esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Cutting Parameters and MQL on Surface Finish and Work Hardening of Inconel 617
###### Abstract  
Inconel 617 is a nickel-based superalloy that is a primary candidate for use in next-generation nuclear applications such as the Gen IV Molten Salt Reactor (MSR) and Very-High-Temperature Reactor (VHTR) due to its corrosion and oxidation resistance and high strength in elevated temperatures. However, Inconel 617 machinability is poor due to its hardness and tendency to work harden during manufacturing. While the machinability of its sister grade, Inconel 718, has been widely studied and understood due to its applications in aerospace, there is a lack of knowledge regarding the behaviour of Inconel 617 in machining. To address this gap, this paper investigates the influence of cutting parameters in the turning of Inconel 617 and compares the impact of Minimum Quantity Lubrication (MQL) turning against conventional coolant. This investigation was performed through three distinct studies: Study A compared the performance of commercial coatings, Study B investigated the influence of cutting parameters on the surface finish, and Study C compared the performance of MQL to flood coolant. This work demonstrated that AITiN coatings performed the best and doubled the tool life of a standard tungsten carbide insert compared to its uncoated form. Additionally, the feed rate had the largest impact on the surface roughness, especially at high feeds, with the best surface quality found at the lowest feed rate of 0.075 mm/rev. The utilization of MQL had mixed results compared to a conventional flood coolant in the machining of Inconel 617. Surface finish was improved as high as 47% under MQL conditions compared to the flood coolant; however, work hardening at the surface was also shown to increase by 10-20%. Understanding this, it is possible that MQL can completely remove the need for a conventional coolant in the machining of Inconel 617 components for the manufacturing of next-generation reactors.  
Inconel 617; MQL; machining performance; tool wear; coated carbide tools Article
# Mapping Temperate Forest Phenology Using Tower, UAV, and Ground-Based Sensors
## 2 Materials and Methods
### Above-Canopy, UAV-Based Measurements  
We used a Mavic Pro outfitted with the stock 12.3 MP RGB camera with a 35 mm equivalent lens with an ISO range of 100-1600 and \(<\)1.5% distortion focus (DJI, Shenzhen, China). (Figure 1). We used DroneDeploy (DroneDeploy; San Francisco, CA, USA) to plan the flight path around the tower with 88% of front and side overlap and a flight altitude of 100 m. The white balance of the drone's onboard camera was set to a fixed color temperature of 6000K. After data collection, we used PhotoScan (Agisoft; St. Petersburg, Russia) to create orthomosaic images with spatial accuracy of 1-2 m and resolution of 0.03 m. These data were rasterized and clipped to a rectangular bounding box around the plots of approximately 5.5 ha (37.9225, -78.276 to 37.9325, -78.275) for further analysis.  
We then derived Green Chromatic Coordinate (GCC) values from the red, green, and blue channels of the UAV orthoimagery, with GCC calculated using the following equation [20]:  
\[\text{GCC}_{\text{UAV}}=\text{DNG}/(\text{DNR}+\text{DNG}+\text{DNB}) \tag{1}\]  
where DN represents the digital number (0 to 255) for the red (DNR), green (DNG), and blue (DNB) channels. GCC is less sensitive to differences in canopy illumination or in differences among cameras [20]. Orthoimage reconstruction was not fully successful for the 7 May 2018 (DOY 127) orthoimage, resulting in the removal of approximately half of the cropped scene (including four camera plots).
# Spherical-Model-Based SLAM on Full-View Images for Indoor Environments
## 5 Validation
### Accuracy Validation with a Decrease in the Field of View  
In this section, we validate the inherent disadvantages of limited-view based methods. We discuss the decreased field of vision and how the performance of our system would behave. Similar to Section 5.2, we designed a route and collected full-view images for tracking and mapping. To decrease the field of view from full-view images, we decreased the range of _theta_ since the full-view image was presented in the spherical model (Figure 15). Thus, we obtained limited-view images with desirable view angles. We tested our system with (_theta_ = 160, 140, 120, 100, 80 degrees) views, and the experiment was conducted ten times. Each time, the limited-view was generated randomly, so the tracking result was different depending on the features. The accuracy with different _theta_ values is listed in Table 2.  
