esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Pixel Scale Changes in NDVI and NPP  
The NDVI and NPP trends were also analyzed based on the pixel data to discover their changing trends more comprehensively. Although the trends in NDVI and NPP exhibited no significant trend from 2000-2016, they indicated a high degree of spatial heterogeneity on a per-pixel analysis, as shown in Figure 6. In general, NDVI and NPP showed insignificant changes in most regions, accounting for 75.15% and 75.40%, respectively. The areas for NDVI and NPP with increased change (extremely significant increase + significant increase) accounted for 23.46% and 24.32% of the area, respectively. Spatially, the regions of increased change in NDVI were mainly distributed in the prairie areas in the midlands and north of the Yangtze River headwater region, and scattered in the midlands and north of the Yellow River headwater region. The regions of increased change in NPP were mainly distributed in the midlands and north of the Yangtze River headwater region, and the midlands and east of the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Vegetation Type Slope**} & \multicolumn{2}{c}{**NDVI**} & \multicolumn{2}{c}{**NPP**} \\ \cline{2-4}  & **2000–2014** & **2000–2016** & **2000–2014** & **2000–2016** \\ \hline Configerous forest & 0.0025 \({}^{\ast}\) & 0.0011 & 2.35 \({}^{\ast}\) & 0.72 \\ Strub & 0.0028 \({}^{\ast\ast}\) & 0.0012 & 2.9 \({}^{\ast}\) & 1.60 \\ Prairie & 0.0019 \({}^{\ast\ast\ast}\) & 0.0011 \({}^{\ast\ast}\) & 0.65 \({}^{\ast\ast}\) & 0.4 \({}^{\ast}\) \\ Meadow & 0.0026 \({}^{\ast\ast}\) & 0.0009 & 2.03 \({}^{\ast\ast}\) & 1.05 \\ Alpine vegetation & 0.0018 \({}^{\ast\ast}\) & 0.0009 & 0.83 \({}^{\ast}\) & 0.55 \\ \hline \multicolumn{4}{l}{\({}^{\ast}\) \({}^{\ast}\), \({}^{\ast\ast}\), and \({}^{\ast\ast\ast}\) represent 0.05 \(<\)\(p\)\(\leq\) 0.1, 0.01 \(<\)\(p\)\(\leq\) 0.05, and \(p\)\(\leq\) 0.01, respectively.} \\ \end{tabular}
\end{table}
Table 1: Slopes of NDVI and NPP changes in different vegetation types.  
Yellow River headwater region. In addition, only a small percentage showed a decreasing trend of approximately 1.39% and 0.29% of the Sanjiangyuan National Park area for NDVI and NPP.  
Figure 4: Temporal variations of the NDVI for the Sanjiangyuan National Park (**a**), the Yellow River headwater region (**b**), the Yangtze River headwater region (**c**), and the Lancang River headwater region (**d**).  
Figure 5: Temporal variations of the NPP for the Sanjiangyuan National Park (**a**), the Yellow River headwater region (**b**), the Yangtze River headwater region (**c**), and the Lancang River headwater region (**d**).  
The Yangtze River headwater region had the largest proportion of extremely significant increase in NDVI (13.58%), and the Yellow River headwater region had the largest proportion of extremely significant increase in NPP (23.24%), whereas the Lancang River headwater region showed no significant change for either NDVI or NPP (Tables 2 and 3).
## 3 Proposed Handover Scheme
### Position Tracking  
In Step 1 of the workflow described in Section 3.1, the position tracking is executed when the RSSI from the current AP decreases significantly. The position of the BSN user is obtained by fusion of inertial sensor-based kinematic tracking scheme and WLAN RSSI-based tracking scheme. A Kalman filter [22] is used as the fusion tool to compensate the differences of the two tracking scheme and improve the positioning accuracy.  
The state vector of the Kalman filter is expressed as \(X_{k}=[s,v,a]^{T}\), where \(s,v,a\) are the target position, velocity, and acceleration respectively. Each of them is a two-dimensional vector along the x-axis and y-axis, _i.e_., \(s=[s_{x},s_{y}]^{T}\), \(v=[v_{x},v_{y}]^{T}\), \(a=[a_{x},a_{y}]^{T}\). The system state transition function of the filter can be expressed as  
\[X_{k}=F_{k}X_{k-1}+w_{k} \tag{1}\]  
where \(F_{k}\) is the state transition matrix and \(w_{k}\) is the process noise. We utilize the Wiener-process acceleration model (WPAM) [23], where the acceleration is a Wiener process and \(F_{k}=[1,T,T^{2}/2;0,1,T;0,0,1]\). This system state transition function performs the kinematic tracking.  
