esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Thus, GNN can significantly improve the performance of NIDS by utilizing the inherent graph structure of network traffic. However, GNN-based IDS still has not achieved the desired reliability and stability level, as the model's input data has not been optimized before training. Previous research has mainly focused on creating graph data based on network topology or only using a component of the graph data, such as nodes or edges. Meanwhile, both nodes and edges are crucial elements of the graph that need to be simultaneously exploited for the model to learn contextually relevant information. Therefore, in this study, we propose a model called the flow-node graph neural network (FN-GNN) to design graph data from network flows. In this proposed model, the graph data are formed using a completely new approach. Specifically, the set of the most important features of flows is represented as nodes. Simultaneously, the edges of the graph are formed by utilizing the correlation between flows that share the same source IP address. This approach helps generate graph data that already contain information about the relationships within them. Additionally, this method preserves the maximum amount of network information since it considers the entire dataset as a whole rather than treating each data point independently. Thus, this approach is the most appropriate and effective for network flow data. Furthermore, our research also proposed a new graph model architecture based on a combination of existing models, GCN and SAGEConv. This combination helps the new model overcome the limitations of previous models and significantly enhances the performance of network attack detection. We implemented the model on two benchmarkdatasets, CIC-IDS2017 and UNSW-NB15. The experimental results show that the accuracy of the proposed model reached 99.76% for the CIC-IDS2017 dataset and 98.65% for the UNSW-NB15 dataset. We summarize our contributions as follows:  
* We proposed the FN-GNN model, a novel approach to represent the network flow data into nodes and edges in graph data. * We proposed a new graph model architecture combined with the GCN and SAGEConv models, which significantly improve the performance of the intrusion detection system. * The proposed model was applied to two standard datasets and we supplied the simulation results to prove the effectiveness of the proposed model. The remainder of this paper is organized as follows. Section 2 reviews the relevant literature and related work. Section 3 provides background information necessary for understanding the research, including an overview of the NIDS system and GNN models. Our proposed FN-GNN model is introduced in Section 4. Section 5 describes the experimental setup, detailing the datasets and evaluation methodology used to assess the model's performance. Section 6 presents and analyzes the experimental results, evaluating the effectiveness of the FN-GNN model compared to the existing method. Finally, Section 7 concludes the paper while also discussing limitations and outlining potential future work.
# Distributed Global Function Model Finding for Wireless Sensor Network Data
## 1 Introduction
The main contributions of this paper are summarized as follows: (1) we present the function finding algorithm using gene expression programming for wireless sensor networks data (FF-GEP) in order to prevent GEP from the local optimum; (2) we solve the generation of the global function model in distributed function finding and provide a global model generation algorithm based on unconstrained and nonlinear least squares (GMG-UNLS); (3) on the basis of FF-GEP and GMG-UNLS, we put forward distributed global function model finding for wireless sensor networks data (DGFMF-WSND); and (4) we describe simulated experiments that have been done and provides performance analysis results. The content of the paper is organized as follows. Section 2 discusses prior work related to data mining in WSNs and function mining. Section 3 introduces the function finding algorithm using gene expression programming for wireless sensor networks data. Section 4 emphasizes the distributed global function model finding for wireless sensor networks data. Section 5 represents experiments and performance analysis. Finally, conclusions are given in Section 6.
# Route Availability as a Communication Quality Metric of a Mobile Ad Hoc Network
## 2 Related Studies
### Metrics Used to Assess the Performance of Routing Protocols in MANETs  
A variety of routing protocols have been proposed for MANETs [2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13], and various metrics have been used to assess their performance, such as packet delivery ratio (PDR), throughput, end-to-end delay, routing overhead, path length, packet loss rate, jitter, path lifetime, and mean opinion score (MOS). Table 1 lists metrics used in different references.  
However, packet delivery ratio, throughput, end-to-end delay, path length, and packet loss rate can be observed only during the packet communication phase after a communication route has been established. In a MANET, in which routes are unstable, these are secondary information. If routing control is to be carried out based on these metrics, nodes need to exchange basic information from which these items of information can be calculated. Routing overhead [2; 5; 7] is used for protocols that are designed to improve the performance and stability of the routes to be established but it does not indicate the quality of the routes established. Path lifetime [10; 11], which is based on the remaining battery levels of relay nodes, is an important assessment metric but is not fit for assessing the nonuniformity (represented by repeated connections and disconnections) that the current paper focuses on.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Metrics** & [2] & [3] & [4] & [5] & [6] & [7] & [8] & [9] & [10] & [11] & [12] & [13] \\ \hline Packet loss rate & & & & & & & & & & & & & \\ Throughput & & & & & & & & & & & & \\ End-to-end delay & & & & & & & & & & & & \\ Routing overhead & & & & & & & & & & & & & \\ Path length & & & & & & & & & & & & & \\ Packet delivery ratio (PDR) & & & & & & & & & & & & & \\ Jitter & & & & & & & & & & & & \\ Path lifetime & & & & & & & & & & & & \\ Mean opinion score (MOS) & & & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Metrics used to assess routing protocols in mobile ad hoc networks (MANETs) in different references.  
