esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods  
#### 2.3.1 Overall Methodology  
The overall flow scheme of this paper is shown in Figure 2. It includes the following main steps: (1) Resampling the GlobeLand30 and ERA\(-\)5 data to 500 m using a majority filter and bilinear interpolation in ArcGIS, respectively; (2) Exploring the average annual trend of GPP and LAI using the linear regression equation; (3) Quantifying each land type change using a transfer matrix; (4) Discussing the net changes of LAI and GPP in regions  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variables** & **Product** & \
\begin{tabular}{c} **Spatial** \\ **Resolution** \\ \end{tabular} &
\begin{tabular}{c} **Temporal** \\ **Resolution** \\ \end{tabular} & **Period** \\ \hline LAI & GLASS & 500 m & 8 days & 2000–2018 \\ GPP & PML-V2 & 500 m & 8 days & 2000–2018 \\ LUCC & Globeland30 & 30 m & — & 2000, 2020 \\ TEMP & & & & \\ PRCP & ERA5 & 0.25\({}^{\circ}\) & hourly & 2000–2018 \\ SR & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Satellite products and reanalysis data used in this study.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Site** & **Latitude** (\({}^{\circ}\)N) & **Longitude** (\({}^{\circ}\)E) & **Data Period** & \begin{tabular}{c} **Mean** \\ **Annual** \\ **Precipitation** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **Mean** \\ **Annual** \\ **Temperature** \\ **(\({}^{\circ}\)C)** \\ \end{tabular} & **Altitude (m)** & \begin{tabular}{c} **Vegetation** \\ **Type** \\ \end{tabular} \\ \hline Dangxiong & 30.49 & 91.06 & 2004–2005 & 450 & 1.3 & 295.7 & \begin{tabular}{c} Alpine \\ meadow \\ Forest \\ \end{tabular} \\ Dinghushan & 23.17 & 112.54 & 2005 & 1956 & 20.9 & 493 & \begin{tabular}{c} Alpine \\ Forest \\ \end{tabular} \\ Duolun & 42.05 & 116.28 & 2007 & 275 & 1.7 & 1324 & Grassland \\ Haibei & 37.61 & 101.33 & 2004–2005 & 535 & \(-\)14.4 & 3190 & Swamp \\ Wetland & 37.67 & 101.33 & 2004–2005 & 535 & \(-\)14.4 & 3190 & \
\begin{tabular}{c} Bush \\ Cropland \\ \end{tabular} \\ Haibei Shrub & 37.67 & 101.33 & 2004–2005 & 535 & \(-\)14.4 & 3190 &
\begin{tabular}{c} Bush \\ Cropland \\ \end{tabular} \\ Qianyanzhou & 26.74 & 115.06 & 2003–2004 & 1542 & 17.9 & 100 & Cropland \\ Changling & 44.59 & 123.51 & 2009 & 470 & 4.9 & 171 & Grassland \\ Changbaishan & 42.40 & 128.10 & 2004–2005 & 713 & 3.6 & 738 & Forest \\ \hline \hline \end{tabular}
\end{table}
Table 2: Information about the geography and vegetation of the study sites.  
and provinces; (5) Using the partial correlation equation to analyze the correlation pattern of the correlation GPP and the other variables.
## 3 Methods
### Audit and Threat Model
#### 3.3.2 Indistinguishability  
The main objective of malicious parties \(\mathcal{S}\) and \(\mathcal{D}\) is to run the blind-signature scheme so that the goals of an attack are fulfilled, while on the other hand, \(\mathcal{J}\) has no chance to detect deviations from the original protocol. \"Impossibility\" can be captured formally by the following left-or-right game.  
**Definition 3** (Indistinguishability Game).: _The game consists of the following phases:_  
* \(\mathcal{S}\) _and_ \(\mathcal{D}\) _choose jointly a bit_ \(b\in\{0,1\}\) _uniformly at random,_
* _If_ \(b=0\)_, they install the original blind-signature scheme_ \(\Pi\)_. If_ \(b=1\)_, they install a modified scheme_ \(\Pi^{\prime}\) _implementing the attack._
* \(\mathcal{J}\) _inspects_ \(\mathcal{S}\) _and_ \(\mathcal{D}\) _as described in Definition_ 2_._
* \(\mathcal{J}\) _outputs a bit_ \(b^{\prime}\)_._  
_If_ \(b^{\prime}=b\)_, then_ \(\mathcal{J}\) _wins the game._  
**Definition 4**.: _We say that the schemes \(\Pi^{\prime},\Pi\) are indistinguishable for an auditor \(\mathcal{J}\) if \(\mathcal{J}\) wins the game from Definition 3 with probability \(\frac{1}{2}+\epsilon\), where \(\epsilon\) is a negligible value._
## 4 Discussion  
In order to investigate the carrier occupancy rate of the \(\Gamma\) valley and satellite valley (L valley) at the high bias electric field, an ensemble Monte Carlo simulation based on the theory of photo-activated charge domain (PACD) is developed. Considering the two cores of PACD theory, the impact ionization and recombination radiation process are simulated. In our simulation, the band structure of three valleys is \(\Gamma\) valley, L valley and X valley, respectively. The amount of particles is kept constant at \(3.0\times 10^{4}\). The results of the fitted curve to the carrier occupancy rate and Monte Carlo simulation are plotted in Figure 8.  
