esa-sceva/satcom-chunk-collection huggingface dataset:
# Wavelet-Enhanced YOLO for Intelligent Detection of Welding Defects in X-Ray Films
## 3 Materials and Methods
### Network Architecture  
Given that YOLOv11 is accurate, efficient, and widely recognized, we selected it as the object detection model. The network architecture used in this study is based on the YOLOv11n framework [44], with a modification that replaces some of the traditional convolutional layers with wavelet transform convolutions (WTConv) [45] in the backbone. We refer to this modified model as WT-YOLO. This change is designed to enhance feature extraction capabilities, especially for the complex and elongated weld defects in the dataset. The architecture of the proposed method is illustrated in Figure 2.  
Figure 2: Structure of WT-YOLO for welding defect detection.  
In the original YOLOv11n architecture, the backbone consists of a series of convolutional layers and residual blocks, followed by a detection head that predicts bounding boxes, class probabilities, and objectness scores. The detection head is responsible for classifying defects and localizing them within the image. During training, the network uses a combination of mean squared error for bounding box regression and cross-entropy loss for classification.  
WT-YOLO's backbone is designed to extract hierarchical features from the input images and is based on a modified YOLOv11n architecture. The backbone begins with a standard convolution layer followed by a WTConv layer, which helps capture low-level features while integrating wavelet-based transformations for enhanced multi-scale feature extraction. As the network progresses, subsequent stages alternate between regular convolution and WTConv layers, allowing the model to learn progressively more complex and multi-scale representations of weld defects. The backbone's final stage incorporates a Spatial Pyramid Pooling (SPPF) layer to aggregate features at various scales, followed by a C2 PSA layer that refines these features before passing them on to the head for further processing. This hierarchical processing enables the network to gradually reduce spatial resolution while increasing the number of feature channels, which in turn helps the model learn rich and diverse representations of the weld defects.  
The head of the model leverages the features extracted by the backbone for prediction tasks. It begins with an upsampling operation that doubles the spatial resolution of the feature maps, followed by concatenation with the corresponding features from the backbone. This concatenation allows the network to retain high-resolution details from earlier layers. The upsampled features are then refined through a series of convolutional layers, processing them at different scales. The head concludes with a detection layer that outputs predicted bounding boxes and objectness scores. Overall, WT-YOLO architecture, with its combination of WTConv in the backbone and a multi-scale detection head, is optimized to detect fine-grained defect features efficiently, which is crucial for accurate weld defect detection across varying defect types and sizes.
## 4 Wavelet Transform
### Implementation Process  
As depicted in Figure 6, the initial step of the DWT process commences with data acquisition and preprocessing. Since the quality of the input data significantly influences the subsequent analysis, it is imperative to preprocess the data to mitigate the adverse effects of noise. The second step involves applying the DWT to obtain discrete wavelet coefficients. This step enables the decomposition of the input signal into multiple frequency bands, facilitating detailed analysis. Subsequently, the original waveform is reconstructed using the obtained discrete wavelet coefficients. While these coefficients provide valuable  
Figure 4: Discrete wavelet decomposition process.  
Figure 5: Inverse discrete wavelet reconstruction process.  
insights, especially at lower frequencies, they represent only a portion of the signal. To extract complete and meaningful features, the original signal within each frequency band is reconstructed, allowing for a comprehensive analysis at the corresponding resolution and capturing full-length characteristics rather than fragmented ones. Finally, the reconstructed signals are evaluated using a set of finite numerical features to assess the condition of the electrical equipment. To visualize the results, the energy ratio formula is employed.
## 4 Results and Discussion
### Numerical Simulation of Precipitation Infiltration in the Field Test Site  
#### 4.2.1 Hydrogeological Conceptual Model  
The soil physical structure at depths of 0-1500 cm in the test site could be divided into nine layers (Table 1) as follows (Figure 8): (1) 0-20 cm was a cultivated soil layer; (2) 20-120 cm was a silty Ioam layer; (3) 120-260 cm was a silty Ioam layer; (4) 260-440 cm was a silty Ioam layer; (5) 440-570 cm was a silty clay layer; (6) 570-1160 cm was a silty Ioam layer; (7) 1160-1300 cm was a silty clay layer; (8) 1300-1400 cm was a loam layer; and (9) 1400-1500 cm was a sandy Ioam layer.  
