esa-sceva/satcom-chunk-collection huggingface dataset:
# Impacts of Compound Hot-Dry Events on Vegetation Productivity over Northern East Asia
###### Abstract  
Climate extremes, such as heatwaves and droughts, significantly impact terrestrial ecosystems. This study investigates the influence of compound hot-dry (CHD) events on vegetation productivity in northern East Asia. Four of the most widespread CHD events occurring during the summer from 2003 to 2019 were selected as the focus of this research. We first verified the performance of the Community Land Model version 5 (CLM5) in the region and then conducted factor-controlled experiments using CLM5 to assess the effects of different climate factors on gross primary productivity (GPP) changes during CHD events. Our results show that vegetation productivity exhibits greater sensitivity to CHD events within the transitional climatic zone (TCZ) than in other affected areas. In grassland areas within the TCZ, precipitation deficit is the primary factor leading to the decrease in GPP (explaining 56%-90% of GPP anomalies), while high temperatures serve as a secondary detrimental factor (explaining 13%-32% of GPP anomalies). In high-latitude forests outside the TCZ, high temperature has a more significant impact on suppressing GPP, while the decrease in soil moisture has a synchronously negligible impact on GPP. There are differences in the effects of high solar radiation on grasslands and woodlands during CHD events. It was observed that high radiation benefits trees by increasing the maximum carboxylation rate (V\({}_{\textrm{cmax}}\)) and maximum electron transport rate (J\({}_{\textrm{max}}\)), as well as enhancing photosynthesis, but has a negligible impact on grasses. Furthermore, this study highlights the potential for compound events to impact vegetation productivity more than expected from individual events due to confounding nonlinear effects between meteorological factors. More than 10% of the negative anomalies in GPP during two CHD events in 2017 and 2010 were attributed to these nonlinear effects. These research findings are significant for understanding ecosystem responses to climate extremes and their influence on carbon cycling in terrestrial ecosystems. They can also contribute to more precisely evaluating and predicting carbon dynamics in these regions.  
compound hot-dry events; gross primary productivity; vegetation; northern East Asia 202415549. [[https://doi.org/10.3390/f15030549](https://doi.org/10.3390/f15030549)]([https://doi.org/10.3390/f15030549](https://doi.org/10.3390/f15030549))
# Mode-Matching-Based Sound Field Recording and Synthesis with Circular Double-Layer Arrays
## 1 Introduction
Although the conventional LS and GSVD methods require interior and exterior discretized control points, the proposed method does not, since a mode matching-based method controls the cylindrical harmonic spectrums of the interior and exterior sound fields as control points instead of the sound pressures. As in the GSVD method [39], the proposed method is also formulated in a two-dimensional height-invariant sound field with a two-dimensional Green's function, circular double-layer arrays, and cylindrical harmonic expansion. The proposed analytical method can also be easily extended for three-dimensional sound fields by introducing the three-dimensional Green's function, spherical double-layer arrays, and spherical harmonic expansion. The rest of this paper is organized as follows. Section 2 briefly introduces the conventional LS and GSVD methods. The proposed formulation for circular double-layer source and receiver is analytically derived in Section 3. In Section 4, computer simulations compare the proposal with the conventional  
Figure 1: Comparison of two-dimensional sound field syntheses with a circular array: (**a**) typical interior sound field synthesis in a free-field; (**b**) typical interior sound field synthesis in a reverberant environment; and (**c**) undesired propagation-free sound field synthesis in a reverberant environment. LS and GSCD methods in both the free-field and reverberant conditions. Section 5 finally concludes this paper. **2. Pressure Matching-Based Sound Field Recording and Synthesis without Exterior Propagation with Double-Layer Arrays**  
The conventional LS- and GSVD-based sound field recording and synthesis methods are briefly introduced here for comparison with the proposed approach.
