esa-sceva/satcom-chunk-collection huggingface dataset:
# An INS-UWB Based Collision Avoidance System for AGV
###### Abstract  
As a highly automated carrying vehicle, an automated guided vehicle (AGV) has been widely applied in various industrial areas. The collision avoidance of AGV is always a problem in factories. Current solutions such as inertial and laser guiding have low flexibility and high environmental requirements. An INS (inertial navigation system)-UWB (ultra-wide band) based AGV collision avoidance system is introduced to improve the safety and flexibility of AGV in factories. An electronic map of the factory is established and the UWB anchor nodes are deployed in order to realize an accurate positioning. The extended Kalman filter (EKF) scheme that combines UWB with INS data is used to improve the localization accuracy. The current location of AGV and its motion state data are used to predict its next position, decrease the effect of control delay of AGV and avoid collisions among AGVs. Finally, experiments are given to show that the EKF scheme can get accurate position estimation and the collisions among AGVs can be detected and avoided in time.  
AGV; INS; UWB; collision avoidance +
Footnote †: journal:
## 3 Results
### Charge Transfer Complex in Solution
#### 3.1.2 Stoichiometry and Formation Constant  
Stoichiometry of the complexes was determined graphically by plotting the absorbance (A) of the resulting complex (y-axis) against the molar ratio of the acceptor/donor to complex (x-axis) as indicated by [PERSON]'s method. The donor to acceptor ratio was 1:1 in both cases (Figure 3). The association constant of the CT complex was calculated using the modified Benesi- Hildebrand method. It was evaluated at room temperature (25 \(\pm\) 2 \({}^{\circ}\)C) and at the maximum of CT complex in acetonitrile using the following Equation (1) [23]:  
\[(\mathrm{C_{a}}\ \mathrm{C_{d}})/A=1/K\varepsilon+(\mathrm{C_{a}}+\mathrm{C_{d} })/\varepsilon \tag{1}\]  
where \(\mathrm{C_{a}}\) and \(\mathrm{C_{d}}\) are the initial concentration of acceptor (DDQ or TCNQ) and donor (quinine), \(A\) is the absorbance of the complex formed at the \(\lambda_{max}\), \(\varepsilon\) is the molar absorptivity of the complex, and \(K\) is the association constant of the complex (\(\mathrm{L\ mol^{-1}}\)). When the values (\(\mathrm{C_{a}}\ \mathrm{C_{d}})/A\) were plotted against (\(\mathrm{C_{a}}+\mathrm{C_{d}}\)) a straight line was obtained, from which one may calculate the association constant, correlation coefficient and the molar absorptivity (\(\varepsilon\)) of the CT complex (Figure 4). The Benesi-Hildebrand data is presented in Table 1. The intercept of the fitting line corresponded to \(1/K\varepsilon\). The association constant is calculated from the slope of the line (\(1/\varepsilon\)) and the derived value of \(\varepsilon\). The molar absorptivities were found to be \(0.33\times 10^{3}\) and \(0.3\times 10^{3}\) L mol\({}^{-1}\) cm\({}^{-1}\) for Q-DDQ and Q-TCNQ, respectively. The association constant of the complex in acetonitrile was found to be \(0.1\times 10^{3}\) and \(1.6\times 10^{3}\) L mol\({}^{-1}\) for Q-DDQ and Q-TCNQ, respectively.  
**Scheme 1.** The proposed mechanism for the formation of quinine-DDQ and quinine-TCNQ charge-transfer complexes.  
Figure 3: Molar ratio method ([PERSON]’s method) using (1 \(\times\) 10 \({}^{-3}\) M) for (1) Q-DDQ and (2) Q-TCNQ method) using (1 \(\times\) 10 \({}^{-3}\) M) for (1) Q-DDQ and (2) Q-TCNQ.  
Figure 2: (**a**) Absorbance spectrum of (1) Q-TCNQ (2) 400 ppm Q-DDQ and (3) 400 ppm Q, (4) DDQ blank and (5) TCNQ versus blank. (**b**) Absorption spectrum of (1) 400 ppm of Q-DDQ complex, (2) 400 ppm of donor (quinine), and (3) DDQ versus Blank. (**c**) Absorption spectrum of (1) 400 ppm of Q-TCNQ complex, (2) 400 ppm of donor (quinine) and (3) TCNQ versus Blank.
