esa-sceva/satcom-chunk-collection huggingface dataset:
# IVUS Image Segmentation Using Superpixel-Wise Fuzzy Clustering and Level Set Evolution
## 2 Motivation
### Assignment of Vascular Components  
Despite the extreme difference in the appearance of IVUS images, a coronary cross-section is generally composed of a lumen and a coronary vessel wall composed of three layers: The intima, the media, and the adventitia. Additionally, several types of structures (calcification, soft plaque) and artifacts (ring-down artifacts, guidewire artifacts, shadow artifacts) are included in IVUS images. In this paper, calcification refers to the calcified plaque, and soft plaque includes fibrotic plaque. An example image with different components labeled is shown in Figure 1.  
The adventitia, plaque, and calcification regions are highly echo-reflective and appear as bright tissues in the image, while the lumen appears darker. Calcifications usually produce signal attenuation  
Figure 1: The example intravascular ultrasound (IVUS) image (**left**), and the same image with intravascular structures and artifacts labeled (**right**).  
behind them, resulting in shadow artifacts. The intima and the media are very thin and not always visual in IVUS images. Ring-down artifacts are characterized by a bright halo around the black catheter area in the center of the image.  
In this paper, we consider that the catheter zone \(R_{c}\) consists of pixels whose distance from the image center \(i_{c}\) is less than the catheter radius \(D_{c}\):  
\[R_{c}=\bigcup{i,\;d(i,i_{c})\leq D_{c}}, \tag{4}\]  
where \(d(i,i_{c})\) is the Euclidean distance between pixels \(i\) and \(i_{c}\). The background zone \(R_{b}\) is the region outside the inscribed circle of the image:  
\[R_{b}=\bigcup{i,\;d(i,i_{c})\geq D_{b}}, \tag{5}\]  
where \(D_{b}\) is the radius of the inscribed circle.  
Inspired by the grayscale differences in these image components, as well as their anatomical and spatial features, we propose a ROI assignment algorithm, which is detailed in Section 3.3.
# Voice Deepfake Detection Using the Self-Supervised Pre-Training Model HuBERT
## 3 Methods
### Self-Supervised Pre-Training Speech Models and Fine-Tuning
#### 3.2.2 Fine-Tuning  
Fine-tuning is one of the transfer learning methods suitable for smaller datasets and it has low training costs, which can improve the detection performance for known attacks. Some studies have shown that fine-tuning is beneficial and can prevent overfitting, promoting better generalization [14]. Pre-training only extracts features of natural speech, and fine-tuning, with both natural and deepfake audio data, enables the self-supervised pre-training model to adapt to the downstream task of audio deepfake detection, which helps to improve detection performance.  
The process of fine-tuning is shown in Figure 2b. After pre-training on unlabeled data, fine-tuning was performed on the two training sets with labels. The back-end detection model and the pre-trained HuBERT model were jointly optimized by back-propagation, and the weighted cross-entropy loss function was used to calculate the loss. The speech \(W=[w_{1},w_{2},\cdots,w_{T}]\) of the T-frame was passed through the CNN encoder to obtain the potential speech representation \(S=[s_{1},s_{2},\cdots,s_{T}]\). and then sent to the transformer encoder to obtain the context representation R. In order to reduce the dimension, a fully connected layer (FC layer) was added after the output of the transformer encoder, and the output was sent to the residual blocks of the back-end detection model. In this paper, the ASVspoof 2021 LA training set (same as ASVspoof 2019 LA training set) and the FMFCC-A training set were used for fine-tuning, and the detection performance of the model was tested on different evaluation sets.  
Figure 2: The overall structure of pre-training and fine-tuning.
## 2 Physically Distributed Logically Centralized (PDLC) SDN Architecture and Components
### Distributed Control Plane Instances  
The control plane operates as a distributed system consisting of a cluster of one or more physical servers, as depicted in Figure 2. Each server has the capability to operate multiple control plane instances, also known as the \"Network Operating System\" (NOS) [16; 22].  