When the bias electric field E is less than 2.00 kV/cm, the majority of photogenerated carriers are located in the \(\Gamma\) valley, as shown in Figure 8. When the electric field is equal to 20.00 kV/cm, the majority of the photogenerated carriers transfer to the satellite valley. The data approximates the ending electric field for the negative differential mobility (NDM). Therefore, based on the PACD's formation and transport, the available electric energy is not sufficient to make more carriers generate valley scattering. With increasing electric field bias, the carrier occupancy rate for the satellite valley saturates at 72.16% when the \(\Gamma\) valley mobility is 8000 cm\({}^{2}\)/V \(\cdot\) s.  
The result with Monte Carlo simulation of average drift velocity and differential mobility versus electric field are plotted in Figure 9. When the electric field is less than 2.00 kV/cm, the \(\overline{v}_{\rm d}\) increases with the increase of electric field, the \(\overline{\mu}\) is about 6400 cm\({}^{2}\)/V \(\cdot\) s, and the volt-ampere characteristics of SI-GaAs PCA basically conform to the ohmic contact. With the bias electric field continuing to increase, the velocity field gradually deviates from the linear relationship. When the electric field is 3.50 kV/cm, the \(\overline{v}_{\rm d}\) reaches the maximum value, about \(1.9\times 10^{7}\) cm/s. With the bias electric field increasing, the average drift velocity begins to decrease; the differential mobility becomes negative, and the minimum value is \(-2400\) cm\({}^{2}\)/V \(\cdot\) s. Finally, when the bias electric field is 30.00 kV/cm, the \(\overline{v}_{\rm d}\) is saturated, and its value is about \(0.96\times 10^{7}\) cm/s.  
Figure 8: The carrier occupancy rate for the satellite valley (L valley) using the experiment fitted curve and Monte Carlo simulation.  
Comparing the experimental result with simulation results, the essential difference is that the simulation process is completed under ideal conditions. When the bias electric field is less than 2.00 kV/cm, the inter-valley scattering phenomenon of photogenerated carriers has appeared. In the experiment, the bias electric field when the inter-valley scattering phenomenon occurs is about 2.17 kV/cm.
# Mobility-Multihoming Duality
## 1 Introduction
### Paper Structure  
We start in Section 2 with an examination of existing solutions to mobility and multihoming, focussing on those proposals that are being, or have been, actively considered as potential solutions for use on the Internet. We then provide an exposition of our radical approach in Section 3, examining both the architectural and engineering considerations with a focus on real deployment capability over IPv6. We describe our implementation of this approach in Linux in Section 4. In Section 5, we provide a description of our methodology for our evaluation, with details of the testbed and experiments. The results are presented in Section 6, highlighting our findings and showing the efficacy of our approach. We present a critical evaluation of our approach in Section 7, highlighting challenges for deployment on the Internet. We conclude in Section 8 by stating our key findings, as well as listing some items for future work.
# YOLOv8-WD: Deep Learning-Based Detection of Defects in Automotive Brake Joint Laser Welds
## 3 Model and Optimization  
### YOLOv8 Model  
YOLOv8, introduced by Ultralytics in 2023, represents the latest evolution of the YOLO (You Only Look Once) series. Building upon the foundation of YOLOv5, it incorporates significant architectural and methodological innovations. YOLOv8 refines and extends the ideas initially introduced in YOLOv5, with a primary focus on enhancing the model's accuracy and usability for real-time object detection tasks. This version optimizes both detection precision and computational efficiency, addressing the growing demands of modern applications that require fast and reliable performance in dynamic environments. The advancements in YOLOv8 demonstrate an important step forward in the progression of deep learning models for computer vision, balancing the trade-offs between speed, accuracy, and resource utilization in real-world scenarios.  