In the measurement vector \(z_{k}=[s,a]^{T}\), \(s\) is obtained by the WLAN RSSI-based Horus system [21], while \(a\) is calculated based on readings from the inertial sensors [24]. The observation equation is  
\[z_{k}=H_{k}X_{k}+n_{k} \tag{2}\]  
where \(H_{k}\) is the observation matrix and \(n_{k}\) is the measurement noise that is determined empirically.  
From the output of the Kalman filter, a relatively accurate trajectory of the BSN user is achieved. In addition, it provides the real-time velocity information, which is to be utilized in position prediction.
## 1 Introduction
[14] proved that a mobile phone app can be effectively used for the real-time monitoring of soil quality and conditions in wireless multi-sensing based on the IoT. As far as we know, most smart farms have been developed mainly for environmental monitoring or environment controls of smart farms. To the best of our knowledge, a smart farm in the literature utilized artificial light, such as LED light, for shedding light onto plants. Ref. [15] studied the effects of artificial LED light on metabolism, growth, and photosynthesis in leafy vegetables and some selected plants. However, artificial light cannot be used as the main source of light for photosynthesis for various kinds of plants. Our strategy is controlling the RB periodically, so that the appropriate amount of natural sunlight intensity can be provided for the plants inside the farm. Since the proposed greenhouse uses the natural sunlight, one can grow various kinds of plants which cannot grow under artificial light. We argue that the proposed RB control strategy can be useful in regions with sufficient sunlight intensity, such as Middle East countries. Suppose that we accumulate the RB angle data for one year. Since solar motion repeats every year, we can control the RB according to this accumulated angle data. In this way, we can reduce unnecessary RB adjustments, after running the system for more than a year. As far as we know, the proposed greenhouse system based on autonomous RB controls is novel, since we control RBs for providing optimal sunlight for plants inside the greenhouse. Simple controls are computationally efficient, robust, easy to implement, and easy to debug. Thus, we address simple RB controls, such that optimal sunlight can be provided for plants inside the greenhouse. We perform experiments to demonstrate the performance of the proposed RB controls. This research is organized as follows. Section 2 describes the proposed RB controls. Section 3 presents experiments of the proposed RB controls. Section 4 describes the discussion. Section 5 presents the conclusions.
## 3 Results
### Long-Term Trends  
Figure 6 displays the statistics of the long-term analysis. The color represents the slope of linear regression fit for the annual mean FPAR and the black dots are grids exceeding 90% significance. GIMMS FPAR3g (Figure 6) shows that Western Europe, Eastern America, the Sahel region, part of Eurasia, and the northern high latitudes are the areas with significant increasing trend in FPAR and LAI. Among these areas, changes in Western Europe are due to the afforestation of former arable land [56], an anthropogenic factor that is already included in the current CLM4. The rest of the areas are all transitional ecoregions: the Sahel region is a transition zone of semi-arid grasslands, savannas, steppes, and thorn shrublands lying between the Sahara desert and the Sudanian Savannas [57]; Eastern America and part of Eurasia are transitional zones of coniferous and broadleaf trees; the northern high latitudes are mainly the Arctic tundra ecosystem which is also ecologically transitional due to climate change [58]. As the satellite observations shows, these areas are highly sensitive to global climate change and thus have significant long-term trends.  
The CLM4 simulations are in broad agreements with the increasing trends illustrated by GIMMS3g datasets (Figure 6). However, the model has fewer grid cells exceeding the statistical significance level of 90% (Figure 6). Since long-trends are primarily driven by changes in forcing factors (e.g., CO\({}_{2}\) concentration, precipitation, and temperature), these discrepancies suggest that the CLM4 needs to improve its correlation between climate and vegetation. Annual changes of vegetation growth possibly  
Figure 5: The annual cycle of monthly mean FPAR and leaf area index (LAI) anomalies for (**a**) the global, and (**b**) savannas (2003–2005)  
caused by other factors such as nitrogen deposition and land use and land cover change are beyond the scope of this paper and are detailed in [40]. Although our study does not examine land cover change, extensive FPAR increase in the northern high latitudes suggests dynamic vegetation change (Figure 6(b)). As several studies asserted, shrub expansion exists in Northern Alaska, Siberia, and the Pan-Arctic [59, 60, 61]. It would thus be necessary to employ a dynamic vegetation model (e.g., Dynamic Global Vegetation Model, DGVM) to reproduce expansions of certain plant types. In sum, we have two findings in regards to the CLM4: (1) CLM4 does capture the long trends in FPAR at the global level, but has much smaller significance levels due to its excess variability in its annual values; (2) discrepancies in the northern high latitudes further suggest that CLM-DGVM is required to fully evaluate model's performance for long-term trends.
