esa-sceva/satcom-chunk-collection huggingface dataset:
# YOLO-DFAN: Effective High-Altitude Safety Belt Detection Network
## 4 Results
### Results and Discussion
Compared with YOLOv4, YOLO-DFAN has tremendous advantages in speed, although its accuracy is inferior to that of YOLOv4. The main reason for this is that the idea of the CSPDarkNet network was applied to YOLOv4, strengthening the network feature representation but making the detection speed slow. In conclusion, the proposed method in this paper balances the precision defects of traditional lightweight networks and the speed defects of traditional heavyweight networks in high-altitude safety belt detection, and it completes the object detection with good performance. Figure 7: Results of the original network’s missed samples: (**a**) prediction of YOLOv4-tiny; (**b**) prediction of YOLO-DFAN; (**c**) heatmap of YOLOv4-tiny; (**d**) heatmap of YOLO-DFAN. We conducted ablation experiments on the high-altitude safety belt dataset to verify the effectiveness of the DFAN module and each of the other modules introduced in the paper, as follows (Table 2):  
In the ablation experiments, we conducted experiments on multiple groups of attention modules and each module used in this paper. The proposed DFAN enhanced the high-altitude safety belt detection better than other small attention modules--such as SENet, CBAM, ECANet, and coordinate attention--and could verify the effectiveness of the constructed DFAN. Observing the experimental results of ASPPNet and PANet, introduced separately for the benchmark network, we were able to verify the validity of the effectiveness of these introduced modules. In the ablation experiments, we verified the effectiveness of the modules introduced and proposed in this paper. We further validated the algorithm's effectiveness by employing YOLO-DFAN in experiments on the Pascal voc07+12 dataset. As can be seen from Figure 8, the YOLO-DFAN algorithm also has a good performance on the Pascal voc07+12 dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Detection** & **Map (\%)** & **F1** & **R (\%)** & **P (\%)** & **Rate(s-Picture\({}^{-1}\))** \\
**Network** & & & & & \\ \hline YOLOv4-tiny & 74.03 & 0.76 & 67.33 & 84.44 & 0.0039 \\ EfficientDet & 72.36 & 0.77 & 64.29 & 85.36 & 0.0031 \\ YOLOv4 & 80.28 & 0.82 & 72.28 & 86.05 & 0.0065 \\ YOLO-DFAN & 79.16 & 0.80 & 71.29 & 84.56 & 0.0055 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Belt detection effects of different networks. \begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Map (\%)** \\ \hline YOLOv4-tiny & 74.03 \\ +SE & 75.28 \\ +CSAM & 75.96 \\ +ECA & 74.98 \\ +CA & 76.24 \\ +DFAN & 77.00 \\ +ASPP & 76.23 \\ +PANET & 75.67 \\ +ASPP+DFAN+PANET & 79.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ablation experiments. Figure 8: Results for YOLOv4-tiny and YOLO-DFAN on the Pascal voc07+12 dataset.
###### Abstract  
With rapid economic development and the change in land use patterns, the region faces the environmental challenge of increasing carbon emission risk. The research on analyzing and identifying carbon emission risk of land use is helpful to realize regional sustainable development. This study takes the Yanshan-Taihang Mountain area of Hebei Province as a case study. Based on the remote sensing monitoring data of land use in 2010, 2015, and 2020, the carbon emissions of land use are calculated by direct and indirect calculation methods. Then, the carbon footprint pressure index and land use carbon emission risk index are introduced to analyze the temporal and spatial differentiation characteristics of land use carbon emission risk in the study area. The findings indicate that carbon emissions associated with land use in the study area initially exhibited an increasing trend, followed by a subsequent decline over time. In space, the high-value areas of carbon emissions are mainly distributed in the south of the study area, and the low-value areas of carbon emissions are mainly concentrated in the northeast of the study area. The region experiences significant carbon-cycling pressure and environmental risk, with the proportion of counties in carbon balance areas decreasing from 27.27% in 2010 to 18.18% in 2020, and the proportion of counties in carbon imbalance areas increasing from 72.72% in 2010 to 81.82% in 2020. The carbon emission risk of land use is divided into micro-risk area, low-risk area, medium-risk area, high-risk area, and severe-risk area. From 2010 to 2020, there was an 18.18% increase in the percentage of counties classified as high-risk and severe-risk areas. Additionally, the regions identified as hotspots for land use emissions exhibited a trend in expansion. This phenomenon indicates that these areas have not successfully managed to mitigate environmental pollution or ensure the sustainable use of resources in the context of their economic development efforts. This series of dynamic changes shows that the study area is facing the challenge of increasing carbon emission risk from land use. Governments at all levels should strengthen environmental governance in high-risk areas, implement stricter land use policies, and promote green development and cleaner production to attain a mutually beneficial outcome for both economic development and ecological protection.  