# Adjacent Vehicle Number-Triggered Adaptive Transmission for V2V Communications
## 4 Numerical Results
This is because 64 QAM fully takes advantage of the good channel when the K-factor is fixed to 10, which is actually an ideal condition in the V2V communication. In scenario I with the ideal situation, it is shown that the proposed method cannot show its advantage over conventional method 3 but achieves 90% throughput performance, since the employment of 16 QAM causes the loss of more data transmission. Scenario II with various K-factors is more practical in comparison to scenario I with a constant K-factor. In scenario II, we set different Rician K-factor values depending on the number of connectable vehicles, in order to make the simulation closer to the realistic V2V environment. In Figure 6, the BER and FER performances are shown for conventional methods 1, 2, and 3, as well as the proposed method. In Figure 6a, the BER of the proposed method is lower than that of conventional method 2 and 3 and only higher than that of conventional method 1, which guarantees the low error rate at the expense of a lower data transmission rate. In Figure 6b, the proposed method presents a higher FER performance than conventional method 1 and 2 but a lower one than that of conventional method 3. Regarding the throughput performance in the Figure 7, the proposed method shows lower throughput than conventional methods 1 and 2 when EbN0 is smaller than 6 dB, but it exceeds that of conventional method 1 when EbN0 reaches 6 dB and exceeds that of conventional method 2 when EbN0 reaches 8 dB. The reason for this is that the error rate drops as the EbN0 is sufficiently large and, thus, higher order modulations such as 16 QAM and 64 QAM start to show their advantages in terms of the throughput. Also, the throughput of the proposed method is always higher than that of conventional method 3, because the relatively lower order modulation of 16 QAM is used against the harsh channel situation when the K-factor is a negative value. Meanwhile, the modulation is fixed to 64 QAM in conventional method 3. When EbN0 is larger than 8 dB, the proposed method shows the highest throughput, which means that the proposed scheme with the adaptive transmission may give the benefits for frequently changing topology in the VANET and, thus, improve the continuity and the reliability of the V2V communication.
## 2 Background
### Adversarial Example Generation
#### 2.1.1 Fast Gradient Sign Method (FGSM)  
The fast gradient sign method (FGSM) is an elementary yet effective adversarial attack introduced by [PERSON] et al. [2]. Its computational efficiency stems from its reliance on a first-order approximation of the loss function. For a neural network model characterized by its parameters \(\theta\), let \(L(\theta,x,y)\) denote the loss function, where \(x\) is the input data and \(y\) is the true label. The adversarial example \(x^{\prime}\) can be generated as follows:  
\[x^{\prime}=x+\epsilon\cdot\text{sign}(\
abla_{x}L(\theta,x,y)) \tag{1}\]  
The term \(\
abla_{x}L(\theta,x,y)\) is the gradient of the loss function \(L\) with respect to the input \(x\). It is a vector in \(\mathbb{R}^{d}\), where \(d\) is the dimensionality of the input. Mathematically, this gradient can be expressed as  
\[\
abla_{x}L(\theta,x,y)=\left[\frac{\partial L}{\partial x_{1}},\frac{ \partial L}{\partial x_{2}},\ldots,\frac{\partial L}{\partial x_{d}}\right] \tag{2}\]  
Here, \(\epsilon\) is a hyperparameter that controls the magnitude of the perturbation introduced. In essence, FGSM perturbs each element \(x_{i}\) of the input \(x\) in the direction that maximizes the increase in loss \(L\).
## 4 Experiment and Discussion
The horizontal axis is the measurement distance. As can be seen, the measurement accuracy of the straightness errors and the angular errors of the designed LD-based system were evaluated to be within \(\pm\)1.1 \(\upmu\)m and \(\pm\)2.0 arcsec, respectively, in the measurement distance up to 1.5 m. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**With Active Drift Compensation**} & \multicolumn{3}{c}{**Without Active Drift Compensation**} \\ \cline{2-7}  & **50 mm** & **500 mm** & **1500 mm** & **50 mm** & **500 mm** & **1500 mm** \\ \hline \(\delta x\) [\(\upmu\)m] & 0.11 & 0.71 & 0.88 & 0.44 & 1.82 & 2.91 \\ \(\delta y\) [\(\upmu\)m] & 0.13 & 0.76 & 1.02 & 0.95 & 2.51 & 4.67 \\ \(\theta x\) [arcsec] & 0.08 & 0.06 & 0.11 & 0.55 & 1.16 & 1.35 \\ \(\theta y\) [arcsec] & 0.09 & 0.11 & 0.17 & 0.90 & 0.93 & 1.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Stability tests of the signals with and without the active drift compensation at the positions of 50 mm, 500 mm and 1500 mm. Figure 13: Stability tests of (**a**) the straightness signals and (**b**) the angular signals measured by the detector and the comparison unit when \(s_{2}=s_{3}=50\) mm.
