esa-sceva/satcom-chunk-collection huggingface dataset:
# Laser Beam Welding of CubeSat 1U Structure Parts Obtained by Powder Bed Fusion
## 3 Results and Discussions
### Spot Welds Characterization
Although these values are approximately 33% of the maximum force observed in the tube (-6090 N), the results indicate that each connector-tube weld resists a static load of approximately 200 kgf. These tensile strength levels corroborate those reported by [PERSON] [25] for electrical discharge spot-welded Ti. Compared to a popular alloy in CubeSat construction, the AA7075-T6 aluminum alloy would need to be three times thicker to withstand this level of stress [26]. Regarding deformations, there was also a significant reduction in the maximum deformation of the tube. In both the LBF and the EBF cases, there was a maximum extension of approximately 2 mm. From the current perspective, these values are sufficiently large to resist loading during launching in a typical rocket. According to a Brazilian flight record, the maximum speed is 2071 m/s, attaining an acceleration of 13 g at 25.3 of altitude. This should be the maximum hydrostatic pressure, because the maximum acceleration for the 1 st stage is 7.7 g decreasing continually up to the apogee (264.5 km). For example, considering that the whole CubeSat weighs half a kilogram, at 13 g the overall load could be 60 N when the cube was coplanar to the baseplate. The maximum loading during launching was only 3% of the ultimate force of failure for every connector-to-tube joint. The images in Figure 8 show the fracture surfaces of the SLM (a) and EBM (b) specimens after tensile testing. Looking at both images, similar fracture surfaces were observed, with a region of deformation bounded by the red dotted lines. As the connector and weld nugget had higher hardness values (Figure 6), only the region of the tube adjacent to the weld deformed. The arrows in Figure 8 show the fracture surface, indicating that the weld zone was detached from the tube as the difference in hardness values was high at the interface between the fusion zone and the tube as a stress concentrator. The same failure procedure was reported in other superposed spot-w  
Figure 7: Tensile force (F) versus displacement (d) for two representative cases of SLM and EBM and for the unwelded base Ti tube.
## 4 Discussion
### Discussion of Research Findings
#### 4.1.2 Testing Performance  
The boxplot has become an ideal technique for presenting a 5-number summary (minimum and maximum range values, upper and lower quartiles, and median value), offering a quick analysis of the models' classification performance [28]. Both \(\text{CNN}_{\text{Adam}}\) and \(\text{CNN}_{\text{SCDM}}\) neural networks show a comparatively longer inter-quartile range (IQR) in the sinusoid class, yielding ultimately higher larger error values, resulting from Q3 + 1.5 * IQR, than the rest hepatic tissue structures. Based on Figure 8, two false positive sinusoid classifications in \(\text{CNN}_{\text{Adam}}\) as liver veins have yielded a greatererror variability for the corresponding vein class. The same is true for the \(\mathrm{CNN}_{\mathrm{SCDM}}\) model diagram, where one incorrect classification of a balloon cell as sinusoid (false positive) and another, including a sinusoid misclassified as a ballooning area (false negative), have increased the inter-quartile error range for both class labels. All these performances, along with the error rates, ultimately produce a classification accuracy of up to 95%. It is noted that they show an improvement compared to the results of previous classification approaches [29] and is expected that they will further reduce the overall fat and ballooning prevalence ratio error compared to human visual interpretations [30]. It is also important, that current outcomes suggest a steady improvement in automated detection techniques and emphasize their diagnostic capabilities with respect to semi-quantitative methods.
