esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Microstructural Investigations
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Investigated Area in** & **\%O** & **\%Ba** & **\%Co** & **\%Zn** & **\%Nb** & **Attribution** \\ \cline{3-8} \multirow{4}{*}{Figure _S1_} & \multirow{2}{*}{zone 1} & 61.3 \(\pm\) 0.8 & 19.5 \(\pm\) 0.2 & 1.7 \(\pm\) 0.2 & 0.62 \(\pm\) 0.2 & 17.9 \(\pm\) 0.2 & \multirow{2}{*}{Ba\({}_{8}\)(Co\({}_{0.72}\)Zn\({}_{0.27}\))\({}_{1}\)Nb\({}_{6}\)O\({}_{24}\)} \\  & & (61.53)\({}^{\ast}\) & (20.51) & (1.86) & (0.67) & (15.38) & \\ \cline{2-8}  & & 295.6 \(\pm\) 21 & 203.3 \(\pm\) 0.2 & 4.9 \(\pm\) 0.3 & 1.8 \(\pm\) 0.3 & 13.3 \(\pm\) 0.3 & Ba(Co\({}_{0.7}\)Zn\({}_{0.3}\))\({}_{1/3}\)Nb\({}_{2/3}\)O\({}_{3}\) \\  & & (60) & (20) & (4.66) & (2) & (13.33) & Ba(Co\({}_{0.7}\)Zn\({}_{0.27}\))\({}_{1}\)Nb\({}_{6}\)O\({}_{24}\) \\ \hline Figure 7d & zone 1 & 59.1 \(\pm\) 0.8 & 19.8 \(\pm\) 0.2 & 1.6 \(\pm\) 0.2 & 0.7 \(\pm\) 0.2 & 18.6 \(\pm\) 0.2 & Ba(Co\({}_{0.72}\)Zn\({}_{0.27}\))\({}_{1}\)Nb\({}_{6}\)O\({}_{24}\) \\ (_S1a_) & zone 2 & 59.6 \(\pm\) 1.4 & 19.1 \(\pm\) 0.2 & 4.5 \(\pm\) 0.2 & 1.9 \(\pm\) 0.2 & 14.9 \(\pm\) 0.2 & Ba(Co\({}_{0.72}\)Zn\({}_{0.3}\))\({}_{1/3}\)Nb\({}_{2/3}\)O\({}_{3}\) \\ \hline Figure 8d & zone 1 & 62.1 \(\pm\) 1.1 & 19.3 \(\pm\) 0.2 & 1.7 \(\pm\) 0.2 & 0.6 \(\pm\) 0.1 & 16.2 \(\pm\) 0.1 & Ba(Co\({}_{0.72}\)Zn\({}_{0.27}\))\({}_{1}\)Nb\({}_{6}\)O\({}_{24}\) \\ (_S2_) & zone 2 & 60.9 \(\pm\) 1.0 & 18.9 \(\pm\) 0.1 & 4.2 \(\pm\) 0.1 & 1.4 \(\pm\) 0.2 & 14.6 \(\pm\) 0.1 & Ba(Co\({}_{0.72}\)Zn\({}_{0.3}\))\({}_{1/3}\)Nb\({}_{2/3}\)O\({}_{3}\) \\ \hline Figure 9d & zone 1 & 56.7 \(\pm\) 0.7 & 19.5 \(\pm\) 0.2 & 1.7 \(\pm\) 0.2 & 0.49 \(\pm\) 0.1 & 17.9 \(\pm\) 0.1 & Ba(Co\({}_{0.72}\)Zn\({}_{0.27}\))\({}_{1}\)Nb\({}_{6}\)O\({}_{24}\) \\ (_S2a_) & zone 2 & 59.8 \(\pm\) 1.6 & 19.4 \(\pm\) 0.2 & 4.4 \(\pm\) 0.2 & 1.8 \(\pm\) 0.2 & 14.6 \(\pm\) 0.2 & Ba(Co\({}_{0.72}\)Zn\({}_{0.3}\))\({}_{1/3}\)Nb\({}_{2/3}\)O\({}_{3}\) \\ \hline \hline \end{tabular}  
* Values in italics are those theoretically expected in the case of isolated particles. Deviations are in agreement with the presence of interfaces between phases detected by XRD. Zone 1: light grey area, zone 2: dark grey area.  
