esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model
### Coverage Rate Analysis  
**Theorem 1.** Consider a set of two sinks in an a\(\times\)b rectangle work area \(Z\), and a diagonal line \(l\). If the two sinks lie in on different sides of \(l\), the intersections of the two radio range circles must be outside the rectangle work area, at the same time the corresponding diagonal vertex (in same position direction) must be inside the radio range of the sink, or else only the sink nearer to the centre of rectangle needs to work, the radio radius chooses the distance between the farthest vertex and itself.  
**Proof.** Firstly, assume two sinks were deployed at different sides of the diagonal line [see Figure2(a)], a straight line through the two intersection points E and F, and \(\mid S_{2}\)\(D\mid>\mid S_{2}\)\(C\)\(\mid\), \(\mid S_{I}\)\(B\mid>\mid S_{I}\)\(A\)\(\mid\), so the intersection of AEFB and EDCF always contains the rectangle work area ABCD. If the corresponding diagonal vertex (in same position direction, such as \(S_{2}\) and \(D\)) is inside the radio range of the sink, the radio range of all sinks contains the whole work area. When two sinks are on the same side, the sink nearer to the centre of rectangle uses the distance between the farthest vertex and itself as radio radius, the other three vertices must be in the radio range. It means that the sink has enough transmission power to cover the whole work area. The other sink does not need to work at this time.  
Consider a WSN with two sink nodes, as shown in Figure 2. According to the Theorem 1, the radio radius \(R_{S_{3}}\) in Figure 2(a), \(i\)\(\in\) {1,2}, is given by:  
\[\begin{cases}\left(x_{S_{1}}-x\right)^{2}+\left(y_{S_{1}}-y\right)^{2}=R_{S_{ 2}}^{2}\\ \left(x_{S_{2}}-x\right)^{2}+\left(y_{S_{2}}-y\right)^{2}=R_{S_{2}}^{2}\end{cases} \tag{3}\]  
where (\(x\),\(y\)) is a point in the radio range circle. In order to not only minimize the total transmission power, but also ensure the coverage rate of network, we also define \(f\):  
Figure 2: Illustration of WSNs with two sink nodes. (a) Two sinks at different sides; (b) Two sinks on the same side.  
\[\begin{split}& f=\min\sum_{i=1}^{2}R_{S_{i}}^{n}\\ &\text{s.t.}\\ &\left(x_{S_{1}}-x_{A}\right)^{2}+\left(y_{S_{1}}-y_{A}\right)^{ 2}\leq R_{S_{1}}^{2}\\ &\left(x_{S_{2}}-x_{C}\right)^{2}+\left(y_{S_{2}}-y_{C}\right)^{ 2}\leq R_{S_{2}}^{2}\\ & 0<x_{E},x_{F}<a\\ & y_{E}\geq b\quad y_{F}\leq 0\end{split} \tag{4}\]  
where \(n\) is the path loss coefficient in equation (2). \(R_{S_{i}}\) in Figure2(b), \(i\in\{1,2\}\), is given by:  
\[\left(x_{S_{2}}-x_{C}\right)^{2}+\left(y_{S_{2}}-y_{C}\right)^{2}\leq R_{S_{2}} ^{2} \tag{5}\]  
In this paper, we only analyse the conditions of two sinks, more sinks, and so forth.
# A Performance Benchmark for the PostgreSQL and MySQL Databases
## 2 Overview of PostgreSQL and MySQL
The study concluded that NoSQL databases are better suited to handling large datasets with higher efficiency, while relational databases like MySQL and PostgreSQL still retain their relevance in structured-data scenarios. In another study conducted in 2021 [23], the performance of Redis, MongoDB, and Cassandra is compared using the Yahoo Cloud Serving Benchmark (YCSB) across various workloads, including read, write, scan, and update operations. Redis outperforms the other databases in read-heavy tasks due to its use of volatile memory, making it the fastest for read operations. MongoDB excels in write-heavy and scan-intensive workloads, showing superior efficiency in data loading and short-range scans. In contrast, Cassandra lags in both read and write tasks, with the slowest overall performance. Their study highlights Redis as best suited for read-heavy workloads, while MongoDB is preferable for write-heavy scenarios, with Cassandra being the least optimized of the three. [PERSON] and [PERSON] [24] conducted a study comparing the performance of MySQL and MongoDB in web applications. Their study highlights the limitations of traditional SQL databases, which struggle with scaling, and contrasts this with the flexibility of NoSQL systems like MongoDB. Their experiments focus on common database operations (insert, update, delete) in both transactional and non-transactional modes. MongoDB consistently outperforms MySQL, especially in transactional queries. MySQL's indexing improves search performance but significantly slows down data modification tasks. Their findings suggest that MongoDB's scalability and performance make it a strong choice for applications where speed is crucial. The benchmarking study [25] conducted in 2016 compared the performance of two popular open-source databases, MySQL and MariaDB [26], focusing on their efficiency in handling workloads in a virtualized environment using Xen 4.4 [27]. The researchers employed OLTP-Simple and OLTP-Seats [28], which simulate real-world online transaction processing (OLTP) workloads, such as flight booking systems. They tested the databases with varying levels of threads and workers to observe resource utilization in terms of CPU and memory consumption. Their key findings showed that MySQL outperformed MariaDB in high-thread environments, particularly with 1,000 threads in OLTP-Simple and four workers in OLTP-Seats. Both databases showed similar CPU and memory usage, but MySQL consistently demonstrated higher transaction throughput. Future research aims to explore MariaDB's performance in cluster environments.