As shown in Table 2, as the field of view decreased, the accuracy decreased. When the field of view was less than 100 degrees, some experiments failed to track ('X' in Table 2). Because a limited view means limited information, only limited features from one direction could not retrain a result as accurately as from all directions, especially for sparse environments. The average error showed that wider views resulted in a better performance.  
Figure 14: **(a)** full-view camera on the MAV; **(b)**PTAM performs badly as MAV rotates or in a sparse feature view; **(c)** GPS flight trajectory; **(d)** The flight trajectory by GPS, PTAM, and our system.  
Figure 15: Achieving limited view from full-view images by decreasing theta.
## 3 Results  
### Measured Ex Situ TS  
A total of 172 tracks (water depth \(>\) 5 m) were identified for the six specimens of Chinese sturgeon (Table 4). The mean range of the distributed water depth for all specimens was 5.46\(-\)8.32 m, and the mean range of the TS was between \(-\)31.4 and \(-\)26.9 dB. Figure 4a indicates the relationship of TS and the distributed water depth for the 78 tracks of the No. 6 specimen (Table 4): the TS range was from \(-\)38.1 to \(-\)23.8 dB and the target distributed water depth was 5.11\(-\)8.97 m. No clear trend, for example TS increasing with water depth, was observed (Figure 4a). At 7 m, the TS varied from \(-\)38.0 to \(-\)26.0 dB, which implied that the body posture (i.e., tilt angle) would have a great influence on TS. Figure 4b shows the interval distribution of TS: the interval between \(-\)32.0 and \(-\)30.0 dB had 25 samples, which occupied 32.1% of the entire tracks.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**IP**} & **Body** & **Number of** & **Mean Depth** & **Mean TS \(\pm\)** & **Min, Max** & **Model** \\  & **Length (cm)** & **Acoustic Targets** & \(\pm\) **SD (m)** & **SD (dB)** & **TS (dB)** & **Estimation (dB)** \\ \hline
1 & 89.2 & 9 & 6.21 \(\pm\) 0.52 & \(-\)28.6 \(\pm\) 3.3 & \(-\)34.1, \(-\)23.0 & \(-\)29.2 \\
2 & 85.0 & 66 & 6.94 \(\pm\) 0.47 & \(-\)27.6 \(\pm\) 2.3 & \(-\)32.8, \(-\)22.8 & \(-\)25.8 \\
3 & 76.8 & 11 & 8.32 \(\pm\) 0.35 & \(-\)27.5 \(\pm\) 2.9 & \(-\)32.0, \(-\)23.1 & \(-\)27.1 \\
4 & 91.2 & 5 & 7.17 \(\pm\) 0.98 & \(-\)26.9 \(\pm\) 4.0 & \(-\)32.3, \(-\)21.2 & \(-\)26.7 \\
5 & 92.6 & 3 & 5.46 \(\pm\) 0.31 & \(-\)30.3 \(\pm\) 2.7 & \(-\)31.9, \(-\)27.2 & \(-\)29.1 \\
6 & 74.0 & 78 & 6.81 \(\pm\) 0.90 & \(-\)31.4 \(\pm\) 2.9 & \(-\)38.1, \(-\)23.8 & \(-\)31.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results from the ex situ measurements and the theoretical calculations.  
