esa-sceva/satcom-chunk-collection huggingface dataset:
# SbMBR Tree--A Spatiotemporal Data Indexing and Compression Algorithm for Data Analysis and Mining
## 5 Conclusions  
In this paper, we propose the SbMBR tree, a novel indexing and error-bounded lossy compression method for spatiotemporal data. The proposed method hierarchically divides the data space into several MBRs through Hilbert curves and builds an indexing structure on this basis. The range query algorithm in the SbMBR tree is also presented. For cross-domain data mining and analysis scenarios, mutual information is used to estimate information loss. We evaluate the performance of our proposed methods on three of the most important aspects, including computational efficiency, compression ratio, and data utility. The results are compared with some of the typical indexing and compression algorithms, and the summaries are: (1) the loss control mechanism is unique among existing tree-indexed lossy compression algorithms; (2) the compression algorithm based on the SbMBR tree has slightly better compression performance compared to the quadtree and JPEG algorithms; (3) the overall efficiency of the building and querying SbMBR trees is slightly better than that of quadtree and brute R-tree; (4) the error estimation method based on mutual information proposed in this paper is consistent with the real error. Overall, our results are promising in that the SbMBR tree might be more efficient if it is applied to data sets that contain larger spatiotemporal regions with high local similarities.  
Figure 11: Comparison of SbMBR tree algorithm and JPEG algorithm through by \(1-error\).  
**Author Contributions:** Conceptualization, B.S.; Methodology, R.G.; Validation, X.Z.; Formal analysis, R.G. and X.P.; Investigation, Z.W.; Data curation, R.G., Z.W., X.P. and R.Z.; Writing--original draft, R.G. and Z.W.; Writing--review & editing, B.S.; Supervision, X.Z.; Project administration, B.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The authors extend their appreciation to National Key Research and Development Program of China (International Technology Cooperation Project No.2021 YFE014400) and the National Science Foundation of China (No.42175194) for funding this work.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Safe Path Planning of Mobile Robot Based on Improved A* Algorithm in Complex Terrains
## 3 Improvement of A* Algorithm  
In order to increase the safety of the planned path generated by the conventional A* algorithm, a new environment modeling method is proposed and the evaluation function is improved to keep the path away from obstacles. As a result, the safe path is smoothed without decreasing the safety.
## 2 Methodology
### Target Decomposition for DLP Data (H/\(\alpha\) Decomposition)  
The DLP contains HH/VH or VV/HV scattering matrix coefficients and also yields a 2 \(\times\) 2 coherency matrix--similar to DCP. Consequently, the \(H/\alpha\) decomposition approach can be directly performed on this coherency matrix [1, 22]. Note that, in this study, we use the synthetically generated DLP data obtained by unitary transformation from circular to linear basis:  
\[\left[\begin{array}{cc}S_{HH}&S_{HV}\\ S_{VH}&S_{VV}\end{array}\right]=2\left[\begin{array}{cc}1&j\\ j&1\end{array}\right]^{-1}\left[\begin{array}{cc}S_{LL}&S_{LR}\\ S_{RL}&S_{RR}\end{array}\right]\left[\begin{array}{cc}1&j\\ j&1\end{array}\right]^{-1}. \tag{13}\]  
The coherency matrix is then expressed by the outer product of the Pauli vector in the same manner as given in (4) (H transmit):  
\[\mathbf{k_{DLP}}=\left[\begin{array}{c}S_{HH}\\ S_{VH}\end{array}\right], \tag{14}\]  
\[\left\langle\left[\mathbf{C_{DCP}}\right]\right\rangle =\left\langle\mathbf{k_{DLP}}\cdot\mathbf{k_{DLP}^{\dagger}}\right\rangle\] \[=\left[\mathbf{U_{2}}\right]\left[\begin{array}{cc}\lambda_{1} &0\\ 0&\lambda_{2}\end{array}\right]\left[\mathbf{U_{2}}\right]^{\dagger}, \tag{15}\]  
where \(\left[\mathbf{U_{2}}\right]\) is  
\[\left[\mathbf{U_{2}}\right]=\left[\begin{array}{cc}\cos\alpha&-\sin\alpha e ^{-i\delta}\\ \sin\alpha e^{i\delta}&\cos\alpha\end{array}\right]. \tag{16}\]  
To compute \(H\) for the DLP mode, the same equation of the DCP mode given in (6) is used. Calculation of \(\alpha\), however, requires a different process, given as:  
\[\overline{\alpha}=P_{1}\alpha+P_{2}\left(\frac{\pi}{2}-\alpha\right), \tag{17}\]  
where \(P_{i}\) are calculated in the same manner as (8). Note that the boundary curves of the \(H/\alpha\) plane for the DLP mode are the same as the DCP curves (see Figure 2b).
