esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Evaluation and Findings
### Comparison with Existing Techniques  
In this section, we conduct a comparative analysis of the EHMFFL algorithm against three existing heart disease diagnosis methods. Since the EHMFFL algorithm utilizes a  
Figure 10: Analyzing the ROC curves of the different techniques using the Cleveland dataset.  
Figure 9: CHM analysis for the Statlog dataset.  
heuristic-metaheuristic-driven feature selection algorithm (i.e., PCC-GWO) embedded in an ensemble learning model, we considered the machine learning approach based on LR by [PERSON] et al. (2021) [18] (referred to as ML), an ensemble of different machine learning models, including DT, RF, SVM, KNNs, and ANN, developed by [PERSON] (2021) [23] (referred to as EL), and a sequential heuristic feature selection method by [PERSON] et al. (2022) [28] (referred to as FS). We assess the precision, recall, and accuracy of the different methods, as depicted in Figures 12 and 13, for the Cleveland and Statlog datasets, respectively. The results in Figure 12 indicate that the EL model outperforms the EHMFFL algorithm in terms of recall when using the Cleveland dataset. A similar trend is observed in Figure 13 for the Statlog dataset, where the FS method excels in achieving the highest recall among all the techniques. However, when considering overall performance and emphasizing accuracy as the main metric, the proposed EHMFFL model demonstrates its superiority over all the compared techniques for both datasets, underscoring its effectiveness in heart disease diagnosis.  
Figure 11: Analyzing the ROC curves of the different techniques using the Statlog dataset.  
Figure 12: Comparison of the results of the EHMFFL algorithm with the existing techniques using the Cleveland dataset.
# Digital Simulation and Analysis of Assembly-Deviation Prediction Based on Measurement Data
## 3 Digital Simulation Prediction and Analysis Experiment of Assembly Deviation
### Precision Measurement and Data Processing of Key Components  
#### 3.2.1 Three-Dimensional Precision Measurement of Key Components of the Core Machine  
In this paper, the MetraSCAN 750 TM 1 Elite handheld laser 3D scanning system is applied in combination with Hexagon's GLOBAL CMM for high-precision 1D, 2D and 3D measurement of key components of the core machine. Figure 5 shows how CMM and 3D scanning work.  
The assembly of the micro-combustion engine is required to ensure a sufficient clearance between the rotor blade tip and the guide of the core engine, so all relevant feature dimensions along the dimensional chain where the clearance is located need to be measured.  
The dense point cloud data obtained via laser 3D scanning reflect the 3D shape, the distribution of high and low points along with the exact location of the parts, etc., which makes it suitable for the scanning of complex and curved parts. There are many micro gas turbines with complex curved parts suitable for data extraction via 3D scanning, such as the compressor impeller and turbine impeller used in the combustion engine rotor. Herein, it is verified by repeated scanning of the standard ball with the handheld laser 3D scanning system that the instrument deviation is within 0.025 mm, which meets the need to measure the parts with a less demanding accuracy requirement than 0.05 mm. For the parts with smooth surfaces, the quality of the point cloud obtained via the laser scanner can be compromised. To solve this problem, such methods as powder coating of the part surface can be used to obtain high-quality point clouds [21]. To measure, the part is fixed to  
Figure 4: Calculation results of measurement point 14 of the micro-turbin: (**a**) Before assembly optimization; (**b**) After assembly optimization.  
Figure 5: The division of labor and cooperation between CMM and 3D scanning.  
the fixture and then scanned with a 3D scanner to obtain all the point cloud data of the part. To avoid errors in the scanning results, each part needs to be scanned three times. For those large and complex parts, it is usually necessary to scan the part from multiple perspectives and then combine the point cloud data obtained from multiple angles to determine the complete 3D shape of the part.  
CMM is characterized by sparse measurement points and high measurement accuracy, with the highest accuracy reaching up to 0.001 mm, which makes it suitable for the highly accurate measurement of dimensional and tolerances. There are many key components in the core machine requiring high processing accuracy; for example, the roundness, coaxiality and straightness of the combustion engine rotor must be within 0.004 mm. In this case, the measurement can be performed via the CMM with higher measurement accuracy. To measure, the workpiece is fixed on the CMM platform and then the probe is used to measure high-precision part assembly features with accuracy requirements within 0.04 mm.
