esa-sceva/satcom-chunk-collection huggingface dataset:
# ISAC towards 6G Satellite-Terrestrial Communications: Principles, Status, and Prospects
## 4 Development Trend and Key Technology of ISAC towards 6G ISTNs
### Future Application Scenario
#### 4.1.1 Environmental Sensing and Dynamic Resource Allocation  
In a real-world application, users served by the same BS, satellite, or vehicle are dynamically changing. This situation leads to a reality where traditional static wireless resource allocation can not always be optimal [90]. Through ISAC, the system detects active  
Figure 4: (**a**) Architecture of cognitive satellite–terrestrial network. (**b**) Architecture of cognitive satellite–terrestrial relay networks.  
users within the coverage area and provides relevant information to the edge nodes and control center. Subsequently, the effective resource allocation of different frequency bands is optimized according to the actual situation.  
The BS will utilize the ISAC system to detect the environment within the range [91] and upload the surrounding data to the superior terminal in real time. As for the high-speed moving satellites in ISTNs and various types of vehicles, the radar image information obtained from remote sensing can be used to allocate resources [92]. In [93], the authors proposed an ISAC-assisted random-access dynamic resource allocation scheme for ISTNs. Relay nodes can obtain the situation of users within the scope through signal sensing and use the sensing data to assist in random access. The dynamic users are organized into orthogonal clusters in the frequency domain. The active users are intelligently grouped into distinct orthogonal clusters in the spectral domain. The dynamic bandwidth allocation factor is optimized to improve the overall data transmission rate, surpassing previous schemes in packet loss rate. For spectrum sharing, CR technology can be employed. In a multi-user environment where multiple users share the bandwidth resources, the users can be divided into different levels. When high-priority users have lower traffic demands, the idle bandwidth can be dynamically allocated to lower-level users. Additionally, in dynamic scenarios, it is necessary to design algorithms to meet the optimal energy allocation and improve the efficiency of resource utilization. In [94], a joint design method is proposed to optimize the allocation of subcarriers and transmit power for both sensing and communication tasks. By optimizing the allocation of subcarriers and transmit power, the minimum total power required by the system can be obtained.
## 2 Route Generation Methodology
### Real-Time Scheduling for Breakdown Scenarios  
In the real world, unexpected events such as vehicle breakdowns can be detrimental to servicing operations. In these cases, the remaining portion of the path assigned to a broken vehicle may be forgotten if the breakdown scenario is not properly addressed. When a vehicle breakdown occurs, the higher-level path generation can be used to reassign the remaining paths amongst the unbroken vehicles.  
To effectively handle the breakdown scenario, all vehicles must keep track of path coverage progress. This is because the sections of the paths that have been covered do not need to be covered again by the other vehicles. Therefore, when a vehicle breaks, only the remaining portion of the paths will be used in the higher-level path generation. The higher-level path generation has the capacity to select the optimal start point (beginning or end of the remaining path) for all paths while reassigning vehicles to the lower-level paths, such that the sum of all vehicles' distances is minimized. The path-updating scheme is visualized in Figure 11, and a pseudocode representation of this process can be seen in Algorithm 2.
```
Input: Paths assigned to each vehicle, vehicle locations, broken vehicle(s)
```
```
## Calculate the remaining portion of each vehicle route
```  
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## This portion updates the paths for the serviceable areas at the point of a breakdown
```  
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for vehicle in vehicles do if current_location is servicing
```  
```
use current_location to split servicing_path
```  
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update start_points for servicing_path
```  
```
##Run the higher-level path generation to redistribute the remaining serviceable areas
```  
```
## The broken vehicle is not considered for this operation
``` run higher_level_path_generation without broken_vehicle(s) ```  
**Output:** coverage paths for each vehicle without the broken vehicle  
Figure 10: The Reeds–Shepp method g applied to resolve sharp turns. First, the starting/ending point and the trajectories should be defined (**a**). Then, three-turn radius-sized circles can be used to plan the Reeds–Shepp path (**b**). Finally, the forward/backward path can be made by connecting the tangent points in the reference circles (**c**).
