esa-sceva/satcom-chunk-collection huggingface dataset:
# An Approach to Aid Decision-Making by Solving Complex Optimization Problems Using SQL Queries
## 3 Experimental Design and Results
### First Experiment: Performance of Using SQL Statements
#### 3.1.2 Naive Brute Force Algorithms  
The PSP, MCP, and 3 CG are related to a broader range of optimization problems. After a careful revision of the literature of related problems, it was observed that there are real-life cases of a small size that involve only a few nodes (thirty or forty) in the case of MCP and 3 CG, or for the PS,P a few securities (between twenty and forty), for example in the case of the PSP, Knapsack, Cargo Loading Problem, and Cryptocurrency [6,8]; or the cases of MCP, Signal Processing Problem [7], and Community Detection [5,33]; or in Time Tabling and Channel Assignment and Graph Coloring [3,4,14,29,31]. Hence, this section validates the proposed approach using small random instances that match real-life sizes found in the literature.  
The validation of the performance of the use of SQL to solve optimization problems takes into consideration the following conditions: (1) the cases are small and can be managed by a DBMS; (2) the development of an ad hoc or specialized strategy is not justified; (3) the software developer lacks the time, or skills, to develop programs for finding solutions for the optimization problems; or (4) implementing a naive brute force strategy is expensive, and efficiency is compromised due to the programming language use. As a result, the proper strategies for comparison were naive brute force strategies because they are the most common strategy to be implemented under the previous situations. Their implementation also used the script language Python, and the set of instances was randomly generated using an Erdos-Renyi random graph generator and a modification of the random generator from [PERSON] and [PERSON] [34] to create PSP instances.  
Table 3 summarizes the results derived from the comparison. The experiment set a time limit of 72,000 s for solving the problem instances; the cases that were not solved within such a time limit are named as _Unsolved_. The results were classified according to the problem analyzed, the PSP, MCP, and 3 CG. The first column shows the problem, and the second is the instance data. The third column shows the time in seconds spent by the naive brute force algorithm to solve them. The last column is the corresponding time needed for the respective SQL query.  
Let us point out that both approaches followed an exponential growth in the time needed to find a solution. However, the time needed for running the respective SQL query was significantly lesser than the time needed for the naive brute force algorithm.
## Abbreviations  
\begin{tabular}{l l} \(u_{g}\), \(i_{g}\) & GSC output voltage and current vectors. \\ \(u_{r}\), \(i_{r}\), \(i_{s}\) & Rotor voltage and current vectors, stator current vectors. \\ \(\mathbf{\varphi}_{s}\mathbf{\varphi}_{r}\) & Stator, rotor flux linkage vectors. \\ \(\omega\), \(\omega_{r}\) & Grid and rotor angular frequencies. \\ \(P_{e}\), \(P_{s}\), \(Q_{g}\) & Electromagnetic power, stator output active and reactive powers. \\ \(P_{g}\), \(Q_{g}\) & GSC output active and reactive powers. \\ \(L_{s}\), \(L_{r}\), \(L_{m}\) & Stator, rotor self- inductances, mutual inductance. \\ \(R_{s}\), \(R_{r}\) & Stator, rotor resistances. \\ \(L_{g}\), \(R_{g}\), \(C\) & GSC inductance and resistance, the capacitance of the dc link. \\ \(I_{r}\), \(i_{t}\) & The load current of the rotor side, system total current vectors. \\ Subscripts & \\ \(\alpha\), \(\beta\) & Stationary \(\alpha\), \(\beta\) axis. \\ +, \(-\), \(5\)\(-\), \(7\)+ & Fundamental, unbalanced, \(5\)\(-\) and \(7\)+ -order components. \\ Superscripts & \\ \(\ast\) & Reference value for controller. \\ \(\wedge\) & Conjugate complex. \\ \end{tabular}
## 3 Time-Optimal Trajectory Planning Based on the MPE and MPC
### Problem Formulation  
Time-optimal trajectory planning can be formulated in the MPC framework [13] as follows:  
\[\begin{array}{ll}\text{Find}&\Delta T(l)\text{ and }\mathbf{v}_{e}(l) \text{ for }l=0,1,\cdots,N_{p}-1\\ \text{to minimize}&\sum\limits_{l=0}^{N_{p}-1}\Delta T(l)\\ \text{subject to}&\mathbf{p}_{e}(l+1)=\mathbf{p}_{e}(l)+\Delta T(l)\mathbf{v}_{e }(l)\\ &V(\mathbf{v}_{e}(l),\mathbf{p}_{e}(l))\leq 0\\ &\Delta T(l)>0\\ &\mathbf{p}_{e}(N_{p})=\mathbf{p}_{e}^{\text{tar}}\end{array} \tag{6}\]  
where \(l\) denotes a time step; \(\Delta T(l)\) denotes the time interval of each time step; \(N_{p}\) denotes the prediction horizon length; and \(\mathbf{p}_{e}^{tar}\) denotes the target end point of the end-effector position.  
