esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Related Works: An Overview of Wireless Power Transfer
### Rectifying Circuit  
As discussed in [41; 42], there are two key approaches to WPT--(a) the resonant coupling which includes inductive, and (b) the microwave power transfer (MPT). The use of rectenna as a rectifier is one common key technology for both types of WPTs. The components of the rectenna comprise: an antenna (for receiving microwave power), a low-pass filter (for preventing higher harmonics from reaching the rectifier), the rectifier with diodes and output filter (used to rectify the received microwave signal to DC). Indeed, the importance of the rectifying circuit technology to WPT cannot be overstressed as it finds application in the rectification of radio wave signals to DC conversion in all types of WPT architectures [42; 43]. A theoretical approach based on inductive coupling has been provided in [41; 42; 43]. The beam efficiency is calculated by the Friis free space propagation model in Equation (12). The Friis transmission equation is applied at a far-field since Friis transmission model assumes a plane wave in the far-field while a spherical wave exists in the near-field where WPT is used [42]. Details of the application of Friis transmission model in calculating the received power are further discussed in Section 5 using Equation (12) with some graphical representation of the effect of distance on the received power by the sensing devices. It is noted in [42] that with simple resonant circuits using a resonant coupler (microwave filter) applied to WPT, a large amount of power (running into some kilowatts) can be transmitted without any radiation over mid-length distance at low frequencies (less than 10 MHz). Authors further showed that resonant coupling of WPT is more suitable for commercial applications. Other details on system specifications for the beam WPT, frequency bands for operation of non-beam WPT, regulation, beam efficiency, and WPT parameters are noted in [41; 42; 43].
# Sino-InSpace: A Digital Simulation Platform for Virtual Space Environments
## 3 Key Technologies
### Time-Space Datum  
Time and space domains are basic attributes of an object. Thus, accurate space-time datum is a prerequisite to depict and cognize an object; these datums are maintained by many specialized research institutes [50; 51; 52]. Due to the different sources of data and means of obtaining these data in practical applications, the spatial-temporal coordinate format of the data and model interfaces vary widely. Therefore, it is extremely essential for Sino-InSpace to clarify the time-space reference and perform mutual conversions.  
From observations of the sun to stars, and then to the introduction of the International Atomic Time (TAI) in 1967, the development of the time system has been guided by the pursuit of higher accuracy. The current time system primarily consists of Sidereal Time (ST), Universal Time (UT), Ephemeris Time (ET), Dynamical Time (DT), and Atomic Time. In addition, other time references have been defined, such as Coordinated Universal Time (UTC), which has been adopted within the Sino-InSpace, Global Positioning System Time (GPST), and BeiDou Navigation Satellite System Time (BDT) for convenience. The conversion between time systems when the data are imported or exported is accomplished with the help of the Standards of Fundamental Astronomy (SOFA) Library for ANSI C [53].  
Considering the space datum, the primary scope of human activity is currently in the earth circle; therefore, we considered a coordinate reference system centered on the earth. Nevertheless, the activities at the solar system scale needed to be supported by a coordinate system with a larger scale. These large-scale coordinate systems [54; 55; 56] primarily included the International Celestial Reference System (ICRS), Planetary Centroid Reference System (PCRS), International Terrestrial Reference System (ITRS), Station Coordinate System, and Satellite Coordinate System, as shown in Figure 2.  
In addition, the transitional coordinate system was used to meet the needs of coordinate system conversion. For example, the orbit prediction of an Earth-center satellite is mostly carried out in the J2000 coordinate system (there is a small deviation matrix between it and the Geocentric Celestial Reference System (GCRS)). However, substellar point calculation and coverage analysis are often carried out in ITRS. Therefore, the transformation between these systems requires the celestial intermediate reference system and terrestrial intermediate reference system. The conversion between reference systems in Sino-InSpace is also supported by the SOFA Library [53].  
Figure 2: Space datum of the Sino-InSpace platform.
