esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Analysis
### Performance Testing and Comparative Analysis
#### 3.3.1 Comparative Analysis of Loss Function Performance
In this comparison, the encoder of the Encoder-DNN model mirrors the specifications outlined in Table 7. Following the feature extraction process, a deep learning model utilizing cross-entropy loss is applied for ability level classification; the classifier hyperparameters are delineated in Table 7.  
Meanwhile, the Encoder-Focal model maintains encoder parameters identical to those stipulated in Table 8. This classifier incorporates a deep learning model with five hidden layers and employs the focal loss function described in Equation (4) as the loss function, adhering to the hyperparameter configurations indicated in Table 8.  
As deduced from the data in Table 5, the application of the WAFL loss function devised in this study notably enhances the performance metrics of the capability evaluation model, surpassing the outcomes of the neural network model devoid of the augmented loss function and the incorporation of focus parameters. Furthermore, there is a significant enhancement in the recognition accuracy of low-ability-level samples, achieving a recognition accuracy rate of 92.7% for samples under ability level A. This surpasses the 68.4% and 81.3% recognition accuracy rates achieved by the other two models, substantiating that refining cross entropy through adaptive focus and sample weights augments the emphasis on key samples.
## 1 Introduction
Two potential network topology spaces and two BO surrogate models, namely Gaussian Processes (GPs) and Random Forests (RFs), have been investigated in this paper. The contents of the paper are structured as follows: Section 2 precisely defines the problem at hand and the requirements that the solution must satisfy; Section 3 describes some recent existing approaches aimed at solving similar problems; Section 4 provides a detailed explanation of the experimental set-up; Section 5 reports the achieved results; and finally, Section 6 draws insightful conclusions and describes how this work could potentially be continued in the near future.
## 1 Introduction  
Hot-dip galvanized steel is widely used in automobiles, construction, home appliances and other industries due to its good welding performance and excellent corrosion resistance [1; 2; 3]. In addition, the corrosion resistance of hot-dip galvanized steel is mainly through the physical barrier protection of the surface coating and the electrochemical protection of zinc as an anode for steel under the action of corrosive media [4; 5; 6]. With technological development and industrial upgrading, when appropriate amounts of Mg and Al (usually 1-4 wt%) are added to zinc coating, a Zn-Al-Mg coating is formed, which can greatly improve the corrosion resistance of hot-galvanized steel [7; 8; 9; 10; 11; 12]. Thus, the Zn-Al-Mg-coated steel comes into being.  
Zn-Al-Mg-coated steel, as an upgrade product of hot-dip galvanized steel, not only has a much lower corrosion rate than hot-dip galvanized steel in the same environment but also has a good bending strength ratio, excellent surface hardness, low cost and other advantages. Therefore, Zn-Al-Mg-coated steel is widely used in automotive outer plates, brackets and internal components [13; 14; 15].  
As we all know, resistance spot-welding has become the main welding process of automobile steel plate links due to its fast welding speed, excellent welding quality, high degree of automation and low cost [16; 17; 18; 19; 20; 21]. The coating of Zn-Al-Mg-plated steel is melted first in the process of spot-welding due to the existence of plating, and the Zn element in the coating is the first to evaporate, reducing the temperature of the steel [9; 15; 22]. However, a part of the Al-Mg elements will remain in the weld, the formation process of the melting core is more complex than that of traditional galvanized steel.  
Therefore, improving the quality of the spot-welding joint of Zn-Al-Mg-coated steel and its welding process parameters are key problems. In addition, the main factors affecting the mechanical properties of resistance spot-welding joints are welding current, welding time and electrode loading [23; 24; 25]. Welding current mainly provides heat for the welding process. When the temperature exceeds the melting temperature at the workpiece interface, the welding core begins to produce. Not only is the size of the welding core related to the size of the welding current, but also the welding time and electrode loading have a great impact on it [26; 27].  
In order to explore the influence of Zn-Al-Mg coating on the resistance spot-welded joint properties of HC340 LAD + ZM steel, the optimal welding process parameters of HC340 LAD + ZM steel were explored through an orthogonal experiment design. Then, based on the optimal parameters, HC340 LAD + ZM with Zn-Al-Mg coating and HC340 LA without coating were designed as experimental welding parameters to explore the nucleation process of the spot-welding joint and the influence of the coating on the properties of the spot-welding joint and, further, to study the influence of the Zn-Al-Mg coating on the corrosion resistance of the spot-welding joint.
