esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
To estimate tire pattern noise signals more accurately, convolution and adaptive filter theory was applied, and the results were verified by comparing the measured data with the estimated data from tire pattern images. We also compared the proposed method with the traditional method (simple scan method), and the adaptive filter method was found to more efficiently predict the tire pattern noise. By using this method, we can obtain more precise wave file data from tire pattern images and its sound quality. Although the estimated sound pressure does not exactly correspond to the measured sound pressure, the loudness of both pressures correspond, and the value of \(r^{2}\) is 96. It can be concluded that the proposed method can be used for prediction of sound quality of pattern noise in the early design stage. For better estimation of sound pressure based on adaptive filters, deep learning with more data can be applied. Eventually, using our proposed prediction method, the time and cost spent on development of tire pattern shapes can be effectively reduced and the quality of tires can be improved.  
S.-K.L. contributed to simulation and algorithm development and writing the paper. K.A. contributed to the vehicle experimental test, H.-Y.C. and S.-U.H. supplied test tires and anechoic chamber for tire noise test.  
This work was supported by Mid-career Researcher Program through NRF of Korea grant funded by the MEST (No. 2019R1A2B5B02069400).  
This work was supported by Inha University.  
The authors declare no conflict of interest.
# Small-Target Detection Based on an Attention Mechanism for Apron-Monitoring Systems
## 3 Materials and Methods  
This study presents a model that shows improvements in terms of three aspects: feature extraction, feature fusion, and classification. First, to enable the network to learn to extract the representative features of small targets on airport aprons and provide more effective feature information for the classifier and mask prediction, the proposed method adds an attention module [27; 28; 29] to the feature-extraction network. The dependency relationship and positional relationship in the feature space guide the network to increase the weight of useful features of small targets on airport surfaces, making the network pay more attention to features that are conducive to the detection of small targets and ignore redundant and invalid information. Second, for the feature fusion module, a bidirectional feature pyramid network (BiFPN) is introduced [30; 31] and then compared with the traditional feature pyramid network (FPN). BiFPN uses weighted fusion that enables the network to perform fusion more effectively. Finally, a more effective detection branch structure is proposed by changing the network structure in the original algorithm, thereby further improving the detection accuracy of small targets [32; 33].  
Figure 1: Network structure.
# Dosimetric Optimization of a Laser-Driven Irradiation Facility Using the G4-ELIMED Application
## 3 Results
### Transmission of Unwanted Ion Species  
In all the simulations presented above, only protons were considered. However, depending on the specific target used to produce the proton beam, some heavier, high-energy ions may be generated. The presence of such unwanted ion species could be detrimental for the sample irradiation if they are not properly filtered out by means of the beamline elements, thus affecting the results of the experiment. Therefore, the heavy-ion transmission effects must be studied prior to the proton beam irradiation. The transmissions of carbon ions with different charge states (from C\({}^{1+}\) to C\({}^{6+}\)) were considered at this stage. Such simulations were performed using a Carbon ion source similar to the proton one, but with a maximum cut-off energy calculated using the following empirical formula:  
\[E^{n+}_{cut-off}=E^{p}_{cut-off}.n/2, \tag{1}\]  
where \(E^{n+}_{cut-off}\) and \(E^{p}_{cut-off}=33\) MeV are the maximum cut-off energies for carbon ions with a charge state equal to \(n\) and for protons, respectively.  
The simulations were carried out only up to the exit of the ESS where the C\({}^{1+}\), C\({}^{2+}\), and C\({}^{3+}\) beams were filtered out, while the C\({}^{4+}\), C\({}^{5+}\), and C\({}^{6+}\) beams presented a transmission over the total initial number of carbon ions of 10\({}^{-2}\)%, 3 \(\times\)10\({}^{-2}\)% and 5 \(\times\)10\({}^{-2}\)%, respectively. The simulations could be extended to the whole beamline but, as it is shown in Figure 6, the maximum kinetic energy after the ESS was around 70 MeV for C\({}^{5+}\) ions, and the maximum range in the air for these ions is around 21 cm (according to ICRU range tables [37]). So,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Energy (MeV)** & **After PMQs (\%)** & **After ESS (\%)** & **After Kapton Window (\%)** & **Final (\%)** \\ \hline
18 & 39 & 2.93 & 2.67 & 0.37 \\
20 & 42.7 & 6.38 & 5.77 & 0.91 \\
22 & 37.1 & 6.56 & 6.2 & 0.81 \\
25 & 34.4 & 6.31 & 5.98 & 0.75 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Transmission efficiency values for different energy configurations and at different points along the ELIMED beamline.  
these ions would never be able to reach the irradiation point. Therefore, we can conclude that carbon ions in the given energy range are filtered out in the ELIMED beamline.
