esa-sceva/satcom-chunk-collection huggingface dataset:
# AdaptiveConv2d: A Novel Convolutional Module for Medical Image Segmentation
## 3 Results  
### Comparison Experiment with SOTA Deep Learning Methods  
As shown in Table 1, out of 240 experimental indicators, 228 demonstrated improvements, yielding an overall improvement rate of 95%. Among the six groups of evaluation metrics, the Dice and IoU indicators--critical metrics for evaluating segmentation effectiveness--achieved a 100% improvement rate. The performance metrics used in this study include the Dice coefficient, Intersection over Union (IoU), accuracy, Specificity, Sensitivity, and Precision. These metrics provide a comprehensive assessment of segmentation quality, and their improvements highlight the effectiveness of the proposed AdaptiveConv2d in enhancing model performance. In particular, AdaptiveConv2d showed consistent improvements across all datasets and network architectures, demonstrating its robustness and adaptability. It significantly improved feature extraction, which is critical for medical image segmentation tasks, where fine details and subtle structures are often important. On the HAM10000 dataset, models incorporating AdaptiveConv2d showed significant performance enhancements. Specifically, the Dice coefficient of the UNet-AD model increased from 0.9235 for the baseline UNet to 0.9356, representing a 1.21% improvement. The Intersection over Union (IoU) increased from 0.8604 to 0.8811, an increase of 2.07%. Additionally, accuracy improved from 0.9256 to 0.9381, and Specificity and Sensitivity both increased by 1.93% and 1.24%, respectively. These improvements indicate that AdaptiveConv2d significantly enhances the model's ability to capture fine features and edge information, resulting in more accurate segmentation of skin lesions. In comparison with other SOTA models, such as the baseline UNet, UNet++, nn-UNet, and others, AdaptiveConv2d consistently outperformed traditional methods across the datasets used. For example, on the HAM10000 dataset, AdaptiveConv2d achieved a higher Dice coefficient and IoU when compared with UNet++ (2.05% improvement in Dice and 3.50% improvement in IoU), showing its superiority in handling complex segmentation tasks. These results confirm that AdaptiveConv2d enhances the model's ability to recognize intricate patterns and small details in medical images, thus improving segmentation performance. For the Blood Cell Segmentation Dataset, despite the high baseline segmentation performance due to high image quality and clear target regions, AdaptiveConv2d still led to further improvements. The Dice coefficient of the UNet-AD model increased from 0.9759 to 0.9806, an enhancement of 0.47%, while the IoU increased from 0.9541 to 0.9623, an improvement of 0.82%. Though the improvement is modest, this demonstrates AdaptiveConv2d's capability in refining segmentation results and minimizing errors, even in
# NGLSFusion: Non-Use GPU Lightweight Indoor Semantic SLAM
## 5 Conclusions  
In this paper, we propose an SLAM system for indoor semantic map construction on a mobile robot with limited computational power by combining the pose acquired by visual SLAM with the semantic information acquired by semantic segmentation in a lightweight way, named NGLSFusion. The system includes a fast frame tracking and highly robust VO module, a real-time semantic point cloud construction module, and a lightweight semantic map construction module. These three modules can provide an effective environment map information for robots in unfamiliar environments. Among them, a VO module for fast frame tracking gives accurate globally consistent poses, the robustness of which is verified with publicly available datasets. The optimized semantic segmentation pretraining model gives similar performance for semantic maps built by the robot without GPU to those built with GPU. In future research, the robot will be placed in a dynamic environment for accurate localization and mapping.  
In the VO module, this paper reduces the feature point extraction time by 50% for key frames, and the front-end processing speed of VO is verified to be faster and provide an accurate pose in the subsequent experiments. In the semantic map construction module, this paper achieves a speed and accuracy close to that of GPU construction without using GPU.  
