esa-sceva/satcom-chunk-collection huggingface dataset:
# Review and Comparison of Emerging Routing Protocols in Flying Ad Hoc Networks
## 4 Routing Protocols
### Cluster-Based Routing Protocols
#### 4.3.4 Discussion and Analysis  
To conclude the discussion about above cluster-based routing protocols, Table 4 illustrates the comparison of performance indexes used to evaluate them. In this kind of protocol, the obvious feature is that the network is divided into many clusters, and is managed in a hierarchical way. Thus, the indexes related with clustering is the key factors to the performance of data transmission. Therefore, compared with the routing protocols mentioned above, the indexes of cluster building time and cluster life time are considered during routing design. In fact, the accuracy of clustering is also very important for the efficient transmission of data, but unfortunately none of them takes it into accounted during their routing design. EECA [47] optimized the selection of subcluster heads, and formed a cluster hierarchy structure. However, it made most of the aggregated data stay in the subcluster head which had already remained storing data for a long time, and it didn't consider the limited buffer space of nodes. Therefore, for the practical point, EECA can not be applied to FANET directly. On the basis of this, EALC [49] proposed an adaptive adjusting transmission power method according to the nodes' condition when optimizing the selection of cluster heads. In addition, when using the k-means density clustering algorithm to choose cluster heads, EALC added two parameters to judge neighboring nodes: energy level and distance, which is superior to traditional artificial intelligence technology. Hence EALC is more suitable for FANET than EECA. BICSF [50] is also an artificial intelligence technology. In this paper, the author not only considered cluster management method by using genetic algorithm, but also focused on the communication for clusters and proposed cluster maintenance scheme. The proposed scheme has obvious advantages over other bioheuristic algorithms in terms of energy consumption, cluster construction time, cluster lifetime, and successful transfer rate.  
Through the above study on clusters, we can see that the main factors that affect the design of clustering algorithm are optimization of cluster head choosing, management of network topology, energy saving of member nodes, and network performance under mobile conditions. For the problems that have not been solved, there are the following points.  
(1) Too many UAV nodes will increase the time required for cluster construction.  
(2) Due to incorrect node power adjustment, the energy consumption of the cluster head is accelerated. Therefore, we need a balancing mechanism between nodes, so that the life of the cluster can be increased and help to save energy of nodes in network.
## 3 Calculation Simulation and Analysis
### Performance Analysis  
#### 3.1.1 Stability  
The method above mainly consists of the explicit center difference method and the implicit Newmark method, and the stability of these two methods is analyzed below. The explicit center difference method is a conditionally stable algorithm, and its stability conditions are [17]  
\[\Delta t\leqslant\frac{2}{\sqrt{\rho(\mathbf{M}^{-1}\mathbf{K})}}=\frac{2}{ \omega_{\text{max}}} \tag{25}\]  
where \(\rho(\mathbf{M}^{-1}\mathbf{K})\) is the spectral radius, i.e., the maximum eigenvalue of the \(\mathbf{M}^{-1}\mathbf{K}\) matrix, \(\omega_{\text{max}}\) is the maximum intrinsic vibration frequency of the system, and \(\omega_{\text{max}}\) can be obtained using the eigenvalues of the coefficient matrix.  
According to the simulation conditions, the stability conditions of Equation (1), Equations (20) and (21) are calculated as \(\Delta t\leqslant 3.3333\times 10^{-4}\), \(\Delta t\leqslant 3.3333\times 10^{-4}\) and \(\Delta t\leqslant 3.3324\times 10^{-4}\), respectively. It can be seen that the stability of the equations in the two symmetrized forms is the same, which also reflects that symmetrizing the coefficient matrices of Equation (1) does not change the stability of the original equations from the side.  
For the implicit Newmark method, when \(\alpha=1/2\), \(\beta=1/4\), this method is unconditionally stable. Therefore, the unconditionally stable Newmark method is used in implicit numerical simulations.  
In summary, the stability requirement of the explicit center difference method under the given conditions is about \(\Delta t\leqslant 3.3333\times 10^{-4}\), while the implicit Newmark method is unconditionally stable, and the time step \(\Delta t\) depends on the accuracy requirement.  
Figure 2: Schematic diagram of the shallow water seismic wave field calculation model.
