esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Problem Modeling
### Effect of Propagation on Coverage  
In order to evaluate a certain coloring for the proposed scenario, the effect of both the propagation and the interferences over wireless signals must be incorporated. Regarding the propagation model, in [29], it is defined that radio signal power losses, expressed in dB and considering that antennas are close to the ground (between 1 m and 2.5 m), can be obtained according to the following equation:  
\[P_{loss}=40\log_{10}d+20\log_{10}f-20\log_{10}(h_{t}h_{r}) \tag{1}\]  
where \(d\) is the distance, in meters, \(f\) is the signal frequency, in GHz, and \(h_{t}\) (\(h_{r}\)) is the transmission (reception) antenna height, also in meters. Equation (1) can be simplified for its use in the 2.4-GHz frequency band, and the resulting expression is as follows:  
\[P_{loss}=7.6+40\log_{10}d-20\log_{10}(h_{t}h_{r}) \tag{2}\]  
A particularly interesting case of this propagation model is when a signal with a power equal to the receiver sensitivity is received, that is:  
\[P_{t}+G_{t}+G_{r}-L-P_{loss}=S \tag{3}\]  
where \(P_{t}\) stands for the transmission power (in dBm), \(G_{t}\) (\(G_{r}\)) stands for the transmission (reception) antenna gain (in dB), \(L\) stands for losses due to walls, windows and other obstacles in the propagation (in dB), \(P_{loss}\) stands for propagation losses (in dB) and \(S\) represents the receiver sensitivity (in dBm). Using Equation (2), we can determine the distance, such that signals can be perceived by the receiver. Such a distance (expressed in meters) can be obtained as:\[R=10^{\frac{P_{t}+G_{t}+G_{r}-L-S-7,6+20 log_{10}(h_{b}h_{b})}{40}} \tag{4}\]  
The interest in computing \(R\) is that it allows one to compute the maximum distance between a pair of nodes for them to be connected in the interference graph.
## 5 Establishment, Training, and Testing of Deep Learning Model
### Comparison with Mainstream Models  
To validate the strong applicability and high accuracies of the UNet adopted in this study for pollution detection tasks, a series of comparative experiments were conducted. Four representative semantic segmentation models, LinkNet [48], PSPNet [49],  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Hyperparameter Configurations** & _mIoU (\%)_ & _Acc (\%)_ & _Pre (\%)_ & _Rec (\%)_ & _f1 (\%)_ \\ \hline initial learning rate = 0.0001 & 86.58 & 99.42 & 75.72 & 76.80 & 76.23 \\ batch size = 8 & 82.94 & 98.57 & 73.09 & 75.48 & 73.66 \\ \hline initial learning rate = 0.01 & 79.30 & 97.23 & 67.82 & 74.38 & 69.74 \\ batch size = 8 & 86.34 & 99.37 & 75.47 & 76.63 & 76.01 \\ \hline initial learning rate = 0.0001 & 83.48 & 98.60 & 73.06 & 76.91 & 74.88 \\ batch size = 2 & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hyperparameter optimization experimental results.  
DeepLabV3 [50], and DeepLabV3+ [51], were selected for comparison. To ensure fairness, all models were trained and tested on the same workstation using identical datasets. For clarity of discussion, we focus on two core accuracy metrics: mIoU and f1. The experimental results are recorded in the table below.  
As shown in Table 2, UNet demonstrates clear superiority across both evaluation metrics compared to all other models. It outperforms DeepLabV3 by significant margins of 1.81% in mIoU and 2.70% in f1, while also maintaining advantages of 1.73% in mIoU and 2.32% in f1 against DeepLabV3+. When compared to PSPNet, UNet shows even stronger dominance in segmentation accuracy, with a 2.20% higher mIoU, along with a 1.53% f1 improvement. Even against the closest competitor LinkNet, UNet preserves a consistent lead of 0.92% in mIoU and 0.52% in f1, solidifying its position as the top-performing model with the highest absolute scores in both critical segmentation metrics.
