esa-sceva/satcom-chunk-collection huggingface dataset:
# Open-Pit Bench Blasting Fragmentation Prediction Based on Stacking Integrated Strategy
## 4 Results and Discussion
### Model Performance Analysis and Discussion
In the random forest method, the importance value of the input variable \(T/B\) is slightly higher, at 0.1183, while in the XGBoost method, \(X_{B}\) has a relatively higher importance value of 0.1503. The importance of the input variables \(S/B\), \(H/B\), and \(B/D\) is low, with \(B/D\) having the lowest importance, at 0.0023 in the random forest method and \(S/B\) having the lowest importance, at 0.0335 in the XGBoost method. Among the seven input parameters in this study, it can be concluded that the input parameter \(E\) has the greatest effect on the \(X_{50}\) prediction results, followed by \(X_{B}\) and \(T/B\), while the three parameters \(S/B\), \(H/B\), and \(B/D\) have the least effect on the \(X_{50}\) prediction results. Figure 10: Comparison of importance values results for input parameters. Figure 9: Comparison of assessment indicators for the four models. In the field of rock engineering, the elastic modulus (\(E\)) reflects the stiffness of rock and serves as an indicator of the material's resistance to deformation. The elastic modulus influences wave velocity, which in turn affects the extent of energy dissipation, resulting in variations in fragment size during rock blasting. The in situ block size, representing the fundamental block size of rock mass in its natural state, is determined by the characteristics of joints and fractures. Rock masses with well-developed fractures are more susceptible to fragmentation. Stemming length and burden distance are critical parameters in blast design, influencing the utilization and release of energy by constraining gas escape and controlling the distance between the blast hole and the free face, respectively. Consequently, they affect fragmentation efficiency [49; 50; 51]. Scholars, in related studies, validated that E, XB, and T/B are the primary influencing factors in predicting outcomes [52], which further supports the correctness of the aforementioned analytical results. All of the above results demonstrate that high prediction accuracy can be obtained by constructing a stacking integrated model for the prediction of blasting fragmentation. Previous research on the prediction of blasting effects mainly compared some single prediction models or optimized a single model [53; 54; 55]. Although these methods can achieve good prediction results, they lack the combination of various prediction methods. Additionally, the tuning process of these models is usually quite complex and time-consuming. One major advantage of utilizing the stacking integrated strategy is that it can combine the strengths of multiple single models and allows for the combination of non-homogeneous models. Moreover, the construction process of the model is relatively simple, and the training speed is faster.
## 5 Discussion  
In this paper, we intend to make improvements in three aspects as part of future research: At first, the dataset based on the complex background of _Camellia oleifera_ fruit plantations needs to be continuously expanded to include more images taken under different conditions, especially images of small-object _Camellia oleifera_ fruit, and the complexity of the context needs to be increased to improve the model detection accuracy. Second, to study the expanded dataset, in future research, our study will incorporate multiple image enhancement methods, thus increasing the diversity of image types within the dataset. Then, the network model needs to be further optimized, so advanced optimization algorithms are introduced to improve the detection accuracy and recall to make the improved model more suitable for the detection requirements of specific scenarios. Finally, the improved detection model should be deployed in the _Camellia oleifera_ fruit harvesting robot to  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Evaluation Indicators**} \\ \cline{2-5}  & **[EMAIL_ADDRESS]\%** & \begin{tabular}{c} **Average Single Image** \\ **Detection Time/s** \\ \end{tabular} & \
\begin{tabular}{c} **Speed/** \\ **(Fame/s)** \\ \end{tabular} &
\begin{tabular}{c} **Size/MB** \\ \end{tabular} \\ \hline YOLO v5s & 98.4 & 0.035 & 25.77 & 14.5 \\ YOLO v4-tiny & 89.9 & 0.025 & 40.45 & 23.1 \\ YOLO v5s-EfficientNet & 98.3 & 0.027 & 33.56 & 6.3 \\ Faster R CNN & 94.3 & 3.6 & 0.03 & 108 \\ YOLOv5s-Camellia & 98.8 & 0.014 & 60.98 & 6.3 \\ \hline \hline \end{tabular}  
* Yolov5s-EfficientNet indicates that the backbone network of the original YOLOv5s model is reconstructed of EfficientNet V2.  
