esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Ablation Study Results of Transformer Models
#### 4.1.2 Ablation Study of RoBERTa  
Tables 4 and 5 present the ablation study of the RoBERTa model. Based on the results presented in the tables, the attention mechanism stands out as the most critical component of the RoBERTa architecture for FND. Its removal leads to the highest average performance drop of 0.1688 and a standard deviation of 0.2151 in the datasets. However, positional encoding appears to be the least critical, with its removal resulting in a slight performance improvement on average (\(-\)0.0240), suggesting that RoBERTa's pre-training dynamics may compensate for its absence in certain scenarios. Analyzing dataset-specific results, the optimal configuration for the FakeNewsNet dataset is achieved without positional encoding (accuracy: 0.5500), while the ISOT dataset performs best with the intact baseline configuration (accuracy: 0.9990), indicating a high robustness to architectural changes. Interestingly, for the LIAR dataset, the highest accuracy is observed when the GELU activation function is removed (accuracy: 0.5945), reflecting a nuanced interaction between the choice of components and the characteristics of the dataset. Overall, the average baseline performance across all datasets is 0.6888, with a standard deviation of 0.2245, highlighting the strong and consistent effectiveness of RoBERTa in FND, and underscoring the importance of attention mechanisms while also revealing surprising flexibility in positional encoding.  
\begin{table}
\begin{tabular}{l l c c} \hline
**Dataset** & **Configuration** & **Test Accuracy** & **Test F1** \\ \hline \multirow{8}{*}{FakeNewsNet} & baseline & 0.4750 & 0.4740 \\  & no\_positional\_encoding & 0.5500 & 0.5266 \\  & no\_attention & 0.4750 & 0.3059 \\  & no\_feedforward & 0.5250 & 0.3615 \\  & no\_residual\_connections & 0.4750 & 0.3059 \\  & no\_layer\_normalization & 0.5000 & 0.3871 \\  & no\_dropout & 0.5250 & 0.4932 \\  & no\_gelu\_activation & 0.4750 & 0.4632 \\ \hline \multirow{8}{*}{ISOT} & baseline & 0.9990 & 0.9990 \\  & no\_positional\_encoding & 0.9990 & 0.9990 \\ \cline{1-1}  & no\_attention & 0.5267 & 0.3634 \\ \cline{1-1}  & no\_feedforward & 0.9990 & 0.9990 \\ \cline{1-1}  & no\_residual\_connections & 0.9990 & 0.9990 \\ \cline{1-1}  & no\_layer\_normalization & 0.9990 & 0.9990 \\ \cline{1-1}  & no\_dropout & 0.9990 & 0.9990 \\ \cline{1-1}  & no\_gelu\_activation & 0.9927 & 0.9927 \\ \hline \end{tabular}
\end{table}
Table 4: RoBERTa Ablation Study: Performance Across All Datasets.  
\begin{table}
\begin{tabular}{l c c c} \hline
**Component Removed** & **Avg Impact** & **Std Dev** & **Count** \\ \hline no\_attention & 0.1504 & 0.2303 & 3 \\ no\_layer\_normalization & 0.0513 & 0.0533 & 3 \\ no\_dropout & 0.0311 & 0.0488 & 3 \\ no\_feedforward & 0.0220 & 0.0376 & 3 \\ no\_positional\_encoding & 0.0154 & 0.0212 & 3 \\ no\_cls\_token & 0.0026 & 0.0030 & 3 \\ no\_residual\_connections & 0.0014 & 0.0016 & 3 \\ \hline \end{tabular}
\end{table}
Table 3: Component Importance Summary Across All Datasets (BERT).
# A Review on Internet of Things for Defense and Public Safety
## 4 Operational Requirements
### Network Capabilities  
Military network infrastructures are severely limited by frequent disconnections, partitioning, and fluctuations of radio channel conditions. This can lead to issues in sensing availability and constraints on the usage of transducers. The military IoT networks operate over tactical radios that establish and maintain mobile and fixed seamless C2 communications between operational elements and higher echelon headquarters. Tactical radios provide interoperability with all services, various agencies of the U.S. Government, commercial agencies, and allied coalition forces. High-bandwidth radios that could constitute integrated networks are still under development. For example, Harris Corporation (Melbourne, FL, USA) will deliver the first batch of RF-335M tactical radio systems in September 2017 to U.S. Special Operations Command (USSOCOM) [81].  
