esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Anti-Jamming Decision-Making Algorithm Based on Knowledge Graph Technology
## 5 Simulation Results and Discussion
### Simulation of Decision-Making Result
#### 5.2.1 Simulation of the Decision-Making Result of Known Jamming Patterns  
In this section, we mainly simulate the conventional jamming patterns. Assuming that the jamming sensing module can give the jamming patterns and parameters, we compare whether the anti-jamming strategies and decision-making parameters derived from the communication anti-jamming decision-making algorithm based on the knowledge graph match the prior anti-jamming strategies and parameters to check the accuracy of the decision-making algorithm. The simulation results are shown in Figure 6.  
As seen from the results in Figure 6, the reliability of the decision-making algorithm is less affected by perception error when the jamming patterns have a narrow bandwidth and limited jamming frequency coverage characters (i.e., single-tone jamming, partial band jamming, comb jamming and high-speed collision jamming). It also confirmed that the proposed algorithm has good robustness.  
On the other hand, the jamming bandwidth is so wide that it covers the whole communication frequency band for wideband block jamming. Once there is a large error in the jamming bandwidth perception and the starting and stopping frequency, the probability that the decision-making result is jammed will increase.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Communication timeslot \(T_{s}\) & 100 \\ Communication frequency band & 0 MHz\(\sim\)60 MHz \\ Communication bandwidth & 2 MHz \\ Bandwidth coincidence threshold ratio & 30\% \\ Total number of timeslots & 3000 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation parameters of the decision-making algorithm.  
Figure 5: Recall of frequency domain feature extraction of broadband blocking jamming.  
Besides, for sweep jamming patterns, the FH pattern generated by the decision-making algorithm can induce serious errors when the perception of sweep starting and stopping frequency and its period contain error. Thus, the algorithm accuracy is vulnerable even if the perception error is small.  
In this paper, we assume that the starting and stopping frequencies of sweep jamming are randomly generated. When the starting and stopping frequencies of sweep jamming do not cover the communication frequency band completely, the frequency change strategy will be adopted. In this regard, when the perception error rate is large, the decision-making algorithm might choose the frequency change strategy, and the decision is wrong. On the other hand, if the starting frequency of the sweep jamming is high, the jamming will not completely cover the communication frequency band, and then the proposed algorithm will choose the correct strategy, namely the frequency change strategy. Therefore, the decision accuracy of the algorithm only depends on whether the randomly selected frequency point is covered by the jamming or not. As a result, the decision accuracy curve shows a steady trend.
## 2 Methodology
### Establishment of the Operational Tunnel Model  
This study was conducted in the context of an engineering project pertaining to the downstream portion segment of the No.1 auxiliary tunnel at Liujia Village, situated in the Chuxiong section of the Central Yunnan Water Diversion Project (CYWDP). As is shown in Figure 3a, the CYWDP is a large (I)-type project in China, characterized by its extensive water diversion scope, long tunnel route, and complex geological conditions. Figure 3b shows the surrounding rock of the working face, which is composed of mudstone and exhibits profusion of fractures within the rock mass. The rock mass displays prominent fissures, and the adjacent strata are notably saturated by moisture. In summary, the overall quality of the surrounding rock is deemed suboptimal.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Parameters** & **Learning Rate** & **Number of Estimators** & **Maximum Depth** & **Minimum Child Weight** & **Subsampling Rate** \\ \hline Values & 0.01 & 100 & 7 & 5 & 0.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Values of the optimized hyperparameters in the XGBoost algorithm.  
