esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
Material properties of an FGM vary along a particular direction known as gradation direction. The radial direction is considered as gradation direction in the exponentially graded (EG) shafts with a circular cross-section. Therefore, material constituents of the shaft are distributed along the radial direction of the shaft. The EG shaft is composed of ceramic constituents on the outer layer of the shaft, and the metallic components are present in the inner core of the EG shaft. The outer ceramic layer is made of Zirconia (ZrO2), whereas the inner metallic core is composed of Stainless Steel (SS). The percentage of the ceramic constituents increases, and the rate of metal constituents decreases while moving across the cross-section from the inner core of the EG shaft. Therefore, the material properties such as Young's modulus and Poisson's ratio depends on the position of material constituents. The position of the material properties is determined by the Voigt model [35], which is a simple rule for mixtures of composites. The material properties of a specific layer \(P_{L}\) is given as  
\[P_{L}=P_{m}V_{m}+P_{c}V_{c} \tag{1}\]  
where \(V_{m}\) and \(V_{c}\) are volume fractions of metal and ceramic, respectively, whereas \(P_{m}\) and \(P_{c}\) are material properties of metal and ceramic, respectively. The sum of volume fractions of metal and ceramic is  
\[V_{m}+V_{c}=1 \tag{2}\]  
Since the material properties vary with the temperature, the temperature dependency of the material properties is achieved by using Equation (3) [36].  
\[P(T)=P_{0}\Big{(}P_{-1}T^{-1}+1+P_{1}T+P_{2}T^{2}+P_{3}T^{3}\Big{)} \tag{3}\]  
\(T\) is the temperature in Kelvin. \(P_{-1}\), \(P_{0}\), \(P_{1}\), \(P_{2}\) and \(P_{3}\) are the coefficients of the temperature, which are listed by [PERSON] and [PERSON] [37]. Since the volume fractions of the material constituents are difficult to determine, the material gradation laws are used. Exponential law is employed in the present work, and its detailed explanation is presented in the following subsection.
## 4 Effect of Design Parameters on Strength  
### Effect of W/C Ratio on Compressive Strength and Split Tensile Strength  
An increase in the W/C ratio results in a decrease in compressive strength, which is observed in Figure 2. In the vast majority of cases, it is observed that with an increase in W/C ratio, there is a decrease in strength of concrete and lower W/C ratio results in higher strength of SCC. The most predominant W/C ratio for all grades of concrete is observed in the range from 0.30 to 0.60. Higher-strength in concrete is observed with a lower W/C ratio in the range from 0.20 to 0.50.  
Different grades of concrete exhibit the split tensile strengths mostly in the range from 2.0 MPa to 5.0 MPa in Figure 3. An increase in the W/C ratio results in the decrease in split tensile strength. Most of the split tensile strength from literature possesses a W/C ratio that lies in the range from 0.30 to 0.60. Higher split tensile strength with a range of above 5.0 MPa has a lower W/C ratio in the range from 0.20 to 0.50 and is observed in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Authors**} & \multicolumn{8}{c}{**Design Parameters**} & \multicolumn{8}{c}{**Strength (MPa)**} \\ \cline{2-10}  & **W/C** & **W/B** & **TA/C** & **FA/CA** & **SP (kg)** & **W/S (\%)** & **\% RFA** & **\% RCA** & **fc** & **fs** \\ \hline  & 1.13 & 0.45 & 8.468 & 5.090 & 3.35 & 10.30 & 0 & 0 & 22.21 & 2.71 \\ [PERSON] & 1.13 & 0.45 & 8.788 & 5.050 & 4.35 & 10.01 & 10 & 20 & 28.63 & 3.02 \\  & 1.13 & 0.45 & 8.743 & 5.010 & 4.75 & 10.05 & 20 & 20 & 28.01 & 2.75 \\ (A) [85] & 1.13 & 0.45 & 8.699 & 4.960 & 4.95 & 10.09 & 30 & 20 & 24.03 & 2.97 \\  & 1.13 & 0.45 & 8.654 & 4.920 & 5.35 & 10.13 & 40 & 20 & 27.13 & 3.07 \\  & 0.55 & 0.42 & 4.564 & 2.589 & 5.50 & 9.39 & - & 0 & 25.11 & 2.15 \\ Nieto & 