## 4 ZnO-Based Nanomaterials for Antimicrobial Application in Water Treatment
### Research Methods for Antimicrobial Activities of ZnO Nanostructures
#### 4.1.4 Colony Unit Measurements  
Colony unit measurement, also known as the diffusion plate technique, is often used to count the amount of living bacteria cells. ZnO-based nanomaterials are introduced into agar plates or dispersed as suspensions in specific liquid media. The strains were mounted on agar plates and incubated at 37 \({}^{\circ}\)C for a specific time. Colony forming units are counted using an appropriate counting method. In addition, the colony forming unit (CFU) value can be used to judge the antibacterial ability of ZnO-based nanomaterials [148; 149; 150; 151]. [PERSON] et al. [152] calculated the percentage of bacterial cell reduction (R%) using Equation (1).  
\[\text{R}\%=\frac{CFU_{control}-CFU_{sample}}{CFU_{sample}} \tag{1}\]  
where \(CFU_{control}\) = numbers of \(CFU_{\text{s}}\) per milliliter for the negative control, and \(CFU_{sample}\) = \(CFU_{\text{s}}\) per milliliter in the presence of ZnO dispersion.  
Within a certain range, the above formula can be used to calculate the bacterial concentration, and the antibacterial performance can be investigated based on the calculation results.
## 3 Numerical Model
### Turbulence Model  
The realizable \(k\)-\(k_{\rm p}\)-\(\varepsilon_{\rm p}\) turbulence model is introduced to simulate the turbulence effects of gas-solid flow in high-pressure dense-phase pneumatic conveying in horizontal pipe.  
Gas phase turbulent transport equation:  
\[\begin{array}{l}\frac{\partial}{\partial t}(\alpha_{g}\rho_{g}k_{g})+\
abla \cdot(\alpha_{g}\rho_{g}u_{g}k_{g})=\
abla\cdot\left[\alpha_{g}\left(\mu_{g}+ \frac{\mu_{g,t}}{\sigma_{k}}\right)\
abla k_{g}\right]+(\alpha_{g}G_{k,g}- \alpha_{g}\rho_{g}\varepsilon_{g})\\ +\beta(C_{sg}k_{s}-C_{gs}k_{g})-\beta(\mathbf{u}_{s}-\mathbf{u}_{g})\frac{\mu_{g,t}}{ \alpha_{g}\
u_{g}}\
abla a_{s}+\beta(\mathbf{u}_{s}-\mathbf{u}_{g})\frac{\mu_{g,t}}{ \alpha_{g}\
u_{g}}\
abla a_{g}\end{array} \tag{5}\]  
\[\begin{array}{l}\frac{\partial}{\partial t}(\alpha_{g}\rho_{g}\varepsilon_{g })+\
abla\cdot(\alpha_{g}\rho_{g}u_{g}\varepsilon_{g})=\
abla\cdot\left[ \alpha_{g}\left(\mu_{g}+\frac{\mu_{g,t}}{\sigma_{k}}\right)\
abla\varepsilon_{g }\right]\\ +C_{gs}\alpha_{g}\rho_{g}D_{g}\varepsilon_{g}-C_{1\varepsilon}\alpha_{g}\rho_{ g}\frac{\varepsilon_{g}^{2}}{\kappa^{2}+\sqrt{\mu_{g}}}+C_{2\varepsilon}\frac{\varepsilon_{g} }{\kappa_{g}}[\beta(C_{gg}k_{s}-C_{gs}k_{g})\\ -\beta(\mathbf{u}_{s}-\mathbf{u}_{g})\frac{\mu_{g,t}}{\alpha_{g}\
u_{g}}\
abla a_{s}+ \beta(\mathbf{u}_{s}-\mathbf{u}_{g})\frac{\mu_{g,t}}{\alpha_{g}\
u_{g}}\
abla a_{g}) \end{array} \tag{6}\]  
Solids phase turbulent transport equation:  
\[\begin{array}{l}\frac{\partial}{\partial t}(\alpha_{s}\rho_{s}k_{s})+\
abla \cdot(\alpha_{s}\rho_{s}\mathbf{u}_{s}k_{s})=\
abla\cdot\left[\alpha_{s}\left(\mu_ {s}+\frac{\mu_{g,t}}{\sigma_{k}}\right)\
abla k_{g}\right]+(\alpha_{s}G_{k,g}- \alpha_{s}\rho_{s}\varepsilon_{s})\\ +\beta(C_{gs}k_{g}-C_{sg}k_{s})-\beta(\mathbf{u}_{s}-\mathbf{u}_{s})\frac{\mu_{g,t}}{ \alpha_{g}\
u_{g}}\
abla a_{g}+\beta(\mathbf{u}_{s}-\mathbf{u}_{s})\frac{\mu_{g,t}}{ \alpha_{g}\
u_{g}}\
abla a_{s}\end{array} \tag{7}\]  
\[\begin{array}{l}\frac{\partial}{\partial t}(\alpha_{s}\rho_{s}\varepsilon_{ s})+\
abla\cdot(\alpha_{s}\rho_{s}\mathbf{u}_{s}  
\[C_{D}=\left\{\begin{array}{cc}\frac{24}{R\varepsilon_{s}}\Big{[}1+0.15(Re_{s})^{ 0.687}\Big{]}&Re_{s}<1000\\ 0.44&Re_{s}\geq 1000\end{array}\right.,Re_{s}=\frac{\alpha_{g}\rho_{g}d_{s}\big{|} \boldsymbol{v}_{s}-\boldsymbol{v}_{g}\big{|}}{\mu_{g}} \tag{10}\]  
\[\boldsymbol{F}_{sg}=\beta(\boldsymbol{v}_{g}-\boldsymbol{v}_{s}) \tag{11}\]  
\(\boldsymbol{F}_{sg}\) is the drag force between the gas and solids phases, \(\boldsymbol{v}_{g}\), \(\boldsymbol{v}_{s}\) are the velocity vectors of the gas and solids phases, m/s.