# Experimental Analysis of Transformer Core Vibration and Noise under Inter-Harmonic Excitation
## 1 Introduction  
Power inter-harmonic refers to any harmonic whose frequency is not an integer multiple of the fundamental frequency, and the inter-harmonic whose frequency is less than the fundamental frequency is usually called sub-harmonic [1]. Common main sources are nonlinear loads, inverter speed governors, and sub-synchronous oscillation of wind turbines [2; 3], etc. Among them, the inter-harmonic frequency generated by common nonlinear loads is 0-50 Hz; the inter-harmonic frequency generated by the frequency conversion device is a discrete value related to the sum or difference of the input and output side frequencies, the inter-harmonic generated by the sub-synchronous oscillation of the wind turbine. The frequency is generally less than the synchronization frequency of 50 Hz. As the main equipment of the power system, the transformer is bound to be affected by inter-harmonics. The existence of inter-harmonics will cause many problems, such as premature saturation of the transformer core, severe distortion of the excitation current, increased vibration, and an abnormal increase in noise.  
At present, many scholars have carried out a lot of research on transformer vibration and noise. Through experimental research, literature [4; 5] concludes that the main reason for transformer vibration and noise is iron core magnetostriction; literature [6; 7] studied the vibration and noise of transformer core under power frequency sinusoidal excitation; literature [8; 9; 10; 11] studied the magnetostriction of the material and the magnetization state and vibration of the iron core under DC bias; literature [12; 13; 14] studied the vibration noise of the transformer core under harmonic and DC bias excitation. Many scholars have simulated the vibration and noise values of transformers and reactors through multiphysics coupling software [15; 16; 17]; literature [18; 19] analyzed the influence on the electromagnetic vibrationcharacteristics of equipment by simulating the condition of each harmonic by superimposing harmonics of fundamental wave excitation; literature [20] systematically studied the magnetization characteristics and loss characteristics of electrical steel materials under excitation with inter-harmonics. However, research on the influence of inter-harmonics on power transformers is still rare, so it is very important to measure the vibration and noise characteristics of transformer cores under the action of inter-harmonics.  
In this paper, the inter-harmonic influences on transformer core vibration and noise are studied through experiments. A core model laminated by oriented silicon steel sheets was made, and core vibration and noise experiments were carried out. The effects of inter-harmonic frequency and content on transformer core vibration and noise were analyzed. At present, there is little research on transformer core vibration and noise under inter-harmonic excitation. The experimental platform and experimental methods in this paper can provide a reference on transformer core vibration and noise under inter-harmonic excitation.
# A Depth-Based Hybrid Approach for Safe Flight Corridor Generation in Memoryless Planning
## 4 Free Space Detection  
For classical approaches, the difficulty of the SFC generation problem increases with the environment's clutteredness. We want to generalize the free space partitioning problem for any arbitrary scenarios. Instead of extracting safe corridors by sampling [26] and geometrical inflating [12] as classical approaches, we employ a learning-based network for detecting free space directly from depth images, which requires an equal amount of computing resources for all scenarios.
# Application of Pulsed Laser-TIG Hybrid Heat Source in Root Welding of Thick Plate Titanium Alloys
## 3 Results and Discussion
### Arc Behaviors Observation  
In order to facilitate the observation, bead-on-plate welding experiments were carried out. To detail the comparisons of arc state, the arc behaviors from a side view were captured by a high speed camera. Figure 7 shows the arc behaviors in single TIG and hybrid welding with the _Dla_ 2 mm, respectively. The welding parameters are listed in Table 2. As illustrated in Figure 7, due to the DC current being employed in this experiment, in single TIG welding the arc always discharges stably along the tungsten extension. However, the arc is compressed cyclically with the pulse laser in hybrid welding. In comparison with single TIG welding, the 4 mm distance between the tip of the tungsten and discharge position, is reduced to 2 mm with laser action. This quantitative description proves that with the action of laser pulses, the arc will act at different locations of the molten pool periodically.  