0.55 & 0.42 & 4.550 & 2.589 & 5.50 & 9.41 & - & 20 & 27.65 & 1.61 \\  & 0.55 & 0.42 & 4.537 & 2.589 & 5.50 & 9.43 & - & 40 & 35.86 & 2.12 \\  & 0.55 & 0.42 & 4.523 & 2.589 & 5.50 & 9.45 & - & 60 & 29.20 & 2.31 \\  & 0.55 & 0.42 & 4.510 & 2.589 & 5.50 & 9.47 & - & 80 & 34.29 & 2.10 \\  & 0.55 & 0.42 & 4.496 & 2.589 & 5.50 & 9.50 & - & 100 & 34.17 & 2.52 \\  & 0.50 & 0.38 & 4.339 & 2.461 & 5.80 & 8.87 & - & 0 & 24.78 & 2.07 \\ Nieto & 0.50 & 0.38 & 4.326 & 2.461 & 5.80 & 8.89 & - & 20 & 31.25 & 2.78 \\  & 0.50 & 0.38 & 4.313 & 2.461 & 5.80 & 8.91 & - & 40 & 40.69 & 2.74 \\  & 0.50 & 0.38 & 4.301 & 2.461 & 5.80 & 8.93 & - & 60 & 38.56 & 2.08 \\ \hline \hline \end{tabular}  
W/C = water to cement ratio; W/B = water to binder ratio; SCC = self-compacting concrete; RCA = recycled coarse aggregate; RFA = recycled fine aggregate; SP = superplasticizer; TA/C = total aggregate to cement ratio; FA/CA = fine aggregate to coarse aggregate Ratio; W/S = water to solid percentage; fc = Compressive strength; fs = split tensile strength.  
\end{table}
Table 6: Design Parameters for Family VI (Compressive strength range: 20 MPa to 30 MPa) according to the literature.  
Figure 2: Effect of W/C ratio on various compressive strengths of SCC concrete with recycled aggregate according to the literature.
## 3 Results and Discussion
### Data Analysis and Processing
From the analysis results, it can be observed that a majority of the variables in the dataset exhibit significance, while a minority show strong significance. While the pressure and equivalence ratio also play a role, their effects are less pronounced compared to \(X_{H_{2}}\) and temperature. This suggests that, although they should not be disregarded, they may not require the same level of meticulous control as the other two factors. Moreover, the correlations among these factors are found to be weak, indicating a degree of independence that can be exploited for individual optimization without causing significant disruptions to the other variables. This nuanced understanding of the factors at play provides a solid foundation for further research and practical applications aimed at enhancing LBV.
# Evaluation of the Landsat-8 Albedo Product across the Circumpolar Domain
## 3 Results
### Accuracy of Circumpolar Full-Expression Blue-Sky Albedo
The same 661 site-dates for assessing Landsat albedo were compared with high quality MCD43A3v06 shortwave albedo. At these sites that are both spatially representative and non-representative for the spatial resolution of the MODIS sensors, the comparison reported an RMSE of 0.074 and bias of 0.011 of all sites (0.043/0.012 at spatially representative sites only). The smaller RMSE and biases of 30 m Landsat albedo at the sites including those spatially non-representatives for the MODIS sensors, support Landsat's ability to accurately measure albedo values within heterogeneous landscapes.
## 2 Results and Discussion
### Biphasic Sequestration of Pd and Cu from Products of Catalytic Reactions
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Substrate**} & \multicolumn{2}{c}{**Metal Concentration/ppm 1**} & \multicolumn{2}{c}{**Sequestration**} \\ \cline{2-4}  & **Crude** & **Treated** & **Efficiency/\%** \\ \hline
**4a** & 275 & 0.170 & 99.9 \\
**4b** & 275 & 0.180 & 99.9 \\
**4c** & 275 & 0.230 & 99.9 \\
**6a** & 171 & 1.25 & 99.3 \\
**6b** & 246 & 0.130 & 99.9 \\
**6c** & 290 & 63.2 ** & 78.2  \\  & 290 & 9.01  & 96.9  \\
**8** & 727 & 0.300 & 99.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of palladium/copper sequestration from model reaction mixtures. An azide-alkyne \"click\" reaction between benzyl azide **7** and dimethyl ethynyl carbinol in the presence of 15 mol\(\%\) of Cu led to formation of triazole **8**. Copper sequestration afforded a nearly 2500-fold reduction of residual Cu amount in the reaction product (Table 3), an extraction efficiency that is more than 99.9 %.
