esa-sceva/satcom-chunk-collection huggingface dataset:
# In-Situ Nanoparticles: A New Strengthening Method for Metallic Structural Material
## 5 Optimizing Microstructure
### Inhibiting Segregation  
Deriving from tin, bronze alloys are always used in as-cast condition and segregation of 8 phase at the grain boundary, which is caused by the solute redistribution of the Sn atoms during solidification process, greatly deteriorates the mechanical properties. Since it is very difficult to dissolve the phase through heat treatment, we can only control the segregation through casting process [83; 84; 85]. However, in our recent research, \(\delta\) phase segregation was dramatically eliminated in as-cast Cu-10 Sn-2 Zn-1.5 Fe-0.5 Co alloy in contrast to virgin Cu-10 Sn-2 Zn tin bronze alloy, as shown in Figure 14a. Large amounts of iron-rich nanoparticles densely distributed in the copper play a significant important role in eliminating 5 phase. It was reported that both Sn atoms and nanoparticles do irregular Brown motion in the melt [86], and Sn atoms will inevitably collide with nanoparticles which own relatively large surface area. [PERSON] et al. [67] built the in-situ nanoparticle wall model [67] that densely distributed in-situ nanoparticles near solid/liquid interface can effectively absorb Sn atoms and prevent Sn atoms from escaping away (Black dashed lines: nanoparticle wall, seen in Figure 14c). 3 DAP reconstruction result (Figure 14b) presents that Sn atoms aggregate at the surface of iron-rich nanoparticles, which is consistent with the in-situ nanoparticle wall model. Obviously, this method inhibits \(\delta\) phase segregation by tailoring the migration rate of Sn atoms, which is totally different from solute trapping model in rapid solidification technique [87; 88]. Hence, the in-situ nanoparticle wall model sheds light on a novel and easy way to eliminate segregation which is expected to be applied in reducing segregation of bulk metallic materials.  
Figure 14: (**a**) SEM micrographs showing microstructure of cast Cu-10 Zn-2 Sn and Cu-10 Sn-2 Zn-1.5 Fe-0.5 Co alloys, (**b**) a 3D atom map reconstruction of an iron-rich nanoparticle and (**c**) in-situ nanoparticle wall model: densely distributed nanoparticles near solid/liquid interface inhibit Sn atom segregation [67].  
Figure 13: Grain refinement effect caused by in-situ nanoparticles in Ni-Cr-Mo HSLA steel [58], Q195 steel [33], Tin bronze [64], pure Cu and Cu-Fe-Co alloy [69].
# A New Improved Multi-Sequence Frequency-Hopping Communication Anti-Jamming System
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}  & \\ \multicolumn{1}{l}{MSFH} & \multicolumn{1}{l}{Intelligent Multi-Sequence Frequency Hopping communication framework} \\  & \\ \multicolumn{1}{l}{USRP} & \multicolumn{1}{l}{Universal Software Radio Peripherals} \\  & \\ \multicolumn{1}{l}{UAVs} & \multicolumn{1}{l}{Unmanned Aerial Vehicles} \\ \multicolumn{1}{l}{V2X} & \multicolumn{1}{l}{Vehicle-to-Everything communications} \\ \multicolumn{1}{l}{FH} & \multicolumn{1}{l}{Frequency Hopping} \\ \multicolumn{1}{l}{BFSK} & \multicolumn{1}{l}{Binary Frequency Shift Keying} \\ \multicolumn{1}{l}{CSI} & \multicolumn{1}{l}{Channel State Information} \\ \multicolumn{1}{l}{RL} & \multicolumn{1}{l}{Reinforcement Learning} \\ \multicolumn{1}{l}{DRL} & \multicolumn{1}{l}{Deep Reinforcement Learning} \\ \multicolumn{1}{l}{SDRLA} & \multicolumn{1}{l}{Sequential Deep Reinforcement Learning Algorithm} \\ \multicolumn{1}{l}{MSFH} & \multicolumn{1}{l}{Multi-Sequence Frequency Hopping communication framework} \\ \multicolumn{1}{l}{BER} & \multicolumn{1}{l}{Bit Error Rate} \\ \multicolumn{1}{l}{AAJ} & \multicolumn{1}{l}{Active