land use change; carbon emission risk; spatiotemporal evolution characteristics; Yanshan-Taihang Mountain; Hebei Province +
Footnote †: journal: Applied sciences  
Article
###### Abstract
Further, it assists in 3D modeling of patient-specific implants, saw guides, and drill guides [34; 48]. A conversion of 2D medical images (DICOM) to 3D data (standard tessellation language: STL) requires 3D thresholding techniques for segmenting different forms of tissues. There are several 3D thresholding techniques for the 3D reconstruction of medical images, namely a volume-based approach, contour-based approach, and point-cloud-based approach [49]. However, the most commonly used method is the volume-based [39] that uses a marching cubes algorithm [50]. The volume-based approach involves an elimination of all voxels consisting of the grey scale values in HU that are lower than the selected threshold value t. It includes all the voxels that are equal to or greater than \(t\). The following binary mask \(M_{(x,y)}\) (Equation (1)) denotes these relationships where \(I_{(x,y)}\) depicts the grey scale values of medical images in HU [51]:  
\[M_{x,y}=\left\{ \begin{array}{cc}0&I_{x,y}<t\\ 1&I_{x,y}\geq t\end{array} \right. \tag{1}\]  
Though the collective processes of CT imaging, 3D thresholding, 3D modeling, and AM yield beneficial outcomes of surgical procedures [15; 16; 28; 29; 30; 31; 32; 33], both academics and practitioners have noticeably understudied the potential sources of their cumulative error [52]. Each step of the process can contribute geometric error to the resulting implant [53]. Previous efforts have been made to evaluate inaccuracies through different CT systems and their protocols [37]; different CT system voltage and current [54]; various CT systems and thresholding values [39; 55]; different thresholding values [54; 56]; various thresholding values and software [57]; different AM processes [53; 58]; and various metal AM machines [59], all of which have prompted additional verification studies that account for the whole process chain. The aim of this study is to evaluate cumulative spatial inaccuracies [52] in the process chain of implementing AM from medical imaging involving different CT imaging protocols, 3D thresholding involving various Hounsfield units, and 3D modeling involving patient-specific geometry. The case study follows the process chain of a pre-operative virtual planning for the procurement of an end-use implant to aid practitioners in establishing appropriate clinical practices that can allow treatment and restoration. The study designs and evaluates a QA Phantom in line with sound metrological expertise to validate a MSCT system. In addition, accurate measurement techniques traceable to the SI unit [60] are adopted for the reference measurements. These include a tactile-probing coordinate measuring machine (CMM) [61] and an optical 3D coordinates measuring system which comprises a combination of the structured light [62] and stereo vision [63] techniques. The procedure offers an innovative approach for determining the inaccuracy of an end-use implant involving the whole process chain as opposed to the partial analyses in the literature involving one or few process steps.