## 4 Interruption Performance of HB- vs FB-MMC DC Grids
### Comparison of Fault Current Blocking Performance (Pole-to-Ground Fault)  
In this section, we simulated a pole-to-ground fault at 0.2 s on the DC side of the rectifier to consider the worst-case fault. Figure 7a shows that the peak current magnitude is about 23.5 kA for both topologies. In the case of the HB-MMC system, the fault current of about 11.5 kA is sustained due to the presence of freewheeling diodes. However, in the case of the FB-MMC system, all IGBTs are blocked when a fault is detected, a reverse voltage is generated to block the fault current, and the fault current naturally approaches zero. The DC side voltage during the fault transient state is shown in Figure 7b. In both systems, voltage oscillations are sustained on the DC side.
## 2 Materials and Methods
### Validation of the Method with Ecological Status Metrics
Since both replicates and data from different dates were considered, this increased the variability. As explained above, for sites with more than one independent catchment ending up in the waterbody (NARB, BIGU, ITTI, THAU), for each catchment data included for the correlations were correlated with the values of the metrics for the part of the waterbody most affected by each sub-catchment. When testing the method, all the studied waterbodies were used without splitting them into groups based on salinity (conductivity) levels: this is because a wide range of pressures is required in order to evaluate and determine significant trends in the response of the ecological status metrics to such pressures, which cannot be achieved when splitting into groups because the sample would be too small. However, in this sense, some assays did show similar trends for cases with large enough sample sizes (data not shown). Further work will be developed in the future for this purpose. For validation of the hydrological and morphological pressures, the percentages of coverage by submerged and emerged macrophytes were used as metrics for assessing the impact of these pressures on the ecological status of the lentitic waterbody. These metrics are approved for ecological status assessment in Spain for assessing hydro-morphological pressures [36; 43]. Data used were provided by official sources of the Spanish River Basin Authorities (Table S2). The Spearman correlation coefficient was used to determine the correlation between the pressure levels estimated from land uses and the metric values, since in most cases the data did not follow a normal distribution. For hydrological and morphological pressures, Pearson correlation coefficients were applied, as both studied variables (metric values and pressure levels) were continuous and fitted a normal distribution. In this case, each waterbody had a pressure level and a metric value, in contrast with the land use pressures, where each waterbody had a single pressure level per pressure type with multiple metric values. Linear regression models were also obtained for determining the response of the metric values to the pressure levels. Linear regressions were selected following the recommendations of the WFD when studying the relationship between pressures and assessed ecological status.
# Graph-Level Label-Only Membership Inference Attack Against Graph Neural Networks
## 5 Experiments
### The Impact of the Number of Perturbed Graphs  
We further investigate the impact of the number of perturbed graphs on the attack performance of GLO-MIA, evaluating it on three datasets and four GNN models. The  
Figure 4: Robustness score distributions of members and non-members under different perturbation magnitude scaler sizes (s: scaler size; red: members; blue: non-members).  
Figure 3: Impact of perturbation magnitude on the performance of the attacks (blue line: GLO-MIA; red line: gap attack; x-axis: scaler size; y-axis: attack accuracy).  
results are shown in Figure 5, where the x-axis represents the number of perturbed graphs and the y-axis represents attack accuracy; the red and blue lines represent the attack accuracy of the gap attack and GLO-MIA, respectively. From the figure, we observe that as the number of perturbed graphs increases, the attack performance generally improves and approaches convergence, indicating that increasing the number of perturbed graphs enhances the attack's effectiveness. When the number of perturbed graphs is small (e.g., one to ten), the attack accuracy is relatively low but still effective, which means that GLO-MIA remains a threat even with limited query budgets. In practical applications, if computational resources permit, increasing the number of perturbed graphs can improve the attack accuracy.
# A Water Tank Level Control System with Time Lag Using CGSA and Nonlinear Switch Decoration
## 3 Control System Structure Design
### Controller Design  
We use the third-order CGSA to design the tank level controller and set the bandwidth frequency of the closed-loop system to \(1/T_{1}\). Then the complementary sensitivity function of the tank level control system at this time is also the closed-loop transfer function of the system.  
\[\frac{G(s)K(s)}{1+G(s)K(s)}=\frac{1}{\left(T_{1}s+1\right)^{3}} \tag{7}\]  
By substituting Equation (6) into Equation (7), the final robust controller is obtained as  
\[K(s)=\frac{1}{GT_{1}s(T_{1}^{2}s^{2}+3T_{1}s+3)} \tag{8}\]  
As can be seen from Equation (8), the controller designed using the third-order closed-loop gain-shaping algorithm is in the form of a typical PD controller with an oscillating component in series. It is simple and easy to implement, solving the problem of complex parameterization and the unclear physical meaning of conventional PD controllers.