The electronic temperature, which is considered as a main metric of the average kinetic energy of the micro-particles in the plasma, is an important measurement to study the heat and mass transfer of plasma, and it also reflects the spatial distribution of the arc state from the other side [19]. Figure 8 shows the major difference of distribution of the electronic temperature between TIG welding and hybrid welding. The result shows that the distribution of electronic temperature presents the dispersing state in TIG welding, but the compressing state in hybrid welding. In the hybrid welding process, although the electronic temperature near the region where laser acts is decreased, the high temperature region is compressed directly above the laser keyhole. Compared with TIG welding, the axis of high temperature region in the hybrid welding process changes from a straight line to a curve. In the hybrid welding process, the plasma is located above the laser action point vertically and connects to the electrode obliquely. This phenomenon is also shown in Figure 9. The electronic temperature decreases from the tungsten electrode to the workpiece in the TIG welding process while it decreases firstly and then increases again from tungsten electrode tip to laser action spot in the hybrid welding process. The addition of the laser is the main factor which causes this change of the distribution of the electronic temperature in hybrid welding process. When the laser is added, titanium is vaporized rapidly with low ionization energy, Ti atoms are ionized and thus plasma with recoil pressure is formed. The recoil pressure causes the laser induced plasma to be distributed vertically above the laser keyhole. The titanium atoms are cumulatively ionized due to the collision with high-energy electrons. The laser keyhole will supply plenty of electrons for arc plasma, so the conducting route changes.  
Figure 7: Arc behaviors in different welding process: (**a**) single TIG; (**b**) hybrid welding.
# A Mask-Based Adversarial Defense Scheme
## 3 Materials and Methods
### Mitigating Adversarial Attacks
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Attack Method** & **0** & **1/3** & **1/2** & **3/4** & **4/5** \\ \hline Benign & **84.18\%** & 85.31\% & 84.59\% & 82.65\% & 76.14\% \\ \hline FGSM L1 \(\epsilon=15\) & 63.53\% & 70.17\% & 78.37\% & 82.63\% & **85.30\%** \\ FGSM L1 \(\epsilon=20\) & 63.01\% & 64.82\% & 73.70\% & 79.25\% & **82.14\%** \\ FGSM L2 \(\epsilon=0.3\) & 67.36\% & 75.95\% & 82.46\% & 84.62\% & **87.55\%** \\ FGSM L2 \(\epsilon=0.4\) & 66.23\% & 70.18\% & 78.33\% & 81.89\% & **84.66\%** \\ FGSM Lift \(\epsilon=0.01\) & 48.88\% & 68.87\% & 76.07\% & 81.05\% & **83.74\%** \\ FGSM Lift \(\epsilon=0.02\) & 40.76\% & 53.77\% & 61.30\% & 68.77\% & **70.61\%** \\ BIM L1 \(\epsilon=10\) & 59.02\% & 67.66\% & 79.63\% & 84.71\% & **87.88\%** \\ BIM L1 \(\epsilon=15\) & 58.23\% & 51.27\% & 68.89\% & 80.91\% & **84.91\%** \\ BIM L2 \(\epsilon=0.3\) & 61.86\% & 61.60\% & 75.69\% & 83.21\% & **87.04\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Defense of different mask rates with the grid size of \(8\times 8\) for the training phase and \(4\times 4\) for the test phase, on a VGG16 model with CIFAR-10 dataset. Figure 3: An experiment showing weakened effect of partially removed adversarial vectors generated from CIFAR-10 samples on the accuracy of a VGG16 network.
# An Analysis of Methods and Metrics for Task Scheduling in Fog Computing
## 7 Literature Review of Problem Solution Techniques
### Fuzzy Logic-Based Scheduling Algorithms  
[PERSON] et al. [30] proposed approach involves assigning tasks in the fog layer to the nodes with the least load, leading to a significant decrease in the waiting time required to complete these tasks. The decision-making algorithm applies task scheduling to fog and cloud resources based on fuzzy logic. Furthermore, the study overlooked the essential aspect of energy consumption.  
