esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Underwater wireless communication has gained a great deal of attention in the last couple of decades because of its applications in the military, industrial, and monitoring sectors. Despite the extreme physical and MAC layer difficulties, acoustics are used for various applications among the various modes of underwater communication technologies used. While significant research efforts have been made to address these issues, the bottleneck remains in achieving high bandwidth, high throughput, and data rate. Researchers have begun to look into full duplex (FD) implementation to improve bandwidth efficiency and increase data rate and throughput. Users can send and receive data simultaneously over the FD links, maximizing bandwidth utilization and increasing throughput. As a result, we thoroughly reviewed various FD physical layered LWAC systems and MAC layered protocols for underwater communication. The various problems that the aforementioned systems and protocols have faced, as well as the solutions suggested in previous works to solve each problem, are also highlighted. Various metrics are used to compare the performance of various physical layered FD systems and FD MAC protocols. We also explore some of the open research questions in these FD-physical layered and MAC layered protocols, as well as future research directions. Based on ample information, we suggest a cross-layered architecture based on various IBFD-SI cancellations, DA-CSMA, and FD-MAC protocols. This review provides a broad view of the current FD physical and MAC layered protocols based on acoustic communication, as well as recommendations.  
self-interference (SI) cancellation; FD-cooperative-UWAC systems; spread spectrum (SS); FD-MAC protocols; contention-free; contention-based; hand-shaking (HS); random access; quality of service (QoS) +
Footnote †: journal: Marine Science and Engineering
## 3 Results and Analysis  
### SCB Test and Analysis  
#### 3.1.1 SCB Test  
The loading equipment was a universal material testing (UTM) machine, and the distance between the two fulcrum points at the bottom of the semicircular specimen was 80 mm. The two failure modes adopted different loading methods. Sinusoidal wave was selected for the loading waveform of the medium-temperature fatigue failure test, and the loading frequency was determined by simulating the actual road speed. Considering the highway driving speed (60-120 km/h), when the driving speed was 60 km/h, the road surface was unfavorable, and the corresponding frequency was 10 Hz. According to the preliminary semi-circular bending (SCB) test, the failure load of the specimen with a pre-cut slot was 2500 N and the selected stress ratio was 0.2, so the loading range was 50 to 500 N. The test termination condition was that the specimen was completely fractured. In the low-temperature fracture test, the specimens were subjected to vertical uniform load at a loading rate of 1 mm/min. The test termination condition was that the specimen was completely fractured. The loading conditions are shown in Table 10.  
Figure 6: (**a**) Interface model of CR-augite. (**b**) Interface model of CR-water-augite. (**c**) Interface model of CR-calcite. (**d**) Interface model of CR-water-calcite.
# Technologies Trend towards 5G Network for Smart Health-Care Using IoT: A Review
###### Abstract  
Smart health-care is undergoing rapid transformation from the conventional specialist and hospital-focused style to a distributed patient-focused manner. Several technological developments have encouraged this rapid revolution of health-care vertical. Currently, 4G and other communication standards are used in health-care for smart health-care services and applications. These technologies are crucial for the evolution of future smart health-care services. With the growth in the health-care industry, several applications are expected to produce a massive amount of data in different format and size. Such immense and diverse data needs special treatment concerning the end-to-end delay, bandwidth, latency and other attributes. It is difficult for current communication technologies to fulfil the requirements of highly dynamic and time-sensitive health care applications of the future. Therefore, the 5G networks are being designed and developed to tackle the diverse communication needs of health-care applications in Internet of Things (IoT). 5G assisted smart health-care networks are an amalgamation of IoT devices that require improved network performance and enhanced cellular coverage. Current connectivity solutions for IoT face challenges, such as the support for a massive number of devices, standardisation, energy-efficiency, device density, and security. In this paper, we present a comprehensive review of 5G assisted smart health-care solutions in IoT. We present a structure for smart health-care in 5G by categorizing and classifying existing literature. We also present key requirements for successful deployment of smart health-care systems for certain scenarios in 5G. Finally, we discuss several open issues and research challenges in 5G smart health-care solutions in IoT.  