The thick cohesive soil was recharged by rainfall infiltration, and the land use type was bare land with no crops at the field test site. Soil water was excreted into the atmosphere in the form of evaporation from within a depth of 200 cm [13]. The change in cohesive soil moisture in this depth range was affected by both precipitation and evaporation. The change in cohesive soil moisture in the vadose zone at the depth range of 200-500 cm was only affected by infiltration from cohesive soil layers above 200 cm by piston flow. Due to the high clay content and low permeability of cohesive soil in the depth range of 440-570 cm, there was a certain retardation effect on the precipitation infiltration. Due to this retardation effect and the stable water flux and infiltration rate of the soil, the layers at the depth range of 500-1100 cm were named the unsaturated-saturated variable zone. The depth of the capillary saturation zone roof was affected by the micro-confined groundwater level. The cohesive soil water content in this zone was affected by infiltration downward  
Figure 8: Vertical hydrogeological conceptual model of the field test site.  
from the upper soil layer and infiltration upward from the lower soil layer by means of piston flow in the saturated zone caused by groundwater level elevation. There was a stable saturated zone of micro-confined groundwater in the range of 1100-1500 cm; the cohesive soil water content was affected by infiltration downward from the upper soil layer and infiltration upward from the lower soil layer by means of piston flow.
## 4 Testing  
### Data Collection Setup  
A set of GPS L1 signals was collected to test the proposed calibration approach. In this data collection, an array of three antennas (Antcom L1 GPS antennas due to their relatively stable phase centre) mounted on the top of a vehicle was employed. The three antennas, after amplification, were connected to a three-channel National Instrument (NI) RF front-end in which the channels are synchronized through a common clock. The received signals were then sampled, down converted and stored for post processing. One of the antennas was chosen as the reference antenna and the origin of the body frame coordinate system. The received signal at this antenna was split into two branches and the second branch was connected to a NovAtel SPAN(tm) LCI system (NovAtel Inc., Calgary, AB, Canada), which includes a NovAtel SPAN(tm) enabled GNSS/INS receiver (SPAN SE) and a tactical grade IMU LCI. The low noise and stable biases of the accelerometer and gyro sensors make the IMUwell suited for ground or airborne survey applications. IMU measurements were sent to the receiver where a blended GNSS/INS position, velocity and attitude solution was generated at a rate of 200 Hz [17]. Raw GPS data was also collected under LOS (line of sight) conditions using a NovAtel ProPak-V3 receiver as a base station (fixed on the rooftop of a building located on the campus of the University of Calgary) to provide the navigation data bits and differential positioning. The data collected by SPAN LCI system and the base station file were then fed to the NovAtel Inertial Explorer(r) post-processing software to produce reference positions, velocities and high accurate orientation components.  
The data collection setup and the test trajectory are shown in Figure 4. In order to operate under an open-sky environment, a parking lot was chosen for the data collection. This parking area is almost surrounded by empty fields (low multipath). Circular motion was performed for the calibration process to cover all azimuth angles. In order to cover all possible elevation angles, the circular motion should be repeated during several time intervals with long enough separations to allow satellites to move significantly in the sky.
# Modeling a Pre-Touch Reaction Distance around Socially Touchable Upper Body Parts of a Robot
## 1 Introduction  
Touch interaction with other people provides various positive effects [1; 2; 3; 4; 5; 6], but, due to the COVID-19 pandemic, such interaction is physically and socially limited in the world. In this context, using social robots as a partner of touch interaction is a promising approach to cover the lack of human-human touch interaction since past studies reported several positive effects of such interaction [7; 8; 9; 10; 11; 12; 13; 14; 15]. Even if the effects of touch interaction with such social robots fail to fulfill the needs of interaction with actual people, people's negative situations can be mitigated.  
As described above, many research works reported the effectiveness of touch effects in human-robot interactions (HRIs). However, these studies mainly focused on after-touch situations; dealing with pre-touch situations has received less focus. A few past research works focused on the pre-touch reaction distance for a robot [16] and a virtual agent [17], although they focused on faces. Therefore, knowledge about pre-touch interaction remains limited. Based on human science literature, a part of the upper body (i.e., the shoulders or arms, including elbow and hands) is mainly used in touch interaction with others [18]. Therefore, pre-touch interaction should consider such touchable upper body parts for natural touch interaction.  
