esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Test Experiments
### Length Measurement Experiment
#### 5.5.2 Experimental Procedure  
During the test, we set the scale bar at four positions (Position 1-4). \(D\) represents the distance between the scale bar and the laser tracker. \(H\) represents the measured horizontal angle to HOME of the laser tracker. Measurements were made with the laser tracker positioned and oriented as shown in Figure 14 and described in Table 2. Three repeated measurements were performed in each position.  
The coordinates of two ends of points of the scale bar measured by the laser tracker are shown in Figure 15.  
With the measured coordinates of the two points of scale bar, we can calculate the length (\(L\)) of the scale bar:  
\[L=\sqrt{(x_{B}-x_{A})^{2}+(y_{B}-y_{A})^{2}+(z_{B}-z_{A})^{2}} \tag{10}\]  
where the coordinate of point A of the scale bar is \(A(x_{A},y_{A},z_{A})\), and the coordinate of point B of the scale bar is \(B(x_{B},y_{B},z_{B})\). Then, the coordinate measurement error of the laser tracker can be verified by calculating the difference between the measured length of the scale bar and the calibrated length of the scale bar. The calibrated length (\(L_{0}\)) of the scale bar we used in experiment was \(1049.974\pm 0.010\) mm. Therefore, the coordinate measurement error of the laser tracker can be expressed as  
\[E=\left|\begin{array}{cc}L-L_{0}\end{array}\right| \tag{11}\]  
\begin{table}
\begin{tabular}{p{42.7 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Position Number** & **Calibrated Length of Scale bar/mm** & **Distance from Tracker, D/m** & **Laser Position** & **Description of Scale Bar Position** & **Horizontal Angles, H/deg** \\ \hline
1 & \(1049.974\) & \(5/10\) & Horizontal & \(0\), \(90\), \(180\), \(270\) \\
2 & \(1049.974\) & \(5/10\) & Vertical & \(0\), \(90\), \(180\), \(270\) \\
3 & \(1049.974\) & \(5/10\) & Right diagonal & \(0\), \(90\), \(180\), \(270\) \\
4 & \(1049.974\) & \(5/10\) & Left diagonal & \(0\), \(90\), \(180\), \(270\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Length measurement system test using a laser tracker.  
Figure 14: Length measurement test.  
Figure 13: The experimental setup.  
We substituted the calibrated transit offset error (\(t\)) and transit tilt error (\(i\)) obtained from Sections 5.2 and 5.3 into the error model mentioned in Section 3, and we then compensated for the distance (\(D\)) and angles (\(H\) and \(V\)) measured by the laser tracker. Finally, the compensation coordinates of point A and B of the scale bar could be calculated by using the Equation (1). Therefore, the calibration of the transit offset and tilt errors could be verified by comparing the magnitude of the coordinate measurement error of the laser tracker before and after compensation.
# A Deep Learning Approach to Estimate Soil Organic Carbon from Remote Sensing
## 3 Materials and Methods
### Chilean Soil Organic Carbon Database  
The Chilean Soil Organic Carbon Database (CHLSOC) is the largest and most-comprehensive repository of soil organic carbon (SOC) data in Chile. Created through a collaborative effort involving 39 public and private institutions, CHLSOC represents an unprecedented national initiative.  
Constructed between May 2018 and April 2019, CHLSOC incorporates diverse data sources, including soil surveys, publications, and unpublished research data. The database comprises 13,612 data points, 89% of which were previously unpublished or inaccessible to the scientific community.  
CHLSOC provides valuable insights into the temporal distribution of soil data in Chile. The date of the sample collection are available for more than 90% of the included data, allowing researchers to explore changes in SOC over time and investigate temporal patterns and trends. The majority of points were sampled between 2006 and 2018, with some data dating back to 1959. There are 6900 points relevant to our study in the dataset, as they have been collected in the time frame in which the Sentinel-2 mission was operational.  
In Chile, the distribution of soil and SOC data is highly concentrated in regions that have intensive agricultural and forestry activities, encompassing approximately 25% of  
Figure 1: Spatial distribution of sampled SOC ground truth values for each of the datasets.  
the country's territory [47]. These areas, characterized by high-quality soils and available water resources, have experienced significant land use conversions for agriculture, forestry, and urban development. However, beyond these regions, there is a notable scarcity of soil data, particularly in areas with limited agricultural and forestry activities. Figure 1C shows the spatial distribution of the CHLSOC dataset.  
To facilitate research, land management, and policy-related endeavors, CHLSOC is freely accessible for registered users to download under the Creative Commons Attribution 4.0 International Public License.