By solving (6), the trajectories of \(\Delta T\) and \(\mathbf{v}_{e}\) (i.e., \(\Delta T^{{}^{\ast}}\) and \(\mathbf{v}_{e}^{{}^{\ast}}\)) can be obtained to approach the target \(\mathbf{p}_{e}^{tar}\) in minimum time within the prediction horizon \(N_{p}\). Only the optimal velocity at the first time step (i.e., \(\mathbf{v}_{e}^{{}^{\ast}}\)(0)) was used to calculate the reference \(\mathbf{p}_{e}^{{}^{\ast}}\) for the end-effector position controller, as follows:  
\[\mathbf{p}_{e}^{{}^{\ast}}\leftarrow\mathbf{p}_{e}^{{}^{\ast}}+T_{s}\mathbf{v }_{e}^{{}^{\ast}}(0), \tag{7}\]  
where \(T_{s}\) is the control period of the end-effector position controller. In [7], the initial values of \(\mathbf{p}_{e}^{{}^{\ast}}\) were selected based on the MPE results of the DAM-3R and JAM-3R. \(T_{s}\) was used instead of the optimal time interval \(\Delta T^{\ast}(0)\), which could be greater than \(T_{s}\), so that the reference \(\mathbf{p}_{e}^{{}^{\ast}}\) could be tracked by the controller at each control step.  
It should again be noted that the time-optimal MPC formulation in (6) requires constraints on the maximum performance of the end-effector (i.e., \(V(\cdot)\leq 0\)). It is practically difficult to solve such an MPC problem without having explicit and accurate performance models of the manipulator. In this study, the mathematical performance models for the DAM-3R and JAM-3R were generated based on the MPE results. We note that, in this study, the dynamics of the end-effector position were simplified by using a first-order model in the MPC (\(\mathbf{p}_{e}(l+1)=\mathbf{p}_{e}(l)+\Delta T(l)\mathbf{v}_{e}(l)\) in (6)) thanks to the MPE-based performance models (\(V(\mathbf{v}_{e}(l),\mathbf{p}_{e}(l))\leq 0\) in (6)). This led to a fast and accurate simulation.
# Performance-Based Analysis of Single-Layer Cylindrical Steel Reticulated Shells in Fire
## 6 Effect of Structural Parameters on the Responses of Reticulated Shells During a Fire
### Influence of Fireproof Coating  
The influence of the thickness of the fireproof coating on the fire resistance of a single-layer cylindrical latticed shell was studied by selecting thicknesses of 1.0 mm, 2.0 mm, and 3.0 mm, as shown in Figure 26. The temperature of steel with a fireproof coating was calculated using an iterative formula of temperature rise in steel with a protective layer. The influence of a fireproof coating was considered. Displacement at the midspan node was selected to compare and analyze the influence of the protective layer on the bearing capacity of the latticed shell structure during a fire. We determined whether latticed shell structures would lose their bearing capacity as well. It can be seen that in the early stages of fire, the displacement caused by thermal expansion decreases with an increase in the thickness of the protective layer. When the thickness of the fireproof coating is 0 mm, the reticulated shell structure loses its bearing capacity in 726 s. When a fireproof coating is added, the entire structure remains undamaged, and node displacement decreases with an increase in the thickness of the protective layer. When heat is transferred to the steel components through thermal radiation and convection, the fireproof coating absorbs part of the energy, slows down the speed of the flame and the heating rate of the components, reduces the temperature of the steel components through thermal radiation and convection, and improves the fire resistance of the steel components.
