esa-sceva/satcom-chunk-collection huggingface dataset:
# An Energy Centric Cluster-Based Routing Protocol for Wireless Sensor Networks
## 2 Literature Review
In the head-hand-over technique, a former cluster head delivers the role of cluster head to an electednode. In the piggybacking technique, the value of weight function is sent along with the local data to an associated cluster head. This reduces the control message overhead during cluster-setup phases. Hence, the steady state and the network lifetime are prolonged compared to other protocols. However, the number of neighbor nodes is not considered in defining competition radius along with the distance from the BS, although it uses the number of neighbor nodes while computing a waiting time to become a cluster head. To resolve this problem, an improvement of EADUC called IEADUC has been proposed in [27]. It combines the cluster head-hand-over technique of HUCL with the same cluster-head election technique of EADUC. Meanwhile, it modifies the relay function of a relay-node selection towards the BS. Unlike the EADUC, a relay node is selected on the basis of relay values defined by three factors; the residual energy of a cluster head, number of member nodes associated with the node, and the costs of data aggregation and transmission to a next hop. This achieves an enhanced network lifetime by contrast with the EADUC and HUCL. IEADUC can prolong the steady state and the network lifetime over the protocols. In all the algorithms, the cluster-head elections are based on different weight functions, where the factors are mostly residual energy of a node, number of neighbor nodes, and distance from the BS. But, in these weight functions, the intra-cluster communication distance has also not taken into consideration, whereas it has a significant impact on the overall energy consumption of the nodes. The cluster-heads election are biased by the time \(T\) and competition range of broadcasting a head message. Therefore, a cluster might have a less residual-energy-obtaining node as a cluster head compared to some of the member nodes, i.e., in [19; 20; 21; 22; 24; 25; 26; 27]. On average, a high number of control messages is required to complete a single data transmission to the BS. Although some methods of the protocols are very effective to balance the energy consumption among the nodes, the control messages overhead use an excessive amount of energy of the nodes, which limits the network lifetime as a whole. Table 1 summarizes the comparison between the protocols on the basis of clustering, scalability, control messages overhead in clustering, and energy efficiency that results in steady state and network lifetime.
# Multi-Channel Expression Recognition Network Based on Channel Weighting
## 4 Discussion
### Overall Model Evaluation  
In order to visually measure the accuracy of the model and to summarize the prediction results for the classification problem, a confusion matrix was generated and the entire model was evaluated as shown in Figure 10, which briefly analyses the result graphs generated for the FER2013 dataset.  
Figure 9: Attention visualization. Where (**a**–**d**) are pictures randomly obtained from the dataset, column (II) is the visualization result of adding ACW, column (III) is the visualization of adding the ASA module, and column (IV) is the visualization result of the network MAWNet proposed in this paper.  
It can be concluded that the model performs superiorly in the recognition of expressions such as happy, surprised, natural, and disgusted, but slightly less well in expressions such as fear, sad and angry. It is possible to identify the errors made by the classification model and to understand the types of errors made by the model: on the one hand, the uneven distribution of the dataset makes the recognition accuracy of different expression categories different; on the other hand, the great inter-class similarity of expressions such as fear and sadness makes the recognition accuracy of this category low. In addition, the model can also be optimized for the above problems.
## 4 Classification of Routing Protocols
### Postural-Movement-Based Routing Protocols
#### 4.4.1 On-Body Store and Flood Routing (OBSFR)  
In [21], the authors presented the location based store-and-flood routing protocol (OBSFR), that in the presence of frequent partitioning intends to reduce the end-to-end delay and hop-count. OBSFR is a modified flooding protocol designed for partitioned networks. In the flooding mechanism, a source node sends multiple copies of the data packets towards the destination (sink node) through multiple routes. The network architecture used in this scheme is consists of seven sensor nodes: two at upper arms, two at thighs, two at ankles and one in the waist area, as shown in Figure 13a, redrawn  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multirow{2}{*}{**Protocol**} & \multicolumn{6}{c}{**Characteristics**} \\ \cline{2-7}  & **Goal** & **Metrics** & **Avg. Delay** & **PDR** & **Security** & **Energy Cons.** \\ \hline \multirow{2}{*}{HIT (2004)} & To maximize the network life & Geographic & & & & \\  & and reduce the direct & information \& residual & Low & N/A & Yes & Low \\  & transmissions to the sink & energy of the nodes & & & & \\ \multirow{2}{*}{AnyBody (2007)} & To reduce the direct & & & & & \\  & transmission of the nodes to & Nodes density & N/A & Very High & No & N/A \\ \hline \hline \end{tabular}
\end{table}
Table 3: Cluster-based routing protocols.  
from [21]. Thus forming a mesh topology divided in one or multiple simultaneous network partitions. The sensor node attached at the right ankle is assigned as a sink node (base station). The sink node is responsible for collecting the raw data from the other bio-medical sensor nodes and forward the processed results to the server located outside of the body, resulting in multi-point-to-point routing.  
