esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Development of Aptamer Sensors for the Quantification of Aflatoxin B1
## 2 Colorimetric, Electrochemical, and Fluorescent Aptamer Sensors to Monitor AFB1
### Colorimetric Aptamer Sensor for AFB1
The sensor consisted of two components: Gold nanoparticles, which were modified with streptavidin, acted as the signal indicators; and three hairpin DNA probes, which were biotinylated, were employed for the specific recognition of AFB1. When AFB1 was present, toehold-mediated strand displacement reactions occurred. Subsequently, the hairpins opened and induced recycling. Consequently, the color of the solution changed to red due to the formation of aggregates of nanoparticles through biotin-streptavidin coupling. The colorimetric signal was linear with respect to AFB1 in the range from \(3.1\times 10^{-9}\) to \(3.1\times 10^{-4}\) g-L\({}^{-1}\). Due to the high sensitivity of the sensor, AFB1 was able to be detected at concentrations as low as \(6.2\times 10^{-10}\) g-L\({}^{-1}\). The sensor was capable of detecting AFB1 in complex sample matrices, including rice samples, due to the robustness of the sensor. The proposed method was simple and convenient in operation. The visible signal was observed after several solutions were mixed at room temperature, and provided a reliable, as well as ultra-sensitive, method for monitoring AFB1 in real environments. Figure 3: Schematic illustration for the colorimetric quantification of AFB1 using the colorimetric signal facilitated by peroxidase-mimicking split DNAzyme. Reproduced from [19]. Copyright 2015, Elsevier. Various strategies have been designed for highly rapid and selective determination of AFB1 in aqueous media. For instance, an AFB1-reponsive hydrogel was prepared by [PERSON] et al. for the colorimetric determination of AFB1 with high sensitivity [21]. The hydrogel was composed of an AFB1 aptamer, as the recognition unit, and a pair of complementary DNA chains, which served as cross-links. In order to visually determine AFB1, gold nanoparticles were added into the hydrogel. In the presence of the analyte, an AFB1-aptamer complex formed, resulting in the destruction of the hydrogel. Meanwhile, the gold nanoparticles were subsequently released, resulting in a transition from colorless to red. Platinum nanoparticles (PtNPs) were added into the hydrogel in advance and, thereafter, the hydrogel was integrated with a distance-readout volumetric bar chart chip, in order to decrease the limit of detection as well as expand the approach toward quantitative analysis. Upon addition to AFB1, the hydrogel was subjected to disruption, the and PtNPs were then released. As a consequence, O\({}_{2}\) was generated, which pulled the ink bar in the chip as H\({}_{2}\)O\({}_{2}\) was decomposed in the presence of the PtNPs. A good linear relationship was found to exist between the distance the ink was moved and the H\({}_{2}\)O\({}_{2}\) concentration. The limit of detection of AFB1 was estimated to be as low as \(5.5\times 10^{-10}\) g L\({}^{-1}\). Furthermore, the strategy was applied to monitoring AFB1 in beer through colorimetric detection.
## 1 Introduction
Such location-based approaches, however, often need a device, such as a GPS, to provide the necessary position information. Furthermore, the cost and weight of a sensor will be increased if each sensor is equipped with such a device. Although several researchers have suggested using a virtual coordinate to provide the position information [10], this would incur extra energy consumption due to the communication process, which is necessary for acquiring such position information. In addition, to obtain such virtual coordinate-based position information, global information has to be used, e.g., the maximum ID among all sensors with the maximum hop distance from a specific node. As can be seen from the above, since all existing routing approaches have some disadvantages, it is more important to develop an assistant tool to enhance existing routing approaches than to develop a new routing approach. To the best of our knowledge, this paper is the first one to propose a multi-agent framework that can be used to assist and enhance many existing routing approaches. The merits of this framework include that: (i) it is independent of individual existing routing approaches, so it can assist many existing routing approaches and enhance their performance; (ii) it is decentralised and operated based only on local information, and it does not need to change the physical network topology; and (iii) it does not only suit static WSNs, but also works well in mobile and open WSNs, where sensors are mobile and new sensors may join or existing sensors may leave the network. The rest of the paper is organised as follows. In the next section, a detailed literature review is provided, which includes routing approaches belonging to the aforementioned three categories, especially those approaches developed after the publication of the survey papers in [4,7]. Then, the proposed framework is described in detail in Section 3. A simulation regarding the performance of the proposed framework is given in Section 4. Finally, this paper is concluded in Section 5.