Figure 4: TS of the Chinese sturgeon (Table 4, No. 6 specimen; BL = 74.0 cm) measured in a 10 m deep net cage: (**a**) relationship between the TS and the distributed water depth; and (**b**) interval distribution of TS.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Period**} & \multirow{2}{*}{\(n\)} & \multicolumn{2}{c}{**Female**} & \multirow{2}{*}{\(n\)} & \multicolumn{2}{c}{**Male**} & \multirow{2}{*}{**Reference**} \\ \cline{3-4} \cline{6-7}  & & **Min (cm)** & **Max (cm)** & & **Min (cm)** & **Max (cm)** & \\ \hline
1981–2004 & 376 & 212 & 321 & 257 & 163 & 285 & [29] \\
1998–2005 & 42 & 240 & 320 & 23 & 153 & 284 & [30] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Body lengths of adult Chinese sturgeons below the Gezhouba Dam in the Yangtze River.
## 3 Optimizing Setting Base on CBR
### Improved Case-Based Reasoning
#### 3.2.4 Case Maintenance  
The accuracy of the cases in the case base is directly reflected in the case solution of CBR. Case maintenance stores the new cases and their solutions into a case database, according to a certain strategy. The newly solved case needs to be stored to increase the searching scope of the case base and adapt the case base to the changing working conditions. However, an incorrect case contaminates the case base and greatly affects the accuracy of the CBR algorithm permanently. What is more, the unrestrained storing of new cases increases the calculation time constantly. Therefore, there should be a strategy in judging whether the new case should be retained or not. This paper puts forward the strategy by the following steps:  
1. Experts judge whether the new case is correct or not. The influence of errors, such as input error, can be reduced by expert judgment.
2. Set \(m_{max}\) as the maximum storage amount. Set \(S_{max}\) as the maximum storage similarity. Identify the maximum similarity \(SIM_{max}\) of all the \(SIM(F,F_{(k)})\). Identify the \(m\) as the amount of the latest case base.
3. If the retrieved case with \(SIM_{max}\) is the generated case, delete this case and store the new case in the case base to replace it.
4. If the retrieved case with \(SIM_{max}\) is the original case, make decisions as follows: * If \(SIM_{max}\geq S_{max}\), which means that there are enough similar cases in the case base, save the case to the case base and delete the most similar generated case before. If there are no generated cases in the case library, delete the closest original case. * If \(SIM_{max}<S_{max}\bigcap m\leq m_{max}\), save the new case in the case base. * If \(SIM_{max}<S_{max}\bigcap m>m_{max}\), save the new case in the case base and delete the oldest case (all generated cases are oldest cases; relatively similar ones are deleted first ). The purpose is to update the case base to accommodate the effects of changes in processes, the environment, etc.
# An Overview of Cube-Satellite Propulsion Technologies and Trends
## 6 Radio-Frequency Ion Thruster (RIT)
### Design Considerations and Technologies  
Ion thruster operation may lead to several interactions between the thruster and the spacecraft instruments namely, ionized propellant (plasma), contamination and field interactions. Plasma and contamination interactions comprise of two types of efflux: propellant efflux composed of propellant ions, neutralizing electrons, non-ionized propellant, and a low-energy charge exchange plasma; non-propellant efflux composed of material sputtered from the thruster components and the neutralizer due to ion bombardment. The field interactions are the result of RF field, electrostatic accelerators and the interaction of the plasma plume with the ambient (space) environment. Contamination effects incurred can be mitigated by the use of inert propellants like xenon and krypton, however, it still leaves out the issues due to the plasma interactions [66].  
Figure 5: Schematic of a Radio Frequency (RF) Ion Propulsion System.  
Hollow cathode tubes are regularly used in electron-bombardment ion engines to provide electrons for the neutralization of the ion beam. The hollow cathode assembly consists mainly of the cathode tube that has an insert (electron emitter), orifice plate (present downstream of thruster) that facilitates the flow of exhaust plume, heater that raises insert temperature and a keeper electrode. Major functions of a keeper electrode are to facilitate turning on the cathode discharge, to maintain the cathode temperature and operation when discharge or beam current is interrupted temporarily, and to protect the cathode orifice plate and external heater from ion bombardment [63; 67]. It is of very high importance to study and analyze the wear of discharge cathodes as their failure is deemed to be one of the major life-limiting mechanisms of ion thrusters. The following wear processes are generally seen in discharge cathode assembly: failure of the heater, cathode orifice plate, and keeper electrode [68].  
