esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Analysis of Temporal Correlation between Snow Cover and NDVI  
#### 3.2.1 Temporal Correlation between Snow Cover and Changes in NDVI  
The intra-annual trends in the snow depth and NDVI are shown in Figure 6. The snow depth gradually decreased as the NDVI increased from April to August, when the NDVI reached its peak for the year in the period with the most vigorous growth of vegetation, and the amount of accumulated snow was the lowest during the year. Subsequently, the snow depth gradually started to increase as the NDVI decreased. In addition, we observed a lag in the occurrence of the minimum NDVI compared with the period when the deepest snow cover occurred. The intra-annual correlation between each meteorological element and the NDVI was the opposite of that between the snow cover and NDVI. The trends in the meteorological elements were similar to the changes in the NDVI, where the response of the NDVI to precipitation agreed well with the occurrence of peaks and troughs in the precipitation. There was also a significant lag in the response to solar radiation. Pearson's correlation coefficients were calculated to quantify the effects of each meteorological element on the NDVI for each data set. In particular, the snow cover and NDVI had a significant negative correlation (R = -0.76), and the correlations between the temperature, precipitation, and solar radiation with the NDVI were R = 0.67, 0.77, and 0.21, respectively. The correlations between the temperature and precipitation with the NDVI were significant and negative, and the correlation between the solar radiation and NDVI was not significant and negative. The absolute R values indicated the significance of the correlations. The significance of the four elements on the NDVI was shown in the following order: precipitation > snow depth > temperature > solar radiation. The difference between the effects of precipitation and snow depth (|R|) was small. It should be noted that the correlation indicated by the results of the Pearson correlation test just represented  
Figure 5: Multi-year spatial and temporal variation of NDVI on the QTP: (**a**) spatial distribution of trend changes; (**b**) trend significance; (**c**) time series of changes.  
a mathematical significance. These initial findings suggested that the temporal correlation between plateau snow cover and precipitation with changes in NDVI is highly significant. Temporally, the changes in the snow cover on the plateau were the opposite of the changes in the NDVI. The changes in the NDVI were synchronized with the changes in other elements. The opposite changes in the snow cover and its participation in vegetation eco-hydrological processes were directly related to the effects of snow cover on the phenology of vegetation on the plateau. The snow cover increased during the cold season, and vegetation died due to the lower temperature. The snowpack melted into water in the warm season, which had a positive effect on the growth of vegetation.
## 5 Case Study of Developing Liver Cancer Diagnosis
### The HCC Type of Liver Cancer  
Liver cancer is considered one of the most life-threatening cancers due to its typically late diagnosis and rapid progression. Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, accounting for about 75% to 85% of cases. There is a widely accepted standard for diagnosing HCC-type liver cancer called the Liver Imaging Reporting and Data System (LI-RADS). Our case study develops a medical image analytics system that particularly diagnoses occurrences of HCC using CECT scans. LI-RADS categorizes liver observations into different categories based on their imaging features and the likelihood of representing HCC. The criteria for HCC categorization are summarized in Table 3.  
The imaging features listed in the third column of the table can be directly determined based on the results of organ, lesion, and tumor segmentations, as well as medical features classified by the platform.
# Wavelet-Based Optimum Identification of Vehicle Axles Using Bridge Measurements
## 3 Wavelet Theory  
The idea behind a wavelet analysis [37] is to express or to approximate the signal as a family of functions constructed from dilatations and translations of a single function called the base wavelet. Mathematically, the continuous wavelet transform of a function \(f(t)\) is defined as the integral transform of \(f(t)\) with a family of wavelet functions, \(\psi_{a,b}(t)\):  
\[WT(a,b)=\int_{-\infty}^{+\infty}f(t)\frac{1}{\sqrt{a}}\psi\bigg{(}\frac{t-b}{a} \bigg{)}dt\quad a\in R^{+},\ b\in R \tag{2}\]  
The function \(\psi\) (\(t\)) is generally called the base wavelet and the series of functions \(\psi_{a,b}(t)\) are called daughter wavelets. The daughter wavelets result from shifting and scaling the base wavelet. The scale factor, \(a\), controls the scaling of the function \(\psi(t)\), and is inversely related to its frequency. The shift factor, \(b\), controls the temporal translation of the function or its 'location' in the signal. In practice, wavelet functions can be dilated into infinitely large or infinitesimally small scales (\(a\)). A question that naturally arises is when or how to exit from the process of wavelet transformation; more specifically, what are the most relevant wavelet scales for a particular application?  