Anti-Jamming} \\ \multicolumn{1}{l}{IDFH} & \multicolumn{1}{l}{Intelligent Differential Frequency Hopping communication framework} \\ \multicolumn{1}{l}{MPFH} & \multicolumn{1}{l}{Multi-Mode Frequency Hopping communication framework} \\ \multicolumn{1}{l}{WGMPFH} & \multicolumn{1}{l}{Wide-Gap Multi-Pattern Frequency Hopping communication method} \\ \multicolumn{1}{l}{DFH} & \multicolumn{1}{l}{Differential Frequency Hopping} \\ \multicolumn{1}{l}{PSD} & \multicolumn{1}{l}{Power Spectral Density} \\ \multicolumn{1}{l}{DFT} & \multicolumn{1}{l}{Discrete Fourier Transform} \\ \multicolumn{1}{l}{CRC} & \multicolumn{1}{l}{Cyclic Redundancy Check} \\ \multicolumn{1}{l}{JSR} & \multicolumn{1}{l}{Jamming-to-Signal Ratio} \\ \multicolumn{1}{l}{SNR} & \multicolumn{1}{l}{Signal-to-Noise Ratio} \\ \multicolumn{1}{l}{SJR} & \multicolumn{1}{l}{Signal-to-Jamming Ratio} \\ \end{tabular}
# Conservation Integrals in Nonhomogeneous Materials with Flexoelectricity
## 3 Conservation Integrals Relevant to the Electric Gibbs Function
### Application of Dilatation and M-Integral  
If we specify the functional derivative to be dilatational, i.e.,  
\[\delta=x_{k}\partial_{k} \tag{27}\]  
Using the same manipulations, one finds  
\[x_{k}J_{k}^{G}=\int_{V}x_{k}F_{k}dV=\int_{V}x_{k}\partial_{i}P_{ik}dV=\int_{V} \left[\partial_{i}(x_{k}P_{ik})-P_{kk}\right]dV \tag{28}\]  
with  
\[\begin{array}{l}P_{kk}=g^{E}\delta_{kk}-\sigma_{kj}u_{j,k}-\tau_{mj}u_{m,jk}+ \tau_{jkm,m}u_{j,k}-D_{k}\varphi_{j,k}-Q_{jk}\varphi_{,jk}+Q_{kj,j}\varphi_{j,k }\\ =\frac{n}{2}\left(\tau_{ijl}u_{i,j}\right)_{\lambda}+\frac{n-2}{2}\big{[} \left(\sigma_{ij}-\tau_{ijn,m}\right)u_{i}\big{]}_{j}+\frac{n-2}{2}f_{i}u_{i}- \tau_{mj}u_{m,jk}\\ +\frac{n}{2}\big{(}Q_{ij}\varphi_{,j}\big{)}_{\lambda}+\frac{n-2}{2}\big{[} \left(D_{i}-Q_{ij,j}\right)\varphi\big{]}_{\lambda}-\frac{n-2}{2}\rho_{e} \varphi-Q_{jk}\varphi_{,jk}\end{array} \tag{29}\]  
where \(n\) is the dimension of spatial coordinates. The generalized M-integral for the nonhomogeneous flexoelectric medium with body force and free electric charge is  
\[\begin{array}{l}M^{G}=\int_{V}\Big{[}x_{k}F_{k}+\frac{n-2}{2}f_{i}u_{i}-\tau _{mj}ku_{m,jk}-\frac{n-2}{2}\rho_{e}\varphi+Q_{jk}\varphi_{,jk}\Big{]}dV\\ =\int_{V}\partial_{i}\big{\{}x_{k}P_{ik}-\frac{n}{2}\big{(}\tau_{mj}u_{m,j}+Q_ {ij}\varphi_{,j}\big{)}-\frac{n-2}{2}\big{[}\big{(}\sigma_{ji}-\tau_{jin,m} \big{)}u_{j}+\big{(}D_{i}-Q_{ij,j}\big{)}\varphi\big{]}\big{\}}dV\end{array} \tag{30}\]  
Define the dilatation flux as  
\[Y_{i}=x_{k}P_{ik}-\frac{n}{2}\big{(}\tau_{mj}u_{m,j}+Q_{ij}\varphi_{j}\big{)} -\frac{n-2}{2}\big{[}\big{(}\sigma_{ji}-\tau_{jin,m}\big{)}u_{j}+\big{(}D_{i}- Q_{ij,j}\big{)}\varphi\big{]} \tag{31}\]Thus, the second integral in Equation (30) can be transformed into a surface integral  
\[M^{G}=\int_{V}\partial_{i}Y_{i}dV=\int_{S}n_{i}Y_{i}dS \tag{32}\]  
It can be easy to draw the conclusion from the first integral in Equation (30) that M-integral derived here is not conserved. In absence of body force and free electric charge, a path-independent integral \(G^{M}\) can be obtained by moving the inhomogeneity term and the gradient term to the left side in Equation (30),  
\[G^{M}=M^{G}-\int_{V}\Big{[}x_{k}A^{G}_{k}-\tau_{mjk}u_{m,jk}+Q_{jk}\varphi_{j, k}\Big{]}dV \tag{33}\]  
Furthermore, we neglect the gradient terms in a homogeneous medium, M-integral will reduce to  
\[M^{G}=\int_{S}n_{i}\bigg{[}x_{k}\Big{(}g^{E}\delta_{ik}-\sigma_{ij}u_{j,k}-D_{ i}\varphi_{j,k}\Big{)}-\frac{n-2}{2}\big{(}\sigma_{ji}u_{j}+D_{i}\varphi \big{)}\bigg{]}dS \tag{34}\]  
It is in agreement with the M-integral for elastostatics [25; 52] and electroelastic medium [53].