# A Novel Named Entity Recognition Algorithm for Hot Strip Rolling Based on BERT-Imseq2 seq-CRF Model
## 3 Model Design
### Encoder-Decoder Layer  
The encoder and decoder use LSTM [25] network, which is an improved model of the RNN network. As shown in Figure 5, a forget gate \(f_{t}\), a memory gate \(i_{t}\), and an output gate \(o_{t}\) are added to the RNN unit, which can effectively capture the context information of the sentence. It is a commonly used network to solve the problem of information dependence in longer sequences.  
The forget gate is responsible for deciding whether to discard the information in the unit. The gate reads the output of the previous unit \(h_{t-1}\) and the input of this unit \(x_{t}\). The output is a value between \((0,1)\), '1' means all are reserved, '0' means all are discarded, and transmitted to the cell state.  
\[f_{t}=\sigma\Big{(}W_{f}[h_{t-1},x_{t}]+b_{f}\Big{)} \tag{4}\]  
The memory gate is responsible for determining whether to store new information in the cell unit. The input is still the output of the previous unit \(h_{t-1}\) and the input of this unit \(x_{t}\). The Sigmoid activation function generates a value between \((0,1)\) to determine the degree of information retention. At the same time, a new tanh layer is created to generate a temporary cell state \(\
# IoT-Enabled Smart Cities: Evolution and Outlook
## 4 Open Challenges for IoT-Enabled Smart Cities
### Achieve Transparency and Acceptance  
While the smart cities paradigm has been, for long, shaped by sales pitches of larger technology vendors and system integrators, for this vision of self-sustainability and citizen's quality of life improvement to become a reality, it is necessary to abandon early \"smart city 1.0\" examples such as Masdar or Songdo (with a technology- or marketing- driven approach rather than addressing operational and citizen needs) as well as to widen the pool of services supported by not only taking the perspective of the city authorities, but also allow addressing the specific needs of the citizens [96].  
In this sense, even if there are significant cultural differences globally, it is necessary to set up the mechanisms to voice the citizen's viewpoint in the current debate about IoT instrumentation. For smart city services to be able to make a real difference to citizens, and be well-perceived by all the stakeholders and actors involved in cities' complex ecosystem,citizens must be empowered with the means to have a central role in the creation and design of such services [97].  
The co-creation concept stands for the participatory development of applications and services collaboratively between several stakeholders. For this scenario to be enabled, platforms supporting smart cities have to provide a collection of intuitive tools and enablers that allow the specification and development of innovative applications. Additionally, it is necessary to counter the perception of loss of privacy to foster the platform to be fed by as many city assets as possible, and the information that they generate.  
Another key aspect of the co-creation concept is its capacity to engage heterogeneous stakeholders in boosting smart city potential [98]. Consequently, smart city platforms need to provide enablers associated to the incentives and rewards of participating in the long-term sustainability of the whole platform and smart city ecosystem. In this sense, it is still necessary to develop a security-by-design approach increasing the transparency of all the IoT asset governance flow shifting from the current paradigm of discrete centralized trusted authorities to a paradigm of liquid and decentralized trust of the network as a whole. This shifting responds to the providers' needs and to the consumers' demands. The first because they are reluctant to share some datasets due to uncertainty of how and for what purposes the data is used. The latter because they require reassurance of the data's quality.
# Research and Design of a Decentralized Edge-Computing-Assisted LoRa Gateway
## 4 Experiments
### Results and Analysis  
Figure 5 provides statistical information on the processing time for access requests. The black dashed line represents the maximum acceptable delay for an end device to receive and accept the message. It may be adjusted to meet certain needs. The join request was deemed unsuccessful if the time barrier was exceeded. Once the number of end devices reached 2000, the mean latency began to exceed the barrier. According to the findings, nearly 75% of end devices could effectively receive and accept messages with a 5 s constraint and 1800 end devices.  
Figure 6 depicts the system throughput and CPU utilization of the LoRa gateway and network server while processing application packets from the end devices. The throughput continued to increase until the number of end devices reached 1200, due to underutilized resources. At the same time, CPU usage increased. Once the number of end devices approached 1200, the system throughput stabilized and decreased slightly. This was because as the number of end devices continued to increase, the system became overloaded and the consumption of various resource switches caused a degradation in system performance, thus affecting system throughput. Similarly, the variation in CPU utilization reflected this. The NC module in the LoRa gateway consumed about seven CPU cores. The rest of the cores were occupied by modules used for the operating system and signal processing. However, the processing burden on the network server was alleviated, as the packet processing was now handled by the LoRa gateway with edge computing capabilities.  