As the connectivity of sensors improves, the system can become overwhelmed with the huge volume of data in transit. This increase in volume of data may force an upgrade to a system's network infrastructure to increase bandwidth or, alternatively, it may require to increase the performance of intelligent data filtering. In the commercial environment, network bandwidth and QoS (Quality of Service) challenges are addressed using COTS hardware combined with open virtualization platforms to manage network demands dynamically. These advanced network servers provide both high availability and also new approaches to control and provision network systems by delivering a path to Network Function Virtualization (NFV) [82]. NFV offers the operator the ability to configure the network infrastructure dynamically through sophisticated management protocols. Thus, NFV empowers military commanders to quickly configure data feeds for changing operational requirements, and to manage device and data security throughout the system. For example, the use of OpenStack [83] optimizes different network demands, such as giving priority to certain data flows, or protecting parts of the network from attacks. Commercial solutions can be adapted to the management of data as they pass through various military networks to get to the combat cloud.  
Figure 8: Main characteristics of DMCC-S R2.0.  
Currently, each military force has its own infrastructure, both for connectivity and for the back-office systems. Transitioning to a combat cloud infrastructure will offer greater ability to export both assets and data in the field for joint operations. Also a combat cloud will allow information and control to move forward when appropriate, providing the operational flexibility to deal with coalitions.  
Nowadays, any army in the world can have the network infrastructure needed to handle, process and distribute the massive flow of data that would be generated by a widespread IoT [33]. In order to make effective use of IoT, the devices must be able to connect to global networks to transmit sensor data and receive actionable analytics.
## 2 Globalized Miniaturization Using Variable-Fidelity EM Models and PCA
### Simulation-Based Antenna Miniaturization  
The geometries of compact antennas are decided upon during the initial stage of the design process, where the effects and relevance of specific topological modifications (stubs, slots, resonators, etc.) are validated using initial parametric studies. Notwithstanding, to achieve the best possible miniaturization ratio, a final tuning of the antenna parameters should be realized using rigorous numerical methods. This requires a formulation of a scalar objective function and constraints that will be processed with the optimization algorithm.  
Here, the size reduction problem is formally written as the following nonlinear minimization task  
\[\mathbf{x}^{*}=\operatorname*{argmin}_{\mathbf{x}}A(\mathbf{x}) \tag{1}\]  
where \(\mathbf{x}=[x_{1}\,\dots\,x_{n}]^{T}\) refers to the vector comprising designable antenna parameters (typically, geometry dimensions), whereas \(A(\mathbf{x})\) stands for the antenna size, which, in the case of planar structures, is normally the footprint area.  
The solution to (1) is subject to constraints, which are more often than not of an inequality kind: \(g_{k}(\mathbf{x})\leq 0\), \(k=1\),..., \(n_{g}\). Examples include the acceptable levels of in-band impedance matching, axial ratio, gain, sidelobe levels, etc. Sometimes, one may also consider equality constraints (e.g., requirement such as a specific value of the antenna beamwidth, etc.): \(h_{k}(\mathbf{x})=0\), \(k=1\),..., \(n_{h}\).  
The constraints imposed on the antenna performance are costly as they necessitate executing EM analysis, and their direct tackling is often inconvenient. Implicit handling using a penalty function approach [80] allows for turning the task into an unconstrained one by adding appropriately scaled contributions, proportional to violations of the constraints, to the primal objective. The reformulated task can be written as  
\[\mathbf{x}^{*}=\operatorname*{argmin}_{\mathbf{x}}U(\mathbf{x})=\operatorname*{argmin}_{ \mathbf{x}}\left(A(\mathbf{x})+\sum_{k=1}^{n_{g}+n_{h}}\beta_{k}c_{k}(\mathbf{x})\right) \tag{2}\]  
where \(U\) is the merit function, which combines the main goal (size reduction) and a linear combination of penalty functions \(c_{k}(\mathbf{x})\), with \(\beta_{k}\) being the penalty coefficients; \(n_{c}=n_{g}+n_{h}\) stands for the total number of constraints. As mentioned before, most of the constraints are of the inequality type in practice. The penalty functions quantify the constraint violations, often in a relative manner. Table 1 gathers a few examples of performance-related constraints along with the corresponding penalty terms.
## 3 Materials and Methods
### Neural Network Model
#### 3.6.2 Loss Function, Metrics, and Optimization  
* Loss Function: The binary cross-entropy loss function was used to optimize the model. This function is suitable for binary classification tasks, as it quantifies the difference between the predicted probability and the actual class label [78].
* Evaluation Metric: The model was evaluated using the F1 score, which is a harmonic mean of precision and recall. This metric is especially useful in imbalanced datasets where both false positives and false negatives need to be carefully accounted for [79].