## 5 Computational Experiments and Results  
### Experiment Environment  
Each session follows one path for routing its required traffic emulated as a real time surveillance streaming data and is not allowed to change during the holding time. The session arrivals followed a Poisson process, and the holding time of each session is set to be an exponential distribution with average 10 s. The experiment with average session arrival rates of 0.25, 0.5, 1 and 2, and the corresponding packet arrival rates are 40, 20, 10 and 5. Figure 4 is shown as the experiment environment which QoS requirements are set in 3 \(\times\) 3, 5 \(\times\) 5, 7 \(\times\) 7 and 9 \(\times\) 9 squares in 3 ms, 4 ms, 5 ms and 6 ms, respectively.  
As Table 5 shows, there is average of 100 packets per second in the network of each topology. The size of each transmitting UDP packet is 1,000 bytes at each packet arrival rate. Aside from the control messages of distribution routing protocol, messages such as HELLO messages for sensing the neighbors and TC message for broadcasting topology information are sent at a fixed period of 5 s.  
The estimate the parameters of our routing metric (shown in Table 6), we record each packet delay and packet inter-arrival time and then calculate mean delay and aggregate flow with corresponding queue length (_i.e._, average number of packets) of each link for every second. The number of data fit for power regression is set to be 10, that is, the data used to compute the regression function are the newest 10 records and the interval of each is 1 s. Additionally, the value of \(K\) in the routing algorithm is set as 5 (_i.e._, at most the five shortest or fastest paths for each O-D pair) and the threshold \(\beta\) for admission control heuristic algorithm is set to be 1.3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & \multicolumn{4}{c}{**Value**} \\ \hline Average Holding Time & \multicolumn{4}{c}{10} \\ Average Session Arrival Rate & 0.25 & 0.5 & 1 & 2 \\ Average Number of Active Sessions & 2.5 & 5 & 10 & 20 \\ Packet Arrival Rate & 40 & 20 & 10 & 5 \\ Average Traffic Input & \multicolumn{4}{c}{100 (packets/s)} \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experimental session types.  
Figure 4: Experimental environment (5 \(\times\) 5).  
If the received signal strength exceeds the reception threshold, the packet can be successfully received. If received signal strength exceeds the carrier-sensing threshold, the packet transmission can be sensed. However, the packet cannot be decoded unless signal strength surpasses the reception threshold. Both the transmission range and interference range are calculated by the two-ray ground reflection model according to the reception threshold and the carrier-sensing threshold, respectively.  
This paper focuses on the effectiveness of our routing metric and related parameters at the on-line stage, and its reduction of system-wide impact between each coming session with QoS provisioning. Thus, we use a single channel in the experimental environment to decouple the effect of the channel assignment algorithm and evaluate the performance of our routing algorithm.
## 4 Results of the Experiment
### Analysis of Spatial Variability in Drought
#### 4.3.3 Year-by-Year Changes in Vegetation in Laibin  
The drought classification outcomes for the Laibin vegetation region were analyzed, quarterly from 2021 to 2023. By averaging these results, an overview of the annual drought classes for the region was obtained, as illustrated in Figure 16.  
Figure 15: Changes in the percentageof drought area of vegetation in Laibin by season in the last three years: (**a**) Map of changes in the percentage of drought area of vegetation in Laibin by season in 2021; (**b**) Map of changes in the percentage of drought area of vegetation in Laibin by season in 2022; (**c**) Map of changes in the percentage of drought area of vegetation in Laibin by season in 2023.  
