esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review: Machine Learning for Combinatorial Optimization Problems in Energy Areas
## 2 Background
### Combinatorial Optimization Problem
#### 2.1.2 Maximum Independent Set  
Maximum independent set (MIS) is a typical central problem in distributed graph algorithms, also known as the maximal stable set, which is an independent set that does not belong to any other independent sets. That is to say, an independent vertex set of a graph is a subset of the vertices such that no two vertices in the subset indicate an edge of this graph. Given a vertex cover of a graph, all vertices not in the cover define an independent vertex set. In addition, a MIS is also a dominating set in the graph, and each independent dominating set must be maximally independent. Many meaningful applications can be mathematically established as MIS problems, such as the communication system [127; 128], computational system and graph coloring problem [129]. For handling MIS problems, some early researchers in studying graphs proposed some interesting algorithms [130; 131; 132]. Afterwards, more individuals and groups started to deploy a series of approaches with higher efficiency, such as the ML method [133], small messages rather than large messages [134], local search [135] and exact algorithm [136].
# An Epistemic Utility-Theoretic Model in Fortifying Oil-and-Gas Production Networks
## 2 Model Formulation  
### Tri-Level Defender-Attacker-Operator Problem  
In our tri-level defender-attacker-operator problem, Equations (1)-(13), all parties seek to optimize the transportation cost of the produced crude oil and natural gas liquids (NGL) from the production facilities to the demand nodes. The modeled oil/gas network compromises the pump stations for the crude oil and the direct pipelines for the NGL. Moreover, the limits of production and pipeline capacities are considered. Figure 1 demonstrates the interaction among the players in the tri-level game, wherein the first level problem, Equations (2)-(5), the defender plans the hardening and false information propagation. Figure 1: Tri-level model presentation; with defender, attacker, and operator interactions. A distinguishing of the feature of our model is the explicit recognition of the importance information plays in such a competitive game, as shown in our discussions on shared cognition and epistemic knowledge, where such information is generated at the defender model. Specifically, the defender would release some false information, do specific actions, and pretend that information/actions are classified, or are real strategies, by way of posturing or side information. When applied on top of hardening, this strategy can deflect the attacker from its intended target toward a protected target, resulting in depleting the adversary's limited resources without any compromise on the oil/gas network. In the second-level problem, Equations (6)-(8), while the attacker believes in spending resources to elicit intelligence from the defender, he makes his strategies based upon the received data, without knowing that he is buying false information. The model demonstrates the intricate relationship between game and information within the first and second-level of the tri-level problem, namely the leader and follower levels. The defender and attacker employ the third-level operator problem, Equations (9)-(13), for evaluating their strategies, to ensure they optimized their interests, and dispatch their products to the demand points. The objective function, Equation (1), optimizes the entire system operating cost (OC), including transportation in the first part and the shortage cost in the second part of the equation. The defender and the operator are seeking to minimize the OC with respect to the decision variables in the sets \(\mathbf{\Gamma}^{O}\) and \(\mathbf{\Gamma}^{O}\), respectively, while the attacker maximizes the OC sought by the decision variables in the set \(\mathbf{\Gamma}^{A}\). Note that Appendix A summarizes the model variable notations for the reader's convenience.
## 3 Dynamic Weight Modification Procedure  
The modification of the mechanism described above is based on a different way of calculating the weights. Our modification generally relates to the triggering procedure of the installation of new flows. The previous version of the proposed mechanism maintained constant weights. This article introduces a modification where the weights are not predefined, they can fluctuate based on the link utilization. In the current version, the weights reflects online link load in the network. Due to this approach, paths are calculated on the basis of the state of the loads in the entire network, not on the overload of only one or a few points in the network. This is a very important modification to the previous version of the mechanism compared to the [8]. This allows for a dynamic view of the whole network represented by constantly changing weights. Calculations are performed only for specific link utilization values.  