## 2 General Methods for the Synthesis of 1,2,4,5-tetrazines
### Pinner Synthesis Using Ininoesters
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Entry** & \(\mathbf{R^{1},R^{2}}\) & **Oxidizing Agent** & **Conditions** & **Yield [\%]** & **Ref.** \\ \hline \multicolumn{6}{c}{**INORGANIC OXIDANTS**} \\ \hline
1 & \(\mathrm{R^{1}=R^{2}}\), \(\mathrm{C_{6}H_{5}}\), \(p\)-\(\mathrm{CIC_{6}H_{4}CH_{2}}\), \(p\)-\(\mathrm{C_{3}C_{6}H_{4}}\) & NaNO\({}_{2}\) & AcOH, 0–20 \({}^{\circ}\)C & 98–49 & [40; 53; 54; 55; 56; 57; 58; 59; 60; 61] \\ \hline
2 & \(p\)-NH\({}_{2}\)C\({}_{6}\)H\({}_{5}\) & \(\mathrm{H_{2}O_{2}}\) & AcOH, \(\Delta\) & 60 & [44; 45] \\ \hline
3 & Br, \(p\)-\(\mathrm{C_{3}OC_{6}H_{4}}\), \(p\)-\(\mathrm{C_{6}H_{4}}\), \(\mathrm{C_{6}H_{4}}\) & \(\mathrm{Br_{2}}\) & AcOH, 55 \({}^{\circ}\)C & 27–37 & [62] \\ \hline
4 & \(\mathrm{C_{6}H_{4}C_{6}H_{4}CH_{2}}\), \(\mathrm{Br_{6}H_{4}CH_{2}}\) & \(\mathrm{O_{2}}\) & 10\% aq \(\mathrm{K_{2}CO_{3}}\), rt & 61–91 & [63] \\ \hline
5 & \(\mathrm{SCH_{2}C_{6}H_{5}}\) & \(\mathrm{FeCl_{3}}\) & \(\mathrm{EtOH}\), 25 \({}^{\circ}\)C & 72 & [43] \\ \hline
6 & \(2H\)-Tetrazol-5-yl & \(\mathrm{CrO_{3}}\) & \(\mathrm{H_{2}SO_{4}}\), \(-5\) \({}^{\circ}\)C & 72 & [46] \\ \hline
7 & 3,5-Dimethyl-1\(H\)-pyrazol-1-yl & \(\mathrm{MnO_{2}}\) & DCM, rt & 58 & [21] \\ \hline
8 & -NH\({}_{2}\), \(\mathrm{R^{1}=R^{2}}\) & NaBO\({}_{3}\) & \(\mathrm{H_{2}O}\), 25 \({}^{\circ}\)C, 2 h & 60 & [43] \\ \hline \multicolumn{6}{c}{**ORGANIC OXIDANTS**} \\ \hline
9 & Pyrid-2-yl, 5-Aminopyrid-2-yl & DDQ (**11**) & Toluene & 81 & [47] \\ \hline
10 & \(\mathrm{SCH_{3}}\), \(\mathrm{CH_{2}O}\)n & PIDA (**12**) & rt, 30 min & 70 & [48] \\ \hline
11 & Pyrid-2-yl, 5-Aminopyrid-2-yl & _m_-CPBA (**13**) & DCM, rt, 12 h & 55 & [42] \\ \hline
12 & 2-Pyridyl & F-CAT (**14**) & \(\mathrm{CH_{3}CN}\), 40 \({}^{\circ}\)C, 12 h & 86 & [49] \\ \hline
13 & Pyrid-2-yl, 5-Aminopyrid-2-yl & Benzoyl Peroxide (**15**) & DCM, rt, 12 h & 65 & [42] \\ \hline
14 & Biphenyl-4-yl & Isopentyl nitrite (**16**) & EtOH, reflux, 4 h & 83.5 & [50] \\ \hline
15 & Pyrid-2-yl, 5-Aminopyrid-2-yl & Benzoquinone (**17**) & DCM, rt, 12 h & 71 & [42] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Oxidation of dihydro-1,2,4,5-tetrazines (**1**) to the corresponding 1,2,4,5-tetrazines (**2**)— reagents and conditions.