During the period of 2021-2023, the predominant drought categories experienced in the vegetation area of Laibin City were classified as light drought and moderate drought. Moderate drought conditions were more prevalent in the eastern-central, central, and southwestern sectors of the Laibin City vegetation region. Conversely, localized regions exhibited normal performance with no drought occurrences, while the remaining areas of the region were primarily characterized by light drought conditions.  
The data presented in Figure 17 illustrates the distribution of vegetation areas in Laibin City over the last three years. The analysis reveals that wet areas in the region have been scarce during this period. Conversely, normal areas have shown a relatively consistent presence, comprising 1.52%, 2.82%, and 2.45% of the total area in each respective year. Notably, the predominant category within the vegetation area of Laibin City has been mild drought, encompassing over half of the total area. Specifically, in 2021, 508,174.47 hectares (ha) were classified as mild drought, constituting 52.09% of the area; in 2022, this figure increased to 575,452.26 ha, representing 58.99% of the area; and in 2023, the mild drought area covered 61.7 ha. Light drought areas in 2023 accounted for 619,706.70 ha, making up 65.53% of the total area, marking the largest extent of medium drought observed in the past three years. The significance of the medium drought category is evident, with 46.37% of the vegetation area in Laibin City classified as medium drought in 2021, totaling 452,331.72 ha. In 2022 and 2023, the medium drought areas covered 372,092.67 ha and 331,851.69 ha, respectively, accounting for 38.14% and 34.02% of the total area. Notably, severe drought areas have not been prominently observed in the region over the past three years.  
Figure 16: Map of yearly drought level results of vegetation areas in Laibin City in the last three years.
## 2 Main 3-HP Biosynthesis Pathways for the Construction of Microbial Cell Factories
### 1,3-Propanediol Pathway  
In _Acetobacter_, alcohols are selectively oxidized to the corresponding aldehydes and ketones by alcohol dehydrogenase (ADH), and the aldehydes are further oxidized to the corresponding carboxylic acids by aldehyde dehydrogenase (ALDH) [23]. _Gluconobacter oxydans_ has a native capacity to convert 1,3-PDO to 3-HP. Further, a recent study revealed the relevant enzymes catalyzing this process by UV mutagenesis and transcriptome analysis techniques in _G. oxydans_[24]. In this pathway, 1,3-PDO is converted to 3-HPA by ADH, after which 3-HPA is converted to 3-HP by ALDH. It was reported that ALDH is the rate-limiting enzyme of this production process [24], so balancing the activity of both enzymes is an effective strategy to increase the titer of 3-HP.
## 3 Results and Discussion
### Evolution of Thermophysical Properties
#### 3.2.1 Thermal Conductivity  
The evolution of the thermal conductivity of the inner shaft lining concrete on Day 1, Day 3, and Day 7 after pouring is depicted in Figure 6. The thermal conductivity of the shaft lining concrete decreased significantly with an increase in age. The reason is that the concrete gradually loses water and forms micro pores inside with the increase of age. The shaft lining thickness also significantly affected the thermal conductivity of the inner shaft lining concrete. The measurement results indicate that the thermal conductivity of the shaft lining at a specific age increased with its thickness. This occurrence was most likely caused by the variations in the internal moisture content of the inner shaft linings with different thicknesses, where the greater the thickness is, the more difficult it is to discharge the internal water. The thermal conductivity of the inner shaft lining concrete with three different thicknesses evolved between 0.92 and 2.16 W/(m\(\cdot\)k) within the first 7 d after pouring. Finally, the evolution equation for thermal conductivity with age within the first 7 d after pouring is fitted based on experimental data, as shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Number**} & \multicolumn{2}{c}{**Thermal Conductivity**} & \multirow{2}{*}{**Time (d)**} & \multirow{2}{*}{**Thickness (m)**} & \multirow{2}{*}{**Fitting Equation**} & \multirow{2}{*}{**R-Squared**} \\  & **(W/(m\(\cdot\)k))** & & & & \\ \hline
1 & y & x & 1.0 & \(y=-0.0263x^{2}+0.0450x+1.8913\) & 1 \\ \hline
2 & y & x & 1.2 & \(y=-0.0233x^{2}+0.0083x+2.0650\) & 1 \\ \hline
3 & y & x & 1.5 & \(y=-0.0179x^{2}-0.0433x+2.2213\) & 1 \\ \hline \end{tabular}
\end{table}
Table 2: Evolution equation for thermal conductivity with age within the first 7 d.  