## 3 Fault Model and SH and GH Node Generation Mechanism
### SH Node Generation Mechanism
In this way, we obtain the CFG and the instructions that lead to SH nodes forming, as shown in Figure 6. \begin{table}
\begin{tabular}{l c c} \hline \hline
**SDC Error** & **Occurrence** & **Description** \\ \hline Format error & 10 & SDC error causes the number of RIB columns to change. \\ \hline Link missing & 124 & SDC error causes the original connected link to be marked as disconnected or missing. \\ \hline Weight change & 121 & SDC error causes the link delay of the original connected path to change. \\ \hline False link & 173 & SDC error causes an unconnected path to be marked as connected. We find that of these 173 false links, 101 can generate SH nodes. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Build_RIB SDC error occurrence number and description. Figure 3: The call graph of Build_RIB. As shown in Figure 7, a typical example of a SH node caused by SEU is that during the dynamic cycle 61,316 of the Build_RIB program execution, the 12 th bit of the _next_ instruction (%18) of the basic block %15 in the _readFile_ function is flipped, resulting in a false link between the No. 9 satellite node and the No. 56 satellite node. In the process of  
Figure 4: The CFG and the instructions in the _readFile_ function that lead to SH nodes forming. Figure 5: The basic blocks and the instructions in the _writeFile_ function that lead to SH nodes forming. Figure 6: The CFG and the instructions in the _main_ function that lead to SH nodes forming. path planning, because the link delay of this false link (Node 9 to 56) is 14 ms better than other paths, this false link is adopted by the path planning algorithm, and the next hop to reach Node 56 and its surrounding nodes in the generated routing table is Node 9. In the actual forwarding process, Node 9 cannot directly forward the data packet to Node 56. Therefore, Node 9 finds another link or drops the data packet, resulting in a SH in the satellite network.
## 4 Results and Discussion
### Case Study  
In addition to analyzing performance parameters, we also compare the actual translation of our model to the industry-level Baidu translation system. Here, the accuracy is measured based on the options of native speakers. We randomly select three different lengths of sentences as test cases and show the comparison results in Figure 11. Overall, our model can translate English to Chinese correctly, especially for short sentences and medium-length sentences (case 1 and case 2). However, the accuracy of the translation is not sufficiently good. In addition, the beam search strategy (beam size is 3) is better than the greed search strategy (beam size is 1) when decoding the sentence. In a nutshell, by consuming a relatively short training time, our trained model is competitive with an industry-level product such as the Baidu translation system. In the future, we will use a more bilingual corpus for improved translation quality.
## Materials and Methods
### Discriminant Analysis Algorithms
#### 2.5.3 Partial Least-squares Discriminant Analysis (PLS-DA)  
PLS-DA is a classification method that was developed from the partial least-squares regression (PLSR) technique by adding a threshold to the predicted values. This method is frequently used in NIR spectroscopy analyses. PLS-DA excels at handling data with collinearity, just like the characteristics of NIR spectroscopy. Specifically, PLS-DA extracts latent variables (LVs) from the predictors (\(X\)) and response (\(Y\)) to build a linear relationship between \(X\) and \(Y\), explained as follows:  
\[Y=\ XB+E\]where \(B\) was the matrix of regression coefficients of the PLS-DA model, and \(E\) was the residual matrix that represents the portion of variance not explained in \(X\) and \(Y\). In this way, redundant variables and noise information are eliminated in the residual \(E\), and the useful predictors are assigned greater weights by the regression coefficients \(B\). Therefore, selecting the number of LVs was crucial to avoiding underfitting or overfitting. This study followed the principle to select an appropriate number of LVs, such that the RMSECV did not change significantly or tend to go up when adding LVs to the model. The response variable \(Y\) was assigned with numbers 1 and 2 to represent the cultivars of seed samples (1 for H8 and 2 for H168). However, the predicted value in the model are hardly perfect integers 1 or 2. Hence, a Gaussian distribution of calibration results was fitted to determine a probabilistic threshold as a cut off to discriminate which cultivar the unknown sample belonged to, and more details about calculating the probabilities have been described in previous work in reference [39].
# Mapping Abandoned Cultivated Land in China: Implications for Grain Yield Improvement
## 3 Results  
### Analyzing the Overall Characteristics of Cultivated Land Abandonment in China  
Based on the data on abandoned cultivated land in China (excluding Taiwan province, along with the special administrative regions of Hong Kong and Macao) in 2017, it was found that the total area of abandoned cultivated land was approximately 9.1009 million hectares (including both permanently and seasonally abandoned land), with an overall abandoned rate of approximately 5.57%. Among the provinces, the highest abandoned rate was 21.12% in Fujian Province, and the lowest was only 1.01% in Sichuan Province. In addition, the range of cultivated land abandonment was distributed in 31 provinces and involved 2710 counties of 337 cities (Figure 2), accounting for 95.3% of all counties in the country.  