The controllers within each domain serve as the SDN's core element and realize the state management (Layer S). They are responsible for facilitating programmatic network access to the control logic, allowing for the reading and writing of the network state. Additionally, the control plane instances communicate the network state to other instances within the cluster through distribution I/E, as illustrated in Figure 2. Consequently, the SDN can present an instance of the control application (Layer A) with an abstraction of the physical network state [16; 22].  
Each controller maintains a Network Information Base (NIB) data structure stored in the database, represented by the yellow boxes in Figure 2. That provides an application with a view of the state of the network [16].
## 4 Data  
As this research investigates the long-range dependence and multifractality for ship flow sequences in container ports, we extracted required container ship flow data from the Automatic Identification System (AIS) database. The data span is from 1 January 2013 to 31 December 2017 for three representative container ports in the world--that is, Shanghai, Singapore, and Rotterdam. Figure 1 represents the original data of ship flow at different time scales for these three container ports.  
The port of Shanghai is not only the largest container port in China, but also the world's largest container port. Its shipping routes reach the world's 12 largest shipping areas, and it has established business contacts with more than 500 ports in nearly 200 countries and regions. As the world's second largest container port, Singapore is also the largest transit port in the Asia-Pacific region. The Port of Rotterdam is the largest port in Europe, as well as the European Gateway. Therefore, in this study, the ports of Shanghai, Singapore, and Rotterdam were chosen to represent all the ports around the globe.  
We analyzed and compared the long-range correlation and multifractality characteristics of the ship flow sequences of both Asian and European ports and the gateway ports and transit ports. In general, the ship flow sequences for the three ports depicted different characteristics at different time scales. Among them, the ship flow series of Shanghai and Rotterdam with daily and weekly time scales showed a more significant upward trend than the ship flow series of Singapore port. Ship flow sequences of the three ports with monthly scale fluctuated up and down along the time line, indicating a nonlinear  
\begin{table}
\begin{tabular}{c l} \hline
**Hurst Exponent** & **Long-Range Dependence Characteristics of Time Series** \\ \hline \(H(q)=0.5\) & There is no long-range dependence in time series and the time series is a completely independent process. \\ \(0.5<H(q)\) & The time series has positive long-range dependence and the overall trend in the future will be consistent with past trend. \\ \(H(q)<0.5\) & The time series has negative long-range dependence and the overall trend in the future will be contrary to the past trend. \\ \hline \end{tabular}
\end{table}
Table 1: The relationship between the long-range dependence of time series and the generalized Hurst exponent.  
and nonstationary feature. Generally, these fluctuations are not random, but relate to the seasonal and monthly cycles.
# Smart Facility Management System Based on Open BIM and Augmented Reality Technology
###### Abstract  
With the wave of the Fourth Industrial Revolution, the construction industry is also witnessing the application of numerous state-of-the-art technologies. Among these, augmented reality (AR) technology has the advantage of utilizing existing 3D models and BIM data and is thus an area of active research. However, the main area of research to date has either been in visualizing information during the design phase, where architects and project stakeholders can share viewings, or in confirming the required information for construction management through visualization during the construction phase. As such, more research is required in the application of AR during the facility management (FM) phase. Research utilizing BIM in the FM phase, which constitutes the longest period during the lifecycle of a building, has been continuously carried out but has faced challenges with regard to on-site application. The reason for this is that information required for BIM during the design, construction and FM phases is different, and the reproduced information is vast, so identifying the required BIM data for FM and interfacing with other systems is difficult. As a measure to overcome this limitation, advanced countries such as the US and UK have developed and are using Construction Operations Building information exchange (COBie), which is an open-source BIM-based information exchange system. In order to effectively convert open-source BIM data to AR data, this research defined COBie data for windows and doors, converted them to a system and validated that it could actually be applied for on-site FM. The results of this system's creation and validation showed that the proposed AR-based smart FMS demonstrated faster and easier access to information compared with existing 2D blueprint-based FM work, while information obtained through AR allowed for immediate, more visual and easier means to express the information when integrated with actual objects.  