\end{table}
Table 4: Performance comparison of different target detection algorithms.  
achieve autonomous detection and harvesting, thus demonstrating the practical value of this research.  
Conceptualization, Z.L. and H.R.; methodology, Z.L., H.R. and L.K.; software, G.N.; validation, Z.L., H.R. and L.K.; investigation, Z.L. and H.R.; data curation, Z.L.; writing--original draft preparation, Z.L.; writing--review and editing, H.R., L.K. and Y.T.; visualization, Z.L.; supervision, H.R. and M.L.; project administration, H.R.; funding acquisition, H.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant number 52065207); the National Key Research and Development Program of China (grant number 2022 YFD2202104); and Jiangxi Provincial Forestry Bureau _Camellia oleifera_ Fruit Research Special Project (YCYJZX2023221).  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available from the corresponding author upon reasonable request.  
Thanks to Jiangxi Academy of Forestry for supporting our work!  
The authors declare no conflict of interest. Accurate detection of key body parts of cattle is of great significance to Precision Livestock.
# Ambulance Locations in a Tiered Emergency Medical System in a City
## 2 Materials and Methods
### The Expected Response Time Model  
The ERTM minimizes the demand-weighted expected response time [6]. This can be calculated using decision variables, \(w_{ijk}\), that specify the nearest ambulance, the second nearest ambulance, etc. at every demand point. Variable \(w_{ijk}\) is one if an ambulance located at station \(i\) is the \(k\)th nearest ambulance to demand point \(j\). The model is as follows:  
\[minimize\qquad\sum_{i\in I}\sum_{j\in I}\sum_{k=1}^{p-1}b_{j}t_{ij}(1-q)q^{k- 1}w_{ijk}+\sum_{i\in I}\sum_{j\in I}b_{j}t_{ij}q^{p-1}w_{ijp} \tag{12}\]  
\[subject\ to\qquad\sum_{i\in I}w_{ijk}=1\qquad\qquad for\ j\ \in\ J,\ k=1,\ \ldots,\ p \tag{13}\]  
\[x_{i}\geq\sum_{k=1}^{p}w_{ijk}\qquad\quad for\ i\ \in\ I,\ j\in\ J \tag{14}\]  
\[\sum_{i\in I}x_{i}=p \tag{15}\]  
\[x_{i}\in Z^{+}\qquad\qquad\quad for\ i\ \in\ I \tag{16}\]  
\[w_{ijk}\in\ \{0,1\}\quad\quad for\ i\ \in\ I,\ j\ \in\ J,\ k=1,\ \ldots,\ p \tag{17}\]  
The first term in the objective function (12) is similar to the expected coverage (7). The second term is the probability that demand points will be served by the farthest (i.e., \(p\)th nearest) ambulance. According to constraints (13), every demand point must be served by exactly one ambulance, so the probabilities of being served by the first, second, etc. to \(p\)th nearest ambulance must sum up to one. That is why the probability of using the farthest ambulance is \(1-\sum_{k=1}^{p-1}(1-q)q^{k-1}=q^{p-1}\). Ambulances from a candidate location \(i\) may be dispatched to serve demand point \(j\) only if at least one ambulance is located at \(i\) (14). The remaining constraints were already explained above.
