esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Interspecific Variation in Observed Plant SLA within Plant Functional Types  
The SLA among different species within a given plant functional type in China showed large variability. The observed plant SLA varied from 0.0002 m\({}^{2}\)/g to 0.0997 m\({}^{2}\)/g, with a mean variation coefficient (CV) of 42% across different PFTs (Table 2). The interspecific variations in SLA were relatively small for tropical BET, boreal BDT, and boreal BDS, with the CV values varying from 21% to 29%, but were large for the remaining PFTs, with the CV values ranging from 40% to 59%. Among the different tree and shrub types, temperate BDT and temperate BDS had the largest CV of SLA, respectively. Moreover, temperate plants with a great interspecific diversity showed higher SLA variability than tropical and  
Figure 2: The differences in specific leaf area (SLA) between observation data in China and default values in the CLM4.5 model. The error bar shows the standard deviation. 1, temperate NET; 2, boreal NET; 3, boreal NDT; 4, tropical BET; 5, temperate BET; 6, tropical BDT; 7, temperate BDT; 8, boreal DDT; 9, temperate BES; 10, temperate BDS; 11, boreal BDS; 12, C3 grass; 13, C4 grass; 14, rainfed crop.  
boreal plants in China. Specifically, the CV of SLA for temperate BDT was two times that for boreal BDT.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**PFT** & \begin{tabular}{c} **Maximum SLA** \\ **Value (m\({}^{2}\)/g)** \\ \end{tabular} & \begin{tabular}{c} **Minimum SLA** \\ **Value (m\({}^{2}\)/g)** \\ \end{tabular} & \
\begin{tabular}{c} **Coefficient of** \\ **Variation (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Number of** \\ **Samples (n)** \\ \end{tabular} \\ \hline temperate NET & 0.002 & 0.016 & 42.86 & 69 \\ boreal NET & 0.003 & 0.008 & 40.0 & 10 \\ boreal NDT & 0.005 & 0.024 & 40.0 & 11 \\ tropical BET & 0.004 & 0.027 & 28.57 & 103 \\ temperate BET & 0.0002 & 0.037 & 42.86 & 277 \\ tropical BDT & 0.007 & 0.032 & 41.18 & 9 \\ temperate BDT & 0.002 & 0.081 & 50.0 & 501 \\ boreal BDT & 0.005 & 0.028 & 25.0 & 4 \\ temperate BES & 0.004 & 0.048 & 47.06 & 221 \\ temperate BDS & 0.002 & 0.010 & 59.09 & 454 \\ boreal BDS & 0.004 & 0.025 & 21.05 & 6 \\ C3 grass & 0.002 & 0.082 & 57.14 & 822 \\ C4 grass & 0.003 & 0.052 & 47.37 & 126 \\ rainfed crop & 0.002 & 0.042 & 52.63 & 19 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The variation in specific leaf area (SLA) observations among different plant functional types (PFTs) in China. Abbreviations: NET, needleleaf evergreen tree; NDT, needleleaf deciduous tree; BET, broadleaf evergreen tree; BDT, broadleaf deciduous tree; BES, broadleaf evergreen shrub; BDS, broadleaf deciduous shrub.  
Figure 3: The responses of specific leaf area (SLA) to light gradients for temperate broadleaf deciduous tree (BDT) (**a**), temperate needleleaf evergreen tree (NET) (**b**), and temperate broadleaf evergreen tree (BET) (**c**). More details about the species can be found in Table S2 [(40; 41; 42; 43)].
# Object-Oriented Remote Sensing Image Change Detection Based on Color Co-Occurrence Matrix
## 3 Experimental Results and Discussion
### Determination of the Optimal Segmentation Scale  
In remote sensing images, the scale has a very important significance, as different scales reflect different details of ground objects. Selecting the appropriate scale for certain research applications has been one of the hot issues in the field of remote sensing research. In order to determine the optimal scale, the OGF is used as the evaluation criterion. Figure 6 shows the changes in the OGF curve with the increasing scale, where Figure 6a,b represent Sentinel-2 and Skysat images, respectively. It can be seen that the OGF shows a trend of increasing first and then decreasing with the increase of the segmentation scale. For Sentinel-2, the maximum OGF = 0.71 corresponds to a scale of 41. For Skysat, the maximum OGF = 0.67 corresponds to a scale of 268. It also shows that the optimal scale will increase as the resolution increases.  