s 2020 20, 4047; doi:10.3390/s20144047
## 4 Discussion
Our DL approach also outperformed or achieved the same performance as patient specific models for bladder. Those models rely on PCA to extract principal modes of deformation from landmarks placed on bladder's contour and across several contoured images for each patient being considered. The drawback for clinical use of such approaches is that (i) a different model is required for every patient and organ and (ii) several images per patient are needed to build the model. Concerning alternative DL methods, the current work slightly outperformed our initial conference paper, [PERSON] et al. [24], on bladder segmentation with 3D U-net. This was probably due to the larger training database and/or the multi-class formulation used in this work, since three organs were segmented instead of one. Only 41 of the patients used in our conference paper were kept in this study. This was because the remaining patients had either had their prostates removed or lacked fully annotated scans. New patients were also added. The two datasets were thus different. However, [PERSON] et al. 's work was the closest to this study. Hence, we did a more thorough comparison with their findings. They obtained a higher DSC than we did for all the organs considered in this study. This might be explained by the fact that they used more samples in their training set (300 CT and 300 pseudo CBCT scans compared with 74 CT and 42 CBCT scans). However, it was hard to determine whether this was the only explanation for their better results. Indeed, in Figure 4, we see that the DSC increased more slowly as the number of training samples increased. Interestingly, they ran the patch-wise 3D U-net proposed by [PERSON] et al. on their test set and obtained DSCs of 0.927, 0.860, and 0.816 for bladder, rectum, and prostate, respectively. Those results were higher than the results obtained on bladder (DSC = 0.88) and rectum (DSC = 0.71) by [PERSON] et al. Therefore, their test set might be of a higher quality than ours, which could be a limitation of their approach in clinical practice, where low quality images are common. Another shortcoming is that they reported their results on a dataset that included both CBCT and CT scans (10%). It was therefore unclear how well their method would perform on a dataset containing only CBCT scans (such as ours). As a final remark, their proposed generation of pseudo CBCT scans from clinically contoured CT scans was a powerful tool for solving the problem of CBCT annotations. However, such knowledge of artificial data generation might not be present in all hospitals. To summarize this comparison, we considered the two publications to be complementary, with our strengths being the size of our test set, detailed comparison with registration approaches, and the detailed study of the impact of additional CT scans in the training database.
## 3 Results and Discussions
### Hydrogen Bond Analysis
In addition, the change in the number of hydrogen bonds of different residues also reflected the diversity and difference in the internal structure of the protein. Unlike residues 61-88, it can be seen from Figure 7 that the number of hydrogen bonds between residues 139 and 149 and water was relatively stable after different electric fields were applied, and this change was not clear. Combined with the calculation and analysis of the RMSD parameters of residues 139-149 above, the RMSD curve of this part of the residue moved upward but did not significantly jump under the action of the electric field, and the change amplitude only increased from an initial 0.3 nm to about 0.5 nm, indicating that residues 139-149 became active under the action of the current electric field but did not undergo significant structural changes. This result confirms that the \(\beta\) folding structure of this fragment region is stable, and the conformational changes are subtle. When the electric field is applied simultaneously in the x, y, and z directions, the number of hydrogen bonds increases as a whole, indicating that the conformation of residues 139-149 does change under the action of the electric field, and more hydrogen bonds are formed with water. In addition, the change in the number of hydrogen bonds between residues 169 and 180 and water in Figure 8 also proves the difference within the protein, and it can be found that the hydrogen bond between residues 169 and 180 and water is significantly reduced under the action of the unidirectional electric field, and the number of hydrogen bonds is closer to the reference value after the electric field is increased. This indicates that the internal structure of the protein fragment folds more under a one-way electric field [35], and the hydrogen bond with the water is broken. Combined with VMD images and RMSD parameter analysis, the changes in the number of hydrogen bonds in the three-segment residue confirmed the conformational changes in proteins. We define the hydrogen bond length as the distance between the donor and the receiver. From Figure 4b, we can find that the internal hydrogen bond length of the protein is elongated but not clear under the action of the electric field. We analyzed this phenomenon for the following reasons: First, the hydrogen bonding force inside the protein is strong, and the electric field applied by the experiment is not strong enough to completely prevent the hydrogen bond at all. Second, the residue region analyzed in this article has a strong dipole moment; in addition, because residues 139-149 contain disulfide bonds, which are offset by the electric field coupling of proteins, skeletal changes are also limited. Combined with the previous analysis, we correctly characterized the conformational changes of proteins by analyzing the number and length of the hydrogen bonds of proteins. Figure 6: The change curves of the number of hydrogen bonds between residues 61 and 88 and water under different amounts and directions of electric fields.
