esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results and Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cases** & \multicolumn{3}{c}{**Scenario \#4**} \\ \hline \multirow{3}{*}{Theoretical values} & Time & (0–2.0 s) & (2.0–4 s) \\  & Irradiance (W/m\({}^{2}\)) & [1000,900,800] & [1000,400,900] \\  & P\({}_{\text{MPP}}\) (W) & 637.9 & 460.1 \\  & V\({}_{\text{MPP}}\) (V) & 95.5 & 62 \\ \hline \multirow{3}{*}{PSO results} & P\({}_{\text{PV}}\) (W) & 637.7 & 332 \\  & V\({}_{\text{PVV}}\) (V) & 95.5 & 69.3 \\  & PSO Efficiency & 99.9\% & 72.2\% \\ \hline \multirow{3}{*}{CSO results} & P\({}_{\text{PV}}\) (W) & 637.5 & 333 \\  & V\({}_{\text{PVV}}\) (V) & 95.5 & 68.8 \\ \cline{1-1}  & PSO Efficiency & 99.9\% & 72.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Scenario 4: Comparisons of the PSO and the CSO under dynamic partial shading circumstances. Figure 8: Scenario 4: Output power–voltage under dynamic partial shading circumstances, [1000,900,800] to [1000,400,900]: (**a**) CSO and (**b**) PSO.
# Review of the AI-Based Analysis of Abdominal Organs from Routine CT Scans
## 2 Historical Evolution
### Deep Learning Revolution (2018-2024)
#### 2.3.3. UNet++  
In 2018, [PERSON] et al. introduced UNet++, to enhance medical image segmentation by leveraging a deeply supervised encoder-decoder network with nested, dense skip pathways. The re-designed skip pathways aim to reduce the semantic gap between the feature maps of the encoder and decoder sub-networks. Additionally, integrating deep supervision improves segmentation accuracy, particularly for lesions appearing at multiple scales. The authors evaluated UNet++ on four diverse medical imaging datasets, including lung nodule segmentation in CT scans, colon polyp segmentation in colonoscopy videos, cell nuclei segmentation in microscopy images, and liver segmentation in abdominal CT scans. Their experiments revealed significant performance improvements, with UNet++ achieving an average IoU gain of 3.9 and 3.4 points compared to U-Net and wide U-Net, respectively, highlighting its efficacy across various medical imaging tasks [14].
###### Abstract  
In this study, the challenge of teleoperating robots in harsh environments such as underwater or in tunnels is addressed. In these environments, wireless communication networks are prone to congestion, leading to potential mission failures. Our approach integrates a Human-Robot Interface (HRI) with a network congestion control algorithm at the application level for conservative transmission of images using the Robot Operating System (ROS) framework. The system was designed to avoid network congestion by adjusting the image compression parameters and the transmission rate depending on the real-time network conditions. To evaluate its performance, the algorithm was tested in two wireless underwater use cases: pipe inspection and an intervention task. An Autonomous Underwater Vehicle for Intervention (I-AUV) equipped with a Visual Light Communication (VLC) modem was used. Characterization of the VLC network was performed while the robot performed trajectories in the tank. The results demonstrate that our approach allows an operator to perform wireless missions where teleoperation requires images and the network conditions are variable. This solution provides a robust framework for image transmission and network control in the application layer, which allows for integration with any ROS-based system.  
Keywords:underwater communications; visual light communication; underwater intervention systems; human-robot interaction; human in the loop +
Footnote †: The results are part of the final experimental demonstration of the OPTIHROV project.
# Correction of Thermal Errors in Machine Tools by a Hybrid Model Approach
## 2 Approach  
The objective is to develop a hybrid correction method that combines a list of simulation-generated characteristic diagrams with a structural model-based observer as the decision maker in order to activate the appropriate characteristic regarding the present load situation in the machine tool control system that is used for thermal error correction.  