We regard pre-touch interaction research as an extension of proxemics in touch interaction contexts. People generally keep a certain distance from others, such as keeping a personal distance [19] in conversation interaction contexts, and such knowledge is well known. Many social robots refer to proxemics' knowledge to control their positions wheninteracting with people [20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33]. Inspired by the idea of proxemics, our ultimate goal is to establish the basis of proxemics in the context of pre-touch interaction as pre-touch proxemics. Such basic knowledge will be important for social robots that physically interact with people.  
This study investigated the pre-touch reaction distance from typical touchable upper body parts. For this purpose, first, we conducted data collection to gather people's pre-touch reaction data. Next, we conducted a statistical analysis of the collected data and implemented a pre-touch reaction model to our android robot as shown in Figure 1. Thus, this study aims to identify the minimum comfortable distance in human-human touch interaction around the upper body.
# LSTNet: A Reference-Based Learning Spectral Transformer Network for Spectral Super-Resolution
## 1 Introduction
Specifically, the framework of the proposed method comprises a reference-based super-resolution (RefSR) strategy, in which the spectral structure of a reference HSI can be transferred, through a learning-based approach, in order to create a reasonable reconstruction spectrum. First, in contrast to traditional CNN-based SSR methods, the proposed method couples an MSI with an HSI (Ref-MSI and Ref-HSI, respectively) and utilizes the spectral information to enforce spectral fidelity. More specifically, a spatial attention module is used to compute the spatial relevance between the input MSI and Ref-MSI, and the related spectral structures are transferred from the Ref-HSI to the reconstruction backbone module. Second, multi-level residual-in-residual channel attention is used to reconstruct a backbone module in order to make full use of the global and local residual learning, thereby allowing the information to pass quickly from the bottom to the top level. Furthermore, a channel attention mechanism is used to enhance the channel-wise adaptive learning, through use of which the model can reallocate the weights along the spectral dimension. The main contributions of the proposed methods are summarized as follows:  
1. A novel learning spectral transformer network (LSTNet) for SSR is proposed, which introduces the transformer architecture into the spectral super-resolution problem. More specifically, the LSTNet is composed of a spectral reconstruction module (SRM) and a spectral transformer module (STM), which learn the prior spectral information and reference spectrum. 2. The proposed STM acts as a bridge to transfer the spectral structures learned from the reference image through the attention mechanism. In the STM, a spatial relevance between input MSI and reference MSI was computed to migrate the most relevant spectrum and the spectrum synthesized by the relevance weights, fusing the spectrum from the reference image into an SRM. 3. The proposed SRM learns the prior spectral information of the training data using a channel attention mechanism to adjust the weights between the feature image bands adaptively. Instead of using an average pooling strategy to calculate the feature statistics, dual-channel attention is used to enhance the diversity of features.
## 3 Results
### EEG Data
#### 3.2.9 Result of LBP  
As depicted in Figure 13, a more negative-going deflection for genuine context was observed in the center and left side of the frontal lobe within the time window between 300 and 1000 ms. A repeated measures ANOVA was executed on the time window with factors of channel (60) \(\times\) context (genuine, fake) \(\times\) LBP (Class I, Class II). The findings indicated that the interaction of channel \(\times\) LBP was significant [F (59, 649) = 2.432, \(p\) < 0.01]. Further analysis disclosed that in the FPZ channel of the prefrontal lobe, in comparison to the Class I LBP, the Class II LBP would evoke a more negative amplitude [F (1, 11) = 6.712, \(p\) = 0.025] and a comparable waveform also manifested in the FP1 channel [F (1, 11) = 8.869, \(p\) = 0.013], AF3 channel [F (1, 11) = 9.097, \(p\) = 0.012], and F5 channel [F (1, 11) = 6.195, \(p\) = 0.030]. No other effect was significant.