As is shown in Figure 4, YOLOv8 is an efficient object detection model with three main components: the backbone, the neck, and the detection head. The backbone uses convolutional neural networks (CNNs) to extract both low-level features (edges and textures) and high-level features (object shapes and patterns). The neck network enhances these features by merging multi-scale representations, combining shallow (detailed) and deep (abstract) features to improve the detection of objects of varying sizes. The detection head then outputs bounding boxes, class labels, and confidence scores for object recognition [30].  
YOLOv8 is available in five variants (YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x) to suit different computational needs. The YOLOv8n (Nano) version, optimized for edge devices like the NVIDIA Jetson Nano, balances speed and accuracy in resource-constrained environments. YOLOv8n achieves 161 frames per second (FPS) with a batch size of 1 and 2.8 ms of training time with a batch size of 32, making it ideal for real-time and large-scale applications. These design improvements enhance both detection accuracy and computational efficiency across various platforms.
## 1 Introduction
### Application of the TWA SPME for VOCs  
Despite the advantages of the TWA SPME approach, comparatively few studies were conducted to bring the approach to the field. The studies [24; 25; 26; 27; 28; 29; 30] showed that SPME devices could be used as TWA samplers to access exposure to different volatile (hydrocarbons, formaldehyde, and others) and chlorinated semi-volatile organic compounds [12] at the source. VOCs were also quantified from biomass gasification process streams in fast-moving environments at elevated temperatures such as syngas stream [28; 29] and idling vehicle exhaust [30; 31]. A major challenge with the TWA SPME approach is the influence of the metallic SPME needle assembly on the VOC extraction process, as documented earlier [28; 29; 30; 31]. The metallic surface of the SPME needle (studied using \"broken fiber\", i.e., fiber without coating) had adsorptive properties that were significant compared with the adsorption by the fiber itself. Similarly, [PERSON] et al. [32] evaluated the contribution of the metallic parts first before quantifying five biomarkers (VOCs) such as dimethyl disulfide, dimethyl trisulfide, pyrimidine, phenol, and \(p\)-cresol emitted during aerobic digestion of animal tissue. The current suggestion to overcome this issue for the TWA SPME for quantification of VOCs is the mandatory evaluation of the contribution of mass extracted by a \"broken fiber\" so this effect can be accounted for. Thus, while reproducible, the contribution of metallic SPME parts on the TWA SPME process adds more steps to method development.  
Recent modeling of the TWA SPME process by [PERSON] et al. [22] provided an insightful identification of limitations for the use of retracted SPME fibers and possible means to address them. In their study, they found that a Carboxen/polydimethylsiloxane (Car/PDMS) SPME fiber with a greater size of a protecting needle (23 ga; as opposed to 24 ga) extracted greater amounts of analytes (about 19% more) than the fiber with a smaller protecting needle gauge size. This study suggests that the space between the SPME coating and the inner wall of the protecting needle plays a crucial role in extracting mass, since it allows faster diffusion of analytes not only to the tip of the fiber but also to its sides. The paper also recommends using a 23-ga SPME fiber for quantification of analyteswith lower detection limits. However, no research reported the quantification of major VOCs that are responsible for the characteristic offensive odor downwind from animal feeding operations using a TWA SPME approach.  
In this research, we aimed at addressing two major needs and gaps in knowledge: (1) to minimize or eliminate the need to consider the effect of the metallic SPME needle on air sampling of VOCs with TWA SPME, and (2) to enable SPME technology to be used for odorous VOC quantification in farm environments.
# Design of an Energy Efficient Future Base Station with Large-Scale Antenna System
## 2 System Model
### Large-Scale Multiple-Input Multiple-Output Model
To show the channel hardening effect with LS-MIMO, it is usually required for \(N_{t}>10\) K, which means that the number of TX antennas in the BS should be much more than the number of users [16].
## 3 Application of the New Charging Structure
### Mechanism of the New Charge Structure  
Figure 6 shows that application of the critical distance strategy only changes the distribution of explosives in the air-decked charging structure. The rock-breaking mechanism of the new critical distance-based charging structure is the same as that of the air-decked charging structure. The new  
Figure 6: The new charging structure designed using the critical distance.  
charging structure is more advantageous than the air-decked charging structure because it removes the need for detonating cord and simplifies the charging process. Air-decked technology has been used for many years [25; 26; 27]. Existing research shows that the shock wave generated after an explosive blast acts on the rock surrounding the blasthole, as well as on the rock that it passes by during propagation to the air deck. This produces extensive microfractures on the surrounding rock surface. When the shock waves generated by the explosives at both ends of the air deck collide in the air column, they reflect, rebound, and move towards the bottom of blasthole and the direction of the stemming. This creates a secondary destructive effect on the rock that the shock waves pass by. Expansion and crushing act on the micro-cracks to crush the rock. Generally, the blast energy is redistributed after the air-decked charge is blasted. The explosive energy is first transferred to the air and then transferred to the rock by the air. The air column is used to reduce the initial pressure from the blasting products and increase the duration of interaction between the detonation wave front and the surrounding rock. The increased blasting time increases the rock fragmentation time and the rock is fully destroyed [28; 29].  
