esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Tool Wear
[37; 38] demonstrated that the edge radius \(r_{n}\) of the cutting tool influences tool temperature, tool life, surface roughness, and the microstructure of the machined surface layer. They determined that the optimal edge radius for tool longevity is \(r_{n}=15\)\(\upmu\)m. If the edge radius is smaller, the cutting edge undergoes rapid chipping. Conversely, a larger edge radius leads to increased grain deformation in the surface layer and higher surface roughness. Other researchers [39] also highlighted the significance of the cutting edge radius, identifying it as the primary factor affecting the minimum uncut chip thickness \(h_{min}\). To evaluate the impact of feed rate and cutting speed on the cutting edge radius of the investigated inserts, microgeometry measurements were conducted [40]. The results are shown in Figures 13 and 14. The average cutting edge radii of the new S205 and 6160 inserts are similar, measuring 36 \(\upmu\)m (Figure 13) and 39 \(\upmu\)m (Figure 14), respectively. However, an interesting trend emerged in the wear analysis, for the S205 inserts, the edge radii decreased regardless of the feed rate or cutting speed (Figure 13), while for the 6160 inserts, the edge radii increased under the same conditions (Figure 14). This discrepancy may be due to different wear mechanisms observed in the two insert types, as illustrated in Figures 7-10. In the case of the S205 inserts, more built-up material was found on the rake face. In contrast, for the 6160 inserts, a greater amount of built-up material was observed on the flank face. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Point No.**} & \multirow{2}{*}{**Ni**} & \multirow{2}{*}{**Cr**} & \multirow{2}{*}{**Fe**} & \multirow{2}{*}{**O**} & \multirow{2}{*}{**V**} & \multicolumn{3}{c}{**Weight \%**} & \multirow{2}{*}{**Ni**} & \multirow{2}{*}{**Mo**} & \multirow{2}{*}{**Si**} & \multirow{2}{*}{**N**} \\  & & & & & & & & & & & \\ \hline
1 & & & & 3.39 & & 52.86 & & & 18.86 & 24.89 \\
2 & 2.86 & 1.29 & 0.89 & 5.03 & & 24.81 & & 1.16 & & 43.84 & 20.11 \\
3 & 45.04 & 15.32 & 14.25 & 3.71 & 0.13 & 10.27 & 4.8 & 0.92 & 2.99 & 2.58 & \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Point No.**} & \multirow{2}{*}{**Ni**} & \multirow{2}{*}{**Cr**} & \multirow{2}{*}{**Fe**} & \multirow{2}{*}{**O**} & \multirow{2}{*}{**V**} & \multicolumn{3}{c}{**Atom \%**} & \multirow{2}{*}{**Ni**} & \multirow{2}{*}{**Mo**} & \multirow{2}{*}{**Si**} & \multirow{2}{*}{**N**} \\  & & & & & & & & & & & \\ \hline
1 & & & & 4.59 & & 42.41 & & & & 14.53 & 38.47 \\
2 & 1.12 & 0.57 & 0.37 & 7.24 & & 21.17 & & 0.56 & & 35.93 & 33.05 \\
3 & 36.09 & 13.86 & 12.01 & 10.9 & 0.12 & 17.91 & 2.43 & 0.9 & 1.47 & 4.31 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Chemical EDS analysis of the flank face in points 1–3 from Figure 12 (\(f=0.07\) mm/rev, \(v_{c}=200\) m/min). Figure 14: _Cont._
# Exploring Surface-Driven Mechanisms for Low-Temperature Sintering of Nanoscale Copper
###### Abstract  
As the density of electronic packaging continues to rise, traditional soldering techniques encounter significant challenges, leading to copper-copper direct bonding as a new high-density connection method. The high melting point of copper presents difficulties for direct diffusion bonding under standard conditions, thus making low-temperature copper-copper bonding a focal point of research. In this study, we examine the sintering process at various temperatures by constructing models with multiple nanoparticles and sintering them under different conditions. Our findings indicate that 600 K is a crucial temperature for direct copper-copper sintering. Below this threshold, sintering predominantly depends on structural adjustments driven by residual stresses and particle contact. Conversely, at temperatures of 600 K and above, the activation of rapid surface atomic motion enables further structural adjustments between nanoparticles, leading to a marked decrease in porosity. Mechanical testing of the sintered samples corroborated the structural changes at different temperatures, demonstrating that the surface dynamic motion of atoms inherent in low-temperature sintering mechanisms significantly affects the mechanical properties of nanomaterials. These findings have important implications for developing high-performance materials that align with the evolving requirements of modern electronic devices.  
