esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Mechanical Properties and Damage Constitutive Model of Water-Bearing Coal
###### Abstract  
Many water-related problems are confronted in coal mining, and the mechanical properties of coal inevitably deteriorate due to water-rock interactions. Therefore, it is necessary to study the macroscopic mechanical properties and damage constitutive model of water-bearing coal for safe mining. The uniaxial compression tests of raw coal samples with five moisture contents under four loading rates were carried out. Based on the test, the Drucker-Prager criterion is introduced to describe the failure law of micro-elements; assuming that the strength of micro-elements obeys a two-parameter Weibull distribution, a three-stage damage constitutive model is established. The model, existing model, and test curves were compared, and four indicators were introduced to evaluate the fitting effect. The results demonstrate that the stress-strain curve has a near horizontal step before the elastic stage, and its length is positively correlated with the moisture content \(\omega\). With the increase in \(\omega\), the peak strain increases linearly, and the compressive strength and Young's modulus first increase and then decrease. The loading rate does not change the type of the fitting function between the mechanical parameters and \(\omega\). The three-stage model is more universal and can better fit the full stress-strain curve of water-bearing coal under uniaxial compression.  
water-bearing coal; coal sample; mechanical property; uniaxial compression; damage constitutive model +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Supervised Classification Algorithms  
A classification function maps the input data (features) to the output class or (target labels) by minimizing the prediction error, often learning the complex input-output relationship patterns. Inputs are shown in the form of n-vectors {X1, X2 \(\dots\) Xn} while outputs are represented in the form of finite k class labels {Y1, Y2 \(\dots\) Yk}. Datasets are divided into training and testing sets, and models are trained using training data, while testing data are used for cross-validation to evaluate the performance of trained machine learning models [32]. SVM and ANN supervised classification algorithms are the most widely used algorithms in ML applications, providing valuable insights through cloud computing involving geospatial data.
## 2 Mathematical Formulation of Object Localization and Tracking Algorithms
### TOF Estimation
#### 2.3.3 Determination of Suitable Fitting Window  
In the proposed system, when the amplitude of the ultrasonic echo signal is sufficiently large to be detected, the target is judged to exist by the system, and the TOF is estimated by the proposed algorithm. However, the waveform of the ultrasonic echo signal is always distorted because of different reflective surfaces, which results in the multiple-peaks phenomenon and interference results of TOF estimation. Therefore, instead of fitting the entire envelope data, a suitable fitting window located on the expected peak according to the mathematical properties of the envelope model is better. For example, reconsidering the GN model (7), which has only one peak value, the curve from the peak to the beginning is monotonically decreasing; that is, there is no turning point. Therefore, as shown in Figure 4, the ideal shape of the waveform in the fitting window should be a single peak and monotonically decrease from the peak; otherwise, a large fitting error will be caused, and the accuracy of TOF estimation will be affected, as shown in Figure 5.  
To solve the problem of an incorrect fitting window with multi-peaks, a three-step procedure is proposed in this study. In Figures 4-6, the blue line is the echo envelope, the red line is the result of curve-fitting, \(\hat{I}_{0}\) is the estimated TOF by (23) or (24), \(t_{0}\) is the ground truth of TOF, which is calculated from target coordinate measured manually, \((t_{max},V_{max})\) is the peak value of echo envelope, and \((t^{*},V^{*})\) is the instant that characterizes the onset of echo envelope, which is calculated by the two-maximums algorithm [15]. The size of the fitting window is defined from \(t^{*}\) to \(t_{max}+n\), where \(n\) is a suitable number. The three-step procedure is presented as follows:  
1. Set the maximum amplitude detected for the first time as the initial value \((t_{max0},V_{max0})\), and find the corresponding \((t^{*},V^{*})\) by the two-maximums algorithm.
2. In the \(i\)th iteration, check if the waveform from \(t_{maxi}\) to \(t^{*}\) is a monotonic decrease. If it is a monotonic decrease, then go to step 3.
3. Execute (23) or (24) to obtain the TOF \(\hat{I}_{0}\).  
Through the above three-step process, the suitable fitting window can be found, and the best fitting result can be obtained, as shown in Figure 6.  
Figure 4: Ideal shape of envelope waveform. (Blue line: echo envelope; red line: curve-fitting result.)  
Figure 5: Double peaks phenomenon of envelope waveform. (Blue line: echo envelope; red line: curve-fitting result.)  
