esa-sceva/satcom-chunk-collection huggingface dataset:
# Region-Based Automated Localization of Colonoscopy and Wireless Capsule Endoscopy Polyps
## 2 Materials and Methods
### Feature Extraction  
The ResNet [39] models with depths of 50 layers or 101 layers act as feature extractors for the model to extract features over the entire image. These feature maps are further improved (to better represent the objects) by extracting more features from five different levels of ResNet layers. These levels are chosen so that the spatial dimension of the layer is reduced by half in the bottom-up view of the ResNet model. A top-down architecture with lateral connections is built to extract better feature maps at different scales. This network is called a Feature Pyramid Network (FPN) [40]. ResNet with FPN extracts features from different hierarchical levels with different scales so that each level has information of higher-level and lower-level features.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Database** & **Purpose** & **Resolution** & **\# Patients** & **\# Sequences** & **\# Images** \\ \hline CVC-ClinicDB & Training & \(384\times 288\) & 23 & 31 & 612 SD frames \\ CVC-ColonDB & Testing & \(500\times 574\) & 13 & 13 & 300 SD frames \\ CVC-PolypHD & Testing & \(1920\times 1080\) & - & - & 56 HD frames \\ ETIS-Larib & Testing & \(1225\times 966\) & - & 34 & 196 HD frames \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of colonoscopy databases.
# Linear Precoding Designs for MIMO VLC Using Multi-Color LEDs under Multiple Lighting Constraints
###### Abstract  
Multiple-input multiple-output (MIMO) technology as an efficient approach to improve the transmission rate in visible light communication (VLC) has been well studied in recent years. In this paper, we focus on the MIMO VLC system using multi-color LEDs in the typical indoor scenario. Besides the correlation of the MIMO channel, the multi-color crosstalk interference and quadrangle chromaticity region are also considered to increase the practicality of this system. With the constraints of power, amplitude and chromaticity, an iterative algorithm to minimize mean-squared-error (MSE) is proposed to jointly design the precoder and equalizer. Our proposed algorithm provides an effective method to get the optimal precoder by updating optimization variables iteratively. As the equalizer matrix is fixed at each iteration, the main non-convex precoding design problem is transformed into a convex optimization problem and then solved. With the utilization of multi-color LEDs, our proposed precoding method would be promising to promote the practical applications of high-speed indoor optical wireless communication. Simulation results show that our proposed method owns better performance than conventional chromaticity-fixed schemes and zero-forcing precoding designs.  
Keywords:visible light communication; precoding design; optical MIMO; chromaticity constraint +
Footnote †: journal:
## 5 Control Algorithms for UAV Networking
### Decentralized Control Algorithm
#### 5.2.1 Behavioral Based
Behavior-based decentralized control algorithms play an important role in enhancing the flexibility, scalability, and adaptability of UAV swarms. By making independent decisions based on sensor information and pre-established behavioral rules, each UAV can autonomously coordinate with neighboring UAVs without centralized control. This enables the UAV system to operate effectively even in complex environments with many obstacles or constantly changing conditions. However, this approach also poses some challenges, including design complexity, high requirements for coordination algorithms, and the ability to ensure formation stability in unpredictable situations. Incorporating advanced methods such as artificial intelligence, deep reinforcement learning, or biomimetic algorithms can help improve the performance of decentralized control systems. It opens up many potential research directions and applications for UAVs in real-world missions.
# A Network Device Identification Method Based on Packet Temporal Features and Machine Learning
## 1 Introduction
The Inception-TCN model was designed to extract the packet timing features, and feature fusion was performed using the multi-head self-attention mechanism to generate the feature vectors, which were then used as the device fingerprints for the identification of the network devices. The overall process is shown in Figure 1.
## 2 System Model
### Classifier Characteristics  
The well-known machine learning-based algorithm called a support vector machine is used to classify the received signal. Primarily, an SVM is designed to deal with two-class classification problems. A linear hyperplane is drawn as a decision surface between two classes of data points. The nearest data points to the decision boundary of each class are known as support vectors, hence the name support vector machine. In fact, the decision boundary is drawn by taking support vectors into account. The optimization problem of the SVM in obtaining the decision boundary for training data \(\{(x_{i},l_{i})\}_{i=1}^{N}\) is given as follows [31]:  
\[\begin{split}&\min\Phi(\mathbf{w})=\frac{1}{2}\left\|\mathbf{w} \right\|\\ &\text{such that}\\ & l_{i}(\mathbf{w}^{T}x_{i}+b)\geq 1\ \ \text{for}\ i=1,2,...,N.\end{split} \tag{10}\]  
A test input, \(\mathbf{x}_{j}\), can be classified using the following decision function:  
\[f(\mathbf{x}_{j})=sign\left(\sum_{i}\alpha_{i}K(\mathbf{x}_{i},\mathbf{x}_{j} )+b\right). \tag{11}\]  
To apply an SVM for multi-class classification problems, the SVM can be used in a _one-versus-rest_ approach. In this approach, \(h\) different SVMs are utilized for an \(h\)-class classification problem. The \(h\)th SVM is trained with data points, referring to the \(h\)th class as a positive class and referring to the data points from the remaining \(h-1\) classes as a negative class. The final decision is made according to the classifier with the highest score in the positive class.