###### Abstract  
The beam analysis model and the initial imperfection are predominant factors in analyzing the lateral buckling behavior of submarine pipelines under high temperature. However, the existing beam models do not consider the inhibition provided by the virtual anchor point of the pipeline. In this study, a fixed-fixed beam model is introduced to simulate the lateral buckling response of a submarine pipeline. This model considers the suppression tension provided by the virtual anchor point during the pipeline feeding-in process, which better reduces the buckling response phenomenon of the pipeline. Based on the assumption of rigid-plastic pipe-soil interaction, the analytical solution of the buckling development of pipeline under full-contact repetitive cosine imperfection is derived. The effectiveness of the fixed-fixed model is verified by comparing with the analytical solutions in other literature. The influence of the imperfection model, the temperature difference, and the soil resistance on the pipeline buckling is investigated. Finally, the size effect of the pipeline on the effective axial force development is discussed. The results show that the pipeline undergoes stress vibration under tensile force, and the frequency and density of the pipeline stress vibration increase with the increase of the initial defect amplitude. Increasing the diameter-thick ratio will significantly increase the bending amplitude and bending stress of the pipeline. Attention should be paid to the fatigue damage caused by continuous stress mutations during pipeline buckling, which could further lead to the structural destruction of the pipeline.  
submarine pipeline; lateral buckling; initial imperfection; fixed-fixed beam
# Compact Bistatic Iterative Passive Radar Based on Terrestrial Digital Video Broadcasting Signals
###### Abstract  
Passive radar has become very popular in recent years because it is usually undetectable, and countermeasures used to prevent its functioning are complex and, in general, easily identified. Terrestrial digital video broadcasting (DVB-T) is commonly used as an opportunistic illumination signal because of its large range and widespread deployment, both of which make it applicable to almost all scenarios. This paper presents the design of a compact and robust receiver for passive radar that uses a low number of antenna while achieving high accuracy. In order to do this, we use an iterative algorithm to refine the initial estimations based on time-domain channel information to converge to the true estimations. This is especially effective when the signal-to-noise-ratio (SNR) at the receiver is moderate and/or there are several reflections in the environment that may introduce some error into schemes that perform the angle of arrival or time of arrival for the estimation. The algorithm proposed herein is able to accurately estimate the position of a target with a low SNR.  
passive radar; DVB-T; iterative algorithm +
Footnote †: journal: Applied Sciences
# An Analysis of the 2008 \(M_{\rm s}\) 8.0 Wenchuan Earthquake's Aftershock Activity
###### Abstract  
We investigated the magnitude-frequency relationship and decay pattern of an aftershock sequence using data from the 2008 Wenchuan earthquake. We analyzed the spatial variations in aftershock activity parameters \(b\) and \(p\). The calculated \(b\)-value of the aftershock sequence is 0.89 \(\pm\) 0.02, which is relatively small, probably owing to the absence of small earthquakes in the aftershock catalog. The \(p\)-value, indicating the decay rate of aftershock activity, is 1.05 \(\pm\) 0.02, which is normal. The decay pattern of the Wenchuan aftershock sequence agrees well with the modified Omori law. The \(b\)-value of the aftershock sequence mainly spatially varies between 0.6 and 1.2, and the \(p\)-value varies between 0.6 and 1.8. Although the physical significance of the spatial variations in \(b\)- and \(p\)-values has not been clearly defined, in this study, the physical significance of the \(b\)-value is mainly related to changes in stress, \(P\)-wave velocity, and the density of media in the earthquake area, and that in the \(p\)-value is associated with the fault slip amount during the mainshock; the \(b\)- and \(p\)-values show a strong linear correlation. After the mainshock, stress decreased and increased in areas with large and small \(b\)-values, respectively; the regions with large and small \(b\)-values were associated with low and high \(p\)-wave velocities, respectively. The subsurface media experienced relatively high and low apparent velocities in areas with small and large \(b\)-values, respectively. The amount of fault slip was small and large in regions with small and large \(p\)-values, respectively, exhibiting a linear correlation between the fault slip amount and \(p\)-value. The results indicate that the spatial variations in the \(b\)- and \(p\)-values were related to the physical properties of the media in the earthquake area and focal earthquake mechanism.  