Due to the fact that the network operates in flow forwarding mode, path recalculations do not cause network instability. The proposed mechanism ensures the stability of the network by leaving existing flows to follow their established paths, thereby preventing any network instability even during path recalculations. Newly calculated paths are reserved for the transmission of new flows. Consequently, the network's operation in flow forwarding mode ensures a smooth transition and mitigates any potential disruptions caused by path recalculations.
## 4 Opportunities and Challenges  
### Privacy Concerns  
With the widespread deployment of AMIs, IoTs, and ICT systems, deep integration of the cyber and physical layers of distribution systems raises serious privacy and cybersecurity concerns. Traditional centralized model training frameworks require large amounts of raw data to be transmitted to a central server for effective model training. However, the transmission of raw data in the network is especially vulnerable to cyberattacks.  
One solution to the above-mentioned privacy concerns is federated learning (FL). First introduced in 2016, FL is developed to address these privacy concerns of traditional learning models [77]. FL allows local devices to collaboratively train a global model without sharing their local data [78]. As models are trained locally, raw data are kept locally, which alleviates the computational communication burdens of the centralized model. Several attempts to integrate FL into power system operation and control are explored in [79, 80]. Reference [79] proposes a decentralized federated DRL-based VVC in distribution networks. Specifically, private measurements and policies of virtual power plants (VPPs) are all kept locally to agents, and these local agents only exchange a few scalars with the coordinated agent, significantly enhancing communication efficiency and data privacy. An FL framework is adopted in [80] to detect false data injection attacks (FDIAs). Not only is privacy preserved by training the data locally for the detection of FDIA, but generated models are also deployed locally, enabling efficient detection as communication delays caused by transmitting data between nodes and a central server are alleviated. To further protect data privacy from hackers, the Paillier cryptosystem is integrated with the FL framework by encrypting model parameters exchanged during training.  
Generally, privacy protection in machine learning addresses three objectives: privacy of the data used for learning a model as input, privacy of the model, and privacy of the model output. The related techniques include privacy-preserving probabilistic inference [81] and computing on encrypted data [82], and however, their application to CPDSs remains open. Future development of AI techniques must take into consideration the privacy implications, not only to combat security breaches that can cause data theft but also to motivate the users to share their data for more efficient and reciprocal demand response. Securing privacy in big data analytics for smart grids remains an ongoing challenge that requires consistent attention and combined endeavors for adequate solutions throughout data collection, storage, analysis, and sharing.
# Transformer-Based Visual Object Tracking with Global Feature Enhancement
## 4 The Experiments
### Analysis of GFETrack  
#### 4.2.1 The Ablation Experiments  
The impact of different components on the success rate of the OTB100 dataset is analyzed to gain insights into the proposed tracker's performance. As presented in Table 1, \(M1\) represents the proposed GFETrack, while \(M2\), \(M3\), and \(M4\) indicate variations in the proposed tracker without the FSA module, ECA module, and positional encoding, respectively. \(M5\) represents the baseline tracker.  
Notably, when the ECA module is removed (comparing \(M1\) and \(M3\)), the success rate experiences a significant drop of 1.1%. This decrease underscores the critical role played by the ECA module in focusing on the most salient subset of the object, thereby contributing to the overall tracking accuracy. The ECA module facilitates enhanced feature discrimination, allowing the tracker to prioritize and attend to crucial object-related information. Additionally, comparing \(M1\) and \(M2\) reveals that the FSA module leads to a modest improvement in the success rate of 0.5%. Contrary to initial assumptions, this improvement is not driven  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **FSA** & **ECA** & **Pos** & **Success** \\ \hline M1 & Yes & Yes & Yes & 70.8 \\ M2 & No & Yes & Yes & 70.3 \\ M3 & Yes & No & Yes & 69.7 \\ M4 & Yes & Yes & No & 69.5 \\ M5 & No & No & No & 69.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The ablation experiments on OTB100 dataset. “Yes” indicates the corresponding proposed module is used, while “No” indicates the corresponding proposed module is not used.  
by a direct enhancement of tracking accuracy. Instead, the primary purpose of the FSA module is to reduce the number of parameters and computational complexity. By achieving parameter efficiency, the FSA module indirectly enhances the tracking speed without compromising the success rate. This nuanced understanding emphasizes that certain modules, while not directly contributing to accuracy improvement, play vital roles in optimizing the overall performance and efficiency of the tracker.