As the number of end devices increases, the LoRa gateway and network server demand increasing CPU resources. As a result of all packages and invalid packages being processed and checked by the LoRa gateway, the network server's CPU use was decreased in comparison to the prior LoRa system, in which the network server was responsible for handling all packages. As demonstrated in Figure 7, in addition to conserving CPU resources, the bandwidth of the transmission link between the LoRa gateway and a network server was also preserved. Compared to the conventional LoRa system, 1000 end devices required about 41.1% less bandwidth. As the number of end devices increased, the gap continued to expand linearly. One of the advantages of conserving resources is that the saved resources may be used for more genuine packets, protecting them from invalid or malicious traffic. We built edge intelligence algorithms in the LoRa gateway, which were able to validate the effectiveness of end devices and perform specific data processing,  
Figure 5: Processing time of join requests with the number of end devices.  
effectively reducing the transmission bandwidth of the LoRa network and reducing the energy consumption.
# Semantic 3D Mapping from Deep Image Segmentation
## 3 Segmentation and Mapping in 3D  
As described in the previous section, multiple works on deep 2D image segmentation have their software available as libraries, executable programs, or as ROS nodes, ready to be integrated into any application. These implementations are usually already pre-trained with general-purpose datasets. In this work, Yolact [1] will be used, although any segmentation software can used as long as its output labels in a complete image or separate sub-images, matching each 2D pixel with a class. Figure 1 shows examples of 3D segmentations performed on the image.  
The general architecture of our approach can be seen in Figure 2. RGB-D camera provides an image and its corresponding point cloud. An image \(I\) is a matrix of \(W\times H\) pixels, each containing the three color components \(<r,g,b>\). The point cloud \(P\) is a vector of length \(l\) that contains the tuple \(<x,y,z,r,g,b>\) with the spatial coordinates \((x,y,z)\), and the color components. If the point cloud is registered, the point cloud \(l_{P}\) has a size of \(W\times H\) tuples. Here, pixel position in the image \(p_{I}(i,j)\) corresponds to the point \(p_{P}(k),k=i*W+j\). Those points \(p_{P}\) where it has not been possible to calculate the spatial information will establish their three spatial coordinates to infinity.  
This set of bounding boxes is the input to our 3D segmentation system. This system uses the point cloud corresponding to the segmented image to obtain the 3D position of pixels that belong to the segmentation. The result includes all the detections in the image, and represents them in three different ways: as a point cloud, as a 3D bounding box, and as an octomap. As there is no standard format for 3D segmentation information, offering multiple output formats allow us to cover the most common ones used in other applications, while reducing potential delays due to cascade conversions.  
Color is used as the key to determining which class the detections belong to in the processing. If the chosen representation format is octomap, for example, all voxels representing space occupied by a class's elements will have the same color.  
The semantic mapping system uses octomap representation of detections as input. As explained before, an octomap can include probabilistic information, useful in the mapping process. The result of the mapping will be an octomap containing the probabilities of object classes in different positions in space on the map's reference axis. This map can be stored and loaded for further use by other applications.  
Figure 1: Example of deep image segmentation.
