esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation of Crosstalk between Data Signals and Feed Light in Power-over-Fiber
## 2 Crosstalk between Data Signals and High-Power Feed Light in PWoF  
Figure 1 shows a schematic view of the simultaneous data signal and high-power feed light transmission using a single-core fiber. In PWoF, the feed light is generally continuous-wave (CW), but the power injected into an optical fiber is much higher than that of the data signal. Therefore, as shown in Figure 1, the intensity noise of the feed light is transferred to the phase noise of the data signal in the core [14]. Here, there are two types of intensity noise induced by feed light if we use a multimode oscillation laser such as RFL. The first type is longitudinal mode beating [15]. In multimode oscillation laser sources, the longitudinal mode spacing is determined by the cavity length, and the laser output has multiple oscillation frequencies with the longitudinal mode spacing. The beat signals are then generated between the oscillation frequencies and are expanded to multiples of the longitudinal mode spacing. The other type of noise is mode partition noise [18]. At multiple oscillation frequencies generated by the multimode oscillation, the ratio of the laser gain to each longitudinal mode fluctuates temporally, and the optical intensity of each longitudinal mode fluctuates. For all oscillation modes at the laser output, the intensity of each longitudinal mode cancels out, and no fluctuations in intensity appear. However, as the laser output propagates through an optical fiber with chromatic dispersion, a time delay occurs in the temporal variation of the optical intensity in each longitudinal mode. This causes the fluctuations in the optical intensity in each longitudinal mode to no longer cancel out, and the overall laser output after fiber propagation fluctuates, resulting in an increase in the intensity noise of the laser.
## 4 Resource Management in Heterogeneous Network (RM HetNet)
### Power Allocation (PA)
#### 4.1.1 Throughput
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Characteristics** & **Issue** & **Methodologies** & **Advantages** & **Limitations/Future Work** & **Ref.** \\ \hline Enhance the SE and EE and minimize the total interference. & \multirow{2}{*}{Heuristic algorithm.} & \multirow{2}{*}{\(\bullet\)} & Demonstrates improvements in total interference, EE, SE, and total network throughput. & User association and mobility require attention and consideration. & [98] \\ \hline \hline \multirow{2}{*}{Maximize both the EE and SE.} & Stochastic optimization problem. & \multirow{2}{*}{\(\bullet\)} & -SE and EE can be significantly enhanced. & A high amount of signaling overhead is to be expected. & [99] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._attention and consideration. In [86], an investigation was conducted into a power allocation challenge for energy harvesting D2D communication in a downlink cellular network scenario. The authors intended to increase the network total sum rate by considering factors that include frequency reuse between D2D and cellular networks, quality of service, and energy harvesting for D2D links. Furthermore, the combined BS and sub-carrier power maximum were considered and formulated. For optimal power distribution to D2D and cellular users, it is recommended to use the energy harvesting and gain-based resource allocation (EHGRA) algorithm, which considers both BS power and energy harvesting. The simulation results show that the proposed algorithm performs better than the OJOA algorithm. In addition, a low-complexity method was developed to increase the sum rate of the entire network. Nevertheless, the proposed algorithm can only be used when only one cell and one BS exist. In [87], the authors considered the power allocation problem in NOMA-based D2D communication in a cellular network with imperfect CSI, focusing on downlink single-cell scenarios. The focus is on determining the most efficient power of UEs. In order to achieve optimal power distribution, they introduce a convex programming-based approach. In the scenario where the CSI is not perfect, the authors make use of the Markov inequality and the Marcum Q-function to analyze the channel gains while considering the outage probability constraints. Subsequent convex programming transforms the probabilistic non-convex problem into a frequent convex problem. The proposed algorithm iteratively determines the optimal power allocation coefficients by employing a Lagrangian dual multiplier approach and Karush-Kuhn-Tucker conditions to solve this optimization problem. According to the simulation results, the proposed method outperforms the state-of-the-art in terms of convergence performance and throughput. Nonetheless, UE interferences are not considered, and the network can only support a single cell and base station.
