{3}\) Mathematical Sciences Division and Computing Sciences Division, Army Research Office,  
Army Research Laboratory, P.O. Box 12211, Research Triangle Park, NC 27709-2211, USA  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +1-919-917-5967.  
_Received: 21 May 2014; in revised form: 21 July 2014 / Accepted: 23 July 2014 / Published: 19 August 2014_  
**Abstract:** Based on the recent development of two dimensional \(\ell_{1}\) major component detection and analysis (\(\ell_{1}\) MCDA), we develop a scalable \(\ell_{1}\) MCDA in the \(n\)-dimensional space to identify the major directions of star-shaped heavy-tailed statistical distributions with irregularly positioned \"spokes\" and \"clutters\". In order to achieve robustness and efficiency, the proposed \(\ell_{1}\) MCDA in \(n\)-dimensional space adopts a two-level median fit process in a local neighbor of a given direction in each iteration. Computational results indicate that in terms of accuracy \(\ell_{1}\) MCDA is competitive with two well-known PCAs when there is only one major direction in the data, and \(\ell_{1}\) MCDA can further determine multiple major directions of the \(n\)-dimensional data from superimposed Gaussians or heavy-tailed distributions without and with patterned artificial outliers. With the ability to recover complex spoke structures with heavy-tailed noise and clutter in the data, \(\ell_{1}\) MCDA has potential to generate better semantics than other methods.  
**Keywords:** multidimensional heavy-tailed distribution; \(\ell_{1}\)-norm; major component; \(n\)-dimensional; outlier; pattern recognition; robust principal component analysis
# Action Recognition Network Based on Local Spatiotemporal Features and Global Temporal Excitation
## 3 Method
### Channel Time Excitation Module (CTE)  
The CTE module adaptively excites time-sensitive feature channels by modeling the interdependences of channels in terms of time to strengthen the global temporal information, as shown in Figure 4. Since our CTE module focuses on capturing temporal dynamics, we firstly used a spatial domain global average pooling to compress the spatial information of the given input feature \(X\in R^{N\times T\times C\times H\times W}\),where \(T\) represents the number of segments, as represented in Equation (6):  
\[F=\frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j=1}^{W}X[:,:,:,i,j],F\in R^{N\times T \times C\times 1\times 1} \tag{6}\]  
We reshaped \(F\) into \(F\in R^{N\times C\times T}\) to reason the temporal information. To learn the temporal information of neighboring frames, we used a 1D convolutional with a kernel size of three to perform \(F\) in the temporal dimension, which decreased the amount of feature channels by a ratio \(r\) (\(r=4\)) to control the computational cost and obtained good performance; this is shown in Equation (7):  
\[F_{s}=Conv*F,F_{s}\in R^{N\times\frac{C}{r}\times T} \tag{7}\]  
Another 1D convolution with a kernel size of one was adopted to perform \(F_{s}\), which recovers the amount of channels, and the time-sensitive weight \(P\) is obtained by a Sigmoid activation function \(\sigma\), which is shown in Equation (8):  
\[P=\sigma(Conv_{\text{exp}}*F_{s}),P\in R^{N\times C\times T\times 1\times 1} \tag{8}\]  
Finally, the input feature \(X\) and time-sensitive weight \(P\) were multiplied in a channel-wise way to excite time-sensitive channels. The original background that is beneficial for action recognition was preserved by a residual link, as shown in Equation (9):  
\[X_{out}=X+X\odot P \tag{9}\]  
where \(X_{out}\) is the output feature of the CTE module. Through the above operations, the temporal of input feature \(X\) was enhanced.
# State-of-All-the-Art and Prospective Hydrogel-Based Transdermal Drug Delivery Systems
## 4 Skin and Diffusion through the Skin
### Mechanism of Drug Release through Hydrogels
#### 4.4.1 Diffusion-Controlled Release Mechanism  
The diffusion-controlled mechanism is a prevalent approach for drug release in hydrogels, and it can be further categorized into matrix and reservoir types. Most transdermal applications use hydrogel patches made of either of these types, depending on the type of drug release. The drug is uniformly distributed within the matrix-type system, whereas the polymeric matrix encapsulates the drug core in the reservoir type. In both cases, drug diffusion is influenced by mesh size, which, in turn, depends on external stimuli, crosslinking density, composition, and gel structure [50]. In the swollen state, typical hydrogels have mesh sizes in the range of 5-100 nanometers [76]. This size is significantly larger than that of most small molecules. Consequently, the drug can be retained in a swollen state. The diffusion of drugs in the hydrogel is most accurately described by [PERSON]'s law of diffusion, as delineated below [50].  
\[D_{ip}\frac{dC_{i}}{dx} \tag{3}\]  
where \(C_{i}\) is the concentration of the drug and \(D_{ip}\) is the drug's coefficient of diffusion.