In order to display the results of the optimal segmentation scale more intuitively, the representative regions are intercepted from the study area and are shown in Figures 7 and 8, where Figure 7a1-a3,b1-b3,c1-c3 show the segmentation results of different representative regions of Sentinel-2 images when the segmentation scale is 21, 41, and 61, respectively. Figure 8a1-a3,b1-b3,c1-c3 show different representative regions of Skysat images when the segmentation scale is approximately 100, 300, and 1500, respectively. It can be seen that the smaller scale will lead to over-segmentation, requiring unnecessary calculations and damaging the integrity of the surface, as shown in Figures 7 and 8a1-c1. The larger scale will lead to under-segmentation, and the bareland cannot be segmented from the surrounding vegetation and buildings, as shown in Figures 7 and 8a3-c3. In Figures 7 and 8a2-c2, the  
Figure 4: The location of research areas.  
segmentation results are more closely in accord with human understanding of the earth's surface. Whether qualitative or quantitative, this can prove the effectiveness of the OGF index in the selection of the best scale. Thus, 41 and 268 can be regarded as the optimal scales to obtain the optimal objects in the subsequent experiments.  
Figure 5: (**a1,b1**) Sentinel-2 images of Shenzhen in January 2020 and 2021; (**a2,b2**) Skysat images of Dapeng in October 2021 and May 2022.  
Figure 6: OGF curves of segmentation scales: (**a**) Sentinel-2 image and (**b**) Skysat image.  
Figure 8: Segmentation results of local areas at different scales in Skysat images: (**a1–c1**) scale \(\approx\) 100; (a2–c2) scale \(\approx\) 300; and (**a3–c3**) scale \(\approx\) 1500.  
Figure 7: Segmentation results of local areas at different scales in Sentinel-2 images: (**a1–c1**) scale = 21; (a2–c2) scale = 41; and (**a3–c3**) scale = 61.
# A Review on the Thermal Hazards of the Lithium-Ion Battery and the Corresponding Countermeasures
## 5 Management and Countermeasures
### Battery Management System
[268] proposed a BMS combining liquid cooling and PCM for a fast-charging LIB pack and their results depicted that the improved system controlled the temperature well in an 8C-rate charging battery pack. Figure 23: Schematic of a combined battery management system (CBMS) integrated phase change material (PCM) with forced-air cooling. Adopted from Ref. [263]. Figure 24: Schematic of a CBMS combining PCM and liquid cooling techniques. Adopted from Ref. [266]: (**a**) the numerical model of single battery; (**b**) cross section of the flow channel; (**c**) temperature monitoring points in A-A cross-section; (**d**) the simplified thermal resistance network at steady state, with dotted thermal path absent for the liquid cooling condition. To conclude, it is worth noting that both the application of FR additives and the thermal management on battery are compliant before the appearance of battery hazard, that is, the normal operation process. Even the safety device acts as a protective method for battery thermal hazard. Whereas, once thermal hazard occurs, it is essential to take certain countermeasures such as the warning of hazard and subsequent firefighting methods to reduce the damage inflicted by the hazard and suppress further deterioration.
## 4 Test Results and Discussion
### Analysis of the Joint Surface Wear  
In the study conducted by [PERSON] [26], the surface damage along the lower (vs. upper) surface had a relatively clearer degradation regularity. Hence, as described in Section 3.2, the image binarization method was used to process the top view of the lower block specimen's surface after the cyclic shear in order to distinguish the worn areas (white) from the unworn areas (blue), and the wear area ratio \(r_{w}\) was calculated. Figure 17 shows the wear process of the lower block specimen's surface under 1.0 MPa. This process is consistent with [PERSON]'s results [27].  
As shown in Figure 18, the wear area ratio \(r_{w}\) increased with the increase in the number of shear cycles, the increment rate of the wear area ratio \(r_{w}\) slowed down after five shear cycles, and the value of \(r_{w}\) was close to 100% under 2.5 MPa normal stress after five cycles. Figure 19 shows that asperity wear and debris backfill occurred during the first shear cycle. This result means that, during the subsequent shear cycles, the worn asperities on one side of the surface no longer had close occlusal contact with the hollows on the other side under a lower normal load. Evidently, the normal stress level plays a role in determining the asperity degradation on a joint surface, as mentioned by [PERSON] [28] and [PERSON] [29]. Therefore, some partial areas of the lower joint surface were only slightly worn in the early stage of cyclic shearing. Under 0.4 MPa stress, even if more shear cycles are performed, the wear area ratio will not reach 100%.  