# An Efficient Grid-Based Geocasting Scheme for Wireless Sensor Networks
## 3 The Proposed Scheme
### System Model  
In this study, the network field is divided into \(M\times N\) virtual cells of grid, where \(M\) and \(N\) are both positive integers. Each cell is a square with cell size \(\alpha\), so the area of the cell is \(\alpha\times\alpha\). The cell size \(\alpha=R_{tr}/\left(2\sqrt{2}\right)\) is calculated by using the transmission range \(R_{tr}\). Any node in a cell can communicate directly with others nodes in its neighboring cells. In the proposed scheme, a pair of numbers is used to identify each cell, called grid identification (GID). The GID of each cell is [\(CX\), \(CY\)], and the GIDs of cells in the first row are [1, 1], [2, 1], [3, 1], [4, 1], and [5, 1] from left to right. Similarly, the GIDs of cells in the second row are [1, 2], [2, 2], [3, 2], [4, 2], and [5, 2], and so on.
# Effects of DNA Topology on Transcription from rRNA Promoters in _Bacillus subtilis_
###### Abstract  
The expression of rRNA is one of the most energetically demanding cellular processes and, as such, it must be stringently controlled. Here, we report that DNA topology, i.e., the level of DNA supercoiling, plays a role in the regulation of _Bacillus subtilis_\(\sigma^{\text{A}}\)-dependent rRNA promoters in a growth phase-dependent manner. The more negative DNA supercoiling in exponential phase stimulates transcription from rRNA promoters, and DNA relaxation in stationary phase contributes to cessation of their activity. Novobioan treatment of _B. subtilis_ cells relaxes DNA and decreases rRNA promoter activity despite an increase in the GTP level, a known positive regulator of _B. subtilis_ rRNA promoters. Comparative analyses of steps during transcription initiation then reveal differences between rRNA promoters and a control promoter, Preg, whose activity is less affected by changes in supercoiling. Additional data then show that DNA relaxation decreases transcription also from promoters dependent on alternative sigma factors \(\sigma^{\text{B}}\), \(\sigma^{\text{D}}\), \(\sigma^{\text{E}}\), \(\sigma^{\text{F}}\), and \(\sigma^{\text{H}}\) with the exception of \(\sigma^{\text{N}}\) where the trend is the opposite. To summarize, this study identifies DNA topology as a factor important (i) for the expression of rRNA in _B. subtilis_ in response to nutrient availability in the environment, and (ii) for transcription activities of _B. subtilis_ RNAP holoenzymes containing alternative sigma factors.  
Bacillus subtilis; transcription; ribosomal RNA; DNA topology +
Footnote †: journal:microorganisms
## 2 Methodology
### Preprocessing  
For the passive seismic data \(y\) collected in the field, first, we carried out CWT and calculated its coefficient \(W_{y}\), which represents the finite energy of the data in a concentrated time-frequency picture. CWT is a multiresolution transform method given by [26], as shown in Equation (1):  
\[W_{y}(a,\tau)=\langle y,\psi_{a,\tau}\rangle=\int_{-\infty}^{+\infty}y(t)a^{- \frac{1}{2}}\psi^{*}\bigg{(}\frac{t-\tau}{a}\bigg{)}dt, \tag{1}\]  
where \(a\) and \(\tau\) denote the scale and time shift, respectively; \(*\) is the complex conjugate; \(y,\psi\) denotes the inner product; and \(\psi\) is the mother wavelet.  