In OBSFR, each packet carries a list of the node-IDs showing its path from the source node along with a unique identifier (source_ID, seq_No.). When a node receives a packet, it stores the packet in its buffer, and starts search for the nodes whose IDs are not listed in the received packet's IDs list. Once found one or more such nodes then it forwards the packets using a broadcast mechanism.
# Intelligent Prediction of Aeroengine Wear Based on the SVR Optimized by GMPSO
###### Abstract  
In order to predict aeroengine wear accurately and automatically, as a predictor, support vector regression (SVR) was optimized by means of particle swarm optimization (PSO). The guided mutation strategy of PSO (GMPSO) is presented herein to determine the proper structure parameters of an SVR, as well as the embedding dimensions of the training samples. The guided mutation strategy was able to increase the diversity of particles and improve the probability of finding the global extremum. Furthermore, single-step and multi-step prediction methods were designed to meet different accuracy requirements. A prediction comparison study on spectral analysis data was carried out, and the contrast experiments show that compared with SVR optimized by means of a traditional PSO, a neural network and an auto regressive moving average (ARMA) prediction model, the SVR optimized by means of the GMPSO approach produced prediction results not only with higher accuracy, but also with better consistency.  
support vector regression; particle swarm optimization; guided mutation; spectral analysis; wear prediction +
Footnote †: journal: Applied sciences  
13 September 2021  
10 November 2021  
Footnote †: journal: Applied sciences  
SVR is a statistical learning theory built on the Vapnik-Cherovnenkis (VC) dimension and structural risk minimum principle. When processing data, the SVR has good generalization capability and avoids the defects of falling into a local minimum, over-learning, and relying on sample size [8]. Furthermore, particle swarm optimization (PSO) has been used to optimize the structure parameters of SVRs and the embedding dimension of training samples, which can ensure the accuracy and consistency of the prediction results.  
The rest of the paper is organized as follows. In Section 2, the SVR model and the optimization calculation for relative parameters are introduced. Section 3 presents the improved GMPSO approach and verifies the performance of GMPSO. In Section 4, an SVR optimized by means of GMPSO was used to predict the wear of an aeroengine and achieved better results compared with other algorithms. Finally, in the last section, conclusions are drawn.
## 3 Methodology for Human Navigation Path Simulation via Inverse Reinforcement Learning  
The proposed EEG-Informed Reinforcement Learning (EIRL) framework, as depicted in Figure 4, orchestrates a synergistic integration of neural signal interpretation and autonomous decision-making through three functionally coupled subsystems. The architecture initiates with multi-modal neurophysiological signal acquisition and annotation, where advanced EEG processing techniques transform raw neural data into discriminative cognitive states. These decoded brain patterns subsequently inform an adaptive control interface that dynamically maps neural activation signatures to robotic navigation primitives within structured grid environments.  
Figure 4: The framework diagram of the EIRL method consists of three parts: electroencephalogram signal processing, inverse reinforcement learning, and reinforcement learning.  
Figure 3: Example of raster map path optimization.  
The framework architecture follows a cascaded design philosophy. The first phase focuses on translating raw EEG signals into discrete control commands through optimized neural networks, prioritizing computational efficiency for embedded deployment. Building upon this neurocognitive interface, the second phase employs apprenticeship learning to extract latent decision patterns from human navigation trajectories, with particular emphasis on risk-averse behaviors near hazardous regions. Finally, the derived reward functions are injected into a Q-learning policy optimizer, where dynamic exploration parameters adaptively adjust exploration-exploitation tradeoffs based on environmental risk profiles. This sequential integration ensures that each module's outputs rigorously validate the inputs to subsequent stages.  
Operational efficacy is achieved through a phased optimization protocol emphasizing essential navigational constraints: preservation of movement economy through step minimization, probabilistic guarantees for obstacle evasion, and systematic elimination of environmental interference artifacts. The system iteratively refines action policies by establishing a bidirectional coupling between neural decoding outcomes and environmental feedback. Following trajectory generation under EEG-guided navigation, an inverse reinforcement learning mechanism extracts latent reward functions through feature expectation alignment with expert demonstration patterns. This learned reward model seeds a reinforcement learning pipeline that progressively optimizes path planning strategies through policy iteration, culminating in a validated navigation controller ready for deployment in real-world assistive scenarios.