To do so, a thermo-elastic FE model was generated for the test machine tool DMG Mori DMU 80 evolution and validated by extensive experimental studies including various thermal load cases. Next, the FE model was qualified to generate simulative characteristic diagrams resulting in a \"Characteristic Generator\", Figure 1 left, that also can be validated by the experimental studies. With this, a variety of characteristics for different load cases can be generated and stored in a database, where they are available for selection by the \"Numeric Control\" (NC), Figure 1 bottom. However, there is currently no method available to find and activate the most appropriate characteristics depending on the current load case of the machine tool. In the presented approach, a fast-calculating structural-based model order reduction (MOR [45]) model was build up and validated that is able to simulate current load situations from linked machine data and was used to evaluate the actual correction quality, Figure 1 right. The outstanding feature of the hybrid model is the addition of the observer, Figure 1 center, which continuously ensures with the help of the fast-calculating structural model that a valid characteristic diagram is used for correction in the control system. If the current correction is not sufficient, a suitable characteristic can be selected from the mentioned database, and, if there is none, the missing characteristic can be generated by interpolating existing characteristics or by new FE simulation with the stored load case description. In addition to the two model calculations, the approach includes, therefore, the observer to evaluate to current correction quality, the interfaces to the models and to the process data to automate the calculations, the database of previously calculated maps, and the interface to the numeric control to exchange the current correction diagram during production.  
This paper presents in the following sections the experimental setup as the basis for FE model validation and experimental characteristic diagram generation (Section 3), the FE model and simulative characteristic diagram generation (Section 4), and the design and validation of the structural model (Section 5). The developed methodology was applied and illustrated exemplarily for the machine tool DMU 80 evolution.
# Shear Band Characterization of Clayey Soils with Particle Image Velocimetry
###### Abstract  
Identifying the spatial distribution of deformation and shear band characteristics is important for accurately modeling soil behavior and ensuring the safety of nearby geotechnical structures. However, most research on the shear behavior of soils has focused on granular soil and clay-rich rocks, with little focus on clayey soil, and the entire shearing process from the initial state to failure has not been observed. This study evaluated the spatial distribution and evolution of deformation in clayey soils from the initial state to the post-failure state and the shear band characteristics. Plane strain tests were performed on normally consolidated and over-consolidated clay specimens, and digital images were captured through a transparent side wall for particle image velocimetry (PIV) analysis. PIV was performed to evaluate the displacement and deformation of soil particles. The results show that the shear-strain behaviors of two clays during the shearing process could be divided into four stages: initial, peak, softening, and steady state. Shear bands were observed to form in the softening stage, and the shear band slopes were compared to values in the literature. These results can be used to characterize shear bands in clay as well as predict failure behavior and guide reinforcement at actual sites with soft ground.  
shear band; particle image velocimetry; clayey soil; deformation; digital image analysis Article
# A Robust Lightweight Network for Pedestrian Detection Based on YOLOv5-x
## 3 Method
### Design of Backbone Network
To address these challenges, we incorporated the compact bilinear pooling (CBP) algorithm [24] to enhance network feature fusion and employed the ELU activation function to accelerate network convergence and improve feature expression capabilities. Additionally, we redesigned the bottleneck layer structure of the network. The CBP algorithm is an extension of the bilinear pooling method [25]. The compact bilinear pooling algorithm addresses this challenge through three main steps: feature mapping, bilinear pooling, and compression. In the feature mapping step, two input  
Figure 4: Ghost bottleneck layer. Figure 3: Ghost module. vectors A and B, typically represented with dimensions d1 and d2, are mapped. Bilinear pooling, the core concept of CBP, computes the product of each feature in A with each feature in B and aggregates these products. This results in a high-dimensional feature vector of size \(d1\times d2\). To manage the potential large number of products and reduce computational complexity, CBP employs a compression technique. This compresses the high-dimensional feature vector into a lower-dimensional representation, usually a fixed dimension, suitable for subsequent tasks. This compression step helps improve computational efficiency. CBP excels at capturing high-order feature interactions between input vectors, making it highly valuable in various computer vision and natural language processing tasks. It can significantly enhance performance, especially in tasks that require high-dimensional feature interactions, while reducing computational costs. The compact bilinear pooling algorithm approximates the bilinear pooling method by using a low-dimensional polynomial kernel mapping and extends the bilinear pooling method by employing tensor sketch algorithms for model compression. This approach effectively reduces feature dimensions and computational expenses without compromising the effectiveness of feature fusion. To minimize computational costs, the proposed CBP-G network features two structures: CBP-G with stride 1 simply modifies the activation function of G-bottleneck, whereas CBP-G with stride 2 employs the compact bilinear pooling algorithm, which more effectively integrates residual edges and Ghost modules and uses the more efficient ELU activation function. The architecture of CBP-G with stride 2 is illustrated in Figure 5, with CBP denoting the compact bilinear pooling structure. The design of CBP-GNet maintains the lightweight characteristics of GhostNet while enhancing the model's ability to capture and integrate features from various network layers. This contributes to a more comprehensive understanding of pedestrian appearances under adverse weather conditions. This improved feature fusion effectively combines contextual information, spatial relationships, and scale-related details, which is crucial for accurate pedestrian detection in adverse weather conditions.