# Enhancements and Challenges in CoAP--A Survey
## 4 Enhancements in CoAP
### Congestion Control Mechanisms in CoAP
#### 4.1.6 CoCoA+  
[PERSON] et al. propose an advance congestion control mechanism (CoCoA+) in [7] as another solution for the issues in CoCoA. The CoCoA+ was proposed to overcome the shortcomings of CoCoA. As the weak RTT estimator in CoCoA is ambiguous and effects the overall RTO calculation, CoCoA+ proposes to reduce the impact of weak RTT estimator in the calculation of overall RTO by reducing the value of 'K' (the RTT variance multiplier) from 4 to 1.  
\[RTO_{X}=RTT_{X}+K_{X}*RTTVAR_{X} \tag{3}\]  
Moreover, the weight of weak RTO estimator is limited in the overall RTO calculation. It is reduced from 50% to 25%.  
\[RTO_{overall}=0.25*RTO_{weak}+0.75*RTO_{overall} \tag{4}\]  
In addition, for the weak RTT measurements, the CoCoA+ limits the measurements to be taken from first transmission and first retransmission only.  
These solutions help in avoiding large increments in the overall RTO values. The CoCoA+ is yet unable to select the correct RTO value in case of burst traffic. This is caused due to inaccurate measurements of retransmitted RTT during bursty traffic, hence resulting in spurious retransmissions. In fact, [PERSON] and [PERSON] in [39] evaluated the congestion control performance of default CoAP and CoCoA+ and found out that in various network conditions, CoCoA+ performs significantly worse than default CoAP congestion control mechanism, such as in case of small RTT values and for bursty traffic.
# An Efficient Scalable Scheduling MAC Protocol for Underwater Sensor Networks +
## 4 Problem Definition  
To achieve higher energy efficiency and network throughput, we target to design a collision-free MAC protocol while also addressing the following problems: hidden terminal and spatial-temporal uncertainty, which are described as follows:
# A Novel Deep Reinforcement Learning Approach for Task Offloading in MEC Systems
## 4 Tasking Offloading Algorithm Based on DRL
### The MDP Framework
#### 4.1.2 Action Space  
At the beginning of time slot \(t\), firstly, the agent has to decide whether to process the task, \(u_{m}(t)\), locally or offload it to the edge server; we denote \(p_{m}(t)=1\) (or 0) when the task, \(u_{m}(t)\), is executed locally (or is offloaded to the edge server), and if there is no task at time slot \(t\), \(p_{m}(t)=-1\). Furthermore, if the task is offloaded to the edge server, we denote \(q_{m,n}(t)\in\{0,\dots,N-1\}\) to indicate the ED that the agent chose, i.e., if the task is offloaded to the ED 4, then \(q_{m}(t)=3\). Thus, the action can be represented by \(a_{m}(t)=(p_{m}(t),q_{m}(t))\).
# Fast 3D Reconstruction of UAV Images Based on Neural Radiance Field
###### Abstract  
Traditional methods for 3D reconstruction of unmanned aerial vehicle (UAV) images often rely on classical multi-view 3D reconstruction techniques. This classical approach involves a sequential process encompassing feature extraction, matching, depth fusion, point cloud integration, and mesh creation. However, these steps, particularly those that feature extraction and matching, are intricate and time-consuming. Furthermore, as the number of steps increases, a corresponding amplification of cumulative error occurs, leading to its continual augmentation. Additionally, these methods typically utilize explicit representation, which can result in issues such as model discontinuity and missing data during the reconstruction process. To effectively address the challenges associated with heightened temporal expenditures, the absence of key elements, and the fragmented models inherent in three-dimensional reconstruction using Unmanned Aerial Vehicle (UAV) imagery, an alternative approach is introduced--the neural radiance field. This novel method leverages neural networks to intricately fit spatial information within the scene, thereby streamlining the reconstruction steps and rectifying model deficiencies. The neural radiance field method employs a fully connected neural network to meticulously model object surfaces and directly generate the 3D object model. This methodology simplifies the intricacies found in conventional 3D reconstruction processes. Implicitly encapsulating scene characteristics, the neural radiance field allows for iterative refinement of neural network parameters via the utilization of volume rendering techniques. Experimental results substantiate the efficacy of this approach, demonstrating its ability to complete scene reconstruction within a mere 5 min timeframe, thereby reducing reconstruction time by 90% while markedly enhancing reconstruction quality.  