Using the concept of Shannon entropy, this paper proposes to guide the selection of wavelet scale automatically by selecting the scale that gives greatest contrast or minimum entropy. The energy of the wavelet coefficients at scale \(a\) and location \(b\) is given by:  
\[Z(a,b)=\big{|}WT(a,b)\big{|}^{2} \tag{3}\]  
and the total energy of the function at scale, \(a\) is:  
\[Z_{tot}(a)=\sum_{b=1}^{N}\big{|}WT(a,b)\big{|}^{2} \tag{4}\]  
Figure 1: Four different functions of 16 values. (**a**) one point has value of one; (**b**) three points have values of 0.33; (**c**) sine wave function; (**d**) sine wave function with different frequency.  
where \(N\) is the total number of wavelet coefficients. Then the entropy of the energy function is described using the concept of Shannon entropy as:  
\[E_{ent}(a\ )=-\sum_{b=1}^{N}p_{b}log_{2}p_{b} \tag{5}\]  
where \(p_{b}\) is defined here as the relative wavelet energy at point, \(b\):  
\[p_{b}=\frac{Z(a,b)}{Z_{tot}(a)}\ \text{with}\ \sum_{b=1}^{N}p_{b}=1 \tag{6}\]  
Ref. [38] point out that the entropy function, \(E_{ent}(a\ )\) is bounded by \(0\leq E_{ent}(a\ )\leq log_{2}^{N}\).  
A lower Shannon entropy value corresponds to greater contrast and a higher energy concentration of the signals [36]. Therefore, an appropriate scale, corresponding to minimum Shannon entropy, should yield greater contrast in wavelet energy levels. For the purpose of detecting peaks caused by axles from strain signals in either SHM or BWIM systems, minimum Shannon entropy can guide the selection of the optimum scale of the wavelet transform, identifying the position of the discontinuities caused by the passing axles.
## 3 Results and Discussion  
### Formation Energy  
DFT calculation results can provide theoretical explanations and reasonable predictions for many experimental phenomena. In this section, the formation energies of the PV, and PVH complexes from the second to sixth C layers are carefully calculated. The formation energy is used to evaluate the quantity and forming efficiency of different complexes. For a more comprehensive comparison, the formation energies of a single P atom and vacancy are also calculated.
## 2 The Parameters and Introduction of the Hardening Soil Model with Small-Strain Stiffness Model  
The HSS model was proposed by [PERSON] [20] based on the hardening soil (HS) model, taking into account the nonlinear relationship between the shear stiffness and strain of soil  
Figure 1: Strain limits for soil under conditions associated with various geotechnical engineering projects.  
in the small-strain range. Compared to the HS model, it can more comprehensively reflect the mechanical properties of soil such as small-strain behavior, compressive hardening, and dilatancy. The characteristics of common soil constitutive models are shown in Table 1.  
The HS model contains a total of 11 parameters, while the HSS model adds two additional small-strain parameters based on this, as shown in Table 2. In the Tables 1 and 2, \"\(\surd\)\" indicates that the indicator can be reflected, while \"\(\times\)\" and \"-\" indicate that the indicator can not be reflected.  
Although these parameters can be obtained through indoor testing, they are quite complex, and it is difficult to provide all parameters in geological exploration reports.  
Two of the parameters, \(c^{\prime}\) and \(\varphi^{\prime}\), can be taken from geological exploration reports. This article provides recommended values for \(E_{\text{50}}^{\text{ref}}\), \(E_{\text{eed}}^{\text{ref}}\), \(E_{\text{ur}}^{\text{ref}}\), and \(R_{\text{f}}\) based on indoor testing, while the estimation of other parameters relies on relevant research experience or parameter inversion methods to obtain values.