# Quantification of SPECT Concentric Ring Artifacts by Radiomics and Radial Features
## 2 Materials and Methods
### Radial Features Extraction  
Considering the radial nature of concentric ring artifacts, two more image features were computed: radial contrast (\(R_{Contrast}\)) and radial root-mean-square contrast (\(R_{RMSC}\)) [17]. Custom macros were written for ImageJ software to compute \(R_{Contrast}\)R and \(R_{RMSC}\)R from the 40 SPECT phantom images \(f(x,y,z)\)[18]. The workflow used to obtain radial features is illustrated in Figure 5. Depending on voxel dimension, 8-10 tomographic homogeneous phantom transaxial sections (\(z_{H}\)) were summed together:  
\[f_{z}(x,y)=\sum_{z_{H}}f(x,y,z) \tag{1}\]  
Summed images \(f_{z}(x,y)\) were transformed to polar coordinates \(f_{z}(r,\phi)\), where:  
\[r=\sqrt{x^{2}+y^{2}} \tag{2}\]  
and:  
\[\phi=\tan^{-1}\left(\frac{y}{x}\right) \tag{3}\]  
To obtain the radial profile \(f_{z\phi}(r)\), the images \(f_{z}(r,\phi)\) were summed over the \(\phi\) angular variable:  
\[f_{z\phi}(r)=\sum_{\phi}f_{z}(r,\phi) \tag{4}\]  
This profile was used to compute \(R_{Contrast}\) and \(R_{RMSC}\) as follows:  
\[R_{Contrast}=\frac{Max\big{(}f_{z\phi}(r)\big{)}-\text{Min}\big{(}f_{z\phi}(r )\big{)}}{Max\big{(}f_{z\phi}(r)\big{)}+\text{Min}\big{(}f_{z\phi}(r)\big{)}} \tag{5}\]  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Family Name** & **Identification** \\ \hline Gray-Level Co-Occurrence Matrix (25) & GLCM (3D:mrg) \\ Gray-Level Distance Zone Matrix (16) & GLDZM (3D) \\ Gray-Level Run-Length Matrix (16) & GLRLM (3D:mrg) \\ Gray-Level Size Zone Matrix (16) & GLSZM (3D) \\ Neighbhood Gray-Level Dependence Matrix (17) & NGLDM (3D) \\ Neighborhood Gray-Tone Dependence Matrix (5) & NGTDM (3D) \\ Intensity-Based Statistics (18) & IS \\ Intensity Histogram (23) & IH \\ Intensity Volume Histogram (7) & IVH \\ Morphological (20) & MORPH \\ \hline \hline \end{tabular}
\end{table}
Table 2: SIBEX software radiomics family name and their IBSI identification [16; 17]. 3D = three-dimensional; mrg = merged. The number in rounded brackets refers to the total number of features considered in that family.  
Figure 5: Workflow used to obtain radial features (i.e., \(R_{Contrast}\) and \(R_{RMSC}\)) from SPECT data sets.  
\[R_{RMSC}=\frac{\sigma\big{(}f_{z\phi}(r)\big{)}}{\mu\big{(}f_{z\phi}(r)\big{)}}= \frac{\sqrt{\frac{1}{K}\sum_{k}\big{(}f_{z\phi}(r_{k})-F\big{)}^{2}}}{\frac{1}{K }\sum_{k}f_{z\phi}(r_{k})} \tag{6}\]  
with \(\mu\) and \(\sigma\) being the estimates of the mean and standard deviation of the radial profile \(f_{z}(r,\phi)\), respectively, \(k\) denotes the spatial location and \(K\) the total number of spatial locations.
## 4 Emergency Maneuver Strategies and Blind 60 Spot Management
### Testing, Validation, and Future Directions  
Rigorous testing and validation are crucial to ensure the reliability and safety of emergency maneuver strategies and blind spot management systems.  
Virtual simulations provide a safe and controllable environment for testing emergency scenarios. [PERSON] et al. [92] introduced CARLA, an open-source simulator for autonomous driving research, which enables the creation and testing of complex emergency scenarios.  
While simulations are valuable, real-world testing [93] is essential for validating system performance. [PERSON] et al. [94] described a comprehensive closed-course testing methodology for evaluating autonomous vehicle safety, including protocols for assessing emergency maneuver capabilities. Collecting and analyzing data from emergency events is crucial for improving system performance.  
Emergency systems must function reliably in all weather conditions. [PERSON] et al. [95] proposed a framework for extracting and analyzing critical scenarios from large-scale naturalistic driving data, providing insights for enhancing emergency response algorithms. [PERSON] et al. [96] conducted a comprehensive study on the performance of various sen sor technologies in adverse weather providing valuable insights for designing robust emergency response systems.  
Looking to the future, several promising directions for research and development emerge. Improving sensor capabilities is crucial for enhancing blind spot detection. [PERSON] and [PERSON] [97] discussed the potential of next-generation LiDAR systems in providing high-resolution 3D mapping of vehicle surroundings, including blind spots.  
Vehicle-to-everything (V2X) communication can significantly enhance situational awareness. [PERSON] et al. [98] explored the potential of 5G and beyond technologies in enabling high-bandwidth, low-latency V2X communication for improved cooperative perception and emergency response.  
Future systems must be capable of handling a wide range of emergency scenarios. [PERSON] et al. [92] proposed a meta-learning approach for autonomous driving that can quickly adapt to new and unseen emergency situations, potentially leading to more robust and versatile emergency response systems.  