# Estimating Critical Latency Affecting Ship's Collision in Re-Mote Maneuvering of Autonomous Ships
## 3 Methodology  
### Method of Estimating Critical Latency  
The concept of critical latency that can cause ship collisions has been established based on COLREGs encountering situations, and the estimation technique of critical latency has been proposed using turning circles of the own ship, which can be explained in x-y plane coordinates.
# Thermal-Mechanical Behaviour of Road-Embedded Wireless Charging Pads for EVs
## 3 Material Characterization
### Ferrite  
The modulus of ferrite was measured in 4-point bending, using a procedure based on ASTM C1161 [20]. Measurements were taken at 21 \({}^{\circ}\)C, 60 \({}^{\circ}\)C and 100 \({}^{\circ}\)C using an Instron 1185 with a mounted environmental chamber (3119-6000 series). The crosshead speed was 1 mm/min, as per the standard. Resistive strain gauges [350 \(\Omega\), type FLAD-3-350-11-1 LJC-F from Tokyo Measuring Instruments Laboratory (TML), Tokyo, Japan] were used for strain measurement. The average modulus was 138 MPa and had negligible change with temperature. A [PERSON]'s ratio of 0.25, based on tests conducted by [PERSON] and [PERSON] [21], was used within the numerical modelling.  
To measure the coefficient of thermal expansion, ferrites, with the strain gauges attached, were placed in an oven and heated to a series of target temperatures in 10 \({}^{\circ}\)C increments. At each increment, \(\Delta T\), the ferrites were kept at the target temperature for an hour to ensure complete thermal equilibrium. The coefficient of thermal expansion (CTE) of a material (\(\alpha_{S}\)) can be calculated based on its stress-free output strain (\(\varepsilon_{meas}\)) at different temperatures and the output strain of a known reference material (\(\varepsilon_{R}\)), using Equation (1) [22]. The apparent strain output data for the reference material [stainless steel (SS 400)], which has a coefficient of thermal expansion (\(\alpha_{R}\)) of 11.8 \(\times\) 10\({}^{-6}\)/\({}^{\circ}\)C, were provided by TML [23].  
The apparent thermal strain, measured on six ferrite samples, is presented in Figure 2a. The calculated CTE of ferrite, presented in Figure 2b, was observed to slightly increase with temperature; however, the change was small enough that it was approximated as a temperature constant value of 11 \(\times\) 10\({}^{-6}\)/\({}^{\circ}\)C.  
\[\alpha_{S}=\alpha_{R}+\frac{\varepsilon_{meas}-\varepsilon_{R}}{\Delta T} \tag{1}\]  
The core loss of CF295 ferrite, which varies non-linearly with temperature, magnetic field, and frequency (although in this case, frequency was fixed at 85 kHz), was measured by [PERSON] et al. for the thermal evaluation of a full-scale IPT pad [7]. The same core loss model has been used in this study. Details of the measurement methodology are given in [24]. The measured core loss model was implemented in the Ansys Maxwell solver using the Steinmetz equation, given in Equation (2), where \(f^{\alpha}\) was fixed at 85,000 (the operating frequency of the pad was 85 kHz [7]). \(\kappa\) and \(\beta\) were empirically determined constants which varied with temperature.  
\[P=\kappa f^{\alpha}B^{\beta} \tag{2}\]  
A relative magnetic permeability of 200 was taken from datasheets [25] and was approximated as a constant between the temperatures of 20 \({}^{\circ}\)C and 100 \({}^{\circ}\)C, based on the modelling approach of [6,7]. The thermal conductivity of ferrite was 4.01 W/m\(\cdot\)K and was measured using the transient hot wire method with the Mathis TC 30 [6].
