esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Relative Importance of Different GPP Control Pathways  
CUP \(\times\) GPPmax was identified as being the control variable for about 61.8% of the entire Zhejiang province; the area percentages of the control variables for CUP and GPPmax were 19.7% and 18.5%, respectively (Figure 3a). The control variable for CUP was mainly distributed in some parts of western and central Zhejiang, while the control variable for GPPmax was mainly distributed in central and eastern Zhejiang. The relative importance of the three control variables ranged from 0 to 82%, and most of them were distributed in the range of 5-35% (Figure 3b). The area percentages of the control variables for CUP  
\begin{table}
\begin{tabular}{l c c c} \hline
**Fragmentation Categories** & **Mean Annual GPP (g C m\({}^{-2}\))** & **Mean Normalized Annual GPP (g C m\({}^{-2}\))** \\
**2000** & **2018** & **2000** & **2018** \\ \hline PAT & \(1040.4\pm 319.1\) a & \(1101.6\pm 449.4\) a & \(11,611.3\pm 17,514.1\) d & \(11,337.4\pm 16,718.4\) d \\ TRA & \(1268.7\pm 256.4\) b & \(1516.8\pm 351.1\) b & \(2979.0\pm 4071.6\) c & \(3505.2\pm 4629.4\) c \\ PER & \(1366.5\pm 193.1\) bc & \(1643.4\pm 231.5\) bc & \(1870.6\pm 1726.2\) b & \(2246.4\pm 1487.0\) b \\ EDG & \(1349.8\pm 202.4\) bc & \(1635.2\pm 246.3\) bc & \(2199.1\pm 2681.0\) b & \(2656.5\pm 3158.8\) b \\ INT & \(1412.4\pm 197.6\) c & \(1693.9\pm 213.9\) c & \(1657.0\pm 1222.9\) a & \(1972.6\pm 1478.9\) a \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of mean annual GPP and mean normalized annual GPP among different forest fragmentation categories for 2000 and 2018. The mean normalized annual GPP was calculated as the ratio of the mean annual GPP to the proportion of forest pixels in a forest landscape. The characters indicate the results of multiple comparisons between any two categories. PAT: patch forest, TRA: transitional forest, PER: perforated forest, EDG: edge forest, and INT: interior forest.  
\(\times\) GPP\({}_{\max}\) were maintained at approximately 60% among various fragmentation levels (Figure 3c). However, the control variable for CUP (13.2-27.8%) and GPP\({}_{\max}\) (11.1-25.0%) still accounted for a certain proportion of area among various fragmentation levels. There was no significant (\(p\) > 0.05) difference in the relative importance between any two fragmentation levels, and the mean relative importance of various fragmentation levels ranged between 25% and 28% (Figure 3d).  
Figure 3: _Cont._  
Figure 2: (**a**) Annual GPP dynamics of different fragmentation levels and (**b**) linear trend distribution of annual GPP of forest landscapes in Zhejiang between 2000 and 2018, and the comparison of the annual GPP trend values among different fragmentation levels. The red asterisk indicates the annual GPP trend of PAT is significantly (\(p\) < 0.05) than that of other fragmentation categories. PAT: patch forest, TRA: transitional forest, PER: perforated forest, EDG: edge forest, and INT: interior forest.
# Improvement and Performance Evaluation When Implementing a Distance-Based Registration
## 2 System Environments and Distance-Based Registration  
### System Environments  
A single location area (LA) in a mobile network typically consists of multiple cells. In this study, we assumed that mobile networks were composed of cells of the same shape. In order to ideally express the range of radio waves from the base station, the shape of the cell must be close to a circle. A hexagonal cell was adopted considering the overlap of neighboring circular cells. Figure 1 illustrates a cell with a hexagonal shape.
# Context-Aware Wireless Sensor Networks for Smart Building Energy Management System
## 4 Proposed EMS for SEEB
### Node Power Consumption Measurement  
The power consumption includes power consumed during computation (i.e., data acquisition) and communication. We measured both of them separately to assess the impact of each. In the WSN data acquisition, we used different sensor node configurations with specific sensor modules, as presented in Table 3.  