The results of the trend analysis revealed the overall spatial pattern and trend of the area/rate of abandoned cultivated land in China. As shown in Figure 3, the area of the abandoned cultivated land presented an \"inverted U\" trend from east to west, and a certain latitudinal zonal differentiation law appeared from north to south. The rate of abandoned cultivated land also showed an \"inverted U\" trend in the east-west direction and a \"U\" trend in the north-south direction.  
In the east-west direction, affected by the three altitudinal increases in China's topography, the quality of cultivated land and the availability of water resources gradually declined from east to west, and the land production capacity also gradually dropped, which led to an increase in abandonment. However, due to the lack of employment opportunities in the western region, farmers still had a strong dependence on cultivated land resources, so the phenomenon of abandonment in the central region reached a peak and gradually decreased to the west. Influenced by East Asia's monsoon weather, the moisture and temperature in Chinese agricultural areas progressively decline from the south to the north, while the trends of land use efficiency and agricultural intensification in China generally follow the pattern of being higher in the southern regions and lower in the northern ones, leading to a gradual increase in the area of cultivated land abandonment from south to north. However, the abandoned rate of cultivated land in the southeast coastal area has  
Figure 3: Trends analysis of the abandoned area and abandoned rate of cultivated land in China. Notes: The green lines and spots represent the distribution of abandoned cultivated land in the east-west detection of China, and the blue represent the distribution of abandoned cultivated land in the north-south detection of China.  
Figure 2: The characteristics of abandoned areas and the rate of abandonment in China.  
increased slightly, which manifested that the development of a non-agricultural economy harmed the utilization of cultivated land.
## 4 SFA-YOLO Network
### SFA Method  
The Selective Feature Attention (SFA) mechanism is based on the C3k2 module. It retains part of the original functionality and enhances the ability to select relevant features. The Bottleneck structure in C3k2 compresses critical feature information in specific tasks. This causes performance degradation, information loss, and reduced detection precision. This paper replaces the Bottleneck structure with Partial Convolution (PCONV) and Convolutional Gated Linear Unit (CGLU) modules to solve this problem. This replacement increases the focus on key features.PCONV (Partial Convolution) is a specialized convolution operation designed to handle missing or masked regions in images. It demonstrates superior performance in image-inpainting and occlusion-handling environments. CGLU (Convolutional Gated Linear Unit) is a gated activation mechanism. It selectively activates convolutional feature channels and effectively regulates feature retention and suppression, strengthening the model's representational capacity. It increases the effectiveness of feature selection by combiningPCONV and CGLU:  
1. [leftmargin=*]
2. More effective handling of missing data  
PCONV performs convolution only on valid regions and enables the network to handle images with missing parts. CGLU introduces a gated mechanism that optimizes feature selection by enhancing useful features and suppressing less relevant ones. This combination strengthens the network's ability to recover missing or occluded areas in image restoration tasks.
3. Enhanced feature selectivity  
The gated mechanism in CGLU allows the network to adjust feature activation based on the context of input features. The combination withPCONV enables the network to handle missing data and activates the most valuable features based on the context of the missing regions. The network infers missing features more accurately under partial loss or occlusion.
4. Improved robustness  
CGLU enables the network to adjust the importance of different feature regions dynamically during partial convolution. This allows the network to select more relevant channels in missing areas and improves robustness against occlusion and incomplete data.
5. Stronger learning capability  
The combination ofPCONV's partial convolution and CGLU's gated activation allows the network to extract useful information from images with missing or occluded regions. The gating mechanism controls the selective flow of information. Even with partial data loss, the network still learns the complete structure of the image more effectively.