## 4 Experiments and Results
### Evaluation Measures  
After performing the experiment, we used several measures to evaluate our results. We calculated the confusion matrix for all the themes in the experiment. Each entry in the confusion matrix represents the following: the number of true positive (TP), which is the number of lines where the classification matches the correct theme; the false positive (FP) which represents the number of lines where the classification matches the incorrect theme as positive; true negative (TN), which is the number of cases where the classification matches the correct negative theme; and false negative (FN), which refers to the number of lines where the classification matches the incorrect negative theme.  
We also evaluated the model to assess the performance using accuracy, precision, recall, and F1 score ([PERSON], 2006), based on the result of the confusion matrix.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer** & **Output Shape** & **Number of Parameters** \\ \hline Embedding & (None, 520, 100) & 5,000,000 \\ \hline Dropout layer & (None, 520, 100) & 0 \\ \hline LSTM & (None, 100) & 80,400 \\ \hline Dense & (None, 4) & 404 \\ \hline Total parameters & 5,080,804 & \\ \hline Trainable parameters: & 5,080,804 & \\ \hline Non-trainable parameters & 0 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Details of long short-term memory (LSTM) model.  
_Accuracy_ is the measurement of all lines that have been classified correctly. It consists of all the true positive and negative lines divided by all the lines being classified. It is defined as:  
\[Accuracy=\frac{\#\;\text{TP}\;+\;\#\;\text{TN}}{\#\;\text{TP}+\#\;\text{FP}+\#\; \text{FN}+\#\;\text{TN}}.\]  
_Precision_ implies the number of selected lines that are correctly classified as positive. It consists of all the true positive lines divided by all the lines that are classified as positive. It is defined as:  
\[Precision=\frac{\#\;\text{TP}}{\#\;\text{TP}+\#\;\text{FP}}.\]  
_Recall_ indicates the proportion of actual classes that are correctly categorized as positive. It consists of all the true positive lines divided by all the lines that are actually positive. It is defined as:  
\[Recall=\frac{\#\;\text{TP}}{\#\;\text{TP}+\#\;\text{FN}}.\]  
_F1 score_ is the combination of both precision and recall. It is defined as:  
\[F1=2\times\frac{Recall\times Precision}{Recall+Precision}\.\]
## 3 Results
### Contouring Time Evaluation  
The DL auto-contouring approach required about 40% less time to complete all OARs segmentation for a dataset on average. This was contributed by 55% and 35% less time required for initial auto-contouring and subsequent review and editing processes, respectively. Table 3 illustrates the mean time required for the atlas-based and deep learning auto-contouring processes. The DL approach had statistically significantly shorter time required for auto-contouring and review and editing, and hence the whole process.  
Figure 4: Sagittal view of spinal cord contouring for a 75-year-old man with T2N2M0 nasopharyngeal cancer by manual contouring (**A**), DL (**B**) (Dice similarity coefficient (DSC): 0.48, Hausdorff distance (HD): 188.62 mm, and 95 th-percentile of Hausdorff distance (HD95): 91.27 mm) and atlas-based auto-contouring (**C**) (DSC: 0.86, HD: 6.55 mm, and HD95: 2.34 mm).
## 2 Design Principles
### Operation Mode  
AAOISE was designed to work unattended to support science instruments with power supply, temperature hold, and remote communication continuously for nearly one year. It is suitable for science equipment installation and operation, making the maintenance work easier at the same time. The design principles are sufficient, small, easy to be expanded, and maintainable. The links betweenthe AAOISE and remote inland observatory are very limited due to its special geographical position with severe environment and confined communication satellite bandwidth, so automatically running is AAOISE's most important fundamental daily mode of operation, which is called \"independent work, remote fine-tuning\" [28; 29].  
Under the self-determination situation, several main tasks need to be completed by AAOISE [30; 31]. Firstly, it was designed to finish complete energy production, transport, supply, and protection. Secondly, it provides an appropriate, stable cabin environment (main index is temperature). Thirdly, it responds to the communication needs of the scientific instruments from time to time. Fourthly, it supports mass data storage for scientific instruments and own maintenance. Fifthly, it regularly communicates with remote observation.  