A new field of materials science called Solid State Ionics (SSIs) can offer solutions to overcome issues with the production and delivery of ions to their extraction sites without complications of plasma discharge chamber and all of its essential components [69]. SSIs deal with the preparation, characterization, theory, and application of solids that support ionic conduction [70]. SSI conductors are currently the key elements in the oxygen sensors used in automobile exhaust systems, lithium ion batteries, solid oxide fuel cells, electro-chromic windows and in some superconductors [69]. Table 5 provides the summary of the surveyed RF ion thrusters along with their performance factors. None of these ion thrusters have a space heritage, while one of them is scheduled to fly on the _Lunar IceCube_ mission in 2018.
## 4 Results and Discussion
### PlanetQi-NWP Models Comparison
#### 4.3.2 PlanetIQ-ECMWF Model Comparison
These findings collectively contribute to a comprehensive understanding of the reliability and utility of radio occultation data for atmospheric research and numerical weather prediction. To investigate the variance disparities between PlanetIQ profiles and GFS and ECMWF models, as well as the differences in observational profiles, an f-test and a \(t\)-test were employed, respectively. Table 2 presents the findings from these tests conducted on random samples of mission profiles, with six checkpoints spaced 25 to 50 days apart. The f-test results show no significant differences in variance across all profile comparisons between PlanetIQ profiles and GFS and ECMWF models, with the exception of the GN02.2023.100.00.10.R20 WVP profile. This specific profile exhibits a noteworthy difference in variances between the profiles of the PlanetIQ and the two models. Furthermore, the \(t\)-test findings for the six checkpoints indicate a lack of significant differences between PlanetIQ profile observations and model profiles, except for the WVP profile at the same checkpoint when compared to both models. It is crucial to note that the observed significant difference in WVP is not considered a limitation in the production of PlanetIQ WVP data. This discrepancy is attributed to small temporal and spatial variations. Given that NWP models generate spatial and temporal profiles from disparate observations, which are not necessarily in the same location and time, spatial and temporal interpolation in NWP models may introduce unreliability into the WVP profiles they produce. Figure 20: Boxplot for RMSE values between PlanetIQ and each of the GFS and ECMWF models for each variable.
## 5 Results and Analyses
### Experimental Analyses
#### 5.2.1 Ablation Experiments
(5) -UA: Removing the distance-weighted attention mechanism in PDWA and assigning uniform attention to all path instances resulted in a significant performance drop (1.67 decreases in F1 score and 2.04 decrease in AUC). This confirms that differentially modeling path-pair distances is essential for effective attention allocation and relation reasoning. (6) -RF: Eliminating relation-aware context filtering and reverting to entity-only filtering caused a substantial performance decline (2.40 decreases in F1 score and 2.78 decrease in AUC). This validates our approach of incorporating relation type descriptions during context selection, demonstrating its effectiveness in preserving crucial relation-specific textual evidence that might otherwise be overlooked. (7) -DW: Removing the semantic distance calculation mechanism between document paths led to one of the largest performance drops (2.96 decrease in F1 score and 2.85 decrease in AUC). This strongly supports our hypothesis that explicitly modeling semantic distances between document paths is crucial for effective cross-document reasoning, particularly for complex multi-hop reasoning chains. In summary, the ablation experiments validate the importance of each proposed module, with bridge entities, relation-aware filtering, and distance-weighted mechanisms emerging as particularly critical components. The results demonstrate that the integration of these elements collectively contributes to CLEAR's superior effectiveness in cross-document relation extraction by addressing both context quality and attention distribution challenges.