# A Study of the Thermodynamic Properties of Nd-C and Ce-C TRISO Fission Product Binary Systems
## 3 Results
### Experimental Results  
The reported phase transition temperatures and associated enthalpies of fusion from this work are summarized in Tables 7-10, along with the available data in the open literature. Data are reported for all expected phase transitions with the exception of the low-temperature transition for sample Nd4. No discernible DSC peak or derivative inflection point could be observed for this predicted transition.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Compound** & **Phase Transition** & **Transition Temperature** [\({}^{\circ}\)C] & **References** \\ \hline Nd & aNd \(\equiv\)\(\beta\)Nd & 862 & [22] \\  & Liquidus & 1001.67 & [22] \\ Nd\({}_{0.9}\)C\({}_{0.1}\) & HCP + Nd\({}_{2}\)C\({}_{3}\)\(\rightleftharpoons\) HCP + L & 844.6 & This work \\  & HCP + L \(\rightleftharpoons\) BCC + L & 891.2 & This work \\  & Liquidus & 911.2 & This work \\ Nd\({}_{0.84}\)C\({}_{0.16}\) & HCP + Nd\({}_{2}\)C\({}_{3}\)\(\rightleftharpoons\) HCP + L & 724.1 & This work \\  & Liquidus & 771.2 & This work \\ Nd\({}_{0.8}\)C\({}_{0.2}\) & Eutectic & 731.8 & This work \\ Nd\({}_{0.75}\)C\({}_{0.25}\) & HCP + Nd\({}_{2}\)C\({}_{3}\)\(\rightleftharpoons\) Nd\({}_{2}\)C\({}_{3}\) + L & 726.1 & This work \\  & Liquidus & 762.2 & This work \\ Nd\({}_{0.7}\)C\({}_{0.3}\) & HCP + Nd\({}_{2}\)C\({}_{3}\)\(\rightleftharpoons\) Nd\({}_{2}\)C\({}_{3}\) + L & 718.5 & This work \\  & Liquidus & 976.4 & This work \\ Nd\({}_{0.65}\)C\({}_{0.35}\) & HCP + Nd\({}_{2}\)C\({}_{3}\)\(\rightleftharpoons\) Nd\({}_{2}\)C\({}_{3}\) + L & 825.1 & This work \\  & Liquidus & 995.3 & This work \\ Nd\({}_{0.63}\)C\({}_{0.37}\) & Nd\({}_{2}\)C\({}_{3}\) + L = L & 1100 \(\pm\) 0.5 & [22] \\ Nd\({}_{0.61}\)C\({}_{0.39}\) & Nd\({}_{2}\)C\({}_{3}\) + L = L & 1200 \(\pm\) 0.5 & [22] \\ Nd\({}_{0.6}\)C\({}_{0.4}\) & HCP + Nd\({}_{2}\)C\({}_{3}\) = Nd\({}_{2}\)C\({}_{3}\) + L & - & This work \\  & Liquidus & 1274.6 & This work \\ Nd\({}_{0.55}\)C\({}_{0.45}\) & Nd\({}_{2}\)C\({}_{3}\) + L = L & 1350 \(\pm\) 0.5 & [22] \\ Nd\({}_{0.51}\)C\({}_{0.49}\) & Nd\({}_{2}\)C\({}_{3}\) + L = L & 1500 \(\pm\) 0.5 & [22] \\ Nd\({}_{0.55}\)C\({}_{0.5}\) & Nd\({}_{2}\)C\({}_{3}\) + L = L & 1500 \(\pm\) 0.5 & [22] \\ Nd\({}_{0.4}\)C\({}_{0.6}\) & Nd\({}_{2}\)C\({}_{3}\) + L = L & 1620 \(\pm\) 40 & [24] \\ Nd\({}_{0.33}\)C\({}_{0.67}\) & \(\alpha\)Nd\({}_{2}\)C\({}_{3}\) = \(\beta\)Nd\({}_{2}\)C\({}_{3}\) & 1150 \(\pm\) 20 & [24; 38] \\  & Liquidus & 2260 & [23] \\  & \(\beta\)Nd\({}_{2}\)C\({}_{3}\) - C = L & 2260 \(\pm\) 30 & [24; 38] \\  & \(\beta\)Nd\({}_{2}\)C\({}_{3}\) - C = L & 2290 \(\pm\) 10 & [25] \\ Nd\({}_{0.3}\)C\({}_{0.7}\) & \(\beta\)Nd\({}_{2}\)C\({}_{3}\) - C = L & 2300 \(\pm\) 15 & [25] \\ \hline \end{tabular}
\end{table}
Table 7: Measured phase transition temperatures of Nd-C from this work and the open literature.