Then, we used HOS and the kurtosis criterion to detect the scale of and remove the pure Gaussian noise correlation coefficient from the time-frequency analysis (TFR), leaving the combination of noise and signal. The \(Kurt\) of \(N\) observation coefficients \(W_{y}\) can be calculated by Equation (2):  
\[Kurt_{y}=\frac{\sum_{n=1}^{N}\left(W_{y_{n}-\mu W_{y}}\right)^{4}}{N\sigma_{W_{y }}^{4}}-3, \tag{2}\]  
where \(\sigma_{W_{y}}\) and \(\mu_{W_{y}}\) are the estimated standard deviation and mean value of wavelet coefficient \(W_{y}\), respectively, and \(N\) denotes the sample sequence. HOS measure the degree of Gaussianity, so they have been used as detectors of non-Gaussian signals in Gaussian noise [27]. Equation (3) defines the HOS criterion, which is retained for the Gaussianity measure for distinguishing Gaussian distributions from non-Gaussian distributions:  
\[\left|Kurt_{y}\right|\leq\frac{\sqrt{24/N}}{\sqrt{1-\alpha}}, \tag{3}\]  
where \(\alpha\) is the level of confidence. [PERSON] et al. [28] estimated that the optimal value of \(\alpha\) is 90%.
## 2 Materials and Methods
### Feature Extraction
#### 2.3.4 Acquisition Parameters  
The employed fMRI data were obtained using a variety of data acquisition protocols and scanners. In this regard, MRI system parameters are utilized as features for the subsequent analysis (9 features in total). Specifically, acquisition parameters included the field of view (FOV, 3 features--one for each dimension, x, y, and z), repetition time (TR, 1 feature), echo time (TE, 1 feature), voxel size (3 features corresponding to space dimensions), and number of slices (1 feature). For each clinic, the same parameters were utilized for the acquisition process concerning ASD and TD individuals.
## 2 Design and Implementation of On-Line PD Monitoring System
### Module for Conditioning and Analog-to-Digital Signal Processing of PD Pulses
The piezoelectric transducer built in the ADW was placed about 39.5 cm from the PD source, which means that the acoustic wave velocity in the transformer tank filled with oil was 1452 m/s. Digital signals from the A/D converter then go to the FPGA (Field-Programmable Gate Array) type Intel 10M50 DAF256I7G with 256 MB cache memory type DDR2. In that logic circuit, two operating modes were implemented: basic (monitoring mode) and extended (diagnostic mode). In the monitoring mode, for HF and UHF method, separate data frames of duration time 55.5 \(\upmu\)s were analyzed. Such data frame length was selected for two reasons. First, it is the time corresponding to the angle 1\({}^{\circ}\) of voltage period (20 ms per 360\({}^{\circ}\) which is approximately 55.5 \(\upmu\)s per 1\({}^{\circ}\)), which enables determination of phase-resolved partial discharge patterns (PRPD). Second, the duration time of a single UHF PD pulse usually amounts to less than 2 \(\upmu\)s. It allows to minimize the risk that the UHF PD pulse would not be detected by the monitoring system. In the case of the AE method, the data frames are aggregated in one-second records. It is caused by the fact that the AE pulses of the high-energy partial discharges (e.g., creeping sparks or interturn discharges), which are registered with resonant piezoelectric transducer may have a time duration of even over a dozen milliseconds [24]. The algorithm implemented in the FPGA for each data record (55 \(\upmu\)s for the HF and UHF method, and 1 s for the AE method) determines the following parameters:  
* the number of pulses detected (\(n\)),
* the maximal amplitude of the pulses (\(A_{max}\)),
* the maximal energy of the pulses (\(E_{max}\)),
* the average amplitude of the pulses (\(A_{mean}\)),
* the average energy of the pulses (\(E_{mean}\)). In the diagnostic mode, the FPGA does not do the calculations. Data frames are sent via a fiber-optic Ethernet cable to the multi-channel data concentrator, and afterward to the monitoring system server. The 1-Gigabit Ethernet physical layer (PHY) was made using the Marvell Alaska 88E1512 chip. That system supports Synchronous Ethernet (SyncE) and Precise timing control protocol (PTP) time stamping, which is based on IEEE1588 [101] and IEEE802.1 AS [102] standards. The 88E1512 system on the first side is connected using the RGMII interface to the FPGA chip and on the second it is connected using SERDES interface to the small form-factor pluggable (SFP) optical transceiver.