## 4 Results and Analysis  
### Dataset and Raman Spectral Sequence Decomposition  
The experiments utilized a Senterra Raman spectrometer to scan plant leaf samples, with a laser power of 0.5 mW, a wavelength of 785 nm, and a spectral scanning range of 252-2909 cm\({}^{-1}\). The training dataset was acquired from Raman spectra collected over a period of time, with a length \(L=2000\). The spectral dataset was evenly segmented into \(m\)= 10 sections, with \(n=200\) data points per segment, giving a total of \(mn=L\) points. The originally collected spectral data are denoted as \(S_{j}^{pi}\), and the noisy datasets with randomly added 20 dB and 30 dB noise are denoted as \(X_{j}^{pi}\), where \(p\) indicates the \(p\)th spectrum in the training set, \(i\) indicates the segment number, and \(j\) indicates the data point within the segment. The noisy data and labels were fed into the neural network for training to obtain the Raman spectral denoising model.  
An example segment of the noisy plant leaf spectra is shown in Figure 4 below.  
Figure 4: Noisy spectra of plant leaves.  
Figure 3: Raman spectra prediction model designed with VMD–EWT–EBiLSTM.  
The VMD algorithm decomposes the Raman spectral sequences with initial parameters set as: \(\alpha\) value is set to 4000 to adjust the bandwidth of each modal component to a proper level, \(\tau\) is set to 0.00001, \(\lambda\) is set to 0, \(K\) is initialized as 1, and the threshold of \(E_{de}\) is set to 0.03. The parameters are then iteratively updated using the VMD decomposition algorithm, calculating the decomposition error \(E_{de}\) each time.  
After iterative updates to the VMD algorithm decomposing the Raman sequences, the iterations terminate when the number of modal decompositions \(K\) = 5, with a decomposition error of 0.0267. Therefore, \(K\) = 5 is taken as the number of variational modal decompositions, and the specific decomposition results are shown in Figure 5. The number of modal decompositions in the EWT is determined by the convergence of the center frequency values when decomposing the VMD residual sequence. Finally, when the number of modes N = 4, the center frequency values converge, so the number of modal decompositions in the EWT is taken as N = 4. The specific decomposition results are shown in Figure 6.  
Figure 5: VMD decomposes the original signal.  
Figure 6: EWT breaks down the residual signal.  
The decomposed sequences are categorized into spectral and noise components using the zero-crossing rate and correlation coefficient. The zero-crossing rate of each modal component are calculated as shown in Table 1. The modal components with zero-crossing rates greater than 0.05 and the correlation coefficients less than 0.5 are taken as noise sequences, and the remaining are taken as spectral energy components.
## 3 Results
### Consistency of Resting Parameters  
There were strong ICC\({}_{2,1}\) for resting FBF (ICC\({}_{2,1}\) = 0.919), O\({}_{2}\)Hb (ICC\({}_{2,1}\) = 0.758), HHb (ICC\({}_{2,1}\) = 0.877), and tHb (ICC\({}_{2,1}\) = 0.844), whereas HbDiff (ICC\({}_{2,1}\) = 0.377) was much weaker (Table 5). These were again assessed but separated by sex. For the men, there were strong ICC\({}_{2,1}\) for resting FBF (ICC\({}_{2,1}\) = 0.836), O\({}_{2}\)Hb (ICC\({}_{2,1}\) = 0.732), HHb (ICC\({}_{2,1}\) = 0.873), and tHb (ICC\({}_{2,1}\) = 0.853), but not for HbDiff (ICC\({}_{2,1}\) = 0.115). For the women, the consistency analysis  
Figure 2: Correlations of strain-gauge and NIRS-derived variables. Panel (**A**) depicts the correlation of forearm blood flow (FBF) mL\({}^{-1}\)-dl FAV\({}^{-1}\)-min\({}^{-1}\) and oxygenated hemoglobin (O\({}_{2}\)Hb) % of resting. Panel (**B**) depicts FBF mL\({}^{-1}\)-dl FAV\({}^{-1}\)-min\({}^{-1}\) and deoxygenated hemoglobin (HHb) % of resting. Panel (**C**) depicts FBF mL\({}^{-1}\)-dl FAV\({}^{-1}\)-min\({}^{-1}\) and total hemoglobin (tHb) % of resting. Panel (**D**) depicts FBF mL\({}^{-1}\)-dl FAV\({}^{-1}\)-min\({}^{-1}\) and hemoglobin difference (HbDiff) % of resting. All four correlations were considered poor and not significant.  