### Development of Future Land Cover Change Scenarios  
in the Metropolitan Fringe, Oregon, U.S.,  
with Stakeholder Involvement  
**[PERSON] and [PERSON] ***  
Department of Geography, Portland State University, P.O. Box 751, Portland, OR 97207, USA;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +1-503-725-3162; Fax: +1-503-725-3166.  
_Received: 26 November 2013; in revised form: 26 February 2014 / Accepted: 6 March 2014 / Published: 14 March 2014_  
**Abstract:** We describe a future land cover scenario construction process developed under consultation with a group of stakeholders from our study area. We developed a simple geographic information system (GIS) method to modify a land cover dataset and then used qualitative data extracted from the stakeholder storyline to modify it. These identified variables related to our study area's land use regulation system as the major driver in the placement of new urban growth on the landscape; and the accommodation of new population as the determinant of its growth rate. The outcome was a series of three scenario maps depicting a gradient of increased urbanization. The effort attempted to create a simple and transparent modeling framework that is easy to communicate. The incorporation of the regulatory context and rules and place-specific modeling for denser urban and sparse rural areas provide new insights of future land conversions. This relatively rapid mapping process provides useful information for spatial planning and projects for where and how much urban land will be present by the year 2050.  
**Keywords:** land cover change; urbanization; scenarios; stakeholder participation; GIS
## 4 Results of Simulation
### Simulation Test of Optimal Parameters
#### 4.2.3 Simulation of Energy Consumption on Different Dust Concentrations  
To further understand the influence of dust concentration \(C\), the test takes different \(C\) values to evaluate the energy consumption under the constant of \(T_{p}\) and \(T_{w}\), where setting \(C\) = 1.0 \(\sim\) 6.0 \(\times\) 10\({}^{-3}\) kg/m\({}^{3}\), \(T_{w}\) = 10 s, \(T_{p}\) = 0.1 s. The filter has a service life of 1457 days as \(C\) = 1.0 g/m\({}^{3}\), and there are 559 days as \(C\) = 2.0 g/m\({}^{3}\), as shown in Figure 10a. Studies have found that a higher dust concentration \(C\) will mean a shorter service life of the filter. For energy consumption, this shows that \(C\) has little effect on the fan motor and the pulse-jet air, as shown in Figure 10b. In terms of cost analysis, as shown in Figure 10c, it can be observed that different \(C\) values have little effect on the electricity costs of fan motors and pulse-jet cleaning. However, a higher \(C\) will increase the cost of the filter because it shortens the life of the filter. Thus, a lower dust concentration \(C\) will be an effective operation parameter to reduce costs.  
Figure 9: The simulation of energy consumption on different pulse times. (**a**). The analysis of the pressure drop. (**b**). The analysis of energy saving. (**c**). The analysis of cost.
## 4 Results and Discussion
### Numerical Modelling as a Tool to Improve the AE Clustering: Effect of the Sensor
#### 4.2.2 Effect of the Piezoelectric Sensor on the Pertinent Descriptors  
With the numerical model, we identify relevant parameters such as ZCR and FC or PP1 and PP4, in order to plot a correlation diagram. Without any doubt, two classes of signals can be observed on the diagram (Figures 15a and 16a) independently of the specimen's size: one class with higher FC and ZCR that is more likely to be related to the signals generated by source D\({}_{1}\) (varying more rapidly), and another class with lower FC and ZCR that is more likely to be associated with the signals generated by source D\({}_{2}\) (varying less rapidly). This observation was made without the aid of any kind of classification algorithm and using only two parameters, which were identified with model work. However, all studies above were performed without considering the piezoelectric AE sensor. Therefore, in the last part, we will take into account the sensor effect on the recorded signals. We will consider typical commercial PAC \(\upmu\)80 and R15 sensors from MISTRAS Group France (Sucy-en-Brie, France). Once the signal on the surface of the material is known by modelling, we can determine the signal of sensor responses by multiplying the signal on the surface by the transfer function (Equation (8)) in the frequency domain. After considering the sensor, we observe an overlapping of two classes in the plane  
Figure 14: Evolution of parameters calculated in the frequency domain as a function of distance to the source for 3 geometries (EPL80, EPL3, EPL16) and 2 sources (D\({}_{1}\) (M\({}_{\rm x}\) = 500 N\(\cdot\)um, 0 mm, 0.1 \(\upmu\)s), D\({}_{2}\) (M\({}_{\rm x}\) = 100 N\(\cdot\)um, 0.95 mm, 1 \(\upmu\)s)): (**a**) frequency centroid, (**b**) spectral spread, (**c**) partial powers.  