## 3 Shaping in TSN
### Overview of Traffic Shapers
These latency bounds hold as long as every switch in the path forwards the frame in the reserved cycle. In such a case, the actual queuing delay is not relevant. The QoS offered by CQF is sufficient for some traffic types but it does introduce extra delay that can not be tolerated by HTS traffic besides having loose control on the jitter. TSN standard defines Time-Aware Shaping, as a more general and fine-grained mechanism but under the cost of more complex scheduling. TAS installs a gate on each of the eight egress queues of a port and controls the state of the gate to allow or forbid the transmission of frames. If the gate is closed, then no transmission from the associated queue is possible, and if the gate is open, then frames from the associated queue could be transmitted. In the case that there is another frame in transmission or the opening time of the gate is not enough to fully serialize the frame on the link, the transmission is blocked even if the gate is in the open state. The states of the gates are independent of each other and can be simultaneously opened or closed. They are controlled by a Gate Control List (GCL). Every entry in this list is a tuple, i.e., a timestamp and a state value (O--opened, C--closed). The GCL configuration requires a base time as well, which is a timestamp that defines when to start applying it. Once configured, the port runs through the GCL and applies the states at their respective timestamps until it reaches the end. Then it starts over from the beginning. Unlike CQF, TAS can control the transmission timing of the frames to the queue level, which enables, if properly configured, the temporal isolation of the different traffic types. CQF is much simpler to configure compared to TAS and it tolerates variance in the arrival time of the frames. In addition, it does not require the traffic to be time-triggered as is the case with TAS, which allows CQF to handle cyclic and acyclic streams. An important direction in future research is to study the benefits and pitfalls of the coexistence of different shaper types in the network. For example, combining TAS and CQF shaping mechanisms was investigated in [11]. However, a more detailed analysis is needed to fully understand the implications of different shaper coexistence in the network.
# A Visual Odometry Pipeline for Real-Time UAS Geopositioning
## 2 Related Works  
Simultaneous Localization and Mapping (SLAM) is a promising solution for ground platforms that can be used for aerial platforms [4]. Without a very extended list, some of the SLAM algorithms used in this regard include MonoSLAM [5] and VINS-mono [6]. Researchers have also considered the cross-view matching approaches for positioning [7]. These methods utilize a Convolutional Neural Network (CNN) to extract landmarks from UAS-acquired images and reference satellite images and estimate the best-matching position in a query-to-target strategy [8].  
Figure 1: Overview of our proposed UAS geopositioning pipeline. The top left reference orthophoto from Google Earth is processed to generate the reference landmark dataset in a quad-tree data structure. Our algorithm requires initial geoposition to generate a list of reference landmarks from the database. Geometric relation between reference and UAS landmarks to convert pixel coordinates to latitude and longitude. Kalman filter is applied before output final predicted UAS geoposition to enhance predicted trajectory smoothness. With the new estimated UAS position, the look-up region is dynamically adjusted.