## 2 Study Area and Data
### Data Preparation
#### 2.2.2 Remote Sensing Data  
In this paper, the remote sensing images used were HJ-1A/B CCD data (Small Satellite Constellation for Environmental Disasters Monitoring and Prediction), which has a moderate spatial resolution (30 m), high time resolution (two days), large swath width (360 km or 720 km), and contains four spectral wavebands: 0.45-0.52 \(\upmu\)m (blue waveband), 0.52-0.6 \(\upmu\)m (green waveband), 0.63-0.69 \(\upmu\)m (red waveband), and 0.76-0.9 \(\upmu\)m (near-infrared waveband) [40]. Several CCD images that include the different growth periods of rice were selected to calculate the LAI. Radiometric calibration of the CCD images was performed using the calibration coefficient of each band provided by the China Centre for Resources Satellite Data. Then, the CCD images were geometrically corrected based on a rectified TM image, and atmospheric correction was conducted using the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) model [41].
# Influence of Fatigue Crack Formation and Propagation on Reliability of Steel Members
## 6 Independent Crack Growth in Directions \"a\" and \"c\"
### Analytical Calculation  
To determine the influence of the size and shape of fatigue crack on the cross-sectional resistance, the parametric study was performed [40; 41]. The welded steel beam I 400, representing, for example, longitudinal girder of steel plate girder bridge with a bottom members' deck (Figure 4) and I 800, representing cross girder of the same bridge, were considered in the study. The input strength and geometric characteristics are shown in  
Figure 3: Cross-sectional characteristics of biaxially symmetric I-beam.  
Table 1. The length of the bridge is 30.0 m. The lengths of the longitudinal girders of plate girder railway bridges vary about (1.6-2.5) m. In the case of railway bridge cross-girders, their lengths are 5.5-6.5 m depending on the bridge arrangement.  
Since this is a cross-section of class 1 (I 400) and class 2 (I 800), the Equations (21) and (23) were used for cross-sectional resistance calculation. The results of crack growth influence on plastic section modulus W\({}_{\text{ply}}\) decreasing are shown in Figure 5, and on plastic bending resistance M\({}_{\text{ply}}\) are shown in Figure 6.  
Crack size was chosen from a minimum value (zero value) up to the size defined by the limit values  
\[2\cdot\text{c}\leq 0.8\cdot\text{b}_{\text{f}},\text{ and a}\leq 0.8\cdot\text{t}_{ \text{f}}. \tag{29}\]  
For I 400, the limit values are 2\(\cdot\text{c}\leq 0.8\cdot 0.100=0.080\) m and a \(\leq 0.8\cdot 0.010=0.008\) m, for I 800 the limit values are 2\(\cdot\text{c}\leq 0.8\cdot 0.250=0.200\) m and a \(\leq 0.8\cdot 0.025=0.020\) m.  
From the results obtained, it can be seen that the fatigue crack growth can largely reduce the moment load-carrying capacity. If the maximum possible crack from surface limited by conditions (29) would appear, it would lead to the cross-sectional area loss of 9.97% (I 400), 15.71% for I 800, while the plastic resistance moment would be reduced up to 33.77% (I 400), 55.15% (I 800) respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Input Characteristics** & **I 400** & **I 800** \\ \hline Yield strength—\(\text{f}_{y}\) [MPa] & 235.0/1.0 = 235.0 & 235.0/1.0 = 235.0 \\ \hline Flange width—\(\text{b}_{\text{f}}\) [mm] & 100.0 & 250.0 \\ \hline Flange thickness—\(\text{t}_{\text{f}}\) [mm] & 10.0 & 25.0 \\ \hline Web height—d [mm] & 380.0 & 750.0 \\ \hline Web thickness—\(\text{t}_{\text{w}}\) [mm] & 8.0 & 10.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cross-section characteristics of welded steel beams I 400 and I 800.  