Keywords:neural radiance field; 3D reconstruction; UAV images; multi-view +
Footnote †: journal: Applied
# An Efficient SS-MAC Protocol for IEEE 802.15.4-Based WSNs of Cluster Tree Topology
## 5 Performance Evaluation
### Average Waiting Time in SS-MAC  
Using the expressions derived in Section 4 to analyze the various performances of the SS-MAC under different network sizes, we obtain the average packet waiting time \(E[w]\), session duration \(W_{s}\), and the maximum packet arrival rate \(\lambda_{max}\) of each member node, as shown in Table 3. When \(L=3,N_{t}=3\) and \(N_{m}=3\), the total number of tree nodes and member nodes of the network are 13 and 60, respectively. Each member node in this network can support a maximum packet arrival rate of 7.495 p/s, the session duration \(W_{s}\) is 0.768 s, which is also the time taken by the sink to complete a round of data collection from all nodes of the network, and the average packet waiting time for each member node is 0.444 s. Table 3 also suggests that \(E[w]\) and \(W_{s}\) increase as the topology level \(L\) and the number of member nodes increase, but \(\lambda_{max}\) decreases gradually. Increase in \(L\) leads to an increase in the number of sharable slots and increase in \(N_{t}\) leads to an increase in the length of each sharable slot. As a result of the combined effect of these two parameters, the session duration \(W_{s}\) continuously increases. If the packet arrival rate for each member does not exceed \(\lambda_{max}\), the network can run steadily. Obviously, the SS-MAC protocol can adapt to large-scale WSNs.
# DE-CapsNet: A Diverse Enhanced Capsule Network with Disperse Dynamic Routing
## 4 Experiments
### Results  
The accuracy of prediction is defined as in Equation (11) [16].  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{11}\]  
TP represents the number of true positive samples and TN represents the number of true negative samples. Similarly, FP represents the number of false positive samples and FN represents the number of false negative samples. Table 1 presents a comparison of the accuracy of our model with that of DeepCaps, CapsNet, and other variants of Capsule Network, which showed that we achieved state-of-the art results on CIFAR-10 in the Capsule Network field. Our results exceeded those of all other models in the Capsule Network field on CIFAR-10 and outperformed CapsNet which had seven ensembles on F-MNIST. There was a 3.56% improvement on CIFAR-10 and a 0.88% improvement on F-MNIST compared to the CapsNet results in [4]. Even though our results were slightly below those of the DeepCaps that had seven ensembles on F-MNIST, which does not have complex backgrounds, our model outperformed both DeepCaps with a single model [17] by 1.95% and DeepCaps with seven ensembles by 0.22% on CIFAR-10.  
For our proposed model trained on CIFAR-10, DE-CapsNet had only 11.2 million parameters, while DeepCaps with seven ensembles [17] had \(7\times 7.22\) million parameters and CapsNet [4] had 22.48 million parameters. Our model achieved 92.96% accuracy on CIFAR-10, while DeepCaps with seven ensembles [17] achieved 92.74% and CapsNet [4] achieved 89.40%. Our architectures for CIFAR-10 are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **CIFAR-10** & **F-MNIST** \\ \hline [PERSON] et al. [4] & 89.40\% & 93.60\% \\ HitNet [12] & 73.30\% & 92.30\% \\ [PERSON] et al. [13] & 75.92\% & 92.07\% \\ DeepCaps [17] & 91.01\% & 94.46\% \\ DeepCaps (7 ensembles) [17] & 92.74\% & 94.73\% \\ DE-CapsNet (ND) & 92.33\% & 93.64\% \\ DE-CapsNet & 92.96\% & 94.25\% \\ \hline \hline \end{tabular} Our model achieved state-of-the-art results among capsule domain networks on CIFAR-10, being only slightly worse than seven ensembles of DeepCaps. ND—No Disperse Dynamic routing.  
\end{table}
Table 1: Comparison of the accuracy of our model with that of DeepCaps, CapsNet, and other variants of CapsNets.
## 4 Empirical Studies
### Prompt-BERT Model Classification Effect
A comparison of the classification effects of different classifiers is shown in Table 5.  