## 4 Results and Discussion
### X-ray Photoelectron Spectroscopy (XPS)
linked to the surface concentrations of these two species. By comparing the intensities, it can clearly be seen that by increasingly adding water, the concentration of the surface-oxidized states increased considerably. The Ni\({}^{2+}\) to Ni\({}^{3+}\) ratio decreased considerably, from 3.3 to 1.3. The presence of strong satellite peaks around 864 eV and 880 eV is typical for NiO\({}^{2}\). Satellites are typical for \(d\)-block elements. Satellites arise due to the interaction between unfilled \(d\) orbitals and O \(2p\) orbitals of the oxygen ligand. Satellites arise due to the multi-electron excitation transitions between unfilled Ni \(3d\) orbitals and O \(2p\) orbitals of the oxygen ligand. After a photoelectron is emitted from the nickel \(2p\) site, two final states are possible:  
(1) To screen the core hole, the Ni \(3d\) level is pulled down below the ligand level (O \(2p\)) and one electron is transferred from the ligand into the Ni \(3d\) level. This corresponds to the main line with a final configuration given by c3d\({}^{9}\)L (c denotes a hole in the core level). (2) There is no charge transfer between the ligand and nickel and the system stays in its c3d\({}^{8}\) configuration resulting in the satellite line. The O1s spectra in Figure 6 showed considerable changes in the spectral features. Oxygen spectra for the Ni25 sample had contributions of three different bonding environments Ni\({}^{2+}\)-O (529.8 \(\pm\) 0.2 eV and 531.8 \(\pm\) 0.2 eV) and NiOOH (532.5 \(\pm\) 0.2 eV). The presence of NiOOH at the surface may be attributed to the incomplete reaction of precursor materials. The deprotonation of NiOOH is highly dependent on the pH of the solution and occurs at neutral or moderately alkaline pH conditions (7-11). This could be a possible reason explaining the absence of this component in the O1s spectrum with added water. However, the increase in the surface concentration of Ni\({}^{3+}\)-O was also reflected in this spectrum (Table 3). Figure 6: O1s core level spectra. Table 5 presents the binding energy values of oxygen bonded in the NiO and Ni\({}_{2}\)O\({}_{3}\) states on the catalysts' surface produced by SCS. Hence, it can be suggested, based on the XPS spectra, that the addition of water in the SCS yields higher concentrations of surface-oxidized states. The ratio NiO/Ni\({}_{2}\)O\({}_{3}\) decreased from 4.3 (50 mL of water) to 1.35 (100 mL of water), explaining why nickel catalyst activity in the hydrogenation of maleic acid increased in this row. Thus, the reason for the changing activity was that the NiO structure resulted in the bridging of the oxygen structure. These bridging structures can cause problems in the adsorption of hydrogen on the catalyst's surface and, as consequence, reduces its activity.
# Three-Dimensional Imaging of Metallic Grain by Stacking the Microscopic Images
## 3 Algorithms for 3D Imaging of Grains and Results
### Selection and Binarization  
In order to select a specific MG and image it in 3D, it is necessary to select the boundary surface of the corresponding MG. First, a point inside a specific MGF is read, it is called point-1, and the brightness of the corresponding coordinate is read. Next, a point is read in the boundary area, it is called point-2, and the brightness of the corresponding coordinate is read. Half of the difference between the two coordinates is used as the threshold, and the resulting ones are separated into a double collection of pixels (two levels of color), namely bright and dark. If it is bright, it is replaced by \"1\", and if it is dark, it is replaced by \"0\".  
After fixing the column at point-1, the row with the first 0 is read while shrinking the row. In Figure 5a, the horizontal direction is the column, and the vertical direction is the row. The column number increases as the column moves to the right. On the other hand, the row number increases as the row goes down. Hence, the upward arrow in Figure 5b means that the row becomes smaller. Therefore, the starting point of the boundary can be found by adding 1 to the row where the first 0 occurs. Furthermore, according to the micrograph, voids may occur in the layer or be expressed as black dots (points) due to decidualization. The presence of these dots may confuse the position of the first zero in the row. As a matter of fact, in this study, as can be seen in Figure 5c, all the small \"0\" surrounded by the wide distribution of \"1\" were changed to \"1\". By these measures, the black dots inside each layer in Figure 5b become collectively \"1\", as depicted in Figure 5c, such that the position of the boundary can be clearly found.  