* Optimizer: The ADAM (Adaptive Moment Estimation) optimizer was employed to update the network weights. ADAM is well-suited for deep learning models as it adapts the learning rate during training and efficiently handles sparse gradients [79].
## 2 Modeling of UAV Observations
### The Coupling Model for Crop Parameter Inversion
#### 2.3.2 Calculation of Jacobians  
The outputs of UNL-VRTM provide the Jacobians of the measurements with respect to the surface reflectance. However, in the inversion framework, the Jacobians of the measurements with respect to the crop phenotype parameter (\(\mathbf{x}_{\text{crop}}\)) are required. The Jacobian matrix determines the direction of convergence and the step size when solving the cost function [43]. According to the chain rule of derivative, the sensitivity transfer process of the observation vector \(\mathbf{Y}\) to the crop parameter \(\mathbf{x}_{\text{crop}}\) is expressed by the following equation [44; 45; 46]:  
\[\mathbf{J}=\frac{\partial\mathbf{Y}}{\partial\mathbf{x}_{\text{crop}}}=\frac{\partial\mathbf{ U}}{\partial P}\frac{\partial P}{\partial\mathbf{x}_{\text{crop}}} \tag{3}\]  
where \(\frac{\partial\mathbf{U}}{\partial P}\) is the Jacobian of the observation with respect to the canopy reflectance, and \(\frac{\partial P}{\partial\mathbf{x}_{\text{crop}}}\) is the Jacobian of the canopy reflectance with respect to the crop parameters, which can be calculated by the finite difference method.
# Results and Achievements of the ALLIANCE Project: New Network Solutions for 5G and Beyond
## 6 The SDN/NFV Domain
### Multicore Fibres and Constellation Shaping  
One of the challenges of B5G systems is how to provide such a huge bandwidth increase in the transport networks. The easiest solution would be installing more fibres. However, this is an obsolete technology and would have the biggest economic impact in the long run. Another alternative would be increasing the capacity of the installed fibre plant. This requires a more efficient spectrum usage (spectral efficiency) and/or expanding the available optical bandwidth (multiband communications). This is the preferred solution for the operators in the midterm. A more disruptive technology would be replacing the old fibre plant by a new optical fibre technology. Several candidates are being evaluated such as hollow-core fibres, multimode fibres or MCF. MCF is much more mature than the other alternatives and is much less demanding in terms of digital signal processing than the multimode counterpart.  
In the framework of ALLIANCE project, it has been found that there exists an optimum multicore crosstalk level of about -55 dB/km which optimises the aggregate capacity of MCF [73] (see Table 3). This level sets the maximum number of cores that can be deployed in a given cladding diameter. This finding simplifies dramatically the design of MCFs to be used in future applications.  
On the other hand, we have experimentally demonstrated that a single transponder can operate at any distance, from DCs to transoceanic communications, using tunable probabilistic constellation shaping (PCS) [74] (see Figure 6). This proves the potential for PCS to adapt to any rate/reach requirement using the same hardware platform.  
Figure 5: Conceptual diagram of a converged B5G fronthaul and PON architecture.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Diameter** & **Reach** & **SE** & **XT** & **Cores** \\ \hline \multirow{3}{*}{125 microns} & 100 km & 75 b/s/Hz & \(-\)63 dB/km & 4 \\  & 1000 km & 50 b/s/Hz & \(-\)63 dB/km & 4 \\  & 10,000 km & 25 b/s/Hz & \(-\)63 dB/km & 4 \\ \hline \multirow{3}{*}{200 microns} & 100 km & 220 b/s/Hz & \(-\)55 dB/km & 13 \\  & 1000 km & 130 b/s/Hz & \(-\)55 dB/km & 14 \\  & 10,000 km & 50 b/s/Hz & \(-\)58 dB/km & 14 \\ \hline \multirow{3}{*}{260 microns} & 100 km & 400 b/s/Hz & \(-\)50 dB/km & 24 \\  & 1000 km & 230 b/s/Hz & \(-\)52 dB/km & 26 \\ \cline{1-1}  & 10,000 km & 85 b/s/Hz & \(-\)58 dB/km & 27 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of experimental results from [73]. (Diameter: cladding diameter. Reach: transmission distance. SE: maximum aggregate spectral efficiency. XT: optimum aggregate multicore crosstalk. Cores: optimum number of cores.)