## 3 Multiple RFI Sources Geolocation Method
### RFI Sources Geolocation Estimation
The position of each particle in any iteration represents a possible solution. After each iteration is completed, fitness is used to select and update the individual optimal position and the group optimal position:  
\[\left\{ \begin{array}{l}\mu_{j}^{t+1}=\beta^{t}\mu_{j}^{t}+v_{1}\xi_{1}^{t} \Big{(}\delta_{j}^{t}-\rho_{j}^{t}\Big{)}+v_{2}\xi_{2}^{t}\Big{(}\delta_{g}^{t }-\rho_{j}^{t}\Big{)}\\ \rho_{j}^{t+1}=\rho_{j}^{t}+\mu_{j}^{t+1}\end{array} \right.\quad, \tag{48}\]  
where \(\beta\) denotes the inertia weight, \(v_{1}\) and \(v_{2}\) are the cognitive and group scaling parameters and their value range is \((0,+\infty)\), \(\zeta_{1}^{t}\) and \(\zeta_{2}^{t}\) are two random values selected in the t-th iteration within the range \([0,1]\). According to Equations (47) and (48), the solver of the position optimization problem can be constructed. In summary, the procedure of the RFI sources geolocation estimation method used in this section can be summarized as follow. Firstly, according to the spatial coordinate \({}_{i}\) at the i-th azimuth time of the radar array, the vector \(\mathbf{v}_{i}^{k}\) from the \(k\)-th RFI source to the radar array and the transformation matrix **H**, the optimization problem in Equation(47) can be constructed. Then, the PSO algorithm shown in Figure 6 is applied to solve it.
## 6 Results and Discussion
### Descriptive Statistics of the Experimental Dataset Variables
#### 6.1.4 Water Content vs. UCS  
* A weak to moderate negative correlation indicates that higher moisture content is generally associated with reduced mechanical strength. However, the considerable scatter suggests that additional factors also affect UCS.
# Hi-RCA: A Hierarchy Anomaly Diagnosis Framework Based on Causality and Correlation Analysis
## 4 Experiment
### Evaluation of Anomalous Microservice Locator  
In this subsection, we discuss the effectiveness of _Anomaly Microservice Locator_. In Figure 3, we present the performance of Hi-RCA in anomalous microservice location in different datasets. It shows that Hi-RCA can achieve almost 90% in terms of \(PR@1\), it can effectively locate all root causes in the top three anomaly services, and its performance achieves almost 100% in terms of \(PR@3\).  
Since the anomalous microservice location is the middle process in Hi-RCA, we compare its performance with other baseline methods to illustrate its effectiveness. We choose three different anomaly detectors; for each microservice, its abnormality is quantified as the amount of its anomalous metrics. The root cause microservice is defined as the microservice with the highest abnormality severity.  
**Baseline anomaly detectors.**  
* _Quantile anomaly detector_[47] compares each time series value with historical quantiles. In our experiment, we detect anomalies if their value is above 99% or below 1%.
* _Level-shift anomaly detector_[47] detects a shift of value level by tracking the difference between median values at two sliding time windows next to each other.
* _Cauchy detector_[48] detects an anomaly by comparing the current value and the history-smoothed value in a sliding window.  
As presented in Table 1, it can be observed that our _Anomaly Microservice Locator_ designed based on Kalman filtering achieves the best accuracy. The reason is that a single anomaly detector can only obtain one type of anomaly and fails to capture various anomalies with different symptoms. Without the consideration of noise in data collection, the detection precision is more vulnerable to false alarms.
## 2 Floating PV Power Generation Characteristics  
### Power Generation Characteristics Comparison System (PGCCS)  
To precisely compare and analyze the power generation characteristics of floating and land-based PV systems, depending on the environmental effects, both systems should have the same system specifications and installation conditions, such as PV modules, inverters, the capacity of facilities, installation orientation, and angle [16].  
In this study, 2.5 kW power generation characteristics comparison systems (PGCCS) were installed, as shown in Figure 3. The configuration of the power circuit of the 2.5 kW PGCCS is shown in Figure 4. Table 1 lists the specifications of the PV module and Power Conditioning System (PCS) of the PGCCS installed, as shown in Figure 4. The installation direction of PGCCS was south, and the mounting tilt was fixed at 30 degrees.  
Figure 3: Pictures of PGCCS for floating and land-based PV systems: (**a**) 2.5 kW floating PV, (**b**) floating PV inverter, (**c**) measuring instrument box, (**d**) 2.5 kW land-based PV.  