low-temperature sintering; nanoparticle; surface atomic motion; molecular dynamic simulation 2020
## 4 Experimental Validation
### Results
#### 4.3.3 Time-Varying Cable Tension and Error Analysis  
The proposed algorithm then processed the identified 4 th to 8 th frequencies to calculate the tension by Equation (12). The results are shown in Figure 9a. By comparison, the actual loads are also marked with red color in Figure 9a. The tension calculated by the low-order frequencies changes more than the higher-order frequencies. Meanwhile, each sensor identified the tension using the 8 th order frequency, as shown in Figure 9b. Almost all the sensors get the same results and are close to the actual loads.  
Moreover, the average error (AE) and maximum error (ME) are shown in Table 2 and Figure 10. The definitions of the errors can be expressed as  
\[AE=(\frac{1}{N}\sum_{j=1}^{N}\frac{\left|T_{i,j}-T_{a,j}\right|}{T_{a,j}}) \times 100\%, \tag{13}\]  
\[ME=max_{j=1,2,\cdots,N}\frac{\left|T_{i,j}-T_{a,j}\right|}{T_{a,j}}\times 10 0\%, \tag{14}\]  
where \(j\) is the sampling points varying from 1 to \(N\). It can be seen from Table 2 and Figure 10 that the AE of the TFR-based algorithm is between 0.16% to 0.59%, while the ME within 5.70% usually appears at the boundary of the signals or calculated results by the low-order frequencies (e.g., 4 th order result marked with cyan color in Figure 9a). The error of higher  
Figure 8: STFT of measured acceleration response by Sensor 1.  
Figure 9: Identified tension compared with actual tension. (**a**) Identified tension using Sensor 1 with different frequencies and (**b**) identified tension using 8 th order frequency with different sensors.  
order frequency is lower than that of low-order frequency. Almost all the ME are within 5%, which is in accord with the application requirements in engineering.
## 6 Simulation Environment and Results
### Simulation Environment
\begin{table}
\begin{tabular}{c c} \hline \hline
**Network Parameters** & **Value** \\ \hline ns-2 Version & 2.34 \\ AODV Implementation & SD-AODV \\ OLSRv1 Implementation & UM(University of Murcia)-OLSR \\ OLSRv1 Parameters & Default values \\ OLSRv2 Implementation & JOLSRv2 \\ OLSRv2 Parameters & Default values \\ Simulation Time & 400 s \\ Simulation Area & 1000 \(\times\) 1000 \\ Transmission Range & 250 m \\ Mobility scenario & Random waypoint \\ Radio Propagation Model & Two-ray ground \\ MAC Protocol & IEEE 802.11 DCF \\ MAC Rate & 2 Mbps \\ Confidence interval & 95\% \\ Number of nodes & 20–50 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. AODV, _Ad hoc_ On-demand Distance Vector (AODV); SD, service discovery; OLSR, Optimized Link State Routing Protocol; DCF, distributed coordination function. This parameter defines the maximum number of hops a service discovery request is allowed to go in the network. In order to gain good confidence in the obtained results, we run the simulations 10 times during 400 s with different mobility pattern files.