## 6 Preservation of Muscle Tissue on Tendon Graft  
The residual muscle tissue is routinely scraped from the hamstring tendon during the graft preparation. However, these remnants contain a variety of potent stem cells to enhance graft maturation. [PERSON] et al. analyzed the samples from hamstring tendons and found pericytes to be more numerous in muscle tissue than in tendon [40]. Moreover, the muscle-derived stem cells expressed higher activity of early bone differentiation markers like alkaline phosphatase and bone sialoprotein, while tendon-derived stem cells presented  
Figure 3: Preparation of a periosteal flap to enhance the femoral part of a graft: (**a**) Harvesting of the periosteum below the pes anserinus attachment; (**b**) Free periosteal flap; (**c**) Femoral part of an anterior cruciate ligament graft (G) augmented with a free periosteal flap (PF).  
Figure 2: Harvesting of a semitendinosus tendon graft with an adjacent periosteum: (**a**) Exposed distal semitendinosus (ST) attachment with an adjacent periosteal flap (PF) (marked violet); (**b**) Harvested semitendinosus tendon graft with an adjacent periosteal flap.  
higher levels of late bone differentiation markers such as dental-matrix-protein 1 and osteocalcin. The authors concluded that muscle- and tendon-derived stem cells may act synergistically and sequentially, enhancing subsequent stages of the tendon-bone unit formation.  
In an in vitro experiment conducted by [PERSON] et al., co-culturing myoblasts with hamstring tendon-derived cells upregulated the expression of tendon/ligament differentiation markers and increased the synthesis of collagen units [41]. Moreover, authors analyzed the influence of muscle-secreted factors on the ACL reconstruction in the rat model. The results were equivocal--The tibial tunnel closure did not show any significant differences between the experimental and control groups, whereas the femoral tunnel closure was significantly improved in the reconstructions enhanced with the muscle-derived serum.  
[PERSON] et al. compared ACL reconstruction with the semi-tendon/semi-muscle graft (SSG) and the tendon-only graft in the rabbit model [42]. Although the diameter of the SSG gradually decreased during the postoperative period, the healing and incorporation of the graft were more evident than in the tendon-only group. The vascularity and cellularity of the SSG were significantly greater than in tendon-only graft reconstructions. Nonetheless, the biomechanical testing revealed the significantly lower ultimate failure load, yield load and elongation at failure in the SSG group. Authors concluded that even though muscle tissue enhances biological incorporation of the graft, excessive amount of muscle remnants may weaken the whole graft construct.
# Hypernetwork Representation Learning with the Set Constraint
## 6 Experiments
### Node Classification  
The multi-label classification tasks are carried out on MovieLens and wordnet datasets, since only the two datasets have label information. In addition, we remove the nodes that did not have labels in the two datasets. The node representation vectors are taken as input to train SVM [29] classifier to calculate node classification accuracies.  
Tables 3 and 4 show node classification accuracies on the MovieLens and wordnet datasets. From the two tables, we have obtained the following observations:  
* On the MovieLens and wordnet datasets, the node classification performance of HRSC is superior to that of other baseline methods. For example, for these two datasets, HRSC outperforms other best baseline method (i.e., Deepwalk) by about 1%. Meanwhile, the node classification performance of HRSC exceeds the remaining baseline methods to a certain extent. Experimental results demonstrate that HRSC is effective and robust, since HRSC incorporates the hyperedges into the process of hypernetwork representation learning to learn node representation vectors of better quality than Deepwalk and other baseline methods. Meanwhile, from the above results, we can find that the node classification performance of HRSC exceeds other baseline methods very little, since the categorical attributes on the MovieLens and wordnet datasets are not obvious.
* The node classification performance of GraRep is second only to that of HRSC and DeepWalk, and its node classification performance is nearly the same as that of DeepWalk. The reason for this is that GraRep is an algorithm framework that integrates the global structure information of a graph into the representation learning process, and considers the hyperedges to a certain extent in the process of network representation learning.
* Although SDNE preserves local and global structure information in the process of network representation learning, it does not perform well in the hypernetwork dataset, which indicates that SDNE is not universal.  
In summary, we find that HRSC obtains high-quality node representation vectors by comprehensively considering the hypernetwork topology structure and the hyperedges.