## 4 Comparison of the Modified Godard Algorithm to the Gardner Algorithm
Since the estimates are unbiased and the drift of the timing estimate over time is rather small, averaging the estimates will reduce the jitter significantly. With these considerations, timing jitters above \(-20\) dB will also allow an operation at a BER of \(10^{-3}\).
## 3 Results
### Accuracy Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Authors (Year)** & **No. of Implants** & **Angular Deviation** & **Coronal Deviation** & **Apical Deviation** \\
**Evaluated** & **Type of Support** & **(\({}^{\circ}\), Mean \(\pm\) SD)** & **(mm, Mean \(\pm\) SD)** & **(mm, Mean \(\pm\) SD)** \\ \hline [PERSON] et al. (2013) & 30 & Mucosa & - & 0.89 \(\pm\) 0.44 & 1.08 \(\pm\) 0.47 \\
[29] & 30 & Mucosa & 4.71 & 1.63 & 2.10 \\ [PERSON] et al. (2013) [41] & 111 & Bone & 5.08 & 1.18 & 1.62 \\  & & Tooth & 3.35 & 1.10 & 1.36 \\ \hline [PERSON] et al. & & & & & \\ (2014) [11] & 172 & Mucosa & 4.33 \(\pm\) 1.42 & 1.10 \(\pm\) 0.39 & - \\ \hline [PERSON] et al. & & & & & \\ (2014) [28] & 44 & Bone & 2.49 \(\pm\) 0.97 & 0.71 \(\pm\) 0.399 & 0.77 \(\pm\) 0.382 \\ \hline [PERSON] et al. & 55 & Mucosa & 2.86 \(\pm\) 1.60 & 1.23 \(\pm\) 0.60 & 1.57 \(\pm\) 0.71 \\ (2014) \({}^{\ast}\) [23] (I) & 53 & Bone & 3.79 \(\pm\) 2.36 & 1.60 \(\pm\) 0.92 & 1.65 \(\pm\) 0.82 \\ \hline [PERSON] et al. & 52 & Mucosa & 2.71 \(\pm\) 1.36 & 1.38 \(\pm\) 0.64 & 1.60 \(\pm\) 0.70 \\ (2014) \({}^{\ast}\) [23] (II) & 49 & Bone & 3.20 \(\pm\) 2.70 & 1.33 \(\pm\) 0.82 & 1.50 \(\pm\) 0.72 \\ \hline [PERSON] et al. & & & & & \\ (2014) [42] & 225 & Mucosa & 4.67 \(\pm\) 2.68 & 1.68 \(\pm\) 0.6 & 2.19 \(\pm\) 0.83 \\ \hline [PERSON] et al. (2014) & 13 & Tooth & 3.99 & 0.90 & 1.23 \\
[33] & 18 & Mucosa & 5.14 & - & - \\ \hline [PERSON] et al. (2014) & 43 & Mucosa & 4.06 \(\pm\) 2.82 & 1.12 \(\pm\) 0.65 & 1.36 \(\pm\) 0.64 \\
[34] & 43 & Bone & 3.19 \(\pm\) 1.95 & 1.33 \(\pm\) 0.47 & 1.40 \(\pm\) 0.43 \\  & & Tooth & 2.94 \(\pm\) 1.84 & 1.63 \(\pm\) 0.98 & 1.84 \(\pm\) 1.00 \\ \hline [PERSON] et al. & & & & & \\ (2015) [32] & 150 & Mucosa & 3.926 \(\pm\) 0.414 & 1.963 \(\pm\) 0.232 & 2.288 \(\pm\) 0.269 \\ \hline [PERSON] et al. (2015) & 80 & Mucosa & 3.33 \(\pm\) 2.32 & - & - \\
[30] & 80 & Mucosa & 3.33 \(\pm\) 2.32 & - & - \\ \hline [PERSON] et al. & & & & & \\ (2015) [31] & 104 & Mucosa & 2.819 & 1.368 & 1.587 \\ \hline [PERSON] et al. (2015) & 57 & Tooth & 4.21 \(\pm\) 1.91 & 1.18 \(\pm\) 0.72 & 1.43 \(\pm\) 0.74 \\
[35] & 75 & Mucosa & 2.8 \(\pm\) 1.5 & 0.87 \(\pm\) 0.50 & 1.10 \(\pm\) 0.53 \\ \hline [PERSON] et al. & & & & & \\ (2016) [24] & 90 & Mucosa & 2.7 & 0.8 & 1.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of deviation parameters.