## 4 Experiments
### Experimental Results and Analysis
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model Name** & **Ave Precision** & **Ave Recall** & **Ave F1-Score (\%)** \\ \hline ZORE & 62.39 & 69.51 & 65.76 \\ \hline UnCORE & 80.43 & 49.76 & 61.48 \\ \hline DSNFs-based ORE & 83.67 & 59.12 & 69.28 \\ \hline CTHE (Ours) & 84.74 & 61.39 & 71.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The optimal results of CTHE method on #ORP-Test. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model Name** & **Ave Precision** & **Ave Recall** & **Ave F1-Score (\%)** \\ \hline \hline DepNN & 83.19 & 81.04 & 82.1 \\ \hline Att-based Bi-LSTM & 88.49 & 86.62 & 87.55 \\ \hline Att-based CNN & 89.57 & 87.03 & 88.28 \\ \hline BERT-based Att-Bi-LSTM & 89.37 & 86.95 & 88.14 \\ \hline BERT-based Att-Bi-GRU & 89.81 & 87.88 & 88.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The optimal results of different models on #RC dataset. all triples labeled by humans. According to the results, we know that named entity recognition performance is reliable. However, in open relation prediction, the effect is better for short sentences, and for longer compound sentences, some triples may be lost. For example, the triple President ([PERSON], US) was lost in case II.
## 4 Result and Discussion
### Comparison of Reconstructed Electron Density at 00:00, 06:00 and 12:00
From the comparison of the four sets of electron density curves with the initial and background electron density values in Figure 9a,b, we can find that the initial electron density values are smaller than the background electron density at most of the heights, especially at the height of the electron density maximum, and that the two reconstruction methods are better optimized for the initial electron density values. In Figure 9a, each 16 \(\times\) 16, i.e., 256 grids, represents the electron density at one height plane, and it can be seen in the figure that in the grid planes around the 3000 th grid, the MART0 reconstruction yields much larger values of electron density than the surrounding ones, and the difference in the results of the MART1 reconstruction is even smaller. This is also the case in higher planes, except that the difference is not as large as near the 3000 th grid, which corresponds to the last longitude-height plane in Figure 8b, where the electron density undulates. As can be seen in Figure 10, the reconstruction electron density by MART0 and MART1 are similar; this is because the value of delta obtained with this initial value is 1.01, i.e., the STEC calculated using the initial value is similar to the simulated STEC, and the initial values of MART0 and MART1 are almost the same. As can be seen from Figure 11b, the in-grid electron densities obtained from the reconstruction of the two methods match the background electron density profiles better at the locations we chose, but there are several singularity values larger than the true electron density values in the plane of the peak height of the electron densities in Figure 11a. Therefore, the reconstruction of the electron density is better because the initial value of the MART iteration is closer to the value of the true electron density of the background, which also leads to a value of delta close to 1. Therefore, the reconstruction results of MART1 and MART0 overlap with each other, and the new method is not very useful for improvement. We also reconstructed the electron density at 18:00 using MART0 and MART1, respectively, and the results are similar to those at 12:00.