The remote fine-tuning mainly includes transmitting the remote controlling command and a few control parameters from the inland observatory. The function was realized with the help of the satellite communication channel and the AAOISE inner site control network. Because of this, AAOISE paid rent to the United States iridium satellite for its communication channel. The AAOISE inner site control network is set up based on the ControlNet field bus [32].
# A Novel Feature Selection Based on VMD and Information Gain for Pipe Blockages
###### Abstract
The advantage is that the screening process does not carry out traversal trial, the combination effect of characteristic factors is less considered and the calculation speed is faster [30]. In summary, this paper proposes a novel approach based on VMD, information gain, sound pressure level, and root mean square entropy (RMSE) for identifying blockages in pipelines. First, Butterworth filtering is performed on the collected acoustic signals initially in this study. Then variational mode decomposition (VMD) is implemented on the filtered signals, followed by the simplification and filtering of components based on information gain. Finally, conversion of sound pressure level is carried out on the filtered components, and the RMSE are extracted as the eigenvectors, which are input into four different classifiers k-Nearest Neighbors (KNN), Support Vector Machines (SVM), Extreme Learning Machine (ELM), and Random Forests (RF), thereby achieving the classification and identification of the pipe condition. The present study has great practical significance for the detection of pipe blockage severity. The paper is organized as follows: Section 2 introduces the VMD and information gain methodologies and provides the basic framework of the proposed hybrid model. Section 3 Brief Experimental setup and experiment conditions. Section 4 is pipe blockage methodology which includes feature extraction, selection, and fault identification. Section 4 verifies the diagnostic performance of the proposed method. Finally, Section 5 concludes the work.
## 3 Validation Analysis, Results, and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**References** & \begin{tabular}{c} **Publication** \\ **Year** \\ \end{tabular} & \begin{tabular}{c} **Overall Size** \\ **(mm\({}^{3}\))** \\ \end{tabular} & \begin{tabular}{c} **System Ground** \\ **Size (mm\({}^{3}\))** \\ \end{tabular} & \begin{tabular}{c} **Antenna Size** \\ **(mm\({}^{3}\))** \\ \end{tabular} & \begin{tabular}{c} **Operation** \\ **Range (GMd)** \\ \end{tabular} & \begin{tabular}{c} **Resonant** \\ **Frequency (GHz)** \\ \end{tabular} & \
\begin{tabular}{c} **Bandwidth (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Gain** \\ **(480)** \\ \end{tabular} & **Applications** \\ \hline
[26] & 2020 & 36 \(\times\) 38 & 36 \(\times\) 21.7 & 38 \(\times\) 24.4 & 45.5-5.57 & 5.035 & 30\% & \\
[27] & 2020 & 20 \(\times\) 19 & 10 \(\times\) 19 & 13.5 \(\times\) 10 & 2.4-2.54 & 2.74 & 5.1\% (140 MHz) & 2.1 & WRAN \\
[28] & 2020 & 54 \(\times\) 37 & 5 \(\times\) 54 & 54 \(\times\) 32 & 1.6-22 & 1.9 & 31.5\% (200 MHz) & 1.67 & - \\ \hline
[29] & 2020 & 24 \(\times\) 19 & 8 \(\times\) 8.5 & 16 \(\times\) 16 & 2.33-2.47 & 2.4 & 5.8\% (140 MHz) & 2.26 & - \\
[29] & 2020 & 24 \(\times\) 19 & 8 \(\times\) 8.5 & 16 \(\times\) 16 & 3.68-3.92 & 6.3 & 13.3\% (200 MHz) & 3.14 & Bluetooth/SC NR/WLAN \\  & & & & 5.38-5.75 & 5.66 & 6.0\% (200 MHz) & 3.73 & \\
[30] & 2021 & 260 \(\times\) 208 & 260 \(\times\) 200 & 21 \(\times\) 8 & 32.5-5.63 & 2.45 & 5.8\% (500 MHz) & 4.38 & LTE/WIFI/sub-6 GHz/SC bands \\  & & & & 5.6-4.1 & 5.705 & 6.6\% (140 MHz) & 5.68 & \\ \hline Proposed & - & 50 \(\times\) 10 & 42.5 \(\times\) 10 & 12.5 \(\times\) 7.5 & 2.2-2.74 & 2.46 & 31.22\% (740 MHz) & 2.7 & 5G, B5G, 4G, V2X, ISM band of WLAN, Bluetooth, 4.002-8 & 6.205 & 65.29\% (0930 MHz) & 6.75 & WiFi 6 band and WMAN, Strings/MM Radio \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the proposed antenna with other research literature.