## 2 Biosorbent-Based Extraction Phases
### Recycled Diatomaceous Earth as a Biosorbent Material  
Diatomaceous earth is an amorphous silicate sediment originating from fossilized unicellular microorganisms on algae of the class Bacillariophyceae centricae. This material is composed mainly of silica dioxide and small amounts of aluminum, iron, calcium, magnesium, sodium and potassium. After being used for the filtration and clarification of beer in a brewery, the diatomaceous earth was subject to thermal treatment and then used as the extraction phase for SPME [23]. The FTIR characterization of the material revealed O-H bonds from silanol groups. Moreover, asymmetric stretching was reported assigned to Si-O-Si bonds, frequently found in silicate materials. This biosorbent has been used along with SPME for the determination of PAH's in river water samples by GC-MS [23]. In the comparison with the commercial fibers PDMS/DVB and PDMS, the biosorbent showed better extraction efficiency for most compounds, except for acenapthylene (C\({}_{12}\)H\({}_{8}\)), fluorene (C\({}_{13}\)H\({}_{10}\)) phenanthrene (C\({}_{14}\)H\({}_{10}\)) and pyrene (C\({}_{16}\)H\({}_{10}\)), for which PDMS/DVB was better. In this work, the authors did not provide a possible explanation for the interactions between analytes and the biosorbent. However, a fiber limitation was reported regarding the use of salt in the optimization step. According to the authors, salt particles added to the samples can remain adsorbed in the surface of the extraction phase, causing fiber damage. In this case, if the salt addition is necessary to improve extraction efficiency, a cleaning step with water may be done before the fiber insertion into the GC injection port.  
Other applications of this sorbent include TFME with 96-well plate system for the determination of endocrine disruptors in water samples by HPLC-DAD [24] and with BA\(\upmu\)E in the determination of methyl and ethyl paraben, benzophenone and tricolcarban in water by HPLC-DAD [25] (Table 1). In the work of [PERSON] et al. [24], bisphenol A (BPA), benzophenone (BzP), tricolcarban (TCC), 4-methylbenzylidene camphor (4-MBC) and 2-ethylhexyl-p-methoxycinnamate (EHMC) were successfully determined from environmental water samples. Considering the analyte structure, the authors attribute the extraction efficiency of diatomaceous earth to the O-H moieties presented in the sorbent and the O-H and N-H groups in the target compounds. In this work, information about the extraction phase stability in the presence of organic solvents was provided. After successive extractions, the biosorbent blades were able to be used without expressive loss in the extraction efficiency for at least 20 extraction/desorption cycles. The proposed method exhibited satisfactory analytical performance, with LODs varying between 1 and 8 \(\upmu\)g L\({}^{-1}\) and determination coefficient ranging from 0.9926 to 0.9988.
# Land Cover Mapping in Southwestern China Using the HC-MMK Approach
## 2 Study Area  
Southwestern China (78 deg25'-110 deg21'E, 21 deg09'-36 deg32'N) in this study includes the Chongqing municipality, the Sichuan, Yunnan and Guizhou provinces and the Tibet autonomous region (Figure 1), with an area of 2.33 million km\({}^{2}\), accounting for about a quarter of the total land area of China. It is bordered by Vietnam, Laos, Myanmar, India and Bhutan to the south, India and Nepal to the west, Xinjiang, Qinghai, Gansu and Shanxi provinces to the north, and the Guangxi, Hunan and Hubei provinces of China to the east. Southwestern China is one of most complicated terrain sections, including plateaus, mountains, hills, basins and plains, and can be divided into four geomorphic units: Qing-Tibet plateau, Hengduan mountain, Yunnan-Guizhou plateau and Sichuan basin [34]. The elevation ranges from less than 500 m (Yangtze River) to 8844 m (Mt. Everest in Qing-Tibet plateau) in this region. There are various types of climates in this area, mainly subtropical, temperate and alpine climates. A variety of land cover types and distinct vertical stratification of vegetation classes can be found in this place.