For each configuration in Table 3, we measured the energy consumed during the sampling process without including the consumption of network setup and data transmission.  
In order to measure and optimize the energy consumption in a better way, we considered two different processes in the sensor node: The sampling process, where the node gathers data, and the transmission process, where the node sends the data.  
Figure 5A presents the energy consumed of the different configurations during the sampling process. These configurations offer different combinations of sensor modules (Table 3), which does not include the radio frequency (RF) module. On the other hand, Figure 5B presents the XBee RF power consumption during sleep mode, networking setup, standby mode, and transmission mode. In a typical scenario where node samples and sends data, the total energy consumed should be the sum of the energy consumed of a given configuration (Figure 5A) and the energy consumed in the transmission mode (Figure 5B). At this stage, we measured the WSN node energy consumption using periodic sampling without scheduling the WSN sensor node tasks using the EACRA algorithm that we will present later in this paper.  
Figure 4: WSN architecture deployment at the university campus building.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \begin{tabular}{c} **Arduino** \\ **Nano** \\ \end{tabular} & \begin{tabular}{c} **Humidity** \\ **Temperature Module** \\ \end{tabular} & \begin{tabular}{c} **Sound** \\ **Module** \\ \end{tabular} & \begin{tabular}{c} **Ambient Light** \\ **Module** \\ \end{tabular} & \
\begin{tabular}{c} **PIR** \\ **Module** \\ \end{tabular} &
\begin{tabular}{c} **Door/Window** \\ **Module** \\ \end{tabular} \\ \hline Configuration 1 & ✓ & ✓ & & & & \\ \hline Configuration 2 & ✓ & ✓ & ✓ & & \\ \hline Configuration 3 & ✓ & ✓ & ✓ & ✓ & \\ \hline Configuration 4 & ✓ & ✓ & ✓ & ✓ & ✓ & \\ \hline Configuration 5 & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 3: Different WSN node configuration used in the experiments.
# Feature Point Matching Method Based on Consistent Edge Structures for Infrared and Visible Images
## 3 Experimental Results
### Running Time Performance  
Table 8 presents the Average Running Time (ART) of the multi-modality image matching methods listed in Table 4. The unit of ART is second. All experiments are performed on a desktop with 64 Byte Windows 7 OS. The hardware of the desktop comprises 3.30 GHz Intel i5 4 core CPU and 8G memory. The software of the desktop comprises vs2010 and opencv2.4. DDCE is implemented by C++ and all methods used for comparison are provided by the authors.  
As shown in Table 8, DDCE achieves the second best performance on running time. MMSURF is inherited from SURF and achieves the best performance on running time. The methods including PCEHD, LGHD, and RIFT are time-consuming because phase congruency need to be computed.
# Adaptive Channel-Enhanced Graph Convolution for Skeleton-Based Human Action Recognition
## 4 Experimental Evaluation
### Experiment Settings  
Every experiment was carried out using a deep learning DCU that has the PyTorch framework loaded. Using SGD with Nesterov momentum (0.9), a weight decay of 0.0004, and a warm-up method for the first five epochs to increase training process stability, the model was trained for 65 epochs. The initial learning rate was set at 0.1 and decreased exponentially with a decay rate of 0.1 at epochs 35 and 55. The batch size was uniformly fixed to 64 across all datasets, and the data pretreatment method outlined in reference [32] was employed. For the Northwestern-UCLA dataset, we set the batch size to 64 and applied the data preprocessing method outlined in [25]. The experimental results for the NTU RGB+D 60 dataset (X-sub) indicate that as the learning rate was gradually decreased (0.1, 0.01, 0.001, 0.0001), the model's Top-1 accuracy was 92%, 89.55%, 75.97%, and 40.92%, respectively, with an increase in training time. This suggests that the model did not achieve optimal performance, with excessively low learning rates. Conversely, when the learning rate was gradually increased (0.2 and 0.3), the model's Top-1 accuracy reached 90.86% and 90.4%, respectively. This indicates that while higher learning rates accelerated convergence, they resulted in a slight decline in accuracy. In summary, a base learning rate of 0.1 demonstrated effective performance in training, balancing the model's convergence speed and stability. The method proposed in this study, and the benchmark model corresponding to this method, use the above settings in ablation studies and comparative experiments.