## 3 Results
### Overheating Risk
#### 3.2.3 Comparison of Overheating Risk under Extreme Weather Conditions  
For the severity of the overheating risk, the occurrence of high indoor air temperature during the heatwaves is crucially important for health. Figure 10 shows the April to September hourly indoor air temperature profiles in the old and new buildings under the heatwave summers of 2018 and 2050. As can been seen, the maximum indoor air temperature without the cooling system reaches 38 \({}^{\circ}\)C and 35 \({}^{\circ}\)C in the old and new buildings in 2018, respectively. In the heatwave summer of 2050, the absolute maxima are just under 40 \({}^{\circ}\)C and 36 \({}^{\circ}\)C.  
Table 10 reports the degree hours above 27 \({}^{\circ}\)C and 32 \({}^{\circ}\)C under the two heatwave summers. The usage of the split cooling unit in the living room decreases the degree hours above 27 \({}^{\circ}\)C and 32 \({}^{\circ}\)C in the warmest bedroom to zero in both hot summers. Otherwise, without the cooling system, the degree hours above 27 \({}^{\circ}\)C and 32 \({}^{\circ}\)C are significantly higher than in the average current and future climate. Although the indoor temperature conditions during the heatwaves in the current and projected future climate deviate considerably from the requirements, by using a split cooling unit in the living room with the setpoint of 23 \({}^{\circ}\)C, thermal comfort can be provided in all the spaces where the maximum air temperature is around 27 \({}^{\circ}\)C and 25 \({}^{\circ}\)C in the old and new buildings, respectively, in both current and future climates.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Cases** & **Degree Hours above 27 \({}^{\circ}\)C** & **Degree Hours above 32 \({}^{\circ}\)C** \\ \hline New-Base with cooling, HWS2018 & 0 & 0 \\ New-Base, HWS2018 & 10,238 & 571 \\ New-Base with cooling, HWS2050 & 0 & 0 \\ New-Base, HWS2050 & 13,143 & 1251 \\ \hline Old-Base with cooling, HWS2018 & 0 & 0 \\ Old-Base, HWS2018 & 10,670 & 1815 \\ Old-Base with cooling, HWS2050 & 0 & 0 \\ Old-Base, HWS2050 & 14,328 & 3128 \\ \hline \hline \end{tabular}
\end{table}
Table 10: The April to September degree hours above 27 \({}^{\circ}\)C and 32 \({}^{\circ}\)C, under HWS2018 HWS2050.  
Figure 10: The April to September hourly indoor air temperature profile of the warmest bedroom, base cases, new and old building, HWS2018 and HWS2050. (The temperature levels of 27 \({}^{\circ}\)C and 32 \({}^{\circ}\)C are shown by black dashed lines).
## 4 Activity Recognition Based on Fusion of Point Cloud and Range-Doppler
### Using Only Range-Doppler Data as Input
#### 4.3.1 Three-Dimensional Convolutional Neural Network (3D CNN)  
Because a piece of range-Doppler data is actually a superposition of multiple images in the time dimension, we try to use a 3D CNN to train the range-Doppler data. The structure of the network is shown in Figure 6. As we can see, this network is similar to the convolutional part of the point cloud network. We used the structure of 'Conv3D-Conv3D-Maxpooling' three times, where the parameter settings are consistent with the network in Part B, without the 'TimeDistributed' layer wrapper encapsulating the layers. Then we input the features to the flattened layer and the fully connected layer for classification successively. The network takes eight frames of range-Doppler data as a set of input data, that is, the data with the shape of 8 \(\times\) 32 \(\times\) 32 \(\times\) 1 can be regarded as a three-dimensional single channel stereo image, so the network can be understood as a classification network of three-dimensional images. However, different from the ordinary image classification network, the input data has not only spatial characteristics but also  
Figure 5: The structure of 3D CNN + LSTM network.  
temporal characteristics. The temporal characteristics can be understood as the series between frames, that is, the characteristics reflected by Doppler velocity. This network can better learn the characteristics of dynamic actions rather than only extracting static two-dimensional features.