# A Watermarking Technique for Biomedical Images Using SMQT, Otsu, and Fuzzy C-Means
###### Abstract  
Digital watermarking is a process of giving security from unauthorized use. To protect the data from any kind of misuse while transferring, digital watermarking is the most popular authentication technique. This paper proposes a novel digital watermarking scheme for biomedical images. In the model, initially, the biomedical image is preprocessed using improved successive mean quantization transform (SMQT) which uses the Otsu's threshold value. In the next phase, the image is segmented using Otsu and Fuzzy c-means. Afterwards, the watermark is embedded in the image using discrete wavelet transform (DWT) and inverse DWT (IDWT). Finally, the watermark is extracted from the biomedical image by executing the inverse operation of the embedding process. Experimental results exhibit that the proposed digital watermarking scheme outperforms the typical models in terms of effectiveness and imperceptibility while maintaining robustness against different attacks by demonstrating a lower bit error rate (BER), a cross-correlation value closer to one, and higher values of structural similarity index measures (SSIM) and peak signal-to-noise ratio (PSNR).  
Keywords:digital watermarking; biomedical image; successive mean quantization transform; Fuzzy c-means cluster; confidentiality; robustness; imperceptibility +
Footnote †: journal: Electronics Electronics
# A Hybrid Deep Learning Model for Protein-Protein Interactions Extraction from Biomedical Literature
###### Abstract  
The exponentially increasing size of biomedical literature and the limited ability of manual curators to discover protein-protein interactions (PPIs) in text has led to delays in keeping PPI databases updated with the current findings. The state-of-the-art text mining methods for PPI extraction are primarily based on deep learning (DL) models, and the performance of a DL-based method is mainly affected by the architecture of DL models and the feature embedding methods. In this study, we compared different architectures of DL models, including convolutional neural networks (CNN), long short-term memory (LSTM), and hybrid models, and proposed a hybrid architecture of a bidirectional LSTM+CNN model for PPI extraction. Pretrained word embedding and shortest dependency path (SDP) embedding are fed into a two-embedding channel model, such that the model is able to model long-distance contextual information and can capture the local features and structure information effectively. The experimental results showed that the proposed model is superior to the non-hybrid DL models, and the hybrid CNN+Bidirectional LSTM model works well for PPI extraction. The visualization and comparison of the hidden features learned by different DL models further confirmed the effectiveness of the proposed model.  
protein-protein interactions; deep learning (DL); convolutional neural networks (CNN); bidirectional long short-term memory (bidirectional LSTM) +
Footnote †: journal: Applied Physics Letters
## 3 Experimental Setup  
Software defined radio (SDR) is a rapidly emerging concept in wireless communication system. The main SDR involves using GNU Radio and universal software radio peripheral (USRP) in an Ubuntu system [18]. The experimental setup is shown in Figure 3, where each node is equipped with a laptop running GNU Radio connected to a USRP in the experiment.  
Figure 3: Experimental setup.  
Metal shields are placed between nodes to help attenuate any line-of-sight path. The distance between every two points of S, R and D is 2 m. The noise power is measured about \(-60\) dBm. The source power is fixed on \(-39\) dBm and relay power changes from \(-51\) dBm to \(-39\) dBm (all the power presented in dBm is measured from the experiment). Figure 4 illustrates the different steps of signal processing at the transmitter and relay designed by the authors.
# Antitumor Effects of _Cannabis sativa_ Bioactive Compounds on Colorectal Carcinogenesis
## 3 Bioactive Compounds in the Prevention and Treatment of Colorectal Cancer
### Effects of Cannabinoids in CRC-Associated Mechanisms
#### 3.1.3 Cbg  
CBG, a non-psychoactive cannabinoid, has gained a lot of interest due to its ability to be a partial agonist of CB1 and CB2 receptors [69]. In addition to the main receptors of the endocannabinoid system, CBG also acts as an antagonist at TRPM8, TRPV4, and 5 HT1A and an agonist at TRPA1, TRPV1, and TRPV2 receptors [70].  