## 4 Experimental Results and Discussion
### Vision Capabilities Implementation
#### 4.1.1 Dataset
The innovative augmentation technique proposed hereafter enriches the dataset by simulating _different weather conditions_ in the vineyard (for example, intense sun, fog, rain, haze and so on). Examples of image augmentation for the WGISD dataset with atmospheric simulations are given in Figure 4. In this way, the resulting vision model is expected to be more robust even in strongly over- or under-exposed scenarios, where the lack of detail and luminance/chrominance distortions considerably undermine the inference process. This technique bears a strong resemblance and shares the same objectives of several cutting-edge studies in image enhancement and restoration methods in CV (among others, [28; 29; 30] for intelligent image de-hazing and [31; 32] for image restoration under extreme low-light conditions). The WGISD dataset enhanced with image augmentation has been renamed Weather-Augmented WGISD (WA-WGISD), and its subdivision of images in training/validation and testing is shown in the second row of Table 2.  
Figure 4: Examples of image augmentation for the WGISD dataset. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**Images**} & \multicolumn{2}{c}{**Bounding Box**} \\  & **Train/Val** & **Test** & **Train/Val** & **Test** \\ \hline
**PASCAL VOC 2007** & 5011 & 4952 & 12,608 & 12,032 \\
**PASCAL VOC 2012** & 11,540 & 11,540 & 27,450 & 27,450 \\
**PASCAL VOC 2007+2012** & 16,551 & 16,492 & 40,058 & 39,482 \\
**Single class average** & 827.55 & 824.6 & 2002.9 & 1974.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Dataset subdivision of PASCAL VOC 2007+2012.
# Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights
## 3 State-of-the-Art
### Developmental Trends and Technical Features
#### 3.1.3 Industrial Internet of Things (IIoT)  
The dynamic application of IoT to every aspect of human activities has led to its extension in the industrial sector. This extension can be viewed as the development of IoT for the specific application in industries [22; 23]. These IoT are expected to meet the unique needs of these industries. The IIoT is considered to be a subcategory of IoT that focuses on industrial communication technologies, M2M applications, etc., as explained in [21]. As a subset, the paper presented the similarities between IoT and IIoT. More so, [PERSON] [20] sees IIoT as a technology that is used to achieve the goals of industries through the deployment of some IoT technologies in a specific way that involves the use of intelligent objects on cyber-physical platforms.  
The peculiarity of industrial needs for the application of IoT, among other things, includes quality of service (QoS) [24], and the authors proposed a two-layered architectural distribution of a hybrid IIoT for QoS. One of the aims of IIoT is to provide real-time information to decision-makers about the state of the industry. To achieve this, Reference [25] developed a wireless gateway for the application of IIoT. A framework for the use of Machine Learning (ML) in the processing of generated data from IIoT has been studied in [26].