## 4 Proposed Approach for Forecasting the Wind Power Intervals
### Wind Power Forecasting Model Based on CNN  
In this study, historical wind power data are used as input data for the wind power forecasting. Due to the nonlinearity, non-stationarity, and randomness of the historical wind power data, a bi-level CNN point-based wind power forecasting model based on VMD and PSR is proposed. First, the historical wind power data are smoothed using VMD and decomposed into modes at different scales; each mode is transformed from a one-dimensional sequence into a high-dimensional matrix using the PSR method. Subsequently, each high-dimensional matrix is fed into the first-layer CNN sub-model, and training with a BP algorithm is performed to obtain the forecasted value of each mode. Finally, the forecasted values of the modes and the real values of the modes corresponding to the previous moments are used to create a high-dimensional matrix, which is fed into the second-layer CNN. The final forecasted wind power values are also obtained by training with a BP algorithm. The flowchart of the point-based wind power forecasting model is shown in Figure 3.  
Figure 3: Schematic diagram of the proposed point-based forecasting model. VMD: variational mode decomposition; PSR: phase space reconstruction.  
Figure 2: Interval forecasting mode. PSO: particle swarm optimization.
## 1 Introduction
Routine monitoring methods include ground stress measurement, bolt stress meters, acoustic testing, and geological radar [11; 12; 13; 14]. The monitoring results can better reflect changes in deformation and mechanical parameters on the section, but they have spatial limitations and make it difficult to capture micro-fractures within the rock mass, which are frequently the precursor to macroscopic instability failure in the rock mass. As a 3D monitoring method, microseismic monitoring can detect micro-fractures within the rock mass in advance, allowing for the forecast of risk areas in the rock. [PERSON] et al. The authors in [15] analyzed the microseismic b-values associated with rock mass large deformation and their temporal variation. [PERSON] et al. [16], based on tempo-spatial characteristics of the microseismic events, analyzed the relation between microseismic activities and field construction and revealed the main damage regions. [PERSON] and [PERSON] [17] analyzed the clustering characteristics of microseismic events and developed a time-based function for high-energy fractures in coal mines. [PERSON] and [PERSON] [18] developed a microseismic monitoring method to manage hazards in underground hard rock mines. [PERSON] et al. [19] investigated the focal mechanism of seismic events induced by underground mines using an optimized moment tensor inversion method. [PERSON] et al. [20] quantified the rock damage in the experimental tunnel using the source radius. [PERSON] et al. [21] examined the stability of the surrounding rock of high-slope tunnels by analyzing the spatial and temporal characteristics of microseismic events. [PERSON] et al. [22] classified the dominant factors of clustering microseismic events and proposed a method for studying the large deformation of rock in underground plant caverns based on source parameters. In the present study, the incorporated method involving RFPA3D and MS monitoring technique is applied to analyze the stability of the surrounding rock, aiming at the instability failure problem inside the rock mass caused by the redistribution of ground stress after excavation in the geologically weak section of the #2 diversion tunnel in the Xulong Hydropower Station in the upper reaches of the Jinsha River, and analyzes the progressive instability failure of the tunnel using the RFPA3D numerical modeling software by comparing the spatial and temporal evolution characteristics of microseismic events to reveal the whole process from micro-fracture initiation, growth, and propagation, to penetration within the surrounding rock after tunnel excavation, and identifies potentially unstable areas after tunnel excavation. The research findings can be used to provide technical support for stability evaluation and risk prediction prior to and after excavation of the #2 diversion tunnel in the Xulong Hydropower Station, and can also provide experience for similar hydraulic tunnel excavation.