The objective of establishing a finite element model is to clarify the construction process of a tunnel. The FLAC3D program was employed to perform numerical simulation on the tunnel operation. As shown in Figure 4a, the computational model extends in the x and z directions until 10 times the tunnel's diameter, considering the influence of boundary effects. The tunnel excavation direction, denoted the y-direction, spanned a length of 20 m [42]. The simulation of the tunnel's surrounding rock and lining was accomplished using zone elements, while cable elements were utilized to represent anchor bolts. In addition, the shell elements were employed to simulate the initial support of shotcrete, as shown in Figure 4b. The analysis is based on geological conditions and the buried depth of the tunnel in the No. 2 branch tunnel of the Liujia Village Tunnel in the Chuxiong Section of the Central Yunnan Water Diversion Project. The surrounding rock near five monitoring sections, including CX22 + 710, was used as the research object to conduct inversion analysis. The depth of this tunnel section is 300 m on average.  
In terms of constitutive models, the Mohr-Coulomb model was applied to describe the behavior of the rock mass, while a plastic model was employed for both concrete and anchor bolts. The model included a total of 92,003 grids and 17,413 nodes. Based on the practical monitoring locations in the operating tunnel operation, monitoring points were positioned in the corresponding places in the model, as shown in Figure 4c, so that arch crown (A), shoulder (B-C) and waist (D-E) convergence could be recorded.  
Figure 4: Numerical model in FLAC3D. (**a**) Dimensions and grid partition of model; (**b**) primary support and lining; (**c**) layout of monitoring points.  
Figure 3: Location of CYWDP. (**a**) Location of Chuxiong in Yunnan province; (**b**) condition of the work face in the RBSR tunnel.
## 4 Numerical and Experimental Comparison
### Structural Comparison  
Figure 17 shows a comparison of the measured central deflection of the slab against deflections predicted using the coupled thermo-structural model. The figure shows that the behavior established between tests and numerical models is very similar.  
Here, the percentages of error (%) between experimental and numerical analyses are compared. The difference between the experimental and numerical values, in each of the simulations, is less than 15% in most of the cases analyzed, as shown in Table 5. When steel reaches a critical temperature between 450 \({}^{\circ}\)C and 800 \({}^{\circ}\)C, it loses its strength and stiffness. This critical temperature depends on factors such as the type of steel, the manufacturing process, and the treatments to which it has been subjected. Due to the range in which this physical phenomenon occurs, simulating this behavior is difficult. For this reason,  
Figure 16: Comparison of experimental (solid lines) and FEM (dashed lines) temperatures at zones A and B: (**a**) LWC-1 and LWC-2; (**b**) LWC-3 and NC.  
Figure 17: Comparison of FEM and experimental tests: (**a**) LWC-1; (**b**) LWC-2; (**c**) LWC-3; (**d**) NC.  
the main differences between the experimental values and the numerical values occur between 200 and 700 s. At this point, the ambient temperature in the furnace is between 518 \({}^{\circ}\)C and 700 \({}^{\circ}\)C. In addition, at this temperature steel decking and concrete slab stop working together.  
It should be mentioned that the above findings are based on several assumptions, and the validity of the proposed model is limited to the experimental data. Limitations still exist in this study, and further research may be needed. First, mechanisms of mass transport within LWC create difficult conditions that are hard to simulate due to their complexity. Second, temperature distributions obtained from thermal analysis influence the structural response through thermal expansion and through degradation of material stiffness and strength. Therefore, it is necessary to take into account the heating process at the same time as the structural response to properly capture the effects of material and geometric nonlinearities at large deformations. Notwithstanding these limitations, the results in this study can be used to explain some experimental observations, such as the debonding.
## 5 Conclusions and Future Work  
### Conclusions  
An integrated in silico model of joint pain was developed to assess the efficacy of phytonutrients on five biomarkers of joint pain, namely, PGE-2, TRPV1, CGRP, COX-2, and ROS. Apigenin lowered four of the five biomarkers, whereas hesperidin lowered all five biomarkers of joint pain individually. A combination of apigenin and hesperidin lowered all five biomarkers of joint pain; however, this specific combination synergistically lowered four of the five biomarkers, COX-2, TRPV1, CGRP, and ROS, while additively lowering PGE2.  