\end{table} 
Table 1: Atomic percentages obtained from EDS analyses on areas of interest by moving the analyzing spot. Zones 1 and 2 correspond to light grey and dark grey areas, respectively, mentioned in Figures 6d, 7d, 8d and 9d. These figures were performed on the samples _S1_, _S1a_, _S2a_ and _S2a_, respectively.
## 2 Description of the Russian Arctic Database
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Station**} & \multirow{2}{*}{**Code**} & \multicolumn{3}{c}{**Geographic**} & \multirow{2}{*}{**Geomagnetic Coordinates**} & \multirow{2}{*}{**Supporting Institution**} \\ \cline{3-3} \cline{5-6}  & & **Latitude** & & & **Longitude** & & **Latitude** \\ \hline Amderma & AMD & 69.47 & 61.42 & 65.4 & 137.7 & AARI \\ Baranov & BRN & 79.27 & 101.75 & 74.2 & 174.3 & AARI \\ Barentsburg & BBG & 78.07 & 14.25 & 75.6 & 108.6 & AARI \\ Bely & BEY & 73.30 & 70.00 & 69.5 & 146.8 & IZMIRAN \\ Cheluskin & CCS & 77.72 & 104.28 & 71.3 & 175.0 & AARI \\ Chokurdakh & CHD & 70.62 & 147.89 & 64.8 & 212.4 & HK\(\phi\)HA \\ Dikson & DIK & 73.54 & 80.56 & 67.9 & 155.7 & AARI \\ [PERSON] & HIS & 80.62 & 58.05 & 74.7 & 145.1 & AARI \\ [PERSON] & CKA & 68.50 & 73.60 & 63.3 & 148.2 & AARI \\ [PERSON] & KHS & 71.11 & 66.86 & 67.5 & 143.1 & AARI \\ [PERSON] & KTN & 75.94 & 137.71 & 69.9 & 201.2 & MAGDAS \\ [PERSON] & LOZ & 67.97 & 35.08 & 64.1 & 114.9 & AARI \\ [PERSON] & LOP & 68.25 & 33.08 & 64.1 & 114.1 & IZMIRAN \\ [PERSON] & NAD & 65.53 & 72.50 & 61.8 & 145.6 & IZMIRAN \\ Norilsk & NOK & 69.40 & 88.40 & 64.3 & 162.2 & AARI \\ [PERSON] & PBK & 70.08 & 170.90 & 65.0 & 228.8 & AARI \\ [PERSON] & SAB & 71.42 & 72.13 & 65.2 & 147.1 & AARI \\ [PERSON] & SKD & 66.52 & 66.67 & 63.0 & 141.7 & IZMIRAN \\ [PERSON] & SEY & 70.10 & 72.50 & 64.8 & 147.7 & IZMIRAN \\ [PERSON] & CPS & 68.88 & 180.63 & 64.2 & 235.1 & IKIR \\ [PERSON] & TIK & 71.59 & 128.78 & 65.7 & 196.9 & HK\(\phi\)HA \\ [PERSON] & VIZ & 79.29 & 76.58 & 74.9 & 154.9 & AARI \\ White Sea & WSE & 66.55 & 33.10 & 63.2 & 111.5 & GC/MSU \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Geographic and corrected geomagnetic coordinates of stations in the Russian Arctic for the epoch 2015. Examples of the constructed magnetogram plots are demonstrated below. The presented events are important for the problem of GIC excitation because they are known to be associated with intense GIC bursts, but their physical nature has not been completely clarified.
