esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
In TM mode, there are three resonances at 0.50 THz, 0.67 THz and 0.91 THz for the initial state of (\(y_{2}\), \(y_{3}\)) = (16, \(-\)16) as the black curve shown in Figure 9b. By moving \(y_{2}\) and \(y_{3}\) values symmetrically along positive and negative \(y\)-axis directions, the first and the third resonances vanish while the second resonance remains quite stable and constant. This means that these resonances can be switched from  
Figure 8: Simulated transmission spectra of the RTM device obtained by moving the SRR-2 and SRR-3 meta-atoms along positive and negative \(x\)-axis directions in **(a)** TE and **(b)** TM modes, respectively. Figure 7: **(a–c)** E-field and **(d–f)** H-field distributions of the RTM device with \(x_{2}\) = 24 \(\upmu\)m in TE mode. triple-band to single-band resonance in TM mode. Therefore, this proposed SRRs can be used as an EIT switch in TE mode and multi-band to single-band switch in TM mode by changing \(y_{2}\) and \(y_{3}\) values symmetrically.
# Self-Organized Fast Routing Protocol for Radial Underwater Networks
## 2 Related Work
### Reliable Data Delivery Protocols  
The focus of this set of protocols is on reliability of data delivery. We present three examples. The Multipath Virtual Sink Architecture (MVSA) [43] uses multiple sinks, which connected together form a virtual sink. Instead of caching delay sensitive data, it is forwarded through multiple paths. This protocol increases the data delivery reliability at the cost of an increase in energy consumption. Efficient Data Delivery with Packet Cloning (EDDPC) [44] uses the same concept of multiple copies. However, it is a little bit different because it clones or copies selected data packets when forwarding them on the basis of link quality and channel conditions.  
The Resilient Routing Algorithm for Long-term Applications (RRALA) [45] is based on virtual circuit routing. Multihop connections are established in advance between each source and sink and each packet is associated with a particular connection. If a connection fails during the packet forwarding, another connection, established in advance, is used.
# Gearbox Fault Diagnosis Based on ICEEMDAN-MPE-AWT and SE-ResNeXt50 Transfer Learning Model
###### Abstract  
Because the gearbox in transmission systems is prone to failure and the fault signal is not obvious, the fault end cannot be located. In this paper, a gearbox fault diagnosis method grounded on improved complete ensemble empirical mode decomposition with adaptive noise, a multiscale permutation entropy and adaptive wavelet thresholding (ICEEMDAN-MPE-AWT) denoising method and an SE-ResNeXt50 transfer learning model are proposed. Initially, the vibration signal is denoised by ICEEMDAN-MPE-AWT, the denoised vibration signal is then converted into a Gram angle field (GAF) diagram, and then the parameters are transferred by the fine-tuning transfer learning strategy. Finally, a GAF diagram is input into the model for training to achieve fault extraction and classification. In this paper, the open gear dataset of Southeast University is used for experimental research. The experimental results show that when using the ICEEMDAN-MPE-AWT and when the signal-to-noise ratio (SNR) of the experimental data is \(-4\) dB, the average accuracy of the GASF+TSE-ResNeXt50 and the GASF+TSE-ResNeXt18 can reach 98.8% and 97.5%, respectively. When the SNR is 6 dB, the accuracy of the above two models reaches 100% and 99.3%, respectively. Moreover, when compared to alternative approaches, the noise reduction method in this paper can better remove noise interference so that the model can better extract fault features. Therefore, the method proposed in this article shows significant improvement in noise reduction and fault classification accuracy compared to other methods.  
gearbox; fault diagnosis; transfer learning; ICEEMDAN; MPE; AWT; GASF; transmission system +
Footnote †: journal: Applied Sciences
## 5 Experimental Results
### Performance Metrics
#### 5.2.5 Threat Avoidance Capacity  
The program can eliminate the malicious activities and exposures that negatively affect the performance of the program. Here, different scenarios are generated to determine the threat avoidance effectiveness of the proposed WBCA and competing programs. In Figure 6a, a maximum of 900 different types of threats (Denial of Service, Man-in-the-Middle, Malware, Phishing, and spam) have been generated. The result demonstrates that the proposed WBCA has a 99.32% threat avoidance capacity, whereas the contending programs have 95.52-97.72% threat avoidance capacity. ISF shows the lower 95.52% threat avoidance capacity as compared to other contending programs.  