The development of molecular systems architecture in this study provides a system-wide understanding of biomolecular interactions that characterize a biological phenomenon or a complex disease such as joint pain. In addition, such an architecture can provide insights on the anti-inflammatory and nociceptive effects of apigenin and hesperidin that may lead to improvements in joint pain.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Joint Pain Mechanisms of Action** & **Apigenin** & **Hesperidin** \\ \hline COX-2 Production & Inhibits IKK activation & Inhibits NF-kB \\ Arachidonic Acid Metabolism & Inhibits COX-2 & Inhibits COX-2 \\ PGE2 Signaling & Inhibits PKC activation & Inhibits PLC activation \\ Oxidative Stress Pathway & ROS Production & ROS Production \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary of effects of apigenin and hesperidin on mechanisms of joint pain.
## 3 Research Framework of Internet of Emergency Services
### Computation Paradigms
#### 3.6.1 Cloud Computing
Cloud services are also typically more cost-effective than traditional IT infrastructure, as they can be accessed on a pay-as-you-go basis. Finally, cloud services can be accessed from anywhere, which makes them ideal for emergency response scenarios where responders may be working in different locations. One specific example of a cloud service that has been used in emergency management and response is Amazon Web Services (AWS). AWS provides a range of cloud-based services, including storage, processing, and analytics. In 2017, AWS was used by the Houston Office of Emergency Management (OEM) during Hurricane Harvey. The Houston OEM used AWS to store and process large amounts of data related to the hurricane, including flood maps, weather data, and social media feeds. This allowed emergency responders to quickly identify areas that were most affected by the hurricane and prioritize their response efforts. Another example of a cloud service that has been used in emergency management and response is Microsoft Azure. Azure provides cloud-based services for storage, processing, and analytics, as well as tools for building and deploying applications. In 2018, the Puerto Rico Emergency Management Agency (PREMA) used Azure to support its response efforts following Hurricane Maria. PREMA used Azure to store and process data related to the hurricane, including satellite imagery and social media feeds. This allowed emergency responders to quickly identify areas that were most affected by the hurricane and coordinate their response efforts more effectively. Finally, Google Cloud Platform (GCP) is another example of a cloud service that has been used in emergency management and response. GCP provides cloud-based services for storage, processing, and analytics, as well as tools for building and deploying applications. In 2020, GCP was used by the Australian Government during the bushfires that affected the country. The Australian Government used GCP to store and process data related to the bushfires, including satellite imagery and weather data. This allowed emergency responders to quickly identify areas that were most affected by the bushfires and coordinate their response efforts more effectively. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline  
In conclusion, cloud computing has emerged as a critical technology for managing and responding to emergencies. Cloud services provide emergency response organizations with the ability to store, process, and analyze large amounts of data and share it securely across different agencies and stakeholders. AWS, Azure, and GCP are just a few examples of cloud services that have been used in emergency management and response, and we can expect to see more cloud-based solutions in the future as the Internet of Emergency Services continues to evolve.
## 2 Related Work  
### Classical DV-Hop Algorithm  
The classical DV-Hop algorithm estimates the location of nodes by calculating the distance between the unknown nodes and beacon nodes [10]. It can be divided into three phases:  
Phase 1: Calculate the minimum hop count. Beacon nodes broadcast the packet, containing their location and hop count, to their neighbor nodes \((ID,\ x_{i},\ y_{i},\ hops_{i})\). The hop count value is increased by 1 at each hop. This process continues until all nodes record their minimum hop count and the coordinates of the beacon nodes [11].  