# AISMSNet: Advanced Image Splicing Manipulation Identification Based on Siamese Networks
## 4 Experimental Results
### Performance Comparison of the Proposed Algorithm
The Siamese model's reduction in processing time and parameters compared with the main CNN backbones is due to the tasks that must be performed by the pre-trained models, which are specifically adapted for the detection of splicing manipulations. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Author** & **Method** & **Results** & **Database Training** & **Database Experiments** \\ \hline \multirow{3}{*}{[PERSON], et al. [19]} & Deep Dense Matching & Precision = 91.5\% & \multirow{3}{*}{SUN2012 and MS COCO} & CASIA V2 and Nimble \\  & and Verification Network & Recall = 79.18\% & & 2017 \\  & and Splicing. & F1 Score = 84.91\% & & \\ \hline \multirow{3}{*}{[PERSON], [PERSON] [23]} & \multirow{3}{*}{ResNet50} & Accuracy = 95\% & \multirow{3}{*}{DSO-1 and Columbia} & DSO-1 and Columbia \\  & & F1 Score= 95\% & & \\ \hline \multirow{3}{*}{[PERSON], [PERSON] [24]} & \multirow{3}{*}{MobileNetV2} & Accuracy = 93\% & \multirow{3}{*}{Columbia} & \multirow{3}{*}{Columbia} \\  & & Recall = 93.6\% & & \\ \cline{1-1}  & & F1 Score= 93.1\% & & \\ \hline \multirow{3}{*}{[PERSON], [PERSON] [25]} & \multirow{3}{*}{DenseNet-201} & \multirow{3}{*}{Accuracy = 94.12\%} & CASIA V1, CASIA V2, and CoMoFoD V2 & CASIA V1 and CASIA V2 \\ \hline [PERSON], et al. [26] & AlexNet & Accuracy = 98.79\% & CASIA V1 & CASIA V1 \\ \hline \multirow{3}{*}{[PERSON], et al. [27]} & VGG-19 and error level analysis. & Accuracy = 96.52\% & CASIA V2 & CASIA V2 \\ \hline \multirow{3}{*}{[PERSON], et al. [28]} & ResNet 50 combined with Canny edge detector & Accuracy = 83.7\% & \multirow{3}{*}{CASIA V1 and CASIA V2} & CASIA V1 and CASIA V2 \\  & & F1 Score = 90.4\% & & \\ \hline \multirow{3}{*}{Proposed Method} & Siamese neural network & Accuracy = 98.6\% & \multirow{3}{*}{CASIA V2, Realistic} & CASIA V2, Realistic \\  & and unsupervised & Precision = 97.5\% & & Tampering, Columbia, \\ \cline{1-1}  & K-means clustering & Recall = 95\% & & and Own database & MIC-F220, and Own \\ \cline{1-1}  & & F1 Score = 96\% & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of the proposed scheme and other previously proposed algorithms. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Proposed Siamese** & **VGG19** & **Resnet 18** & **Resnet 50** & **Densenet 121** & **Alexnet** & **GoogLeNet** & **Mobilenet** \\ \hline
5.25 & 140.61 & 11.31 & 24.03 & 7.22 & 58.05 & 5.86 & 2.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the number of trainable parameters (in millions).
# A Metal-Insulator-Metal Deep Subwavelength Cavity Based on Cutoff Frequency Modulation
## 3 Results
### Loss Analysis for the Three Cavities
The light line is indicated by the blue line; (**c**,**d**) Top and side views of the electric field profiles of the fundamental (\(L_{c}=220\) nm) and second-order (\(L_{c}=780\) nm) cavity modes. suppressed by multipole cancellation [26], \(Q_{\text{rad}}\) of the second-order cavity mode became larger than that of the fundamental cavity mode, increasing \(Q_{\text{total}}\). On the other hand, the calculated propagation length was 4.8 \(\upmu\)m and the reported value was \(\sim\)5 \(\upmu\)m in a similar MIM waveguide [14], which is at least an order of magnitude larger than the proposed cavity size.