Air-decked techniques for rock fragmentation have achieved great success. Several researchers [26; 27] have reported that use of air docking can improve the effective utilization of explosive energy and rock fragmentation uniformity, while reducing explosive consumption by 10-30% and decreasing blasting costs. [PERSON] et al. [30] observed the development of a fracture network in thick Plexiglass under the influence of an air-decked explosive, as showed in Figure 7. The shock wave reflected back-and-forth in the air column and the duration of the shock wave effect on the Plexiglass increased by a factor of 2-5. The above analysis indicates that the new charging structure is also suitable for rock-breaking. Furthermore, the distance between explosives in the new charge structure is more standardized, so the problem of detonation failure will be significantly improved.  
Figure 7: Fracture network development in thick Plexiglass [30].
## 4 Experiment and Results
### Generalization Experiments  
While the SD-saliency-900 dataset provides valuable insight with its three steel surface defect categories, its limited scope may not fully represent all possible defect variations. Potential biases in data collection, particularly from specific production environments, could also impact model generalization across diverse industrial applications.  
To comprehensively evaluate the DEPANet's performance, we performed additional tests on the ESDIs-SOD dataset [52]. Following established protocols, we trained the model on 3600 images (1200 per class) and tested on the remaining 1200 images. Table 3 presents a comparative evaluation of DEPANet against 10 methods. The results demonstrate superior performance across all key metrics (MAE, SM, \(E_{max}\), \(F_{max}\), and parameter efficiency), confirming the method's robustness and effectiveness.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Exp.**} & \multicolumn{5}{c}{**DEPANet**} & \multicolumn{5}{c}{**SD-Saliency-900**} \\ \cline{2-9}  & **ResNet34** & **DEFP** & **EFAM** & **CBAM** & **MAE \(\downarrow\)** & **SM \(\uparrow\)** & \(E_{\text{max}}\uparrow\)** & \(F_{\text{max}}\uparrow\)** \\ \hline \#1 & ✓ & X & X & X & 0.0191 & 0.8891 & 0.9231 & 0.8919 \\ \#2 & ✓ & ✓ & ✓ & X & 0.0141 & 0.9267 & 0.9671 & 0.9298 \\ \#3 & ✓ & ✓ & ✓ & ✓ & 0.0132 & 0.9373 & 0.9758 & 0.9396 \\ \hline \#4 & \multicolumn{5}{c}{BCE} & \multicolumn{5}{c}{0.0154} & 0.9015 & 0.9598 & 0.9116 \\ \#5 & \multicolumn{5}{c}{Dice} & \multicolumn{5}{c}{0.0149} & 0.9141 & 0.9674 & 0.9180 \\ \#6 & \multicolumn{5}{c}{BCE + Dice} & \multicolumn{5}{c}{0.0132} & 0.9373 & 0.9758 & 0.9396 \\ \hline \end{tabular}
\end{table}
Table 2: The experimental results of ablation studies conducted using the dataset SD-saliency-900.
# Learning to Segment Blob-like Objects by Image-Level Counting
## 3 Methods
### Morphological Loss  
The morphological loss presented in this section is intended to deal with a problem that is not taken into account by the counting loss (Section 3.1) or the contrast loss (Section 3.3): without further control mechanisms during the training, the segmentation result for a blob-like object does not necessarily show a blob-like structure, but may be scattered into several fragments for objects with unsharp borders. The method presented in Algorithm 1 solves this problem. In the case that objects are expected, a morphological grayscale opening followed by a grayscale closing is used as the annotated image. This means that the morphologically manipulated output is compared to the original output to calculate the loss value. The combined use of the morphological loss, the counting loss, and the contrast loss is shown in Figure 3.  