## 4 Experiments and Analysis
### Ablation Experiments  
For a deeper assessment of how the suggested modules affect the algorithm's efficiency, RetinaNet, YOLOv8m, and YOLOv11m were chosen as the core networks, and we executed evaluation activities on the dataset we proposed. Table 4 shows the changes in various detection metrics after adding different modules to the algorithms. Our suggested modules are observed to markedly enhance performance in all three baseline scenarios, with the YOLOv11m algorithm showing the most significant enhancement, as shown in Figure 8. Employing a multi-scale channel attention system, the iAFF module focuses on both large targets distributed globally and small targets distributed locally, leading to a notable enhancement in precision (an increase of 6.8%)
# Online Intrusion Scenario Discovery and Prediction Based on Hierarchical Temporal Memory (HTM)
## 2 Materials and Methods
### The Intrusion Scenario Discovery and Prediction Framework
#### 2.2.1 Intrusion Action Clustering  
To simplify the associations between actions, the online action clustering is introduced, which can reduce the classes of actions. The definition of intrusion action is modified for the consequential processes.  
Intrusion action, which refers to a group of attack types derived from the raw IDS alerts, can be denoted as a tuple \(A\): \(<\)time, types, cid, srclp, dstlp\(>\). The _time_ field indicates the creating time, the types field contains a group of alert types expressing the same malicious behavior, the cid field indicates the class id, and the fields srclp and dstlp indicate the source IP address and the destination IP address, respectively. The actions are extracted by the intrusion action extracting process.  
The intrusion actions can be compared with each other based on the similarity of the types field. In this paper, the edit distance of two strings is used to calculate the similarity of the actions. Consider two intrusion actions \(A\) and \(A^{{}^{\prime}}\), the types field can be denoted as \(T(X)=\{t_{n}|t_{n}\in X\), \(n=1,2,\ldots,|X|\}\), the similarity of two attack types which are strings can be calculated with Equation (1):  
\[\Delta(tx,ty)=\frac{1}{\mu LD(tx,ty)+1}\qquad(0<\mu\leq 1) \tag{1}\]  
where \(LD\) is the Levenshtein Distance function that returns the distance of two attack types: \(t_{x}\) and \(t_{y}\). The parameter \(\mu\) is a factor that determines the capacity of a potential cluster. Thus, the similarity of action \(A\) and \(A^{{}^{\prime}}\) is calculated with Equation (2):  
\[sim(A,A^{{}^{\prime}})=\frac{\sum_{k=1}^{p}\Delta(A(t_{k}),A^{{}^{\prime}}(t_ {k}))}{p} \tag{2}\]  
where \(A(t_{k})\) indicates the \(k\)th attack-type of \(A\), and \(p=max\) (\(|A|\), \(|A^{{}^{\prime}}|\)), if _sim_ (\(A\), \(A^{{}^{\prime}}\)) = 1, \(A\) is the same as \(A^{{}^{\prime}}\). Based on the _sim_ function, online clustering can be achieved. The _cid_ field will be set to the cluster id for each action in the same cluster.  
For a cluster \(C=\{A_{k}|k\in Z,\ 1\leq k\leq\mid\)C\(\mid\)}, the average similarity between an action \(X\) and cluster \(C\) can be calculated with Equation (3):  
\[\begin{split} avgsim(X,C)=\frac{\sum_{k=1}^{n}sim(X,C[k])}{n} \end{split} \tag{3}\]  
where \(C[k]\) denotes the \(k\)th action in cluster \(C\). If the value of \(avgsim\) (\(X\), \(C\)) is higher than the threshold, then \(X\) will be put into cluster \(C\), otherwise, a new cluster will be created.  
Based on the equations described above, the actions with similar semantics can be merged into one class, and the number of actions will be reduced significantly.
## 5 Swarm-Based Solution
### Routing Problem
#### 5.2.2 Heuristic Information Computation  
A final environmental impact metric is taken into account to ensure that the routing protocol is not exposed to environmental impact, which helps select cluster heads in suitable surroundings and bypasses harmful places to a certain extent. Thus, the following heuristic information is calculated by the final environmental impact as follows:  
\[\eta_{xy}^{2}(t)=\frac{EIM_{y}^{i}(t)}{\sum\limits_{l\in FCHN_{x}}EIM_{l}^{i}(t)} \tag{69}\]  
The candidate relay node with a higher value \(\eta_{xy}^{2}\) has less environmental influences on urgent data packet delivery and is more likely to be chosen.  