# Providing Predictable Quality of Service in a Cloud-Based Web System
## 4 Experiments and Discussion
### Testbed
The service times for both processor and SSD drive were obtained in experiments with a website using WordPress and running on a server with an Intel Core i7 7800X CPU, a Samsung SSD 850 EVO driver, and 32 GB RAM. The database server was modeled as a single queue. The service times for the server were obtained for the same hardware as the web server was using. The experiments were conducted for 12 web servers working in three zones (four web servers in each zone). This configuration is the most efficient for web cloud system with two-layer architecture controlled by cooperating FNRD web switches [43]. As was mentioned earlier, the WCEDF method uses the WCEDF web switch in the region and FNRD web switches in availability zones. In the presented experiments, the WCEDF method was compared with the web cloud-based system which uses on both layers the same distribution strategies--FNRD (configuration marked FNFN), Least Load (configuration marked LL),Round Robin (configuration marked RR), and Path-based routing (configuration marked P). Previously published results of experiments revealed that the configuration FNFN is very efficient and the obtained service times can be even two times shorter than for other popular distribution strategies used in two-layer configurations [42; 43].
## 2 Literature Review
### MILP Models  
Researchers have also adopted MILP formulations to address inspection planning problems, typically focusing on minimizing total quality-related costs while considering various operational constraints. [PERSON] et al. [38] developed an MILP model that simultaneously plans inspection activities and preventive maintenance in linear-deteriorating, serial MSMSs. Their model minimizes a comprehensive cost function encompassing production, maintenance, inspection, scrapping, replacement, and penalty costs. [PERSON] et al. [39] proposed an integrated dynamic lot-sizing and inspection planning model, ensuring that the outgoing product quality meets an acceptable quality level (AQL). Their MILP approach links production and inspection decisions but does not consider multiple inspection technologies. Similarly, the robust MILP model by [PERSON] et al. [40] addresses inspection planning under production uncertainty, using Taguchi loss functions and Monte Carlo simulations to manage variability in manufacturing processes. However, the focus remains on single inspection methodologies. These MILP-based contributions demonstrate the effectiveness of mathematical programming for inspection planning but often operate under simplifying assumptions, such as single-product systems or single-mode inspections.
## Data
### Numerical Weather Prediction Data  
The LDAPS is the numerical weather prediction model operated by the KMA that is a locally optimized version of the UM from the MetOffice of the United Kingdom (UK). The UM LDAPS provides a 3-hourly weather forecast over a 1.5 km grid using three-dimensional variational data assimilation (3 DVAR) with optimized parameterizations for the physical processes such as radiation, boundary layer, convection, and the land-atmosphere interactions. The land surface variables are produced by the UK MetOffice Surface Exchange Scheme (MOSES), which computes the momentum, thermal energy exchange, water flux, and CO2 flux, based on the land-atmosphere interaction scheme in the land surface model (LSM). We extracted the variables air temperature, land surface temperature, soil temperature at 10 cm depth, relative humidity, and wind speed at noon local time every day from 2013 to 2019.
## 3 Simulation Analysis
### CEEMDAN-SVD Simulation
According to the evaluation principles of \(SNR\) and \(RMSE\), the CEEMDAN-SVD algorithm has the best denoising performance, indicating that the method can effectively filter out low-frequency noise and high-frequency noise and is more suitable for the analysis of low-\(SNR\) signals.
# Preparation and Properties of Epoxy Composites with Multi-Scale BN Sheets
## 3 Results and Discussion
### Breakdown Performance  
Figure 4 shows the relationship of \(\log\left[-\ln\left(1-p\right)\right]\) and \(\log\left(E\right)\) of EP composites. The Weibull parameters \(\alpha\) and \(\beta\) are shown in Table 1. The \(\alpha\) is found to be strongly affected by the filler of nano-BN in the EP composites and the voltage ramping rate. The electrical breakdown strength of the material is 77.1 kV/mm when the additional amount of nano-BN is 20%, which is higher than that of M-BN/EP composite. The barrier forms by small-scale BN, and hinders the formation of conduction pathway. Thus, the breakdown strength of the 10%N-BN/EP and 20% N-BN/EP composites improve remarkably.  
In the theory of electrical strength, usually referred to as free-volume theory [29], breakdown is initiated by free electrons accelerated by electric field in the largest of the holes present in the amorphous phase of all polymers. Breakdown starts when the energy gained by free electrons in field \(E\) on free path of length \(l_{e}\) attains with probability \(P=1\)  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**EP Composite** & \(\mathbf{\alpha}\) & \(\beta\) \\ \hline M-BN/EP & 74.2 & 2.5 \\
10\% N-BN/EP & 76.5 & 0.5 \\
20\% N-BN/EP & 77.1 & 1.0 \\
30\% N-BN/EP & 73.6 & 0.81 \\
40\% N-BN/EP & 73.2 & 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Breakdown strength data of EP composite.  
