esa-sceva/satcom-chunk-collection huggingface dataset:
# Unexpected-Behavior Detection Using TopK Rankings for Cybersecurity
## 4 Network User Profiling Using TopK Rankings  
In accordance with the NIST, an IDS that uses anomaly-based detection has profiles that represent the normal behavior of any of the following: user, host, network connection, or application. Then, it is compared to real-time activity in order to detect a significant difference [7].  
In [17], a profiling method is proposed based on building TopK rankings of accessed services from network traffic captured at the host. Each service is represented by the 3-tuple <remote IP address, transport protocol, remote port>. This profiling process is carried out within a secure environment where it can be guaranteed that the host is used only by the expected user and there is no malware, virus, trojan, or any other malicious software installed. This method produces a profile structure constituted by a list of TopKs denoting the normal behavior of a user at their computer.  
Each TopK in the profile represents the top \(K\) most accessed services based on total transferred bytes, during a timeframe \(f\). A new TopK is calculated every \(\Delta t\) seconds. Each TopK is overlapped with the previous ones as is illustrated in Figure 5.  
Additionally, this profiling method offers a mechanism to determine how similar a given TopK ranking is to the profile, returning a value in the range \([0.0\cdots 1.0]\), where 0.0 and 1.0 denote, respectively, totally different and identical.
## 4 Experimental Evaluation  
The capabilities of the developed IoT device and the entire environment for constant monitoring of beehives with the real-time detection of varroosis through the analysis of video data stream were evaluated in a series of experiments. Within these experiments we investigated:  
* The effectiveness of bee identification (segmentation of ROIs, Section 4.1) and _Varroa destructor_ detection (the classification of bee images, Section 4.2);
* Time performance of both operations (Sections 4.3 and 4.4);
* The impact of camera resolution on both detection processes (Section 4.5);
* Device resources consumption (Section 4.6).
## 2 Materials and Methods
### Discrete Wavelet Transform (DWT)
This splits detailed spaces to yield more prime decomposition. [PERSON] (1992) [52] explained the wavelet packet transformation equation as the following:  
\[\left\{ \begin{array}{c}v_{2i}^{j}(x)=\sqrt{2}\sum\limits_{k}h_{k}v_{i}^{j}(2 x-k)\\ v_{2i+1}^{j}(x)=\sqrt{2}\sum\limits_{k}g_{k}v_{i}^{j}(2x-k)\end{array} ;\;i=0,1,2, \ldots and\;k=0,1,\ldots m\right. \tag{22}\]  
where the two filters \(h_{k}\) and \(g_{k}\) are associated with the scaling function \(\varphi_{jk}(x)\) and the base wavelet function \(\Psi_{jk}(x)\)[49]. Wavelet entropy represents the non-uniformity of states, which is an ideal parameter for measuring the ordering of unsteady signals [53]. It can also provide information about the dynamic process and the signal potential. When the coefficient matrix of the wavelet transformation is represented by a probability distribution, the calculated wavelet entropy represents the randomness of the pattern [54]. The wavelet packet decomposition is an orthogonal function, which means the total energy entropy of the original signal should be the summation of the coefficient energy entropy [32]. The wavelet entropy energy can be defined as in Equation (23), where \(P_{i}=E_{i}/\sum_{i=1}^{n}E_{i}\) is the percentage of coefficient energy of the original signal [55]:  
\[EN=-\sum\limits_{i=1}^{n}P_{i}logP_{i} \tag{23}\]  
In this study, the norm entropy was used to analyze the pressure signal. In an orthonormal basis entropy, \(s\) is the signal, \(s_{ij}\) is the coefficient of \(s\), and E is the entropy function such that \(E(0)=0\) and \(E(s)=\sum_{i}E(s_{i})\). This entropy formula was used in MATLAB to calculate norm entropy. The concentration in \(l^{p}\) is a norm where \(1\leq P<2\). Now, \(E(s)=\left|s_{i}\right|^{p}\) so \(E(s)=\sum_{i}\left|s\right|^{p}=\left|\left|s\right|\right|_{p}^{p}\) for the norm entropy method [48]. The wavelet entropy can find small or abnormal frequencies, and wavelet entropy can thus find different characteristics of multiphase flow. Therefore, this study aims to characterize a two-phase flow pattern using norm entropy based on wavelet packet decomposition of the pressure signal. This method followed the steps shown in Figure 5.  