###### Abstract  
The mineralization of benzene, toluene, ethylbenzene, and xylene (BTEX) into inorganic substances by microorganisms may affect the water-rock interaction. However, few studies have quantitatively analyzed the processes. To quantitatively reveal this mechanism, in this study, nitrate and toluene were taken as the typical electron acceptor and BTEX, respectively. Based on hydrogeochemical theory, the mechanism and mathematical model were established. In addition, the model was verified with a toluene mineralization experiment. The mechanism model demonstrated that H\({}^{+}\) was the main factor in the dissolution or precipitation of CaCO\({}_{3}\). The mathematical model derived the equations quantitatively between the amount of toluene mineralization, CaCO\({}_{3}\), and some biogeochemical indicators, including temperature, microbial consumption, and other major ions in groundwater. According to the model, the amount of dissolved CaCO\({}_{3}\) increased with the increasing proportion of completely reduced nitrate. For a complete reaction, the greater the microorganisms' consumption of toluene was, the smaller the precipitation of CaCO\({}_{3}\). CaCO\({}_{3}\) dissolution was a nonmonotonic function that varied with temperature and the milligram equivalent of other ions. Furthermore, the validation experiments agreed well with the mathematical model, indicating its practicality. The established model provides a tool for assessing the biodegradation of toluene by monitoring the concentration of groundwater ions.  
BTEX mineralization; toluene; denitrification; carbonate dissolution and precipitation; mechanism; model +
Footnote †: journal: Applied Sciences
# Research on Path Planning Multiple Mobile Robots Based on the LAPGWO Algorithm
## 4 Results  
In this section, simulation experiments will be carried out in a warehouse environment to verify the feasibility and advancement of the proposed algorithm. All the codes in this paper are written in MATLAB 2021a. All the experiments are run on a Lenovo desktop computer equipped with a Core i5-12500H CPU with a frequency of 2.5 GHz and 16 GB of memory. The parameter settings of the algorithm are shown in Table 1. Many preliminary experimental tests have been carried out to obtain a reasonable interval range regarding the values of key parameters, such as the chaotic intensity \(\mu\), the repulsive force gain coefficient \(\rho(t)\), and the disturbance intensity coefficient \(\eta\). The selection of specific optimal parameters will be explained in the subsequent content.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Setting Values** \\ \hline Population size & 60 \\ Search dimension \(n\) & 30 \\ Maximum number of iterations \(T\) & 600 \\ Chaotic interval \(\mu\) & 3.57–4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Algorithm parameter setting.  
Figure 2: Flowchart of the algorithm.  
In the simulation experiments of this section, the advancement of the algorithm proposed in this paper is mainly verified. The core objective is conducting a horizontal comparative analysis by comparing it with efficient multi-agent path planning algorithms. The comparative algorithms are the grey wolf optimizer (GWO) algorithm, and the LM-GWO algorithm proposed in reference [26], the CWSO algorithm proposed in reference [28], and the DSA-EGA algorithm proposed in reference [31]. Thirty simulation tests are carried out, and each algorithm's average path length, running time, and standard deviation are recorded. Furthermore, whether the algorithm in this paper can generate an approximately optimal path planning for the multi-agent system under a single time window constraint is examined.
# A Novel Traffic Classification Approach by Employing Deep Learning on Software-Defined Networking
## 3 Materials and Methods
### Assess  
It is the final phase of the SEMMA methodology. One of the most critical steps in machine learning models is their evaluation, which means determining their performance. Metrics provide information about the performance and accuracy of each model [44]. In this section, the performance of the four models is evaluated using test data, which means data that were not used during the training phase. Multiple metrics were employed to measure their performance and assess their predictive capability. Different evaluation metrics exist depending on the type of technique applied. In this case, the classification metrics used were Accuracy (2), Precision (3), Recall (4), and F1-Score (5), which were used to gain a broader understanding of each model's behavior and how well it fits the data.  