## 4 Experiments and Results
### Ablation Experiment
#### 4.4.3 Ablation Experiment on Attention Mechanisms  
To validate the impact of different attention mechanisms on model performance, ablation experiments were conducted on various attention types, with results shown in Table 4. The first row represents the baseline without any attention mechanism. The second and third rows indicate the addition of SE attention and CBAM attention, respectively. The fourth row incorporates CA attention, the fifth row adds EMA attention, and the sixth row integrates the proposed multi-scale linear spatial attention (MLSA). The results demonstrate that compared to mainstream attention mechanisms (SE, CBAM, CA, EMA), the proposed multi-scale linear spatial attention significantly enhances detection accuracy. Relative to the baseline without attention, multi-scale linear spatial attention improved AP by 1.8%, increased recall by 2.0%, and reduced MR\({}^{-2}\) by 1.9%. SE attention showed limited improvements because it focuses solely on channel-wise interdependencies while ignoring spatial features. CBAM attention, though leveraging large-kernel convolutions for spatial feature extraction, fails to address long-range dependencies. In contrast, the proposed multi-scale linear spatial attention effectively balances spatial-channel interactions and long-range dependency modeling, substantially boosting overall performance.
# Thermal Investigations on Carbon Nanotubes by Spectroscopic Techniques
## 3 Results and Discussion
### Spectral Distance Evaluation  
Figure 3a,b shows the spectral distance (SD) behavior with respect to temperature for CNTs, respectively for the recorded IR and Raman spectra, together with the fit of the model (brown line). The SD analysis procedure was applied to the whole investigated spectral range for each temperature spectrum.  
Each data point represents the value of the deviation of the spectrum at a given investigated temperature from the respective spectrum measured at 25 \({}^{\circ}\)C. From the evaluation of the SD applied for each spectrum as a function of temperature an increasing sigmoidal trend was obtained. In order to extract quantitative information, a curve fit through Equation (2) was performed for the two profiles. From this analysis it emerged that the curve, for the IR spectra, presents a point of inflection equal to \(T_{0}=206.5\)\({}^{\circ}\)C and for the Raman spectra presents a point of inflection equal to \(T_{0}=206.28\)\({}^{\circ}\)C. Such a method furnishes the same value of inflection point for both the spectroscopic techniques employed.
# Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods
###### Abstract  
In ultra-wideband (UWB)-based wireless ranging or distance measurement, differentiation between line-of-sight (LOS), non-line-of-sight (NLOS), and multi-path (MP) conditions is important for precise indoor localization. This is because the accuracy of the reported measured distance in UWB ranging systems is directly affected by the measurement conditions (LOS, NLOS, or MP). However, the major contributions in the literature only address the binary classification between LOS and NLOS in UWB ranging systems. The MP condition is usually ignored. In fact, the MP condition also has a significant impact on the ranging errors of the UWB compared to the direct LOS measurement results. However, the magnitudes of the error contained in MP conditions are generally lower than completely blocked NLOS scenarios. This paper addresses machine learning techniques for identification of the three mentioned classes (LOS, NLOS, and MP) in the UWB indoor localization system using an experimental dataset. The dataset was collected in different conditions in different scenarios in indoor environments. Using the collected real measurement data, we compared three machine learning (ML) classifiers, i.e., support vector machine (SVM), random forest (RF) based on an ensemble learning method, and multilayer perceptron (MLP) based on a deep artificial neural network, in terms of their performance. The results showed that applying ML methods in UWB ranging systems was effective in the identification of the above-three mentioned classes. Specifically, the overall accuracy reached up to 91.9% in the best-case scenario and 72.9% in the worst-case scenario. Regarding the F1-score, it was 0.92 in the best-case and 0.69 in the worst-case scenario. For reproducible results and further exploration, we provide the publicly accessible experimental research data discussed in this paper at PUB (Publications at Bielefeld University). The evaluations of the three classifiers are conducted using the open-source Python machine learning library scikit-learn.  
UWB; NLOS identification; multi-path detection; NLOS and MP discrimination; machine learning; SVM; random forest; multilayer perceptron; LOS; DWM1000; indoor localization +
Footnote †: journal: Applied Sciences
# Ontology-Based Production Simulation with OntologySim
## 7 Technical Description  
### Basic Building Block of the Ontologysim  
The basis of the OntologySim is the Python library Owlready2. The ontology framework is used to store the data and the status of the production. A more detailed description of how Owlready works can be found in Section 2.1 and References [17; 26].  
Figure 12: OntologySim pipeline.  