Figure 5: Evolution of the maximum temperature on the inner shaft lining with thickness and the time to reach the maximum temperature in the shaft lining.
# Using Noise Level to Detect Frame Repetition Forgery in Video Frame Rate Up-Conversion
## 3 Proposed Noise-Level Detection
### Periodicity Detection  
The FR forgery is automatically identified by using spectrum analysis to detect the periodicity of the standard-deviation curve. The discrete Fourier coefficient \(\xi[k]\) of \(\sigma[t]\) is first computed as follows,  
\[\xi[k]\ =\ \sum_{t=0}^{N-1}\sigma[t]e^{-j\frac{2 kt}{N}}\ 0\leq k\leq N-1 \tag{13}\]  
where \(N\) is the video length, and Equation (13) is implemented by FFT. Then, the amplitude spectrum is obtained as follows,  
\[\eta[k]\ =|\xi[k]| \tag{14}\]  
Figure 3: Illustrations of standard-deviation curve for both original and forged _Foreman_ sequences.  
finally, the DC components of \(\eta[k]\) are removed as follows,  
\[\alpha[k]\ =\eta[k]-\frac{1}{N}\sum\limits_{k=0}^{N-1}\eta[k] \tag{15}\]  
as shown in Figure 4, by Equations (13)-(15), we analyze the standard-deviation curves of the original and forged _Foreman_ sequences in Figure 3. It can be seen that the amplitude spectrum of the original standard-deviation is small and distributed evenly while there is a large spike on the spectrum amplitude of the forged one. The spike is much higher than other coefficients, which can prove the periodicity of the standard-deviation curve, therefore determining whether the video is forged by FR according to this spike.  
To realize automatic detection, we need to measure the two Fourier spectrums shown in Figure 4. So the peak-mean ratio (PMR) of the amplitude spectrum is defined as follows,  
\[R=\frac{\max\{\alpha[k]\ |k=0,1,\ldots,N-1\}}{\frac{1}{N}\sum\limits_{k=0}^{N-1} \alpha[k]} \tag{16}\]  
where \(\max\{\cdot\}\) denotes the maximum value of input set. The amplitude spectrum of the forged video has an isolated large spike, so the \(R\) value is large. The amplitude spectrum of the original video is small and evenly distributed, and therefore there is a small \(R\) value. PMR can be used as an index to determine whether the video is forged by FR. By setting an appropriate threshold, automatic detection can be realized by the following hard threshold decision,  
\[\text{FR}=\begin{cases}on&R\geq Thr\\ off&R<Thr\end{cases} \tag{17}\]  
where \(Thr\) is a preset threshold, _on_ represents the existence of FR forgery, and _off_ denotes no FR forgery. PMR can also be used to evaluate the performance of the detection algorithm. A large PMR value indicates that the detection algorithm provides a significant spike, which can improve the detection accuracy. Conversely, a small PMR value indicates that the detection algorithm provides a spike with small amplitude, which means a possibility of mistakes in detection. In the proposed FR forensic method, the threshold \(Thr\) is only a parameter, and it is important for the accuracy of detection. Based on the PMR distribution on the testing video database, the cross-validation proposed by [6] is used to select a proper threshold. According to the experimental results in Section 4.2, \(Thr\) is set to be 5.  
Figure 4: Illustrations of Fourier spectrum for both original and forged _Foreman_ sequences.
# Cleaner Processes for Making Laundry Soap from Vegetable Oils and an Essential Oil
## 3 Results and Discussion
### Confirmation of the Presence of Essential Oil in Soap
#### 3.2.2 EDX Results  
Elemental analysis of soaps indicates the presence of essential oils in soaps even after 8 months of their manufacture (Table 3).  