Figure 4: Model of typical steel plate girder bridge with bottom member members’ deck on railway line \(\dot{\text{Z}}\)lina-Martin with girders I 400 and I 800 used in parametric study.
## 5 Conclusions  
This study demonstrated that while laser incisions healed somewhat slower than scalpel incisions, they offered advantages in terms of hemostasis. The femtosecond laser-treated implants installed in the tibias of New Zealand White rabbits exhibited excellent osseointegration, with comparable ISQ values, micro-CT radiologic analyses, and histologic analyses when compared to SLA-treated implants. The BIC results also indicated higher values for the femtosecond laser-treated implants compared to the SLA-treated implants, suggesting potential benefits for initial implant fixation.  
The findings indicate that femtosecond laser surface treatment of titanium implants can achieve similar osseointegration compared to traditional SLA surface treatments. This supports the potential for femtosecond laser-treated implants to be used effectively in clinical settings, offering a predictable and efficient alternative to existing surface treatment methods. The ability to produce uniform nanostructures with minimal contamination risk further enhances the viability of femtosecond laser treatment as a preferred method for dental implant surface modification.  
Conceptualization, W.-S.D., K.-B.-D.S. and Y.-T.S.; Data curation, J.-M.L.; Funding acquisition, J.-M.L.; Investigation, W.-S.D., K.-B.-D.S. and Y.-T.S.; Resources, J.-H.H., J.-H.L. and H.-D.K.; Supervision, K.-B.L. and J.-M.L.; Writing--original draft, W.-S.D.; Writing--review and editing, S.-M.H. and Y.-G.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) [grant no. 2022R1C1C2007040], the Korea Institute for Advancement of Technology (KIAT) grant funded by the Ministry of Trade, Industry and Energy (MOTIE) (P0017662), the National Research Foundation of Korea (NRF) grant funded by the Ministry of Education (MOE) (2022 RIS-006), Republic of Korea.  
The research protocol of this study was reviewed and approved by the Institutional Animal Care and Use Committee (Approval number: KMEDI-22090202-00).  
Not applicable.  
The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
## 2 Related Work
### Data Visualization  
The visualization layer handles a variety of tasks, such as operational dashboards, control and governance, data analytics, portal and mobile usage, and application programming interface (API) gateways. Operational dashboards display all of the relevant sensors, devices, and machinery in real time. They also include the basic standard operating functions of the manufacturing units. Control and governance are controlled from the command center, based on the real-time monitoring or anomaly detection of machines and sensors [31]. Data analysis is the process of examining data to identify patterns or trends. Data analysis and machine learning algorithms are also used to perform predictive and preventative actions. Analytics and metrics for portal and mobile usage are presented on graphical user interfaces on consumer applications or mobile devices. The API gateways display enterprise applications for demand forecasting, inventory management, traceability, and other purposes and they also participate in the orchestration of business process management (BPM).
## 4 Hierarchical Cache Management (HCM) Policy
### DRAM Cache Replica Management  
The replicas in the DRAM cache are managed by an LRU queue (denoted as \(Q_{d}\)), with LRU serving to capture the temporal locality of requests. However, DRAM can only provide cache capacity in the range of tens of gigabytes. In heavy traffic scenarios, cache replacement in \(Q_{d}\) occurs very frequently, leading to short residency times for replicas in the cache, making it difficult for them to be hit by subsequent requests. To address this, we allocate a portion of DRAM space for a virtual cache queue (denoted as \(Q_{v}\)), which uses an LRU queue to track the replicas recently replaced in \(Q_{d}\). The purpose of \(Q_{v}\) is to observe the request access patterns of replicas over a longer time scale. The term \"virtual\" indicates that the replica data in \(Q_{v}\) are not actually stored in DRAM; it is only used to maintain the historical cache replacement records of DRAM.  
