esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Review of Related Works and Analysis of Unsolved Problems
Using the MAUT, the space of situations can be expressed in the model and, following the recognition of an adequate space, the information is provided by the model (e.g., the state of the context and the description of the expressed situation space). The model then calculates the degree of confidence with which the situation has occurred. The calculated reliability is compared with the confidence limit (a comparison of the calculated reliability with an individual situation of a certain threshold, which allows comparing the results calculated for different situations). The dynamic properties of transportation are analyzed in many studies by revealing important properties of the contextual data [40]. Transportation and recording of information from dynamic objects involve heterogenic communication structures, and the importance of the context can differ in real-time updates. The situation assessment relates to the evaluation of the broader spectrum of given data, rather than that from an individual sensor. The background to this approach comprises the methods of recognition of context-aware data by classifying primary data sources that are obtained from different kinds of wireless sensors, such as speed gauges, accelerators, temperature gauges, radar positioning systems, and video provision systems. An Approach for the Development of a Smart Service Provision System Applicable to the Management of Cargo Transportation Processes  
The objective of this research was the development of the extensible infrastructure of a smart service provision system for drivers in cargo transportation. The aim was to avoid accidents, assist in accident investigations, and/or assist in avoiding traffic congestion. An approach to the development of the adaptable smart service provision system is presented in Table 2. All layers are required for context-aware information recognition during the cargo transportation management process. Investigations of the development of services related to vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication are also important in our proposed approach. These processes provide decision support related to some of the levels of the hierarchy of decision making in cargo transportation management, with the aim of achieving the requirements of sustainable development. Not all of these layers are described in this paper. However, we analyzed some of the layers in more detail as presented in the following sections.
# MSEANet: Multi-Scale Selective Edge Aware Network for Polyp Segmentation
## 2 Related Work
### Parallel Attention Model  
Attention mechanisms have garnered significant interest for their ability to enhance feature representation, making them a valuable component in image segmentation tasks. Self-attention [18], in particular, enables models to prioritize critical features while filtering out irrelevant information. For instance, a stepped network [19] has been introduced for real-time polyp segmentation in colonoscopy images. This network employs four blocks for spatial feature extraction, integrating a dual attention module within each block and utilizing a multi-scale fusion module to consolidate features across scales. However, such models often suffer from slow processing speeds and limited ability to effectively integrate global contextual information. Recently, models incorporating parallel attention structures have demonstrated promising results. For example, the Parallel Reverse Attention Network (PraNet) [9] leverages a reverse attention mechanism to focus on polyp regions while employing a recursive feature aggregation module to capture global contextual information effectively. Similarly, PRAPNet [20] is an improved deep learning model designed to further enhance polyp segmentation performance through its Parallel Residual Atrous Pyramid structure.  
The combination of parallel structures and attention mechanisms not only accelerates network processing but also enhances focus on critical aspects such as edge features and global contextual information. This capability is particularly advantageous in scenarios where polyps exhibit high visual similarity to the background or have blurred boundaries. However, efficiently extracting edge features and seamlessly fusing them with contextual information remain significant challenges. To address this, this study proposes a network model based on a parallel attention structure, leveraging its robust contextual fusion capabilities and flexible module integration to achieve improved polyp segmentation.
# Bounce: A High Performance Satellite-Based Blockchain System
## 3 Bounce Protocol
### Straw-Man Multi-Satellite Protocol
Thus the satellite for slot \(i\) will send its own Commit Record only if it receives at least one Sending Station message with  
Figure 1: Components of the Bounce protocol: Each Sending Station may send one or more Merkle Tree roots to the satellite in charge of a slot. The satellite signs a sequence of zero or more roots and sends them to Broadcast Ground Stations which make the roots and the underlying Merkle Tree widespread (i.e., available to the user community). the multisigned Commit Record of slot \(i-1\). If no such Sending Station message arrives, then that satellite will send a \"negative\" Commit Record indicating failure. Note that even one working Broadcast Ground Station is enough to make Commit Record information widespread. Broadcast Ground Stations play the role of a relay. They do not execute a consensus protocol. * The Merkle tree contains invalid transactions, reflecting double-spending or over-draft spending. _Response:_ The block could still be committed. Payment transactions will be handled at _Accumulator_ nodes as described in Section 4.3. Any node that maintains a view of the full ledger can become an accumulator node. * By design, Mission Control is supposed to assign just one satellite to each slot. If Mission Control became malicious, it might allow forks. _Response:_ By construction, Mission Control will be governed by a small set (say 20 to 100) of reputation-conscious individuals. If a Mission Control person misbehaves, that bad behavior will be reflected in its simple-to-interpret signed assignments of roles. That would discredit that human administrator, see Section 3.3.3.