Wenchuan earthquake; aftershocks; Gutenberg-Richter relationship; Omori law +
Footnote †: journal: Applied  
0000-0001-447-4400-0000-0000-0000-0000-0000-0000-0000-00000-00000-0000-0000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-000000-00  
studying the magnitude-frequency relationship of aftershocks and their decay patterns, we can more thoroughly understand the characteristics of aftershock activities, which is valuable for predicting strong aftershocks and mitigating aftershock losses. In this study, we mainly investigated the features of the spatiotemporal variation in aftershock activities using the aftershock sequence of the Wenchuan earthquake based on the above two statistical models and discussed the relationship between the aftershock activity parameters, the focal mechanism of the earthquake, and the physical properties of the media in the earthquake area.
## 3 Results and Discussion
### Chemical Profile
\begin{table}
\begin{tabular}{c c c c c} \hline
**Parts** & **Methods** & **TPC (mg GAE/g extract)** & **TFC (mg RE/g extract)** & **PBD ( mmol TE/g extract)** \\ \hline \multirow{4}{*}{Flowers} & HAE & \(32.18\pm 0.40\)\({}^{\text{b}}\) & \(28.48\pm 0.40\)\({}^{\text{d}}\) & \(2.10\pm 0.08\)\({}^{\text{a}}\) \\  & MAC & \(34.27\pm 0.40\)\({}^{\text{a}}\) & \(28.38\pm 0.45\)\({}^{\text{d}}\) & \(2.14\pm 0.13\)\({}^{\text{a}}\) \\  & UAE & \(32.32\pm 0.40\)\({}^{\text{b}}\) & \(28.41\pm 0.50\)\({}^{\text{d}}\) & \(2.08\pm 0.02\)\({}^{\text{a}}\) \\  & SOX & \(30.96\pm 0.17\)\({}^{\text{c}}\) & \(32.50\pm 0.67\)\({}^{\text{b,c}}\) & \(1.68\pm 0.02\)\({}^{\text{b,c,d}}\) \\ \hline \multirow{4}{*}{Leaves} & HAE & \(23.57\pm 0.30\)\({}^{\text{f}}\) & \(31.60\pm 0.74\)\({}^{\text{c}}\) & \(1.91\pm 0.09\)\({}^{\text{a,b}}\) \\  & MAC & \(25.20\pm 0.26\)\({}^{\text{e}}\) & \(28.33\pm 0.14\)\({}^{\text{d}}\) & \(1.66\pm 0.07\)\({}^{\text{c,d}}\) \\  & UAE & \(24.39\pm 0.14\)\({}^{\text{e,f}}\) & \(32.95\pm 0.35\)\({}^{\text{b}}\) & \(1.84\pm 0.06\)\({}^{\text{b,c}}\) \\  & SOX & \(27.93\pm 0.25\)\({}^{\text{d}}\) & \(34.61\pm 0.29\)\({}^{\text{a}}\) & \(1.52\pm 0.11\)\({}^{\text{d,e}}\) \\ \hline \multirow{4}{*}{Roots} & HAE & \(18.40\pm 0.37\)\({}^{\text{g}}\) & \(2.54\pm 0.09\)\({}^{\text{e}}\) & \(1.29\pm 0.09\)\({}^{\text{e,f,g}}\) \\  & MAC & \(19.27\pm 0.25\)\({}^{\text{g}}\) & \(2.45\pm 0.12\)\({}^{\text{e}}\) & \(1.26\pm 0.05\)\({}^{\text{f,g}}\) \\ \cline{1-1}  & UAE & \(16.49\pm 0.39\)\({}^{\text{h}}\) & \(2.45\pm 0.07\)\({}^{\text{e}}\) & \(1.49\pm 0.09\)\({}^{\text{d,e,f}}\) \\ \cline{1-1}  & SOX & \(17.36\pm 0.19\)\({}^{\text{h}}\) & \(2.62\pm 0.06\)\({}^{\text{e}}\) & \(1.17\pm 0.08\)\({}^{\text{g}}\) \\ \hline \end{tabular} Values are reported as mean \(\pm\)S.D. PBD: Phosphombolybdeum assay; HAE: Homogenizer assisted extraction; MAC: Maceration; UAE: Ultrasound assisted extraction; SOX: Soxhlet. GAE: Gallic acid equivalent; RE: Rutin equivalent; TE: Trolox equivalent. Different superscripts (a–h) in the same column indicate significantly differences among the tested extracts (\(p<0.05\), by ANOVA assay).  