augmented reality (AR); building information modeling (BIM); facility management (FM); mobile application +
Footnote †: journal: Applied Sciences
# A Review on Internet of Things for Defense and Public Safety
## 3 Target Scenarios for Mission-Critical IoT
### Surveillance  
Security cameras and sensors, combined with sophisticated image analysis and pattern recognition software, ease remote facility monitoring for security threats. In the case of marine and coastal surveillance, using different kinds of sensors integrated in planes, unmanned aerial vehicles, satellitesand ships, make possible to control the maritime activities and traffic in large areas, keep track of fishing boats, and supervise environmental conditions and dangerous oil cargos.  
Other examples can be the monitoring of hazardous situations: combustion gases and preemptive fire conditions to define alert zones, monitoring of soil moisture, vibrations and earth density measurements to detect dangerous patterns in land conditions or earthquakes, or distributed measurement of radiation levels in the surroundings of nuclear power stations to generate leakage alerts.
## 2 Materials and Methods
### Apparatus for Determining Residual Shear Strength  
For the purposes of this research, tests of residual shear strength were conducted on selected soil samples using two apparatuses: the ring shear apparatus (RS) and the direct shear apparatus (DS).  
The RS used in the research is based on the original design developed by Bromhead, 1979 [12] and is manufactured by Wykeham-Farrance Engineering Limited (Figure 3). The sample is ring-shaped with an internal diameter of 70 mm, an external diameter of 100 mm, and a height of 5 mm. The drainage of the sample is facilitated by two bronze porous discs attached to the bottom of the lower part and the top of the shear box.  
In the ring shear apparatus, during testing, the cross-sectional area of the sample remains constant, and thus, shear stresses are induced by torque.  
The formula used to calculate residual shear strength is:  
\[\tau_{R}=\frac{3(F_{1}+F_{2})L}{4\pi\big{(}R_{2}^{3}-R_{1}^{3}\big{)}}, \tag{1}\]  
where  
\(\tau_{R}\)--residual shear strength, kPa;  
\(F_{1}\), \(F_{2}\)--force measured at the ends of the beam for transmitting torque, N;  
\(R_{1}\), \(R_{2}\)--internal and external diameters of the sample, mm, respectively;  
\(L\)--length of the torque beam, mm.  
For determining the residual shear strength parameters using the direct shear test in a shear box, an apparatus manufactured by Matest in Italy was used (Figure 4). The sample was square-shaped with the dimensions of 60 \(\times\) 60 mm, and the initial height of the sample was 21 mm.  
Figure 3: (**a**) Bromhead’s ring shear apparatus. (**b**) Three-dimensional model of the ring shear apparatus (Source: manufacturer’s manual). Legend: 1, 2—horizontal force measurement cell, 3—frame for transmitting vertical load, 4—touchscreen display, 5—vertical force measurement cell.  
The formula used to calculate residual shear strength in the direct shear test is:  
\[\tau_{R}=\frac{P}{A} \tag{2}\]  
where  
\(\tau_{R}\)--residual shear strength, kPa;  
\(P\)--horizontal shear force, N;  
\(A\)--the cross-sectional area of a sample, mm.
# Nucleation Mechanism and Rupture Dynamics of Laboratory Earthquakes at Different Loading Rates
## 3 Results
### Effect of Loading Rate on the Features of Stick-Slip Events  
#### 3.2.1 Shortening in Recurrence Interval  
The recurrence interval is defined as the time interval between the two adjacent stick-slip events, as illustrated in Figure 6a. The effect of the loading rate on the recurrence interval is shown in Figure 6b. The recurrence intervals decrease with the increase in the  
Figure 4: Macroscopic shear stress drop–displacement curve at different loading rates. The black squares represent the shear stress drop at each fault slip. The blue and red lines represent the upper and lower limits of the stiffness fluctuation of the system, respectively. The inset shows the definition of shear stress drop and the fault slip defined as the fault displacement during a slip event. The fault slip is derived from DIC measurements.  