As can be seen from Table 1, the classification accuracy of the FCNN, DNN, and TextCNN models is 0.9210, 0.9318, and 0.9548, respectively, after short text extension, while the accuracy of the BERT model is 0.9620, which is because BERT uses a bidirectional Transformer architecture, which can consider the contextual information at the same time, whereas, in contrast, the CNN and other neural networks can only capture local features within a fixed window. This indicates that the BERT model has higher accuracy and stronger feature extraction ability when dealing with the short text classification task. The BERT-RNN model further adds the RNN layer on top of BERT, and its classification accuracy is 0.9632. This shows that by fusing BERT and an RNN, not only the in-depth understanding of contextual information in keyword expansion is retained in BERT but also the RNN captures the sequence data with the help of the RNN. This shows that by fusing BERT and the RNN, not only does BERT retain the ability to understand contextual information in keyword expansion, but also, with the ability of the RNN to capture the temporal dependency in sequence data, the deep semantic information of the text can be extracted more effectively to improve the accuracy of classification. Further, the Prompt-BERT-RCNN model, which introduces the cue learning template, achieves the most outstanding performance in terms of accuracy and weighted average F1 score, reaching 0.9784 and 0.9782, which are 1.19% and 1.22% higher than the BERT-RCNN model, respectively. This result indicates that Prompt can provide explicit task guidance and contextual information for the BERT-RCNN model and better activate the potential of the pre-trained model, which effectively improves the classification performance of the model. It can be seen that Prompt-BERT is proposed in this paper. The -RCNN model performs the best among many models and is able to achieve effective classification of short medical texts.
# Multi-Domain Communication Systems and Networks: A Tensor-Based Approach
## 3 Simultaneous Signal Processing Across Domains
### Tensor-Based Receiver Designs
#### 3.2.3 Applications of other Tensor Decompositions
\begin{table}
\begin{tabular}{l l} \hline
**Tensor Tool** & **Example of Applications** \\ \hline PARAFAC (CP) & Model received signal in DS-CDMA and develop blind receiver methods [11] \\ Tucker Decomposition & Data mining, Computer Vision, finding low rank structures in high dimensional data [32] \\ PARATUCK & Semi blind receivers for joint channel estimation and data detection in MIMO OFDM CDMA systems [12] \\ Tensor Train Decomposition & Reducing storage complexity in Big Data applications [32], space–time coding for MIMO OFDM relay systems [40] \\ Tensor Inversion & Joint multi-domain equalization in systems such as MIMO GFDM [9] \\ Tensor EVD using [PERSON] product & Multi-linear system theory [22], finding tensor channel capacity [25] \\ Block Constrained PARAFAC & Blind multi-user detection and equalization for over-sampled, DS CDMA and OFDM systems [41] \\ PARAFAC with Linear Dependencies (PARALIND) & Blind receiver for MIMO OFDM in the presence of carrier frequency offset [42] \\ \hline \end{tabular}
\end{table} 
Table 4: Tensor tools and applications.
## 9 Future Research Directions and Challenges
### Fusion of RF with Other Sensor Data  
The mobility and the issue of receiving diverse information with different frequencies is a challenge that is missing in the literature. Localisation algorithms are expected to be implemented in real-time, with each measurement coming in its own time. In fingerprinting, most existing works rely on fixed target localisation and datasets consisting of RF features. However, in either the online or offline step, other sensor data such as IMU and images can be used effectively with radio-based data sets. They can either narrow down the searching area in the online phase or directly fuse with fingerprints in the offline step. From a theoretical point of view, there is still room for localisation improvement in accuracy and robustness in a data fusion scheme. For instance, to the authors' knowledge, there is no study on fusing promising measurements provided by 5G with images, LIDAR, etc. Several combinations of the sensor data and their performance under different situations and parameters need to be researched.
# Structural Methodologies for Distributed Fault Detection and Isolation
## 5 Case Study
### Discussion  
The two proposed algorithms for designing distributed diagnosis systems provide a solution with maximum possible detectability and isolability that can be achieved for a system given a set of measurements. Unlike previous work, such as [14; 15], our proposed methods were based on system models expressed as equations and, therefore, did not need to use the temporal response and event ordering in the diagnosis, all of which are derived properties and, therefore, require additional computation. Using a purely structural approach reduced the overall diagnosability of the system for the given set of measurements. However, it also reduced the number of assumptions we needed to make about the fault characteristics, such as the order of events in the diagnoses subsystems (which can be error-prone), and we did not have to analyze in detail the subsystem dynamics.  