## 2 Subgrid Stress Tensor Equation Closure and Calibration  
### Subgrid Stress Tensor Equation  
We base this research on the incompressible Navier-Stokes equations, as follows:  
\[\frac{\partial u_{i}}{\partial x_{i}}=0, \tag{1}\]  
\[\frac{\partial u_{i}}{\partial t}+u_{k}\frac{\partial u_{i}}{\partial x_{k}}= -\frac{1}{\rho}\frac{\partial p}{\partial x_{i}}+\
u\frac{\partial^{2}u_{i}}{ \partial x_{k}\partial x_{k}}. \tag{2}\]  
Summation over repeated indices is implied.  
After spatial filtering denoted by an overbar, an additional term called subgrid stress tensor appears in the following momentum equation:  
\[\frac{\partial\overline{u}_{i}}{\partial t}+\overline{u}_{k}\frac{\partial \overline{u}_{i}}{\partial x_{k}}+\frac{\partial\overline{u^{\prime}_{i}u^{ \prime}_{k}}}{\partial x_{k}}=-\frac{1}{\rho}\frac{\partial\overline{p}}{ \partial x_{i}}+\
u\frac{\partial^{2}\overline{u}_{i}}{\partial x_{k}\partial x _{k}}.\]  
Hereinafter, cumulants [19], or central moments [20], are used to abbreviate the following notation:  
\[\overline{a^{\prime}b^{\prime}}=\overline{ab}-\overline{ab},\ \ \ \ \overline{a^{\prime}b^{\prime}c^{\prime}}=\overline{abc}-\overline{ab^{\prime}c^ {\prime}}-\overline{bc^{\prime}a^{\prime}}-\overline{c^{\prime}a^{\prime}b^{ \prime}}-\overline{ab}\overline{c}.\]  
By combining (1) and (2) and applying a filtering operator, one can obtain the following differential equation for the subgrid stress tensor:  
\[\begin{split}&\frac{\partial\overline{u^{\prime}_{i}u^{\prime}_{ i}}}{\partial t}+\frac{\partial}{\partial x_{k}}\left[\overline{u^{\prime}_{i}u^{ \prime}_{i}}\overline{u}_{k}+\overline{u^{\prime}_{i}u^{\prime}_{i}u^{\prime}_ {k}}+\frac{1}{\rho}\left(\overline{p^{\prime\prime}u^{\prime}_{i}}\delta_{jk}+ \overline{p^{\prime\prime}u^{\prime}_{j}}\delta_{ik}\right)-\
u\frac{\partial \overline{u^{\prime}_{i}u^{\prime}_{j}}}{\partial x_{k}}\right]\\ &=-\overline{u^{\prime}_{i}u^{\prime}_{k}}\frac{\partial\overline {u}_{i}}{\partial x_{k}}-\overline{u^{\prime}_{i}u^{\prime}_{k}}\frac{\partial \overline{u}_{i}}{\partial x_{k}}+\frac{1}{\rho}\rho^{\prime}\left(\frac{ \partial u^{\prime}_{i}}{\partial x_{j}}+\frac{\partial u^{\prime}_{i}}{ \partial x_{i}}\right)-2\
u\frac{\partial u^{\prime}_{i}}{\partial x_{k}} \frac{\partial u^{\prime}_{j}}{\partial x_{k}}\end{split} \tag{3}\]The terms in this equation can be interpreted as follows. On the LHS, under the divergence symbol, there are advection by a resolved flow field, turbulent transport due to subgrid-scale velocity fluctuations, turbulent transport due to subgrid-scale pressure fluctuations and molecular transport, respectively. On the RHS, the first two terms represent the subgrid stress tensor production. This is followed by a pressure-strain correlation which acts as redistribution between energy components in the principal frame of \(\overline{u_{i}^{\prime}u_{j}^{\prime}}\). The last term in (3) represents the subgrid stress viscous dissipation. Turbulent transport terms, the pressure-strain correlation and the dissipation tensor require closure models.
## 2 Constellation Design  
### Mission Aims and Technical Requirements  
Currently, low-latitude regions suffer a significant inadequacy of observation coverage because the observation focus of most polar-orbiting remote sensing satellites is the medium and high latitudes. To overcome the challenges associated with spatial information acquisition in low-latitude regions, HSC aims to establish a comprehensive hybrid-sensor satellite system capable of providing high-frequency and extensive observations in low-latitude regions when fully operational. The key technical requirements of the constellation include high spatial coverage, high frequency, and an application-oriented approach. As the main design concept is to enhance observational coverage, a focus on high spatial coverage is essential. Given the primary focus on ocean observation and, particularly, medium-size ship identification, the requirements, such as small-inclination orbit, a design concept of coverage priority, a sensor combination of multispectral, hyperspectral, SAR and ship AIS, onboard image processing technology for ship detection, swaying capability, spatial resolution, and swath width, were carefully considered during the planning, design and construction of HSC.