# A Comparison Study on the Metabolites in PC-3, RWPE-1, and Chrysin-Treated PC-3 Cells
###### Abstract  
Prostate cancer is frequently diagnosed and the leading cause of death in men worldwide. Prostate-specific antigen (PSA) blood tests and biopsies are the primary methods for diagnosing prostate cancer; however, their accuracy is less than 50%. Therefore, there is a need to develop diagnostic tests that minimize patient discomfort during examination and adequate biomarkers that are more accurate, sensitive, and specific for the detection of prostate cancer. This study investigated the application of metabolomics to identify biomarkers in prostate cancer biofluids. In addition, changes in prostate cancer metabolite levels induced by chrysin, a natural anticancer compound, were evaluated and compared with those in non-treated prostate cancer cells. Gas chromatography-mass spectrometry (GC-MS)-based metabolomic profiling was performed to investigate the differences in metabolic alterations among prostate cancer, normal prostate, and chrysin-treated prostate cancer cells. Pairwise comparisons of the extracellular fluid metabolomics were performed using principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and Student's \(t\)-test. The results revealed significantly different patterns among the metabolite groups, including alcohols, amino acids, carboxylic acids, organic acids, sugars, and urea. The RWPE-1- and chrysin-treated PC-3 (PC-3 Chr) cell groups showed similar tendencies for 23 metabolites, while the groups showed significant differences from the PC-3 group. Most amino acids showed higher concentrations in PC-3 cells than in the normal cell line RWPE-1 cells and PC-3 Chr cells. Our results revealed that GC-MS might be an effective diagnostic tool to detect prostate cancer and contribute to finding new tumor markers for prostate cancer as the basis for new ideas.  
Keywords:prostate cancer; metabolomics; RWPE-1; PC-3; chrysin; GC-MS +
Footnote †: journal: Medical Sciences
## 3 Experimental Results and Analysis
### Microanalysis Results for the MICP-Treated Soil  
#### 3.5.1 SEM Morphology Analysis  
We selected MICP-treated soil and control group samples with ages of 7 days, 14 days, and 28 days to conduct SEM (Scanning Electron Microscopy) experiments. Using a knife, we made two parallel scratches on each soil sample; the scratches were approximately 0.5 mm deep and 2 mm apart. We broke each sample along the scratches to obtain an undisturbed SEM sample. After drying a sample, we coated it with gold to make it conductive. We then used the large-chamber tungsten filament scanning electron microscope (Zeiss EVO18, Oberkochen, Germany) at the Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, to capture a sufficient number of SEM images at an appropriate magnification. This allowed us to examine the internal soil microstructure and analyze the MICP reinforcement mechanism at the microlevel. The surface micromorphology of the soil samples at different periods was observed and analyzed.  
We specifically identified clearly generated calcium carbonate crystals in the spaces between the soil particles after 7 days (Figure 7). These calcium carbonate crystals exhibit obvious regular shapes and are mostly bulk crystals. According to this figure, calcium carbonate crystals serve as a filling material between the soil particles, which results in increased soil cohesion.  
In contrast to treated sandy soil, where calcium carbonate occurs as separate granules adjacent to two or more sand particles, in soft clay, the calcium carbonate forms as cement mineral granules among clay particles. These observations indicate that in soft clay, microbes can induce calcium carbonate precipitation around soil particles, thereby enhancing soil cohesion.  
Upon examining the soil sample after 28 days, we found locally concentrated calcium carbonate generation in the soil (Figure 7). The microbes in soft clay are relatively activated and effectively induce calcium carbonate precipitation around soil particles, thereby realizing soil reinforcement.
## 4 Conclusions  
In this study, WHRA performance degradation was assessed using a machine-learning approach. Our feature selection-based GA-GRNN demonstrates powerful probabilistic-based nonlinear regression for practically constructing the curves of RUL by predicting CVs. The concluding remarks are as follows:  
Figure 12: LR score (CV) for the RUL of the WHRA obtained by the LR model.  
Figure 13: Reverse plot of Figure 6 of the synergy position across 369 days (2,819,678 data).  
1. This method provides a GA-GRNN that mimics the relaying of original feature data to determine the CV for a call-for-maintenance RUL model. Using the GA-GRNN, the CV can be determined and adjusted when performance degradation occurs. GA-GRNN can calculate the CV score and determine an ultimate degradation trend when a different machine is running.