loading rate. Previous studies [22] showed that there is a power-law relationship between the recurrence interval and the loading rate:  
\[T_{R}=m\left(V_{L}\right)^{n} \tag{3}\]  
where m is the scaling constant, and n is the power-law exponent. Importantly, n determines the time-dependent effect of frictional strength: n \(>-1\) corresponds to the time-dependent weakening effect; n \(=-1\) indicates that the static stress drop is not affected by the recurrence interval, and there is no time-dependent strengthening; n \(<-1\) means the time-dependent strengthening of the fault strength [40]. For this work, the experimental results in Figure 6b are fitted as follows:  
\[log\ T_{R}=nlog\ V_{L}+m \tag{4}\]  
The n is fitted as \(-1.201\), indicating a time-dependent strengthening of the fault strength. A recent study [23] found that n is equal to \(-1.252\) through stick-slip experiments on granite blocks simulating a fault, which is similar to our results.  
Figure 5: Vertical stress evolution for faults at different loading rates.  
Figure 6: **(a)** Shear stress versus time for the case with a loading rate of 0.5 \(\upmu\)m/s, where \(T_{R}\) is the recurrence interval of a stick–slip event. (**b) \(T_{R}\)** as a function of loading rate. The dotted line is the fitted curve based on the data, while the solid line is the reference line with a slope of 1 which is used to distinguish whether the fault is strengthened or not.
# A Parametric Computational Study of RC Building Structures under Corner-Column Removal Situations
## 2 Description of the Study  
### Description of the Building and Test  
A full-scale RC building structure, purpose built for the experiment and previously analysed by [PERSON] et al. [28] and [PERSON] et al. [27], was used for the computational analysis and parametric study described here. The building consisted of two floors and \(2\times 2\) ways with \(0.30\times 0.30\) m\({}^{2}\) columns and flat-slabs that were \(0.20\) m thick. The distance between column edges was \(5\) m and the floor-to-ceiling height was \(2.8\) m. B500S reinforcement steel bars (yield stress of \(500\) MPa) were used for the reinforcement of slabs and columns. The design of the building followed the requirements of Eurocodes [38; 39; 40]. A more detailed description of the reinforcement bars of the concrete elements can be found in [PERSON] et al. [28]. Table 1 gives the concrete mechanical properties of slabs and columns obtained on the day of the test, which have been used in the FE model validation.  
The building was subjected to a sudden corner-column removal [28]. In addition to the self-weight of the structure, a superimposed uniformly distributed load was considered on each floor. That load was equal to \(5.3\) kN/m\({}^{2}\) and applied to a tributary area of \(5\times 5\) m\({}^{2}\) on the corner bay where the column was to be removed. At the edges of this corner bay, a facade load of \(0.56\) kN/m was also applied on the first floor simulating the load of a hypothetical outer wall. The resulting load configuration represents a design load case under accidental scenarios [28; 36]. Figure 1 contains a sketch and a photograph of the loads (in red in the sketch) and the building prepared for the test.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mechanical Property** & **Element** & **Result [MPa]** \\ \hline \multirow{2}{*}{Compressive strength(f\({}_{\text{c}}\))} & 1 st slab & 30.5 \\  & 2 nd slab & 31.1 \\ \hline \multirow{2}{*}{Tensile strength(f\({}_{\text{ct}}\))} & 1 st slab & 2.44 \\  & 2 nd slab & 1.83 \\ \hline \multirow{4}{*}{Elastic Modulus(E)} & Ground floor columns & 29,275 \\  & 1 st slab & 28,810 \\ \cline{1-1}  & 1 st floor columns & 29,403 \\ \cline{1-1}  & 2 nd slab & 33,119 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical properties of concrete for columns and slabs.