indicated similar results as the men, such that strong values were observed for resting FBF (ICC\({}_{2,1}\) = 0.947), O\({}_{2}\)Hb (ICC\({}_{2,1}\) = 0.777), HHb (ICC\({}_{2,1}\) = 0.887), and tHb (ICC\({}_{2,1}\) = 0.830), but again not for HbDiff (ICC\({}_{2,1}\) = 0.577) (Table 5).
## 3 EQPD-MAC Protocol Development
### Algorithm
After receiving the Tx-beacon, Sensor 10 will broadcast an Rx-beacon with the sensor's address indicating its availability to receive and relay the data packet. Otherwise, it will simply go to sleep and wait for the next round of data packet transmission. When Sensor 15 receives Rx-beacon, it examines the beacon and checks whether it contains the sensor's address. After that, the priority data packet will be transmitted by Sensor 15. Next, the sensor will wait for an ACK as a receipt for the successful transmission of the packet and goes to sleep. The ACK packet is overheard by Sensor 5, and it will directly receive the priority packet from Sensor 10 after performing CCA. Sensor 5 will route the priority packet to the sink and after receiving the intended priority packet the sink sends an ACK packet.
## 3 Proposed Algorithm
### Correntropy and Correntropy-Induced Metric  
Correntropy [12] provides a generalized similarity measure between two arbitrary data points \(\mathbf{x}=(x_{1},x_{2},\ldots,x_{d})\) and \(\mathbf{y}=(y_{1},y_{2},\ldots,y_{d})\) as follows:  
\[C(\mathbf{x},\mathbf{y})=\mathbf{E}[\kappa_{\sigma}(\mathbf{x},\mathbf{y})], \tag{1}\]  
where \(\mathbf{E}[\cdot]\) is the expectation operation, and \(\kappa_{\sigma}(\cdot)\) denotes a positive definite kernel with a bandwidth \(\sigma\). The correntropy is estimated as follows:  
\[\hat{C}(\mathbf{x},\mathbf{y},\sigma)=\frac{1}{d}\sum_{i=1}^{d}\kappa_{\sigma }(x_{i},y_{i}). \tag{2}\]  
In this paper, we use the following Gaussian kernel in the correntropy:  
\[\kappa_{\sigma}(x_{i},y_{i})=\exp\left[-\frac{(x_{i}-y_{i})^{2}}{2\sigma^{2}} \right]. \tag{3}\]  
A nonlinear metric called CIM is derived from the correntropy [12]. CIM quantifies the similarity between two data points \(\mathbf{x}\) and \(\mathbf{y}\) as follows:  
\[\mathrm{CIM}(\mathbf{x},\mathbf{y},\sigma)=\big{[}1-\hat{C}(\mathbf{x}, \mathbf{y},\sigma)\big{]}^{\frac{1}{2}}, \tag{4}\]  
where, since the Gaussian kernel in (3) does not have the coefficient \(\frac{1}{\sqrt{2\pi\sigma}}\), the range of CIM is limited to \([0,1]\).  
In general, the Euclidean distance suffers from the curse of dimensionality. However, CIM reduces this drawback since the correntropy calculates the similarity between two data points by using a kernel function. Moreover, it has also been shown that CIM with the Gaussian kernel has a high outlier rejection ability [12].  
Figure 3 shows the landscape of CIM for the case in which \(\mathbf{x}=[x_{1},x_{2}]^{\top}\) and \(\mathbf{y}=[0,0]^{\top}\).