However, there are still some problems in the semantic map construction method presented in this paper. While pursuing real time, the requirement of segmentation accuracy was abandoned. In the future, some other networks, such as PSPNet, will be tried as a method to improve segmentation accuracy and real-time performance, where the robot returns to the beginning, which has been ported to the robot device used in this paper and tested in large and small environments, respectively, and the performance of each detection is good.  
Conceptualization, L.W. and L.J.; methodology, L.W.; software, L.W.; validation, L.W., L.J. and Y.L.; formal analysis, B.L.; investigation, L.J.; resources, L.J.; data curation, L.J.; writing--original draft preparation, L.W.; writing--review and editing, L.J. and B.T.; visualization, L.W.; supervision, H.L.; project administration, L.J.; funding acquisition, L.J. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the National Key R&D Program of China grant (No.2019 YFB1310000), National Natural Science Foundation of China grant (No.51874217), and Hubei Key R&D Program Project of China grant (No.2020 BAB098).  
Not applicable.  
**Data Availability Statement:** Computer Vision Group--Datasets-RGB-D SLAM Dataset and Benchmark (tum.de) (TUM RGB-D dataset). SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite (princeton.edu) (SUNRGD dataset).  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Volatile Compounds Analysis Using GC-qMS
#### 3.4.3 Acids  
Fatty acids can be degraded to produce volatile acids during wine fermentation, and different yeast strains have been reported to regulate the metabolism of fatty acids under wine fermentation. As a result, the volatile acid composition could be altered in different strain-fermented wines [39]. In the present study, seven volatile acids were detected in these passion fruit wines (Table 3). These wine samples contained a similar content of the total volatile acids. It has been reported that volatile acids might induce an unpleasant aroma in wine when their level is higher than their threshold [3]. In the present study, six volatile acids were found to have a higher concentration than their threshold in these passion fruit wine samples. This indicated that the unpleasant fatty, cheesy, pungent and rancid flavor might be incorporated into the overall aroma of these passion fruit wines [21; 35]. It should be noted that these passion fruit wine samples exhibited a significant difference in the level of octanoic acid, isovaleric acid, hexanoic acid and 9-decenoic acid. These fatty acids could enhance the rancid, cheesy and fatty aromas in the passion fruit wine due to their high OAV. For example, the CY passion fruit wine showed the highest concentration of octanoic acid, whereas its lowest concentration was found in the ES-fermented wine. The other volatile acids did not show a concentration difference in these different strain-fermented passion fruit wine samples.
## 2 Background Preliminaries
### Low-Complexity AMiDS  
The building of scalable AMiDS to protect the sensing nodes in large, complex systems and environments requires robust yet simplistic and computationally inexpensive elements or constructs. Hence, the idea is to use low-complexity machine learning algorithms to build scalable and computationally inexpensive AMiDS models. The building of scalable and less costly AMiDS models often involves the use of low-complexity machine learning algorithms such as PCA and oSVM. Low-complexity security mechanisms are highly  
Figure 1: Connected nodes in an MioT environment.  
Figure 2: Connected nodes in a CioT environment.  
desirable when considering the protection of intelligent sensing devices with low power and computation cost requirements. This paper uses low-complexity machine learning algorithms, i.e., PCA and oSVM, to construct AMiDS models to protect IoT sensing nodes in CioT systems and MioT environments.  
Given the stringent requirements of CioT and MioT in terms of their low latency, power, and computation overheads, the low-complexity ML algorithms have emerged as a viable option due to their low resource utilization and computation overhead compared to medium- and high-complexity models, such as the Random Forest and deep neural networks (DNNs). In particular, the PCA algorithm is inherently a dimensional reduction algorithm with low memory and CPU utilization. It uses statistical approaches to find data points with the highest variance, thus reducing the feature space by removing redundant and unnecessary features and organizing the remaining features in a new feature space known as the principal space [18]. oSVM, a variant of the SVM, uses kernel functions to reduce the calculations required without reducing the input dimensions. In addition, the SVM has a low bias and high variance and is suitable when the dataset is small.