Figure 16: Curves of parameter c vs. normal stress: (**a**) 0\({}^{\circ}\) and 180\({}^{\circ}\) analyzed directions, (**b**) analyzed direction between 0\({}^{\circ}\) and 360\({}^{\circ}\) at an interval of 30\({}^{\circ}\).  
Figure 17: Using the image binarization method to process the worn (white area) and unworn areas (blue area) of the surface under 1.0 MPa.  
A joint surface wear fitting formula under cyclic shear was proposed to reveal the relationship between the shear cycles, normal stress, and wear area ratio \(r_{w}\). The fitting formula can be rewritten as follows:  
\[r_{w}=\frac{k\cdot n}{1+j\cdot n}\;with\;r_{w}\;\in\;[0,1] \tag{5}\]  
where \(n\) is the number of shear cycles, \(k\) is the first derivative value of the wear area ratio curve at point 0, and \(k\) and \(j\) follow an approximate linear correlation, as shown in Figure 20.  
Figure 19: Local damage evolution under low normal stress.  
Figure 20: Curves of parameters \(k\) vs. \(j\) in the joint surface wear fitting formula.
# A Dynamic Surface Gateway Placement Scheme for Mobile Underwater Networks +
## 4 PADP Algorithm Description
### Clustering Algorithm  
In the process of optimizing, it is certain that the sensor nodes in one layer are not directly connected to the surface nodes, instead they can be connected by relay. So, we need to consider the connectivity between nodes. The disjoint-set data structure partitions sensor nodes in Layer 1 into disjoint sets referred to as _clusters_ according to their locations and transmission range \(d\). The Aalgorithm 1 uses three basic functions. MAKE-CLUSTER(\(x\)) creates a new cluster with a single member \(x\). This operation requires that \(x\) is not already in any other cluster. UNION(\(x,y\)) unites the clusters containing \(x\) and \(y\) into a new cluster. FIND-CLUSTER(\(x\)) returns a pointer to the representative of the (unique) cluster containing \(x\). The scheme requires \(O(n\log(n))\) time, where \(n\) stands for the number of nodes.  
```
1. G: the entire set of sensor nodes
2. E[G]: the cluster of directly connected sensor nodes
3. p[x]: parent of node x
4. rank[x]: rank of node x (the term rank is used instead of depth since it stops being equal to the depth if path compression (described below) is also used.)

Clustering algorithm:
1. for each node i \(\in\) G
2. do MAKE-CLUSTER(i)
3. for each edge (i, j) \(\in\) E[G]
4. do if FIND-CLUSTER(i) \(\
eq\) FIND-CLUSTER(j)
5. then UNION(i, j)

MAKE-CLUSTER(x):

1. p[x] \(\longleftarrow\) x
2. rank[x] \(\longleftarrow\) 0

UNION(x, y):

1. LINK(FIND-CLUSTER(x), FIND-CLUSTER(y))

LINK(x, y):

1. if rank[x]\(>\)rank[y]
2. then p[y] \(\longleftarrow\) x
3. else p[x] \(\longleftarrow\) y
4. if rank[x] = rank[y]
5. then rank[y] \(\longleftarrow\) rank[y] + 1

FIND-CLUSTER(x) with path compression:

1. if x \(\
eq\) p[x]
2. then p[x] \(\longleftarrow\) FIND-CLUSTER(p[x])
3. return p[x]
```  
**Algorithm 1** Operations of disjoint-set data structure.
# Deep Visual Waterline Detection for Inland Marine Unmanned Surface Vehicles
## 4 Experiments and Evaluation
### Comparison to Other Alternative Approaches
#### 4.4.2 Quantitative Assessment on Robustness  
Then, according to Equations (3)-(6), we have calculated the precision-recall metrics and FP-irrelevance metrics respectively corresponding to the visual results presented in Figure 8. The quantitative comparisons on these evaluated metrics are shown in Table 5.  