* _Accuracy_ is the model's precision in predicting accurate outcomes, calculated by dividing the number of accurate predictions by the total number of predictions. \[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}\] (2)
* _Precision_ indicates how precise the model is in identifying positive cases. It is calculated by dividing the number of correctly classified positive instances by the total number of cases classified as positive. \[Precision=\frac{TP}{TP+FP}\] (3)
* _Recall_ is the proportion of positive cases correctly identified by dividing the number of correctly classified positive instances by the total number of positive cases. \[Recall=\frac{TP}{TP+FN}\] (4)
* _F1-Score_ aims to achieve a balanced performance in classifying positive and negative cases. It is a combined measure of precision and recall that balances both metrics. This metric was helpful because there was an imbalance in the classes. \[F1\text{-}Score=2*\frac{Precision*Recall}{Precision+Recall}\] (5)
# Safety and Energy Implications of Setback Control in Operating Rooms during Unoccupied Periods
###### Abstract  
Health care facilities are high energy-demanding buildings. The energy-saving potential is limited due to safety regulations, especially in critical care areas like operating rooms (ORs). Reducing the supply airflows during unoccupied periods, also called ventilation turdown or setback, is accepted as an energy efficiency measure as long as it does not compromise the pressure relationship. In addition, temperature and relative humidity setbacks can introduce further energy savings. This work aims at studying the effect that a setback has on the OR-positive pressure and the savings achievable in both the energy supply and CO\({}_{2}\) emissions. Towards this target, five tests are performed in two ORs of a public hospital during the summer, winter, and midsseason. A setback is applied on the basis of an occupancy sensor, and the pressure difference from the OR adjacent spaces is monitored. The outdoor and supply air conditions and airflows, as well as fan energy consumption, are measured. Punctual pressure relationship losses are observed during the occupied periods due to doors opening but not during ventilation setback operations. The energy savings achieved accounted for 75% of the natural gas consumption and 69% of the electricity in the ORs. The yearly estimations imply economic savings of near 20,000 EUR and more than 100 tons of CO\({}_{2}\) emissions.  
health care facilities; hospitals; operating rooms; energy efficiency measures; unoccupied periods; setback; turndown; pressure relationship +
Footnote †: journal: Applied Physics Letters
# Stress Analysis and Spalling Failure Simulation on Surrounding Rock of Deep Arch Tunnel
## 3 Simulation of Spalling Failure  
### Test Materials and Methods  
The Miluo granite was used for the experimental simulations and was machined into cubic (100 mm \(\times\) 100 mm \(\times\) 100 mm) specimens containing arched holes (arches are 25 mm high, \(f/B=1/2\), and 16.7 mm, \(f/B=1/3\), 12.5 mm, \(f/B=1/4\); straight walls are 25 mm high and 50 mm wide). The UCS of the granite is 144.77 MPa, and the modulus of elasticity is 42.67 GPa. These specimens were machined to an accuracy in accordance with ISRM standards, i.e., a recommended standard tolerance of 0.0175 mm and a vertical tolerance of 0.025 mm on each side as a datum. The prepared specimens were tested on a true triaxial test system with a loading rate of 2000 N/s. The stress path of the test process is shown in Figure 4. Firstly, the stresses in X, Y, and Z directions were increased with 2000 N/s to the set initial stress level and maintained for 120 s after reaching the initial stress level. The stress in the Y direction was kept constant, and in the X direction (axial direction of the hole), the loading method was changed from force to displacement-controlled loading to keep the axial displacement constant (plane strain problem) [64]. Subsequently, the stress in the Z direction was continuously increased with 2000 N/s. To ensure that only partial failure occurs on the hole sidewall, the loading rate was kept unchanged, and step loading was exerted. The loading was stopped when the failure area of the hole walls basically occurred along the X direction, and the stress in the Z direction was kept unchanged. When the hole wall reached stability, the stresses in the X, Y, and Z directions were released to 0 MPa at a rate of 20 mm/min.  