\end{table} 
Table 1: Total phenolic (TPC), flavonoid (TFC) and antioxidant capacity of the tested extracts.
## 3 Methods
### Calibration
#### 3.3.4 Estimating the Extrinsic Calibration  
After scaling the point cloud, we can utilize the fact that an _ego_ coordinate system is now computable, with directional vectors oriented _upwards_ and along the rails (see Figure 1). First, we compute the optimized directional \(x\)- and \(y\)-vectors as:  
\[\varepsilon_{e,x} =\mathbf{R}(0,0,\hat{x})\cdot\varepsilon_{e,x}^{init}\,, \tag{18}\] \[\varepsilon_{e,y} =\varepsilon_{e,x}\times\varepsilon_{e,z}\,, \tag{19}\]  
Figure 4: Finding the rails within the optimized histogram, as illustrated in Figure 3: The DBSCAN algorithm is used to identify the cluster center, after which the two centers closest to the Line of Sight (LoS)’s \(y\)-coordinate at the nearest camera position (i.e., the leftmost position in Figure 3) are selected.  
where \(\mathbf{R}(0,0,\hat{\mathbf{a}})\) is the rotational matrix with zero roll and pitch, and \(\hat{\mathbf{a}}\) is the yaw angle. With these conditions, we have established a right-handed coordinate system. However, up to this point, we have only determined the directional vectors of this system. The origin lies at \(M_{d}^{r}\), which is not ideal, as \(M_{d}^{r}\) can vary between consecutive frames. Therefore, we shift the origin to a more practical location, as illustrated in Figure 5.  
Given that we already know the \(y\)-coordinates of the _ego-rails_, we position the origin between these rails and below the camera. The new origin is thus defined as:  
\[\underline{Q}_{ego}=\begin{bmatrix}x^{cam}\\ y_{l}^{cr}+0.5\cdot(y_{r}^{cr}-y_{l}^{cr})\\ 0\end{bmatrix}\,. \tag{20}\]  
Please note that this formula applies only before the point cloud is scaled. If the point cloud has been scaled, \(y_{l}^{cr}\) and \(y_{r}^{cr}\) must also be scaled before applying this formula. Here, \(x^{cam}\) represents the \(x\)-coordinate of the camera's origin in the _ego_ coordinate system, where the origin is still located at \(\underline{M}_{d}^{r}\). After shifting the origin to \(\underline{Q}_{ego}\), the resulting origin lies directly beneath the camera and at the center of the ego-track, as shown in Figure 5.  
The algorithmic process for the scaling and extrinsic calibration step is presented in Appendix A, Algorithm A3, along with the complete pipeline described in Algorithm A1.
## 2 Materials and Methods
### Determination of the Volatile Compounds of the Oil  
The volatile fraction of the oil has been determined by the headspace SPME method right after production. Sampling was performed by solid-phase microextraction applying a Supleco 24 Ga, 2 cm, 50/30 \(\upmu\)m DVB/CAR/PDMS Stableflex fiber directly on the headspace of 2 mL of poppy seed oil at room temperature for 30 min. This fiber type has already been tested and advised by [PERSON] and her co-workers [12] in their previous study. After sampling the manual SPME holder, it was taken immediately into the GC-MS instrument. Volatile compounds were separated using 30 m \(\times\) 0.25 mm HP-5-MS nonpolar column (film thickness 0.25 \(\upmu\)m) using a GC 6890 N, detector: 5975 Inert mass selective detector (Agilent Technologies). The injector temperature was 230 \({}^{\circ}\)C in splitless mode. Oven temperature was programmed as follows: the initial temperature was 60 \({}^{\circ}\)C, then it was raised by 3 \({}^{\circ}\)C/min until 240 \({}^{\circ}\)C and held for 5 min. The ionization voltage was 70 eV. The carrier gas was helium with a constant flow of 1 mL/min.  