# Rapid Deployment Method for Multi-Scene UAV Base Stations for Disaster Emergency Communications
## 4 Simulation Results and Discussion
### Efficiency Analysis of the Algorithm  
In a rectangular area with dimensions of 500 m \(\times\) 500 m, UAVs are randomly deployed. Within area R, there are, respectively, 6, 8, 10, and 12 targets that require priority emergency  
Figure 4: Fitness curve of iteration number and the Euclid distance between UAVs and target deployment points. (**a**) Fitness curve for 500 m \(\times\) 500 m area UAV deployment (eight UAVs); (**b**) fitness curve for 1500 m \(\times\) 1500 m area UAV deployment (eight UAVs); (**c**) fitness curve for 2500 m \(\times\) 2500 m area UAV deployment (eight UAVs); (**d**) fitness curve for 4000 m \(\times\) 4000 m area UAV deployment (10 UAVs).  
communication. Simulation experiments are conducted to compare the single search CPU average computation times of the SD algorithm, SAIBFGS algorithm, LAESGA algorithm, and SGA algorithm. The aim is to validate the efficiency of these algorithms in solving the problem at hand. Each algorithm is run 10 times for different numbers of targets, and the average values are obtained, as shown in Figure 5 for statistical analysis.  
As depicted in Figure 5, the line search method, such as the SD algorithm and SAIBFGS algorithm, showcases remarkable advantages in terms of computational efficiency when compared to genetic algorithms. The improved genetic algorithm, compared to the standard genetic algorithm, demands less computational time, thereby conserving computing resources.
## 3 Model and Preliminaries
### Weight Matrix Construction  
Weight matrix construction is an essential topic in STARIMA models. In the STARIMA model, the weight matrix is time invariant and equal for the same neighbor order. In the LSTARIMA model, a time variant weight matrix was introduced to improve traffic prediction accuracy with lower AR and MA orders. Furthermore, the speed difference was used to construct the weight matrix in the LSTARIMA model [13]. The speed of a road is an important character of traffic flow, but is not the essential one in terms of impact to surrounding roads. It is obvious that a road that outputs only one vehicle will not impact neighbor roads at the same level as saturated roads with the same speed. The traffic output amount to the neighbor roads in a time slot has more impact on the future traffic state of neighbor roads. Thus, the output vehicle number during a time slot was used in the weight matrix construction instead of speed in this paper.  
For all pairwise road sections \((i,j)\) with spatial lag \(h\), the corresponding \(w_{ij}^{(h)}(t)\) is defined as follows:  
\[w_{ij}^{(h)}(t)=\left\{\begin{array}{cc}1&h=0,i=j\\ \frac{Q_{ij}(t)}{\sum Q(t)}&h=1,\sum Q(t)\
eq 0\\ \frac{Q^{(h)}{}_{ij}(t)}{\sum Q^{(h)}(t)}&h\
eq 1,\sum Q^{(h)}(t)\
eq 0\\ 0&\sum Q^{(h)}(t)=0\end{array}\right. \tag{6}\]where \(Q_{ij}(t)\) is the number of vehicles running from road \(j\) towards road \(i\) at time slot \(t\) and \(\sum Q(t)\) is the sum of vehicle numbers from directly connected roads to road \(i\) at time slot \(t\). \(Q^{(h)}{}_{ij}(t)\) is the number of vehicles on the \(h\) order neighbor road \(j\) towards \(h-1\) order neighbor of road \(i\) at time slot \(t\), and \(Q^{(h)}{}_{ij}(t)=0\) if \(j\) is not the \(h\) order neighbor of \(i\): \(\sum Q^{(h)}(t)\) is the sum of \(Q^{(h)}{}_{ij}(t)\).
# Improved Ant Colony Algorithm for the Split Delivery Vehicle Routing Problem
## 1 Introduction
To prevent a traditional ant colony algorithm from falling easily into a local optimum, a recent study [20] improved the pheromone update strategy and prevented the algorithm from entering a local optimum prematurely by adjusting the pheromone content. Experiments show that the max-min ant colony algorithm has the best pheromone processing performance. These methods focus on how to keep the algorithm from falling into a local optimum but seldom pay attention to how to make an algorithm that has fallen into a local optimum jump out of the local optimum. In addition, as far as we know, in previous research on the ant colony algorithm for solving the SDVRP, designing different selection strategies for different types of customer points has not been considered. To address these issues, we have made improvements to the ant colony algorithm. The main contributions of this paper are as follows:  
* For the state transition process of the ant colony algorithm, we propose an adaptive selection threshold to replace the commonly used fixed threshold, which enables the algorithm to better balance convergence speed and global search ability. * We designed new pheromone updating rules by referring to the max-min ant colony algorithm. To improve the existing methods, we added a pheromone backtracking mechanism and an adaptive pheromone volatile factor to improve the ability of the algorithm to jump out of a local optimum. * Unlike the common single strategy of customer selection, ours divides customers into initial customers and noninitial customers. We also designed selection strategies for each type of customer, so that the selection of customer nodes is more targeted. * We applied the proposed improved ant colony algorithm to SDVRP instances of various scales for a comprehensive evaluation. The experiments show that our method can effectively solve problems of different sizes and its performance is competitive compared with other heuristic algorithms. The structure of this article is organized as follows. Section 2 introduces the related literature of the SDVRP. Section 3 introduces the mathematical model of the SDVRP. Section 4 presents our proposed improved ant colony algorithm in detail. In Section 5, we apply the improved ant colony algorithm proposed in this paper to small-scale, medium-scale and large-scale test sets, and then discuss the experimental results. Finally, Section 6 summarizes this paper.