# Efficacy of Herbal Extracts-Based Nano-Formulations in Extending Guava Fruit Shelf-Life
###### Abstract  
The guava (_Psidium guajava_ L.), known as 'Apple of the Tropics' is a rich source of polyphenols, pectin, dietary fiber, and carotenoids. Guava comes under climactic types of fruits; due to its high transpiration and respiration rate it experiences high post-harvest losses. Giloy leaf and ajwain seed herbal extracts-based nano-formulations (NFs) were synthesized using biopolymer sodium alginate and CaCl\({}_{2}\), viz., T1 (Alginate: CaCl\({}_{2}\)), T2 (Alginate: CaCl\({}_{2}\): Ajwain extract), and T3 (Alginate: CaCl\({}_{2}\): Giloy extract). Antibacterial and antioxidant activity of the NFs were examined to check their efficacy as antibacterial agents, which was checked against _E. coli, P. aeruginosa_ and _B. cereus_ bacterial culture. The zone of inhibition against bacteria ranged from 6 \(\pm\) 0.42 mm to 14 \(\pm\) 0.92 mm. Antioxidant activity was 93.27%, 71.67%, and 67.04% for T2, T3 and T1 NFs and 89.90% and 67.05% for ajwain and gloy extracts. NFs treated fruits showed minimum loss in physiological weight, firmness, and color change compared to control fruit (uncoated). Physiological loss in weight ranged from 3.16 to 17.21% and 3.23 to 15.57% and fruit firmness ranged from 4.47 to 8.41 kg/cm\({}^{2}\) and 4.84 to 8.37 kg/cm\({}^{2}\) during storage at 25 \(\pm\) 2 \({}^{\circ}\)C (incubation) and 32 \(\pm\) 2 \({}^{\circ}\)C (room temperature), respectively. Among NFs, T2 showed the best results in preventing ripening and maximum loss of quality was observed in control (uncoated) fruits. Thus, NFs are an effective method of extending the shelf-life of fruits and ajwain based NFs increased shelf life of guava from 4-5 days to 7-8 days. Fruits storage at 25 \(\pm\) 2 \({}^{\circ}\)C showed better results compared to storage at 32 \(\pm\) 2 \({}^{\circ}\)C. Thus, NFs treated fruits storage at lower temperature controls the ripening related changes maximally.  
Keywords:nanoformulations; guava; antibacterial; shelf-life +
Footnote †: journal:
## 4 Discussion
In addition, although GRF can certainly be measured most accurately with dynamometers/ force-plates [40], the size and weight of these devices disturb the skiing equipment and, thereby, the performance of the skier. To avoid this, we used pressure insoles here, which do not always indicate the magnitude of GRF accurately [41]. On the other hand, direct measurement of the pressure on the soles, an important parameter when skiing [23; 35], is especially useful for dealing with asymmetries in pressure on different parts of the foot. To assess a more precise magnitude of the GRF, we performed biomechanical modeling of this parameter based on the acceleration of the center of mass monitored by three-dimensional kinematics, as in our previous studies [11; 42]. As is true of virtually all studies on alpine skiing, generalization of our present results, despite the relatively large size of our study population, is not straightforward. The snow, terrain and weather were nearly ideal during our testing and similar studies under varying conditions are now required.