## 3 Dual Sparsity Pruning Federated Learning Framework
### Dynamic Aggregation and Sparse Pruning
#### 3.3.2 Server-Assisted Evaluation and Dynamic Pruning  
In practical federated training scenarios, edge devices often operate under varying environmental conditions that affect their communication quality, and the computational  
Figure 2: Pipeline of the scale-based model aggregation. The red and yellow lines represent the weights that the three clients aggregate together.  
resources of edge devices may be affected by different environments, leading to fluctuations in training time and performance. To mitigate the impact of heterogeneous factors on model training, we propose an adaptive sparsity adjustment mechanism based on the gradient change ratio. This mechanism enables devices to dynamically adjust their sparsity ratios throughout the training process. The detailed procedure for dynamic sparsity adjustment is presented in Algorithm 2. Our proposed approach allows for adaptive sparsity ratio adjustment while maintaining model convergence, thereby enhancing both training efficiency and resource utilization.  
```
1:Initial sparsity ratio \(p_{i}\), sparsity increment step \(\Delta p\), the threshold of gradient change rate \(\varphi\), the window size \(W\).
2:for global round \(t=1,2\), \(\cdots,T\)do
3: Calculate the model gradients \(g_{t}\).
4: Compute the gradient change rate \(\rho_{t}\): \(\rho_{t}=\frac{\|g_{t}-g_{t-1}\|_{2}}{\|g_{t-1}\|_{2}}\).
5: Use a sliding window \(W\) to assess the gradient change rate: \(avg_{\rho}=\frac{1}{W}\sum\limits_{i=t-W+1}^{t}\rho_{i}\).
6:if\(avg_{\rho}<\varphi\)then
7: Increase \(p_{i}\): \(p_{i}\gets p_{i}+\Delta p\)
8:else
9: keep \(p_{i}\) invariable.
10:endif
11:endfor
```  
**Algorithm 2** Dynamic Sparsity Adjustment
## 3 Proposed Framework
### PSI-CNN Model
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Stage** & **Layer Name** & **Number of Filters** & **Size of Feature Map (H \(\times\) W \(\times\) D)** & **Filter Size** \\ \hline \multirow{4}{*}{Conv stage 1} & Input layer & & 224 \(\times\) 224 \(\times\) 3 & 3 \(\times\) 3 \\  & Conv1-1 & 64 & 224 \(\times\) 224 \(\times\) 64 & 3 \(\times\) 3 \\  & Conv1-2 & 64 & 224 \(\times\) 224 \(\times\) 64 & 3 \(\times\) 3 \\  & Pooling1 & - & 112 \(\times\) 112 \(\times\) 64 & - \\ \hline \multirow{4}{*}{Conv stage 2} & Conv2-1-1 & 128 & 112 \(\times\) 112\(\times\) 128 & 3 \(\times\) 3 \\  & Conv2-1-2 & 128 & 112 \(\times\) 112 \(\times\) 128 & 3 \(\times\) 3 \\  & ImageResizing2 & - & 112 \(\times\) 112 \(\times\) 112 \(\times\) 128 & 3 \(\times\) 3 \\  & Conv2-2-1 & 128 & 112 \(\times\) 112 \(\times\) 128 & 3 \(\times\) 3 \\  & FeatureFusion2 & - & 112 \(\times\) 112 \(\times\) 128 & - \\  & Pooling2 & - & 56 \(\times\) 56 \(\times\) 256 & - \\ \hline \multirow{4}{*}{Conv stage 3} & Conv3-1-1 & 256 & 56 \(\times\) 56 \(\times\) 256 & 3 \(\times\) 3 \\  & Conv3-1-2 & 256 & 56 \(\times\) 56 \(\times\) 256 & 3 \(\times\) 3 \\  & Conv3-1-3 & 256 & 56 \(\times\) 56 \(\times\) 256 & 3 \(\times\) 