# Automated Data Acquisition in Construction with Remote Sensing Technologies
## 4 Integration of Various RS Technologies
### RFID Integration with Other Sensory Data  
Only a few technology sets are available for indoor positioning. The reasons for this poor diffusion include high costs compared to the value achieved and technical limitations such as precision, reliability, and performance of such systems. In this way, some researchers have investigated the integration of a wireless sensor network (WSN) with the RFID. This approach provides new capabilities in order to have a better estimation by facilitating the identification of materials located in the scene and estimating their position at the same time [26,27]. The integration of the RFID technology with other RS technologies can also improve onsite information for having efficient management of construction operations during a project lifecycle [27].
## 4 Discussion
correlation between coercivity, volume, and diameter of small microstructure defects [32]. All these theories describe an increase in magnetic hardness with the rising volume and diameter of microstructure defects. Since all other micromagnetic parameters strongly correlate with coercivity, they similarly depend on the microstructure defects' diameter and volume. The clear difference in the results of magnetic measurements between the nonirradiated and irradiated conditions correlates well with the results of the mechanical tests and can be explained by the occurrence of microstructure defects due to neutron irradiation (matrix features at low fluence and Cu-rich precipitates at high fluence). Small microstructure defects impede Bloch-wall movement due to the foreign body effect and, therefore, cause an increase in magnetic hardness, as illustrated by the magnetic parameters in Figures 13-16. The foreign body effects describe the interaction between Bloch walls and small microstructure defects [31]. The higher the volume of small microstructure defects (matrix features), the higher the mechanical properties and the stronger the foreign body effect. The smaller change in mechanical properties at increased neutron fluence can be explained by an increase in the microstructure defects' (Cu-rich precipitates) diameter and a smaller increase of the volume of defects, which, in turn, similarly influence the magnetic properties. The foreign body effect and the interaction between Bloch walls and microstructure defects both weaken; thus, magnetic properties increase more slowly. For the irradiated eastern RPV steel 15 Kh2 NMFA, we observed that parameters derived from both magnetic methods reached saturation. For western RPV material A508 Cl2, the MAT descriptor continuously increased, but most 3 MA-X8 parameters reached saturation. Another conclusion is that by applying different nondestructive magnetic techniques, a similar correlation was found between mechanical properties and the modification of magnetic parameters. This finding is encouraging for future practical applications of magnetic NDE. The innovative part of this study is the combination of the results obtained using both magnetic methods, followed by a training procedure to determine targeted material properties (hardness, DBTT, and yield strength). Excellent correlation coefficients and impressively low RMSEs were achieved for the prediction and quantification of hardness, ductile to brittle transition temperature, and yield strength of the tested specimens (Figures 17 and 18). Correlation coefficients larger than 91% were obtained between nondestructively predicted and destructively determined mechanical properties (Tables 5 and 6). Therefore, the two magnetic methods resulted in a strong correlation between their outcomes, and their combination can be used to precisely predict different mechanical properties.
# Model Predictive Control of Grid Forming Converters with Enhanced Power Quality
## 4 Results and Discussion
### Sampling Effect  
As the sampling time (\(T_{s}\)) decreases, the rejection of disturbances usually improves. However, when (\(T_{s}\)) becomes too small, the computational effort increases dramatically. Therefore, the optimal choice is a balance of computational burden and power quality. Figure 20a,b show two points; initially, they confirm that the frequency control algorithms are significantly influenced by sampling time values. Secondly, they show how samplings affect the dynamic changes when a load step is applied. The presented results show the behaviour of the predictive control implemented using periodic control for the grid forming application. It can be seen that the deviation in the case of 40 kHz sampling is high and varies between 4 kHz and 4.5 kHz, while in the 80 kHz the variance is between 4 kHz and 4.15 kHz.