## 3 Algorithms
### Contour Parallel Algorithms  
As was mentioned in the paragraphs above, the high maximum curvature of direction parallel paths is suspected to impede part quality due to fractures and twists of fibers. Additionally, surfaces with holes inevitably lead to travel motions in most situations. To avoid high curvatures and travel motions, the notion of contour parallel algorithm is introduced. These generally follow the boundary or contour and produce long segments with low curvature. They can also open the opportunity of load adequate path planning from a manufacturing perspective, similar to direction parallel paths, especially if the computed vectors of maximum principle stress closely follow a certain pattern in a subarea of the surface. This pattern can be extracted by the user and will then be followed by the path as the contour is traced and repeated inwards.  
One general observation is to be made when dealing with contour parallel algorithms: \(\phi\) can have several local maxima, making a spiral-like traversal nontrivial. Further description on why the local maxima lead to the impossibility for a trivial solution to the continuous path planning problem can be found in [37].
## 3 Problem Description
### Optimization Objective
#### 3.3.2 Weighted Average Response Time and Penalty  
Due to the significant variation in runtime values across different sub-tasks, using makespan as the reward metric may not be ideal. In contrast, the average response time is used as a metric. The Weighted Average Response Time is calculated as follows:\[reward_{2}=\sum_{i=1}^{N}\sum_{j=1}^{m_{i}}f(P_{i,j})(SAT_{j}^{i}-AT_{j}^{i}) \tag{2}\]  
where \(f(P_{i,j})\) is a function that is related to priority. This function is used to control the impact of priority on the rewards, and its parameters can be customized based on the requirements of the scenario.  
The weighted average response time provides an overall measure of scheduling efficiency. However, it may not accurately reflect the appropriateness of scheduling for individual tasks. In some cases, the network may prioritize the scheduling of certain tasks while leaving others idle for extended periods.  
To address this issue, a Penalty is introduced. For a timeout (e.g., k times the average response time or a user-set value) sub-task, its Penalty is defined as \(penalty_{j}^{i}\). The new reward is calculated as follows:  
\[reward_{2}=\sum_{i=1}^{N}\sum_{j=1}^{m_{i}}f(P_{i,j})(SAT_{j}^{i}-AT_{j}^{i})+ \sum_{(i,j)}^{Out}penalty_{j}^{i} \tag{3}\]  
where \(Out\) represents the set of timeout sub-tasks.
## 3 Results and Discussion
### Correlation Analysis
According to correlation analysis results, temperature increase displays a good positive correlation with stone rotational speed (R = 0.67 (\(p\) = 0.003)). Consistent with ANOVA results, chickpea flour temperature increases as stone rotation speed increases. With respect to energy consumptions, the correlation analysis found a strong positive correlation with chickpea moisture content (R = 0.74 (\(p\) < 0.001)). As highlighted in Section 3.2, the sample with the highest moisture content (i.e., 12%) showed the highest energy consumption and the highest milling time. The significant effect of moisture content can also be observed in this case, where chickpeas with the highest moisture content (i.e., 12%) have a more plastic behavior during milling, leading to a higher strain of the electric motor with the subsequent increase in energy consumptions [12; 20]. Concerning average power, the correlation analysis highlighted a strong positive correlation with stone rotational speed (R = 0.92 (\(p\) = 0.001)). Furthermore, these results are consistent with the ANOVA. In particular, average power increases as the stone rotational speed increases. Finally, Pearson Correlation analysis found a good correlation between specific milling energy and chickpea moisture content (R = 0.71 (\(p\) = 0.001)). Specific milling energy represents the energy required to produce 1 kg of chickpea flour. The results reported in Tables 1 and 4 highlight that specific milling energy significantly increases as chickpea moisture content increases. This result is consistent with the ANOVA results. The explanation of these results is related to the fact that dry raw materials are more brittle and less resistant than more humid and conditioned ones, which show higher plasticity and are more difficult to mill [12; 20].