## 3 Proposed Joint Resource Optimization for NOMA-Enabled Vehicular Networks
### Solution Variants for Reducing Computational Complexity
#### 3.3.2 Reformulation with Utility Function and Load Balancing
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Step** & **Description** & **Rationale** \\ \hline S1 & Load Balancing & Prioritizes an even distribution of users across RSUs to prevent congestion and reduce the reliance on NOMA channels, indirectly minimizing intra-channel interference. \\ \hline S2 & \begin{tabular}{c} VU-Channel \\ Assignment \\ \end{tabular} & \
\begin{tabular}{c} Each RSU independently manages resource allocation, including channel assignment and OMA-NOMA switching, to ensure scalability and adaptability to local network conditions. \\ \end{tabular} \\ \hline S3 & Power Control &
\begin{tabular}{c} Optimizes transmit power for each user at the RSU level, ensuring that QoS constraints are met while minimizing the total power consumption. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Three-step hierarchical approach for the reformulated solution variant. In the formulation, the objective function is almost the same as Equation (9) except that the outermost summation over all RSUs is no longer needed. Also, and the constraints related to the association are not needed since it is already taken care of by P. (13). For the constraints that are taken from the original optimization problems, the only necessary change is to remove \(\forall b\) since P. (14) is executed for each RSU. The optimal solution from P. (14) is then passed to the second-phase QoS-constrained optimal power control problem that is already presented in Section 3.2.2.
# Parent and PHY Selection in Slot Bonding IEEE 802.15.4e TSCH Networks
## 3 Parent and PHY Selection Heuristic
### Heuristic
For the former, the time complexity is \(O(|N|\cdot|P|\cdot|M|)\) (with \(N\), \(P\) and \(M\) being the total set of nodes, the set of possible parents and the set of available PHYs respectively), while for the latter, the time complexity is \(O(c\cdot|N|\cdot|P|)\), where \(c\) represents the maximum number of iterations needed to achieve convergence (i.e., the while loop at line 5). Therefore, the total time complexity of the heuristics is \(O(c\cdot|N|^{2}\cdot|M|)\), as \(|P|\leq|N|\). To limit the influence of \(c\) on the computational complexity of the algorithm, a maximum bound can be set on the number of allowed iterations. Our experiments (cf., Section 5) show that the number of iterations needed for convergence are consistently below five (with an average of 3.5 iterations) in the evaluated scenarios. This analysis proves the computational efficiency of the heuristic compared to the time complexity of the centralized GA scheduler, that is, \(O(|N|!\cdot|N|^{2})\), as explained in detail in our previous work [7].
# Preprocessing for Multi-Dimensional Enhancement and Reconstruction in Neural Video Compression
###### Abstract  
The surge in ultra-high-definition video content has intensified the demand for advanced video compression techniques. Video encoding preprocessing can improve coding efficiency while ensuring a high degree of compatibility with existing codecs. Existing video encoding preprocessing methods are limited in their ability to fully exploit redundant features in video data and recover high-frequency details, and their network architectures often lack compatibility with neural video encoders. To addressing these challenges, we propose a Multi-Dimensional Enhancement and Reconstruction (MDER) preprocessing method to improve the efficiency of deep learning-based neural video encoders. Firstly, our approach integrates a degradation compensation module to mitigate encoding noise and boost feature extraction efficiency. Secondly, a lightweight fully convolutional neural network is employed, which utilizes residual learning and knowledge distillation to refine and suppress irrelevant features across spatial and channel dimensions. Furthermore, to maximize the use of redundant information, we incorporate Dense Blocks, which can enhance and reconstruct important features in the video data during preprocessing. Finally, the preprocessed frames are then mapped from pixel space to feature space through the Dense Feature-Enhanced Video Compression (DFVC) module, which improves motion estimation and compensation accuracy. The experimental results demonstrate that, compared to neural video encoders, the MDER method can reduce bits per pixel (Bpp) by 0.0714 and 0.0536 under equivalent PSNR and MS-SSIM conditions, respectively. These results demonstrate significant improvements in compression efficiency and reconstruction quality, highlighting the effectiveness of the MDER preprocessing method and its compatibility with neural video codec workflows.  
video coding; preprocessing technology; neural network +
Footnote †: journal: Applied