## 5 Signal and Noise Evaluation  
As defined above, each frame in our ATRIS 3D system is built of cumulative per-pixel sum of \(N\) sub-frames taken with positive or negative signs according to the control bit of the modulation sequence. Unlike in communication systems where a receiver extracts the transmitted signal from the observed noise-like temporal signal, in our case \"signal\" and \"background noise\" are two spatially combined groups of pixels. To build the range map, we must find the coordinates of \"signal\" pixels in captured images.  
In the following we evaluate the intensity of the \"signal\" pixel in comparison to all the rest (background) pixels regarding the applied modulation scheme.
###### Abstract
Significant effort has been spent to achieve good-quality reconstruction with scarce tomographic geometries determined by the limited availability of location to install detectors (usually confined to ports of the vacuum vessel). The development of various techniques incorporating a priori information (mainly related to the magnetic configuration) played an essential role in ensuring a good spatial resolution. The temporal resolution that can be achieved in bolometry is suitable for studying rapid emissivity evolution. Bolometry became an increasingly requested diagnostic during experimentation, and, therefore, an increased amount of data should be processed. Therefore, the demand for high-speed computational methods is increasing quickly. The method based on neural networks represents a good solution for machines where a solid reconstruction database has been accumulated (by using a traditional approach, e.g., ICO on JET). However, for new machines, the implementation of very fast or even real-time algorithms is still at the beginning. The code used in [6] is implemented in MATLAB, taking input data from the JET database formatted and organized accordingly. The execution time needed by MATLAB in the hardware platform used for the evaluation requires around 63 s, but the target execution time to obtain a real-time performance is 25 ms. Reducing the execution time implies that acceleration techniques are essential to approaching real-time performance.
# Novel Online Optimized Control for Underwater Pipe-Cleaning Robots
###### Abstract  
Due to the particularity of the jacket structure of offshore platforms and the complexity of the marine environment, there have been few effective localization and autonomous control methods for underwater robots that are designed for cleaning tasks. To improve this situation, a fusion bat algorithm (BA) online optimized fuzzy control method using vision localization was developed based on the constraints of the underwater operational environment. Vision localization was achieved based on images from a catadioptric panoramic imaging system. The features of the pipe edge and the boundary of the area covered by biofouling were obtained by image processing and feature extraction. The feature point chosen as the \"highest\" point of the boundary was calculated by projection transformation to generate the reference path. The specialized fuzzy controller was designed to drive the robot to track the reference path, and an improved bat algorithm with dynamic inertia weight and differential evolution method was developed to optimize the scale factors of the fuzzy controller online. The control method was simulated and further implemented on an underwater pipe-cleaning robot (UPCR), and the results indicate its rationality and validity.  
underwater robot; automation; vision localization; fuzzy control; bat algorithm +
Footnote †: journal: Applied Sciences
## 4 Cellular Network Reconstruction and Validation
### Automatic Generation of the Network Database
The overlapping period is very relevant because it allows us to perform analyses and verification concerning the data integration process. As a matter of fact, we were indeed able to easily recognize 63,839 cells appearing in both datasets. An example is shown in Figure 11. The cell identified by _type_net_ = 'GSM', _mcc_ = 655, _mmc_ = 2, _code_area_ = 1011, _ci_ = 10,503 (grey polygon) was reconstructed using 37 observations of OpenCellID and 87 observations of the u-blox dataset (the orange and green polygons, respectively). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **CELL** & **REGISTATION AREA** & **SubPLMN** & **PLMN** \\ \hline \multirow{3}{*}{GSM} & 1,553,523 & 45,970 & 811 & \\  & (1,553,523) & (35,157) & (743) & \\ \multirow{3}{*}{UMTS} & 2,001,145 & 43,147 & 794 & 993 \\  & (693,622) & (29,139) & (746) & (925) \\ \multirow{3}{*}{LTE} & 2,240,032 & 48,098 & 607 & \\  & (864,673) & (40,787) & (559) & \\ \hline \multirow{3}{*}{} & 5,794,700 & 137,215 & 2,212 & \\  & (3,111,818) & (105,083) & (2048) & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Overall network summary: total number of elements in our database (polygons in brackets). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Tech.** & **Observations** & **Valid Observations** & **\% Neighbors** & **Valid Neighbors** & **\%** \\ \hline
1 & GSM & 26,896,809 & 26,840,087 & 99.79 & 0 & 0 & 0 \\
1 & UMTS & 6,195,903 & 6,177,024 & 99.70 & 0 & 0 & 0 \\
1 & LTE & 9,859,665 & 9,848,455 & 99.89 & 0 & 0 & 0 \\
2 & GSM & 2,522,483 & 2,258,522 & 89.54 & 9,476,328 & 6,768,116 & 71.42 \\
2 & UMTS & 62,318 & 55,723 & 89.42 & 431,416 & 102,568 & 23.77 \\ \hline
1 & & 42,952,377 & 42,865,566 & 99.80 & 0 & 0 & 0 \\
2 & & 2,584,801 & 2,314,245 & 89.53 & 9,907,744 & 6,870,684 & 69.34 \\
1\&2 & 45,537,178 & 45,179,811 & 99.21 & 9,907,744 & 6,870,684 & 69.34 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Measurements (1 = OpenCellID, 2 = u-blox).