2. Using LR as a predictive machine health model denotes 1 in advance and 0 only after run-to-failure maintenance; this is time consuming and incurs a high cost, and is not feasible for predicting health statuses in major practical industrial problems. This study successfully demonstrated feature extraction with a GA-GRNN, with the ability to update the trend from the initial healthy CV to the final failure CV with data from each day.
3. This study provides a methodology based on data-mining methods, including data preprocessing, feature extraction, feature selection, normalization, artificial intelligence algorithms with GA, and evaluation with feature-based GRNN. The GA-GNN successfully determined the CV of real machinery to predict degradation. Our future study will focus on using a long short-term memory network for more efficient training of the labels of machine statuses.  
[PERSON] conceived, analyzed the GA-GRNN data associated with the LR data, and wrote the paper; [PERSON] performed the GA-GRNN data analytics; and [PERSON] performed the LR and feature engineering program.  
This research received the funding from the Ministry of Science and Technology.  
The authors thank part of the Ministry of Science and Technology for financially supporting this research under Grant MOST 106-2221-E-018-013 MY2. The authors also thank [PERSON] for the experiment set-up in their laboratory of our mechatronics engineering department and the initial data cleaning by [PERSON] when we were a team for a project in 2018 with a robotic issue.  
The authors declare no conflict of interest.
## 4 Path Ambiguity Solution
### Pseudorange Comparison  
When a user receives two or more DVB-T signals with similar or exactly the same propagation delays, it can exploit the visibility of a single GNSS satellite to solve the path ambiguity problem by comparing the ranges. This method is described by considering the case of two DVB-T signals with the same propagation delay. Therefore, two possible positions are calculated.  
In Figure 8, we suppose that the GNSS satellite is at position, \(\vec{S}\{X_{s}(t_{i}),Y_{s}(t_{i}),Z_{s}(t_{i})\}\), with velocity, \(\vec{v_{s}}\), at time \(t_{i}\). The receiver is at position, \(\vec{P_{1}}\{X_{1}(t_{i}),Y_{1}(t_{i}),Z_{1}(t_{i})\}\), with velocity, \(\vec{v_{1}}\). Besides this, the receiver may also produce a wrong position estimate, which is \(\vec{P_{2}}\{X_{2}(t_{i}),Y_{2}(t_{i}),Z_{2}(t_{i})\}\) with velocity, \(\vec{v_{2}}\). Therefore, at each time, \(t_{i}\), the receiver can calculate the distance between itself and the observed satellite. Taking into account the two estimated positions, the two ranges, \(d_{1}\) and \(d_{2}\), are evaluated as:  
\[d_{1}=\sqrt{(X_{s}(t_{i})-X_{1}(t_{i}))^{2}+(Y_{s}(t_{i})-Y_{1}(t_{i}))^{2}+(Z _{s}(t_{i})-Z_{1}(t_{i}))^{2}} \tag{15}\]  
Figure 7: True and estimated trajectories.  
and:  
\[d_{2}=\sqrt{(X_{s}(t_{i})-X_{2}(t_{i}))^{2}+(Y_{s}(t_{i})-Y_{2}(t_{i}))^{2}+(Z_{s }(t_{i})-Z_{2}(t_{i}))^{2}} \tag{16}\]  
When the pseudorange comparison method is active, the user can compare \(d_{1}\) and \(d_{2}\) with \(\rho^{{}^{\prime}}=\rho+n\), which is the range derived from the GNSS signal, while \(n\) is the error source, which is modeled with a Gaussian distribution. It is worth mentioning that the local clock error can be estimated when the user is in open sky. Therefore, the range can be obtained instead of the pseudorange. The distance differences can be computed as:  
\[\Delta d_{1}=d_{1}-\rho^{{}^{\prime}} \tag{17}\]  
\[\Delta d_{2}=d_{2}-\rho^{{}^{\prime}} \tag{18}\]  
If one of them is above a given threshold, the corresponding position can be marked as fake, and this path will be discarded in the further calculation. We choose three times the standard deviation of the satellite range error as a reasonable threshold, considering that the distance difference corresponding to the correct path will be less than the threshold, with a probability of \(99\%\).