# Heat Generated during Dental Implant Placement: A Scoping Review
## 4 Discussion
Additionally, recent advancements in surgical template design, such as adding an irrigation channel, also facilitate cooling during osteotomy preparation. The dental literature remains divided on the choice of drilling material: metal or zirconia [38; 39; 40; 41; 42; 43; 44; 45; 46]. It is known that a prolonged thermal stimulus can inflict the greatest damage to bone at equivalent levels of heating [38; 39], with a consensus on the direct correlation between osteotomy depth and heat generated. The average force used during bone preparation is 1.2 kg [47], and while the literature suggests that load may influence heat generation [50], reports have indicated that a small load of 1.2 kg and 2.4 kg can beused without problem [48]. When using a piezoelectric device, the load must be modified to no more than 400 g and, in clinical practice, the piezoelectric must be handled with extreme care [54; 78], supported by a cooled irrigant [75]. Guided surgery is gaining popularity in modern implant dentistry due to its increasing precision and reliability. Despite the broad consensus that surgical guides can lead to an increase in bone temperature [83; 90], studies such as that by [PERSON] et al. [82] indicate that the use of these devices at the time of implant placement does not adversely affect bone implant integration, considering long-term implant survival results. Significant advancements include the development of open guides [67; 91], the use of cooled irrigants [117], and the possibility of adding irrigation channels [95; 96; 97]. Modern implant planning software lacks tools for adding additional irrigation channels. In virtual planning, using a fixation pin as an additional channel is the easiest way to add this secondary irrigation [96]. Nevertheless, flapless surgery seems to limit this improvement. Further research is needed to better understand bone heating during guided surgery. For temperature measurement methods, infrared thermography appears to be more accurate than thermocouples during drilling. At the same time, real-time temperature monitoring during implant placement could help clinicians better control bone temperature during surgery, reducing the risk of thermal injury [139]. Temperature monitoring is also relevant in others branches of medicine [140]. It is conceivable that one day the same technology could be adopted in the dental field. Real-time temperature monitoring could aid clinicians in reducing bone heating and associated complications. Finally, conflicting indications exist regarding cutter wear. While [PERSON] et al. suggested that a bur used for 600 times remains acceptable [124], other studies have reported signs of drill wear [125]. It is advisable to maintain a record card for each drill, documenting the number of uses and the type of bone it is used on. For instance, drills may wear out more quickly when used on denser bone types like type D1 compared to type D4 bone. Importantly, it is recommended not to exceed 50 uses per drill to ensure optimal performance and minimize the risk of heat generation.
# HVdc Circuit Breakers: Prospects and Challenges
###### Abstract  
The integration of offshore wind farms has revitalized the interest in multi-terminal high voltage direct current (M-HVdc) transmission grids. HVdc breakers' importance has increased as M-HVdc grids are now a commercial truth. Several HVdc circuit breaker technologies have been developed, published, and appeared as prototypes for HVdc networks. This paper summarizes the HVdc breaker technologies from the last two decades, distributed mainly in literature. A comparison of various state-of-the-art HVdc breakers is presented. Further, areas are identified where further research and development are required. The goal is to provide primary challenges and prospects in the HVdc breaker field.  
dc grid protection; HVdc circuit breaker; multi-terminal HVdc transmission; power system protection +
Footnote †:  
the small inductance, which demands fast interruption technology [18]. Thus, the recognition of the M-HVdc grid as a reliable and efficient network is forcefully subjected to the disposal of HVdc circuit breakers (DCCBs), making them one of the keys empowering technology [19; 20; 21; 22; 23].  