# Different Formulations to Solve the Giesekus Model for Flow between Two Parallel Plates
## 4 Results
### Advantages and Disadvantages for Each Formulation  
The formulation based on the Schleiniger and Weinacht formulation [10] is advantageous in terms of the velocity cost in obtaining the flow components, because the system of equations is solved in a semi-analytical way. Another advantage of this formulation is the possibility to obtain the flow variables without the contribution of the solvent; that is, flow for the purely polymeric fluid (\(\beta=0\)). The disadvantage appears in the direction of convergence towards the adequate pressure gradient for the ideal flow. This disadvantage is related to the term inside the square root of Equation (21), it can becomes negative, and the solution is no longer real. This behavior appears for high values of Weissenberg _Wi_, Reynolds number \(Re\), and the mobility parameter \(\alpha_{G}\).  
The formulation based on the independent variable change is advantageous for analytically obtaining the main components of the desired flow. Another advantage: there is no restriction for the values of the flow variables, except those already found by [PERSON] and [PERSON] [10] (such as \(\alpha_{G}=0.5\)). The disadvantage is the computational cost, since it was not possible to obtain an analytical expression for this. It is necessary to interpolate a new distribution for the tensor \(T_{xy}\) to find an equally spaced domain of \(y\), then it is numerically integrated to the expression (29), and finally the streamwise velocity component can be obtained. Another disadvantage of this formulation is the limitation of the flow simulation for purely polymeric fluids (\(\beta=0\)). The limitation arises from the equation for the derivative of velocity (29), where the right-hand side terms are divided by \(\beta\).  
The solution obtained by high-order simulation is advantageous because simplifications are not adopted in the equation system that models the flow, so the problem is solved, considering all of the terms in the equation system. The disadvantage of this formulation is the computational cost, since many CPU hours are necessary to obtain the solution for each case. The adopted code uses domain decomposition parallelization, high-order methods for approximating the spatial derivatives, and a classical fourth-order Runge-Kutta method for the temporal derivative.  
The simulations were carried out in a computer Intel Xeon E5-2680v2 2.8 GHz. The wall time required for the Schleiniger and [PERSON] [10] and the independent variable change formulations were less than 5 s. The high-order simulations were performed using 16 cores and the wall time was about 20 h.
# Interconnected Government Services: An Approach toward Smart Government
###### Abstract
Data transfer from one part of public administration to another poses the problem of privacy protection. The main challenge is integrating heterogeneous computer systems within a public administration's jurisdiction from a technical, financial, and privacy perspective. During the last couple of years, the importance of these efforts has become enormous. ICT adoption in government organizations mandates that information flows smoothly between an organization and its constituent parts [8]. On the other hand, implementing such solutions poses the threat of privacy violations when transferring data. It can accidentally release policy-sensitive data, with the risk of data misuse and challenges regarding the ownership of data [9]. The current situation in public administration inspired us to conduct the research presented here and propose a model that should provide better interoperability among various information systems through database integration. Our solution allows for the processing of input data (according to defined service procedures) from heterogeneous databases without leaking sensitive data. Therefore, the main contribution is a model suggesting the application of several innovative technologies to achieve better problem-solving, the optimal utilization of resources, and policy innovation. We have used a (di)graph theory model to confirm the efficiency of the proposed solution on a specific public administration policy (e.g., the child-benefits policy [10]). After modeling the current state and the proposed solution, we compared the corresponding digraph models using digraph weights, which are related to the time required for collecting the documentation/documents. This paper is organized as follows: Section 2 provides an overview of the e-government research. A focus is its transformation into a smart government with potential challenges (such as data privacy) based on existing literature. Section 3 presents a new approach to information system modeling that introduces an integration layer for existing databases and services and suggests the application of several innovative technologies. In Section 4, we perform an efficiency test of the proposed solution using graph theory to model the current and proposed state. We compare the time required to collect documents and process citizen requests, and we validate the effectiveness of the proposed model. In Section 5, we discuss the results. Section 6 concludes the paper, summarizing the main contributions.
## 4 Experiments and Analysis
### Impact Assessment of Different Prompt Templates  
In this paper, we add prompt information before each sentence, and to evaluate the influence of different prompt templates on the performance of the PTKRE model, we design three new prompt templates as follows:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **F1-Score** \\ \hline PTKRE-Att & 90.19 \\ PTKRE-Pos & 90.31 \\ PTKRE-ChatGPT & 90.26 \\ PTKRE & 90.45 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of ablation experiments using BERT.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **F1-Score** \\ \hline PTKRE-Att & 91.10 \\ PTKRE-Pos & 91.24 \\ PTKRE-ChatGPT & 91.18 \\ PTKRE & 91.38 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of ablation experiments using RoBERTa.  