## 4 Security in Time-Critical CPSs
### Security as a Prerequisite for Timing Predictability  
Time predictability is based on assumptions regarding the considered system and surrounding it environment. One of the common assumptions in embedded systems is systems' input integrity, i.e., that the inputs are not forged, deleted, injected or modified by an adversary.  
However, the possibility of an adversary to physically access the system is not captured by the classical assumptions of time predictability in ESs. Thus, time predictability has an implicit dependency on the input data security. Given the shift from embedded systems to CPSs, a concern regarding the assumption covering security rises even higher, as the system becomes more open and complex. For CPSs in industrial context, increased connectivity of these systems makes securing the data integrity more challenging. It also brings new attack surfaces and possible vulnerabilities unless security is not addressed properly at the design time. For instance, once the time predictability of the autonomous haulers in the considered example is analysed, it can be guaranteed under a set of assumptions, which includes the assumption regarding the sensor data integrity being intact. An autonomous hauler receives command information regarding its movement from the control centre. The assumption is also that the hauler has local intelligence that allows it to sustain a temporary loss or disruption of control information, e.g., caused by a failure in the communication channel [39]. To make its own decisions about its current actions, a hauler requires an updated map of the quarry and the approximated location of the other vehicles. If command information, e.g., the speed, acceleration and direction, can be forged by an adversary, the time predictability of the system verified at the design time will not hold any more. One more possible scenario can be created by a failure occurring in the communication channel due to communication quality degradation or jamming, the time predictability of the system can be jeopardised, e.g., by forged sensor information or map. Hence, to support time predictability in time- and safety-critical industrial CPSs, the systems' security must also be supported.
# Monitoring of Preload Variation of Linear Guide Positioning Stage Using Artificial Neural Network
## 3 Dynamic Characteristics of Positioning Stage
### Stage Vibration against Linear Guide Preload and Feed Rate  
As demonstrated in the previous section, the vibration behavior of the stage in motion can be appropriately related to the structural rigidity, which is determined by the preload state of the linear guides. This also implies that the preload state of the linear guide can be examined by observing the variation in the vibration of the stage. Based on the data collected from the motion tests of stage, the distribution of vibration RMS values and the raw data points are depicted by a response surface methodology, as shown in Figure 13. The response surface plots clearly show the dependence of the RMS vibration levels on the stage rigidity and feed rate.  
Figure 12: Schematic of dynamic model and angular motion of stage: (**a**) dynamic model of stage [41] and; (**b**) derivation of angular acceleration [42].  
Figure 13: Response surfaces of vibration RMS levels against the stage rigidity and feed rate.  
In addition, the vibration level continued to increase as the feed rate increased. This indicates that the vibration caused by increasing the feed rate was more dominant in affecting the RMS vibration level. The RMS vibration levels of the highest preloaded stage (case I) decreased significantly as compared to the lowest preload stage (case III). However, for stages II and IV with different combinations of sliding carriages at low and high preload amounts, the rigidity of the stage was lower than that of the highest preload stage (case I), and higher than the lowest preload stage (case III). The results of this vibration analysis indicate that the RMS vibration level of the positioning stage generated during movement is related to the preloaded status of the linear guides.