## 3 The Proposed Protocol
### Tree Route Establishment  
MAs move through a tree-structured route to visit LDSs and collect the data records using a hop-count-aware DFS algorithm. According to Algorithm 3, a CH that knows no CHs are around forwards a reply beacon toward the sink to form the tree path. This beacon is included by data type, the sender's location address (SID), hop count and time stamp. Hop count is used to avoid loops and recognise an optimal path for MAs to move through. This is incremented by each intermediate LDS until the sink is reached. A NDN routing is used to forward the beacon messages. Each intermediate LDS which receives the beacon message checks PIT to find a matching record. Similarly to [33], the LDS updates PIT and passes the beacon's information to FIB to check the message forwarding strategies if this is a new beacon. FIB allows the forwarding of the beacon and incrementation of \(hop\_count\) if the LDS has a closer Euclidean distance than the beacon sender to the sink. Otherwise, the beacon is discarded. The beacons are forwarded until the sink is reached. An LDS still becomes a _joint_point_ if receives a beacon matching PIT records from LDSs which have farther distances to the sink.  
```
Data: beacon (data_type, SID, hop_count, time_stamp); /*beacon forward.*/ if\(LDS.CH==CH\)then  Broadcast beacon;  end if /*untilsink is not reached.*/ while\(LDS\
eq sink\)do /*beacon is received from farther area.*/ ifbeacon.SID\(\geq LDS\)then /*This is a new beacon.*/ ifbeacon\(\
otin\)\(LDS.PIT\)then /*Matches FIT.*/ ifbeacon\(\in\)\(FIT\)then  hop_count++;  forwards beacon;  end if  end if /*A true beacon, but a copy is already available.*/ else  joint.flag\(\leftarrow\) true;  end if  end if /*A wrong beacon from closer area is received.*/ else  Discard beacon;  end if  end
```  
**Algorithm 3**Tree establishment.
# A Study of Breast Cancer Classification Algorithms by Fusing Machine Learning and Deep Learning
## 2 Related Work
### Deep Learning
In recent years, research on breast cancer pathology image classification based on deep learning has developed rapidly, especially the wide application of convolutional neural networks built on large datasets in natural language processing, object recognition, image classification recognition, etc., laying a solid foundation for the application of CNN in breast cancer pathology images. Since Dr. [PERSON] and other researchers made public the BreakKHis breast cancer dataset and introduced the pathology image dataset in 2015, a series of research results have been achieved in breast cancer recognition using convolutional neural networks based on this dataset. By adopting six feature descriptions such as local binary pattern (LBP), gray-level co-generative matrix (GLCM), and different classification algorithms such as support vector machine and random forest, the recognition accuracy rate reached 80-85%. Dr. [PERSON] architected single-task and multi-task convolutional neural networks to predict malignant tumors, increased the recognition rate to 83%, concluded that the recognition rate was independent of the magnification, and, at last, achieved an accuracy rate of 86.3% on breast cancer pathology images. However, the accuracy of the deep learning model for automatic recognition of breast cancer pathology images is not yet as high as expected. Pathological tissue image classification recognition differs from traditional image classification recognition (e.g., recognition of dogs and cats) in terms of image characteristics and dataset size. Pathological tissue images have characteristics such as differential ambiguity, feature diversity, cell overlap phenomenon, and uneven color distribution, especially when the small size of the current pathological tissue image dataset and the uneven number of benign and malignant samples will affect the recognition rate. Therefore, improving the dataset and designing a reasonable learning model, as well as effectively improving the automatic recognition capability, are all important research directions.