In addition to the above metrics, jitter [6] and MoS [9; 12; 13] are important assessment metrics for applications such as video streaming in which user's quality of experience is of great importance. However, these have not been proposed as metrics to assess the nonuniformity in MANETs.
## 4 Notification-Based Querying Method (NOP Query) Proposal
### Static NOP Query Chain (SQC)  
The SQC is a static schema of the query expression that describes the elements of the query and the corresponding values and associations between them, represented as an NOP Chain. Consider a hypothetical query that finds a \"red car on the left below a white truck\" expressed in the Listing 1 using the SQL-like format. Currently, the \"NOP Query language\" is a prototype under development.  
```
SELECTLEFTELOW(Object[1],Object[2]) FROMDataStream WHEREObject[1].label='car' ANDObject[1].color='red' ANDObject[2].label='truck' ANDObject[2].color='white'
```  
Listing 1: Query example for finding a red car to the left below a white truck.  
Based on this query, Figure 5 presents an overview of the SQC creation process. Using the Symbol Table, the Lexical Analyzer examines the query expression and separates the elements and their corresponding values. The query can be represented in two predicates because two vehicles are recognized _FBEs_: \"red car left below\" and \"white truck\". Considering these elements, the resulting tokens belong to _FBEs_ and their attributes, spatial relations, and the action \"find\". In the first step, the Notification Chain Assembler (NCA) assembles the Static _NOP_ Query Chain with _Attributes_ for object names/labels (car and truck) and colors (red, white).  
In the second step, the NCA assembles the corresponding _Premises_ for labels, colors, and position values (\(X\) and \(Y\)), following the scheme shown in Figure 6, using a 2D coordinate system of the computer vision:  
* _Premise_ 1: \"car left truck\", \"_Attribute X (FBE 1) \(<\) Attribute X (FBE 2)\";_
* _Premise_ 2: \"car below truck\", \"_Attribute Y (FBE 1) \(>\) Attribute Y (FBE 2)\";_
* _Premise_ 3: \"_Attribute Color (FBE 1)_ = red\";_
* _Premise_ 4: \"_Attribute Label (FBE 1)_ = car\";_
* _Premise_ 5: \"_Attribute Color (FBE 2)_ = white\";_
* _Premise_ 6: \"_Attribute Label (FBE 2)_ = truck\".  
In the third step, the NCA creates the _Condition_ with all these _Premises_ and sets the conjunction operator. In the fourth step, the NCA creates the _Rule_ with the associated _Condition_, _Intigation_, and _Action_ elements. Finally, the _Method_ is created to display a warning message if the query is matched. The result of the SQC process with all these entities and expected states required for query matches is shown in Figure 7.  
Figure 5: Overview of the process for creating the static NOP query chain.
# Augmented Reality for PCB Component Identification and Localization
## 5 Results
### Experiment 2
#### 5.2.3 Component Identification Accuracy and Confidence  
In this study, identification confidence refers to the proportion of correctly identified components averaged across participants for each component size category and viewing angle. It serves as a quantitative measure of how reliably users can locate and identify components using the AR interface. Components were grouped into three size categories: Small (Area \(\leq\) 0.05 cm\({}^{2}\)), Medium (0.05 cm\({}^{2}\) < Area \(\leq\) 0.15 cm\({}^{2}\)), and Large (Area > 0.15 cm\({}^{2}\)). The dataset included seven Small, four Medium, and one Large component. However, this size distribution was a consequence of the available PCB layout rather than a controlled design choice, resulting in an unintentional imbalance, particularly the presence of only one large component, which limits the generalizability of size-related findings.  
The grouped bar chart (Figure 16) shows that Medium-sized components maintained consistently high confidence scores across all angles, suggesting they are reliably identified regardless of viewing perspective. Small components exhibited a decrease in confidence at more oblique angles, with the lowest confidence observed at 45\({}^{\circ}\), indicating that identification becomes more difficult as visibility decreases. The single Large component demonstrated a trend of increasing confidence at steeper viewing angles. However, this observation remains preliminary and cannot support generalizable conclusions due to the limited representation of large components in the dataset. While the results overall suggest that both component size and viewing angle influence identification performance--medium-sized components exhibiting greater robustness and small components being more angle-sensitive--interpretations regarding large components should be viewed as indicative rather than conclusive.