Wavelet Packet Analysis of the Experimental Data  
In this study, a pressure transducer provided time-domain pressure fluctuations, which were analyzed using wavelet packet analysis. As mentioned previously, the existing experimental setup only provides slug flow, and the dispersed bubble flow regime and pressure signal also show specific characteristics for each kind of flow regime. The data acquisition system collected pressure transducer  
Figure 4: Wavelet packet analysis decomposition tree. signals with a sampling frequency of 100 Hz. Overall, 10,000 data points were used in 1-D wavelet analysis using MATLAB (details can be found in the above sections). The MATLAB processed data were taken into account for further analysis.
## 3 Results  
### Search, Screening, and Quality Assessment Outcomes  
A total of 4487 studies were retrieved across the databases (Web of Science, PubMed, SPORTDiscus, and Scopus; Figure 1). In addition, 49 studies were identified via other methods. Following the removal of duplicate records (\(n=1727\)), a total of 2760 studies remained for screening. In turn, 780 studies were excluded based on their title and abstract, and 1980 full-text articles were sought for retrieval. Of these, 221 records were inaccessible, and 1009 were excluded using Rayyan. A total of 971 studies were excluded based on full-text review. Consequently, 17 studies were included in the systematic review.  
Table 3 provides the quality scores of the 17 included studies, where nine studies were classified as'very good' and eight studies were classified as 'good' [25].
# Link Connectivity and Coverage of Underwater Cognitive Acoustic Networks under Spectrum Constraint
## 3 System Model  
Herein, we first give the problem definition in Section 3.1. Then, we introduce the network model in Section 3.2, and the underwater channel model in Section 3.3.
# Multi-Mobile Agent Trust Framework for Mitigating Internal Attacks and Augmenting RPL Security
## 4 Experimental Works and Results
### Experimental Setup  
The proposed model was simulated using the Cooja network simulator. A lightweight SLF was utilized as the trust model in the fog layer. A modified Cooja platform based on Contiki OS was used for the implementation [12]. This OS was installed on a laptop with an Intel(r) CoreTM i5-3317U CPU running at 1.70 GHz with 8 GB RAM. The suggested model was tested with various scenarios, including small and large networks. With the same initial energy of 100 J, we set up a network with 30 to 120 sensor nodes. Data and control messages were randomly sent between nodes. The number of malicious nodes ranged from 3 to 12 with a ratio of 1:100. Table 2 summarizes the parameters used in the simulation and experimentation along with the set values.
# Design and Applications of GLANCE: GLanceable Alarm Notification for a User Centered Experience
## 3 Results of the 1 st Macro-Phase: The GLANCE Project
### Third Iteration: Final Release of the Proof-of-Concept and Definition of a General Formal
#### 3.3.2 GLANCE Generalization
Arrows at the extremes of the synthetic component act as affordances for its behavior (anyway, in the default ambient behavior the notifier would autonomously and continuously scroll the list, moving from element to element). This scenario corresponds to monitoring a set of elements S = {E\({}_{i}\), \(i\) = 1,..., n}, where each E\({}_{i}\) acts as a source of multiple notifications and can hence be viewed as an alternative solution for the hybrid situation of Case 3 in Section 3.2.2: The advantage is the absence of limit in the number of scrollable elements, but its disadvantage is the loss of a global overview and the lack of direct access to individual regions. It is worth noting that the notifier allows one to scroll sets of elements (each managed as in the basic model discussed for network management), according to two-level multichannel scenarios;* The display in Figure 9 illustrates the use of _display tabbing_, showing notifications of incoming email, organized in categories (e.g., inbox, spam, work, etc.) represented by tabs. This solution has the advantage of offering a glanceable global overview and guaranteeing an easy direct access to individual categories (tabs) in an interactive tool (again, in the default ambient behavior the notifier would autonomously and continuously move from tab to tab). The disadvantage is the upperbound on the number of categories determined by the limitation on the display width. Another disadvantage can be the possible contiguity of red/orange and green tabs, which would require additional visual cues for tabs with major/critical notifications (e.g., thicker borders);
* The display in Figure 9 illustrates the combined use of the two mechanisms in a two-level multichannel scenario in which individual channels are associated to tabs and are further structured into subcategories handled by the horizontal scrolling of the display. It must be observed that the utilization of such mechanisms allows one to overcome the limitations on the threshold given in Table 6. For example, a TLC network partitionable in subnetworks may be managed either by assigning subnetworks to different tabs or by simply scrolling the subnetworks. In summary, the two additional mechanisms of horizontal scrolling and tabbing extend GLANCE monitoring/overviewing capability and allow one to manage structured notification spaces. The features of GLANCE can be the basis for customizable multichannel notifiers built and configured onto multisource prioritized notification dispatchers. As the discussion about the example scenarios showed, configuration choices should be on a case-by-case basis, taking into account the characteristics of the monitored notification sources and the degree of complexity of the notification space, in order to select the best combination of overviewing mechanisms.