## 4 Results and Analysis
### Tracking Result Analysis
#### 4.3.1 Quantitative Evaluation
Another reason is that when the target rotates, the traditional object tracking algorithms cannot resist this  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Video** & **Evaluation Metrics** & **Ours** & **KCF** & **CSK** & **TLD** & **DSST** & **HCF** & **SiamFC** & **SiamRPN** \\ \hline \multirow{4}{*}{Plane1} & AUC (\%) & **79.8** & 67.6 & 62.5 & 37.5 & 68.0 & 72.0 & 70.8 & 72.8 \\  & Precision score (\%) & **99.0** & 88.6 & 77.4 & 30.2 & 94.3 & 97.3 & 94.6 & 96.7 \\  & Success score(\%) & **100.0** & 86.0 & 74.0 & 25.0 & 93.0 & 97.0 & 93.0 & 94.0 \\  & FPS & 14 & 95 & **106** & 13 & 76 & 21 & 16 & 13 \\  & AUC(\%) & 75.3 & 59.2 & 55.0 & 36.8 & 67.1 & 70.4 & 72.7 & **77.3** \\  & Precision score(\%) & 82.0 & 51.0 & 49.0 & 9.0 & 64.0 & 69.0 & 73.0 & **87.0** \\  & Success score(\%) & 98.0 & 85.0 & 83.0 & 11.0 & 89.0 & 91.0 & 97.0 & **99.0** \\  & FPS & 15 & 90 & **  
change and still update the original trajectory, generating errors and causing the object tracking bounding box to drift, significantly reducing tracking accuracy. The SiamRPN algorithm uses multiple anchor points and scales to overcome these changes and achieve appealing tracking results. The remaining four video sequences (Car3, Car4, Car5, and Car6) involve occluded object sequences. Many classical tracking algorithms cannot continue tracking the object when the moving object is occluded, while only our algorithm still tracks the object. This may be because the classical tracking algorithms cannot deal with long-time object occlusion and thus track the wrong object, eventually losing the object. In contrast, our algorithm combines the Kalman filter with occlusion detection to determine whether the object is occluded. When the object is occluded, we no longer utilize the KCF algorithm to track the occluded object and employ the adaptive Kalman filter to track the occluded object by manually setting the noise covariance Q and R values, which are initially set to 2 and 0.01, respectively. The precision score, success score, and AUC of various tracking algorithms are illustrated in Figures 12 and 13 and Table 4. The analysis of the above observations indicates that since our algorithm can solve the problem of occluded objects, it can track the object when it re-appears. This is why it has a high tracking accuracy, while the competitor algorithms have lower tracking results and lose the object due to occlusion. In summary, objects in satellite videos easily encounter occlusion due to their small size and large image width, with the object occlusion problem being a significant difficulty in object tracking. Thus, solving the object occlusion problem is the key to successful tracking, while for the unoccluded case, the object is tracked according to the original method.
## 4 Experiments
### Experimental Results of Single-Scale Organ Information Dataset
#### 4.4.2 Experimental Results on Skin Lesion Segmentation Datasets  
Table 3 presents the experimental results of MINTFormer and other advanced models on skin lesion segmentation datasets. MINTFormer achieved the best performance in terms of the Dice coefficient, reflecting its strong segmentation capability to accurately delineate skin lesion areas. Figure 11 shows the visual comparison of lesion segmentation results on the ISIC2017 dataset between MINTFormer and other advanced models. In Figure 11a,b, the background of the lesion areas is simple, making it easier for the models  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**ACDC**} & \multicolumn{5}{c}{**Kvasir-SEG**} \\ \hline