###### Abstract  
Various statistical distributions, such as Weibull, log-normal, and exponential functions, are frequently employed to interpret the dielectric breakdown (BD) strength data of insulating materials, including cross-linked polyethylene, low-density polyethylene, polypropylene (PP), and polyethylene. This study aimed to determine a suitable statistical distribution for analyzing the dielectric BD strength data of PP insulators before and after thermal degradation. Dielectric BD strength tests were conducted on thermally deteriorated PP insulators under various degradation conditions. Additionally, a coefficient of determination was employed to assess the compatibility between the dielectric BD strength data and the statistical distribution of PP insulators before and after thermal degradation. The test results indicate that the coefficient of determination for alternating current BD strength data was 0.955 in the log-normal distribution before degradation and 0.929 in the Weibull distribution after degradation. Consequently, in the analysis of the PP insulation breakdown data, the log-normal distribution was found to be suitable for data before degradation, while the Weibull distribution was deemed suitable for data after degradation. These results can lead to lower errors in the power system design process, enhancing reliability when analyzing the BD strength data of insulation materials.  
AC breakdown strength; dielectric breakdown; polypropylene (PP); degradation; Weibull distribution; goodness of fit +
Footnote †: journal:  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON] Reliability Assessment of Statistical Distributions for  
Analyzing Dielectric Breakdown Strength of Polypropylene. _Appl. Sci._**2024**, _14_, 3. [[https://doi.org/10.3390/app14010003](https://doi.org/10.3390/app14010003)]([https://doi.org/10.3390/app14010003](https://doi.org/10.3390/app14010003))  
Appl. Sci. 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Machine Learning Based Energy-Efficient Design Approach for Interconnects in Circuits and Systems
## 3 ANN-Based Design Methodology for Low-Power Interconnect
### Proposed Design Methodology
#### 3.3.3 Time-Domain Optimization  
The time-domain simulation is performed with the optimal structure observed in the frequency domain. The EH and the EW presented for the eye-opening determine the quality of the signal in this simulation. While the EW is sensitive to the jitter and the signal transition, the EH is directly proportional to the signal amplitude at a specific frequency. Hence, the reduction in voltage to save power will degrade the EH that harms the signal integrity, and can not meet the design requirements. Thus, the optimal design that gives maximum EH provides the designer with the highest margin to minimize the transmitter supply voltage satisfying the minimum EH requirement. Consequently, the power consumption is minimized with the minimum allowable voltage estimated by the following Equation (7).  
\[V_{TX,MIN}=V_{SUPPLY}\frac{EH_{MAX}}{EH_{MIN}} \tag{7}\]  
where:  
\(V_{TX,MIN}\): Minimum allowable transmitter supply voltage  
\(V_{SUPPLY}\): The supply voltage in design conditions  
\(EH_{MAX}\): Maximum eye height  
\(EH_{MIN}\): Minimum eye height in design conditions
# Towards Lightweight URL-Based Phishing Detection
## 4 Evaluation  
Our phishing detection engine was evaluated with a dataset of \(\sim\)380,000 benign and phishing URLs. This dataset included 305,737 benign URLs and 74,436 phishing URLs. We note that the use of an imbalanced dataset for this evaluation allowed us to evaluate the selected classifier in a realistic scenario [11]. This holds true as real traffic has proportionally more benign URLs compared to the phishing ones.  
