esa-sceva/satcom-chunk-collection huggingface dataset:
# The Ongoing Greening in Southwest China despite Severe Droughts and Drying Trends  
[PERSON]  
1 Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disaster, School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China; [EMAIL_ADDRESS] (X.C.); [EMAIL_ADDRESS] (J.C.); [EMAIL_ADDRESS] (R.G.); [EMAIL_ADDRESS] (M.G.); [EMAIL_ADDRESS] (X.W.); [EMAIL_ADDRESS] (S.Z.); [EMAIL_ADDRESS] (C.L.); [EMAIL_ADDRESS] (Y.X.)  
[PERSON]  
1 Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disaster, School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China; [EMAIL_ADDRESS] (X.C.); [EMAIL_ADDRESS] (J.C.); [EMAIL_ADDRESS] (R.G.); [EMAIL_ADDRESS] (M.G.); [EMAIL_ADDRESS] (X.W.); [EMAIL_ADDRESS] (S.Z.); [EMAIL_ADDRESS] (C.L.); [EMAIL_ADDRESS] (Y.X.)  
Qingyun Yan  
2 School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China; [EMAIL_ADDRESS] [EMAIL_ADDRESS] [EMAIL_ADDRESS] [EMAIL_ADDRESS] [EMAIL_ADDRESS] [EMAIL_ADDRESS] 2020121001  
decreased by 101.54 Tg C, which accounted for about 50% of the net carbon sink in the terrestrial ecosystem in China [16].  
However, the phenomenon of greening on a global scale has been widely reported. A rising question is that although the destructive effects of these drought events on vegetation in the short-term are obvious, do they have a significant impact on long-term changes?  
In Southwest China (SWC), severe drought events that occurred between 2009 and 2012 had a very large impact on the region's ecosystems [17; 18; 19]. Due to the severity of the drought, a numbers of studies investigated the meteorological mechanism, intensity, extent, and duration of the drought; and the destruction in vegetation, the economy and society. As far as the ecosystem is concerned, this damage is all-round, including cropland and natural vegetation.  
At present, nearly 10 years have passed since the drought events, and long-term vegetation records can be used to describe the subsequent vegetation development in the area. Therefore, the objective of this study is to answer the rising question: will drought events affect long-term vegetation trends? If not, what are the factors dominating the trend of vegetation greening in the area? In the following context, we will identify the severity of drought events in different areas of SWC from 2009 to 2012 and the changes in dryness and wetness in SWC, compare the response of vegetation to drought events and the impact of drought events on long-term vegetation trends, and investigate the main driving factors that affect vegetation trends in SWC.
## 5 Simulation Experimental Results and Discussions
### Comprehensive Balance Method Analysis
#### 5.4.2 Tetrahedral Mesh  
Factor C: Based on the analysis of R-values, factor C is the most important factor for all four indices. From the analysis of \(p\)-values, factor C exhibits a very important effect on the computation time and a significant impact on the weight reduction ratio. Based on the influence of factor C on various indices and the engineering objective of weight reduction, C1 is selected.  
Factor D: According to the analysis of R-values, factor D represents the most crucial factor for compliance, maximum stress, and the weight reduction ratio, but it is a minor factor for computation time. From the analysis of k-values, \(k_{2}\) is selected for compliance, maximum stress, and the weight reduction ratio. From the analysis of \(p\)-values, factor D has a significant effect on the weight reduction ratio and computation time. Based on the impact of factor D on different indicators and the engineering goal of weight reduction, D2 is selected.  
Factor E: According to the analysis of R-values, factor E is a minor factor for compliance, maximum stress, and the weight reduction ratio, and it is a secondary factor for computation time. From the \(k\)-value analysis, the values of \(k_{1}\), \(k_{2}\), and \(k_{3}\) for factor E for the compliance and weight reduction ratio are not significantly different. From the \(p\)-value analysis, factor E has a significant impact on the computation time. Based on the effect of factor E on various indicators and the need to reduce the computational cost, E3 is selected.  
Figure 10: Finite element stress map of the geometric reconstruction model No. 9 via the hexahedral mesh (unit: MPa).  
Based on the above analysis, the optimal solution for the tetrahedral mesh is C1D2E3, which corresponds to a mesh size of 3.5 mm, a filter radius of 5.6 mm, and a penalty factor of 3.5. This optimal solution corresponds to test 3 in Table 10, and the finite element stress map is illustrated in Figure 11.  