## 4 Materials and Methods
### Solid-Phase Microextraction (SPME) for Sample Preparation  
Serum sample extracts were prepared using SPME devices, which consisted of thin stainless steel blades coated with a 1:1 mixture of HLB and PS-DVB-WAX particles. All samples were block randomized across two 96-well plates, and 200 \(\upmu\)L of each serum sample was added to its respective well. After gentle agitation of the as-prepared samples for 30 min at 500 rpm, samples were ready for SPME extraction. Prior to extraction, SPME devices were mounted on the Concept-96 manual kit and conditioned in a mixture of 1:1 methanol:water (\(v/v\)) for 30 min at 1500 rpm. Following conditioning, the device was rinsed in water for 15 min at 1500 rpm. Immediately after device pre-conditioning and rinsing, agitated serum samples in PBS were exposed to the blades for 1 h at 1500 rpm for extraction of metabolites onto the solid phase coating. After extraction, the device was rinsed in a mixture of 90:5:5 (\(v/v/v\)) water:methanol:acetone for 10 s at 500 rpm to remove any loosely attached matrix components. Metabolites extracted onto the devices were desorbed for 1 h at 1500 rpm in 600 \(\upmu\)L of 4:3:3 methanol:acetonitrile:water (\(v/v/v\)) + 0.1% ascorbic acid. For optimal compatibility with the LC-MS methodology, the desorption solution was diluted with 400 \(\upmu\)L of 45:30:25 sopropanol:acetonitrile:methanol (\(v/v/v\)) to produce a final extract composed of 3:3:3:1 isopropanol:methanol:acetonitrile:water) (\(v/v/v/v\)). A pooled quality control (QC) sample was prepared by combining 10 \(\upmu\)L of each sample extract. The pooled QC was injected approximately every 10 sample injections during instrumental acquisition.
## 1 Introduction
Recent studies indicated that the global interannual variability in terrestrial ecosystem fluxes and in the atmospheric CO\({}_{2}\), are strongly influenced by forest fire emissions [32, 33]. It is noted that China's forests have greatly contributed to the carbon sink in the global carbon budgets during the last five decades [34]. However, recent increases in forest fires in China have already caused an increase in CO\({}_{2}\) emissions [35, 36]. The forest fire risk prediction is very important for fire management and fire protection strategies planning. The occurrence and spread of forest fire are basically determined by the fuel property, weather conditions, and terrain features. A prerequisite for the strategy planning is the ability to identify forest fire risk zones across both broad areas and local sites. Forest fire risk zones are locations where a fire is likely to start, and from where it can easily spread to other areas [37]. In this paper, we primarily present an overview on the status of detecting forest fire hot spots and fire areas by using satellite sensors in China over the past three decades, and analyze a few developed algorithms used for detecting fires with greater accuracies. We illustrate a forest fire emission model used to estimate gas and aerosol emissions in China. We also evaluate forest fire risk potential models that have been used for predicting the forest fires occurrence in China.