When the number of threats is maximized up to 1800, the threat avoidance capacity of the proposed WBCA is not highly affected. Despite the increase in the number of threats, the proposed WBCA shows 99.11% threat avoidance capacity. On the other hand, the contending programs are highly affected by maximizing the number of threats. The contending programs show the threat avoidance capacity of 93.81-95.04% depicted in Figure 6b. The main reason for getting the higher threat avoidance capacity is to use Blockchain technology that greatly reduces the potential threats.
# Adaptive Hierarchical Density-Based Spatial Clustering Algorithm for Streaming Applications
## 3 Methodology
### Cluster Hierarchy Condensation  
In this stage, the preceding process's large and convoluted tree is condensed into a smaller tree with more data tied to each individual node. We cycle over the hierarchy, using the minimum cluster size as an important metric, to determine whether the split is just noise, or if it is greater than the minimum cluster size, we consider it a legitimate cluster split and leave it in the tree [21].
## 2 Microbial Induced Carbonate Precipitation by _Sporosarcina pasteurii_  
There are various urease-producing bacteria in nature, and their characteristics are different [8,17], as shown in Table 1.  
After exposure to high temperatures, concrete becomes porous, has strong alkalinity and many cracks, and the cracks are rich in oxygen. For the application purpose of this article, the ideal urease-producing bacteria should have the following characteristics: (1) Adaptability to the alkaline environment; (2) Aerobic, microaerobic, or facultative  
\begin{table}
\begin{tabular}{c c c c} \hline
**Microorganism** & **Urease Activity** & **Basophilic** & **Aerobic** & **Pathogenic** \\ \hline _Sporosarcina pasteurii_ & high & ✓ & ✓ & ✗ \\ _Proteus vulgaris_ & low & moderate & ✓ & moderate \\ _Proteus mirabilis_ & low & moderate & ✓ & ✓ \\ _Helicobacter pylori_ & high & ✗ & ✓ & ✓ \\ \hline \end{tabular}
\end{table}
Table 1: Characteristics of different urease-producing bacteria.  
anaerobic; (3) secrete high activity urease; (4) Bacteria and their metabolites are non-toxic, harmless, and non-pathogenic.  
Previous research [8; 17] showed that _Sporosarcina pasteurii_ can survive in a weak alkaline environment and produce high activity urease, they are aerobic bacteria, and the bacteria themselves and their metabolites are non-toxic, harmless, and non-pathogenic. In conclusion, _Sporosarcina pasteurii_ is the ideal urease-producing bacteria.  
_Sporosarcina pasteurii_ can secrete urease during its metabolic process; this urease will decompose urea in the surrounding environment into NH4+ and CO32-, then mineralize and deposit CaCO3 in the presence of Ca2+.
## 3 Results
### Sine Sweep Test Results
#### 3.3.3 Effect of Heat Treatment Temperatures on the Damping Capacity of WMD  
The NiTi WMD fabricated with a relative density of 0.31 was heat-treated at 150 \({}^{\circ}\)C, 200 \({}^{\circ}\)C, and 250 \({}^{\circ}\)C for 30 min and then subjected to a 1 g sine sweep test from 5 to 200 Hz. Figure 20 shows the transmissibility function of the NiTi WMDs heat-treated with different temperatures. In general, heat treatment makes the resonant peak shift to higher frequencies and alters the acceleration transmissibility of the vibration-isolation system, except for the case of 150 \({}^{\circ}\)C. Heat treatment at 200 \({}^{\circ}\)C yields a wider resonant peak with a lower peak transmissibility value, whereas heat treatment at 250 \({}^{\circ}\)C gives rise to a narrow resonant peak with a higher peak transmissibility value.  
Figure 19: Characterization of vibration isolation performance of NiTi WMDs fabricated with different densities: (**a**) natural frequency; (**b**) peak transmissibility; (**c**) loss factor.  
Figure 18: Effect of the mesh density on the acceleration transmissibility of the vibration-isolation system subjected to the 1 g sine sweep test.  