To validate the reliability and feasibility of the optimal solution obtained using the comprehensive balance method, a comparative study was conducted with the gooseneck chain's finite element model, which was verified experimentally. The specific results are provided in Table 12, and the weight reduction ratio is calculated based on the volume of the original model.  
According to Table 12, it can be seen that the optimal solutions for both hexahedral and tetrahedral meshes obtained using the comprehensive balance method satisfy the static loading requirements and outperform the experimentally verified finite element models in terms of the weight reduction ratio and maximum stress. In particular, the optimal solution for the hexahedral mesh has the highest weight reduction ratio and the lowest maximum stress, indicating the potential for further weight reduction and providing more space for subsequent iterative optimization. This result also confirms the feasibility and benefits of the recommended standards for TO factors, geometric reconstruction criteria, and the geometric reconstruction approach.
# Random Access Performance of Distributed Sensors Attacked by Unknown Jammers
## 3 RA Performance Analysis
### Success Probability of Preamble Transmission
#### 3.1.2 Derivation of \(\overline{m}\)  
Let \(G_{0}\) and \(G_{r}\) denote the arrival rates of the initial PT and the \(r\)-th preamble retransmissions, respectively. Let \(S_{P}\) be the throughput of PT, and let \(r_{\rm max}\) be the maximum number of preamble retransmissions allowed. Let \(q_{A}\) denote the probability that SN-A receives AI successfully. The relationship between \(G_{0}\) and \(G_{r}\) is then:  
\[\begin{cases}G_{0}=S_{P}+G_{r_{\rm max}}\{1-u_{\bullet}(r_{\rm max})q_{A}\}, \\ G_{1}=G_{0}\{1-u_{\bullet}(0)q_{A}\},\\ G_{2}=G_{1}\{1-u_{\bullet}(1)q_{A}\},\\ \quad\vdots\\ G_{r_{\rm max}}=G_{r_{\rm max}-1}\{1-u_{\bullet}(r_{\rm max}-1)q_{A}\}.\end{cases} \tag{15}\]  
The composite offered traffic is therefore \(G=\sum_{r=0}^{r_{\rm max}}G_{r}\). From (15), \(G_{r}\) is given by:  
\[G_{r}=\frac{G\prod_{i=0}^{r-1}\{1-u_{\bullet}(i)q_{A}\}}{1+\sum_{j=1}^{r_{\rm max }}\prod_{i=0}^{j-1}\{1-u_{\bullet}(i)q_{A}\}}. \tag{16}\]The average number of retransmissions \(\overline{m}\) to measure the corresponding average power level in the unit of \(\mu L\) is then given by:  
\[\overline{m}=\sum_{r=0}^{r_{\max}}m_{r}\frac{G_{r}}{G}=\frac{1+\sum_{r=1}^{r_{ \max}}\left[m_{r}\prod_{i=0}^{r-1}\{1-u_{\bullet}(i)q_{A}\}\right]}{1+\sum_{r= 1}^{r_{\max}}\prod_{i=0}^{r-1}\{1-u_{\bullet}(i)q_{A}\}}. \tag{17}\]  
It should be noted the success probability \(u_{\bullet}(r)\) in (13) is a function of \(\overline{m}\), while \(\overline{m}\) as expressed in (17) is a function of \(u_{\bullet}(r)\). Therefore, (13) and (17) are to be solved recursively in order to get \(\overline{m}\).