To guarantee timely delivery of urgent data, pick the forwarding node with the greatest relaying speed among candidates. Relaying speed is heuristic information calculated by Equation (70).  
\[\lambda_{xy}^{2}(t)=\frac{Rd_{y}(t)}{\sum\limits_{l\in FCHN_{x}}Rd_{l}(t)} \tag{70}\]  
The candidate relay that has a higher value of \(\eta_{ij}^{1}\) offers more speed in the transmission of real-time data and has more chance of being the next relay. Selecting the neighbor node with the highest relaying speed at each hop will reduce the delivery time.  
The suggested energy consumption load metric is heuristic information that is considered since energy consumption balance is a critical difficulty in designing an energy-efficient routing algorithm for WSNs.  
\[\beta_{xy}^{2}(t)=\frac{ECL_{y}(t)}{\sum\limits_{l\in FCHN_{x}}ECL_{y}(t)} \tag{71}\]  
The neighbor node with a higher value of \(\beta_{xy}^{2}\) has a higher residual energy after sending all its traffic load messages, and thus has a more chance of being selected as the next forwarder. Some crucial data packets may be lost as a consequence of the lossy links in WSNs, wasting energy, and adding to the delay caused by the need to retransmit missing packets. One main thing affecting end-to-end latency, packet delivery rate, and energy efficiency is packet loss. The lossy feature of wireless connections across it may be described by the \(PRR\). As a result, the following is how the path quality is defined. As a result, the connection quality is regarded as heuristic data, which are defined as:  
\[\gamma_{xy}^{2}(t)=\frac{PRR_{xy}}{\sum\limits_{l\in FCHN_{x}}PRR_{xl}} \tag{72}\]The forwarder node with a greater value of \(\gamma_{xy}^{2}\) has a better link quality and has more opportunity to be chosen as a relay node.  
In resource-constrained WSNs, the real-time routing algorithm should identify next-hop nodes that give the best energy/latency trade-off. Therefore, hop counts to the sink should be regarded as a heuristic for routing decisions.  
\[\delta_{xy}^{2}(t)=\frac{1\Big{/}mhc_{y}^{s}}{\sum\limits_{l\in FCHN_{x}}\frac {1}{mhc_{l}^{s_{l}}}} \tag{73}\]  
A neighbour node with a greater \(\delta_{xy}^{2}\) value is closer to the sink node than the other candidates and has a better probability of becoming the next forwarder.
## 1 Introduction  
The importance of transitioning to sustainable development cannot be overstated, as evidenced by the United Nations' release of the \"Transforming our world: the 2030 agenda for attainable development\" document in 2015. This document outlines 17 Sustainable Development Goals (SDGs) and 169 sub-objectives that aim to address a wide range of sustainability issues [1]. As one of the key stakeholders in global sustainability, the international shipping industry plays a crucial role in achieving these goals. The SDGs are a globally agreed policy guide that seeks to promote environmental and social sustainability through actionable steps. Given its significant impact on the environment, the shipping industry's participation in sustainable development is essential for the achievement of the SDGs [2]. The shipping industry is responsible for a significant portion of global carbon dioxide emissions, with emissions from international shipping alone accounting for 870 million tonnes in 2018. Additionally, the industry emits significant amounts of nitrogen oxides and sulphur oxides, which can harm both human health and the environment. Unless serious efforts are made to reduce emissions, it is estimated that emissions from shipping could increase by 150-200% by 2050. As a result, regulatory organisations like the International Maritime Organisation have set a goal to reduce greenhouse gas emissions by at least 50% by 2050. To achieve this goal, the shipping industry needs to find sustainable alternatives to fossil fuels like Heavy Fuel Oil, which is currently the main source of power. However, these alternatives must also be economically viable and comply with existing regulations. A variety of shipping companies and associations have been actively researching these issues to find the best alternative solutions for a viable and greener future. To address these challenges, many organisations and associations in the shipping industry are conducting research and providing guidance on the best practices and strategies for transitioning to more sustainable operations [3]. The involvement of these organisations is essential to ensure that the industry moves towards a more sustainable future in a coordinated and efficient way. The transition toward greener shipping operations has become a central focus in maritime research and policy. A detailed overview of decarbonisation technologies--ranging from advanced navigation systems and optimised hull designs to alternative fuels and energy-efficient propulsion systems--is presented in [4]. Similar to the direction of this work, this study offers a comparative analysis of these technologies based on their emission reduction potential and economic feasibility, contributing valuable insights to the broader discussion on sustainable maritime practices.