While significant progress has been made in emergency maneuver strategies and blind spot management for autonomous vehicles, numerous challenges and opportunities remain for improvement. Continued research and development in areas such as advanced sensor technologies, AI-driven decision-making algorithms, and comprehensive testing methodologies will be crucial in creating safer and more reliable autonomous driving systems capable of handling the complex and unpredictable nature of real-world driving environments.
## 3 Related Work
### Power Control in THz Communications
Moreover, it introduces an optimization algorithm tailored to the THz channel, with the objective of minimizing transmission power by optimizing trajectory paths for both uplink and downlink transmissions between UAVs and users. The study derives equations governing UAV locations and transmission power optimization for individual users. Furthermore, it formulates analytical models addressing capacity, outage probability, and bit error rate (BER), considering factors influencing performance.
# Analysis of the Influence of Flood on the L4 Combination Observation of GPS and GLONASS Satellites
## 3 Site, Data, and Rainfall Description  
Data sets collected during the flood at Zhengzhou ZHNZ station, China (34.5\({}^{\circ}\) N, 113.1\({}^{\circ}\) E) from DOY 193 to DOY 204, 2021 are used. The station is operated by the Crustal Movement Observation Network of China. Both GPS and GLONASS signals can be received by a geodetic dual-frequency receiver. The sample rate of data sets is 30 s. The surrounding environment of the receiver is dry soil. The geographical location of the GNSS ZHNZ observation station located in Zhengzhou City, Henan Province, China is shown in Figure 2.  
The accumulative rainfall from DOY 193 to DOY 204, 2021 in Zhengzhou, China is obtained by the weather station, as presented in Figure 3. The rainfall data can be downloaded from the website: [[https://q-weather.info/weather/57083/history/](https://q-weather.info/weather/57083/history/)]([https://q-weather.info/weather/57083/history/](https://q-weather.info/weather/57083/history/)) (12-23 July 2021). The weather station was established by the National Meteorological Information Center of China. The distance between the weather station and the GNSS observation station is about 5 km.  
Figure 2: The geographical location of ZHNZ station, located in Zhengzhou City, Henan Province, China.  
From Figure 3, it can be seen that the rainfall mainly occurred on DOY 200 and DOY 201, 2021. From DOY 200, the rainfall began to increase sharply, and the peak of rainfall reached 188.72 mm on DOY 201. The rainfall began to decrease from DOY 202 and stopped on DOY 204, 2021. The flood is caused by the heavy rain which lasted for two days from DOY 201 to DOY 202, 2021. In addition, it should be noted that the flood is not only caused by the rainfall, but also caused by the discharge of water from the adjacent reservoir. The geographical location of the reservoir and the GNSS station are demonstrated in Figure 4.  
Figure 4: (**a**) The blue and green dots are the GNSS station and the reservoir; (**b**) The arrow in the subplot indicates the reservoir management station; (**c**) The view of the reservoir at 8:00 a.m. on DOY 202, 2021; (**d**) The hourly rainfall on DOY 202, 2021.  
Figure 3: Accumulative rainfall from DOY 193 to DOY 204, 2021 in Zhengzhou, China.
## 3 Results and Discussion  
### Spray Deposition  
#### 3.1.1 Total Deposition on the Crops and Losses to the Ground  
The samples of the wheat plant were used to measure the total spray deposition per plant. The filter papers were used to measure the spray deposition in different wheat canopy and losses to the ground, and the recovery rate was calculated from the amount of the total deposition and additive. The total deposition of the UAV was not significantly different from the other sprayers (Table 1). Among the four sprayers, the EAP sprayer achieved the highest total deposition and the KMB sprayer achieved the lowest (Table 1). Compared with the SPB and KMB sprayer, the UAV and EAP sprayer had significantly higher recovery rates. The dose transfer process studied showed the transfer of pesticide from the spray tank to the target organism and in this process, losses of drift and run-off had great influence on deposition [24]. In this study, the SPB and KMB sprayer had relatively lower depositions and, correspondingly, those two sprayers had the higher run-offs, which were 0.39 and 0.50 \(\upmu\)g/cm\({}^{2}\) (Table 1). Similar to other studies [25; 26], it could be that high-volume spraying easily leads to run-off. In Table 1, Columns 4 and 5, compared with other sprayers, UAV had the lowest losses to the ground.