Our results suggest (Table 6), that our detection engine continues to perform very well using this imbalanced dataset. The high accuracy shows that our proposed model shows little confusion in classifying URLs, either benign or phishing. Moreover, during our experiments the model's sensitivity was slightly higher than precision. This means that our model tends to produce false positives more frequently than false negatives. This is an acceptable trade-off, as frequent false positives would only mean irritation to the user, when benign web sites are classified as phishing. On the contrary, a higher false negative rate would mean that the model would miss active phishing attacks, which could lead to a security breach.  
The performance of our work in detecting phishing URLs over time was further evaluated by classifying phishing URLs on a daily basis for six consecutive days (i.e., 5-10 May 2020), as well as individual days after several months, i.e., 15 November 2020 and 25 April 2021 (see Table 7). During this experiments, every day we used the latest URLs provided by PhishTank at the time of testing. These included all the confirmed online and active phishing attacks by the PhishTank community at the time of our experiment. Our results were compared with the performance of Google Safe Browsing (GSB), which is the default protection that is offered by the most popular web browsers at the time of our experiments, namely Chrome, Firefox and Safari. To access the most up to date threat  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metric** & **Value** \\ \hline
**Precision** & 97.40 \\
**Sensitivity** & 99.06 \\
**F-measure** & 98.22 \\
**Accuracy** & 99.29 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Optimised Random Forest results on the 380,000 mixed records dataset.  
Figure 2: High-level description of the methodology applied to Random Forest model.  
information from GSB we used its online query functionality instead of relying to local browser caches, which might not contain latest GSB data [7]. Our experiments, which are summarized on Table 7, suggest that our phishing detection engine outperforms GSB, i.e., on average \(\sim\)94% accuracy compared to \(\sim\)45%.
###### Abstract
To answer this research question, this work tries to find out whether deploying smart traffic lights at intersections in small cities can contribute to a relevant reduction in CO\({}_{2}\) emissions in the vicinity of these intersections. It also aims to study the potential gains in average speed and waiting time (stopped at the intersection), which determine the total time spent in traffic and therefore impact people's well-being. The work focuses on small cities, as their traffic profiles and driving patterns are very different from those of heavily congested cities currently addressed by most similar studies [32]. The findings of this study can be important to justify the adoption of smart traffic lights in small cities. The research methodology adopted to deal with the research question is a quantitative modelling method tested by computational simulation, tailored to the specific characteristics of a typical intersection in a small Portuguese city, using representative traffic profiles, accurate vehicle kinematics, realistic driver behaviour, and a precise estimation of CO\({}_{2}\) emissions. Furthermore, the smart routing algorithm is designed from the start to optimize CO\({}_{2}\) emissions rather than traffic flow, and it does not rely on any V2V or V2I communications, which require the installation of new hardware/software in vehicles. All in all, the main contributions of this article are  
* We propose a novel microscopic traffic simulation framework, designed to simulate realistic vehicle kinematics and driver behaviour, and accurately estimate CO\({}_{2}\) emissions. * We propose a smart routing algorithm for traffic lights, especially designed to optimize CO\({}_{2}\) emissions at intersections. * We evaluate the impact of this algorithm on reducing CO\({}_{2}\) emissions and improving traffic flow, by simulating its behaviour and outcomes with different traffic profiles.
## 2 Energy Management and Review of Surveys
### Different EM Structures  
Generally, the MG structures are divided into [8; 12; 13]: AC, DC, and AC/DC hybrid MGs.  
The operation modes of MGs are addressed as grid-connected and islanded ones [13]. In addition, the interconnection architectures of multi-MGs (MMGs) are addressed as [8; 9]: radial, mesh, and daisy-chain.  
Furthermore, different EMS schemes or structures are mentioned as:  
* Centralized [8; 9; 11; 12];
* Decentralized [8; 9; 11; 12];
* Hybrid [8; 9];
* Distributed [8; 11; 12];
* Hierarchical [11]: primary control: local supervision, voltage and current control, and power-sharing control; secondary control: power quality control, power flow control, and synchronization control; tertiary control: economic dispatching and optimization, MG supervision, and generation forecasting [12];
* Nested [8]. The most important missions of control for EMS in MGs are [11]:
* Power quality/reliability regulation (including low power factor, voltage imbalance, unreliable power supply, and voltage/frequency offsets);
* Spinning reserve;
* Energy shifting;
* Peak shaving [11];
* Energy arbitrage;
* Black start.  