###### Abstract
For this reason, in this study, instead of relay on the standards, Finite Element Simulations of the STBF configuration have been used to determine the stress tensor and how it varies during the loading cycle. Subsequently, a multiaxial fatigue criterion, i.e., the Findlay one [27], was used to relate the force applied during the test and the fatigue limit. In this way, it is possible for a given material and a given gear geometry, to determine the level of force \(F_{lim}\) that will theoretically lead to the failure of the sample in \(1M\) cycles. By way of example, the gear geometry according to [18] was used, but the procedure could be generally applied to any gear geometry. The previously determined level of force \(F_{lim}\) was successively applied to the same gears which were, however, shot peened before the fatigue tests. The residual stresses induced by the shot peening have been determined with a non-linear dynamic simulation of the surface treatment itself. The pre-conditioned samples were virtually tested in accordance to the wrought gears (not peened) applying the level of force obtained before. The FEM results were analysed with the same approach exploiting the Findley criterion. The comparison of the equivalent stress (or better called damage parameter DP) for the wrought and the shot peened gear allowed the quantification of the impact of shot peening on the tooth root bending resistance. Moreover, the high level of detail of the simulations (meshing size of 10 \(\upmu\)m), has allowed us to relate the slight shift of the crack nucleation position and orientation to the non-homogeneous residual stress field induced by the shot peening treatment.
## 3 Short-Circuit Current Calculation Considering Power Fluctuation  
### Output Power Fluctuation Analysis of PV Stations  
Among the DGs in distribution network, distributed photovoltaic stations (PVs) are dominant, and different PVs in the same distribution grid usually have a consistency, which corresponds to the intensity of solar radiation. The Ausgrid dataset can be obtained from the official website [30]. The historical data include output power curves of nearly three hundred household rooftop photovoltaic system. The yearly output power of the rooftop photovoltaic system of customer 2 is selected by us for the analysis and clustering. Based on the Ausgrid dataset, the distributed rooftop PV output is converted to standardized output data at half-hour intervals, and the PV output characteristics of single-family residents are shown in Figure 2.  
As shown in Figure 2, the PV power output in all four seasons of spring, summer, fall, and winter have obvious randomness. Cluster analysis of the output curves shows that the distributed PV output is the largest from 10:00 to 14:00, when the corresponding light intensity is also the largest. This is a typical sunny day output characteristic, but there is also a more obvious volatility on a daily basis. Other categories of PV output are irregular, depending on the environmental factors during the day.  
Figure 2: Cluster analysis of PV output power: (**a**) spring season; (**b**) summer season; (**c**) autumn season; (**d**) winter season.
# Closed-Form Method for Atmospheric Correction (CMAC) of SmallSat Data Using Scene Statistics
## 3 Results
### Estimation Error
Color balance is a valuable indicator of potential problems that could occur through use of machine analyses. (**b**) CMAC clearing of the image provides the color balance conferred by the TOAR image. The features within both are darkened through hypothesized diffuse shading from aerosol particles. (**c**) Sen2 Cor correction of the same clip displaying color balance problems and residual haze.
## 3 Numerical Tests
### Couette Flow
#### 3.1.1 Effect of Degree  
In order to investigate the effect of degree of function \(D\) on the PINN-predicted result, three hard constraints with three different degrees (i.e., \(k\) = 1.0, 2.0, 3.0) and the same scaling factor (i.e., \(a\) = 1.0) are formulated. The maximum streamwise velocity is used in this flow problem to quantitatively measure the prediction accuracy of PINN, and the comparison between the PINN-predicted results and analytic solutions at different Reynolds numbers is listed in Table 1. As can be seen, the relative errors of the maximum axial velocity between the PINN-predicted results with first-degree hard constraints and analytic solutions are remarkable. However, as the constraint degree increases to 2.0, the relative errors are significantly reduced below 0.1% at most of the examined Reynolds numbers between 139 and 1250 and slightly increased when the Reynolds number increases to 2000. As for the cases with the third-degree hard constraints, the relative errors are larger than those with the second-degree hard constraints but much smaller than those with the first-order hard constraints. Therefore, the best degree of hard constraints for the Couette flow is 2.0. For both second- and third-degree hard constraints, the relative errors generally increase with the Reynolds number. Besides higher prediction accuracy, the PINN with second- and third-degree hard constraints exhibits better convergence properties than that with the first-degree hard constraints, as shown in Figure 2.