**Architecture** & **Dice** & **RV** & **MYO** & **LV** & **Architecture** & **Dice** & **SE** & **SP** & **ACC** \\ \hline U-Net [4] & 87.55 & 87.10 & 80.63 & 94.92 & BDG-Net [60] & **91.50** & **68.53** & 92.74 & 74.11 \\ \hline Att-Unet [61] & 86.75 & 87.58 & 79.20 & 93.47 & KDAS3 [62] & 91.30 & 66.03 & 93.93 & 76.25 \\ \hline nnUNet [36] & 90.91 & 89.21 & **90.20** & 93.35 & U-Net++ [63] & 82.10 & 44.62 & 84.48 & 72.01 \\ \hline UNetR [64] & 88.61 & 85.29 & 86.52 & 94.02 & Polypy-SAM++ [65] & 90.20 & 67.29 & 96.82 & 73.56 \\ \hline TransUNet [7] & 89.71 & 88.86 & 84.53 & 95.73 & U-Net [4] & 81.80 & 64.28 & 86.90 & 70.21 \\ \hline Swin-Unet [35] & 90.00 & 88.55 & 85.63 & 95.83 & TGA-Net [66] & 89.82 & 62.53 & 84.89 & 75.43 \\ \hline CSWin-Unet [20] & 91.46 & 89.68 & 88.94 & 95.76 & PEFNet [67] & 88.18 & 59.98 & 76.32 & 66.34 \\ \hline ST-Unet [68] & 89.73 & 87.65 & 82.11 & 94.39 & TransNetR [69] & 87.06 & 56.45 & 79.54 & 78.67 \\ \hline UNetFormer [70] & 89.09 & 88.92 & 87.88 & 95.03 & ResUNet++ [71] & 81.33 & 44.60 & 77.30 & 72.66 \\ \hline MINTFormer & **92.11** & **90.62** & **89.80** & **95.90** & MINTFormer & **92.54** & 66.14 & **98.64** & **79.23** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results on ACDC dataset and Kvasir-SEG dataset, best result is highlight in red, and the second best result is highlight in blue.  
to capture the features of the lesion areas. Observing the highlighted regions, although most models can mark the approximate location of the lesion areas, not all models can capture the boundary details of the lesion areas. In Figure 11a, MINTFormer not only accurately marks the lesion areas but also avoids mislabeling the surrounding interference information as lesion areas. In contrast, SwinUnet and Missformer incorrectly label the interference information. In Figure 11b, all models can mark the approximate location of the lesion areas, but only MINTFormer can more accurately restore the shape of the lesion areas. In Figure 11c-e, the background of the lesion areas becomes more complex, which further tests the models' ability to filter redundant information. It can be seen that in Figure 11c-e, except for MINTFormer, other models struggle to mark the location of the lesion areas accurately. The marked lesion areas by other models significantly deviate from the ground truth.
# Hybrid FSK-FDM Scheme for Data Rate Enhancement in Dual-Function Radar and Communication
## 5 Simulation Results
### Example 1: Single Communication Receiver  
In the 1 st case, the radar target is fixed at \(\theta_{r}=0^{\circ}\) while the communication receiver is placed at \(\theta_{c}=-50^{\circ}\) as shown in Figure 5. The signal received at the communication receiver is shown in Figure 6.
# Noise Reduction Using Active Vibration Control Methods in CAD/CAM Dental Milling Machines
## 3 Experimental Result
### Analysis of Noise Characteristics of Dust Collectors  
Table 4 indicates that the vibration frequency at point A was 59.4 Hz when the noise from the dust collector was recorded and measured. It approached 60 Hz around the hose of the dust collector, which is where the center and the most powerful energy sound pressure level (dB) was observed. Seeing that 60 Hz and the noise of harmonics accord with the number of revolutions of the compressor  
Figure 4: Dust collector noise level recording points.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Noise SPL (dB(A))** & **Front** & **Left** & **Right** \\ \hline Not using dust collector & 71.2 & 72.4 & 67.8 \\ Using dust collector & 80.2 & 82.5 & 78.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Noise level of the milling machine using and not using a dust collector. SPL = sound pressure level.  
inside the dust collector in the bottom of the milling machine, it is supposed that the noise was generated because the vibration of the compressor was transmitted to the dust collector case.  
Figure 5 shows the power spectrum analyzed using data recorded around point A, where the noise level of the dust collector is the highest. In this spectrum, frequency changes according to the length of the noise period, and it can be seen that the noise is generated at a constant level and concentrated in the frequency band of 60 Hz. As a discrete tone, the noise of the dust collector has a fundamental frequency of nearly 60 Hz and a regular sound pressure variation to bring about the sense of pitch consisting of multiple harmonics. When it comes to frequency, this noise has a line spectrum (Figure 5).  