Compounds were identified by comparing their mass spectra to those of authentic compounds as well as by using mass spectra libraries (NIST, Wiley). Linear retention indices (LRI) were determined based on \(n\)-alkane hydrocarbons analyzed before in the same GC-MS conditions using the method of [PERSON] and [PERSON] [22]. LRI values were also compared to the authentic compounds as well as to the previous literature data.
# Steering Pull Model and Its Sensitivity Analysis
## 5 Results and Verification
### Verification  
For verification, the steering pull distance of the vehicle is measured through field tests. In order to measure the steering pull, a global positioning system (GPS) is adopted. The verification experiments are performed through the following procedures. First, a GPS system is mounted on the roof of the vehicle and the driver drives the vehicle straight on the test road. The GPS system measures the driving path of the vehicle. By repeating the measurement and averaging the measured driving paths, the reference line of the steering pull is obtained. Second, the driver drives the vehicle along the reference line, and when the vehicle reaches 80 km/h, the driver releases the steering wheel. The vehicle starts to deviate from the reference line, and by repeating the measurement and averaging the deviation, the pulling line is obtained. Finally, the steering pull distance of the vehicle can be obtained by comparing the pulling line and the reference line.  
The GPS measurement system consists of a GPS, a GPS data acquisition device, and a laptop. The program for the data acquisition and creating the reference and pulling lines is coded in LabVIEW. The experimental setup for the steering pull measurement is shown in Figure 11 and the specifications of the measurement system are described in Table 2.  
Figure 12 shows the theoretical estimation and the experimental verification results. As the simulated results, the measured steering pull distance increases with the increase of the vehicle moving distance. There are slight differences between the model and the verification results, but the differences can be neglected considering the uneven road condition of the verification experiments. Throughout the entire steering pull distance, the model provides very accurate estimation results.
# Spatial and Temporal Sampling Properties of a Large GNSS-R Satellite Constellation
## 2 Discussion of Orbital Parameters, Sampling Process, and SpOCK
### SpOCK and Its Operation  
Although there are a multitude of functional mission simulation tools available for commercial and research applications, the Spaecraft Orbital Characterization Kit (SpOCK) can better predict the performances of and provide data for mission inputs that operate in a manner similar to CYGNSS. The central capability of SpOCK is a high accuracy numerical propagator of spacecraft orbits and computations of ancillary parameters [18]. The C-based programming of SpOCK allows for data variables, including CYGNSS specular points, to be easily added or subtracted in accordance with user preferences.  
When creating constellations for SpOCK simulations, users can select the orbital parameters of individual satellites as well as controlling data processing tools. In text-based input files, users first describe how many satellites will be in the constellation, then provide both the individual sets of orbital parameters of each satellite and the antenna pattern used by all satellites. These inputs are read by SpOCK's C-based simulator, and outputs are provided at a user-specified time delta in the form of multiple data files. Among these outputs is the list of constellation's detected specular points, data that is necessary for CYGNSS's atmospheric measurements and may not be reported by other simulation tools. Using these written output files, various performance metrics can be calculated in other programs.
# Comparative Evaluation of Remote Sensing Platforms for Almond Yield Prediction
## 2 Materials and Methods
### Application of Machine Learning Regression Models
#### 2.4.1 Feature Selection Process  
Feature selection plays a key role in the ML pipeline. Selecting the most suitable features improves the model's performance, reduces computational efforts, and assists in the interpretation of the results. By selecting the most relevant features, more accurate, efficient, and interpretable models can be achieved, facilitating better decision making and a better understanding of the underlying data patterns. Herein, for the selection of the best possible features, the _bestFeatures_ script [43] was used. This is a tool for identifying the best possible combination of features for fitting a ML model. This method uses cross-validation (CV) to evaluate different feature subsets and their corresponding performance scores (\(R^{2}\)). The CV method partitions the dataset into training and testing subsets, multiple times (folds). For each fold, part of the dataset (testing) is always unseen by the algorithm. It then computes the cross-validated score for each combination of features and tracks the maximum score achieved, effectively controlling the problem of overfitting. The method also ensures a low level of correlation related to the features. The output of the method includes the \(R^{2}\) score and error metrics, corresponding to the best feature combination. For the current analysis, this method was used considering a 5-fold CV, and a combination of 1 to 8 features, determined by the script's best performing approach. The analysis was performed separately for each dataset (agronomic features, climate features, and VIs, from the three different platforms), as well as for mixed datasets (e.g., climate features + VIs), depending on the vegetation data acquisition platform. Table 3 lists the selected features, according to the type of features considered.