# Lightweight Integrity Preserving Scheme for Secure Data Exchange in Cloud-Based IoT Systems
## 4 Proposed Scheme
### Proposed Scheme Details
#### 4.2.3 Dynamic File Support  
Within the IoT-cloud system calculations, smart device users can regularly retrieve and modernize external information for an array of reasons. Therefore, encouraging information dynamics within public integrity checking systems is essential [17; 42; 43]. An encouraging information dynamic system should involve the following stages. To start the process:  
* The smart device user submits an appeal for the file to the semi-trust server. The semi-trust server then obtains the appeal and passes it on to the IoT-cloud system.
* The IoT-cloud system then transmits the file to the semi-trust server and the semi-trust server passes it on to the smart device user.
* The smart device user then alters the file, returns the file to IoT-cloud system, and transmits the altered file to the semi-trust server including the information version parameter _ver_. Within this context, _ver_ has the value of 0 or 1:
* If the _ver_ has a value of 0, this indicates that the file has not been altered.
* If the _ver_ has a value of 1, this indicates that it has been altered.
* Upon receiving the file, the semi-trust server considers the _ver_ value to identify the next steps. When the _ver_ has a value of 0, then the file has not been altered and no further steps are required. However, when the _ver_ has a value of 1:
* The semi-trust server must create new keys (\(Ck\), \(Vk\)) for the altered file and then re-calculates \(Ch\) and \(\sigma\).
* Once this has been completed, the semi-trust server submits a file appeal to the IoT-cloud system to substitute the new file for the old file and the new \(\sigma\) for the old \(\sigma\).
# Three-Dimensional Inversion of the Time-Lapse Resistivity Method on the MPI Parallel Algorithm
## 3 Synthetic Example Tests  
### Theoretical Model and Forward Modeling Response  
The time-lapse resistivity method is commonly used to monitor the migration process of underground fluid, in which the fluid often migrates from one geological unit to another with groundwater. In this study, a time-lapse model is established to simulate the fluid migration process.  
Figure 4 shows the sections of the time-lapse model. The anomalous bodies in the time-lapse model are two low-resistance prisms. The left prism remains unchanged, whereas the prism on the right diffuses along the positive direction of the X-axis, simulating the continuous migration of underground fluids over time. The time-lapse model includes three different time points, labeled T\({}_{1}\), T\({}_{2}\), and T\({}_{3}\). A schematic diagram of the model in the X-Z and X-Y directions at these three times is shown from left to right. The resistivity of background A is 100 \(\Omega\cdot\)m, whereas the resistivities of abnormal bodies B\({}_{1}\) and B\({}_{2}\) are 10 \(\Omega\cdot\)m. The size of B\({}_{1}\) is fixed, with dimensions of 5 m in the X-direction, 6 m in the Y-direction, and 5 m in the Z-direction. The distance between B\({}_{1}\) and B\({}_{2}\) is 6 m. B\({}_{2}\) remains fixed at 6 m in the Y-direction and 5 m in the Z-direction, but its size in the X-direction increases over time, reaching 4 m, 5 m, and 6 m at T\({}_{1}\), T\({}_{2}\), and T\({}_{3}\), respectively.  
The same observation system was used for forward modeling of the time-lapse model at T\({}_{1}\), T\({}_{2}\), and T\({}_{3}\). Figure 5 shows a schematic diagram of the observation system. The three-pole device is used for measurement on the ground surface. The electrodes are arranged as a rectangular square array, the red triangle represents the position of the transmitting electrode, the blue dot represents the position of the receiving electrode, and the black solid line box represents the projection of the anomalous bodies B\({}_{1}\) and B\({}_{2}\) on the ground surface. When each transmitting electrode is powered, all adjacent receiving electrodes in the X-direction are activated to ensure that enough information in the three-dimensional space is obtained. Gaussian-random errors of 5%, 5%, and 20% were added to the observation data at T1, T2, and T3, respectively, which simulated significant noise disturbance in the data. At last, there were a total of 194 sources, with 23,760 data points obtained at a single time, and 71,280 data points obtained at three times.  
The forward modeling and inversion use the same grid generation method, which is obtained through many forward and inversion tests. First, we must ensure that all the transmitting and receiving electrodes are on the grid nodes; second, infinity is set to more than 3 times the size of the observation area; finally, we must ensure that there are at least 1-2 lines with 3-5 points on each line above the anomalous body.