# Spatial Deformation Calculation and Parameter Analysis of Pile-Anchor Retaining Structure
## 3 Calculation of Retaining Structure Deformation
### Deformation Prediction Formula  
Based on the expressions of the strain energy for the crown beam, waist beam, pile, and anchor cable, as well as the potential energy from the active and passive soil pressures, the total potential energy of the pile-anchorbeam support system can be expressed as Equation (15):  
\[\Pi=W_{D}+W_{Z} \tag{15}\]The principle of minimum potential energy [24; 25; 26] states that the critical state of a structure corresponds to the first derivative of the total potential energy being 0. Therefore, the optimal equilibrium state of the structure can be determined by solving for the stationary value of the potential energy. The expression for the minimum potential energy principle is given by Equation (16):  
\[\frac{\partial\Pi}{\partial\delta_{0}}=0 \tag{16}\]  
The displacement of the crown beam in the middle of the foundation pit, denoted as \(\delta_{0}\), can be determined by solving Equation (16):  
\[\delta_{0}=\frac{T_{pa}-T_{pp}}{2T_{gb}+2\sum\limits_{i=1}^{M}T_{wbi}+2T_{sb}+ 2\sum\limits_{i=1}^{M}T_{mi}} \tag{17}\]  
In this formula, \(T_{pq}\) is the unit active external potential energy of retaining structure. \(T_{pb}\) is the unit passive external potential energy of the retaining structure. \(T_{gb}\) is the unit deformation strain energy of the crown beam. \(T_{wbi}\) is the unit deformation strain energy of the \(i\)-th row of waist beams. \(T_{sb}\) is the unit deformation strain energy of the pile. \(T_{mi}\) is the unit strain energy of the \(i\)-th row of anchor cables. After calculating the displacement of the crown beam in the middle of the foundation pit towards the foundation pit, denoted as \(\delta_{0}\), the deformation curves of the crown beam and the horizontal deformation curves of each pile can be determined. Ultimately, the spatial deformation of the retaining structure can be determined.
## 2 The Hot Stamping Process  
Hot stamping is a thermomechanical forming process with intended transformation both in microstructure and macrostructure. Figure 1 represents a basic hot stamping process. Hot stamping is a complex manufacturing process. The basic steps of hot stamping are as follows:  
* Heating--Blank is heated up to the austenitization temperature. This temperature has significant influence on the blank properties, the processing time, and the cost-efficiency of hot stamping.
* Forming and Quenching
* Forming--Provides pressing for macrostructure transformation and must be finished before the beginning of the martensite transformation.
* Quenching--Immediately after finishing forming, the transformation from austenite into martensite occurs here. The morphologies of these microstructures (austenite and martensite) are very different and thus possess different mechanical properties  
In the automotive industry, the manufacturing of structural components from ultrahigh-strength steels (UHSS) is rapidly increasing to enhance safety and reduce fuel consumption. Low form-ability and substantial spring-back put constraints on the formation of UHSS at room temperature. Therefore, hot stamping is the best alternative and has been widely used to date. The complexity of the hot stamping process originates from high working temperature and simultaneous quenching, and the need to control the processing parameters, during both phases, precisely to achieve desired phase fraction percentage and hence desired properties. Like other steels, Boron steel (22 MnB5) in the austenite phase has low flow stress at elevated temperatures. This process has the capability to take advantage of this aspect of boron steel and produce parts with high tensile strength, minimum spring-back, and reduced sheet thickness.  
Process designers must look for cost-effective solutions that help in decision making and improve the efficiency of the process. Hot stamping involves both materials design and structural design. Careful design of the hot stamping process can produce automotive structural parts with desired mechanical properties with acceptable quality, maximum output, and minimum cost subject to the limitations and constraints of the hot stamping mill. The challenges associated with the design of the hot stamping process are attributable  
Figure 1: A Schematic of the Hot Stamping Process.  
to the complex nature of the process, which involves a large number of processing parameters, complex relationships, constraints and bounds, and the hierarchical nature of PSPP relationships. In this work, we attempt to address some of these challenges by developing a design method supported by an ANN-based prediction model and design space exploration of the hot stamping process to realize an end product. The next section presents the Design Space Exploration Framework (DSEF).