## 4 Experimental Results
### Evaluation Metrics

Table 1: \(Cont.\)include different aspects of unlearning efficiency and the tradeoff between resources and privacy. The metrics are elaborated on below:  
* Model accuracy (A) is evaluated before and after the unlearning process to assess how well the model retains its predictive power after removing specific data points [24]. * Unlearning efficiency (\(U_{e}\)) is measured as the time required to remove the influence of a data point from the model [25]. \[U_{e}=t_{end}-t_{start}\]  
where \(t_{start}\) and \(t_{end}\) represent the start and end times of the unlearning process. * Residual influence (\(R_{inf}\)), in terms of the remaining influence of the unlearned data on the model, this is determined by comparing the model parameters before and after unlearning [25; 26]. \[R_{inf}=\frac{||\ \theta_{before}-\theta_{after}\ ||}{||\ \theta_{before}\ ||}\]  
where \(\theta_{before}\) and \(\theta_{after}\) are the model parameters before and after unlearning, respectively. * Communication overhead (\(C_{overhead}\)) represents how much data is exchanged between the nodes and the server of the central during the process of unlearning [4]. \[C_{overhead}=\sum_{n=1}^{N}S_{n}\]  
where \(S_{n}\) represents the size of the data sent by node \(n\), and \(N\) is the total number of nodes. * Convergence time (\(T_{convergence}\)) is measured as the number of rounds required by the global model to restore its stability following unlearning [27]. \[T_{convergence}=min\Big{\{}t:\parallel\theta_{G}^{(t)}-\theta_{G}^{(t-1)}\ ||< \epsilon\Big{\}}\]  
where \(\theta_{G}^{(t)}\) is the global model at round \(t\), and \(\epsilon\) is a small threshold indicating convergence. * Unlearning accuracy (UA) measures the precision with which the unlearning procedure has erased the influence of a particular datum. It is measured by assessing how the model output differs with and without the particular datum. Unlearning accuracy determines whether the unlearning mechanism has indeed reversed the model influence of the requested data points or otherwise negatively affected the remaining model [28]. * Computational cost (CC) concerns the computational requirement to unlearn; it comprises computation time and memory utilization [12]. \[CC=t_{cpu}+m_{usage}\]  
where \(t_{cpu}\) represents the CPU time consumed, and \(m_{usage}\) represents memory usage during the unlearning process. A lower computational cost indicates that the algorithm is efficient in terms of resource consumption.
# Image Deblurring Based on an Improved CNN-Transformer Combination Network
###### Abstract  
Recently, using a CNN has been a common practice to restore blurry images due to its strong ability to learn feature information from large-scale datasets. However, CNNs essentially belong to local operations and have the defect of a limited receptive field, which reduces the naturalness of deblurring results. Moreover, CNN-based deblurring methods usually adopt many downsample operations, which hinder detail recovery. Fortunately, transformers focus on modeling the global features, so they can cooperate with CNNs to enlarge the receptive field and compensate for the details lost as well. In this paper, we propose an improved CNN-transformer combination network for deblurring, which adopts a coarse-to-fine architecture as the backbone. To extract the local features and global features simultaneously, the common methods are two blocks connected in parallel or cascaded. Different from these, we design a local-global feature combination block (LGFCB) with a new connecting structure to better use the extracted features. The LGFCB comprises multi-scale residual blocks (MRB) and a transformer block. In addition, we adopt a channel attention fusion block (CAFB) in the encoder path to integrate features. To improve the ability of feature representation, in the decoder path, we introduce a supervised attention block (SAB) operated on restoration images to refine features. Numerous experiments on GoPro and RealBlur datasets indicated that our model achieves remarkable accuracy and processing speed.  