Phase 2: Calculate the average hop distance among nodes. The beacon node receives data frames from other beacon nodes, stores their coordinates and hop count information, and then forwards the frames. The average hop distance can be calculated by Equation (1).  
\[Hopsize_{i}=\frac{\sum_{i\
eq j}^{n}\sqrt{\left(x_{i}-x_{j}\right)^{2}+\left(y _{i}-y_{j}\right)^{2}}}{\sum_{i\
eq j}^{n}H_{ij}} \tag{1}\]  
where \(Hopsize_{i}\) represents the average hop distance of beacon node \(i\). \(\left(x_{i},\ y_{i}\right)\) and \(\left(x_{j},\ y_{j}\right)\) represent the coordinates of beacon nodes \(i\) and \(j\). \(H_{ij}\) represents the minimum number of hopsbetween beacon nodes \(i\) and \(j\). \(n\) represents the number of beacon nodes. The unknown node calculates the distance to the beacon node based on the number of hops and the average hop distance among all the beacon nodes, which can be calculated by Equation (2).  
\[d_{iu}=Hopsize_{i}\times H_{iu} \tag{2}\]  
where \(d_{iu}\) represents the distance between the beacon node \(i\) and the unknown node \(u\), and \(H_{iu}\) represents the minimum number of hops between beacon node \(i\) and unknown node \(u\).  
Phase 3: Calculate coordinates using the least squares method. After the previous two phases, the unknown nodes obtain the estimated distances to all beacon nodes. The system of linear equations can be constructed as expressed in Equation (3).  
\[\left\{\begin{aligned} (x_{u}-x_{1})^{2}+(y_{u}-y_{1})^{2}& =d_{1u}^{2}\\ (x_{u}-x_{2})^{2}+(y_{u}-y_{2})^{2}&=d_{2u}^{2}\\ \vdots&\\ (x_{u}-x_{n})^{2}+(y_{u}-y_{n})^{2}&=d_{nu}^{2}\\ \end{aligned}\right. \tag{3}\]  
where \((x_{1},\ y_{1})\), \((x_{2},\ y_{2})\), \(\ldots\), \((x_{n},\ y_{n})\) represent the coordinates of \(n\) beacon nodes, and \((x_{u},\ y_{u})\) represents the coordinate of unknown node \(u\). Rewrite Equation (3) in the form of \(AX=b\). Then, the unknown node coordinates can be calculated using the least squares method [12].
# Reducing Video Coding Complexity Based on CNN-CBAM in HEVC
###### Abstract  
High-efficiency video coding (HEVC) outperforms H.264 in coding efficiency. However, the rate-distortion optimization (RDO) process in coding tree unit (CTU) partitioning requires an exhaustive exploration of all possible quad-tree partitions, resulting in high encoding complexity. To simplify this process, this paper proposed a convolution neural network (CNN) based optimization algorithm combined with a hybrid attention mechanism module. Firstly, we designed a CNN compatible with the current coding unit (CU) size to accurately predict the CU partitions. In addition, we also designed a convolution block to enhance the information interaction between CU blocks. Then, we introduced the convolution block attention module (CBAM) into CNN, called CNN-CBAM. This module concentrates on important regions in the image and attends to the target object correctly. Finally, we integrated the CNN-CBAM into the HEVC coding framework for CU partition prediction in advance. The proposed network was trained, validated, and tested using a large scale dataset covering various scenes and objects, which provides extensive samples for intra-frame CU partition prediction in HEVC. The experimental findings demonstrate that our scheme can reduce the coding time by up to 64.05% on average compared to a traditional HM16.5 encoder, with only 0.09 dB degradation in BD-PSNR and a 1.94% increase in BD-BR.  
HEVC; intra-frame; CNN; CBAM; CU partition Article
# Methods and Applications of Space Understanding in Indoor Environment--A Decade Survey
## 3 Taxonomy of Input Data Types
### Taxonomy Discussion  
Summarizing, the created taxonomy showed that there is virtually no limitation or minimal requirement for the input data complexity to perform room segmentation and recognition successfully. The range of inputs stretched from trivial series of numbers (one dimension) and matrixes of values (two dimensions), through the spatial location of measurements (three dimensions), to complicated mixes of different information with an unlimited number of features. This variety allowed researchers to choose the data structure that best suited the hardware capabilities of their equipment. The least powerful devices generated simplified measurements, such as power usage readings or radio signal strength. We can observe that the more computational power the sensor has, the more complex data it returns. Worth mentioning is a visible change in the approach, from offline data gathering and later processing (if the device was not powerful enough) to edge-computing and transfer to the cloud of only already deducted conclusions. Mobile platforms with better processors were capable of online analysis of even complicated visual input or continuous readings, such as video streams from cameras or LiDAR-based distance calculations. Nevertheless, we can summarize that there is not yet enough computational power on mobile platforms to process complex spatial data, such as point clouds, in a fully online manner. The best industrial laser scanners generate so much data, and their processing is so complicated, that neither data analysis on the device nor instantaneous transfer to the cloud is possible. In such a scenario, the data were only collected during the acquisition phase and processed later, offline and using dedicated machines.