3 \\  & ImageResizing3 & - & 56 \(\times\) 56 \(\times\) 3 & - \\  & Conv3-2-1 & 256 & 56 \(\times\) 56 \(\times\) 256 & 3 \(\times\) 3 \\  & Conv3-2-2 & 256 & 56 \(\times\) 56 \(\times\) 256 & 3 \(\times\) 3 \\  & Conv3-2-3 & 256 & 56 \(\times\) 56 \(\times\) 256 & 3 \(\times\) 3 \\  & FeatureFusion3 & - & 56 \(\times\) 56 \(\times\) 256 & - \\  & Pooling3 & - & 28 \(\times\) 28 \(\times\) 512 & 3 \(\times\) 3 \\ \hline \multirow{4}{*}{Conv stage 4} & Conv4-1-1 & 512 & 28 \(\times\) 28 \(\times\) 512 & 3 \(\times\) 3 \\  & Conv4-1-2 & 512 & 28 \(\times\) 28 \(\times\) 512 & 3 \(\times\) 3 \\  & Conv4-1-3 & 512 & 28 \(\times\) 28 \(\times\) 512 & 3 \(\times\) 3 \\  & ImageResizing4 & - & 28 \(\times\) 28 \(\times\) 3 & - \\  & Conv4-2-1 & 512 & 28 \(\times\) 28 \(\times\) 512 & 3 \(\times\) 3 \\  & Conv4-2-2 & 512 & 28 \(\times\) 28 \(\times\) 512 & 3 \(\times\) 3 \\  & Conv4-2-3 & 512 & 28 \(\times\) 28 \(\times\) 512 & 3 \(\times\) 3 \\  & FeatureFusion4 & - & 28 \(\times\) 28 \(\times\) 512 & - \\  & Pooling4 & - & 14 \(\times\) 14 \(\times\) 512 & - \\ \hline \multirow{4}{*}{Conv stage 5} & Conv5-1-1 & 512 & 14 \(\times\) 14 \(\times\) 512 & 3 \(\times\) 3 \\  & Conv5-1-2 & 512 & 124 \(\times\) 14 \(\times\) 512 & 3 \(\times\) 3 \\  & Conv5-1-3 & 512 & 14 \(\times\) 14 \(\times\) 512 & 3 \(\times\) 3 \\  & ImageResizing5 & - & 14 \(\times\) 14 \(\times\) 3 & - \\ \cline{1-1}  & Conv5-2-1 & 512 & 14 \(\times\) 14 \(\times\) 512 & 3 \(\times\) 3 \\ \cline{1-1}  & FeatureFusion5 & - & 14 \(\times\) 14 \(\times\) 512 & 3 \(\times\) 3 \\ \cline{1-1}  & Pooling5 & - & 14 \(\times\) 14 \(\times\) 512 & - \\ \hline FC stage & Fully connected & - & 1 \(\times\) 1 \(\times\) 1024 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: A detailed summary of the PSI-CNN structure
# On Deploying the Internet of Energy with 5G Open RAN Technology including Beamforming Mechanism
## 4 Implementation of Beamforming in Open RAN Networks
### Zero-Forcing Implementation
The ZF precoding condition of generating \(\mathbf{W}\) precoder for user \(k\) is orthogonal to all the other users' channel vectors \(\mathbf{h}_{k}\), such that there is no interference from other users where \(\mathbf{h}_{k}^{*}\mathbf{w}_{k^{\prime}}~{}=~{}0~{}\perp~{}k~{}\
eq~{}k^{\prime}\); the received SINR for the \(k\)-th user is determined as follows:  
\[\gamma_{ZF,k}~{}=~{}\frac{P_{k}|\mathbf{h}_{k}^{*}\mathbf{w}_{k}|^{2}}{\sigma_{n}^{2}} \tag{8}\]
# Bridge Crack Detection Based on SSENets
## 3 Experimental Results and Ablation Study
### Experimental Results  
In order to fairly test the performance of SSENets, we choose to compare with the model proposed by [PERSON] et al. [25] and several traditional classification models for comparison. We guarantee that all models in the test apply the hyperparameters mentioned in Section 3.1. The experimental results are shown in Table 1. Compared with other models, SSENets achieves a higher detection accuracy [38] of 97.77, which proves the SSENets could perform better on bridge cracks dataset.