image deblurring; coarse-to-fine strategy; transformer; CNN-transformer combination Article  
Image Deblurring Based on an Improved CNN-Transformer Combination Network  
202313311. [[https://doi.org/10.3390/app13010311](https://doi.org/10.3390/app13010311)]([https://doi.org/10.3390/app13010311](https://doi.org/10.3390/app13010311))
# Parameter Optimization Method for Metal Surface pBRDF Model Based on Improved Strawberry Algorithm
###### Abstract  
To study the polarization reflection characteristics of metal surfaces, a parameter optimization method for the polarization bidirectional reflection distribution function (pBRDF) model of metal surfaces based on the improved strawberry algorithm has been proposed. Firstly, the light scattering characteristics of metal surfaces were analyzed and a multi-parameter pBRDF model was constructed. Then, the working mechanism of the strawberry optimization algorithm was investigated and improved by introducing the chaotic mapping and Levy flight strategy to overcome the shortcomings, such as low convergence rate and easily falling into local optimum. Finally, the method proposed in this paper was validated by simulating open-source data from references and the obtained ones with a self-built experimental platform. The results show that the proposed method outperforms those by nonlinear least squares, particle swarm optimization and the original strawberry algorithm in fitting the detected degree of polarization (DOP) data, indicating the modeling accuracy was significantly improved and better suited to characterize the polarized reflection properties of metal surfaces.  
metal surface; pBRDF; strawberry optimization algorithm; parameters retrieval Article
## 2 STRC Shear Wall Failure Modes and Small Sample Database Establishment
### Data Collection  
Currently, there is limited research on STRC shear walls, and the experimental period is long and costly. It is difficult to establish a sufficiently large database by collecting data from existing studies, resulting in a small sample database. To predict the failure modes of STRC shear walls, we established a database containing 133 STRC shear walls based on existing relevant studies [29; 30; 31; 32; 33; 34]. To ensure the accuracy of the prediction results, we followed strict rules during data collection. Specifically, the STRC shear walls included in this database meet the following conditions:  
1. The steel tubes are arranged in the boundary unit or the middle of the wall.
2. The STRC shear walls are single-layered with a symmetrical rectangular cross-section and no openings.
3. The vertical reinforcement and steel tubes of all STRC shear walls are continuous, without lap splices.  
Figure 1: Schematic diagram of failure mode of STRC shear walls: (**a**) flexural; (**b**) flexure–shear; (**c**) shear; (**d**) sliding shear.  
(4) The loading method for testing is cyclic loading.  
(5) Walls without special structures, such as hidden steel frames, hidden steel trusses, or shear keys, are excluded from the study.  
The collected data cover three types of wall failure modes: shear failure, bending failure, and flexural-shear failure, ensuring that the established model has universal applicability. However, it is evident that the sample size of this database is insufficient, and data augmentation techniques need to be employed to further expand the database.
## 5 Simulation Results
The mean number of serially connected multipolling sequences for each combined case is presented in Figure 8.  
As shown in Figure 8, the proposed node insertion algorithm performs excellently in deriving, on average, fewer than two serially connected multipolling sequences that cover the Wi-Fi nodes in all cases. It can be seen that the number of serially connected multipolling sequences derived by the proposed node insertion algorithm tends to decrease as the number of Wi-Fi sensors increases in the sensor networks and converges to only one as the number of nodes increases in the sensor networks. This significant reduction in the multipolling frame transmission overhead results in each sensor being granted more wireless bandwidth to transmit uplink data, extending the lifetime of Wi-Fi sensor networks. Figure 7: Sample Wi-Fi sensor network and formation of serially connected multipolling sequence with \(l=30\) and \(R=r\). In Figure 9, the mean number of previously added nodes in the sequences is presented, from which the connectivities are considered for the proposed node-insertion algorithm to select the next nodes. As the number of previously added nodes from which the connectivities are considered to select the next nodes increases, the proposed node-insertion algorithm requires more time to derive serially connected multipolling sequences that cover the nodes. The proposed node insertion algorithm requires more time to derive serially connected multipolling sequences as the number \(l\) of nodes and the transmission range \(R\) increase. For the worst case of \(l=1000\) and \(R=1.5r\), the proposed node insertion algorithm took less than 0.8 s to derive the serially connected multipolling sequences that cover the nodes for each case of 50 random Wi-Fi sensor networks using a computer of 1.6 GHz CPU and 8 GB RAM. Figure 8: Results of mean numbers of serially connected multipolling sequences.