# A Lattice Boltzmann Method and Asynchronous Model Coupling for Viscoelastic Fluids
## 5 Conclusions  
In conclusions, we have employed a coupled lattice Boltzmann method, with a generalized asynchronously coupling scheme, to simulate the Oldroyd-B fluids when the Weissenberg number is increased. This coupled method includes two types of equations, one is Lattice Boltzmann equation of the solvent and the other is constitutive equation of the stress tensor. The two equations are solved using two different time steps--one is of macroscopic scale and the other is of mesoscopic scale. For viscoelastic fluids at a higher _Wi_ number, the computational stability of asynchronously coupling is greatly affected by the polymer stress as the elasticity interaction becomes relatively large. Therefore, we have presented a generalized technique for time-stably advancing of the coupled lattice Boltzmann method, which can improve the numerical stability of the simulation at a higher _Wi_ number. The main idea of the new method is that one can add an adaptable number of micro solver time steps before the transmission of data between the coupling variables.  
We have shown that the numerical results, as calculated by the new method, are in good agreement with analytical results of the two-dimensional channel flow. In addition, for viscoelastic fluids through contractions at _Wi_ = 5, we have found that the prediction of our presented method can reproduce the same numerical results that were obtained by previous literatures. The main advantage of our presented method is that it could be use to simulate the Oldroyd-B fluids when the _Wi_ number is increased. Furthermore, our new method could also be easily implemented, which keeps the traditional advantageous of LBM. Hence, the coupled lattice Boltzmann method that was described in this paper could be seen as an alternative numerical technique for simulating the viscoelastic fluid.  
This work was supported by the National Natural Science Foundation of China (Grant Nos. 11601411,11601410), the Natural Science Basic Research Plan in Shaanxi Province of China (Program No. 2017 JQ1029), the Scientific Research Program Funded by Shaanxi Provincial Education Department (Program No. 15 JK1313), and the Scientific Research Foundation of Xi'an Polytechnic University (BS1435).  
[PERSON] developed the numerical method, analyzed the data and drafted the manuscript. [PERSON] of the study and finalized the manuscript. [PERSON] the scratch numerical experiments, and edited the manuscript. All the authors read and approved the final version of the manuscript.  
The authors declare no conflict of interest.
# Driving Behavior Modeling Based on Consistent Variable Selection in a PWARX Model
## 3 Analysis and Modeling of Driving Behavior
### Definition of Input Candidates and Output  
Candidates for input variables  
* Range (m): \(u_{1}=x_{l}-x_{f}\);
* Range rate (m/s): \(u_{2}=v_{l}-v_{f}\);
* KdB: \(u_{3}\);
* Jerk (m/s\({}^{3}\)): \(u_{4}=\Delta a_{f}/\Delta t\)
* Inverse of time to collision (rate of increase in visual angle to leading car) (s\({}^{-1}\)): \(u_{5}=\dfrac{v_{l}-v_{f}}{x_{l}-x_{f}}\);
* Time headway (time deference between cars) (s): \(u_{6}=\dfrac{x_{l}-x_{f}}{v_{f}}\).  
Output variable  
* Speed (m/s): \(y=v_{f}\).  
KdB is a risk-feeling index which is defined as the logarithm of the time derivative of the leading car's back area projected onto the ego driver's retina [54]. To express KdB, \(u_{1}\) and \(u_{2}\) are used as follows:  
\[u_{3}=\begin{cases}10\times\log(-c),&\text{if }c<-1\\ -10\times\log(-c),&\text{if }c>1\\ 0,&\text{if }-1\geq c\leq 1\end{cases} \tag{11}\]  
where \(c\) is denoted by  
\[c=4\times 10^{7}\times\dfrac{u_{2}}{u_{1}{}^{2}}. \tag{12}\]  
An intuitive interpretation of KdB is that the level of dangerous situation the driver faces increases as the value of KdB increases. Before identification, all variables were normalized.
## 3 Results  
### DPP Test Network  
To implement the proposed data model, a Fabric test network was deployed. The blockchain runs the smart contract described in Section 2.4 The components of the test network are presented in Figure 5.  
Figure 4: The chaincode module dependency diagram.  
Figure 5: The DPP blockchain test network.  