According to [PERSON], [PERSON] [65], and [PERSON] et al. [66], estimated ground stresses at burial depths of 500 m, 650 m, and 800 m were used to simula  
Figure 4: True triaxial loading stress path [64].  
characteristics of tunnel walls. The vertical stresses of surrounding rock at burial depths of 500 m, 650 m, and 800 m are calculated according to the following empirical formula:  
\[\sigma_{v}=0.027H \tag{26}\]  
The horizontal stress is calculated by the following:  
\[\sigma_{\rm{Hmax}}=6.7+0.0444H \tag{27}\] \[\sigma_{\rm{Hmin}}=0.8+0.0329H \tag{28}\]  
where \(\sigma_{v}\) is the vertical stress, \(\sigma_{\rm{Hmax}}\) and \(\sigma_{\rm{Hmin}}\) are the maximum and minimum horizontal stresses, \(H\) is the burial depth, respectively. The initial ground stresses at burial depths of 500 m, 650 m, and 800 m are listed in Table 2. The three initial horizontal stresses of rock specimens in the true triaxial test are shown in Figure 5. The \(\sigma_{\rm{X}}\), \(\sigma_{\rm{Y}}\), and \(\sigma_{\rm{Z}}\) represent the stresses in the X, Y, and \(Z\) directions, respectively.
# Conflict and Computation on Wikipedia:
## Appendix C Relaxation Time, Mixing Time, Decay Time, Trapping Time  
By the [PERSON] theorem [96], the relaxation time, \(\tau\), defined in Equation (1) above, provides an upper bound on the mixing time, \(\tau^{\prime}(\epsilon)\), the maximum time it takes an arbitrary initial condition to be no further than a small distance \(\epsilon\) from the stationary distribution, where distance is defined as the maximum absolute value difference for any state occupation probability. In particular, we have  
\[(\tau-1)\log\left(\frac{1}{2e}\right)\leq\tau^{\prime}(\epsilon)\leq\tau\log \left(\frac{1}{e\pi_{\text{min}}}\right), \tag{1}\]  
where \(\pi_{\text{min}}\) is the smallest value in the stationary probability distribution; of characteristic order \(10^{-1}\) in the HMMs considered in this paper. These relationships hold when the HMM is \"reversible\" and \"irreducible\"; in empirical work, such as that presented here, those conditions are almost always satisfied. For example, they hold when states in an HMM have a small self-loop probability, no matter how small, and a directed path exists between any two states; both of which are true by default when using a Dirichlet prior and are true by inspection in our EM fits. This justifies a useful and intuitive interpretation of \(\tau\) as a measure of how quickly an arbitrary initial condition converges to the average, as well as suggesting more sophisticated measures that take into account the allowable level of deviation (\(\epsilon\)) and inhomogeneities in the stationary distribution itself (\(\pi_{\text{min}}\)).  
The relaxation time \(\tau\) is related to another natural quantity, the decay time of the second eigenvector. The time constant for decay, \(\tau_{d}\), is related to \(\lambda_{2}\) as  
\[\lambda_{2}^{t}=e^{-t/\tau_{d}}, \tag{10}\]  
which implies that  
\[\tau_{d}=-\frac{1}{\ln\lambda_{2}}. \tag{11}\]  
On doing a Laurent series expansion around \(\lambda_{2}\) equal to unity, we find  
\[\tau_{d}=\frac{1}{1-\lambda_{2}}-\frac{1}{2}+\mathcal{O}(\lambda_{2}-1)+\ldots, \tag{12}\]  
which implies that, to zeroth order in \(\lambda_{2}-1\) (i.e., when relaxation times are long), \(\tau_{d}\approx\tau-1/2\) as used in Equation (2). All of the machines in this paper are in the regime where the relaxation time and decay time are nearly equivalent.  
In contrast to relaxation, mixing, and decay time, trapping time is an empirically-measured quantity. To compute trapping time, you define a set of internal states of interest, and then use the Viterbi algorithm to reconstruct the maximum-likelihood path through a particular time series. Trapping time is then defined as the average length of time the system spends in the set of interest before it leaves. In this paper, we track trapping time for the two main subspaces as defined by the sign structure of the second eigenvector.