## 3 PSO Based on ABC Search (F-WAPSO)
### Improved PSO and Its Convergence Analysis
#### 3.1.1 Improved PSO (WAPSO)
If \(c_{1}\) is too small, it is difficult for particles to find the global optimal solution only according to their own experience. Therefore, the \(c_{1}\) factor plays a key role in finding the global optimal solution for particles. At the end of PSO iterations, the historical optimal solution of each particle gradually converges to the individual historical optimal solution, which has a slightly worse quality than the global optimal solution. In this study, the acceleration factor \(c_{1}\) was changed into an adaptive factor to increase the influence of the global historical optimal solution and improve the overall performance of the algorithm. The improved velocity update formula is shown in Equation (6):  
\[v_{ij}(\tau+1)=\omega v_{ij}(\tau)+c_{1}(\tau)r_{1}\big{(}gbest_{j}(\tau)-x_{ ij}(\tau)\big{)}+c_{2}r_{2}\big{(}pbest_{ij}(\tau)-x_{ij}(\tau)\big{)} \tag{6}\]  
where \(c_{1}(\tau)=2\times ht\tau\times c_{1}=2\times\frac{\epsilon^{\tau}-\epsilon^ {\tau}}{\epsilon^{\tau}+\epsilon^{\tau}}\times c_{1}\) and \(c_{1}\) becomes a hyperbolic tangent time-varying function. With an increasing number of iterations, \(c_{1}\) becomes larger, making the probability of the particle approaching the global historical optimal solution increase in later iterations. The improved PSO is termed adaptive PSO (APSO). Dynamic \(\omega\) can improve the optimization speed and make the population find the global optimal solution more quickly. In this study, the linear decreasing inertia weight is introduced as shown in Equation (7). The inertia weights decrease linearly. At the beginning of iterations, large inertia weights are used to accelerate the convergence speed. At the end of iterations, small inertia weights are used to improve the search accuracy:  
\[w(\tau)=w_{\text{min}}+(w_{\text{max}}-w_{\text{min}})\times\frac{\tau_{\text{ max}}-\tau}{\tau_{\text{max}}} \tag{7}\]  
where \(\tau_{\text{max}}\) is the maximum number of iterations, \(\omega_{\text{min}}\) and \(\omega_{\text{max}}\) are the minimum and maximum inertia factors, respectively. Typically, \(\omega_{\text{min}}\) = 0.4 and \(\omega_{\text{max}}\) = 0.9 [20]. The APSO with linear decreasing inertia weights is named WASPO, Table 1 depicts the calculation steps of WASPO. \begin{table}
\begin{tabular}{l} \hline \hline \multicolumn{1}{l}{**WAPSO**} \\ \hline Parameter: \(\tau_{\text{max}}\), number of particles, \(\omega\), \(c_{1}\), \(c_{2}\) \\
1. Initialize the swarm \\
2. Evaluate the swarm \\
3. \(\tau=1\) \\
4. while \(\tau\leq\tau_{\text{max}}\) do \\
5. for every particle \(i\) do \\
6. Update all particles’ velocities and positions according to Equations (5) and (6) \\
7. if \(f(pbest_{i})\leq f(x_{i})\) then \(pbest_{i}=x_{i}\) \\
8. end if \\
9. end for \\
10. Update \(gbest\) \\
11. \(\tau=\tau+1\) \\
12. end while \\
13. return \(gbest\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The steps of WASPO.