## 4 Experiments and Results
### Baseline  
The main algorithms used in this work include IAC, IPPO, and MADDPG. In SSDs, the agent has its own reward function for independent learning. Therefore, we assign the A3C algorithm and PPO algorithm to each agent as a baseline for comparison. Moreover, global information is used in both SR and AEN, so we conducted a control experiment with MADDPG which also applies global information.  
The A3C algorithm is an excellent reinforcement learning algorithm that combines the PG algorithm (actor network) and the Q-learning-based algorithm (critic network). Therefore, A3C greatly improves the efficiency of policy learning. Additionally, through multi-environment parallel processing, the sampling rate, and the running speed is increased.  
The PPO algorithm is based on the AC architecture (each agent has an actor network and a critic network). However the algorithm realizes the reuse of data through importance sampling. Another innovative point of the algorithm is that it limits the update range of the policy. The PPO determines whether to optimize or not by calculating the relationship between the new policy and the old policy. This idea ensures that each gradient update is in an optimal direction.  
MADDPG algorithms are extensions of DDPG in multi-agent systems. The algorithm trains a critical network using global information based on the settings of centralized critic and decentralized policy and predicts their next actions by modeling the policies of other agents. It uses the predicted action to find a self-action that maximizes the joint Q-value. The algorithm has an excellent performance in a multi-agent environment, and has been compared to most RL algorithms as a baseline.
## 3 Results and Discussion
### Electrochemical Behavior of the Modified Electrode  
The electrochemical behavior of the developed electrodes is represented in Figure 2. As seen in Figure 2A, the modified electrode with MWCNT, GCE/MWCNT, brought about a broad cathodic current in the presence of H\({}_{2}\)O\({}_{2}\) starting from \(-\)0.3 V. An increase in the cathodic current was observed with GCE/MWCNT/MNPs-CH upon the addition of H\({}_{2}\)O\({}_{2}\), starting from \(-\)0.5 V in Figure 2B. No change in the current was observed with the addition of H\({}_{2}\)O\({}_{2}\) in solution when GCE/MWCNT/MNPs-TEOS were used as the working electrode in Figure 2C. However, when GCE/MWCNT/UN-MNPs were utilized (Figure 2D), with the addition of H\({}_{2}\)O\({}_{2}\), a high reduction peak appeared. Compared to GCE/MWCNT, the reduction in H\({}_{2}\)O\({}_{2}\) starts at lower potential magnitudes (\(-\)0.2 V) in GCE/MWCNT/UN-MNPs. Thus, it can be concluded that the naked electrocatalytic activity of UN-MNPs toward a reduction in hydrogen peroxide is the highest. The reduction in H\({}_{2}\)O\({}_{2}\) takes place by electron transfer from the electrode. Therefore, the UN-MNPs act as wires, which facilitate the electron transfer to H\({}_{2}\)O\({}_{2}\). Overall, the peroxidase-like activity of the UN-MNPs stems from their electron transfer ability by reducing  
Figure 1: SEM image and elemental mapping images representing the constituent of (**A**) naked magnetic nanoparticles (UN-MNPs), (**B**) chitosan-modified MNPs (MNPs-CH), (**C**) MNPs modified with tetraethylorthosilicate (MNPs-TEOS); (**D**) SEM image representing the uniform distribution of UN-MNPs drop-cast on glassy carbon electrode (GCE)/multi-walled carbon nanotubes (MWCNT) to form GCE/MWCNT/UN-MNPs.  
substrates and H\({}_{2}\)O\({}_{2}\). Based on the obtained results, it can be concluded that the MNPs that were exposed directly to H\({}_{2}\)O\({}_{2}\) possess a higher electrocatalytic effect.
# Benchmarking Anomaly Detection Methods for Extacardiac Findings in Cardiac MRI
## 4 Results and Discussion
### Unsupervised Self-Supervised Methods  
Similar to feature modeling approaches, self-supervised methods yielded competitive results within the unsupervised category, with DAE [16] emerging as one of the best-performing methods overall (Table 2). Examining the corresponding anomaly maps in Figure 7, it is evident that DAE achieves impressive pixel-level results, particularly when lesions resemble noise (e.g., sample C.III). Likewise, PII [24] demonstrated strong pixel-level performance, as illustrated in Figure 7 and reported in Table 2. Since PII is explicitly trained to segment (synthetic) abnormalities, the binary cross-entropy loss helps achieve good specificity at the pixel level. However, because PII is trained with artificially generated squared-shaped abnormalities, it tends to overfit to this shape, often producing square-like patterns in anomaly maps. This limitation is not observed in DAE, as its network is trained to reconstruct a noise-free version of the input normal image, thereby implicitly learning the normal distribution. Nonetheless, DAE appears to slightly overfit to the Gaussian noise distribution added during training, limiting its generalizability to certain anomalies.  