It was found that the prominent band of the frequency spectrum obtained from the microphone and the main peak of the vibration frequency obtained from the vibration test coincided at about 60 Hz. Based on these results, the vibration generated from the dust collector was confirmed as the main noise source. Vibration and noise generated from precipitators are considered as noise caused by vibration transferred from compressors. Generated causes, locations, and results can be analyzed as shown in Figure 6 depending on test results.  
As a consequence of analysis of noise characteristics, the noise from the dust collector is caused by vibration transmitted from around the hose located on the side. The structure of the dust collector (see Figure 7) makes it difficult to add a noise reduction device (such as rubber/damper) inside.  
Therefore, we propose a noise control method using anti-vibration as the most efficient method to reduce the undesired noise.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Dust Collector** & **A** \\ \hline Sound pressure level (dB(A)) & 82.5 \\ Vibration frequency (Hz) & 59.4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The sound pressure level of the dust collector at the recording point A.  
Figure 5: Frequency analysis and noise sound spectrum (linear scale).
## 2 Machine Learning Methods
### Tree-Based and Ensemble Learning Models  
Decision tree (DT) is a versatile machine learning algorithm that can successfully perform classification and regression tasks. It is also a machine learning algorithm that is adaptable to multi-output tasks, has the ability to model complex data sets, and has simple applicability. To build a decision tree, the algorithm iteratively splits the data into smaller and more homogeneous subsets. The partitioning process is performed by selecting a feature that best separates the data set at each step. This process continues until leaf nodes are obtained [17].  
The random forest (RF) algorithm is a multi-directional machine learning algorithm that combines multiple decision trees with the ensemble method. This algorithm can solve both classification and regression problems. It divides the training data set into n subsets and predicts between these decision trees by installing a decision-tree algorithm in each cluster [18].  
The adaptive boosting (AdaBoost) algorithm is an ensemble learning method used to obtain a stronger model by successively training weak learners in regression problems. Adaptive boosting basically aims to correct errors by giving more weight to incorrectly predicted instances at each iteration [19].  
The gradient boosting regressor (GBM) algorithm is a powerful ensemble learning method that combines successively generated weak learners (usually decision trees) to minimize errors. It is particularly effective in regression problems requiring high accuracy, given its progressive error correction structure [20].  
The extra trees regressor (ETR) algorithm generates predictions by averaging a large number of decision trees, similar to random forest; however, it can provide lower variance and faster training time due to its completely randomized selection of attribute separation when building trees [21].  
The bagging regressor (BR) method trains separate models on multiple subsets generated by random samples from the data set and averages their outputs. In this way, model variance is reduced, and more stable and generalizable predictions are obtained [22].  
The histogram-based gradient boosting regressor (HGBR) algorithm divides the continuous variables into histograms during the preprocessing phase of the data set, allowing the model to run faster and with more memory efficiency. This structure is especially optimal for large-scale data sets [23].  
The extreme gradient boosting (XGBoost) is a high-performance gradient boosting algorithm that includes advanced features such as regularization and parallel computing. It is one of the preferred methods in many machine learning competitions, due to its robustness in the contexts of extreme learning and computational efficiency [24].
# Design of a Compact Multiband Monopole Antenna with MIMO Mutual Coupling Reduction
## 3 Antenna Implementation and Measurement Results
### The Compact Multiband Monopole Antenna  
Figure 14 shows the measured and simulated reflection coefficients of the compact multiband monopole antenna. The measurements indicate good agreement with the simulations, validating the design of the antenna. The antenna exhibits multiband characteristics with reflection coefficients below \(-10\) dB at the targeted frequency bands. However, at 2.46 GHz, the reflection coefficient is only \(-10.2\) dB, and it is almost the same at 3.47 GHz. Additionally, within the highest band, from 4.4 GHz to 7.1 GHz, the measured bandwidth is 400 MHz less than the simulated bandwidth.  