## 2 Conceptual Framework Building
### Conceptual Framework  
Utilizing the paradigm of relationship development and the anomaly-seeking approach, this study has elaborated on the various influencing factors through a systematic literature review to integrate the conceptual framework of decision-making capability and strategies of evacuation behaviours. As individuals with high-risk perceptions are more inclined to affirm the severity of the contingency and positively influence the choice of evacuation behaviour strategies [70]. Thus, responses to cues are derived from the aspect of risk perception. In terms of emotions, although the panic theory is inapplicable to the study of behavioural choice nowadays. However, to a certain extent, emotional responses in evacuation also reveal the relationship between emotions and behavioural choices [45]. Thus, the emotional response to receiving emergency cues and the emotions in the face of a crowd both originate from the emotional aspect. As an extension of cognitive abilities, the safety knowledge aspect contributes to people's familiarity with emergency safety facilities. In addition, we used the eight survivability factors already proposed as an extension of personality variables [71]. This includes a leadership factor with the habit of gathering and organising people, a problem-solving factor with good strategic problem solving, an altruistic factor with the ability to care for and help others, a stubborn personality with the ability to stick to one's attitudes or habits, a habit of conforming daily behaviour to social norms of etiquette, the ability to maintain calm emotional regulation in stressful situations, self-transcendence with a sense of responsibility to society and positive behavioural aspects of maintaining or improving oneself in everyday life [71]. Consequently, various sources were used in this study to construct measures of evacuation behavioural strategies, as shown in Table 1. The framework which consists of a level of emergency knowledge, risk perception, emotion of response, and survivability was developed to explain evacuation behavioural strategies. Figure 1 illustrates the main structure of the conceptual framework for bridging the knowledge gap in current evacuation behaviours research. As the evacuation efficiency of a crowd can be significantly influenced by the evacuation behavioursof individuals in the group [72], evacuation behaviours are influenced by decision making [28]. Considering this, it is assumed that \"risk perception, emotion, survivability and knowledge of emergency response have an impact on evacuation efficiency\", and that the components of decision-making capability have an impact on evacuation behaviours at the metro platform. Figure 2 demonstrates the structural model of the impact of the components of decision-making capability on evacuation behaviours.
## 3 Results
### Implant Description and Distribution
#### 3.1.2 Two-Stage Surgery  
Seventy-four individuals, comprised of 31 (42%) men and 43 (58%) women underwent two-stage surgery that involved 253 implants. Their age range was 19-77 years (mean 55 \(\pm\) 13 years), and 17 (23%) were current smokers. Of the 253 implants, 118 (47%) were in the lower jaw and 135 (53%) in the upper jaw. The implant diameter ranged from 3.5 mm to 6 mm (mean 4.05 \(\pm\) 0.4 mm), and the implant length ranged from 8 mm to 16 mm (mean 12.4 \(\pm\) 1.6 mm). The follow-up time ranged from 12 to 181 months (mean 41 \(\pm\) 42 months). The gingival index at the last follow-up was 0 for 177 (70%) implants and 1 for 76 (30%) implants. Crestal bone loss ranged from 0 mm to 5.28 mm: the mean mesial crestal bone loss was 0.96 \(\pm\) 0.82 mm, and the mean distal bone loss was 0.94 \(\pm\) 0.87 mm (\(p>\) 0.05). The follow-up period was divided as for one-stage surgery. The mean mesial and distal crestal bone loss for the short follow-up period were both 0.9 \(\pm\) 0.7 mm and increased slightly to 1 \(\pm\) 1 mm and 1.1 \(\pm\) 1.1 mm respectively during the long follow-up (\(p>\) 0.05).
## 3 Results
### Effect of PEF Technology on the Phenolic and Tocopherol Composition of VOO
At the beginning of the malaxation stage (time 0) PEF oils have approximately twice the concentration of both phenolic compounds than the control oils. After 30 min of malaxation, although the contents of oleacein and oleocanthal remained significantly higher in PEF oils, the differences with control oils were reduced to 50%. Finally, after 45 min of malaxation, both PEF-treated and control oils exhibited very similar contents. Thus, the amount of these secoriidoid derivatives in VOO extracted with PEF was very high already from the beginning of the malaxation stage, while in control oils those contents progressively increased along malaxation. These results suggest that the PEF treatment allows a more efficient and rapid extraction, biosynthesis and/or absorption of the main secoriidoid phenolics in the oil. Thus, the use of PEF technology would decisively enhance the incorporation of phenolic compounds into the oil at short malaxation times. On the contrary, the use of PEF technology in extraction processes with long malaxation times probably does not provide significant benefits in terms of phenolic content. According to these results, a malaxation time of 30 min after the PEF treatment of the olive pastes seems to be the best option in terms of phenolic content. It is important to highlight that the phenolic content of VOO is determined by the content of phenolic glycosides present in the olive fruit and the activity of the endogenous enzymes \(\beta\)-glucosidase and polyphenol oxidase, which regulate the content of secoriidoid derivatives in the oil [2; 9]. Thus, if due to genetic or environmental factors the olive fruits have a low phenolic content or the activity levels of key enzymes within the phenolic metabolism are not adequate, no technological treatment will allow obtaining oils with an optimal phenolic content from those fruits. According to the few published data on the effect of PEF technology on the phenolic composition of VOO, there seems to be a strong varietal component.