# Bayesian Survival Analysis Model for Girth Weld Failure Prediction
## Survival Prediction Model
### Girth-Weld Failure Causes
Therefore, using the theory and quantitative model of describing the causes of a pipeline's defects, combined with a survival analysis and other prediction methods, the Bayesian survival-analysis model of girth weld can be established and can be used to analyze the influence of different factors on the life of girth welds and predict their mean survival.
## 2 Materials and Methods
### Deep Neural Network-Based Channel Estimation and Signal Detection
#### 2.5.2 Long Short-Term Memory (LSTM)-Based Channel Estimation  
Among artificial neural networks (ANNs), the outputs of specific nodes in the recurrent neural network (RNN) are served back in a recursive way to affect the input to the same node. Current outputs of RNN can recall memory because they are based on prior computations. Meanwhile, disappearance gradient is an issue in a naively designed RNN, but LSTM architecture can efficiently overcome it. LSTM contains new gates that permit gradient control and defense of long-range dependencies to overcome the vanishing gradient problem. It contains three layers: input layer, hidden layer, and output layer. Input layer communicates with input data, while the LSTM cells execute the function of the hidden layer. The prediction results are shown in the output layer [11; 12].
## 4 Discussion  
The improved positioning accuracy and convergence time of PPP and PPP-RTK when using triple-frequency observations is an interesting topic. The time series of the positioning error statistical results of the triple-frequency scheme are shown in Figure 10 and Table 4, respectively. It is evident from Figure 10 that PPP-RTK exhibited significantly improved positioning performance compared with PPP-AR. For triple-frequency PPP-RTK, remarkable improvements of 76.13% in horizontal positioning accuracy and 69.83% in vertical positioning accuracy were observed when compared with PPP-AR. Comparing Figures 5 and 10, it is evident that the disparity in positioning accuracy between the dual-frequency and triple-frequency solutions of PPP-RTK was relatively minor. In contrast, PPP-AR exhibited a substantial difference in complex environments. Compared with dual-frequency PPP-AR, triple-frequency PPP-AR demonstrated improvements of 23.84% in horizontal accuracy and 17.41% in elevation accuracy. Unfortunately, triple-frequency PPP-AR is still unable to achieve stable and reliable positioning. Compared with dual-frequency PPP-RTK, triple-frequency PPP-RTK exhibited improvements of 19.61% in the horizontal direction and 2.67% in the elevation direction. Since dual-frequency PPP-RTK demonstrated outstanding performance, the contribution of multi-frequency observations appears to have been less pronounced.  
Table 5 presents the positioning availability of dual-frequency and triple-frequency PPP-RTK. Compared with the dual-frequency scheme, the percentage of horizontal positioning errors less than 5 cm improved from 92.83% to 96.36%, and the percentage of errors less than 10 cm improved from 99.69% to 99.89%. Similarly, the percentage of elevation  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **N (cm)** & **E (cm)** & **U (cm)** \\ \hline PPP-AR & 8.9 & 8.7 & 24.2 \\ PPP-RTK & 2.2 & 2.0 & 7.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The statistical values of convergence time and positioning error for two triple-frequency algorithms for vehicle kinematic positioning, tested in the Wuhan area.  
Figure 10: The time series of positioning errors for PPP-AR and PPP-RTK triple-frequency solutions in the north, east, and up directions. The yellow dashed line represents the outage of GNSS.  
positioning errors less than 10 cm increased from 96.46% to 98.83%, and the percentage of errors less than 20 cm increased from 99.61% to 99.93%. Multi-frequency PPP-RTK can significantly enhance accuracy, particularly for users who demand centimeter-level 3D positioning accuracy. In such scenarios, multi-frequency PPP-RTK demonstrates exceptional performance. However, for users with slightly lower accuracy requirements who prioritize reliability, the benefits of employing multi-frequency observations may not be readily apparent.