## 2 Facility Layout Problem with Relative Positioning Constraints
### Reducing Redundant Inequalities by Exploiting Relative-Positioning Constraints Structure
#### 2.2.2 Redundancy in Within-Boundary Constraints  
The within-boundary constraints are also reduced by using the relative-positioning structure. The constraints \(w_{i}\leq x_{i}\) in (4) only needs to be imposed on the left-most departments, i.e., for \(\forall i\) s.t. \(\sum_{j=1}^{N}\mathcal{H}_{ij}=0\). In the similar way, the minimal set of within-boundary constraints can be reduced as follows:  
\[w_{i}\leq x_{i} \text{for }\forall i\text{ s.t. }\sum_{j=1}^{N}\mathcal{H}_{ji}=0\] \[x_{i}\leq W-w_{i} \text{for }\forall i\text{ s.t. }\sum_{j=1}^{N}\mathcal{H}_{ij}=0\] \[h_{i}\leq y_{i} \text{for }\forall i\text{ s.t. }\sum_{j=1}^{N}\mathcal{V}_{ji}=0\] \[y_{i}\leq H-h_{i} \text{for }\forall i\text{ s.t. }\sum_{j=1}^{N}\mathcal{V}_{ij}=0\]  
Suppose \(N_{left}\), \(N_{right}\), \(N_{upper}\), \(N_{lower}\) are the number of the most left, right, upper, and lower departments, respectively, the number of inequalities can be reduced to \(N_{left}+N_{right}+N_{upper}+N_{lower}\). Assuming that the non-overlapping condition (14) holds, this number becomes, at most, less than \(2(N+1)\), which is less than the original \(4N\) inequalities.
## 5 Conclusions  
The present work reported welding of single-sided T-joints of 8 mm thick AH36 shipbuilding steel with three optical setups using process fibers with core diameters of 200 \(\upmu\)m, 300 \(\upmu\)m and 600 \(\upmu\)m. The current study found that:  
1. Full fusion in one welding pass was produced with all three process fibers studied.
2. Penetration depth and width of the weld both primarily depend on the beam diameter. The parameter with the greatest influence on the depth of the weld is the power density of the beam, while the width of the weld is determined by the diameter of the focused beam. The width of the weld bead only has a minor correlation to the diameter of the beam on the surface.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **200 \(\upmu\)m Process Fiber** & **300 \(\upmu\)m Process Fiber** & **600 \(\upmu\)m Process Fiber** \\ \hline \(\alpha\) & 6\({}^{\circ}\) & 6\({}^{\circ}\); 10\({}^{\circ}\) \\ Beam offset & 1 mm & 1 mm & 1\(-\)1.5 mm \\ F\({}_{\text{PP}}\) & \(-\)4 mm & \(-\)4 mm & \(-\)2\(--\)6 mm \\ \hline \hline \end{tabular}
\end{table}
Table 5: Acceptable limits for beam positioning for producing full penetration.  
Smaller spot sizes provide an advantage in penetration depth at the same welding speed and power but are prone to producing undercuts. Due to the narrowness of the weld, the positioning of the beam has to be extremely accurate to avoid the weld missing the root of the joint.
* Welds produced with 600 \(\upmu\)m process fiber were less prone to undercut formation and had more favorable shape of the weld toe than welds produced with 200 \(\upmu\)m and 300 \(\upmu\)m process fibers.
* Process fiber with core diameter 600 \(\upmu\)m produced welds with the highest quality and was least sensitive to changes in beam positioning.
* Using beam delivery fibers with larger core diameters has a favorable effect on achieving full fusion in T-joints. Reduced energy density on surface increases the width of the weld throughout the penetration and produces smoother junctions of weld bead and base material.  
Authors gratefully acknowledge [PERSON] for assistance with the experiments and [PERSON] for the help with the metallography. Authors would like to thank the project PAMOWE of Academy of Finland for financial support.  
[PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] contributed materials/analysis tools; and [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflicts of interest.
## 4 Discussion
### Sources of Uncertainty  
Although the present results clearly show the high accuracy of the rice-mapping procedure, it is appropriate to recognize several potential limitations. Errors usually occurred at the perimeter between rice and non-rice fields (Figure 9), leading to omission errors in the rice field category. The mixed pixel effect in the boundaries along the field edge contributed to the discrepancy. This effect has been reported in rice mapping studies and is still a source of spatial uncertainty [3; 15; 20; 22; 50; 73]. For the commission errors in the rice field category, the contribution was mainly from non-rice fields (such as small roads, levees, or bunds) classified as rice fields. Bunds are similar to rice fields, as their water levels and soil moisture also fluctuate seasonally.