# Wind Source Localization System Based on a Palm-Sized Quadcopter
## 4 Wind Source Localization Experiment  
### Experimental Condition  
The WSL experiments were conducted in an indoor area depicted in Figure 4a, measuring 4.0 m \(\times\) 3.0 m \(\times\) 3.0 m. The experimental area was equipped with a ceiling camera (BSW50 MBK, Buffalo Inc., Aichi, Japan, 30 fps) capable of observing the entire area and recording the trajectory of the quadcopter. The recorded quadcopter videos were analyzed using a deep-learning-based video analysis tool [24].  
Because this study is the first step in WSL, the problem was simplified and limited to a search in a two-dimensional plane. Therefore, the altitudes of the quadcopter and the wind source were set at the same height. The altitude was approximately 1 m above the ground, and the quadcopter began its search 2 m away from the wind source.  
A fan (DR-HTF007, Dreo, Hong Kong, China) was installed as the wind source, configured with the following three scenarios:  
* Scenario 1: Windless condition (fan powered off);
* Scenario 2: Wind speed under 2 m/s;
* Scenario 3: Wind speed over 2 m/s.  
The wind speeds for Scenarios 2 and 3 are the set values for the wind source. To figure out the environmental wind profiles for Scenarios 2 and 3, wind speed maps were constructed according to the procedure shown in Figure 4b. The wind speed distribution for each scenario was measured in the x and y directions at 0.5 m intervals using a commercially available wind sensor (testo 405i, Testo SE & Co. KGaA, Titisee-Neustadt, Germany) and calculated by linear interpolation. Wind speed was measured at each measurement point for 60 s, and the mean value was defined as the wind speed at that point. Environment wind maps at the altitude where the quadcopter conducted localization for Scenarios 2 and 3 are shown in Figure 4c,d. Figure 4c,d clearly show that wind speeds decrease with distance from the wind source in both scenarios. As shown in Figure 4c,d, the wind speed decreases as one moves away from the wind source in both scenarios. The wind speed at 2 m, which is the starting point of the search, is approximately 0.5 m/s, which is relatively  
Figure 3: Flowchart of the constructed anemotaxis algorithm and the definition of the movement direction of the quadcopter.  
low in both scenarios. Ten repeated experiments were conducted under these conditions. Successful localization was defined as the quadcopter moving to a position sufficiently close to the wind source. Localization failure was defined if more than 2 min elapsed from the start of localization or if the quadcopter moved outside the exploration area. The quadcopter system is evaluated based on metrics including localization success rate, localization time, and upwind movement rate derived from the experimental results.
## 3 Methodology
### Proposed Framework
#### 3.1.1 Modified Multi-Head Self-Attention (MMHSA)  
The utilized modified multi-head self-attention (MMHSA) [53] module extracts low-frequency information with a reduced cost compared to a typical MHSA module. This module is composed of h parallel self-attentions to derive features from the input X, with subspaces of x\({}_{1}\), \(\ldots\), x\({}_{\text{h}}\) to be fed to each head. The self-attention (SA) for input X can be calculated using Query, Key, and Value layer parameters (W\({}^{\text{Q}}\), W\({}^{\text{K}}\), W\({}^{\text{V}}\)) as follows:  
\[\text{SA}(\text{X})=\text{Attention}(\text{X}\cdot\text{W}^{\text{Q}},\text{ P}_{\text{s}}\ (\text{X}\cdot\text{W}^{\text{K}}),\text{ P}_{\text{s}}\ (\text{X}\cdot\text{W}^{\text{V}})), \tag{1}\]  
The layer parameters in typical attention can be calculated using \(softmax\Big{(}Q\ K^{T}/\sqrt{d}\ V\Big{)}\), where d is the hidden dimension of the transformer. In the modified version in Equation (1),  
Figure 2: (**a**) Overall architecture of the proposed Wet-ConViT model, (**b**) convolutional block, and (**c**) transformer block.  
there is an addition P\({}_{8}\) parameter, which involves average-pooling downsampling operation with stride s. This parameter reduces the spatial dimension before the attention module and reduces the computational cost as well.  