The Replica Information Table (RIT) in Figure 1 is used to record the attribute information corresponding to each locally cached replica, including but not limited to the replica's popularity, redundancy, and cache distance. Specifically, the popularity value can be set as the number of request hits for the replica at the current node, the redundancy value can be set as the number of replicas cached upstream of the node, and the cache distance is used to record information such as the number of hops or the delay between the current node and upstream service nodes. When a data request hits any node, the corresponding content attributes of the replica are retrieved from its local RIT and added to the header fields of the returned data packet. Along the content delivery path, intermediate nodes can read and update these header fields, such as incrementing the cache distance field hop-by-hop or increasing the redundancy field value by 1 after the data are cached locally.  
When a new replica is inserted into \(Q_{d}\), it is directly placed at the tail of \(Q_{d}\) according to the LRU rule, and its corresponding popularity and redundancy information is recorded in the local RIT, as described earlier. When a new request hits a replica in \(Q_{d}\), the replica is first moved to the tail of \(Q_{d}\), and its local popularity information is updated accordingly. When a replacement occurs in \(Q_{d}\), the replaced replica is further processed by the SSD cache admission filter described in Section 4.2. Specifically, replicas that pass through the filter are written to the SSD cache, while those that do not pass the filter are removed from DRAM, and the corresponding entry is inserted into the virtual cache queue \(Q_{v}\).
# Ontology with Deep Learning for Forest Image Classification
## 4 Deep Learning Architectures
### VGG-16 Architecture  
VGG-16 is a CNN network that has received huge attention in the area of computer vision due to its high classification accuracy of 92.7% when implemented on 1000 images of 1000 different categories on the ImageNet dataset [33]. The 16 in the VGG-16 architecture represents 16 convolutional layers with learnable parameters. Overall, it is composed of 13 convolutional layers, 5 pooling layers, and 3 dense layers, which gives a total of 21 layers; however, it has only 16 weight layers with learnable parameters. What is unique about VGG-16 is that it disregards a large number of hyper-parameters and instead uses 3 \(\times\) 3 filter convolutional layers with stride 1 and max-pooling layers of 2 \(\times\) 2 filters.
# Path Planning and Collision Avoidance in Unknown Environments for USVs Based on an Improved D* Lite
## 3 Improved D* Lite
### Adjacent Node Expansion and Steering Angle Limitation  
In practical applications, obstacle detecting sensors (e.g., Lidar, ultrasonic and stereo camera) can obtain detailed environmental information around the USV, which is much more substantial than the information on the eight adjacent nodes required by D* Lite. In addition, affected by limited steering maneuverability and inertia, USVs can only turn a limited angle each time. It is difficult to allow a USV to accurately turn an integral multiple of 45 degrees each time, which the original D* Lite constrains. To increase the flexibility of steering angles, we first expand the number of adjacent nodes from 8 to 24 and 48, respectively. Second, we add a restriction for the steering angle each time to simulate a more realistic scenario for USVs.
# Joint Deployment Optimization of Parallelized SFCs and BVNFs in Multi-Access Edge Computing
## 6 Conclusions  
The integration of multi-access edge computing (MEC) and parallelized service function chains (P-SFCs) can provide low-delay network services for mobile users by deploying virtualized network functions (VNFs) to process user requests in parallel. Multiple backup VNFs (BVNFs) are deployed near these VNFs to satisfy reliability requirements. In this paper, we studied the problem of maximizing the number of accepted user requests in the resource-limited MEC network. We first proposed a new approach to enhance the reliability of P-SFCs and reduce the number of idle BVNFs. Then, we modeled the dynamics of delay and reliability caused by VNF parallelization and BVNF deployment and formulated the joint deployment problem of P-SFCs and BVNFs. Next, we designed an approximation algorithm to find a near-optimal deployment of P-SFCs and BVNFs by finding the critical VNF, target sub-flow, and edge server and determining the split and merge of sub-flows. Experimental results based on real-world datasets show that our proposed algorithm outperforms two representative algorithms.  
For future work, we would like to extend our research in two directions. The first direction is to study a multi-objective optimization problem by considering the trade-offs of reliability, delay, and resource preemption. Meanwhile, the second direction is parallel scheduling and congestion control in a dynamic MEC network.  
Conceptualization, Y.H. and J.L.; methodology, Y.H.; software, Y.L.; validation, Y.H.; formal analysis, Y.H. and J.L.; investigation, Y.L.; resources, Y.L.; data curation, Y.H.; writing original draft preparation, Y.H.; writing--review and editing, Y.H. and J.L.; visualization, [PERSON] and Y.L.; supervision, [PERSON] and J.L.; funding acquisition, Y.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.