```
if\(n_{e}=0\)then return\(0\)\(\triangleright\) Not applied without expected objects else  low \(\leftarrow\) mean(lowest \(p\%\) of values in \(\mathbf{I}\)) \(\triangleright\) Smoothed minimum  high \(\leftarrow\) mean(highest \(p\%\) of values in \(\mathbf{I}\)) \(\triangleright\) Smoothed maximum for\(0<h\leq H\)do for\(0<w\leq W\)do \(\mathbf{I}_{h,w}^{\text{(norm)}}\leftarrow(\mathbf{I}_{h,w}-\text{low})/(\text{ high}-\text{low})\)\(\triangleright\) Soft scaling endfor endfor \(\mathbf{I}^{\text{(morph)}}\leftarrow\text{morph\_close}\big{(}\text{morph\_ open}\big{(}\mathbf{I}^{\text{(norm)}}\big{)}\big{)}\) for\(0<h\leq H\)do for\(0<w\leq W\)do \(\mathbf{I}_{h,w}^{\text{(diff)}}\leftarrow\max\Big{(}\mathbf{I}_{h,w}^{\text{(norm)}}- \mathbf{I}_{h,w}^{\text{(morph)}},0\Big{)}\) endfor endfor \(\text{diff}\leftarrow\text{mean}\big{(}\mathbf{I}^{\text{(diff)}}\big{)}\cdot m\)\(\triangleright\) Difference per \(m\) pixels returndiff endif
```  
**Algorithm 1** Computation of the morphological loss for object counting. The inputs are the image \(\mathbf{I}\), the expected number of objects \(n_{e}\), the percentage \(p\) of extreme values to use for the smoothed minimum and maximum calculation, and the scaling factor \(m\)
# Echelle Grating Spectroscopic Technology for High-Resolution and Broadband Spectral Measurement
## 6 Discussions and Outlook
For a given grating size, the immersed echelle has an n-fold increase in resolving power compared with the common echelle [121; 122; 123]. At some observatories, both the echelle and the immersed echelle are used in the spectrometer to observe different wavebands [124; 125]. In the future, the echelle and cross-dispersion will continue to serve as a significant platform for the spectrometer family. It holds the promise of more applications in environmental monitoring, mineral exploration, metallurgy, biomedicine, and other fields. Furthermore, echelle spectrometers in various observatories around the world will be used to explore more diversified scientific discoveries.
## 4 Results and Discussions
### Optimization of Hip Mass Ratio  
After obtaining the optimal value for the leg mass ratio \(m_{per}\), in this section, we refer to the parameters of the human body structure and take \(m_{per}=0.3\) as the fixed mechanism parameters of the bipedal robot. Keeping the ratio of the robot rod length \(l_{hyper}=0.2\) and \(l_{hper}=0.4\) unchanged, the optimal mass ratio of hip to body, \(m_{hper}\), is analyzed.  
Figure 7 shows the variation in the full-range energy consumption \(COT_{ave}\) when \(m_{hper}\) varies in the range of [0, 0.6]. The \(COT_{ave}\) decreases with an increase in \(m_{hper}\). When the proportion of the robot's hip mass to the body mass is large, the bipedal robot's mechanism exhibits superior performance in terms of full-range energy consumption under the given objective.  
Figure 8 shows that when \(V=\) [0.3, 0.4, 0.5, 0.6], the \(COT\) corresponding to the different step lengths changes with \(m_{hper}\). The \(COT\) decreases with an increase in \(m_{hper}\) at different walking speeds when the step length is short. For long step lengths, when the walking speed is large, such as \(V=0.6\) and \(D=0.9\) as shown in Figure 8d, \(m_{hper}\) does not considerably affect the \(COT\). At small walking speeds, the \(COT\) first decreases and then increases with an increase in \(m_{hper}\), when \(V=0.3\) and \(D=0.8\) as shown in Figure 8a; \(COT_{min}\) is 0.024825 for \(m_{hper}=0.6\).  
When the \(m_{hper}\) is small, such as \(m_{hper}=0.2\) as shown in Figure 8d, the \(COT\) is large when the step length is short (\(D=0.3\)) and small at the long step length, i.e., \(D=0.9\). With an increase in \(m_{hper}\), the full-range energy consumption significantly decreases for shorter step lengths. However, the energy consumption of long step length walking remains nearly unchanged with aan increase in \(m_{hper}\).  
As shown in Figure 8, the value of longer step length remains relatively stable as \(m_{hper}\) changes. When \(m_{hper}\) is larger, the value of \(COT\) for shorter step length is relatively  
Figure 6: Optimal step length and \(COT\) with \(m_{per}=0.08\): (**a**) optimal step length; (**b**) optimal \(COT\).  
Figure 7: The variation in \(COT\) when \(m_{hper}\) varies in the range of [0, 0.6].  
smaller. Therefore, for larger \(m_{hyper}\), the designed walking gait achieves optimal full-range energy consumption.  
Figure 9 shows the variation in the optimal step length with the walking speed when \(m_{hyper}=0.3\) and the \(COT\) values correspond to the optimal step length. The optimal step length increases with na increase in walking speed.