To provide a clearer insight into this phenomenon, the natural frequency, peak transmissibility values, and dissipation factors of NiTi WMDs are calculated and plotted as a function of heat treatment temperatures, as shown in Figure 21. As the heat treatment temperature rises, the natural frequency of the WMD decreases slightly at first, then increases to higher levels, indicating an improvement in the damper's stiffness. Meanwhile, the peak transmissibility value decreases to its minimum when the WMD is heat-treated at 200 \({}^{\circ}\)C and then increases to higher values. Accordingly, the NiTi WMD achieves its maximum loss factor of 0.717 as it is heat-treated at 200 \({}^{\circ}\)C.
## 2 Overview of Positioning Principles Using UWB and Wi-Fi RTT Radio Signals  
UWB and Wi-Fi RTT are two positioning technologies that have gained attention due to their high accuracy and broad applications. In this section, we will explore the fundamental principles of positioning using these two types of radio signals, focusing on two different timing measurement methods: the Time of Flight (TOF)-based positioning method and the Time of Arrival (TOA) and Time Difference of Arrival (TDOA)-based positioning methods.
# Wire-Grid and Sparse MoM Antennas: Past Evolution, Present Implementation, and Future Possibilities
## Why MoM?
### MoM Hybridization
The author then used this technique for broadband analysis of antennas radiating in the presence of electrically large conducting bodies (platforms). The results of using this method are presented, and they showed significant savings of both computer memory and CPU time [109]. In 2011, MoM was comprehensively hybridized with other methods (e.g., FEM, PO, or UTD) and accelerated with the multilevel fast multipole method (MLFMM) [110]. A technique for combining the FEM solution for volume electric fields and MoM (or boundary integral solutions for the cut-off boundary) to solve open boundary problems was presented in [111]. This technique was based on a highly parallelized domain decomposition infrastructure.
# Advances in Biomaterials for Breast Reconstruction
## 3 Scaffold Guided Breast Tissue Engineering
### Components of Bioink for Breast Tissue
#### 3.2.1 Nature Derived Biomaterials  
Nature-derived biomaterials that can be used for breast tissue regeneration consist of collagen, alginate, chitosan, hyaluronic acid (HA), and decellularized extracellular matrix (dECM) [75]. Alginate and chitosan provide an appropriate microenvironment for adipocytes, including adipose-derived stem cells (ADSCs) by means of cell viability and proliferation, leading to adipogenic differentiation [76; 77]. DECM is a hydrophilic matrix extracted from tissues via a decellularization process. The components of dECM  
Figure 4: Reconstruction of breast with a personalized three-dimensional (3D) bioprinted adipose tissue construct using stem cells derived from autologous tissue. Based on [70].  
contain glycosaminoglycans, proteins, polysaccharides, fibronectin, and collagen type I and II. These components provide a biomimetic microenvironment for cell adhesion, proliferation, and differentiation [78]. Although the dECM of adipose tissue has been shown to promote the formation of adipose tissue of the breast, there remains the limitation that the degradation rate of dECM exceeds the rate of new ECM formation [79; 80; 81]. Nature-derived components have common limitations such as instability and difficulty in modification, but they provide a biological and chemical microenvironment.
## 7 Conclusions and Future Works  
In this paper, we have presented an PI-based multi-RIS-assisted system model for HSC with HWI. We have designed and compared different phase shift optimization strategies of the RISs, and obtained a phase shift set that can maximize SE, remove Doppler spread, and keep delay spread at a very low level. Furthermore, we have derived the closed-form expression of SE. We have also compared the performance of different numbers of RISs with HWI in terms of SE and delay spread. The LoS channel model has been extended to the Rician case to show rationality in practice. Numerical evaluations revealed that when the number of RISs increases from 2 to 16, the range of average spectral efficiency and delay spread are from 4 to 4.6 Bit/s/Hz and from 0.7 us to 2.5 us, respectively. Therefore, the tradeoff between SE and delay spread should be carefully considered for multi-RIS communication systems. For future works, one promising research direction is using RISs as Doppler generators to obtain Doppler diversity [36; 44]. In particular, an RIS can be used not for aligning the direct and cascaded links, but for creating suitable Doppler shifts [12], which can achieve Doppler diversities to enhance the transmission quality. Besides, it is important to optimize the phase shift set under the assumption that the Rician channel exists. Another possible research direction is hardware degradation in RISs. The RIS and transceiver HWI considered in this paper do not relate to usage times. In practice, however, hardware degradation caused by usage time cannot be ignored [45], which is challenging but worth studying in-depth in the future. Moreover, it is necessary to pay attention to the security of the storage of the phase shift set. In this paper
## 4 Health Benefits of Probiotic Supplementation in Diseased Dogs
### Gastrointestinal Diseases and Abnormalities
#### 4.3.4 Other Diseases Associated with Diarrhea or Prevention of Gastrointestinal Infections  
The effect of the oral supplementation of _Lactobacillus murinus_ LpP2 to dogs suffering from canine distemper virus-associated diarrhea was also studied. In detail, an intervention of LpP2 (5 \(\times\) 10\({}^{9}\) CFU/day) administered to dogs for five days improved the clinical score of the disease, stool consistency, stool output, appetite, and mental health. The results suggested that LpP2 could be used to manage canine distemper virus-associated diarrhea [71].  