## 6 Verification and Results
### Episode 3: Security Integrity (req: ii, viii)
#### 6.3.3 Summary  
The verification of the security integrity objectives is summarised as follows:  
**Req ii ✓**: We confirmed that the architecture supports flow-based encryption by observing encrypted and non-encrypted traffic traversing ISP-B. End-to-end traffic tests also confirmed that each flow was tagged with two different types of inner NSH encryption headers.  
**Req viii ✓**: The architecture is resistant to simple packet injections and to the manipulation of flow  
Figure 10: Flow-based encryption test in episode 3.  
rules. However, this packet integrity is established by using IPsec over the encrypted links. Hence, it requires that every Virtual Link is encrypted and that the Compute Nodes are not compromised. A possible solution in order to ensure packet integrity to non-encrypted data are to add an integrity key to the NSH header.
# Vegetation Loss Measurements for Single Alley Trees in Millimeter-Wave Bands
## 1 Introduction
In the terahertz domain, there are some interesting works related to speckle patterns [12], spatiotemporal wave synthesis [13], and sampling of the terahertz field [14]. In sub-terahertz, conducted measurements in the urban micro scenario showed the foliage loss to be no greater than 11 dB [15]. Similarly, in mmWave, the loss of vegetation is elaborated in [16]. In the series of measurements at 110-170 GHz, various categories of foliage were checked: for example, forest, single tree, hedge, tree trunk, and various species. Based on the measurement data, the authors extended the maximum exponential decay (MED) model by adding a new parameter, namely, the plant area index (PAI). This parameter was used to characterize the density of the foliage. The root mean square error (RMSE) between the measured PL data and the fit of the model was 5.6 dB. Taking into account the above analysis, we see that further measurements are needed. We observed that in the literature, foliage is considered in two ways: (i) as a continuous woodland, according to the ITU-R.P833 document [17], or (ii) as a single tree. Thus, in our work, we concentrate on the new case, where we carry out measurements of foliage loss in a tree alley. In principle, our measurement points are located between trees in the tree alley at the same distance. This paper is organized as follows. In the next section, additional attenuation in vegetation is analyzed based on the radiative transfer equation. In Section 2.2, the tree alley perspective is analyzed, and later on, the statistical models are described in 2.3. The measurement campaign is described in detail in Section 3. The measurement results are presented in Section 4, followed by discussion in Section 5. The paper is summarized and conclusions are drawn in Section 6.
###### Abstract  
A predictive control method using injected offset voltage to achieve neutral point (NP) voltage balance of three-phase three-level neutral point clamped (NP) rectifiers, without employing a weighting factor, is proposed in this study. One of the biggest problems with the three-level NP rectifiers is the dc link capacitor voltage imbalance. Therefore, it is necessary to maintain the balance of the NP voltage in addition to synthesize the three-phase sinusoidal input current by control methods. Conventional predictive control methods for the NP rectifiers have used a weighting factor in a cost function that determines the control ratio of the input currents and the capacitor voltage balance. As a result, it is burdensome to empirically redesign the weighting factor when the rectifiers' parameter values and control conditions change. Unlike the conventional methods, the proposed approach without the weighting factor can significantly eliminate differences between two DC capacitor voltages by utilizing an offset voltage, which is generated by using the difference between the upper and lower capacitor voltages. Consequently, the proposed approach using the offset voltage injection can control the input currents and retain the balance of NP voltage. Simulation and experiments are presented to verify the correctness of the NP voltage balancing of the proposed control method.  
neutral point clamped rectifier; predictive control method; offset voltage injection +
Footnote †: journal: Applied Physics Letters
## 3 Experimental Results
### Comparative Experiments
#### 3.4.3 Loss Function Analysis  
To clearly describe the trend of loss function changes during the model's iteration process, the training and validation loss value curves for the baseline model and the aforementioned SOTA algorithms have been plotted, with a total of 200 iterations, as shown in the following Figure 9.  
Figure 8: Feature map comparison.  
From the figure, it can be observed that the loss curves for all models converge and trend towards smoothness. The YOLOv5 algorithm exhibits the smallest convergent values for both training and validation losses, utilizing binary cross-entropy for confidence and classification losses while employing CIOU loss for localization loss, resulting in higher convergence accuracy. However, considering its relatively low model accuracy, it may be overfitting the training and validation sets, leading to poor performance on the test set. Moreover, due to the simplicity of the model, with a floating-point operation count of only 109.6G, it struggles to effectively capture the key features of the data.  