There is a substantial difference between the needs and necessities of dc and ac breakers. Mainly because of the unavailability of natural zero crossings in dc systems. The DCCBs would interrupt dc fault currents swiftly and dissipate vast amounts of energy stored in the dc grid's inductances. Today, only transfer and load switches are in use for the HVdc applications. However, DCCBs are available for low and medium-range dc applications. HVdc CBs are not commonly available, or proposed DCCBs have minimal current breaking capability. Numerous DCCB designs have been proposed based on parallel and series connections of classical ac interrupters, charging units, semiconductors, resonance circuits, and varistors--each concept associated with certain drawbacks and advantages. Most of the ideas investigated only a single or few aspect of many design requirements, but no literature has given an overall picture.  
The objective of this paper is to provide a comprehensive overview of HVdc circuit breaker technologies, including recent significant attempts in the development of modern HVdc circuit breakers, and, by this, to revive the discussion on this very topic. HVdc breakers from technology giants like ABB, Siemens, GE, etc. are assessed and a detailed performance-based comparison is provided, not available in the literature. Functional analysis of each technology is presented. Additionally, different technologies based on derived information from literature are compared. Finally, recommendations for the improvement of circuit breakers are presented.
## 2 Path Planning Algorithm Based on the Markov Decision Process (MDP)
### Transition Probability  
The ocean currents predicted by a regional ocean model are used in the simulation, but uncertainties between the prediction and the real ocean currents always exist. The transition probability \(P(s^{\prime}\,|\,s,\,a)\), which denotes the probability of reaching the state \(s^{\prime}\) from \(s\) by taking an action \(a\), is introduced into the MDP model to describe the uncertainties. When the AUV takes an action \(a\), a random number \(\mathbf{v^{\prime}}\) is added to the local ocean current \(\mathbf{v_{2}}\) to decide which grid the AUV can reach next. This process is repeated and the total number to arrive at the adjacent grids is counted. Then, this number is divided by the total number of repetitions to obtain the transition probability \(P(s^{\prime}\,|\,s,\,a)\). By applying the above simulation to all the grids, the transition probability matrix can be obtained.
## 5 Applications and Services
### UAV Design
#### 5.2.3 Solar-Powered UAVs  
Toward more sustainable and carbon-neutral environments, solar energy has been extensively utilized in the energy sector [154], as well as in solar-charged batteries [155]. Solar-powered UAVs are expected to see increased usage in the future due to advancements in solar panels and microcontrollers [156]. The solar-powered RISeUAV has also garnered attention in the wireless communication community as a means of providing supplementary propulsion power [157]. The optimal joint active and passive beamforming, along with the energy-constrained RISeUAV trajectory, is derived in closed form, while also optimizing the UAV's size, weight, power limitations, reflecting elements, and flying/hovering time.
## 4 Discussion  
This study provides essential information on the use of arc light signals emitted during electric arc welding for current estimation. The results obtained are consistent with previous studies, as referenced in [1; 22; 47]. In this study, based on previous ANFIS based welding estimation studies [47; 49], a new arc light current estimation approach is presented. [PERSON] et al. [47] developed a method for power consumption estimation with sound data. In the study, the proposed method estimates current using arc light. In previous studies in the literature, power consumption estimation with sound data [47] and weld bead width and penetration depth estimation with IR thermal image data [49] were performed using ANFIS. Our findings are consistent with the studies [47; 49] supporting the applicability of ANFIS in welding systems.  
The study highlights the critical role of arc light in the welding process and demonstrates that a successful estimation model can be created between arc light and current.  
Figure 17: Performance analysis of the ANN model with test data: (**a**) comparison of target FCS with FSANNCO for 2.50 mm electrode; (**b**) comparison of target FCS with FSANNCO for 3.25 mm electrode.  
Weld quality, especially in processes requiring manual welding, is related to the welder's experience and skill in visual perception. Therefore, visual perception has the advantage of providing a wealth of information without contacting the workpiece or the welding circuit [6]. During the welding process, welders manually adjust the welding distance speed and other parameters depending on the arc light, and these changes affect the current drawn from the electrode welding machine. In this context, our study shows that estimating current using arc light can automate the process independently of human factors and that this approach can be beneficial for monitoring the dynamics of the welding process and detecting anomalies. Additionally, it can also be used to enhance efficiency, reduce error rates, and ensure process control in welding automation, robotic welding, and artificial intelligence-based welding systems. In the study, it is observed that ANFIS models created with training and test data provided high compatibility with FCSs but showed a certain deviation and delay against sudden changes.  