Figure 5d is a diagram explaining the algorithm for finding the coordinates of the boundary after finding the starting point of the boundary by the algorithm of Figure 5c. We know the positions of the 'first 0' and 'last 1'. The presence or absence of \"1\" and \"0\" is checked in four adjacent pixels. There are four cases. Point-1 moves to the upper side and  
Figure 5: Algorithms for selection, binarization, and boundary extraction. (**a**) Select a structure and background; (**b**) Boundary extraction with voids; (**c**) Boundary extraction without voids; and (**d**) Boundary extraction algorithm.  
stops at the boundary of \"1\" and \"0\", so it is the fixed condition. We can assume four cases, including this fixed condition, right upper, and right side will be 1-1, 0-1, 0-0, 1-0. In the case of 0-1, the boundary is to the right, as shown in the following case of the breakpoint in Figure 5d. In the case of 1-1, the boundary will be right-side up, as shown in the following case of 0-1 in Figure 5d. In the case of 0-0, as shown in Figure 5d, the peak at the following arrows. The MATLAB command boundary is used.
## 4 Performance Analysis
### Experimental Results and Discussion
#### 4.4.5 Different Learning Rates  
To explore the influence of different learning rates on the model, a comparison of three different learning rates is conducted on our proposed model. To be specific, the learning rates set on the CCKS2020 dataset are 1 \(\times\) 10\({}^{-6}\), 1 \(\times\) 10\({}^{-5}\), and 5 \(\times\) 10\({}^{-5}\), respectively, and the learning rates set on the CCKS2019 dataset are 1 \(\times\) 10\({}^{-6}\), 1 \(\times\) 10\({}^{-5}\), and 2 \(\times\) 10\({}^{-5}\), respectively.  
The experimental results on both datasets with different learning rates are illustrated in Figures 6 and 7, respectively, and the following conclusions are obtained from the analysis of the results. Firstly, our model achieves the optimum performance with a learning rate of 1 \(\times\) 10\({}^{-5}\), and the F1 values on CCKS2020 and CCKS2019 are 90.02% and 90.40%, respectively. Secondly, excessive learning rate leads to failure of model convergence. Specifically, when the learning rate increases to 5 \(\times\) 10\({}^{-5}\) and 2 \(\times\) 10\({}^{-5}\) on CCKS2020 and CCKS2019, the F1 value drops by 2.47% and 2.73%, respectively. Finally, a learning rate that is too small will cause model to fall into a local optimum rather than a global optimum. To be specific, the F1 values decrease by 3.91% and 0.85% when the learning rate decreases to 1 \(\times\) 10\({}^{-6}\) on CCKS2020 and CCKS2019, respectively.
## 4 Experiment and Result Analysis
### Experimental Settings
#### 4.1.2 Implementation Details  
We choose BERT-Base as the pre-trained model on Conll04 and ADE datasets. However, since SciERC is a dataset related to the scientific field, and SciBERT is a BERT model trained on a large amount of scientific corpus, we choose SciBERT as the pre-trained model on the SciERC dataset. In Equation (10), the dimension of the entity length representation vector is set to 20 which is consistent with our baseline model SpERT [26]. In Equations (13) and (14), the dimension of the local context length representation vector is set to 200 which is selected in [25; 50; 100; 150; 200; 250] based on the development set. As shown in Figure 3, the F1 of NER and RE are the highest when the dimension of the local context length representation is 200. Referring to SpERT [26], the relation filtering threshold is set to 0.4, and the upper bound for sample calculation of relation pairs in each sentence is 100. On the Conll04 dataset, the batch size is set to 2, the learning rate is set to \(5\times 10^{-5}\), and the dropout rate is set to 0.5. On the SciERC dataset, the batch size is set to 4, the learning rate is set to \(5\times 10^{-5}\), and the dropout rate is set to 0.5. On the ADE dataset, the batch size is set to 8, the learning rate is set to \(6\times 10^{-5}\), and the dropout rate is set to 0.5.  
Figure 3: F1 values for named entity recognition (Entity) and relationship extraction (Relation) when the local context length representation vector with dimensions of 25, 50, 100, 150, 200 and 250 (on Conll04 development set).