# UY-NET: A Two-Stage Network to Improve the Result of Detection in Colonoscopy Images
## 3 Method
### Intersection over Union (IoU) and Average Precision (AP)  
The two types of metrics commonly used to assess object detection and localization are IoU and AP. In the present study, IoU was calculated by dividing the intersection of the ground truth and predicted regions by the union of the ground truth and predicted regions. In other words, it measured the overlap ratio between the two regions. The Equation (1) is shown below: (GT stands for the ground truth region, and PD stands for the predicted region).  
\[\text{IoU}=\frac{\text{GT}\cap\text{ PD}}{\text{GT}\cup\text{ PD}} \tag{1}\]  
Figure 4 depicts the intersection and union between the ground truth and predicted regions. The red area represents the ground truth, while the yellow area represents the predicted region.  
Figure 3: Framework of UY-Net.  
AP was calculated as the area under the precision-recall curve. The predicted targets were evaluated based on IoU calculations. If the value of IoU was greater than a predefined threshold, then the target would be considered a true positive (TP). If the value of IoU was below the threshold, the target would be considered a false positive (FP). Both TP and FP represented the states in the confusion matrix (see Figure 5). In the present study, the threshold for AP was set within a specified range. For example, the IoU threshold was set from 0.25 to 0.75 with an interval of 0.05, denoted as (AP@[0.25:0.05:0.75]). If the IoU threshold was 0.50, it would be referred to as AP50.  
Precision was the proportion of predicted targets that were true targets (also known as the Positive Predictive Value: PPV). Recall was the proportion of targets that were correctly predicted as targets (also known as Sensitivity). Precision (2) and Recall (3) equations are shown below.  
\[\mathrm{Precision}\;=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FP}} \tag{2}\]  
\[\mathrm{Recall}\;=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FN}} \tag{3}\]  
The calculation of AP involved selecting the maximum precision corresponding to each change in Recall. Then, these recalls were considered as calculation points. The Equation of AP (4) is shown below:  
\[\mathrm{AP}=\sum_{k=0}^{k=n-1}[\mathrm{Recalls(k)-Recalls(k+1)}]\times\mathrm{ Precisions(k)} \tag{4}\]  
where Recalls (n) = 0, \(\mathrm{Precisions(n)}\) = 1, n = Number of Thresholds
# Experimental Assessment of the Spatial and Temporal Distribution of Non-Contact Tonometer Airflows
## 4 Discussion
### Comparison between the Airflow Pressures of the CorvisST and ORA  
It is evident from the present study that there are significant differences in the temporal and spatial distribution of the CorvisST and ORA airflow pressure. The most distinct differences were the maximal pressure at an 11 mm distance, the pressure change during the increasing phase and the area affected. In total, the CorvisST airflow is stronger, shorter and of a higher magnitude than that of the ORA.  
There have been numerous studies comparing the CorvisST and ORA [53; 54; 55; 56]; it could be shown that to some extent CorvisST dynamic corneal response characteristics correspond to the ORA measures. However, comparisons between the two devices are limited as the pressure applicated to the cornea differs significantly. Furthermore, the CorvisST applies the same pressure to each eye, whereas the ORA applies a controlled increase in airflow pressure during the inward deformation of the cornea, followed by a defined decrease in pressure through the outward phase of the deformation [4; 5].