Figure 4: Breakdown strength data of BN/EP composite.  
Figure 3: Thermal conductivity curve of BN/EP composite.  
the barrier energy \(E_{\mu}\). Then all electrons with this energy hop over \(E_{\mu}\), separating the traps in the compact structure [30]. The relationship is shown in Equation (5). The \(l_{e}\) becomes shorter after N-BN added, which result to the \(E_{b}\) higher. So, the breakdown strength data of N-BN/EP composites is higher than that of M-BN/EP composites.  
\[E_{b}=\frac{E_{\mu}}{el_{e}} \tag{5}\]  
when the concentration increases to 30%, the nanoparticles are likely to have agglomeration effect. The charge accumulated around the agglomeration BN will form the charge package, which resulting in the increase of local electric field and the formation of conduction channels [31; 32].
# BRNN-LSTM for Initial Access in Millimeter Wave Communications
## 4 Simulation Results and Performance Evaluation  
The proposed BRNN-LSTM prediction scheme is simulated in Figure 4 over various time steps, which shows high approximation between the ground truth and the prediction pattern. This shows that the proposed scheme achieves high accuracy. Thereby, it can successfully predict the subsequent beam indices with high success probabilities, once the network is sufficiently trained. Moreover, the accuracy of the proposed scheme is further studied by computing the loss function as presented next.  
_Loss Function:_ The training objective aims to reduce the loss function, i.e., mean square error (MSE) between the prediction vector of the proposed model \(\hat{Y}\) and the actual ground truth \(Y\) at the upcoming time step generated from a sample of \(U\) data points on all variables. This function is evaluated at every time step \(t\) as  
\[Loss\big{(}Y_{u},\hat{Y}_{u}\big{)}=\frac{1}{U}\sum_{u=1}^{U}\big{(}Y_{u},\hat {Y}_{u}\big{)}^{2}. \tag{12}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Parameter** & **Value** \\ \hline LSTM layers & No. of cells in LSTM layers & 4: [50,50,50,50] \\ \hline Dense layer & Activation function & ReLU \\ \hline \multirow{3}{*}{Other Hyper-parameters} & Batch size & 16 \\  & Epochs & 350 \\  & Dropout rate & 0.2 \\ \hline \multirow{3}{*}{Training Parameters} & Optimizer & RMSprop \\  & Learning rate & 0.001 \\ \cline{1-1}  & Drop-out regularization rate & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter Settings for the BRNN-LSTM Model.  
Figure 4: Beam index prediction over time.  
The reduced loss functions are computed over 350 Epochs, as depicted in Figure 5, which shows that the proposed scheme yields high accuracy and success probability. This in turn highly impacts the beam access procedure. This is shown next, by evaluating the proposed scheme for key metrics in beam access versus major existing schemes, i.e., access times and energy consumption.
## 5 Performance Evaluation
### Communication Overhead
#### 5.2.2 Temporal Communication Overhead Per Node  
Figure 7 shows the comparison result of ours and the scheme [23] in terms of temporal communication overhead per node when \(k\) ranges from 0 to 600, and \(T\) ranges from 0 to 6000 mins.  
In Figure 7, our scheme reduces significantly the packet size sent per node to almost three orders of magnitude than the scheme [23], due to the high overhead of public key encryption. During temporal aggregation, if the process of exchanging random between communication nodes is ignorable, then every node sends its serial encrypted packet formed as \(\{E\parallel H\parallel R\parallel t\}(1\leq t\leq T)\) to the aggregator, so the packet size is \(T\cdot(|E|+|X|+k\cdot|R|+|H|+128)\) bits, while in our scheme, one node's temporal aggregation is computed synchronously before being reported to the aggregator by \(k\) communication nodes, and they sends the local temporal aggregation packet size of \(|x|+k\cdot|R|+128\) bits to the  
Figure 6: Variety of spatial communication overhead per node with the value k.  
Figure 7: Variety of temporal communication overhead per node with the values k and T.  
aggregator every \(T\) timeslot, so aggregating one node's temporal consumption in \(T\) serial time slots costs \(k\cdot(|x|+k\cdot|R|+128)\) bits. Hence, when \(k\ll T\), ours overhead is always lower significantly lower than the scheme in Reference [23]. Just as the description above, we shorten the number of the communication nodes in Reference [23] into \(k\), and the performance evaluation shows the proposed collection of modular addition and masking keys from PRF saves much computation and communication overhead compared with traditional public key encryption without compromising individual privacy.