# Non-Uniform Input-Based Adaptive Growing Neural Gas for Unstructured Environment Map Construction
## 3 Agng-Nu
### Implementation of AGNG-NU  
AGNG-NU adopts the dynamic response deletion mechanism, which dynamically sets the maximum age value of different regions according to the density of the input environment in order to solve the defect of mistaken deletion of effective nodes in dense regions. Then, it also adopts the adaptive adjustment mechanism of neuron parameters, so that location weight parameters can be dynamically and adaptively adjusted according to the non-uniform input environment. Finally, with the links of competition, coordination and neuron growth, the construction of the topology map in the non-uniform input environment is realized.  
Algorithm 3 shows the AGNG-NU method. Line 1 shows the initialization of the algorithm; lines 2 to 20 show the iterative process of the algorithm, of which lines 3 to 5 show the competition stage, where the closest winning node, the second closest node, and the node connected to the winning node are picked out. Lines 6 to 8 show the coordination, neuron weight adjustment, and deletion stages, in which the winning node and connecting edges are adjusted to make it more approaching the input information. Lines 9 to 19 show the neuron growth stage; when the cycle reaches a certain number of times, a new node is added, which reflects the dynamic growth and adaptation of the topology structure with the increase in input signals.  
Compared with traditional algorithms, AGNG-NU can solve the problems of inaccurate topology structure, excessive aging of connecting edges, and excessive deletion of nodes in non-uniform input environment. A dynamic response deletion mechanism is proposed, and the maximum age value is dynamically adjusted according to the density of regional sample points. The generated nodes and their topology can meet the needs of environment description. Meanwhile, an adaptive adjustment mechanism of neuron parameters is proposed to make the node position more suitable for the described feasible area. AGNG-NU can dynamically update and adaptively adjust the topology structure with the input of environmental information, better describe the unstructured characteristics of the environment, and construct an environmental topology map, which is suitable to be applied to the problem of dynamic optimization and decision making based on the environment.
# Avoiding Obstacles via Missile Real-Time Inference by Reinforcement Learning
## 2 Missile Dynamics and Environment Modeling
### Missile Dynamics  
The missile equations of motion is given by the pseudo-5 DOF model as follows:  
\[V_{m}=\left|\vec{V}_{m}\right| \tag{1}\]  
\[\psi=\frac{a_{m_{y}}}{V_{m}\cos\theta_{m}} \tag{2}\]  
\[\dot{\theta}=-\frac{a_{m_{z}}+g\cos\theta_{m}}{V_{m}} \tag{3}\]  
\[\psi_{m}(t)=\int_{0}^{t}\dot{\psi}_{m}dt+\psi_{m}(0) \tag{4}\]  
\[\theta_{m}(t)=\int_{0}^{t}\dot{\theta}_{m}dt+\theta_{m}(0) \tag{5}\]  
\[C_{b}^{n}=\begin{bmatrix}\cos\psi_{m}\cos\theta_{m}&-\sin\psi_{m}&\cos\psi_{ m}\sin\theta_{m}\\ \sin\psi_{m}\cos\theta_{m}&\cos\psi_{m}&\sin\psi_{m}\sin\theta_{m}\\ -\sin\theta_{m}&0&\cos\theta_{m}\end{bmatrix} \tag{6}\]  
\[V_{m}=C_{b}^{n}V_{M}^{b} \tag{7}\]  
\[R_{m}=R(0)+\int_{0}^{t}C_{b}^{n}V_{m}^{b}dt \tag{8}\]  
where \(\theta_{g}\) and \(\psi_{g}\) are the elevation look angle and the azimuth look angle, respectively, \(\lambda_{y}\) and \(\lambda_{z}\) are the line of sight angle of the y-axis and z-axis under inertial frame, respectively, \(V_{m}\) is the velocity vector of a missile, \(R_{c}\) is the line of sight vector, and \(\theta_{m}\) and \(\psi_{m}\) are the pitch and yaw attitude of the missile, respectively. The geometry of the model is showed in Figure 1.  
Figure 1: Geometry of the pseudo-5 DOF missile model.