Conversely, CutPaste [32] exhibited weaker results, particularly in the coronal and sagittal subdatasets (Table 2). This performance is reflected in its anomaly maps (Figure 7), which show considerable instability, potentially linked to the synthetic abnormalities generated during training. Unlike PII, which applies Poisson interpolation to better blend extracted patches into target images, CutPaste does not apply any fusion technique. As a result, the network can more easily distinguish the pasted patches, increasing the risk of overfitting. Moreover, the high anatomical variability in this dataset appears to demand more complex statistical distributions, which could be better modeled using kernel density estimation if sufficient data were available. These challenges contribute to CutPaste's inferior pixel-level and image-level classification performance compared to the other two self-supervised methods.
# A Dual-Band 8-Antenna Array Design for 5G/WiFi 5 Metal-Frame Smartphone Applications
## 4 Experimental Results and Discussion
### Radiation Performance  
To validate the radiation performances, the proposed antenna underwent measurements in a microwave anechoic chamber. Figure 9 illustrates the simulated and measured theta-polarized and phi-polarized radiation patterns of the antenna at 3.5 GHz, 4.9 GHz, and 5.5 GHz. The close alignment between simulated and measured results indicates favorable radiation characteristics at the three representative frequencies (3.4-3.6 GHz, 4.4-5.0 GHz, and 5.15-5.85 GHz), highlighting promising radiation performance for mobile communications.  
Figure 9: Simulated and measured radiation patterns in xoy-plane and yoz-plane at (**a**) 3.5 GHz, (**b**) 4.9 GHz, and (**c**) 5.5 GHz.
## 4 Simulation Setup, Result Analysis, and Discussion
### Average Packet Drop  
We analyzed the simulation result of the proposed SDAA protocol and compared it with the other contending standard secure MAC protocols, including SFSC, SDA, and EEST. Based on the result of the average packet drop in the network due to malicious node attacker (MNA) in the network. The average packet drop in the network is determined as the number of packets dropped due to MNA attacks experienced in the network. Figure 6 depicts the results of the average packet drop for the proposed SDAA approach and the other contending approaches. As depicted in Figure 6, when the number of MNA attackers increases from 1 to 18, the packet drops in the network were observed at 34 with the proposed SDAA protocol and 36, 41, and 44 for SEFSC, EEST, and SDA, respectively. Hence, the packet drop of the proposed SDAA protocol is 4-20% lesser than other competing approaches. The reason for fewer average packet drops for our proposed SDAA protocol is the result of the robust authentication method and secure aggregated data optimized method deployed in CBND, as compared to the other contending approaches, such as SEFSC, SDA, and EEST, which did not utilize CBND and secure authentication methods in their networks.  
Figure 5: Average delivery ratio varying number of attackers.
# Broadcasting in Stars of Cliques and Path-Connected Cliques +
###### Abstract  
Broadcasting is a fundamental information dissemination problem in a connected network where one node, referred to as the originator, must distribute a message to all other nodes through a series of calls along the network's links. Once informed, nodes assist the originator by forwarding the message to their neighbors. Determining the broadcast time for a node in an arbitrary network is NP-complete. While polynomial-time algorithms exist for specific network topologies, the problem remains open for many others. In this paper, we focus on addressing the broadcasting problem in network topologies represented by specialized clique-based structures. Specifically, we investigate the windmill graph \(Wd_{kj}\), which consists of \(k\) cliques of size \(l\) connected to a universal node, and extend our study to the star of cliques, a generalization of the windmill graph with cliques of arbitrary sizes. Our primary objective is to propose an efficient algorithm for determining the broadcast time of any node in an arbitrary star of cliques and to rigorously prove its optimality. Additionally, we broaden the scope by examining the broadcasting problem in path-connected cliques, a topology featuring \(k\) cliques of varying sizes sequentially connected along a path. For this structure, we develop a computationally efficient algorithm that leverages clique sizes and adjacency to optimize broadcast strategies, with broader implications for understanding communication in block graphs.  
Keywords:broadcasting; algorithm; star of cliques; path-connected cliques; windmill graphs +
Footnote †: This article is a revised and expanded version of a paper presented at IW0 CA 2024: [PERSON]; [PERSON] Broadcasting in Stars of Cliques and Path-Connected Cliques. _Applications_**2025, _18, 76_.