An alternative task scheduling approach was introduced, where tasks are selected from a set and transferred to a fog node in a sequential list [71]. This study uses a fuzzy logic-based scheduling method that guarantees task deadlines. The priority values determine the degree of urgency for executing the tasks. However, the study did not consider energy consumption and costs. The authors also overlooked the potential benefits of using cloud computing to reduce system costs.  
[PERSON] et al. [51] examine how user preferences are affected by the constraints of fog nodes and prioritize the reduction of delay and energy consumption. They propose an approach to task scheduling that uses ranked lists and considers user preferences and fog node constraints. The method utilizes a linguistic and fuzzy quantified proposition to prioritize the fog nodes.  
[PERSON] et al. [75] present a QoS-aware approach to placing applications that prioritize the different location requirements for applications, considering the multiple users' expectations. The proposed method evaluates fog instance capabilities based on the current state. Fuzzy logic determines the expected rating for application placement requirements, considering multiple user expectation parameters and the capability rating of fog instances, which is determined by their various state parameters. They aim to maximize the overall gain in Quality of Experience (QoE) for the user by prioritizing a less congested network, ensuring sufficient allocation of resources, and minimizing the time taken to process applications. The authors did not take into account the energy consumption of fog nodes.
## 3 Proposed Methodology
### Bresenham's Line Algorithm  
Subsequently, these control point pairs are processed using the Bresenham line drawing algorithm [18]. As already discussed, this algorithm calculates the optimal sequence of grid coordinates that follow the line that connects a pair of points. We can use this sequence to scan the image from one control point to the other along the normal of the curve. As each line is followed, the coordinates of the traversed pixels are registered. This operation is repeated for all pairs of control points. The result is a set of pixel coordinates and their respective pixel values, which are then stored in columns stacked next to each other in the order each line crosses the curve from left to right. Each column is then interpolated using linear interpolation to a standard length, so that all stacked columns to fit an array of height \(2H+1\), where \(H\) represents the total height of the text. This array is essentially an approximate mapping of the text from the 2D manifold defined by the text's curvature to the 2D Euclidean space of the final image. In other words, it serves as a semi-processed transformation into a rectangular shape, capturing the essential structural information of the image based on the defined curvature and is used for further processing steps to refine the final image output. Figure 6 shows the semi-processed image. Most of the text curvature has been corrected, OCR software and algorithms can already discern the majority of the text content at this stage, and the final alignment step will address the finer details.
# Nickel and Cobalt Price Volatility Forecasting Using a Self-Attention-Based Transformer Model
## 2 Literature Review
When used with time series data, the attention mechanisms of the transformer architecture allow the model to focus on the most important data, enabling it to capture long-term relationships between time steps accurately. TFTs are particularly well-suited for applications that require the projection of multiple time steps into the future, and they can handle large amounts of datasets. They have been shown to achieve state-of-the-art performances on a variety of time series forecasting tasks, including univariate and multivariate forecasting. In addition to forecasting, TFTs can be utilized for various time series assessment tasks such as anomaly detection and the interpolation of missing values. They are versatile, and can process many types of time series data including both typical and atypical time series. Recent studies that have utilized TFTs include those by [24; 25; 26] which have applied them to a variety of applications ranging from wind speed prediction to tourism demand. In this study, we focus on forecasting the volatility of the two most critical metals used for fossil-free energy, namely Nickel and Cobalt.