ZCR/FC or in the plane PP1/PP4, which highlights the crucial influence of the piezoelectric sensor on the AE features (Figures 15 and 16). The values of the pertinent descriptors are strongly affected by the sensor, mainly by the R15 sensor. Nevertheless, even if the pertinent descriptors are affected by the sensor effect, they also contain information on the source. This result shows the requirement to take into account several descriptors. Therefore, it would be very useful to know the pertinent descriptors on the surface of the material in order to choose the most pertinent descriptors for the unsupervised classification. The results of the modelling work will improve the robustness of the segmentation of AE signals.  
Figure 15: Correlation diagram between frequency centroid and zero crossing rate without sensor effect with a perfect point-contact sensor (**a**), and considering the effect of a \(\upmu\)80 sensor (**b**) and R15 sensor (**c**).
# R-PreNet: Deraining Network Based on Image Background Prior
## 2 Related Works  
The objective of the single-image deraining task is to recover a rain-free image from its rain-corrupted counterpart. The video-based deraining task can use video to obtain consecutive multiple frames of images, and use the temporal nature of the continuous images to obtain the position information of rain streaks and the background information of rain streaks occlusion positions, thus achieving the video-based deraining task. However, the task of removing rain from a single image is challenging due to the absence of information regarding the positions of rain streaks and the occluded background, complicating the reconstruction of a rain-free image.  
Existing approaches proposed for this task can be primarily categorized into two classes: model-driven methods and data-driven methods.  
Figure 1: Image deraining in the real world. PReNet [7] and R-PReNet were trained on RainTrainH. (**a**) is a real rain image, (**b**) is the result image after using PReNet to remove rain, and (**c**) is the result image after using this algorithm to remove rain. This images show that R-PReNet can effectively remove rain streaks while retaining better background textures and maintaining the basic tone of the original image.
# Collaborative Robotics: Enhance Maintenance Procedures on Primary Flight Control Servo-Actuators
## 2 Experimental Setup
In addition, due to the complexity and variety of the tasks required, it has been necessary to equip the robot with a Force-Torque (FT) sensor and the In-Sight 7802 industrial 2D camera from Cognex, both depicted in Figure 2(b). The faulty unit is mounted inside a test bench that provides the hydraulic pressure, while an Industrial Measurement System (IMS) collects the measurements from the EHSA through specific aeronautic cables. These data are transmitted to the Central Computer (CC) inside which an algorithm controls the sequence of actions the robot must perform on the servo-actuator. On the other hand, the industrial manipulator constantly exchanges data with the FT sensor, the camera, and the signal light tower via TCP/IP communication protocol [20]. The schematic of the main subsystems is reported in Figure 4.  
To improve software usability, a Graphical User Interface (GUI) has been developed. To use the manipulator, the technician must only specify three parameters to launch the robot feature extraction or the adjustments sequence:  
* **EHSA type**: type of servo-actuator under test. Each model requires different test procedures, so the Central Computer must know which sub-program to run;
* **Robot**: necessary to define the proper static IP address of the manipulator with which tests will be performed;
* **Task**: since the proposed algorithm is used both for maintenance and health features extraction for Prognostics and Health Management (PHM) purposes, different sequences can be launched by the operator. Knowing this information, the central computer extracts, from configuration files, the instructions to send to both the robot and the test bench. All the commands have been defined in three main configuration files:  
1. **Macro sequencer**: high-level sequencer which controls all the tests, adjustments, and single actions to be commanded. This configuration controls both the test bench and the UR10, taking into account the EHSA type and the task selected by the operator;
2. **Robot sequencer**: low-level sequencer in which are defined all the single robot movements;
3. **EHSA sequencer**: low-level sequencer that sets the test bench and the unit under test to desired levels of pressure, currents, ram positions, or voltages inputs. Figure 4: Overview of the communication network implemented to allow autonomous maintenance on electro-hydraulic servo-actuators.