## 3 Results
### Input Data Intercomparison
#### 3.2.1 Net Radiation (RNET)  
In the CO station (Table 4, Figures S1 and S24), EXP1 and EXP2 have quite similar results, with maxima slightly lower for EXP2, while EXP3 cuts both minima and maxima, resulting in a lower range. Experiments \"b\" have maxima slightly larger than those of experiments \"a\". This is not surprising, considering that net radiation is inversely proportional to albedo, since albedo of vineyards(0.23, experiment \"a\") exceeds the one of irrigated crops (0.18, \"b\"). EXP1b/2b have distributions closer to the observations, and the uncertainty of the mean value for EXP1a is about 20 W m\({}^{-2}\) and rises to about 25 W m\({}^{-2}\) for EXP3a. Both values are reasonable, especially considering that the typical measurement error of a radiometer (that measures the most important component of net radiation, e.g., the incident solar radiation) in standard commercial sensors is 20-30 W m\({}^{-2}\).
## 2 Related Works
The authors of [25] test teleultrasound over multiple network conditions, including LTE, 5G, and Ethernet. They suggest that this particular teleoperation domain is rapidly approaching feasibility over such network technologies. The authors in [26] explore various areas for improvement related to robotic surgery, for example, in instrument motion scaling. They tested various configurations to determine their relative merits and benefits. Their tests indicated the potential to achieve benefits to task times in telesurgery by using specific configuration sets for motion scaling. The work by [PERSON] et al. presented in [27] illustrates the potential for telerobotic surgery to be conducted over 5G cellular networks. They performed surgery on 12 patients of various ages. They measured an average of 28 ms during these surgeries, conducted with a 100% success rate. These tests were conducted between a host hospital where the patient and surgical robot were located and five test hospitals located in different cities where the surgeon was located. Similarly, in [28], the authors used 5G for telesurgery experiments across a separation distance of 300 km between the host and client. They used a video camera and reported that all the surgeries were successfully completed. The network latency was approximately 18 ms throughout these tests for control communication and about 350 ms for the video camera feed. [PERSON] et al. and [PERSON] et al. [9; 10] presented a demonstration system and experiments for a remote mobile machinery control system utilizing 5G radios and a digital twin with a hardware-in-the-loop development system. A 5G test network was used by harnessing the network within a virtual demonstration environment, with remote access from a distance of 500-600 km. Virtual private networks were utilized to better represent real-life scenarios. The haptic remote control experimental results indicated that with a suitable edge computing architecture, an order-of-magnitude improvement in delay over the 5G connection compared to existing LTE infrastructure was achieved, resulting in an achieved latency as low as 1.5 ms.  
To summarize our literature review, we were unable to find any study that could provide an in-depth latency analysis of telerobotics investigating the contributing factors to the end-to-end latency in typical telerobotics applications. The work we are presenting in this paper thus explores network latency for a larger variety of network topologies and separation distances compared to the work presented thus far in the scientific literature. It also conducts an in-depth latency component anal ysis to further illuminate the contributing factors to the end-to-end latency in typical telerobotic applications.
# Segmenting Individual Tree from TLS Point Clouds Using Improved DBSCAN
## 4 Discussion
### Individual Tree Segmentation Analysis of Complex Forest  
In the mixed forest, the recall, precision, and F1-score of segmentation are 87.37%, 94.32% and 0.91, respectively. While in the artificial forest, the recall, precision, and F1-score of segmentation are 92.70%, 95.93% and 0.94, respectively. The segmentation effect is better for simple forest types, and relatively poor for individual tree segmentation of forked trees, multi-trunk trees and other complex trees. Because in the result of segmentation, the trunk that belongs to one tree may be mistaken for multiple trees, resulting in the phenomenon of false segmentation. In the case of more complex trunks, the choice of parameters \(\varepsilon\) and \(MinPts\) is more important. However, the appropriate parameter \(\varepsilon\) can still bring the point clouds at the trunk together. When the distance between multiple trunks belonging to the same tree is much smaller than the distance between different trees, the improved DBSCAN algorithm will obtain the appropriate value of the parameter \(\varepsilon\), so our method can achieve good segmentation effect in this case. However, the total segmentation effect is not as good as the simple case of individual trunk. Figure 13 shows an example of partially complex tree segmentation.  