German Shepherd (n = 36, 18 females, 18 males) and Yorkshire puppies (n = 36, 18 females, 18 males) were vaccinated for distemper and parvovirus at 6 weeks of age, and were vaccinated for distemper, hepatitis, leptospirosis, parainfluenza, parvovirus at 8 weeks and 12 weeks of age. These puppies were randomly grouped to fed with diet supplemented with either of the probiotic, _L. rhamnusus_ MP01 (\(\sim\)9 log\({}_{10}\) CFU/day) or _L. plantarum_ MP02 (\(\sim\)9 log\({}_{10}\) CFU/day) (two probiotic groups) or diet without probiotics (control group) for 8 weeks (started at 5 weeks of age). Each group (two probiotic groups and one control group) had German Shepherd (n = 12, 6 females, 6 males) and Yorkshire puppies (n = 12, 6 females, 6 males). GI infection (symptons such as diarrhea in the presence or absence of fever or vomiting) during the duration (2 months) of the study. _L. rhamnus_ MP01 and _L. plantarum_ MP02 interventions effectively prevented gastrointestinal infections in German Shepherd and Yorkshire puppies compared to that of the control group, and also increased the SCFAs concentration in the feces of dogs of probiotic group [72].  
Since only single study are available for distemper virus-associated diarrhea [71], and for the prevention of gastrointestinal infections in puppies [72], more studies are needed to suggest the health promoting probiotic effect in those cases.
###### Abstract  
Shield tunneling under rivers often requires monitoring riverbed deformations in near real-time. However, it is challenging to measure riverbed deformation with conventional survey techniques. This study introduces a comprehensive method that uses the Global Positioning System (GPS) of the USA and the BeiDou navigation satellite system (BeiDou) of China to monitor riverbed deformation during the construction of twin tunnels beneath the Hutuo River in Shijiazhuang, China. A semi-permanent GPS network with one base station outside the river and six rover stations within the river was established for conducting near real-time and long-term monitoring. The distances between the base and the rover antennas are within two kilometers. The network was continuously operating for eight months from April to December 2018. The method is comprised of three components: (1) Monitoring the stability of the base station using precise point positioning (PPP) method, a stable regional reference frame, and a seasonal ground deformation model; (2) monitoring the relative positions of rover stations using the carrier-phase double-difference (DD) positioning method in near real-time; and (3) detecting abrupt and gradual displacements at both base and rover stations using an automated change point detection algorithm. The method is able to detect abrupt positional-changes as minor as five millimeters in near real-time and gradual positional-changes at a couple of millimeters per day within a week. The method has the flexibility of concurrent processing different GPS and BeiDou data sessions (e.g., every 15 minutes, 30 minutes, one hour, one day) for diffident monitoring purposes. This study indicates that BeiDou observations can also achieve few-millimeter-accuracy for measuring displacements. Parallel processing GPS and BeiDou observations can improve the reliability of near real-time structural deformation monitoring and minimize false alerts. The method introduced in this article can be applied to other urban areas for near real-time and long-term structural health monitoring.  