# Reduced-Complexity Tracking Algorithms on Chip for Real-Time Location Estimation
## 1 Introduction
Under a stationary environment, as compared with software implementation for location tracking, the proposed chip training, decision, and tracking approach can not only implement location accuracy which is similar to the KF tracking approach, but it possesses computational complexity and provides better performance than that of non-tracking approaches. An approach with low complexity and high performance is proposed. Compared to the software implementation and the previous research, because of its low complexity and faster processing speed, the proposed approach on a chip has more benefits for LBS services or IoT applications.
# M-Healthcare Model: An Architecture for a Type 2 Diabetes Mellitus Mobile Application
## 2 Analysis
### Telehealth  
Telecare is a part of telehealth. Telecare focuses on the therapeutic side, while telemedicine covers the prophylactic, preventive, and therapeutic aspects [26]. One of the functions of telehealth, and a major requirement in providing health services, is patient monitoring and scheduling. The coverage of telehealth, telemedicine, and electronic health (e-health), telecare, and m-health is described by [PERSON] et al. [27].
# Ultra-Wideband Bandpass Filters Using Tapered Resonators
## 3 Design of Tapered Resonators  
In this section, a procedure for the design of short-circuited resonators based on the analysis given in Section 2 will be introduced. For ETTL stubs:  
\[Z_{0i2}=Z_{0i1}e^{\xi z}, \tag{15}\]  
where \(Z_{0i1}\) is the impedance at \(z=0\), \(Z_{0i2}\) is the impedance at \(z=l_{min}^{exp}\), and \(k\) is the tapered ratio defined by \(k=(1/z)\ln(Z_{0i2}/Z_{0i1})\). The minimum length of the ETTL stub will be determined using (2), as shown in Figure 2.  
For LTTL stubs:  
\[Z_{0i2}=Z_{0i1}(1+mz), \tag{16}\]  
where \(Z_{0i1}\). is the impedance at \(z=0\), and \(Z_{0i2}\) is the impedance at \(z=l_{min}^{lin}\).  
The wavelength \(\lambda\) is not constant along the taper. Hence, to determine the effective wavelength, the effective dielectric constant \(\varepsilon_{e}\) of the ETTL is approximated as the average effective dielectric constant of wide and narrow lines [22] then  
\[\varepsilon_{e}=\frac{\varepsilon_{e1}+\varepsilon_{e2}}{2}, \tag{17}\]  
where \(\varepsilon_{e1}\) is the effective dielectric constant of the line with characteristic impedance \(Z_{0i1}\), and \(\varepsilon_{e2}\) is effective dielectric constant of the line with characteristic impedance \(Z_{0i2}\). The effective wavelength is then given by  
\[\lambda=\frac{c}{f_{0}\sqrt{\varepsilon_{e}}}, \tag{18}\]  
where \(f_{0}\) is the center frequency, and \(c\) is the velocity of light. In the same manner, the above procedure can be applied for the LTTL shown in Figure 3.  
Table 1 shows the elements that can be used in the design of both the exponential and the linear tapered short-circuited shunt resonators for three different \(k/\beta_{0}\) ratios.  
Figure 3: Linear TTL short-circuited stub.  
Figure 2: Exponential TTL short-circuited stub.
# Load Balancing Strategies for Slice-Based Parallel Versions of JEM Video Encoder
## 2 Description of the New Characteristics of the Joint Exploration Test Model (JEM)
### Picture Partitioning
The following parameters are defined in order to obtain efficient partitioning in a QTBT tree:  
* _CTU size:_ The root node size of a quadtree; the same concept as in HEVC. * _MinQTSize:_ The minimum allowed size of the leaf node in the quadtree. * _MaxBTSize:_ The maximum allowed size of the root node in the binary tree. * _MaxBTPeph:_ The maximum allowed depth of the binary tree. * _MinBTSize:_ The minimum allowed size of the leaf node in the binary tree. MaxBTSize and minQTSize are two factors that are critical to the R/D performance and the encoding time. In JEM, these two parameters of the current slice are set adaptively larger when the average CU size of the previous encoded picture is larger, and vice versa for only the P and B slices [17]. At the transform stage, only the lower-frequency coefficients are maintained for transform blocks with sizes (width or height) larger than or equal to 64. For example, for an M \(\times\) N transform block (where M is the width and N the height), when M is larger than or equal to 64, only the left M/2 columns of transform coefficients are retained. Similarly, when N is larger than or equal to 64, only the top N/2 rows of the transform coefficients are retained. This behavior can be skipped using skip mode for large blocks. Figure 3: JEM QTBT partition schema. The proposed QTBT approach in JEM uses more partition types than HEVC in order to adapt the resulting partition tree to the contents of the scene. It is guided in this task by a trade-off between rate reduction and distortion reduction, and as we will see in the next section, this is a computationally expensive task. The whole video frame is first partitioned into equally sized (up to \(256\times 256\)) CTUs, and each CTU is then further partitioned into CUs based on the scene contents, i.e., the time needed to process a whole CTU depends on the complexity of the underlying scene in each CTU. A summary of the main differences between HEVC and JEM related to picture partitioning is shown in Table 1.