# A Deep Reinforcement Learning Floorplanning Algorithm Based on Sequence Pairs +
###### Abstract
Moreover, significant progress has been made in the application of reinforcement learning to task scheduling [32], vehicle routing problems [33], graph coloring [34], and more. Recently, integrating reinforcement learning into electronic design automation (EDA) has become a trend. For example, the Google team [35] formulated macro-module placement as a reinforcement learning problem and trained an agent using reinforcement learning algorithms to place macro-modules on chips. [PERSON] et al. [36] utilized the Q-learning algorithm to train an agent that selects the best neighboring solution at each search step. [PERSON] et al. [37] introduced cooperative learning to address floorplan and routing problems in chip design. [PERSON] et al. [38] proposed a deep reinforcement learning method for VLSI placement parameter optimization. [PERSON] et al. [39] utilized iterative reinforcement learning combined with simulated annealing to place modules. [PERSON] et al. [40] employed graph convolutional networks and reinforcement learning methods for floorplanning under fixed-outline constraints. This paper proposes a deep reinforcement learning-based floorplanning algorithm utilizing sequence pairs for the floorplanning problem. The algorithm aims to optimize the area and wirelength of the floorplan. To evaluate the effectiveness of our algorithm, we conduct experiments on the internationally recognized benchmark circuits MCNC and GSRC, comparing our approach with simulated annealing and the deep Q-learning algorithm proposed by [PERSON] et al. [36]. In terms of dead space on the MCNC benchmark circuits, our algorithm outperforms simulated annealing and the literature [36] by an average improvement of 2.7% and 1.1%, respectively. Additionally, concerning wirelength, our algorithm shows an average improvement of 9.1% compared to simulated annealing. On the GSRC benchmark circuits, our algorithm demonstrates an average improvement of 7.0% and 3.7% in dead space to simulated annealing and the literature [36], respectively. Furthermore, for wirelength, our algorithm exhibits an average improvement of 8.8% over simulated annealing. These results validate the superior performance and robustness of our algorithm in handling ultra-large-scale circuit designs.
# DARI-Mark: Deep Learning and Attention Network for Robust Image Watermarking
## 3 Proposed Framework
### Attack Layer  
To enhance the algorithm's robustness to distortion attacks, DARI-Mark adds an attack layer between the watermark embedding network and the watermark extraction network. It simulates attacks that are commonly encountered in actual communications. Figure 4 shows the flow chart of the attack layer, which simulates four common attacks: JPEG compression (quality factor \(Q=50\)), salt-and-pepper noise (noise density \(s=0.04\)), Gaussian noise (standard deviation \(\sigma=0.1\)), and sharpening (sharpening radius \(R_{\text{S}}=10\)), forming a distinguishable attack layer.  
Figure 3: Embedding network: responsible for watermark embedding.  
The probability of each attack is randomly assigned in each iteration to improve the generalization ability while satisfying the condition that the total probability must be 1.0. In each round of iterative training, attack methods are randomly selected. The network parameters are continuously updated by the loss function and optimization algorithm to generate a robust watermark extraction network.
## 2 Pixel-Level Equalized Memory Matching Network
### Pixel-Wise Memory Storage and Update  
To prevent memory explosion and accumulation of segmentation template errors, inspired by [25], a mask quality evaluation module (MQEM) is introduced for evaluating the segmentation results of each frame and determining whether the frame can be added as a reference frame to the memory library. Since the hardware cannot withstand the increasing memory requirements, when the memory reaches a certain limit, we perform dynamic updates to prevent memory explosion problems.  
Figure 2: Pipelines of the equalized memory matching method.  
The model consists of a fractional encoder, four convolutional layers, and two fully connected (FC) layers. The same feature extraction network as the memory network can be used to share the convolutional network parameters. Query image \(I^{i}\in R^{H0\times W0\times 3}\) and its segmentation mask \(M^{i}\in R^{H0\times W0\times 2}\) act as inputs to the evaluation model, and the feature map \(f^{i}\in R^{H0/16\times W0/16\times C}\) is obtained. \(f^{i}\) is then fed into the convolutional layers followed by the FC layer, and the fraction \(S^{A}\) is finally output. The process is as follows.  
\[\begin{array}{l}f^{i}=Enc(I^{i}\oplus M^{i})\\ S^{A}=Fc(Conv(f^{i}))\end{array} \tag{5}\]  
where \(\oplus\) indicates the concat operation, and \(i\) denotes the index of the current query frame.  