# GIS-Based Flood Assessment Using Hydraulic Modeling and Open Source Data: An Example of Application
Due to its versatility, the capability for both 1D and 2D modeling, geospatial data integration, and extensive functionalities for flood and hydraulic structure analysis, HEC-RAS, an Open Source solution, is utilized in other studies for flood simulation [48], flood prediction [49], and mapping [50], assessing territorial susceptibility to floods [51], or evaluating the feasibility of hydraulic constructions [52; 53]. Although GIS methods, LiDAR data, and hydraulic modeling are widely used tools in flood analysis, most studies focus on large river basins or urban areas, with a lack of detailed research applied to small and medium-sized basins, such as the Cigher River. Furthermore, the impact of floods on agricultural land is often underestimated, and the methodologies used are not always reproducible, which limits their applicability to other regions. Based on these general premises, this study starts from the following working hypothesis: the study area, characterized by complex physical-geographical conditions, has been affected by major hydrological events (flash floods and severe flooding) with significant environmental and societal impacts in the years 1970, 1981, 1995, 2000, 2005, 2006, 2008, 2014, 2020, and 2022 [54]. In this context, the region's vulnerability to these hydrogeomorphological events is emphasized, and through GIS technologies and hydraulic modeling in HEC-RAS, flood-prone areas can be accurately identified, enabling the development of viable solutions for proactive and careful risk management and disaster prevention. The aim of this study is to conduct a detailed flood risk analysis on a section of the Cigher River, a tributary of the Crisul Alb River, using hydraulic modeling in HEC-RAS and open-source data. Specifically, the study has the following objectives: (1) to identify flood-prone areas based on different hydrological scenarios, (2) to assess the potential impact of floods on agricultural land, and (3) to propose a reproducible methodology that integrates geospatial data and hydraulic modeling techniques, making it applicable to future studies or territorial planning. This study aims to contribute to a better understanding of hydraulic and hydrogeomorphological processes in the Cigher River basin and, based on the obtained results, to the development of sustainable solutions for flood risk management. The study stands out for its novelty and originality, integrating modern hydraulic modeling techniques in HEC-RAS and GIS with open-source data, which was applied for the first time in the flood-prone Cigher basin. Additionally, by analyzing diverse hydrological scenarios, evaluating flood impact on agricultural land, and adopting a systematic approach, the study proposes a reproducible, accessible, and applicable methodology for other similar river basins. It also offers valuable insights for flood risk management, territorial planning, and the promotion of sustainable flood prevention strategies.
## 5 Discussion  
The phenology- and pixel-based paddy rice mapping algorithm [11] based on flood signal detection is well developed and has been successfully applied to MODIS and Landsat data to produce regional and national rice maps. However, its application to Sentinel-2 data, which has 10-m bands, likely causes a severe salt-and-pepper effect. In addition, algorithms to exploit optical data are not robust when applied to monsoon regions where cloud cover is frequent. The PODS algorithm proposed in our study has three advantages: an object-based classification that uses fields as the basic classification unit to reduce the salt-and-pepper effect; integrating microwave data with optical data to obtain as many positive flood signals as possible in cloudy and rainy regions; and using the PhenoRice algorithm to estimate the transplanting date to address uncertainty in the critical transplanting phase in subtropical/tropical regions. The performance of the PODS algorithm depends on three modules: object extraction, transplanting date estimation, and flood signal detection.
## 3 Simulation Results
### Case wWhen Forecasting Error Follows A Normal Distribution  
We assumed that the demand at time t follows an AR (1) process as mentioned in Equation (23).  
\[\mathrm{D_{t}}=\ \mu+\phi\mathrm{D_{t-1}}+\ \varepsilon_{t} \tag{23}\]  
where \(\mu\) is a positive constant, \(\phi\) is the autoregressive parameter and \(\varepsilon_{t}\) is Gaussian i.i.d with null mean and a standard deviation of 5 while \(\phi\) was allowed to vary between \(-0.9\) and \(0.9\). The value of \(\mu\) used for the simulation is 150.  
The TLF on the retained sample averaged over 100 replicates and then over the quantiles which include the following values namely 85%, 90%, 95% and 99%, versus the autoregressive parameter \(\phi\) assumed to vary between -0.90 and 0.90 are made.  