To analyze the power generation characteristics, the PV output and meteorological data, as summarized in Table 2, were collected.  
The analysis period was six months from 1 July 2017 to 31 December 2017, and the analysis time was from 7:00 to 19:00 during the daytime when solar power is generated. Because the PGCCS was installed at the end of June 2017, it was analyzed based on the results of operation over the six months from 1 July 2017 to 31 December 2017. As the climate during spring and autumn exhibits the same characteristics, in Korea, all the different seasonal characteristics for these seasons could be covered during this period of analysis. The analysis was performed in the daytime because the module temperature was influenced by solar irradiance during the daytime and this directly affected the system efficiency. Night-time was excluded because the deviation in the PV module temperature characteristics reduced when it was included in the analysis time.
## 7 Conclusions  
We implemented our IAPS on mobile devices by combining machine learning with traditional image-processing techniques to incorporate image scene information. First, we constructed a general scene recognition dataset and a lightweight network, ShuffleNet. The model was converted using TensorFlow Lite and deployed on a mobile device. Subsequently, the image contrast and color saturation were adjusted using the GPUImage-ContrastFilter and the GPUImageSaturationFilter on the GPU of the device. A local mean-square error filter algorithm and a log curve were employed for skin smoothing and whitening. Even though the accuracy of the model is 70%, the aesthetic quality of the IAPS-processed image is high, which verifies the effectiveness of IAPS. Finally, we validated the functionality of the system using an anonymous questionnaire survey with 100 participants. The findings show that most of the participants believed that the aesthetic beauty of the processed images was satisfactory, indicating that the proposed IAPS is more aligned with the public aesthetic standards. However, our work still faces challenges, such as limited improvements in image aesthetics; moreover, personalized user needs was not considered. These challenges require further investigation in future research.  
Conceptualization, L.S.; Methodology, X.Z.; Validation, P.Y.; Writing--original draft, Z.H. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Phone Model** & **Redmi k30** & **HONOR 30 Pro** & **OPPO Find X** \\ \hline CPU & Snapdragon 730G & Kirin 990 & Snapdragon 845 \\ RAM (GB) & 8 & 8 & 8 \\ Operating System & Android 11 & Harmony 2.0.0 & Android 10 \\ Processing Time (ms) & 63 & 57 & 52 \\ Memory Occupation (MB) & 73 & 70 & 71 \\ CPU Occupation (\%) & 12 & 13 & 9 \\ Stability & 3000 & 3000 & 3000 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance testing on different phones.  
**Funding:** This work was supported in part by the National Natural Science Foundation of China under Grant No. 62072159 and No. 61902112, the Science and Technology Research Project of Henan province under Grant No. 222102210011 and No. 232102211061.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Permission for faces was obtained in the study.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Solution Algorithm  
Note that the proposed line planning model with resource constraints becomes NP-hard. Although it is a linear integer programming model that can be solved by commercial solvers, it is not an effective method for real-scale cases. Heuristic algorithms and meta-heuristic algorithms [39, 40] are widely used to solve various complex engineering problems. Due to integrating the multi-period strategic-level line planning decision with resource constraints, it will be a challenging task to design neighborhood rules. Fortunately, we can use the linear relaxation technique to deal with the complex constraints first. Then, a round heuristic [41, 42] is designed to obtain a satisfied solution to the original problem.  
There are four main steps of the proposed heuristic algorithm: first, the linear relaxation solution of the original problem is obtained by a commercial solver, and round the variables \(n^{up}(t),\ n^{dourn}(t)\) of the first time period, \(t=1\); second, count the number of passengers on each riding arc, and filter out the integer variable \(x\) and update the number of passengers on each riding arc; third, round the remaining variables \(x\) according to the remaining resources and passengers on the riding arc, then update the number of passengers on each riding arc; and fourth, check the line capacity and remove overloaded passengers. The detailed Algorithm 1 is described as follows:  
```
Input: LP solution \(\left(x^{*,up},x^{*,up},y^{*},n^{*,up},n^{*,down}\right)\) Output: IP solution \(\left(x^{*,up},x^{*,up},y^{*},n^{*,up},n^{*,down}\right)\)
1 set \(n^{*,dourn}(t=1)=round\left(n^{*,dourn}(t=1)\right),n^{*,up}(t=1)=round(n^{*,up }(t=1))\)
2 for\(t\in T\)do
3 calculate \(n^{*,up}(t)\) \(n^{*,up}(t)=n^{*,up}(t-1)-\sum_{t\in\omega}\sum_{f\in F}x^{*,up}_{t,f,k}(t-1) \cdot f\cdot k\) + \(\sum_{t\in dourn}\sum_{f\in F}x^{*,dourn}_{t,f,k}(t-\mu)\cdot f\cdot k\)
4\(\overline{L}^{up}:=\emptyset\) // check whether the variable x is an integral value
6 set riding arc weights \(\pi_{a}\coloneqq\sum_{p:\hat{a}\in\pi_{d}(p)}y^{*}_{p},n^{\prime}_{\hat{a}}=\pi _{\hat{a}}\)
7 for\(l\in\mathcal{L}^{up}\)do
8 for\(f\in F\)do
9 for\(k\in K\)do
10 if\(x^{*,up}_{l,f,k}(t)=1\)then
11\(x^{*}_{l,f,k}(t)\coloneqq 1\)
12\(\pi_{a}\coloneqq\max\{0,\pi_{a}-\delta\cdot f\cdot k\}\ for\ \hat{a}\in l\)
13
14 end if
15 if\(x^{*}_{l,f,k}(t)\) is fractionalthen
16\(\overline{L}^{up}\coloneqq\overline{L}^{up}\cup\{l\}\)