## Materials and Methods  
### Materials  
Stearic acid, Brij S20, Fluorescein isothiamate (FITC, purity \(\geq\) 90%, HPLC), Silymarin (SLM), Silibin (Sb, purity \(\geq\) 98%, HPLC), Sodium chloride (NaCl), Lipase from porcine pancreas, Pepsin from porcine gastric mucose, Bile salts, Sodium Hydrooxide (NaOH), Calcium chloride (CaCl\({}_{2}\)), Cholesterol, Lecithin, Phosphate buffered saline (PBS) bioperformance certified, Mucin from porcine stomach, Sodium azide, Chlorpromazine, Indomethacin, dl-\(\alpha\)-Tocopherol acetate (Vitamin E), d-Mannitol, d(+)-Glucose anhydrous, Acetone high performance liquid chromatography (HPLC) grade, Acetonitrile HPLC grade, Methanol HPLC grade, Dimethyl sulfoxide (DMSO) HPLC grade, Formic acid analytical grade, Hydrochloric acid (HCI) analytical grade, and 1,7-octadiene 98%, Dichloromethane (CH\({}_{2}\)Cl\({}_{2}\)) were purchased from Sigma-Aldrich (Milan, Italy). Capryol 90, Luroglycol 90, Labrafac PG, Labrafac WL 1349, Labrafil M 1944 CS, and Precirol ATO 5 were a gift of Gattefosse (Saint Priest, France). Oleic acid was purchased from Carlo Erba Spa (Milan, Italy). Hemp oil and borage oil were from Galeno srl (Comeana, Prato, Italy). Water was purified by a Milli-Q\({}_{\text{plus}}\) system from Millipore (Milford, CT, USA). Phosphotungstic acid (PTA) was from Electron Microscopy Sciences (Hatfield, PA, USA). The 96-well Multi-Screen PAMPA filter plates (pore size 0.45 \(\upmu\)m) were purchased from Millipore Corporation (Tullagreen, Carrigtwohill, County Cork, Ireland). Dialysis kit was from Spectrum Laboratories, Inc. (Breda, The Netherlands).
# Chipless Radio Frequency Identification (RFID) Sensor for Angular Rotation Monitoring
## 4 Measurements  
A prototype of the sensor has been manufactured (Figure 8) and tested. The measurements have been performed, by using a two-port Vector Network Analyzer (Keysight--E5071C) and a dual-polarized horn antenna (Flann DP240), placed 40 cm from the tag. The tag has been rotated manually every 5 degrees. The cross polar backscattering is represented by the measured \(S_{21}\). This signal contains the response of the tag as well as the response of the environment. For this reason, the measured raw signal has been improved, by subtracting the environment response \(\left(S_{cross}^{tag}=S_{21}^{tag}-S_{21}^{tag}\right)\). The cleaned signal \(S_{cross}^{tag}\), measured for all of the azimuthal angles has been used to generate a map similar to the one already obtained by using the simulated data.  
The chart mapping the distance among the encodings of the different angular positions obtained by using the measurements is illustrated in Figure 9. It is apparent that the map measurements  
Figure 8: Prototype of the chipless RFID sensor for angular rotation monitoring comprising a set of \(4\times 4\) unit cells with four-dipole elements in each one.  
Figure 7: (a) Chart mapping the distance for the four-dipole structure when the value of _thres\({}_{2}\)_ is set equal to 3 dB and when (b) it is 2 dB. Resolution step of 3 degrees.  
agree to simulated data (Figure 5), with the only exception being angular positions close to 90 degrees. This small discrepancy could be due to some errors in the alignment with the angular reference positions or the non-perfect orthogonality between the antenna and the tag axes. Theoretically, the rotation speed is not an issue for the monitoring process. In practice, for typical rotation speeds of wind generators (5-20 rpm) or robotic arms, even a non-dedicated hardware and software platform can provide the reading. For very high rotation speeds (drilling machines or turbines), the data acquisition may require a dedicated hardware and software platform in order to guarantee real-time measurements.