# SBNNR: Small-Size Bat-Optimized KNN Regression
## 4 Proposed Method
### Instance Selection  
Instance selection plays a pivotal role in the development of more robust and generalizable models, particularly in instance-based methods like KNN. Its fundamental objective is the removal of redundant data, a process of paramount importance when dealing with small datasets. In such limited datasets, the efficient curation of instances becomes crucial, as individual data points can yield disproportionate influence, thereby significantly impacting both the accuracy and generality of the resulting model.  
In essence, instance selection serves as a strategic filter, sieving through the data landscape to identify the most informative and representative examples. This selective process not only streamlines computational efficiency but also guards against the adverse effects of overfitting. Small datasets are particularly susceptible to overfitting, where models may excessively adapt to idiosyncrasies within individual data points, leading to poor generalization on unseen data.  
By carefully curating a subset of instances, instance selection aims to strike a delicate balance between retaining essential information and eliminating redundancy. In doing so, it empowers models like KNN to better discern meaningful patterns, reduce sensitivity to noise, and enhance predictive performance, all while navigating the challenging terrain of data scarcity.  
Deciding on the best combination of instances leads to a search of a \(2^{m}\) space, where \(m\) is the total quantity of training data. Therefore, using evolutionary algorithms such as the bat algorithm can be an excellent way to search in this space.
## 2 System Description
### Microgrid Operation  
The MG operates in three modes (normal, islanded, and restoration). We consider an additional MG operation scheme known as \"preparation mode\", which is specifically designed to increase the resilience of the MG loads for external grid failures by the proactive scheduling of the MG's resources and devices (Figure 2).  
In its normal operating mode, the MG provides power to its loads with the lowest possible operating costs. Based on the predicted renewable energy generation and load, the MG determines the schedules for generating resources and power exchange with the external grid considering several physical constraints, such as the capacity of a point of common coupling and ramp rates of generating units. For the BESS, frequent charge/discharge cycles can be restricted in the normal operating mode to prevent the battery cells from degrading rapidly [24].  
MG operation during the event periods is referred to as emergency mode. The main objective of the MG in this mode is modified for supporting critical loads using local resources  
Figure 1: The MG model considered in this study.  
Figure 2: MG operation modes according to event phases [15; 19] and the schematic of the preparation mode of the MG operation.  
because the MG is separated from the external grid. The physical constraints of the generator should be considered, as in the normal mode. However, some constraints can be relaxed to handle emergency scenarios. For example, the restriction on the charge/discharge cycles of BESS should no longer be considered in the emergency operation mode as the survival of the load has a higher value than that of maintaining the BESS lifespan.  
The preparation mode of the MG operation is developed as a proactive microgrid management strategy to consider both the normal and emergency mode operation conditions that can effectively improve the survivability of loads. Under the warning of an external grid outage, the MG can enter the preparation mode and change the operating states, and it can generate levels of its resources for the ongoing power supply to the load after interruption. However, supporting all loads for the duration of the event may not be economical or even viable, given the constrained local resources of the MG. Therefore, the loads should be split into critical and non-critical loads and treated differently based on their importance in the preparation mode. Therefore, a requirement for satisfying the critical loads is a hard constraint of the problem, and meeting the non-critical loads is treated as a soft constraint. Violating a constraint incurs a penalty in the objective function.
# Simulation Analysis of Knee Ligaments in the Landing Phase of Freestyle Skiing Aerial
## 1 Introduction  
In the absence of trauma, the knee joint can operate effectively for decades while being subjected to high mechanical loads. The knees of athletes have a shorter life expectancy. After professional athletes retire, their knee joints often exhibit damage due to overwork. For example, the rate of knee joint injury in freestyle skiing aerialists in Chinese national team is close to 85% and higher in retired athletes. Studies have shown that instantaneous impact in the vertical direction will damage the cartilage of the knee joint, while long-term repeated impact will cause strain damage to the stress concentration region of the cartilage [1]. Other studies have shown that when the knee joint flexes at a certain angle, shear or torsion stress caused by instantaneous movement of the tibia may damage the cruciate ligaments [2; 3], while instantaneous inversion or erosion of the knee may cause the medial or lateral collateral ligaments to be damaged [4; 5]. In addition, the athletes' ankle joints are essentially locked in snowshoes, which do not provide sufficient cushioning at the moment of landing, resulting in greater impact force to be absorbed by the knee joints. For these reasons, freestyle skiing aerialists suffer a high rate of knee injuries.  