In contrast, the baseline model and the proposed algorithm use binary cross-entropy loss for classification while employing DFL loss and CIOU loss for bounding box regression, transforming the regression problem into a classification problem. Although the convergent values are not as good as those of other models, the proposed LDKA-NET maintains a small parameter count while possessing a greater model complexity, enabling it to better capture the complex features within the data without overfitting to the noise present in the training data. This results in optimal model performance and better generalization capability.  
Both the baseline model and the algorithm in this paper use binary cross-entropy loss for classification, while bounding box regression employs DFL loss and CIOU loss, transforming the regression problem into a classification problem. Although the convergence value is not as good as other models, the LDKA-NET in this paper maintains a large model complexity while keeping the parameter count low, allowing it to better capture the complex features in the data instead of overfitting to the noise in the training data, resulting in optimal model performance and good generalization ability.
## 2 Characterization and Modeling of the Fault Ride-Through Process in Doubly Fed Wind Turbines
### Characterization of the New Energy Unit Fault Ride-Through
In summary, the fault ride-through process of new energy units presents typical staged dynamic characteristics, and its control modeling needs to adopt a multi-stage coupled modeling approach. By establishing the stage division criterion based on the fault characteristics, constructing the dynamic equations of each stage, and designing the smooth switching logic and parameter synergistic optimization, we can realize the accurate characterization of transient nonlinear characteristics, active support capability, and dynamic regulation characteristics of new energy units, which provides a theoretical foundation for improving the accuracy and generality of modeling of new energy units. It provides a theoretical basis for improving the accuracy and versatility of new energy unit modeling.
## 3 Research Design and Methodology, and Model Integration
### AHP Analysis
Inconsistencies in pairwise comparison responses could affect the reliability of the outcomes. The value of \(\lambda_{\max}\) is always greater than or equal to the number of elements \(n\), and the closer \(\lambda_{\max}\) is to \(n\), the more consistent matrix \(A\) is deemed. Perfect consistency is achieved when \(\lambda_{\max}=n\). Therefore, the consistency index and ratio, derivable using Equations (8) and (9), are critical for assessing whether the importance assigned in pairwise comparisons is consistent. \[CI=\frac{\lambda_{\max}-n}{(n-1)} \tag{8}\]  
\[CR=\frac{CI}{RI} \tag{9}\]  
In the context of the AHP analysis, '\(n\)' signifies the number of elements being compared within a single layer. The term '\(RI\)', which stands for random index, varies with the dimension of the matrix. The \(RI\), as presented in Table 4, is derived from the \(CI\) calculated by randomly generating integers from 1 to 10 and forming a reciprocal matrix. Consequently, the Consistency Ratio (_CR_) measures the relative consistency of this study's _CI_ against the _RI_, indicating inconsistency. A critical aspect for ensuring the AHP analysis validity is the response consistency, typically ascertained by focusing on responses where the _CR_ value is less than 0.1 (_CR_ < 0.1). To maintain analytical reliability, responses with _CR_ values exceeding this threshold are generally excluded.
## 4 The WBANs Communication Stack
### Routing Protocols
#### 4.3.4 Mission-Critical Routing Protocols
This is crucial for maintaining survivability and efficiency in a harsh environment. The particular interest is that, how much benefit and overhead can be achieved when different organizations collaborate to support critical applications without violating organization induced constraints. In this work, a new metric is defined to model different levels in an organization-aware network using hierarchical addresses, and organizationally shortest path (OSP). Also, the proposed metric is backward compatible with other famous protocols such as optimized link state routing (OLSR) protocol. Simulation results demonstrate that the proposed method provides survivability and efficiency in battlefield environment while keeping the cross-organization data transfer at a low level. To meet the stringent requirement of reliably transmitting data for critical disaster evacuation and military applications, the authors in [104] propose a lightweight and energy-efficient joint mechanism for packet loss recovery and route quality awareness in WSNs. In this protocol, overhearing feature for characterizing the wireless channels as an implicit acknowledgment (ACK) mechanism is used as proposed in [105]. In addition, the protocol allows for an adaptive selection of the routing path, based on a collective cooperation within neighborhood. After a detailed survey on WBAN communication stacks, in the next section, open issues, challenges, and opportunities are explored and discussed.