Table 1 gives the tick loss values of each SS measurement method for the four CSL levels and for a lead time of one week and four weeks assuming demand that follows the AR (1) process with normal distribution and \(\phi\)= \(-0.70\) or \(\phi\) = 0.70. Values in the blood indicate the minimum tick loss for a given CSL level. For example, the value of tick loss in the case of \(\phi\) = 0.70 and lead time of one week is 2.05 at an 85% level when we apply the CEVT method. This value decreases to 0.22 when CSL is 99%. We choose the lead time of four weeks to imply a minimum value of tick loss of 7.06 for the 95% level and 0.95 for 99% level. We can remark the sensitivity of tick loss to various factors, such as the value of \(\phi\), lead time, and CSL level. It appears that the CEVT method provides the most performance in the majority of cases followed by the FHS method and GARCH. This points out that there is an autocorrelation in the estimating of error variability that can be explained by the fact that the prediction model does not correspond exactly to the demand generation process. Traditional methods cannot provide good results by comparison with GARCH-based methods. The SES method offers the worst performance in terms of TLF.
# Performance of Payement Temperature Prediction Models
###### Abstract  
Appropriate asphalt binder selection is dependent on the correct determination of maximum and minimum pavement temperatures. Temperature prediction models have been developed to determine pavement design temperatures. Accordingly, accurate temperature prediction is necessary to ensure the correct design of climate-resilient pavements and for suitable pavement overlay design. Research has shown that the complexity of the model, input variables, geographical location among others affect the accuracy of temperature prediction models. Calibration has also proved to improve the accuracy of the predicted temperature. In this paper, the performance of three pavement temperature prediction models with a sample of materials, including asphalt, was examined. Furthermore, the effect of calibration on model accuracy was evaluated. Temperature data sourced from Pretoria were used to calibrate and test the models. The performance of both the calibrated and uncalibrated models in a different geographical location was also assessed. Asphalt temperature data from two locations in Ghana were used. The determination coefficient (\(R^{2}\)), Variance Accounted For (_VAF_), Maximum Relative Error (_MRE_) and Root Mean Square Error (_RMSE_) statistical methods were used in the analysis. It was observed that the models performed better at predicting maximum temperature, while minimum temperature predictions were highly variable. The performance of the models varied for the maximum temperature prediction depending on the material. Calibration improved the accuracy of the models, but test data relevant to each location ought to be used for calibration to be effective. There is also a need for the models to be tested with data sourced from other continents.  
temperature prediction models; asphalt binder; climate resilience; pavement temperature 2023  
1
# Helicopter Safe Landing Trajectory after Main Rotor Actuator Failures
## 1 Introduction
The optimal control method has been widely used and verified with flight tests for autorotation problems [18; 19; 20; 21], the main procedure of which consists of discretizing an optimal control problem first and turning it into a nonlinear programming problem. Then the nonlinear programming problem is solved by numerical algorithms. In order to describe the geometry constraints caused by actuator stuck failure, a general swashplate geometry is described. For each actuator case, a new reconfiguration solution for the control mixer is presented. By giving up vertical control, the attitude control is guaranteed, and the influence on vertical control due to geometry constraints brought by the new reconfiguration is derived. With the swashplate geometry defined and the control mixer reconfigured, a modified helicopter dynamic model can be formulated. The modified model is based on a nonlinear three-degree-of-freedom rigid-body helicopter dynamic model, and control inputs of the model are determined by each actuator failure case. Safe landing trajectories are then obtained by optimal control method. The UH-60 is taken as a sample model, and results are presented for various flight speeds and heights for all actuator failure cases. The safe boundaries of forward speed when the actuator failure takes place are also explored by employing speed sweeps. This paper is organized as follows. Section 2 defines the control geometry, deriving a general mathematical description of the control geometry. Solutions of reconfiguration of the control mixer are also presented. In Section 3, the helicopter dynamic model is formulated with constraints subject to each actuator failure case. In Section 4, the safe landing problem is formulated by optimal control method. In Section 5, results with various initial conditions of each failure case are shown and discussed. Finally, conclusions are presented in Section 6.