The strength of this study compared to previous works in the literature lies in the use of arc light for welding current estimation and the use of the ANFIS method for current estimation. However, the study also has limitations. Increasing the number of datasets at different current levels suggests that the diversity of results can be expanded. Additionally, the diversification of various filtering and artificial intelligence methods, as well as different arc welding techniques and applications, could enhance the scope and richness of the study.
## 5 WST Applications
### Precision Agriculture  
The development of WST applications in precision agriculture (see Figure 1) makes possible to increase efficiencies, productivity and profitability while minimizing unintended impacts on wildlife and the environment, in many agricultural production systems. The real time information from the fields will provide a solid base for farmers to adjust strategies at any time. Instead of take decisions based in some hypothetical average condition, which may not exist anywhere in the reality, a precision farming approach recognizes differences and adjusts management actions accordingly [70].
## 5 Simulation Results--BER Performance  
Let us see the simulation results for proposed algorithm (9). An assessment of the BER performance of FTN signals at bit rates higher than the Nyquist barrier by a factor of 2-3 was carried out using the simulation method to calculate the energy costs for algorithm (9). Channels with additive noise and constant characteristics and channels with Rayleigh fading of signals were considered transmission channels.  
FTN signals based on RRC pulses with a duration \(T_{\mathrm{s}}=8T\) and a roll-off factor \(\beta=0.3\) of the frequency response of the shaping low-pass filter were used in the simulation of packet transmission. A high level of intersymbol interference in FTN signals, caused by the superposition of adjacent signals on each other, was observed under these conditions. The values of error probability of the FTN signals were obtained depending on the signal-to-noise ratio. The signal-to-noise ratio is understood as the ratio \(E_{b}/N_{0}\), where \(E_{b}\) is the energy of the received signal (for the binary alphabet). The sample size (number of transmitted bits) was equal to \(10^{6}\) in BER measurements. The modulation type of FTN signals was selected as BPSK.
# Convolution-Transformer Adaptive Fusion Network for Hyperspectral Image Classification
## 3 Experiments and Analysis
### Experiment Result
#### 3.2.2 Experiment Results on DFC2018 Dataset  
Figure 8 shows the visually comparison of different methods on the DFC2018 dataset. One can observe that CTAFNet obtained more accuracy classification map than the other methods. Moreover, the classification map generated by CTAFNet is the cleanest, with little  
noise in buildings and roads, while the classification map generated by other methods has noticeable noises. Table 6 shows the experimental results of different methods in the DFC2018 test set. The experimental results demonstrate that the proposed CTAFNet method achieves the best performance, with 92.59% pixel accuracy and 82.10% mIoU. Specifically, the pixel accuracy and mIoU of CTAFNet is 1.47% and 2.50% higher than the second-best model SS3 FCN, respectively. SegFormer has the worst performance, achieving only 73.44% pixel accuracy and 58.11% mIoU. In terms of classification accuracy of each class, CTAFNet obtained the best score in 12 of the 17 classes. Among them, IoU in residential buildings, major thoroughfare, and cars was far higher than the second place (7.46%, 10.93% and 8.57% higher, respectively). \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**Class** & **UNet** & **DeepLab v3+** & **SegFormer** & **Swin-UNet** & **UNet-m** & **SS3 FCN** & **ENLFCN** & **SSDGL** & **FSSANet** & **CTAFNet** \\ \hline Buildings & 85.47 & 85.53 & 63.47 & 82.89 & 82.64 & 83.86 & 85.17 & 81.66 & 84.93 & 86.63 \\ Vegetation & 94.87 & 94.99 & 93.09 & 95.94 & 95.45 & 95.71 & 95.25 & 95.72 & 95.8 & 95.72 \\ Roads & 81.17 & 82.09 & 66.95 & 81.07 & 80.04 & 83.93 & 80.77 & 82.01 & 81.62 & 84.64 \\ Water & 72.11 & 68.78 & 30.75 & 75.52 & 76.74 & 74.65 & 76.89 & 75.23 & 75.06 & 78.42 \\ Cars & 48.94 & 47.74 & 32.34 & 38.4 & 47.17 & 60.44 & 47.56 & 57.38 & 45.84 & 61.64 \\ \hline pixel acc. & 93.86 & 94.08 & 88.51 & 93.72 & 93.61 & 94.71 & 93.88 & 94.2 & 94.09 & 95.07 \\ mIoU & 76.52 & 75.83 & 57.32 & 74.76 & 76.41 & 79.72 & 77.13 & 78.4 & 76.65 & 81.41 \\ \hline \end{tabular}
\end{table} 
Table 5: Classification results for the AeroRIT test set. Figure 7: Visually comparison of different methods on the AeroRIT test set.