## 3 Results
### Spatial Distribution  
The spatial and temporal distribution of grassland production along the China-Mongolia Railway in Mongolia from 2006 to 2015 was obtained by inversion of the optimal exponential model based on MSAVI, Y = 3.594 * exp(5.224 * X) (Figure 2).  
From 2006 to 2015, the spatial distribution of grassland production showed an increasing trend from the southeast to the northwest along the main railway line. The grassland production of the Dornogovic and Dundgovic provinces in the south was significantly lower than that of the Tov, Hentiy, and Bulgan provinces in the north. According to the geographic location of the study area described in Figure 1 and the spatial distribution results of grassland production described in Figure 2, three different levels of grassland production can be recognized in the study area. The area along the Zamyu Und-Sainshand-Choir Railway were mainly orange-red patches, indicating that the area had the lowest grassland production, which was obviously lower than other areas along the railway. The area along the Choir-Ulaanbaatar Railway were mainly light green patches, indicating that grassland production in this area was at a medium level. The area along the Ulaanbaatar-Sukhbataar Railway were mainly dark green patches, indicating that grassland production in this area was the highest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameters** & **Model Types** & **The Inversion Model** & **R2** & **Sig.** & **RMSE (kg/ha)** & **Accuracy (\%)** \\ \hline \multirow{3}{*}{EVI} & Linear model & Y = \(-\)19.490 + 384.791 * X\({}_{1}\) & 0.46 & 0.000 & 423.55 & 66 \\  & Exponential model & Y = 4.257 * exp(12.647 * X\({}_{1}\)) & 0.63 & 0.000 & 466.53 & 63 \\  & Multivariate model & Y = \(-\)16.655 + 436.870X\({}_{1}-0.049\)X\({}_{2}\) & 0.47 & 0.002 & 423.91 & 66 \\ \hline \multirow{3}{*}{MSAVI} & Linear model & Y = \(-\)27.370 + 165.323 * X\({}_{2}\) & 0.57 & 0.000 & 370.00 & 71 \\  & Exponential model & Y = 3.594 * exp(5.224 * X\({}_{2}\)) & 0.72 & 0.000 & 279.09 & 78 \\  & Multivariate model & Y = \(-\)18.070 + 239.539X\({}_{1}-0.177\)X\({}_{2}\) & 0.61 & 0.000 & 383.40 & 70 \\ \hline \multirow{3}{*}{NDVI} & Linear model & Y = \(-\)13.940 + 204.158 * X\({}_{3}\) & 0.56 & 0.000 & 335.37 & 73 \\  & Exponential model & Y = 5.728 * exp (6.300 * X\({}_{3}\)) & 0.68 & 0.000 & 306.68 & 76 \\  & Multivariate model & Y = \(-\)3.192 + 264.166X\({}_{1}-0.119\)X\({}_{2}\) & 0.59 & 0.000 & 308.76 & 75 \\ \hline \multirow{3}{*}{PsnNet} & Linear model & Y = 0.482 + 0.403* X\({}_{4}\) & 0.68 & 0.000 & 389.21 & 69 \\  & Exponential model & Y = 12.701 * exp(0.010 * X\({}_{4}\)) & 0.66 & 0.000 & 322.82 & 74 \\ \cline{1-1}  & Multivariate model & Y = 16.944 + 0.481X\({}_{1}-0.106\)X\({}_{2}\) & 0.70 & 0.000 & 375.42 & 70 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Grassland production estimation model based on remote sensing.  
Figure 2: The spatial distribution of grassland production along the China–Mongolia Railway in Mongolia from 2006 to 2015.
## 3 Results and Discussion
### Low-Field NMR Spin-Spin Relaxation (T\({}_{2}\)) Measurement

Table 1: _Cont._Figure 2: Distributed T\({}_{2}\) relaxation time of black rice with different milling degrees (A: 0%, B: 0.57%, C: 1.27%, D: 4.16%, E: 6.97%, F: 10.19%) under different stages (untreated samples (**a**), soaked for 30 min (**b**), cooked for 10 min (**c**), 20 min (**d**), 40 min (**e**)), and peak area A\({}_{21}\) of black rice cooked for 40 min (**f**). Different superscript letters in the bar graph represented statistically significant differences (\(p<0.05\)). samples (A and B) after cooking for 20 min, indicating easier cooking properties for milled samples (C-F). After cooking for 40 min, the water content of all samples reached basically the same level, ranging from 59.29% to 62.80%, indicating complete cooking and maximum possible grain water absorption. Correlation analysis showed a positive linear correlation (R\({}^{2}\) = 0.9429) between water content and low-field NMR parameters (sum peak area). This fully demonstrated the quantitative reliability of low-field NMR data in measuring water concentration and distribution during cooking as all the water was detected [36; 37].