# Correction of Field-Measured Wind Speed Affected by Deterministic Interference Factors
## 3 Wind Speed Revise
### NLDI Site
#### 3.1.3 Experimental Results  
The dominant wind direction of the Pearl River delta region is southeast wind to northeast wind. Therefore, the wind revision in incident directions ranging from \(0^{\circ}\) to \(180^{\circ}\) (\(0^{\circ}\) due north; clockwise rotation is positive) is sufficient to satisfy the revised requirements for gales in all typhoon measurements. The wind speeds at the NLDI site, the upstream reference point at the same height, and the wind speed influence coefficient (\(C_{V1}\)) are illustrated in Figure 6. The figure shows that the \(C_{V1}\) derived from the WTT and the CFD are consistent in the overall variation trend, but the former results are greater than the latter ones, quantitatively.  
The WTT results revealed that the measured wind speeds were increased in different degrees due to the existence of the island under wind directions from \(20^{\circ}\) to \(100^{\circ}\). However, it decreased under the other wind directions. For the CFD simulated results, the wind directions that corresponded to the increased wind speeds were \(30^{\circ}\) to \(90^{\circ}\). Apparently, the WTT results were more sensitive due to the complex flow patterns, such as backflow and  
Figure 6: Description of experimental results: (**a**) relationship between wind speed and wind direction of WTT; (**b**) relationship between wind speed and wind direction of CFD; (**c**) variation of \(C_{V1}\) with wind direction of NLDI.  
reattachment in valleys and depressions, which can be reproduced in the WTT but difficult to simulate in CFD.  
The least square method was implemented to fit the functional relationship between \(C_{V1}\) and wind directions by WTT and CFD. It provided guidance on correction when using the meteorological data collected at NLDI in the follow-up work. The polynomial fitting results of \(C_{V1}\) were provided as follows:  
\[\left\{\begin{array}{ll}C_{V1-\text{WTT}}=0.000001008x^{3}-0.0003129x^{2}+0. 02439x+0.6809&(R^{2}&=0.9943)\\ C_{V1-\text{CFD}}=0.0000007333x^{3}-0.0002258x^{2}+0.01763x+0.6818&(R^{2}&=0.98 33)\end{array}\right. \tag{4}\]  
According to the measured wind speed of NLDI (Figure 3), the second maximum mean wind speed of 39.1 m/s under the wind direction \(84^{\circ}\) with the time of 14:47 should be revised. The \(C_{V1}\) corresponding to WTT is 1.12, and the \(C_{V1}\) corresponding to the numerical simulation is 1.00 according to Equation (4). Affected by the terrain, a slight acceleration effect was observed at the site under the sensitive wind direction, and the corrected wind speeds were less than or equal to the on-site measured ones. The correction effect of WTT on the measured wind speed was more significant.
# Experimental Study of See-Saw Mode Nano-Vibration on Orifice-Type Restrictors
## 4 Discussion
Because 17.7 and 25.3 kHz are approximately twice and three times the relationships with 7.9 kHz, 7.9 kHz can be considered the fundamental frequency of the normal random nano-vibration of the restrictor; 17.7 kHz is the double frequency, and 25.3 kHz is the triple frequency. * More results of the normal nano-vibration PSD of the restrictors were obtained when \(p_{a}\) was 0.1, 0.2, 0.3, 0.4 and 0.5 MPa. From these results, the fundamental frequency of the nano-vibration was consistently around 9.4 and 10.6 kHz (double-ring-grooved style restrictor), and 7.9 kHz (double-U-grooved style restrictor), which fully supports the notion that the obtained frequency is an intrinsic vibration model, which is unrelated to air-supply pressure.
# Advanced Cooperative Formation Control in Variable-Sweep Wing UAVs via the MADDPG-VSC Algorithm
## 4 Flight Validation and Performance Analysis
### Experimental Platform Overview  
The validation of the MADDPG-VSC algorithm was conducted through a flight validation platform composed of L-30A variable-sweep wing UAVs. This platform was designed to test the effectiveness of the algorithm in cooperative control tasks. The platform was configured to balance hardware performance with algorithmic demands, ensuring reliable and precise flight control.