By concatenating the SAs of the parallel subspaces, the MMHSA can be calculated as follows:  
\[\text{MMHSA(x)}=\text{concatenate(}\sum_{i=1}^{\text{h}}\text{SA}_{\text{i}}( \text{x}_{\text{i}}))\text{W}^{\text{O}} \tag{2}\]  
where W\({}^{\text{O}}\) is the output layer parameter. To further reduce the computational cost, a shrinking ratio parameter reduces the data dimension within a pointwise convolution. These modifications can improve the efficiency of the self-attention module, which is a critical factor in the computational complexity of the model.
## 2 Optical Inspection Technologies in Railways
### Sensor Systems
#### 2.3.9 Wheels  
Wheels are an essential component of the railroad system. Various defects such as cracks, fatigue, scaling, spalling, flat spots, cavities, and indentations can occur [68]. Some damage types are shown in Figure 9. Repeated monitoring of these signs of degradation is therefore important in order to counteract the risk of failure. Laser scanners and cameras are frequently used here as sensors. Ref. [69] shows the combined use of laser profile measurements and evaluation using machine vision. Overall, the combination of different sensors and evaluation using AI has improved the detection of surface defects. The W-Inspect system from MERMEC uses high-resolution cameras and ad hoc lighting for automated inspection. The surface of the wheel is inspected while the train is moving [46]. SelectraVision's laser and camera-based system inspects train wheels up to a driving speed of 150 km/h. The system is locally installed and able to measure the profile of a wheel with an accuracy of 0.5 mm [47]. A complete overview of wheel testing is given in [70].  
By using camera sensors, it is also possible to detect defects without thickness or depth. Laser-based systems, on the other hand, make it possible to detect surface wear. The use of AI can support the automation of detection here, see also Figure 14 [69]. Wayside camera/laser systems (except when they are permanently installed) have a clear advantage over other counterparts as strain gauges because they can be relocated. However, these systems need proper calibration before functioning [70]. Changing light conditions represent a major challenge. Reflections, shadows, and motion blur make detection difficult.
## 4 Implementation of RUL Prediction Based on Proposed Method
### Experimental Verification and Analysis
#### 4.4.2 Comparison Before and After Feature Clustering Reduction
According to Table 8, compared with the sensitive feature set, the MAE of the optimal feature set was reduced by 27.8%, and the RMSE was reduced by 25.6%. Therefore, feature redundancy removal from the sensitive feature set can effectively improve the prediction accuracy of the bearing RUL in the later stages. This further confirms the effectiveness and necessity of the proposed method.
## 2 Evolution of AI-Driven 6G Network Architecture
### RAN Architecture Evolution
Figure 2 illustrates potential options for computing resource exposure based on GPC and dedicated hardware. Dedicated hardware is typically designed for specific communication or AI functions, and its computing resources are not able to be exposed. In this case, AI capabilities can only be used to optimize the performance of the RAN itself. On the other hand, GPC-based solutions leverage virtualization technologies to flexibly construct virtualized communication and AI functions from general-purpose computing resource pools. This approach allows for the dynamic adjustment of computing resource allocation ratios according to changing workload. Additionally, idle computing resources can be exposed to third-party applications, creating new revenue models and improving resource utilization. Meanwhile, data exposure requires greater caution. Currently, there are no high-quality datasets specifically for wireless networks. Most of the existing datasets were either generated through simulations or collected as long-term statistical data via network management systems. This limitation arose for several reasons: (1) Lack of standardized interfaces: The industry lacks unified standardized open interfaces, making cross-device data collection challenging. Although O-RAN has defined standardized open interfaces such as O1, O2, E2, and A1, not all vendors have adopted the O-RAN model. (2) Privacy and security concerns: Wireless data, especially user-related data, inherently involve privacy and security requirements. Without robust security mechanisms, opening such data to third parties may lead to risks such as unauthorized tampering and information leakage. (3) Ownership disputes: Data openness may involve the interests of multiple stakeholders, including equipment vendors, operators, and service providers. Disputes over data ownership and usage rights may arise. Addressing these challenges is crucial for unlocking the potential of AI in the next-generation RAN. The exposure of computing resources and data will be pivotal in determining the application scope and influence of network AI. Figure 2: Possible computing resource exposure options based on GPC and dedicated hardware.