# Sensitivity Estimation for Differentially Private Query Processing
## 5 Experiments
### Experimental Setup
#### 5.1.4 Competitors  
In the following experiments, our methods are called \"Sampling-SE\" and \"Sketch-SE\", which stand for sampling-based sensitivity estimation and sketch-based sensitivity estimation, respectively. The competitors to our methods include elastic sensitivity (ES) [10] and residual sensitivity (RS) [11].  
Figure 5: Join structure of queries on the TPC-H (Q1–Q3) and Facebook datasets (Q4–Q7).  
(1) ES: Elastic sensitivity is a smooth upper bound of local sensitivity. ES is computed based on the frequency of the most frequent join attribute in each relation.  
(2) RS: Residual sensitivity is a smooth upper bound that is tighter than ES. It is computed based on the maximum boundaries of the residual queries.
## 3 Research Objectives and Methodology
### Research Hypotheses  
Based on previous studies, literature search, and preliminary data analyses, it is possible to formulate several hypotheses that will be tested in the proposed research. The present scientific research is based on the assumption that the increasing share of electricity generation from renewable sources--particularly solar and wind--has a fundamental impact on the market dynamics in the daily wholesale market. Based on the analysis of previous studies, the continuous processing of historical data and the current developments in European power systems, the following hypotheses were formulated:  
**Hypothesis 1 (H1).**_It is assumed that there is a systematic negative relationship between hourly generation from wind and solar resources and the price of electricity that is statistically significant and replicable across multiple countries and time periods. Hours with high RES generation are expected to experience a decrease in market price, with this effect being most pronounced in countries with higher penetration of variable resources._  
**Hypothesis 2 (H2).**_It is assumed that there is a threshold level of combined wind and solar generation beyond which the probability of price extremes, especially negative ones, increases significantly. This non-linear dynamic should be identifiable by frequency analysis and segmentation of the data by production intervals. The effect is also expected to depend on the season and time of day._  
**Hypothesis 3 (H3).**_The predictive ability of simple linear trends between daily RES generation (solar and wind) and electricity price is assumed to vary significantly between years. In years with extreme price developments (e.g., during an energy crisis) or with an increasing share of RES, the value of the coefficient of determination \(R^{2}\) is expected to be higher, while in stable years the relationship is expected to be weak or statistically insignificant. The analysis of annual regression models should reveal the temporal variability of the predictive power of these relationships._  
**Hypothesis 4 (H4).**_Significant regional differences in the impact of RES generation on price volatility are assumed to exist, conditioned by the degree of system flexibility and the interconnect-edness of individual national markets. Countries with better developed flexibility measures (e.g., accumulation, demand management, cross-border exports) are expected to show lower volatility even at comparable levels of RES generation._  
**Hypothesis 5 (H5).**_We believe that it is possible to statistically identify predictive patterns of negative price incidence based on a combination of production and time factors. It is assumed that multivariate analysis (e.g., regression models and decision rules) can be used to identify rules such as \"if RES generation exceeds a certain threshold during a weekend in the summer, the probability of negative prices is XX%\". These prediction schemes can serve as a basis for developing pricing strategies and optimising trading behaviour in the daily market._
# A Novel Method for 3D Lung Tumor Reconstruction Using Generative Models
## 3 The Proposed Model
### Lung Segmentation
#### 3.1.2 Discriminator Architecture  
The discriminator network plays a pivotal role in differentiating between the masks created by the generator and the genuine masks. The Earth Mover's (EM) distance is used as an effective and smooth measure to gauge the difference between the true and generated mask distributions. The discriminator approximates the EM distance by evaluating the gap in expected values between samples from the model's distribution \(p_{z}\) and the actual distribution \(p_{real}\). This is conducted by analyzing the differences in the outputs from \(D_{x\sim p_{z}}(G(x))\) and \(D_{x\sim p_{real}}(x)\). Unlike the conventional discriminator in a Generative Adversarial Network (GAN), which is typically tasked with classification, our discriminator assumes a regression function. Its goal is to approximate the EM distance function precisely. In tasks like image generation with GANs, the aim is often to create images that closely mimic real ones. However, in lung