# A Review of Harmonic Detection, Suppression, Aggregation, and Estimation Techniques
## 3 Harmonic Suppression Methods
### Passive Power Filter  
By the end of the last century, researchers [27] proposed a power filter composed of capacitors and inductors to include harmonics and reactive power compensation; as a result, the power factor of the grid was improved. A passive power filter deploys the principle of resonance to create a low-impedance route for specific frequency components to filter out the harmonics from the power system [28]. The key advantages of the passive power filter are low cost, structural simplicity, and ease of implementation. Passive power filters may need many improvements, such as the parameters of components strongly affecting the filter performance of the source power filter. When the power signal's frequency is changed, over-resonance occurs, and filtering performance will decrease eventually [29; 30]. Due to over-resonance, harmonic signals are amplified. In addition, the capacity of PPF is limited when the harmonic signals are too large; it may overload and damage the filter.
# Tensile Specim Circular Grid Pattern and AI-Based Strain Calculation Method
###### Abstract  
During tensile testing of materials, strain measurement is conducted using either contact or non-contact methods. Contact methods offer high accuracy and precision but are limited by the specimen's thickness and dimensions, whereas non-contact methods minimize damage to thin specimens and allow measurements in various environments, though they require longer preparation and calculation times. This paper proposes a circular grid marking pattern and a strain prediction algorithm using artificial intelligence (AI), which simplifies the preparation process and allows strain prediction without additional equipment. The circular grid pattern can be arranged in various configurations from \(1\times 5\) to \(5\times 7\), and a laser marker, which requires minimal time, was used to engrave the pattern on the specimen to shorten the preparation time. The AI model, trained on image-based data, enables strain calculation regardless of the specimen's gauge length and size, and allows measurement of local strain as well as gauge-length strain. The reliability of this concept was verified by applying it to tensile testing.  
artificial intelligence (AI); grid circle pattern; image processing; measurement strain; tensile test +
Footnote †: journal: Applied Sciences
## 4 Discussion
### Flow Regimes and PM\({}_{2.5}\) Distribution
#### 4.1.1 Parallel Wind  
In the symmetrical street canyon, the predominant flow regime is characterized by a parallel flow along the street axis. This flow pattern leads to the accumulation of traffic pollutants, resulting in a consistent increase in PM\({}_{2.5}\) concentration along the street axis, with the highest concentration occurring at the pedestrian level [42].  
In asymmetric street canyons, due to the height difference between buildings, the airflow accelerates near higher buildings when passing through the canyon. In the parallel wind direction specifically, the streamlines near the taller buildings on the vertical central plane were denser, thereby promoting the dispersion of particulate matter. The movement pattern of this airflow velocity results in relatively lower concentrations of PM\({}_{2.5}\) near taller buildings in asymmetric street canyons.
# Vegetation Growth Response and Trends after Water Deficit Exposure in the Loess Plateau, China
## 3 Results
### Sustainable Development of Vegetation Growth in the Loess Plateau  
#### 3.3.1 Determining the Future Growth of Vegetation  
The sustainability of future changes in vegetation and meteorological drought was determined by trend analysis and Hurst index calculations. Figure 9 shows that 93.90% of the Loess Plateau exhibited improved and weakly reversed changes (improvement and anti-sustainability) in moisture condition, with 0.02% classified as strong anti-sustainability, 58.57% as medium anti-sustainability, and 35.32% as weak anti-sustainability. However, 6.10% of the area is expected to transition from dry to wet. Although the majority of the area is expected to become wetter, the potential for anti-sustainable changes should be addressed. Between 2000 and 2020, vegetation restoration improved in 98.59% of the total area of the Loess Plateau, with 59.24% experiencing improvement, but with weak anti-sustainability, and 28.70% experiencing weak persistence. Therefore, the future trend of vegetation restoration requires attention, particularly in improved and anti-sustainable areas. Analyzing the causes of vegetation restoration and improving the external environment of vegetation growth is crucial for forestry development and scientific management. Vigilance and concern for the health of vegetation restoration are necessary to ensure sustainability.  
Figure 8: Time scales (**a**) and Rmax (**b**) for the response of various vegetation species to water surplus and deficit.  
Figure 9: Sustainability of vegetation growth and changes in moisture conditions during the growing season in the Loess Plateau. (**a**,**b**) represent the trend and sustainability of NDVI and SPEI07, respectively.