With the quality evaluation module, the memory network can optionally add those frames whose mask quality score is greater than the threshold \(\sigma\) to ensure that the stored information is more accurate and reliable and improve the efficiency and quality of information storage. Therefore, the negative impact of noisy data on memory network is avoided.  
To prevent memory explosions, the size of the storage needs to be limited and dynamically updated to accommodate new scenarios. The memory is dynamically updated when it reaches a particular storage limit so that different video scenes can be handled. As a measure of consistency with time between each reference frame and the current frame, we calculate the time consistency score \(S^{C}\) in the following manner:  
\[S^{C}_{k}=e^{-|i-k|}, \tag{6}\]  
where \(k\) and \(i\) represent the index of a reference frame and current frame, respectively.  
Using the time consistency score \(S^{C}\) and the accuracy score \(S^{A}\), we can calculate the score for each reference frame in the memory bank as follows:  
\[S^{R}_{k}=S^{A}_{k}+S^{C}_{k}, \tag{7}\]  
where \(k\) is the index of a reference frame, and \(S^{A}_{k}\) and \(S^{C}_{k}\) are the accuracy score and consistency score of the reference frame with index \(k\), respectively.  
The reference score is used as a basis to move out those memory frames with low scores so that the memory storage can be dynamically updated.
# Smart Agriculture Applications Using Deep Learning Technologies: A Survey
## 6 Discussion
### Future of Deep Learning in Agriculture  
Agriculture is one of the complex areas of application, as each region has its climatic conditions, nature, and features that differ from the other areas. Therefore, there is an urgent need for technology to distinguish the elements of interest and analyze the collected data. This requires a considerable amount of data to explore, considering changes in real-time. Therefore, in this regard, deep learning is one of the most important technologies that can carry out these functions using appropriate algorithms such as CNN and RNN. When an algorithm is fed with field data, including climate parameters, soil types, weather patterns, and other factors, it builds a probabilistic model before making any decision. Early and accurate identification is essential in tracking different diseases before bearing food or financial losses. After sorting through images of sick plants spanning a decade, this algorithm can determine the type and severity of the disease. The same applies to the course of the weather. The main advantage of a deep learning model is that the software creates the selected feature on its own without provision. Unsupervised learning makes us better equipped and informed to work in the unpredictable and constantly changing real-time environment. It is essential because the significance of the IoT continues to grow, and most of the data generated by humans and machines are unstructured and unclassified. Deep learning outperforms traditional methods, such as ANN, SVM, RF, etc. Automatic feature extraction with deep learning models is more efficient than conventional feature extraction.
# Precise Positioning of Autonomous Vehicles Combining UWB Ranging Estimations with On-Board Sensors
## 5 Experiments and Results
### Real Tests Setup
Nevertheless, as this RTK-SN position has an error lower than 5 cm under good GNSS condition, it is the best approach to verify the accuracy and robustness of the UWB developed solution.
## 3 Design Optimization Method of FLSP Based on Load-Deformation Coordination
### Predicted Large Deformation
#### 3.1.2 The Simplification and Stiffness Calculation of the FLSP  
The connection between the FLSP and the primary lining is complicated. The following assumptions were made to simplify the analysis:  
1. Without considering the foundation bearing capacity, it was assumed that the FLSP bore all of the surrounding rock loads, and that the design was biased towards safety;
2. The bending moment between the FLSP and the primary lining was ignored;
3. The role of the FLSP is to limit the sinking of the arch foot, so the axial force of the FLSP was ignored, and only the lateral bearing effect was considered;  
Figure 11: Flow chart of design optimization of FLSP.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Deformation Level** & **One** & **Two** & **Three** \\ \hline Small, mid-span (B \(\leq\) 12 m) & 100–200 & 200–300 & 300–400 \\ Large-span and above & 150–250 & 250–350 & 350–450 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Reference reserved deformation for extruded surrounding rock tunnel (unit: mm).  
Therefore, only the shear force between the FLSP and the primary lining was considered. Several standard diameters and wall thicknesses of FLSPs applied in engineering practice were selected, and combined with Formula (7) to obtain the lateral stiffness of the FLSP.