In order to establish the internal force within the knee joint, a number of researchers have conducted in vitro experiments and with cadavers [6; 7]. Other researchers have used the inverse dynamics technique to analyze knee movement [8; 9]. Most researchers choose finite element analysis [10], which is possibly a more objective method of obtaining the specific movements of components within the knee joint. In this study, finite element simulation method had been adopted to analyze the knee joint at phase of landing, so as to obtain the stress of cartilage and ligament during landing buffering.
# Complexity of Hamiltonian Cycle Reconfiguration
###### Abstract  
The Hamiltonian cycle reconfiguration problem asks, given two Hamiltonian cycles \(C_{0}\) and \(C_{t}\) of a graph \(G\), whether there is a sequence of Hamiltonian cycles \(C_{0},C_{1},\ldots,C_{t}\) such that \(C_{i}\) can be obtained from \(C_{i-1}\) by a switch for each \(i\) with \(1\leq i\leq t\), where a switch is the replacement of a pair of edges \(uv\) and \(wz\) on a Hamiltonian cycle with the edges \(uw\) and \(vz\) of \(G\), given that \(uw\) and \(vz\) did not appear on the cycle. We show that the Hamiltonian cycle reconfiguration problem is PSPACE-complete, settling an open question posed by [PERSON] et al. (2011) and [PERSON] (2013). More precisely, we show that the Hamiltonian cycle reconfiguration problem is PSPACE-complete for chordal bipartite graphs, strongly chordal split graphs, and bipartite graphs with maximum degree 6. Bipartite permutation graphs form a proper subclass of chordal bipartite graphs, and unit interval graphs form a proper subclass of strongly chordal graphs. On the positive side, we show that, for any two Hamiltonian cycles of a bipartite permutation graph and a unit interval graph, there is a sequence of switches transforming one cycle to the other, and such a sequence can be obtained in linear time.  
Keywords: bipartite permutation graphs; chordal bipartite graphs; combinatorial reconfiguration; Hamiltonian cycle; PSPACE-complete; split graphs; strongly chordal graphs; unit interval graphs +
Footnote †: journal:
## 3 Experiments and Results
### Custom Models
#### 3.3.1 SimpleNet  
We first build a NN relying on the standard 2D-Convolution operation. Aiming at the simplest model possible, we construct its architecture by avoiding skip connections, pooling operations, etc. Therefore, we rely solely on stacking multiple convolutional layers together. More in details, convolutional layers are concatenated, alternating between stride-1 layers and stride-2 layers, up until an 8 \(\times\) 8 pixels embedding is obtained. Stride-1 convolutional layers are straightforward convolutions that keep the resolution of the embedding untouched. On the other hand, stride-2 convolutional layers reduce embedding size by a factor of 2. The width of the NN--i.e., number of filters of convolutional layers--duplicates at each stride-2 layer, to avoid information compression issues. Finally, a global average pooling is applied to the 8 \(\times\) 8 pixels embedding, and a classification layer is added to obtain the prediction over the input image. SimpleNet architecture is shown in the upper section of Figure 1.
## 2 Theoretical Model and Hypotheses
### The Moderating Effect of Information Security Awareness  
Information security awareness (ISA) is defined as employees' overall understanding of potential information security-related issues and possible consequences and understanding what steps should be taken to address these issues [37]. ISA is the degree of users' protection of information systems, establishing security and protection mechanisms to ensure unpublished data sets or information [38]. When individuals or organizations encounter serious threats and loss of effectiveness in their protection systems, increased awareness of information security can alleviate system abuse or inadequate information security cognition. Marketing management can spread security notions to employees by training, communication, rewards, and participation, which can enrich employees' ISA [31]. In other words, once employees have more incentives, understanding, and direction to learn and follow their company's security instructions, they will have a higher sense of the importance of information security.  
ISA is a necessary component that effectively impacts users' ISI. [PERSON] et al. [37] presented new insights into individuals' security behaviors in a study involving 20 organizations from the U.S. and Ireland, which found that when employees had higher information security awareness, it led to higher security behaviors. ISA was essential in managing illicit security behaviors and intentions in these organizations. [PERSON] et al. [23] investigated how ISA affected individuals' ISI in a study in the Northwest U.S. that included 231 samples working at a university and found that security-aware employees were familiar with their security practices and guides, and through ISA, their ISI improved. Cultivating information security awareness can enhance employees' organizational security intentions and behaviors [24]. To test the moderating effects of ISA, the current study hypothesized the following:  
**Hypothesis 4.**_ISA positively moderates the relationship between ISM and ISI in the container shipping industry; the positive relationship is stronger when ISA is perceived to be higher than lower._  
**Hypothesis 5.**_ISA positively moderates the relationship between ISM and RSC in the container shipping industry; the negative relationship is weaker when ISA is perceived to be higher than lower._