## 2 Battery Pack Cooling Mathematical Model  
### Air-Cooled Battery Pack Structure Introduction  
According to [PERSON] et al. [16; 17], the heat dissipation efficiency of a battery pack can be improved by using a parallel air supply system. Therefore, as shown in Figure 1, a parallel air supply cooling structure was used in this paper. There were \(M\times N\) 18,650 batteries in the battery pack. The battery was surrounded by baffles that regulated the flow of air. The cooling air flowed in from the inlet and through the wedge-shaped baffle of the divergence plenum (\(DP\)) and entered the cooling channel (\(CC\)) to cool the battery. Then the air flowed through the wedge-shaped baffle of the convergence plenum (\(CP\)) and flowed out of the battery pack. Figure 2a is a vertical view of the parallel air-cooled BTMS, where \(M\) battery cells in each column are one battery unit, that is, there are \(N\) battery units. Figure 2b is a left view of the parallel BTMS. The minimal widths of the battery units (\(d_{0}\)) in the battery pack were the same. The angle of the plate to the convergence (\(\theta_{\text{cp}}\)) and divergence (\(\theta_{\text{dp}}\)) plenums, as well as the minimal widths of the convergence (\(w_{\text{cp}}\)) and divergence (\(w_{\text{dp}}\)) plenums, affected the temperature field and the volume of the battery pack. When simulating the temperature field in the battery pack, computational fluid dynamics (CFD) was generally used for calculation because of its high accuracy. Compared with the CFD method, the mathematical model analyzed the heat dissipation process of the battery pack from the mechanism, clearly clarified the principle of the heat dissipation of the battery pack, and optimizing the battery pack with the mathematical model saved a lot of simulation time.
## 2 Main Concepts
### Artifacts
#### 2.4.2 Photometric Artifacts: Noise, Saturation, and Texture Loss  
Physical limitations of the sensor and exposure differences between LDR images can introduce random noise, which is more evident in underexposed regions (Figure 3b), and saturation or texture loss in overexposed/underexposed areas (Figure 3a), where the original information is irrecoverable. Deep convolutional networks have been used both for noise removal [66; 67; 68; 69] and for plausible texture synthesis through dedicated spatial modules and explicit noise injection at the input to handle varying levels [41; 45; 70; 71]. Such strategies combine feature learning with attention to context to reconstruct fine details and maintain tonal consistency throughout the HDR.
# Hom Antenna on Chip Operating at 180 GHz Using the SiGe CMOS Process
## 2 Antenna Design
### Parameter Analysis  
After the architecture is completed, some parameters are adjusted. This article will conduct parameter analysis on the feed width L\({}_{6}\) and the top layer width W\({}_{3}\), as shown in Figure 4. First, the feed width L\({}_{6}\) is analyzed for parameters.  
Figure 3: Comparison of three types of reflection coefficient.  
Figure 2: Evolution of Antenna Design. (**a**)Type1; (**b**) Type2; (**c**) Type3.  
Figure 5: (**a**) L\({}_{6}\) parameter analysis return loss comparison; (**b**) \(W_{3}\) parameter analysis return loss comparison.  
When the width of the upper layer W\({}_{3}\) is 475 \(\upmu\)m. As shown by the green line in Figure 5b, return loss will be lower than \(-\)10 dB at 169.88 GHz, the deepest point is \(-\)18.5647 dB, and the bandwidth is 169.88-179.35 GHz. When the width of the upper layer W\({}_{3}\) is 480 \(\upmu\)m. As shown by the red line in Figure 5b, return loss will be lower than \(-\)10 dB at 179.09 GHz, the deepest point is \(-\)16.5554 dB, and the bandwidth is 168.30-177.88 GHz.  
After parameter analysis, when the width of the W3 top layer is 455 \(\upmu\)m, the reflection coefficient has the lowest value and the center frequency is closest to 180 GHz. Therefore, the results of the S parameter analysis when the top layer width W\({}_{3}\) is 455 \(\upmu\)m are used as the antenna top layer width of the antenna in this article in Figure 5b.  
From Tables 2 and 3, we can see that the wider the feed width, the narrower the bandwidth, and the larger the lowest point. The wider the width of the top layer, the frequency will move to a lower frequency, and the larger the lowest point will be. In summary, the analysis and optimization of antenna parameters is a complex but critical process that can significantly enhance the antenna's performance, ensuring the efficiency and reliability of wireless communication systems.