The measured and simulated radiation patterns of the compact multiband monopole antenna are shown in Figures 15 and 16, respectively. The patterns demonstrate good omnidirectional coverage in the azimuth angle (XY plane) and stable performance across the different frequency bands. However, the gain decreases noticeably as the frequency increases. The elevation angle (YZ plane) also exhibits a similar trend and is not smooth.  
Figure 14: The measured and simulated reflection coefficients of the compact multiband monopole antenna.  
Figure 13: The prototype antennas, the network analyzer, and the measurement environment.  
The gain decrease is likely due to the loss from the FR4 PCB and the SMA connector as the frequency increases.
# Impact Wear Behavior of the Valve Cone Surface after Plasma Alloying Treatment
###### Abstract  
Valves are prone to wear under harsh environments, such as high temperatures and reciprocating impacts, which has become one of the most severe factors reducing the service life of engines. As a lightweight ceramic, CrN is considered an excellent protective material with high-temperature strength and resistance to wear. In this study, a CrN coating was applied onto the valve cone surface via double-layer glow plasma surface metallurgy technology. The formation process, microstructure, phase composition, hardness, and adhesion strength were analyzed in detail. Impact wear tests were conducted on the valve using a bench test device. The SEM and EDS results showed that the CrN coating evolved from an island-like form to a dense, cell-shaped surface structure. The thickness of the coating was approximately 46 \(\upmu\)m and could be divided into a deposition layer and a diffusion layer, from the outer to the inner sections. The presence of element gradients within the diffusion layer proved that the coating and substrate were metallurgically bonded. The adhesion strength of the CrN coating measured via scratch method was as high as 72 N. The average Vickers hardness of the valve cone surface increased from 377.1 HV\({}_{0.5}\) to 903.1 HV\({}_{0.5}\) following the plasma alloying treatment. After 2 million impacts at 12,000 N and 650 \({}^{\circ}\)C, adhesive wear emerged as the primary wear mode of the CrN coating, with an average wear depth of 42.93 \(\upmu\)m and a wear amount of 23.49 mg. Meanwhile, the valve substrate exhibited a mixed wear mode of adhesive wear and abrasive wear, with an average wear depth of 118.23 \(\upmu\)m and a wear amount of 92.66 mg, being 63.7% and 74.6% higher than those of the coating. Thus, the CrN coating showed excellent impact wear resistance, which contributed to the enhancement of the service life of the valve in harsh environments.  
double-layer glow plasma surface metallurgy technology; valve cone surface; microhardness; adhesion strength; impact wear +
Footnote †: journal: Applied Sciences
# Delay-Sensitive NOMA-HARQ for Short Packet Communications
## 3 Delay-Sensitive NOMA-HARQ  
As can be seen in Figure 1a, in S-NOMA-HARQ, each \(i\)-th user conducts its retransmission with its maximum power in the new time slot. This causes new arriving packets to be delayed when retransmission is requested. We propose a delay-sensitive NOMA-HARQ (D-NOMA-HARQ) designed for delay-sensitive applications that avoid the excess delay due to retransmissions and adhere to the packet deadlines. More specifically, user \(i\) conducts its retransmission with its new arriving packet non-orthogonally. That is, when retransmission is requested from user \(i\), it will superimpose the retransmission packet and new packet with power fractions \(\alpha_{i}\) and \(\tilde{\alpha_{i}}\), respectively, where \(\tilde{\alpha_{i}}=1-\alpha_{i}\). The D-NOMA-HARQ scheme for two users with a maximum one retransmission, is shown in Figure 1b.
# A Physical-Layer Watermarking Scheme Based on 5G NR
## 3 System Performance Simulation
### System Performance in the Fading Channel  
In the fading channel, the strength of the signal changes at the receiver. In some cases, signal fading is more serious, so it is difficult to complete identity authentication. Therefore, in the fading channel, the result of channel estimation using DMRS is investigated, and a threshold is set. If the modulus of channel response is lower than the threshold, the channel is considered to be fading seriously, and identity authentication is abandoned. In this paper, the threshold is set to 0.7.