* Original Prompt: [ent1] and [ent2] are related in the sentence through <mask> <mask> sentence:
* Prompt 1: In this sentence, the relation is <mask> <mask>:
* Prompt 2: [ent1] and [ent2] are related in the sentence through <mask> <mask>
* Prompt 3: In this sentence, the relationship between [Ent1] and [Ent2] is <mask> <mask> sentence:  
In the experiments, we used RoBERTa as the pre-trained language model and explored the impact of different prompt templates on the performance of the PTKRE model. The relevant experimental results are presented in Figure 3:  
1. Template 1 removed entity-related information from the original prompt, retaining only the \"<mask>\" token and the sentence starting token \":\". The experimental results show that removing entity-related information impairs the model's performance, resulting in an overall F1 score decrease of 0.26 to 0.34 compared to the original template.
2. Template 2, based on the original template, removed sentence starting information \"sentence\", which indicated the starting position of the sentence where RoBERTa extracts entity relationships from the data. Compared to the original template, the experimental results show that Template 2's overall F1 score decreased by 0.09 to 0.15, indicating relatively more minor damage to the model than removing entity-related information.
3. Template 3 retained all the information but used a different phrasing while conveying the same meaning as the original template. Template 3's overall F1 score decreased by 0.13 to 0.3 compared to the original template.  
These experimental results illustrate that different prompt templates significantly impact the model's final performance. This study confirms that adding entity-related information and sentence-starting position information to the prompt template enhances the model's performance. Furthermore, different phrasings also influence the model's performance.
# Decentralized Robust Control of a Network of Inverter-Based Distributed Generation Systems
## 2 Methodology
### State Space Representation of the System in the Overlapping Decomposition Framework
#### 2.3.1 Overlapping Decomposition for Two Inverters  
In this section, a brief introduction to overlapping decomposition is provided [33]. Two interconnected IBDGs are considered; their dynamic equations are presented in Equation (3) and their state space representation is expressed as follows (the matrices \(B\), \(C\), and \(D\) are not displayed for the sake of brevity):  
\[\dot{x}=Ax \tag{5}\]  
where \(x=[i_{inv_{1}}\ v_{load_{1}}\ i_{12}\ i_{inv_{2}}\ v_{load_{2}}]^{T}\) and the matrix \(A\) is expressed as follows:  
\[A=\left[\begin{array}{cccc}0&-\frac{1}{L_{1}}&0&0&0\\ \frac{1}{C_{1}}&0&-\frac{1}{C_{1}}&0&0\\ 0&\frac{1}{L_{12}}&-\frac{R_{12}}{L_{12}}&\frac{1}{L_{12}}&0\\ 0&0&0&0&-\frac{1}{L_{2}}\\ 0&0&\frac{1}{C_{2}}&\frac{1}{C_{2}}&0\end{array}\right] \tag{6}\]  
By rearranging the five components of the state vector \(x\) into two overlapping vectors \(\bar{x}_{1}=[i_{inv_{1}}\ v_{load_{1}}\ i_{12}]^{T}\) and \(\bar{x}_{2}=[i_{inv_{2}}\ v_{load_{2}}\ i_{21}]^{T}\) with \(i_{21}=-i_{12}\), and concatenating them into a new state vector \(\bar{x}=[i_{inv_{1}}\ v_{load_{1}}\ i_{12}\ i_{inv_{2}}\ v_{load_{2}}\ i_{21}]^{T}\), the extended state space equation is represented as follows:  
\[\dot{x}=\bar{A}x \tag{7}\]  
Figure 3: Extended block diagram of inverter #\(i\) connected to inverters #\(k\in\mathcal{N}(i)\).  
where the matrix \(\bar{A}\) is expressed as follows:  
\[\bar{A}=\left[\begin{array}{cccccc}0&-\frac{1}{L_{1}}&0&0&0&0\\ \frac{1}{C_{1}}&0&-\frac{1}{C_{1}}&0&0&0\\ 0&\frac{1}{L_{12}}&-\frac{R_{12}}{L_{12}}&0&-\frac{1}{L_{12}}&0\\ 0&0&0&0&-\frac{1}{L_{2}}&0\\ 0&0&0&\frac{1}{L_{2}}&0&\frac{1}{C_{2}}\\ 0&\frac{1}{L_{12}}&0&0&-\frac{1}{L_{12}}&-\frac{R_{12}}{L_{12}}\end{array}\right] \tag{8}\]  
Therefore, duplicating the shared state \"\(i_{z}\)\" of the original state space vector results in the extended state space model of the system represented by the matrix \(\bar{A}\) in Equation (8). This extended model is generalized to the case of \(n\) inverters in the next subsection.