## 2 Nanotechnology and Nanomedicine
### Drug Delivery System
#### 2.7.1 Drug Loading Efficiency and Encapsulation Efficiency  
Loading efficiency and encapsulation efficiency are of paramount importance in nanomedicine, these two parameters refer to the mass ratio of drugs to nanoparticles and utilized drugs (amount of drug fed) after the drug loading process respectively [2]. However, nature, charges, structures, and physicochemical properties of the nanocar materials influences the loading efficiency, likewise mass of the drug fed, drug solubility, structural affinity of the drug, mechanism of drug-loading and other experimental conditions determine the final encapsulation efficiency [137; 138]. In the most part, high loading efficiency is difficult to get compared to high encapsulation efficiency for some nanoparticles, although nanoparticles with high loading content are obtained using methods with high encapsulation efficiency. Moreover, crystallization, dipole interaction, physical attraction, precipitation, covalent bonds, coordinate bonds and hydrogen bonds result in high encapsulation within the matrix of nanoparticles [2; 139]. Despite the general significant increase in the size of the nanoparticles after loading as reported by a considerable number of researchers [18; 22; 140; 141], it has been observed that the drug loading and encapsulation do not have any significant effect on the morphology of the nanoparticles. Hence, the shape and nature remains unaltered while the size increases after loading depending on the existing bond interactions between the nanoparticles and the type of drug loaded [11; 17]. Drug molecules consolidation onto a nanoparticle occurs during nanocar synthesis followed by adsorption of the drug leading to drug incubation within the nanocar [142]. Thus CSCaCO\({}_{3}\)NP is capable of loading drug molecules in large quantity due to its large surface area, functional group endings and porosity through the process of adsorption [143]. The negatively charged CSCaCO\({}_{3}\)NP usually demonstrates minimal drug lost during the loading process [10]. A study explained the high loading content of CSCaCO\({}_{3}\)NP to be attributable to its charge potential, therefore positively charged drugs could be efficiently encapsulated since the electrostatic interactions between the different molecules was amplified [10]. [PERSON] et al. [18] reported a decrease in the percentage entrapment by CSCaCO\({}_{3}\)NP as the amount of drug decreases and vice versa although, this is not always so as the condition varies with the type of drugs used.
## 8 Conclusions  
The proposed architecture considers very specific restrictions of applying sensor networks in clinical situations. The seamless integration among the different levels of the system architecture enables mobile node and network configuration. Likewise, this system architecture permits the interaction of its components to function efficiently within the parameters of this study.  
Simulation results of this study indicate that:  
* The routing algorithm based on fixed tables supported by the IEEE 802.15.4 standard functions well under the restrictions imposed by the case study.
* The MSP430 architecture performs well under the parameters described in this case study.
* Although the set-up time of IEEE 802.15.4 can vary significantly, it is small enough to be used to monitor medical applications.
* The presence of additional star topologies employing the same channel does not significantly affect the network set-up time.
* The performance of the transmissions in this case study is good; however, for critical applications, we suggest employing more energy saving techniques.
* The experimental results validate the proposed scenarios for medical applications in wireless sensor networks.  
Future research will employ hardware we developed in conjunction with a hybrid protocol such as PANDORA for patient home monitoring.
# A Survey on Node Clustering in Cognitive Radio Wireless Sensor Networks
## 5 Node Clustering in WSNs vs. CR-WSNs  
Although the objective of clustering in CR-WSNs is similar to that in WSNs (i.e., scalability, coverage, extended lifetime, robustness, better data collection, and aggregation), clustering in CR-WSNs is more challenging, because PUs are unlikely to cooperate with SUs. New cognitive radio network-related challenges are node deployment, CH selection, PU arrival prediction, channel allocation, increased complexity, error rate, and PU interference.  
WSNs are generally designed for a single channel, whereas CR-WSNs require multiple channels. If any PU claims the channel currently used by CR-WS nodes, the channel must be vacated immediately within a prespecified time.  
In addition to the clustering challenges faced by WSNs, CR-WSNs have to protect the right of the incumbent users to access licensed channels. The responsibilities of the CHs and CMs are different in CR-WSNs than in WSNs. It is even more difficult to handle unpredictable and frequent channel-status changes in CR-WSNs. Therefore, clustering schemes for WSNs cannot be directly implemented in CR-WSNs.  
Trust and security should be more controlled in CR-WSNs, because some malicious or selfish nodes can intrude on the communications by interfering with the licensed channels or not changing the channel within the prespecified time period. In addition, a malicious node might report false sensing results about the channel status.  
Workload for a CH in CR-WSNs is much greater than in WSNs. CHs in CR-WSNs have to focus on inter-cluster and intra-cluster communications, as well as take care of available channel information in the central spectrum database and channel availability information from neighbors and/or any other CHs. This workload is unnecessary in WSNs.  