## 2 Materials and Methods
### Data Source and Processing
The 8-day composited ET data during 2015 were measured by an eddy covariance system in a cropland in Korla (41\({}^{\circ}\)36\({}^{\prime}\)N, 86\({}^{\circ}\)12\({}^{\prime}\)E) [55]. The 10-day composited ET data in 2015 were measured by an eddy covariance system in a shrubland in Jinghe (44\({}^{\circ}\)37\({}^{\prime}\)N, 83\({}^{\circ}\)33\({}^{\prime}\)E) [56]. Aridity is generally expressed as a function of precipitation, potential evapotranspiration and temperature. The UNEP proposed a method to classify the climate zones based on the indicator metrics of the aridity index (AI). The AI dataset ([[https://cgiarcsi.community/](https://cgiarcsi.community/)]([https://cgiarcsi.community/](https://cgiarcsi.community/)), accessed on 1 August 2021) produced by [PERSON] and [PERSON] was used in this study to classify areas into five climate zones in Xinjiang (Figure 3) [57]. The proportion of areas in each climate zone to the total area was 36.9%, 50.2%, 10.1%, 1.4%, and 1.4% for hyper-arid, arid, semi-arid, dry sub-humid, and humid zones, respectively. Areas such as the Gobi Desert were considered as non-vegetated areas and were excluded from analysis. The proportion of vegetated areas in each climate zone to the total area was 8%, 59%, 27.7%, 3.3%, and 2% in the hyper-arid, arid, semi-arid, dry sub-humid, and humid zones, respectively.
## 5 Coto Laurel Case Study
### Model Validation  
To test and validate the proposed model, a comparison between the active power calculated using the proposed model and the real data measured by the Coto Laurel's POI's meter was performed. To perform the simulation, the yearly average irradiance per minute was used as an input parameter. To improve accuracy and testing time, the simulation was carried out in the OP5700 simulator from Opal-RT Technologies. After running the simulation, the average power per minute was obtained, which closely matches the average power data measured at the Coto Laurel farm. Figure 10 shows the simulated mean power (blue), the measured mean power data (orange), and the absolute error (green) for each minute. The average error during the day is approximately 3.05%. It is important to highlight that at the beginning and at the end there are the highest errors of the model, this happens due to the transient that occurs in the simulation due to the change in irradiance from 0 to a certain value. However, the model demonstrates to be accurate during the rest of the day. Results from this study validate the accuracy of the model describing the dynamics of Coto Laurel.
# UAV-BS Site Planning Based on Circular Coverage Strategy
## 2 Background and Related Work  
The development of UAV communication is inseparable from research on UAV communication system models and their performance. In UAVCNs, the UAV can be used as an air user to receive ground information, and can also be equipped with communication equipment as a wireless relay or air base station to expand network coverage. Relevant research is shown in Table 1. As aerial users, UAVs are used in the Internet of Things on a large scale to collect sensor data from ground equipment [7]. UAVs equipped with base stations as relays are used in ultra-dense scenarios, which can strengthen the signal of mobile users and provide long-distance communication. UAV-BSs are more used to assist the ground network in unloading data volume and emergency communication direction [8]. UAV communication is the first choice for low-altitude network composition due to its convenience and low environmental impact.