## 4 Our Findings
### Publication Frequencies by Year  
As shown in Figure 3, our results revealed a significant trend in the included studies' yearly publication frequency. According to the data, just 22 pieces of research were published in 2020, the year with the fewest publications, but, in the years that followed, there was a noticeable rise, with 39 publications in 2021 and 37 in 2022. After reaching  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Keyword** & **Count** \\ \hline Technology/Technologies & 4378 \\ Security & 11,518 \\ Systems & 3697 \\ Blockchain & 16,400 \\ Distributed & 2112 \\ Privacy & 4454 \\ Information & 5438 \\ Control & 2881 \\ Encryption & 1633 \\ Detection & 7043 \\ Prevention & 381 \\ Attack & 5450 \\ Vulnerability/Vulnerabilities & 8422 \\ Protocol & 1901 \\ Transaction & 5249 \\ Network & 6848 \\ Bitcoin & 1971 \\ Ethereum & 4672 \\ Smart & 13,760 \\ Internet & 2375 \\ Cryptocurrency/Cryptocurrencies & 1221 \\ Strategies & 554 \\ Strategy & 590 \\ Mitigation & 385 \\ Cryptography & 642 \\ IoT & 633 \\ Tether & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Keyword frequencies of the included studies.  
58 publications in 2023, the rising trend continued, culminating in a noteworthy peak of 118 publications in 2024. This dramatic increase in publications, especially in 2024, suggests that interest in the topic is expanding and that research is moving more quickly. The rise might be explained by the growing importance of blockchain technology, its uses, and the security issues that surround it, which have drawn more attention from both researchers and industry professionals. This pattern emphasizes how dynamic and quickly changing the area is, which reflects how crucial it is to solve new problems and advance our understanding of blockchain and related fields.
## 5 Discussion
### Assessing the Current Remote Sensing of Pasture Monitoring
Generally, the combination of instruments significantly provides a platform to constrain remote sensing trade-offs in an integrated way to fix specific errors or limitations associated with sensors and the target environment. The saturation of biomass (in sparse vegetation and/or peak growing season) associated with vegetation indices [45; 223], soil background and topography influence on SAR sensitivity [45], homogeneous canopy associated with LAI [32; 135] or with 3-D point cloud photogrammetric mapping [116; 193], and spatial, temporal, and radiometric resolution drawback can be addressed using appropriate modelling involving hyper-temporal, multispectral, visible and SAR to improve the accuracy of the model. The SAR backscatter is not sensitive to soil background when the vegetation canopy is low. At the same time, the visible and NIR bands of optical instruments enable the absorption of more radiation than soil, resulting in a higher reflectance for denser canopy areas and lower reflectance values for bare soil. Proximal hyperspectral data were resampled to Sentinel-2 surface reflectance and combined with RTM PROSAIL to estimate LAI, thereby confounding the homogeneous assumption related to RTM [32]. Furthermore, using 3-D point cloud photogrammetric to estimate pasture height and biomass from vegetation volume can be confounded with trees, shrubs, and other land use types, making this approach prone to error. Hence, multispectral bands (i.e., NIR) are included to map land cover or mask the non-pasture community [43; 82; 83; 181].
# Analysis of Uncertainty in Conveyor Belt Condition Assessment Using Time-Based Indicators
## 1 Introduction
Most studies indicate that belt wear evolves non-linearly, with increasing rates of defect formation and area growth, particularly in later life stages [3; 6]. This implies that older belts degrade faster, requiring more frequent inspections and timely repairs or replacements of segments. Predictive models based on canonical correlation analysis [14] and non-linear regression [13] enable forecasting of the critical point, when degradation reaches levels necessitating belt shutdown according to user-defined replacement strategies [21; 22]. The aim of this study is to analyze the influence of the transported material type--overburden, lignite, or a mixture--on the rate of core-damage accumulation in Type-St belts. Diagnostic data were obtained from the DiagBelt+ system, which facilitates the assessment of steel-cord condition without dismantling the belt [23]. Over 100 belt-loop scans were used, comprising segments of varying length, age, and operational history. For each segment, the transported material type, belt age, Resurs (percentage of expected operating time), and failure density and area were determined. This approach enables identification of differences in degradation rates depending on material type and supports formulation of technical recommendations for belt selection, discharge-point design, maintenance scheduling, and replacement strategies in open-pit mines [24; 25; 26; 27; 28].