Figure 12: Under-segmentation error. When \(\varepsilon=3\) (>0.72), (**a**) six trees are not correctly segmented, (**b**) three trees are not correctly segmented.
## 1 Introduction
This feature enables the extraction of detailed information, such as the distribution and fluctuation of the refractive index in a transparent medium [21]. Consequently, a two-dimensional map of refractive index changes can be obtained, facilitating highly precise and sensitive temperature calculations. In the present work, we employ an off-axis digital holographic interferometry technique [22] integrated with a Mach-Zehnder interferometer based setup for temperature measurement of a transparent medium. This technique offers fast measurements and higher accuracy compared with other available interferometry techniques, making it most suitable for our intended purpose. Considering the small size of the flame as a transparent medium in our experiment, the Mach-Zehnder interferometer setup is preferred over the lens-less Fourier Transform digital holography (LLFTDH) as used in [23]. Our experimental layout is simplified and cost-effective compared with the quadriwave lateral shearing interferometer (QWLSI) methods, which require specific two-dimensional gratings [17; 24; 25]. The measurement speed is limited only by the camera's frame rate and the laser's power, enabling the capture of fast-changing phenomena. Importantly, the presented method simplifies the calculation process compared with previous approaches [26; 27; 28; 29; 30; 31], making it computationally efficient. We calculate the temperature coefficient of the refractive index [32] of air and establish the relationship between the phase changes and temperature variations based on the temperature coefficient of the refractive index. This approach avoids the burden of complicated mathematical inversions, such as the inverse Abel transformation, convolution back projection, iterative reconstruction techniques, as well as additional work with the Lorentz-Lorentz equation [23] and Gladstone-Dale relation [15; 33; 34].
## 3 DenseNet-Hybrid Control Plane Architecture
### DenseNet-Hybrid SDN Infrastructure  
The idea of deploying SCs is to enhance the coverage area and capacity offered by macro-cells by adding more base stations with smaller coverage. The SCs are responsible to connect the sensor devices as well as other Internet of Things (IoT) devices to the mobile network. The macro-cell site usually acts as a gateway site for aggregating the traffics of a set of SCs and provides connectivity to the core network.  
Due to the capacity concern of mobile networks, fiber-based technology can be a good option for the mobile backhaul. However, due to implementation cost (e.g., laying fiber to all the SCs) and the nature of the area to cover, it may not be very cost-effective. On the other hand, a multi-hop wireless backhaul, by interconnecting SCs with wireless links (e.g., microwave or mm-Wave backhaul links), can provide a cost-effective solution. The deployment of SCs in mobile backhaul networks can greatly enhance network capacity by reusing the radio spectrum [1].  
In this context, by maintaining the global view of the network, SDN can provide huge advantages for managing the network and to provide better resource allocation. Even though SDN provides better manageability of the network, in wireless networks, where control messages are sent over in-band channels, the performance of the SDN-based network may degrade due to channel impairments (e.g., the loss of control messages). In general, in mmWave mobile backhaul, mesh topology is an ideal candidate to interconnect the SCs where SC base station is placed on the street furniture (e.g., a lamp post). In such a scenario, due to obstructions, all the SCs may not be connected directly to the aggregation point (e.g., macro-cell).  
Only one SC may be connected directly to the aggregation point and act as a gateway node for other SCs that are in the coverage area of the macro-cell. The SDN controller may be placed in the aggregation point (see Figure 1). Communication between the SDN controller and the SCs will be maintained via gateway SC, which is a realistic in-band SDN deployment.  
As Figure 1 illustrates, we propose a hybrid control plane architecture for DenseNet. In this way, our architecture attempts to preserve the benefits of both worlds (i.e., centralized and distributed). Specifically, we propose to maintain a centralized control logic to preserve the benefits of canonical-SDN (i.e., simple network management, programability) under reliable control plane conditions, whereas the distribcharge of acting under unreliable conditions to quickly react to failures and to avoid the inefficient use of an inaccessible (or hard to reliably reach) centralized control logic. In this way, network nodes are modified to be able to dynamically switch among both modes of operation.