## 1 Introduction  
Global Navigation Satellite Systems (GNSS) are frequently applied to capture athletes' position, velocity and acceleration (PVA) in team sports [1,2] and individual sports, such as running/locomotion [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], orienteering [16], sailing/rowing [17], ski jumping [18], cross country skiing [19, 20, 21], snowboarding [22, 23], and alpine skiing [24, 25, 26, 27, 28, 29, 30, 31]  
In the sport of competitive alpine skiing, there are three main demands on a valid and practicable measurement system for capturing skiers' kinematics: (1) it has to fulfill high accuracy standards in order to detect the small but substantial differences between athletes' trajectories [32, 33]; (2) it should cause minimal interference with the athletes' competitive skiing [34]; (3) it should allow the largest possible capture volumes in order to be able to analyze entire competitions and/or training runs.  
In order to meet these requirements, to date a number of different methods have been suggested: non-differential GNSS methods [31, 35], real-time differential GNSS methods [25, 36, 37] and post-processed, differential GNSS methods [30, 38]. While some applications use only the American GPS system [34, 38], other applications combine GPS with the Russian (GLONASS) system [25, 28, 30]. Furthermore, some differential methods use the L1 frequency only [28], while others use frequencies L1 and L2 [25, 30].  
Despite the large number of GNSS applications in alpine skiing and sports in general, no study has ever comprehensively compared the available GNSS methods with respect to position accuracy. Hence, the magnitude of position accuracy of the applied methods is largely unknown. Furthermore, signal obstruction can lead to partial or total loss of satellite signals and loss of differential solutions. Due to the relatively high dynamics in alpine skiing, the time needed to acquire a differential positioning solution is crucial.  
Therefore, this study assessed position accuracy and the time taken to compute differential GNSS solutions for five typical GNSS methods commonly used in sport science, in the case of alpine ski racing under three different satellite signal obstruction conditions. The goal of this study is to investigate which GNSS methods meet the accuracy requirements of dynamic sports applications in difficult satellite signal obstruction conditions, such as alpine skiing. Consequently, the study consisted of two main steps: In the first step, the position solutions were compared to an independent video-based photogrammetric reference system during one turn of a giant slalom course. In the second step, the most accurate and consistent GNSS method from Step 1 was defined as the reference method and was compared to the other methods over the larger capture volume of an entire giant slalom run. In addition, the time required to compute differential GNSS solutions was assessed for each method and condition.
## 2 The Proposed DLSODC-GWM Technique
### IRD-Based Object Detection Module
#### 2.1.4 Loss Function  
The IRD technique utilizes anchors that are intensively sampled on distinct scale feature maps as trained samples. Most of its samples are easy negative samples that do not comprise the object, and only some positive instances that comprise the object. As a result, the imbalance of positive as well as negative samples are magnified. Numerous easy negative samples have minimum promotion effects on the concept from the entire trained procedure and simulate the final detection efficiency of the method. The focal loss function  
Figure 1: Schematic representation of RefineDet architecture.  
(LF) was dynamic cross-entropy (CE) LFs. According to the novel function, the CE-LF was redesigned. The weight factor \(\alpha\) and modulation factors \((1-p_{t})^{y}\) are established, in which the \(\alpha\) factor balances positive as well as negative samples, and the modulation factor \((1-p_{t})^{\gamma}\) modifies the weight of easy and hard samples to make the method effort on hard examples under the trained procedure and enhance the accuracy. The focal LFs are as follows:  
\[FL(p_{t})=-\alpha_{t}(1-p_{t})^{\gamma}\;\text{log}\;(p_{t}), \tag{1}\]  
where,  
\[p_{t}=\left\{\begin{array}{ll}p&\text{if}\;y=1\\ 1-p&\text{otherwise},\end{array}\right. \tag{2}\]  
where \(y\) refers the ground truth class labels containing if the sample was foreground or not. When it can be foreground, the label has 1, else it can be \(-1\). Besides, \(p\in[O,1]\) defines the type probabilities of foreground samples with label \(y=1\), for instance, the forecast possibility of a sample comprising the objects.  
\[\alpha_{t}=\left\{\begin{array}{ll}\alpha&\text{if}\;y=1\\ 1-\alpha&\text{otherwise}.\end{array}\right. \tag{3}\]  
While the weight factor, \(\alpha\) weakens the result of the easy sample loss value on the entire network loss and balances the effect of positive, as well as negative samples, on the networks.