In WSNs, a basic parameter for CH selection is residual energy. In case of energy depletion, the CH responsibility switches to another node, so that a CH's energy depletion does not break network connectivity. On the other hand, in CR-WSNs, if CHs have dedicated hardware, network lifetime depends upon the CH's energy. Therefore, energy conservation for the CH is important. The active duty cycle of nodes in WSNs is very low (as low as 1%) [51]. However, the active duty cycle is high in CR-WSNs, because CR-WS nodes have to monitor PU activities and report to the CH.
## 3 Algorithmic Framework
### Experimental Results
The performance of the approach is illustrated in Figure 3b, revealing that the majority of optimal solutions, comprising 764 mixtures (equivalent to 94.9% of the total), fall within the range of 0.6322 to 0.6484 TMFV (with 0.6322 being the minimum TMFV). This outcome underscores the effectiveness of the GA in generating solutions closely aligned with the Min fitness function within a short timeframe. Conversely, on the basis of the allocation of best solutions, only 51 mixtures (comprising 5.1% of the total) lie within the range of 0.6485 to 0.6607 TMFV (with 0.6587 being the maximum TMFV). Regarding the cost of mixtures, a series of 1000 separate experiments suggest a 1.6976 EUR/L minimum and a 1.7457 EUR/L maximum mixture cost. The difference between the minimum and maximum costs is 0.0481. Meanwhile, the average mixture cost across all experiments stands at 1.7036 EUR/L, with a standard deviation of 0.0077 (as depicted in Figure 4a). The allocation of Best Cost solutions is presented in Figure 4b. For most best solutions, 877 mixtures (\(951=504+245+129+73\)) or 95.1% are between 1.6976 EUR/L and 1.7219 EUR/L (minimum mixture cost 1.6976 EUR/L). The above results confirm the GA's effectiveness in providing fuel costs close to the minimum value. Based on the best solutions, the allocation of only (\(49=38+8+2+1\)) mixtures (4.9%) was between 1.7220 EUR/L and 1.7463 (maximum mixture cost 1.7457 EUR/L).
# Resource-Efficient Multicast URLLC Service in 5G Systems
## 3 Analyses of Possible Solutions and Their Adaptation to mURLLC
### The Feedback Scheme  
For each multicast stream, the gNB can configure one out of three feedback schemes. With the first scheme, which, hereafter, we call No feedback, the gNB does not allocate uplink resources for the feedback transmission. Thus, conditional HARQ cannot be used with this scheme. In contrast, with the second scheme, called _ACK/NACK feedback_, the gNB allocates a separate uplink resource for each UE such that the UE can send a positive (ACK) or negative (NACK) acknowledgment for each transmission. With the third scheme, called _NACK-only feedback_, the gNB configures a single uplink resource where only the UEs that have failed to decode the transmission send NACK. Thus, with this scheme, the gNB schedules an HARQ retransmission but does not know which UEs require it, which complicates transmission parameters selection for subsequent TXs.  
For each multicast stream, the gNB can select a feedback scheme depending on the stream QoS requirements and the number of available TXs. Specifically, for the one TX case (i.e., for strict latency requirements), the gNB can use either No feedback or NACK-only feedback schemes. In the case of the No feedback scheme, to provide high reliability, the gNB shall select a very robust MCS, which leads to huge resource consumption. In contrast, the usage of the NACK-only feedback scheme allows selecting proper MCS by taking into account UE feedback at the cost of moderate uplink overhead. For two or more TXs, the gNB can use either the NACK-only or ACK/NACK feedback schemes. Comparing these schemes, the ACK/NACK feedback scheme allows reducing resource consumption for HARQ retransmissions with respect to the NACK-only feedback scheme because the gNB knows the set of UEs that have failed to decode previous TXs. However, ACK/NACK feedback requires higher UL resource consumption, which scales linearly with the number of UEs in the multicast group. In Section 4, we use system-level simulations to study the influence of the used feedback scheme on the overall resource consumption in different scenarios and provide recommendations for selecting the feedback scheme for mURLLC traffic.