The total number of MSO sets was exponential in terms of the system measurements, and the MSO set selection was equivalent to the set covering problem. Therefore, the MSO-based algorithm had high computational cost especially for large-scale systems. The algorithm guaranteed that the subsystems shared a minimum number of measurements between the subsystems, implying that we minimized the communication of measurement streams across subsystems of the global system. This is important because sending data between subsystems is costly in large-scale systems. Moreover, it is  
Figure 14: A hypothesis test can achieve a zero false positive rate and detect \(f_{E240}\) in \(4^{\prime}\):\(48^{\prime\prime}\) using residual \(r_{a11}\). The step function represents the detection time.  
Figure 13: Residual \(r_{a11}\) for 24 h when each sensor in the ADAPT-Lite system has an additive noise with a normal distribution, \(N(0.0.1)\), and \(f_{E240}=0.25\) occurs at \(t=20\) h.  
straight forward to extend the MSO-based approach to robust distributed diagnosis by considering residuals' robustness performance in the selection process [32; 33].  
The equation-based algorithm found a minimal set of equations from neighboring subsystems that guaranteed the maximum possible detectability and isolability that can be achieved for the system given a set of measurements. The number of equations was significantly smaller than the number of MSO sets. Therefore, the second algorithm was computationally more efficient. Moreover, the second algorithm did not need to use the global model in the design process of the supervisory system. This makes the algorithm very feasible for large-scale complex systems. However, it did not guarantee that the number of shared variables among the subsystems was globally minimum.
## 2 Review
### Dynamic Spectrum Access: Signal Processing and ML/DL Approaches
The spatio-temporal modeling of cognitive radio systems using multi-dimensional signal processing concepts was presented in [66]. Here, directional sensing was exploited using three-dimensional infinite impulse response (IIR) filters as beamformers. Similar directional sensing approaches using multi-dimensional filters were presented in [67; 68; 69]. Furthermore, antenna array systems were employed for directional sensing in [70; 71; 72]. In [73], a DFT-based multibeamformer was employed for real-time directional sensing. Compared to the multibeamformer employed with ADFT in this study, exact DFT was employed in [73] with 16 simultaneous beams at 2.4 GHz. We note that the upper limit of the channel capacity that can be achieved with DSA [25] is not changed using the ADFT instead of the DFT.
## 3 Proposed Methodology
### System Model  
Mobile ad-hoc networks (MANETs) are the most prevalent kind of wireless system, due to their flexibility and many characteristics, including being infrastructure-less and self-configuring. MANETs are faced with several difficulties, including constrained devices, restricted bandwidth, and link failure caused by a change in topology that is very dynamic,  
Figure 1: Block diagram of the proposed method.  
constrained devices, scalability, fault tolerance, security risks, and the maintenance routing protocol. For information to be sent from a single node to a different one in MANETs, routing is a fundamental issue. Since the development of commercial mobile ad-hoc networks, routing has been the subject of analysis. Many MANET routing protocols are currently being developed to provide accuracy, speed, stability, dependability, scalability, resiliency, and QoS-awareness, along with routing protocols for an enormous amount of dynamic network topology. The defined network model serves as a basis for the implementation of the proposed approaches. In any network, routing is the process of choosing a path. Numerous machines, referred to as nodes, and the pathways or links that connect them, make up a computing network. There are numerous ways in which two nodes in a linked network might communicate with each other. The process of choosing the optimal path while adhering to pre-set rules is referred to as routing. The blockchain is a distributed database that operates on a peer-to-peer (p2p) basis, similar to a ledger, wherein a list of ever-expanding entries, known as blocks, are connected and secured through the use of public-key cryptography. Using blockchain technology, instead of adding to the centralized database as in a traditional centralized system, new information is added to a block and made available to all nodes in a distributed network. A hash value is created, typically using the safe hash cryptographic technique 256 bits (SHA256), which identifies each block in a blockchain. Security is controlled to safeguard sensitive data and prevent hackers and other unauthorized individuals from accessing them. As the reliance on blockchain networks grows, a major concern is blockchain security. The goal of blockchain security, a risk-management strategy, is to safeguard transactions and, by extension, the entire blockchain network. To enable video transmission through the nodes, several novel features have been incorporated into the proposed algorithm. The operational concept of the proposed algorithm is displayed in Figure 2. First, the input video is fed to the proposed module. In order to achieve effective video streaming on both sides, two Linux modules are used. The video stream is thus sent to the proposed MANET infrastructure, and it is appropriately received at the output side. Then, the simulation settings of the network are adjusted to extract features from the videos to test them, and finally, they are secured to save the information.