The structure of the OntologySim is shown in Figure 13 below. Around the entire ontology, wrapper methods have been designed to standardize access to the ontology. The search queries are realized via unique IDs and via iteration through the connections. This approach has high-speed advantages over SPARQL and SWRL (Semantic Web Rule Language). Building on top of the wrapper methods, simulation, loggers, agents, and KPI modules are implemented. These modules form the basis for ontology-based simulation and contain, in addition to the ontology, the core logic of the simulation. To start the simulation, either config files or ow files are loaded or a request to the webserver is sent. Nevertheless, the wrapper methods are generalized, so that only the config or ow is required to instantiate a simulation run. The agent integration is structured in such a way that standard agents, such as FIFO, Shortest Queue, etc., are pre-implemented, and their programmed agents can be easily created via a predefined interface.  
Based on the basic simulation, a web server is integrated, which enables more targeted access to simulation data and lays the foundation for the visualization, as shown in Figure 13. The designed API interface is implemented with Flask, which is a stateful service due to the discrete running simulations. The basic functions of the API are to call KPIs, log data, and create and configure simulations. Together with the simulation module and the web server, this forms the backend of the software structure [64; 65]. A frontend Framework, here React, is used for the visualization of the simulation. The data is retrieved from the Flask web server using Ajax calls and stored in the Redux Store. The Redux Store serves as the basis for displaying KPI diagrams, event logger tables, and simulations. The connection between frontend and backend is enabled using AJAX calls.
# A Lifetime Maximization Relay Selection Scheme in Wireless Body Area Networks
## 7 Conclusions and Future Work  
### Conclusions  
In this paper, a relay selection scheme is proposed by formulating an optimization problem that considers both the energy consumption rate and residual energy of each node to maximize the minimum lifetime of the nodes in the network. To solve this NP-hard optimization, a heuristic iterative solution is then designed to rapidly address the problem. The simulation results show that the heuristic solution significantly reduces the time complexity with a only 0.5% performance degradation when compared with the optimal values of the optimization, which indicates that the proposed scheme is suitable for a real-time dynamic WBAN system. We evaluate the performance of the proposed scheme on a human-body-shaped model and a general WBAN model specified in the IEEE 802.15.6 standard with respect to energy difference degree, network range and total number of nodes, respectively. It can be illustrated in the simulation results that the proposed scheme always outperforms existing relay selection algorithms in terms of network lifetime of WBANs. In addition, the proposed relay selection algorithm does not violate the topology restriction of IEEE 802.15.6. Furthermore, we make a brief discussion on how to implement our proposed scheme in a real WBAN system to validate the feasibility. In conclusion, LMRSS is an effective and suitable relay selection method to enhance the network lifetime of WBANs.
## 3 Results
### Effects of W/C Ratios on the Microstructure of SLMKPC  
Figure 7 exhibits the micro-morphologies of SLMKPC samples with different W/C ratios after 28 d of curing. When W/C = 0.2, the SLMKPC sample was composed of a large number of rod-like crystals, which were closely spliced together to form a thin and dense sheet structure consisting of a few micro-cracks (Figure 7a). When the W/C ratio increased to 0.25, a large number of columnar hydration products existed in the hardened SLMKPC sample with a high degree of crystallization (Figure 7b). These hydration products were tightly packed with each other, no cracks were detected in the microstructure, and the highest compressive strength was obtained. When W/C = 0.3-0.4, the fracture and crystallization degrees of hydration products were the worst; hence, the overall structure of the sample was loose due to the existence of small cracks and voids (Figure 7c-e). When W/C = 0.35-0.4, crystals ruptured into granular ones (Figure 7d,e); hence, the compressive strength was low due to the loose crystal structure (Figure 5).  
Figure 6: Influence of W/C ratios on the phase composition of SLMKPC: (**a**) After 3 d of curing, (**b**) after 28 d of curing, (**c**) TOPAS quantitative analysis of the phase compositions of SLMKPC at different W/C ratios.