# Intersection Routing Based on Fuzzy Multi-Factor Decision for VANETs
## 4 Results
### Simulation Setting  
IRMFD was simulated using an NS2 simulator, with a mobility model consisting of an area of 3000 m \(\times\) 4000 m. This was constructed using SUMO and formed a map shown in Figure 6a, which contained 28 junctions and 43 bidirectional road segments with four lanes. Figure 6b shows the view of an intersection. The initial positions and destination of vehicles are randomly distributed. The simulation parameters are shown in Table 5. The vehicles moved with a speed ranging from 5-20 m/s. The transmission range was set to 250 m and the period of the traffic light was set to 50 s. The number of vehicles varied from 100 to 400, and the packet sending rate was set to 4 pkt/s. The simulation time duration was 1000 s.  
Figure 5: Routing based on fuzzy multi-factor decision (IRMFD) protocol model.  
Figure 6: Simulation city roadways (**a**) and intersection structure (**b**).
# Seismic Random Noise Attenuation via Low-Rank Tensor Network
## 2 Materials and Methods
### Problem Statement and Modeling  
For the 3D seismic image denoising problem, we represent recorded images \(\mathcal{Y}\in\mathbb{R}^{n_{1}\times n_{2}\times n_{3}}\) as the superposition of clear 3D seismic images \(\mathcal{X}\in\mathbb{R}^{n_{1}\times n_{2}\times n_{3}}\) and some additive random noise \(\mathcal{N}\in\mathbb{R}^{n_{1}\times n_{2}\times n_{3}}\), and the random noise attenuation model can be expressed as follows:  
\[\mathcal{Y}=\mathcal{X}+\mathcal{N}. \tag{8}\]  
The goal of seismic image denoising is to recover the clean seismic image \(\mathcal{X}\) from the noisy seismic image \(\mathcal{Y}\) in the presence of random noise \(\mathcal{N}\). As proven in [19; 26; 47], performing LRA operation on a noisy data matrix or tensor can effectively suppress random noise, and LRTA methods based on WTNNM have achieved excellent results in image denoising [40]. Meanwhile, in order to deal with the boundary roughnesscaused by LRTA, the TV regularization is introduced as a constraint on the denoised result of LRTA methods [26; 48]. Thus, the seismic image denoising problem can be formulated as minimizing an objective function that incorporates the data fidelity and the regularization terms, as follows:  
\[\begin{split}&\min_{\mathcal{X},\mathcal{N}}\alpha\|\mathcal{X} \|_{\mathbf{w},\mathcal{B}}+\mu\|\mathcal{X}\|_{TV}+\gamma\|\mathcal{N}\|_{F}^ {2}\\ &\text{s.t.}\quad\mathcal{Y}=\mathcal{X}+\mathcal{N},\end{split} \tag{9}\]  
where \(\alpha\), \(\mu\), and \(\gamma\) are trade-off parameters to balance all three terms, \(\|\mathcal{X}\|_{\mathbf{w},\mathcal{B}}\) denotes the weighted tensor nuclear norm (WTNN) of \(\mathcal{X}\) with the weight vector parameters \(\mathbf{w}=[w_{1},w_{2},\cdots,w_{\min(n_{1},n_{2})}]\), and \(\|\mathcal{X}\|_{TV}\) denotes the TV regularization defined as follows:  
\[\|\mathcal{X}\|_{TV}=\tau_{x}\|\mathcal{D}_{x}\mathcal{X}\|_{1}+\tau_{y}\| \mathcal{D}_{y}\mathcal{X}\|_{1}+\tau_{z}\|\mathcal{D}_{z}\mathcal{X}\|_{1}, \tag{10}\]  
where \(\mathcal{D}_{x}\), \(\mathcal{D}_{y}\), and \(\mathcal{D}_{z}\) are the finite-difference operators along the three dimensions, and \(\tau_{x}\), \(\tau_{y}\), \(\tau_{z}\) are weights that control regularization strength for three directions, respectively.