In CRC, CBG treatment had positive results in cell survival, apoptosis, oxidative stress, and in vivo tumor growth. As for cell survival, CBG was reported to induce a significant decrease in the viability of CACO-2 cells at 30 uM after 3 h of incubation (\(p<0.001\)). However, after 48 h of incubation, CBG effects on cell viability were significantly decreased even for lower concentrations (3, 10, and 30 uM). In HCT116 cells, a similar trend was observed, and this effect was repressed in cells where TRPM8 was suppressed [12]. In HCT116 cells, purified CBG exhibited antiproliferative potential at concentrations 2.5, 5, and 10 uM [42]. Furthermore, an increase in CHOP mRNA and caspase-3 activity validated the apoptosis-inducing effects of CBG on CACO-2 cells. Additionally, [PERSON] et al. [12] demonstrated that CBG could increase the production of ROS in CRC cells, reduce tumor growth in a xenograft model, and prevent abnormal crypt foci from developing in an AOM-induced model.  
Despite the small number of studies evaluating CBG's effects on CRC, there is some evidence suggesting its potential as a chemopre event. Its potential for the treatment of breast cancer and melanoma has also been highlighted [33], but additional research is required to elucidate its anticancer mechanisms and its potential in CRC.
# SbMBR Tree--A Spatiotemporal Data Indexing and Compression Algorithm for Data Analysis and Mining
## 3 Proposed Methodology
### Indexing Algorithm  
Algorithm 2 presents the tree-building algorithm for an SbMBR tree. To implement it, we use the bottom-up tree construction method. First, we regard spatiotemporal data as several \(1\times 1\) MBRs. Then, Algorithm 1 is continuously used to merge MBR to the upper layer, where the maximum error of each layer increases in the form of multiplication. At  
Figure 3: Three-dimensional Hilbert curve.  
the same time, the relationship between the upper and lower layers is established. Until all MBRs are merged into one MBR, the MBR is the root node of the SbMBR tree.
```
Input: spatial data \(D\), the maximum error of leaf nodes \(x\) Output: the SbMBR tree
1. \(T{\leftarrow}\varnothing\)
2. \(p{\leftarrow}\)transform \(D\) into MBRs whose size is \(1\times 1\) and the maximum error is \(x\)
3. \(T.\)addNode(\(p\))
4. **while**\(p.\)size > 1 do
5. \(x{\leftarrow}2x\)
6. (\(q\), \(R\))\({\leftarrow}\)merge_MBR(\(p\), \(x\))// Algorithm 1
7. \(T.\)addNode(\(q\))
8. \(T.\)addEdge(\(R\))
9. \(p{\leftarrow}q\)
10. **end while**
11. **return**\(T\)
```
**Algorithm 2**Build SbMBR tree  
A range query receives a query rectangle range and maximum acceptable maximum error and returns a set of MBRs who can fill the space of the rectangle range and meet the requirement of maximum error.  
Algorithm 3 describes the range query algorithm. We mainly use recursive methods to realize error-controllable range queries. \(I\) is the intersection of the MBR of the current node and the query range. When \(I\) is empty, no MBRs are meeting the query requirement, so an empty set is returned. When \(I\) is not empty and \(eps\) of the MBR of the current node is less than or equal to the given \(eps\), \(I\) has satisfied the requirement, so \(I\) is returned. Notably, when the root node has no child, even if the error does not meet the condition, only I can be returned. After the above conditions are eliminated, recursion calling is required.
```
Input: the root node of a subtree root, rectangle range \(R\), maximum error \(eps\) Output: A set of MBRs contained in range \(R\)
1. \(S{\leftarrow}\varnothing\)
2. \(I{\leftarrow}R\cap root.MBR\)
3. **if**\(I=\varnothing\)then
4. **return**\(\varnothing\)
5. **endif**
6. **if**\(root.MBR.eps\leq\)\(eps\) or the root node has no child then
7. **return**\(I\)
8. **endif**
9. **for**\(ch\) in all the children of \(root\)do
10. \(S{\leftarrow}S{\cup}\)range_query(\(ch\), \(R\), \(eps\))//recursive function call
11. **end for**
12. **return**\(S\)
```  
**Algorithm 3**Range query