The computational stress, privacy, scalability, resilience, and communication infrastructure are important issues for EMS that should be discussed [8]. In addition, the functionality of EMS in different views of information modules, scheduling and control, and resilience operation should be addressed [8].  
In addition, it is required to compare the different types of EMS control strategies in terms of information access, communication information, function in real-time, the feature of plug and play, expenditure, the structure of the grid, size (number of nodes), tolerance during the fault, infrastructure final nodes, operation flexibility, bandwidth and latencies, quality of service (QoS), connectivity, safety measures, and individuality [12].  
The control configurations for ESSs are aggregated, distributed, and hybrid [11].  
The strategies to attain the control objectives are addressed as [11]:  
* Active and reactive power (PQ);
* Voltage-frequency (V-f);
* Droop control;* Conventional control techniques for MGs: PI/PID controller, model predictive controller (MPC), linear quadratic regulator (LQR), and intelligent control techniques for MGs (particle swarm optimization (PSO), fuzzy logic control (FLC), artificial neural networks (ANNs), and reinforcement learning (RL) methods) [11].
# CARA: A Congestion-Aware Routing Algorithm for Wireless Sensor Networks
## 3 Congestion-Aware Routing Algorithm
### Route Evaluation Parameters
#### 3.1.1 The Forward Rate  
Because sensor nodes have a limited range of transmission, most nodes often cannot communicate directly with sink nodes, requiring additional nodes to forward data. Therefore, the routing protocol needs to consider the spatial distance relationships between the forward node, the neighbor node, and the sink node during routing decisions.  
If a packet sent to the sink is in node \(i\), node \(i\) must send the packet to a forward node to avoid forwarding it back. According to the definition of [28], the forward neighbor set \(F(i)\) of node \(i\) can be expressed as  
\[F(i)=\left\{j\Big{|}d_{j,sink}\leq d_{i,sink},d_{i,j}\leq R\right\} \tag{1}\]  
In the definition, the distance from node \(i\) to the sink is \(d_{i,sink}\); the distance from node \(j\) to the sink node is \(d_{j,sink}\); the distance from node \(i\) to node \(j\) is \(d_{i,j}\). \(R\) is the node's maximum transmission distance.  
To select the next-hop node from the forward neighbor set, we define the forward rate. As shown in Figure 1, \(j^{\prime}\) represents the intersection of the line _isink_ and the arc, the center of which is the sink node, and the radius is \(d_{j,sink}\). Therefore, we define the forward distance \(d_{f}(i,j)\) from node \(i\) to node \(j\) as  
\[d_{f}(i,j)=d_{i,j}=d_{i,sink}-d_{j,sink},j\in F(i) \tag{2}\]  
The forward distance \(d_{f}(i,sink)\) from node \(i\) to the sink is  
\[d_{f}(i,sink)=d_{i,sink} \tag{3}\]  
Based on the forward distance \(d_{f}(i,j)\) and \(d_{f}(i,sink)\), the forward rate \(r_{f}\) of node \(i\) to node \(j\) is  
\[r_{f}(i,j)=\frac{d_{f}(i,j)}{d_{f}(i,sink)}=\frac{d_{i,sink}-d_{j,sink}}{d_{i, sink}} \tag{4}\]  
The forward rate expresses the spatial distance relationship between node \(i\), node \(j\), and the sink. The higher the forward rate of the node, the closer the packet is to the sink node after forwarding to the next-hop node. In the free space model, the node prefers to select the next-hop node with a long single-hop transmission distance to reduce the number of hops on the path and the transmission delay.  
The forward rate reflects the spatial relationship between the nodes and causes packets to flow directly to the sink, reducing network latency and energy consumption. Therefore, if the routing selection only considers the forward rate, the node should transfer packets to the forward node with the highest forward rate.