segmentation, the challenge is different. The mask generated by the generator varies in intensity across a continuum from 0 to 1, in contrast to the mask, which is strictly binary, consisting entirely of 0 s and 1s. This difference could cause the discriminator to focus too narrowly on distinguishing between real and generated masks, possibly missing other important aspects. To avoid this, we do not directly input the generator's output or the real masks into the discriminator. Instead, we use a modified approach where the discriminator's input includes lung slice images. These images are altered versions of the original CT scans, modified using segmented and actual masks. This technique allows the discriminator to evaluate the relationship between the generated masks and the original lung
# Recent Improvements in the Development of Soft Grippers
## 3 Dexterous Manipulation
### Performance Evaluations
#### 3.3.2 Evaluation of Dexterous Soft Grippers  
Dexterous grippers can be evaluated based on their level of dexterity, their shape, their characteristics, their performance, and even the control algorithms. The most significant evaluation methods proposed in the literature are as follows:  
* Kinematic dexterity level [57]: it is an evaluation based on the grasping and manipulation capabilities of robotic hands. Depending on whether the grasping and manipulation are made with the fingertips or the whole hand, the dexterity is evaluated from 0 to 1, with the weighting shown in Figure 5a.  
* FFP index [84]: it is an evaluation based on the shape or the form, the features, and the performance of robotic grippers. It evaluates dexterity from 0 to 300 based on the existence or absence of various characteristics. The weighting is shown in Figure 5b.
* Taxonomy of benchmark tasks [85]: it is an evaluation based on the ability of the grippers to perform tasks that measure physical, dexterity, and functional capabilities. The hierarchy of tasks is given in Figure 5c.
* Open grasp suite [86]: it is an evaluation focused on the control algorithms of classical grippers. It is based on simulations with software developed by the authors.  
Figure 5: Evaluation of dexterous grippers. (**a**) Kinematics dexterity level [57]; (**b**) FFP index [84]; (**c**) Taxonomy of benchmark [85].
## 5 Conclusions  
In this paper, FRP grid-PCM-composite-reinforced RC beams are modeled and analyzed by the finite element software ABAQUS, and the effects of the main influencing factors on the flexural performance of FRP grid-PCM-composite-reinforced RC beams are investigated. The following conclusions are obtained:  
1. The finite element model established based on bond-slip cohesive contact is feasible and effective. It can fit the test phenomenon and the magnitude of bending load capacity better and can be used to simulate the bending specimens of FRP grid-PCM-composite-reinforced RC beams.
2. An FRP grid can effectively improve the flexural stiffness and bearing capacity of reinforced concrete beams and has a non-linear relationship with the amount of FRP grid reinforcement and the elastic modulus of the grid.
3. When debonding failure occurs in the reinforced beams subjected to bending, the FRP grid strain does not change significantly with the grid shape, but it is more affected by the amount of grid reinforcement and the type of grid material, and the greater the amount of grid reinforcement and the grid elasticity modulus, the lower the grid strain when debonding occurs.
4. The formula for calculating the debonding strain and the formula for calculating the flexural capacity of FRP grid-strengthened reinforced concrete beams with interfacial debonding failure were established. Based on the experimental data collected, the formulas were verified, and the results show that the peeling strain formula and flexural capacity formula proposed in this paper can provide a theoretical basis for the calculation or assessment of the flexural capacity of FRP grid-strengthened reinforced concrete beams.
# Impact of Texture Information on Crop Classification with Machine Learning and UAV Images
## Materials and Methods
### Classification Methods and Feature Extraction
#### 2.3.2 Support Vector Machine  
SVM is a machine learning algorithm for finding the optimal decision boundary of training data located at the boundary of classes [38]. The SVM classifier is known to be effective for classification with a limited amount of training data [39]. The main concept of SVM is to solve the optimization problem which maximizes the margin between decision boundaries [40]. To solve non-linear optimization problems, kernel functions such as radial basis function (RBF) are commonly used [39]. When the RBF kernel is used, the parameters of cost and gamma should be optimally determined. Large values of cost and gamma result in overfitting to the training data, yielding poor generalization ability of the classifier [41]. In this study, these two hyper-parameters were determined using a grid search based on 10-fold cross-validation of training data [42].