# Applying Game Theory to Teach the PERT Scheduling Method
## 5 Analysis of Results
### Quantitative Analysis  
#### 5.2.1 Preliminary Analysis  
To conduct the quantitative analysis, 26 closed questions focused on the PERT method for construction scheduling were asked of the students in a pre- and post-test. To classify the areas of knowledge to be assessed, the 26 questions were grouped into four categories, each fundamental for understanding the PERT method. The first category, \"Identification of activities\", grouped 6 questions and sought to measure the students' ability to identify activities in a construction project. The second category, \"Duration of activities\", also included 6 questions to assess the student's ability to determine the duration of activities in a construction project. The third category, called \"Critical Path\", included 7 questions, and its objective was to assess whether the students could determine the critical path of a project. Finally, the fourth category, \"Variability and Decision\", which also consisted of 7 questions, sought to measure students' understanding of the concepts associated with variability (typical of the PERT method) and decision-making (typical of Game Theory).  
From the results obtained and shown in Table 6, it is possible to notice that the correct answers by the students in the post-test were percentage-wise higher than the correct answers obtained by the students in the pre-test in each category. However, to evaluate whether these differences were statistically significant, it was necessary to perform other statistical tests, so the normality of the data was previously verified. To do so, Table 6 shows the results of the Shapiro-Wilk Normality test, where the \"Critical Path\" and \"Variability and Decision\" dimensions, together with the total number of responses, were normally distributed (\(p>0.05\)), in which case the t-Student test for paired samples was applied. The other dimensions, not passing the normality test, were analyzed using the Wilcoxon test.  
Table 6 shows that all categories had a percentage difference greater than 8% in favor of the post-assessment, where category I \"Identification of activities\" had the minor difference (8.1%), while category IV had the most significant percentage difference between the pre- and post-assessment (20.8%). The minor difference in category I could be explained because \"Identification of activities\" is a task that is transversal to any project scheduling method, so it is unnecessary to have used Game Theory to improve. It was different for the category \"Variability and Decision\", which, with a difference of more than 20% improvement between the pre- and post-test results, showed that those subjects more closely linked to probabilistic methods (such as PERT) could benefit more from topics associated with decision-making, which was widely addressed through Game Theory during the class. Overall, the total difference was more significant than 15% between the pre- and post-test at the end of the learning process of the PERT method using Game Theory.
# Experimental and Numerical Dynamic Identification of an Aerostatic Electro-Spindle
## 4 Numerical Results
### Damped Natural Frequencies and Damping-Factor Identification
#### 4.7.3 Comparison of LSCEM and LDM for Numerical Simulations  
When LDM and LSCEM are compared for the numerical simulations, the damped frequencies obtained with LSCEM differ by less than 1.6% with respect the frequencies obtained with LDM, apart from the case with \(p_{s}\) = 0.3 MPa. The estimations of damping factors differ by much more--up to 80%. This parameter is more sensitive to variations in the air gap and pressure.
# A Review of Smart Grid Anomaly Detection Approaches
###### Abstract  
The size of power grids and a complex technological infrastructure with higher levels of automation, connectivity, and remote access make it necessary to be able to detect anomalies of various kinds using optimal and intelligent methods. This paper is a review of studies related to the detection of anomalies in smart grids using AI. Digital repositories were explored considering publications between the years 2011 and 2023. Iterative searches were carried out to consider studies with different approaches, propose experiments, and help identify the most applied methods. Seven objects of study related to anomalies in SG were identified: attacks on data integrity, unusual measurements and consumptions, intrusions, network infrastructure, electrical data, identification of cyber-attacks, and use of detection devices. The issues relating to cybersecurity prove to be widely studied, especially to prevent intrusions, fraud, data falsification, and uncontrolled changes in the network model. There is a clear trend towards the conformation of anomaly detection frameworks or hybrid solutions. Machine learning, regression, decision trees, deep learning, support vector machines, and neural networks are widely used. Other proposals are presented in novel forms, such as federated learning, hyperdimensional computing, and graph-based methods. More solutions are needed that do not depend on a lot of data or knowledge of the network model. The use of AI to solve SG problems is generating an evolution towards what could be called next-generation smart grids. At the end of this document is a list of acronyms and terminology.  
smart grid; cyber physical systems; anomaly detection; artificial intelligence