###### Abstract  
In tasks such as intelligent surveillance and human-computer interaction, developing rapid and effective models for human action recognition is crucial. Currently, Graph Convolution Networks (GCNs) are widely used for skeleton-based action recognition. Still, they primarily face two issues: (1) The insufficient capture of global joint responses, making it difficult to utilize the correlations between all joints. (2) Existing models often tend to be over-parameterized. In this paper, we therefore propose an Efficient Hierarchical Co-occurrence Graph Convolution Network (EHC-GCN). By employing a simple and practical hierarchical co-occurrence framework to adjust the degree of feature aggregation on demand, we first use spatial graph convolution to learn the local features of joints and then aggregate the global features of all joints. Secondly, we introduce depth-wise separable convolution layers to reduce the model parameters. Additionally, we apply a two-stream branch and attention mechanism to further extract discriminative features. On two large-scale datasets, the proposed EHC-GCN achieves better or comparable performance on both 2D and 3D skeleton data to the state-of-the-art methods, with fewer parameters and lower computational complexity, which will be more beneficial for application on computing resource-limited robot platforms.  
action recognition; deep learning; skeletal sequences; graph convolution; depth-wise separable convolution Article
# Effects of Bogies on the Wake Flow of a High-Speed Train
## 5 Results and Discussions  
### Aerodynamic Forces  
In this section, comparisons of the aerodynamic forces of the three high-speed train models are conducted to determine the effects of the bogies on the external flow of the high-speed trains. In the current simulations, the flows are fully developed after 1.0 s and, thereafter, the aerodynamic forces acting on the train fluctuate around the mean values. Therefore, the averages of the next 5000 time  
Figure 11: Frequency determined by DMD.  
Figure 10: The first six DMD modes of the wake flow of a cylinder.  
steps are taken to count the time-averaged aerodynamic forces of the three models and the drag coefficients; the results are shown in Table 3. The comparisons of the results of the real model and the simplified model, as well as the results of the trailing car for the semi-simplified model and the simplified model show that the drag forces from the bogies are as large as those from the other parts of the train. That is, in the real model, the drag forces from the six bogies contribute approximately 50% to the total drag forces of the train. As it is well known, the drag forces of the train result from the external flow. The bogies are located at the bottom of the train body where the flow is very complex. These bogies protrude from the smooth bottom of the train and are affected by the incoming flow, which results in unavoidable large drag forces.  
Apart from the average forces of the high-speed train, the existence of the bogies also has a large effect on the oscillation of the aerodynamic forces acting on the train. The time histories of the aerodynamic coefficients of the trailing cars of the three train models are shown in Figure 12. The blue line represents the real model, the orange line represents the semi-simplified model, and the green line represents the simplified model. It can be seen that the aerodynamic forces acting on the trailing car of all the three models oscillate around the average values. However, the amplitudes of the oscillations are quite different. The oscillations of the aerodynamic forces of the simplified model are rather weak compared to the strong oscillations in the real and semi-simplified models. Moreover, the oscillation amplitudes of the forces are larger in the real model than in the semi-simplified model. These differences can be further confirmed by the standard deviations of the aerodynamic coefficients, as shown in Table 4. Obviously, these differences are the result of the different configurations of the bogies in the train, which leads to the conclusion that the existence of the bogies in the high-speed train not only affects the average aerodynamic forces, but also introduces great disturbances to the flow field and consequently results in strong unsteady characteristics in the flow field.
# Blind Image Separation Method Based on Cascade Generative Adversarial Networks
## 3 Experiments  
To test the performance of the method, we selected natural images and remote sensing images as datasets. For the natural image datasets, we compared the results of the proposed method with the results of the classic BIS method, called non-negative matrix factorization (NMF) [5], fast independent component analysis (FastICA) [22], and the state-of-the-art network generation methods, NES and the method of [PERSON] et al. [23]. In the remote sensing image datasets, because of a lack of BIS methods for remote sensing images, we compared the datasets with four dehazing removal methods (the color attenuation prior (CAP) [24], dark channel prior (GDCP) [25], gated context aggregation network (GCANet) [26], and MOF model [15]).