## 1 Introduction
The violation of this basic assumption, caused by distortion within the brain tissue during surgery, results in a mismatch between preoperative and intraoperatively acquired imaging, which can be reported as brain shift [28; 29], and is expected to a larger extent after durotomy [23]. Generally, iUS is believed to improve resection control in tumor surgery. However, [PERSON] et al. described a rate of 18% of discordance between 2D iUS and postoperative imaging with regards to the extent of tumor resection of parenchymal brain lesions [30]. Additionally, 2D ultrasound (either 5 MHz phase array for deeply located or 10 MHz for superficial lesions) was found to be less reliable than 1.5T iMRI in detecting small remnants of a size of less than 1 cm\({}^{3}\)[31] and imaging quality diminishes during tumor resection [32]. Similarly, when using navigated 3D ultrasound in glioma surgery, [PERSON] et al. found a high specificity and sensitivity of 95% before resection, which decreased over the course of the operation and sensitivity was as low as 26% after resection, while specificity remained at 88% [33]. Nonetheless, the authors of the mentioned studies pointed out, that the benefits of iUS outbalance the shortcomings, provided the surgeon is aware of its limitations [30; 31; 32; 33]. In our institution, navigated 3D ultrasound became fully implemented into the workflow of most cranial procedures. For tumor resection, navigated i3D US images are acquired routinely after craniotomy and as a resection control after tumor removal. Since the initial ultrasound acquisition is conducted before dural opening, one would assume the influence of brain shift to be moderate. However, displaying the ultrasound images as an overlay to the preoperative MRI during surgery conveys in some cases the impression of object incongruency, although this method only allows a gross estimation. Different aspects contributed to the decision to acquire the first i3D US before dural opening. First, the dural layer protects the brain and allows a gentle probe swiping over the surface without direct contact to the cortex, while it does not cause significant artifacts. Second, the tough dural layer also reduces the influence of probe pressure and brain pulsation. Finally, we aimed to address the question of how the imaging modalities MRI and iUS resemble each other, respectively,differ under optimal conditions of a minimized brain shift, which is given at the earliest possible operative stage right after craniotomy. To specify this, we prospectively obtained navigated i3D US datasets in a series of 35 cases of brain metastasis, which shared the feature to have more delineated boundaries in US than, e.g., gliomas, as described by [PERSON] et al. before [34]. We compared i3D US to preoperative MRI, analyzing the tumor volume, shape, and distance of segmented objects.
# Predicting the Aggregate Mobility of a Vehicle Fleet within a City Graph
## 1 Introduction  
Predicting the mobility of vehicles can be of great help to smart city management (e.g., location-based services, sensing, etc.) and smart vehicle applications (e.g., transportation systems, communications, etc.) [1; 2; 3; 4; 5; 6].  
Depending on the application and domain, the prediction can be done based on the data of each element (disaggregated data) or using aggregated values for a given region or way. Although disaggregated data can potentially lead to more accurate results, personal mobility data may not always be available Storing this type of information long-term or sharing it with third parties may lead to privacy and legal issues. Thus, this work focuses on directly applicable and privacy-preserving models on aggregated data.  
In recent years, many real-world datasets have been published, and they have become the norm in GNN-based and broader ML-based traffic forecasting studies [7]. Evaluating with real data eliminates any potential questions about the validity of the data and the applicability of the model in real scenarios. However, traffic simulations have two potential benefits. Firstly, simulations have the ability of modeling unseen graphs, such as a planned road topology [7]. Secondly, evaluating predictive models on synthetic and extreme scenarios can help to better understand them and discover their limitations [8].  
Taking into account that asset (vehicle) mobility in a city can be formalized using the graph that defines the streets (edges), intersections (nodes), and directions (associated with edges), in this work we address the prediction of _aggregated_ asset distribution along the nodes of this graph, based only on past aggregated distribution history. Aggregated patterns are a result of individual asset behavior, which in real-life scenarios can be very complex. This in turn hinders the ability to reason about the emergent behavior or to construct extreme scenarios that test the limits of a given model. Hence, this paper focuses on a very simple mobility model based on Markov Chains (MC). This model is parameterized by a transition matrix, which can be easily tuned. Then, we propose three prediction schemes: a method based on a Markov Chain (MC) model [9], and two methods based on Multilayer Perceptrons (MLP) [10] and graph neural networks (GNNs) [11].  
The rest of this paper is organized as follows. Section 2 presents relevant works in the areas of mobility models, mobility prediction, and graph neural networks (GNNs). In Section 3, we formalize the problem as a time series prediction task. A first prediction scheme based on a Markov Chain model is developed in Section 4. In Section 5 two alternative neural network prediction schemes are designed: one based on a multilayer perceptron (MLP) and the other on graph neural networks (GNNs). Simulations performed to illustrate the applicability of the proposed schemes are presented in Section 6. Concluding remarks and future lines of work to be addressed are stated in Section 7.