The changes occur due to the temperature during saponification, the mixing time, the mass of essential oil and due to the volatilization of some components of the essential oil included in the soap.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & & & \\ \hline \multirow{2}{*}{**Saponification Type**} & \multirow{2}{*}{**Atoms**} & **Soap Without** & **Soap + Neem** & **Soap + Tea Tree** & **Soap + Thyme** \\  & & **Essential Oil** & **Essential Oil** & **Essential Oil** & **Essential Oil** \\ \hline \multirow{3}{*}{Hot saponification} & C & 39.36 & 39.57 & 39.81 & 39.71 \\  & Na & 9.98 & 9.42 & 9.33 & 8.79 \\ \cline{1-1}  & O & 50.66 & 51.01 & 50.86 & 51.50 \\ \hline \multirow{3}{*}{Cold saponification} & C & 39.70 & 39.85 & 39.92 & 40.49 \\ \cline{1-1}  & Na & 9.27 & 9.29 & 9.20 & 8.62 \\ \cline{1-1}  & O & 51.03 & 51.31 & 50.88 & 50.89 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Elemental analysis of soaps.  
Figure 3: FTIR spectra of soaps formulated by hot saponification.  
Compared to standard soaps (without essential oil), soaps containing essential oils contain more C atoms regardless of how saponification is performed: hot or cold. Hot-formulated soaps contain between 39.36 and 39.81% C atoms and cold-formulated soaps are richer in C atoms (39.70-40.49%). This fact demonstrates that cold-formulated soaps are a better option for formulating an ecological soap.  
In addition, the decrease in the percentage of O in soaps containing an essential oil can be explained by the higher volatility of some of its components; some of the volatile components that also contain O atoms evaporate during hot saponification or during ripening, during the 8 months of storage, until the EDX analysis is performed.  
The graphs related to the EDX analysis of each soap are presented in the Supplementary Materials, in Figures S1 and S2.
## 4 Current Status and Identified Needs
### Level 2 Bio-Geophysical Land Products  
A large number of bio-geophysical parameters are derived from the SR products currently provided by ESA optical land imaging sensors (see, in particular, the products systematically generated in the frame of CGLS to monitor the vegetation, the water cycle, the energy budget and the terrestrial cryosphere [8]). A full review of Cal/Val needs for all relevant land bio-geophysical products is out of scope for the present paper, and we instead focus our attention on a subset of these parameters, including key terrestrial CSVs, which are retrieved from ESA optical imaging sensors in the solar spectrum domain, namely:  
* Land Surface Albedo is defined as the ratio of the radiant flux reflected from a unit surface area into the whole hemisphere to the incident radiant flux of hemispherical angular extent. Albedo measures include black-sky albedo, defined in the absence of a diffuse component (no atmospheric scattering), white-sky albedo, under isotropic illumination, and blue-sky albedo, under ambient conditions. Long-term historical observations from the Proba-V mission and the current S3 measurements from OLCI and SLSTR are being used within the C3S to derive coarse resolution global land surface albedo products [142]. * Fraction of absorbed photosynthetically active radiation (fAPAR) is defined as the fraction of solar photosynthetically active radiation (PAR) reaching the surface in the 400-700 nm spectral region that is absorbed by vegetation. fAPAR can be considered at a given time (e.g., instantaneous fAPAR) or daily integrated. All vegetation elements contribute to PAR absorption, both green elements, as well as other non-green elements such as trunks, senescent material and flowers. Satellite-based estimates of
## 3 Methods
### Experimental Environment Configuration, Training Setup, and Data Augmentation
#### 3.3.3 Data Augmentation Techniques  
In this study, we employed various data augmentation techniques to enhance the generalization and robustness of the model. These techniques include HSV color space augmentation (random adjustments of hue, saturation, and brightness), geometric transformations (rotation, translation, scaling, shearing, and perspective transformations), flipping (horizontal and vertical flips), as well as composite augmentation techniques (Mosaic and Mixup). The specific parameter settings were as follows: a hue adjustment range of 0.015, a saturation adjustment range of 0.7, a brightness adjustment range of 0.4; for geometric transformations, the translation range was \(\pm\)0.1, and the scaling range was \(\pm\)0.5; the probability of horizontal flipping was 0.5. These data augmentation techniques generated more diverse training samples during the training process, thereby significantly improving the model's performance in practical applications.