###### Abstract  
The aim of this study was to evaluate the changes in tear osmolarity and matrix metalloproteinase-9 (MMP-9) and their relationships with ocular discomfort in patients after femtosecond laser-assisted cataract surgery (FLACS). This retrospective observational case series reviewed medical records of 51 eyes of 51 patients who underwent FLACS. Tear osmolarity and MMP-9 were evaluated preoperatively and at 1 day, 1 week, and 1 month postoperatively. Tear osmolarity \(\geq\) 310 mOsm/L and MMP-9 \(\geq\) trace positive were defined as abnormal results. The ocular surface disease index (OSDI) was evaluated preoperatively and postoperatively at 1 month. Tear osmolarity level and the number of eyes with abnormal tear osmolarity results did not change significantly up to 1 month after surgery. MMP-9 level and the number of eyes with abnormal MMP-9 results increased at 1-day postoperatively and then decreased at 1-month postoperatively (all \(p<\) 0.001). Among the OSDI subscales, ocular discomfort was aggravated (\(p<\) 0.001) but the visual function score improved at 1 month postoperatively (\(p<\) 0.001). Ocular discomfort aggravated after FLACS without an increase in either tear osmolarity or MMP-9. Ocular discomfort which patients commonly experience after FLACS may be due to etiology other than development or aggravation of dry eye disease.  
f +
Footnote †: : 2021: 11, 11878. [[https://doi.org/10.3390/app112411878](https://doi.org/10.3390/app112411878)]([https://doi.org/10.3390/app112411878](https://doi.org/10.3390/app112411878))
## 2 Research Status of Key Technology for Environmental Perception of Intelligent Excavators
### Research Status of Multi-Sensor Information Fusion Technology
[68] made full use of the complementary advantages of LiDAR data and optical imaging to extract different building features from the two data sources and fuse them to form the final complete building boundary. This method can automatically and accurately extract building boundaries of complex shapes and has strong robustness in complex environments. In terms of the outdoor environmental perception of robots, [PERSON] et al. [69] proposed a positioning algorithm based on the fusion of camera and LiDAR. By combining the depth information of LiDAR with the color texture information of the image, a feature point matching relationship is established. This method solves the localization problem of outdoor robots under the influence of complex environmental factors such as light and ground fluctuation. [PERSON] et al. [70] fused visual information, LiDAR information, and odometer information to solve the problem of timely positioning and accurate composition of mobile robots in unknown environments. Experiments proved that multi-sensor information fusion can make the composition more accurate and robust. In terms of unmanned road environmental perception, [PERSON] et al. [71] fused LiDAR depth information by mapping it to two-dimensional camera images and realized real-time pedestrian detection through cluster analysis and support vector machine classification. [PERSON] et al. [72] proposed a real-time vehicle detection method based on the fusion of radar and vision sensors. Hough transform and Chebyshev's theorem were used to filter the non-target points in the LiDAR data channel, significant vehicle images were generated in the image data channel, and the vehicle position was located by segmentation method. Table 4 summarizes the methods proposed in the literature [15; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76]. At present, most of the multi-sensor information fusion technologies use LiDAR and cameras for fusion, and the mode is relatively simple. More model data need to be added for the necessary supplement to express the measured target more comprehensively. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Literature** & **Keywords** & **Major Contribution** & **Time** \\ \hline \multirow{4}{*}{[15]} & Monocular camera, binocular stereo camera, & Using the monocular camera, binocular stereo camera, and LiDAR as environmental sensing equipment, an autonomous intelligent excavator & \\  & binocular stereo camera, & based on a comprehensive sensing system was developed. Through multi-sensor information fusion, obstacles and targets in the mining scene & \\  & and LiDAR & were detected, and real-time three-dimensional modeling of mining results & \\ \cline{1-1} \cline{2-4}  & & was achieved. & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Research status of multi-sensor fusion technology.