17 end if
```  
**Algorithm 1**The improved round heuristic algorithm.
## 1 Introduction
Therefore, it can work in harsh environments outside a laboratory. It can also produce both 780 nm and 852 nm lasers, so we could conveniently perform laser cooling and trapping, as well as manipulating both Rb and Cs atoms simultaneously, it is useful in the Rb-Cs biatomic interferometer and cold RbCs dimer experiments. Most importantly, the spots produced by this system have good beam quality and a wide tuning range, which make it a scheme that is more promising for application. Considering all of the advantages, the biatomic interferometer and cold RbCs dimer experiments can be performed in unstable environments, such as an airborne system and a system in the space station.
## 2 Versatile Video Coding (VVC)
### Inter Prediction  
The main contribution regarding inter picture prediction in VVC is the introduction of the merge mode along motion vector differences (MVD) that is used for finer representation of motion parameters and the adaptive motion vector resolution [8]. Similar to HEVC, the merge mode adopts candidates from spatial, temporal, and zero motion vectors. Further, it adopts a pairwise average vector, and a history-based vector [5]. VVC has achieved better precision accuracy than its predecessors by performing motion prediction at the sub-coding unit level. Moreover, affine motion compensated predictions [30] have been previously performed for handling zooming operations, rotation, and transformation motions. Here, two or three motion vectors were used to describe a sub-prediction unit. Therefore, triangle partitioning was applied at the CU level, where CU was divided into two triangles. This approach allowed more precise predictions within a CU.
# Model Test on Grouting Properties of Alluvial Filler Soil
## 7 Engineering Characteristic Analysis of Grouting Soil
### Observation and Analysis of Slurry Diffusion  
#### 7.4.1 Visual Observation of Slurry Diffusion  
A full-section excavation of grouting soil was carried out to observe the shape, size, and distribution radius of slurry veins. The reinforcement effect of grouting and the distribution of slurry were determined by full section excavation directly and effectively, as shown in Figure 14.  
As shown in Figure 14, the grouting reinforcement and slurry filling characteristics are given as follows:  
(1) The slurry effectively filled most of the voids in the alluvial filler soil, and the splitting grouting phenomenon was produced in the clay, and the gravel voids were filled.  
(2) The density of the slurry in the horizontal layer distribution was denser than that of the vertical distribution, which indicates that the horizontal splitting is easier to produce than the vertical splitting. After the slurry split the soil, there was a slurry channel crack with high intensity, so these split slurry cracks do not affect the overall strength.  
(3) The slurry was diffused along the fissure spreading direction; the slurry in the fissure is packed tightly with a filling thickness of 2.0 cm and partially filled with gravel-shaped cement with a diameter of about 2.0-5.0 cm. In the loose place of gravel filling, it can be seen that the cement stone and angular gravel are condensed into lumps with a length of 3.0-5.0 cm.  
Figure 14: Diffusion of slurry in the excavation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Grouting** & **Density** & **Water Content** & **C** & \(\varphi\) & **Compression** & **Compression** \\
**Sequence** & **(kN/m\({}^{3}\))** & **(\%)** & **(kPa)** & **(\({}^{3}\))** & **Coefficient** & **Modulus (MPa)** \\ \hline Before grouting & 19.9 & 12.7 & 28.70 & 21.50 & 0.20 & 6.65 \\ After grouting & 21.0 & 10.4 & 43.76 & 21.96 & 0.12 & 10.28 \\ \hline \end{tabular}
\end{table}
Table 3: Details of dynamic model tests.  