The test network comprises three distinct organizations, namely _Service Provider_, _Manufacturer_, and _Supplier_. Each organization is equipped with a singular peer, a state database, and a certificate authority (CA). The Service Provider organization is responsible for the management of the proposed system. It is also responsible for deploying the _ordering service_ and for managing the _channel_. The same organization runs the DPP service gateway. Ensuring uniformity, each peer has an identical version of the installed chaincode. Communication among clients, peers, and the ordering service occurs through RPC utilizing SSL/TLS certificates provided by the certificate authority.  
To execute a transaction like creating a _Product_ asset, the peer runs the smart contract (chaincode). After validation, the transaction is sent to the ordering service, typically a single node. Multiple transactions can be sent to different peers simultaneously. The ordering service organizes transactions into blocks based on the network's consensus protocol, then distributes them to all peers. Each peer validates the block, storing data in their state databases. The communication takes place through a _channel_. This is a private communication pathway (a subnet) that enables secure and confidential interaction between specific network members. It restricts access to transactions, ensuring that only authorized participants can view and engage with the data exchanged within the channel. This mechanism allows organizations to conduct sensitive transactions without exposing them to all participants in the broader network.  
The _state database_ serves as a key-value store for tracking the ongoing status of the ledger, housing the current values of all ledger assets and the live state of any deployed smart contracts on the platform. Fabric supports two peer state database options. The default state database embedded in the peer node is LevelDB. This database option stores the data as basic key-value pairs and supports key, key range, and composite key queries. Alternatively, CouchDB serves as an optional configurable state database, enabling data modeling on the ledger as JSON and facilitating rich queries against data values rather than retrieving data just by keys. With CouchDB, indexes can be deployed inside the chaincode package to enhance query efficiency and enable the fast querying of large datasets [27].  
Accessing the peers directly from the client application is restricted. Instead, the blockchain interacts with the external environment through a Java (Spring Boot) REST API (i.e., DPP service gateway). The integration of the Java application with the ledger network was accomplished using the Fabric SDK [28].
# Method for Estimating the Optimal Coefficient of L1C/B1C Signal Correlator Joint Receiving
## 1 Introduction
The correlator joint tracking for unequal distributions of data and pilot power remains in the stage with an equal weight combination (1:1 combination) and power ratio combination, which does not cover the introduction and verification of the positioning module. To meet the trends of high-precision users, this paper proposes an entire correlator joint tracking and positioning structure, primarily based on the data and pilot signal models with an unequal power allocation. The optimal coefficients of the correlator joint for carrier loop are deduced based on the equivalence of the correlator joint and the discriminator joint. Next, a joint code tracking accuracy model is established for MBOC and BOC signal, and the optimal coefficients of the code loop are determined with the aid of numerical simulation. Finally, the optimal coefficients estimated in this paper are verified by the errors in the carrier loop discriminator, the code loop discriminator, the signal carrier-to-noise ratio and the positioning error. The positioning accuracy improvement ratio of the satellites that participate in the positioning of the BDS3 constellation and the Japanese Zenith satellite (QZSS) constellation was quantified using the correlator joint optimal coefficient and compared with the traditional coefficient.
# Escaping Printable Encoded Streams to Embed Out-of-Band Data
## 5 Discussion and Results
### CRC Embedding  
In the first run of tests, we considered adding an 11 bits CRC to blocks of data encoded in printable form with Base41 [1]. The method adds three octets to the Base41 encoding of the block; thus, if the block has size \(n\) octets (\(8\,n\) bits), then the Base41 encoding inflates it to \(1.5\,n\) octets, adding the CRC leads to \(1.5\,n+3\) octets with an overload of \(\frac{3}{15\,n+3}\times 100\%\). On the other hand, an 11 bits CRC on a block of \(8\,n\) bits represents an overload of \(\frac{11}{8\,n+11}\times 100\%\). Analogous formulas can be derived for 14 bits CRC and employing Base45 unused sequences.  
We performed the computation of the overload for blocks of sizes 128, 256, 512, and 1024 bits (or 16, 32, 64, and 128 octets, respectively). Table 2 shows the resulting overloads for CRCs embedded into Base41 and Base45 encodings as proposed, comparing them with the classical overload had when embedding a CRC of (11 and 14 bits, respectively). From these data, it may be seen that the increase in overload is quite limited and feasible for an application level error detection and data protection from unintentional modifications.  
Figure 4: Possible encoding of parameters with separators \(\mathrm{S}_{d}\) and \(\mathrm{S}_{t}\).  
Figure 5: Identifying types of parameters with separators.