# Path Planning of Intelligent Mobile Robots with an Improved RRT Algorithm
## 2 Improved RRT Path Planning Algorithm
### Collision Detection and Kalman Filter Redundancy Point Elimination
The black solid line is the Kalman filtered path: Start \(\rightarrow\) p\({}_{4}\rightarrow\) p\({}_{6}\rightarrow\) Final, and the gray dashed line is the initial path, where it can be seen that the number of nodes between the start point and the target point is reduced significantly, which shortens the frequency of nodes adjusting their own position in path planning. q is the collision detection node of node \(p_{3}\), which adjusts its position through collision detection, and combines with the variable steering angle strategy to get the next child node \(p_{4}\). In the process of path planning, in addition to ensuring the path is optimal, it is also essential to guarantee the smoothness of the path. The higher the smoothness, the higher the controllability. This paper uses Bezier curves for path smoothness optimization. Bezier curves can generate smooth, continuous curves, reducing sharp corners and discontinuities in the path, while also avoiding unnatural movements or acceleration changes. In robot path planning, smooth paths can reduce sudden changes in the robot's motion, thereby improving the stability and accuracy of movement. In this paper, a second-order Bezier curve is used to give three control points, \(P_{1}\), \(P_{2}\), and \(P_{3}\), where \(P_{1}\) and \(P_{2}\) form a first-order Bezier curve while \(P_{2}\) and \(P_{3}\) form a first-order Bezier curve, i.e:  
\[ \begin{cases}P_{1,2}=(1-t)P_{1}+tP_{2}\\ P_{2,3}=(1-t)P_{2}+tP_{3}\end{cases}  \tag{9}\]  
On the basis of generating two first-order points through Equation (9), a second-order Bessel point \(p_{3}^{2}\) is generated through Equation (10). \[p_{3}^{2}=(1-t)P_{1,2}+tP_{2,3} \tag{10}\]  
to obtain a second-order Bezier curve:  
\[p_{3}^{2}=(1-t)^{2}P_{1}+2t(1-t)P_{2}+t^{2}P_{3} \tag{11}\]  
Figure 4: Principle of redundant node removal and collision detection. With t taken at equal intervals from 0 to 1, the path of the final curve after second-order Bezier smoothing is shown in Figure 5. In this figure, the time values from (a) to (d) are 0.182 s, 0.479 s, 0.708 s, and 1 s.
# Reimagining Historical Exploration: Multi-User Mixed Reality Systems for Cultural Heritage Sites
## 3 Results and Discussion
### Limitations  
While the present work offers significant advancements in the use of mixed reality (MR) for cultural heritage, it is not without limitations. One of the primary challenges lies in the reliance on GPS-dependent localization for outdoor environments, which can be affected by signal accuracy, especially in urban areas with tall buildings or in remote regions. This limitation may result in minor misalignment between the virtual and real-world models, affecting the overall user experience. Additionally, the non-location-based system, while useful for indoor environments, lacks the contextual accuracy provided by GPS-based methods, potentially reducing the sense of immersion. Furthermore, the current implementation does not incorporate advanced haptic feedback or environmental interactions, which could further enhance the sense of presence and engagement.  
Future work can address these limitations by integrating more robust localization methods, such as combining GPS with Simultaneous Localization and Mapping (SLAM) [28] or with geo-referenced image matching technologies for improved precision [29]. Expanding the application to include adaptive virtual guide behaviors, such as real-time responses to user queries or actions, could make the experience more interactive and personalized, or a real-life guide connected to the app with live chat could be used [30]. Furthermore, conducting larger-scale user studies with diverse demographics will provide deeper insights into user preferences and areas for improvement. By addressing these aspects, the proposed system could evolve into a more versatile and comprehensive platform for the preservation and promotion of cultural heritage through MR technologies.