Usually, for a robust waterline detection approach, both high precision and high recall are desired in any water environments. From Table 5, we see that the edge detection method (Canny edge detector) attains the same desirable recalls as ours when rail, ripple or shadow is emerging, whereas the corresponding precisions are much lesser than ours. Moreover, under foggy weather conditions, despite both of the two compared methods obtain high precision, the recall of the edge detection method is only half of ours. Obviously, for the same scenarios, the effectiveness of the edge detection method is more sensitive to environmental noises than ours. The reason is that a large number of pixels irrelevant to a waterline are also selected by the edge detection method, while correct pixels are annotated in an estimated image-map. For instance, there are 54,182 false positives (FP) also marked as black pixels in the upper image of Figure 8a. Moreover, although the number of irrelevant pixels is rather small (only 16 false positives) owing to low illumination caused by fog, many true positives are still missing in the result by the edge detection method that induces unsatisfactory recall (just 41.9%). Then, the measured irrelevances shown in Table 5 indicate that most of irrelevance metrics on the edge detection method are negative, which means those irrelevant pixels (false positives) selected by this method scatter around the real waterline. On the contrary, our irrelevance metrics are positive, implying that our false positives as a whole are more approximate to the ground truth.  
Actually, in practical waterline detection tasks, the edge detection method as well as many other alternative approaches such as waterline detection based on image segmentation generally employ necessary image preprocessing (e.g., image denoising) to eliminate those irrelevant pixels induced by environmental noises and guarantee high precision and high recall at the same time. However, these approaches relying on image preprocessing influence the efficiency of waterline detection tasks more or less due to extra computational costs. Instead, our approach can straightforwardly distinguish candidate waterline segments from raw images captured by USVs, since the proposed approach adopts an end-to-end paradigm based on deep learning, in which there is no preprocessing procedure. Therefore, as far as a single estimated image-map is concerned, our approach has better capacity of resisting environmental disturbance in the absence of image preprocessing, which is also demonstrated by the visual comparisons presented in Figure 8.
## 4 Reliable and Secure Probability Analyses  
In relay-based systems where the trustworthiness of relays may be questionable, ensuring the secure transmission of sensitive data from the source to the intended recipient without compromising confidentiality is crucial. A common approach in this scenario involves transmitting under a pre-established secrecy rate, as outlined by [36].  
In our study, we consider an imperfect link between the source and relays, implying that the relayed information may contain errors. The recipient, aiming to retrieve the original data, must deal with these corrupted versions. This extraction process at the destination mirrors the characteristics of the CEO problem. However, a definitive rate-distortion boundary for this CEO problem has not been established. Therefore, the secrecy transmission rate discussed by [37] is not utilized in our methodology. Instead, we adopt the \"reliable and secure probability\" (RSP) metric. This metric, based on the principle of outage probability, is recommended as a relevant physical layer (PHY) security parameter by [38]. The mathematical representation is as follows:  
\[P_{\text{RSP}} =P_{\text{out}}^{\text{UR}}-P_{\text{out}}^{\text{D}}\] \[=\underbrace{\Pr\{\text{disruption at }\text{UR}_{1}\cap\text{ disruption at }\text{UR}_{2}\}}_{P_{\text{out}}^{\text{UR}}}\] \[-\underbrace{\Pr\{\text{disruption at }\text{UR}_{1}\cap\text{ disruption at }\text{UR}_{2}\cap\text{ disruption at }\text{D}\}}_{P_{\text{out}}^{\text{D}}}, \tag{8}\]  
where \(P_{\text{out}}^{\text{UR}}\) encapsulates the probability of disruptions at the untrusted relay nodes, specifically \(\text{UR}_{1}\) and \(\text{UR}_{2}\), and \(P_{\text{out}}^{\text{D}}\) represents a similar metric for the destination node. RSP measures the likelihood of successful message recovery at the destination while ensuring the message remains unintelligible to untrusted relays. In untrusted relay networks, the basic consideration is to utilize relays to enhance transmission reliability while minimizingthe security threats posed by untrusted relays. Therefore, we introduced the RSP metric to assess the balance between security and reliability.  
A higher RSP value indicates that the probability of the untrusted relay experiencing an outage is high, while the probability of the destination experiencing an outage is low, signifying the high-security performance of the system. Conversely, a lower RSP value suggests that the probability of the untrusted relay experiencing an outage is low, and the probability of the destination experiencing an outage is high, indicating low reliability and security performance. Additionally, a lower RSP value can also imply that the probability of both the destination and the untrusted relay experiencing an outage simultaneously is high, meaning the system's reliability is low, or that the probability of both the destination and the untrusted relay experiencing an outage simultaneously is low, indicating the system's security performance is low.