BeiDou; change point detection; GPS; riverbed; shield; structural health monitoring; twin tunnels
# MCRCNet: A Bearing Fault Diagnosis Method for Unknown Faults Based on Transfer Learning
## 2 Preliminaries
### Reconstruction Convolution
After the spatial reconstruction unit, the channel reconstruction unit is added to realize the reconstruction of features by segmenting, transforming, and fusing features from the perspective of channels. The segmentation operation splits the input spatial refined feature  
Figure 2: Channel reconstruction unit. \(X^{\omega}\) into two parts--one part is the channel number \(\alpha C\), the other part is the channel number \((1-\alpha)C\)--and then the number of channels of the two groups of features is compressed by the \(1\times 1\) convolution kernel to obtain \(X_{up}\) and \(X_{low}\), respectively. The transformation operation takes the input \(X_{up}\) as the input of \"rich feature extraction\", performs GWC (Gate-Way Convolution), and PWC (Point-Wise Convolution), respectively, and then adds to obtain the output \(Y1\). The input \(X_{low}\) is used as the supplement of \"rich feature extraction\", performs PWC, and obtains \(Y2\) by taking the union of the obtained result and the original input. The fusion operation uses a simplified SKNet approach from adaptation to merge \(Y1\) and \(Y2\). Specifically, the global average pooling is first used to combine the global spatial information and channel statistical information to obtain the pooled \(S1\) and \(S2\). Then, Softmax is performed on \(S1\) and \(S2\) to obtain the feature weight vectors \(\beta_{1}\) and \(\beta_{2}\). Finally, the feature weight vectors are used to obtain the output \(Y=\beta_{1}\times Y_{1}+\beta_{2}\times Y_{2}\), and \(Y\) is the extracted feature of the channel. A schematic diagram of the channel reconstruction unit is as follows. After completing the design of the two units, the two units are fused and placed in a sequential manner. Specifically, for the intermediate input feature \(X\) in the bottleneck residual block, the spatial refinement feature \(X^{\omega}\) is first obtained by the spatial reconstruction operation, and then the channel refinement feature \(Y\) is obtained by the channel reconstruction operation. This method makes full use of the spatial redundancy and channel redundancy between features, and finally, reduces the redundancy between the intermediate feature maps and enhances the feature representation of the convolution operation.
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 3 Current Gridded Forest Biomass Maps
### AGB Maps from Other Data Sources
#### 3.5.5 Global Forest Biomass Carbon Map from 1993 to 2012  
[PERSON] et al. (2015) derived global forest biomass carbon estimates from 1993 to 2012 with a spatial resolution of 0.25 degrees, from the empirical relationship between the AGB tropical regions map from [PERSON] et al. (2013) and the VOD data estimated from a series of passive microwave data including Special Sensor Microwave Imager (SSM/I), Advanced Microwave Scanning Radiometer for Earth Observation System (AMSR-E), FengYun-3B Microwave Radiometer Imager (MWRI) and Windsat. The [PERSON] et al. (2013) biomass map uncertainties added error to the final estimated results. The accuracy of the biomass map in this study was difficult to assess due to its coarse spatial resolution.
# An End-to-End Video Steganography Network Based on a Coding Unit Mask
## 3 Experimental Results
### Number of Kernel Parameters Comparison among Different Steganography Networks
#### 3.2.3 Extraction Accuracy of Hiding Secret Message  
Ensuring the message of the receiver is the same as the sender is an important indicator to test the effectiveness of the steganography algorithm. ACCU is the accuracy of the bit-to-bit comparison of received and sent messages. From the fourth row in Table 2, the proposed P_F32_2 has the ACCU value of 0.994 in bold, which is the highest in the four models, and the proposed P_F16_3 and model SteganoGAN both have the ACCU value of 0.993, which is the second highest. Note that the decoder of P_F32_2 has more hidden features that can decode hidden messages more accurately. Compared with P_F32_2, the proposed P_F16_3 takes two third parameters, which achieves basically equivalent message extraction accuracy owing to its third feature extraction network channel, as shown in blue box of Figure 5. Therefore, PyraGAN has a good performance in extracting messages.  
In general, the proposed model PyraGAN is the best in terms of capacity, imperceptibility, and accuracy compared with the existing two excellent models of HiDDeN [21] and SteganoGAN [22]. In addition, the proposed model P_F16_3 uses the minimum parameters, of which the occupied memory is only 220 K.