(4) In the clay and silty sand filling zone, there were network-like massive cement stones with a length of 5.0-8.0 cm.
# AVSS: Airborne Video Surveillance System
## 6 Evaluation
### Simulations  
In this section, the placement algorithm we devised is evaluated in various ways. First, the global and local score change along with increasing number of UAVs is provided. The time consumed to obtain each placement is also discussed, and the effect of different \(\alpha\) value is shown.
# Damage Detection in Beam Bridges Using Quasi-static Displacement Influence Lines
## 4 Experimental Example  
For the above numerical example, only one quasi-static displacement IL through each cross-section is applied to evaluate the performance of the proposed method. In this section, a model bridge with a cross-section consisting of three T-shaped beams is taken as an example. The assessment of the proposed method is expanded to use the displacement ILs in consideration of the transverse load distribution.
## 3 Results
### Genomic Distribution of M. longifolia TPS Genes  
The 63 _TPS_ genes were mapped to nine scaffolds of _M. longifolia_ genome sequence assembly based on their localization information (Figure 3). The distribution of these genes is uneven, for example, only two _TPS_ genes mapped onto scaffold3 and scaffold6, while 19 _TPS_ genes clustered on scaffold9. The clustered distribution of some subfamily members was also observed, such as nine _TPS-b_ genes clustering on scaffold11 and 16 _TPS-e_ genes clustering on scaffold9. Tandem duplication and segment duplication are common phenomena related to the increase in gene copies in plants. In this study, tandem duplication and segment duplication of _TPS_ genes were also analyzed. Seven tandem duplicates and 3 segment duplicates of _TPS_ genes were observed in the _M. longifolia_ genome sequence assembly, and it contained a total of 30 _TPS_ genes (Figure 3). The duplication events occurred in the _TPS-a_, _TPS-b_, and _TPS-e_ subfamilies.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Species**} & \multicolumn{6}{c}{**Subfamily**} & \multirow{2}{*}{**Total**} \\ \cline{2-2} \cline{4-7}  & & **a** & **b** & **c** & **e** & **f** & **g** \\ \hline _M. longifolia_ & 13 & 22 & 5 & 18 & 1 & 4 & 63 \\ _O. teruiflorium_ & 14 & 12 & 7 & 2 & 1 & 7 & 43 \\ _S. inidium_ & 21 & 5 & 6 & 3 & 0 & 7 & 42 \\ _S. inultrilumina_ & 32 & 21 & 5 & 2 & 1 & 3 & 64 \\ _S. s. splendens_ & 52 & 30 & 7 & 7 & 2 & 6 & 104 \\ _A. thaliana_ & 22 & 6 & 1 & 1 & 1 & 1 & 32 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of _TPS_ subfamily gene numbers in _M. longifolia, A. thaliana_ and other Lamiaceae plants.  
Figure 2: Phylogenetic analysis, subfamily classification, gene structure and conserved domains in _M. longifolia_ TPSs. The black rectangles represent exons and the lines represent introns. The coding sequences of the conserved N-terminal domain, C-terminal domains, RR(X)8W motif, NSE/DTE motif, RXR motif, and DDXXD motif are represented in green, orange, purple, blue, gray, and red, respectively.
###### Abstract  
Hydrodynamic fingering induced by gel formation shares common features with growing biofilms, bacterial colonies, and the instability of a confined chemical garden. Fluid displacement with gel formation is also essential in various engineering applications, including CO\({}_{2}\) leakage remediation from storage reservoirs and enhanced oil recovery. We conducted Hele-Shaw cell displacement experiments for a miscible fluid system using skim milk and aqueous citric acid solution. This study aimed to investigate the effects of gel film formation on the fingering instability of a miscible fluid system and develop a mathematical model of the sequential growth of gel film formation at the fingertip. We found that the gel film formation thickens with time, resulting in instability at the interface. A distinctive fingering pattern, resembling tentacles, appears where miscibility is suppressed, and the growth of the finger is localized at the fingertip. The finger width remains constant with increasing flow rate, whereas the number of fingers increases linearly before the fingers merge. The gap width significantly limits the finger width. Finally, a mathematical model of sequential film thickness growth for a bubble-like fingertip structure was developed. This model is based upon the interplay between the diffusion of citric acid through the existing gel film formation and elongation of the fingertip. The model provides an understanding of the fundamental mechanism of the growth of the bubble-like fingertip.  
gel film; finger instability; miscible; finger number; film thickness; finger width; mathematical model +
Footnote †: journal: Applied Sciences