# Research on Rock Strength Test Based on Electro-Hydraulic Servo Point Load Instrument
## 3 Rock Sample Preparation and Test Method  
### Rock Sample Preparation  
Three representative rock samples were selected for quantitative analysis, namely, fine-crystalline granite and coarse-crystalline granite with high strength and large differences in grain size and morphology, and siltstone with moderate strength and uniform grain size.  
Both granites were taken from Qingchuan County, Guangyuan City, Sichuan Province. The coarse-crystalline granite is mainly gray-white with black mica and white quartz embedded in it, the grains are large and evenly distributed. Neither block structure nor joints and fissures are developed, and the integrity of the block is good. The fine-crystalline granite has small grains of black mica in its mineral composition, showing a uniform distribution of dots, and the rock sample is mainly black and white, with fine grain structure, blocky structure, dense structure, and hard texture. The siltstone was taken from an abandoned mine in Wuhua District, Kunming City, Yunnan Province, and has a dark red color, blocky coarse-grained sandy structure, and is relatively homogeneous.  
To ensure the accuracy of the test results, the different sizes of each rock sample were taken from the same rock block with uniform texture, and each rock block was checked for macroscopic defects to ensure that there were no obvious cracks or alteration areas. The different sizes of rock samples were cut and core-drilled along the same direction. For the square samples of the point load test, the three rock samples were cut into square specimens with side lengths of 20, 30, 40, and 50 mm using an automatic type infrared-controlled cutting machine (DQ-4A). For the cylindrical samples of the uniaxial compression test, firstly, a core drilling machine (ZS-100) was used to core the rock block, and then an automatic-type infrared-controlled cutting machine (DQ-4A) was used to cut the core sample into cylindrical specimens of 50 mm in diameter and 100 mm in height. To ensure the stability of the test data and reduce the degree of dispersion, a face grinding machine (SHM-201) was also used to smooth the section of the cut rock sample, so that the size error of the test rock sample was controlled within the range of \(\pm\)0.1 mm. A total of 56, 64, and 57 specimens of fine-crystalline granite, coarse-crystalline granite and siltstone were used for the point load test, while a total of 6, 5, and 6 specimens were used for the uniaxial compression test, respectively. Table 2 lists the specific sizes and quantities.
# A Sensor for Characterisation of Liquid Materials with High Permittivity and High Dielectric Loss
## 4 Simulation Analysis
### Building the Frequency Function for Lossless Samples  
Since there are too many curves, only a portion of the simulated results are presented in Figure 8. For the unloaded case, the resonant frequency is 2.974 GHz. For other values of \(\epsilon^{\prime}_{\rm r}\), the resonant frequency shifts to lower side. This reminds us that the real part \(\epsilon^{\prime}_{\rm r}\) of the permittivity can be retrieved by observing the frequency shift \(\Delta f_{\rm r}\) relative to the unloaded condition for the lossless sample. From the simulated results, the frequency shift \(\Delta f_{\rm r}\) versus \(\epsilon^{\prime}_{\rm r}\) is plotted in Figure 9, under the condition of the lossless case (\(\tan\delta=0\)). The fitted quartic equation is  
\[\begin{array}{c}\Delta f_{\rm r}=f_{0}-f_{\rm r}=-2.00691\times 10^{-4}+0.00342 \epsilon^{\prime}_{\rm r}-6.27316\times 10^{-5}\epsilon^{\prime}_{\rm r}{}^{2} \\ +6.83277\times 10^{-7}\epsilon^{\prime}_{\rm r}{}^{3}-2.91958\times 10^{-9} \epsilon^{\prime}_{\rm r}{}^{4}\end{array} \tag{5}\]  
Here, the coefficient of determination \(R^{2}\) is 0.9998, showing a sufficiently good fit for data retrieval.  
Figure 8: (dB) magnitude of sensor for \(\epsilon^{\prime}_{\rm r}\) range from 5–90.