# 3D Global Path Planning Optimization for Cellular-Connected UAVs under Link Reliability Constraint
## 3 Methodology
### Problem Definition and Formulation
#### 3.2.2 Objective Function
\(r_{mask}\) is defined as  
\[r_{mask}=r_{i}\cup r_{j}\ \ \ \ \forall i,j\in n_{BS}\ and\ i\
eq j, \tag{6}\]  
where \(n_{BS}\) is the number of BSs and \(r\) is the reliable zone function, which is defined as  
\[r=\max\biggl{(}1-\frac{d_{BS}}{r_{rz}},0\biggr{)}\times\biggl{(}\frac{r_{rz}} {r_{rz}-d_{BS}}\biggr{)}, \tag{7}\]  
where \(r_{rz}\) is the radius of the reliable zone, and \(d_{BS}\) is the 3D distance between the computed point and a BS, for example, for the \(i^{th}\) point, it can be calculated as  
\[d_{BS}=\sqrt{\bigl{(}x_{d}^{i}-x_{BS}\bigr{)}^{2}+\bigl{(}y_{d}^{i}-y_{BS} \bigr{)}^{2}+\bigl{(}z_{d}^{i}-z_{BS}\bigr{)}^{2}}, \tag{8}\]  
where \(x_{BS}\), \(y_{BS}\), and \(z_{BS}\) are the coordinates of the considered BS antenna in space, and \(x_{d}^{i}\), \(y_{d}^{i}\), and \(z_{d}^{i}\) are the coordination of the \(i^{th}\) point at space. The penalty terms for no-coverage zones, obstacles, and no-fly zones are defined as  
\[P_{nc}=\sum_{n_{BS}}\max(1-\frac{d_{BS}}{r_{nc}},0), \tag{9}\]where \(r_{nc}\) is the radius of the spherical zone around the BS that there is no reliable coverage. It is generally accepted that the shorter the distance to the BS, the higher the probability of serving users with reliable signals. Meanwhile, in a close range to the BSs center, the signal quality is unreliable due to the antenna propagation pattern, especially in the boundaries of the main lobe. Another reason to consider this parameter is to avoid collision with the BS tower,  
\[P_{ob}=\sum_{n_{ob}}\max(1-\frac{d_{ob}}{r_{ob}},0), \tag{10}\]  
where \(d_{ob}\) is the distance of the computed point to the center of the obstacle, \(r_{ob}\) is the radius of the obstacle, and \(n_{ob}\) is the number of obstacles in the problem,  
\[P_{nf}=\sum_{n_{nf}}\max(1-\frac{d_{nf}}{r_{nf}},0), \tag{11}\]  
where \(d_{nf}\) is the distance of the computed point to the center of the no-fly zone, \(r_{nf}\) is the radius of the no-fly zone, and \(n_{nf}\) is the number of no-fly zones in the problem.
## 5 Results and Discussion
### Analysis of the Thermal Performance of the Storage Tank during Discharge with a c-h Ratio of 0
#### 5.2.3 Exergy Efficiency  
The internal exergy efficiency of the storage tanks during the discharge process for E4, E5, and E6 is shown in Figure 15. When discharging at different flow rates, the storage tank with porous obstacles installed at both the top and bottom outlets has the highest exergy efficiency among the experiments in the same group, followed by the tank with the obstacle installed near the bottom.  
Figure 14: Temperature contour map at a flow velocity of 0.3 m/s for c–h ratio 0: (**a**) Porous obstacle in the middle; (**b**) Porous obstacle near the bottom; (**c**) Porous obstacles near both the bottom and top.  
Figure 15: Exergy efficiency: (**a**) at a flow rate of 0.1 m/s; (**b**) at a flow rate of 0.2 m/s; (**c**) at a flow rate of 0.3 m/s.  
Under the influence of flow rate and thermal conduction, the exergy efficiency of the storage tank is highest at a flow rate of 0.2 m/s. At the same discharge state, at the final moment of 0.3 m/s, the exergy efficiency of Type e and Type f is higher than that at 0.1 m/s. This differs from the discharge when the initial c-h ratio is 0.38. However, this phenomenon is also caused by excessive axial thermal conduction due to the prolonged discharge time.  
Under high flow rates, the storage tank with a porous obstacle at the bottom still maintains a relatively high exergy efficiency. As the flow rate increases, the difference in exergy efficiency between Type c, Type e, and Type f becomes larger. The difference in exergy efficiency between the tanks is smallest at 0.1 m/s and largest at 0.3 m/s.