Figure 6: Curve-fitting result of envelope waveform in the desired region. (Blue line: echo envelope; red line: curve-fitting result)
# Instrument Detection and Descriptive Gesture Segmentation on a Robotic Surgical Maneuvers Dataset
## 2 Materials and Methods
### Evaluation Method
#### 2.5.2 Instrument Detection  
In this work, YOLOv4 was used to detect the tip of the instruments, using the images as an input. YOLO (You Only Look Once) is a popular single-shot object detector known for its speed and accuracy. This model is an end-to-end neural network that makes predictions of bounding boxes and class probabilities all at once. This model is composed of three parts: backbone, neck, and head. The backbone is a pretrained CNN that computes feature maps from the input images. The neck connects the backbone and the head. It consists of a spatial pyramid pooling module and a path aggregation network, which merges the feature maps from various layers of the backbone network and forwards them as inputs to the head. The head processes the aggregated features and makes predictions for bounding boxes, objectness scores, and classification scores. Yolov4 has already proven to provide good results for surgical instrument detection [37].  
The experimental setup for instrument detection includes two scenarios:  
* **Leave-One-Supertrial-Out (LOSO)**: for this setup, we only used images of videos performing _pea on a peg_ for training the network, and then we incorporated images of videos performing _post and leave_ for testing. This setup can be used to evaluate the robustness of the method for different experimental scenarios.
* **Leave-One-Trial-Out (LOTO)**: for this setup, we used images of videos performing _pea on a peg_ and _post and sleeve_ for training and testing the network.
# Bacterial Foraging-Based Algorithm for Optimizing the Power Generation of an Isolated Microgrid
## 2 Description of the Isolated MicroGrid
### Optimization Problem  
We defined the mono-objective optimization problem associated with the optimal power generation as:  
\[min\,\Phi=\sum_{\tau=1}^{24}F(\vec{p}(\tau))\qquad\vec{p}(\tau)\in\mathbb{R}^{4} \tag{18}\]  
subject to the constraints:  
\[\begin{array}{lll}h_{1}(\vec{p}(\tau))&=&P_{1}(\tau)+P_{2}(\tau)+P_{3}(\tau)+P _{4}(\tau)=P_{L}\\ g_{1}(\vec{p}(\tau))&=&SOC^{min}-SOC(\tau)\leq 0\\ g_{2}(\vec{p}(\tau))&=&SOC(\tau)-SOC^{max}\leq 0\end{array} \tag{19}\]  
with the bounds:  
\[\begin{array}{lll}0&\leq&P_{1}(\tau)\leq DG_{nominal}\\ 0&\leq&P_{2}(\tau)\leq SOC(0)\times B_{cmax}-SOC^{min}\times B_{cmx}\\ 0&\leq&P_{3}(\tau)\leq P_{pv}(\tau)\\ 0&\leq&P_{4}(\tau)\leq P_{wind}(\tau)\end{array} \tag{20}\]  
where \(DG_{nominal}\) is the nominal capacity of the DG system and \(B_{cmax}\) is the maximum capacity of the battery system; with proposed values of 5000 kW and 2000 kW, respectively. Values of \(P_{pv}\) and \(P_{wind}\) were taken from [30]. Indeed, values of \(P_{pv}(\tau)\) and \(P_{wind}(\tau)\) (where \(P_{pv}\) is the photovoltaic output power and \(P_{wind}\) is the wind output power) are based on studies conducted on real data. For the photovoltaic resource, two-day solar irradiation data collected in Celestun (Mexico) was used. For the wind resource, data collected in Celestun (Mexico), Ambewela (Sri Lanka), and Madrid (Spain) for several heights was used. In this study, these ranks are used as input values for the TS-MBFOA and NTS-MBFOA at each run to find the minimum value optimizing the IMG.  
Table 3 summarizes the initial power in Watts (W) and percentages (for the ESS) of each resource in the IMG per hour.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Time**} & \(\mathbf{L_{P}}\) & \(\mathbf{P_{1}}\) & \(\mathbf{P_{2}}\) & \(\mathbf{P_{3}}\) & \(\mathbf{P_{4}}\) \\ \cline{2-6}  & **Watts** & **Watts** & **\%** & **Watts** & **Watts** \\ \hline
00:00 & 2500 & 5000 & 0.950 & 1 & 1 \\
01:00 & 2500 & 5000 & 0.949 & 1 & 500 \\
02:00 & 2850 & 5000 & 0.737 & 1 & 750 \\
03:00 & 2950 & 5000 & 0.630 & 1 & 600 \\
04:00 & 2850 & 5000 & 0.482 & 1 & 1000 \\
05:00 & 2500 & 5000 & 0.629 & 1 & 700 \\
06:00 & 2150 & 5000 & 0.653 & 1 & 350 \\
07:00 & 2250 & 5000 & 0.480 & 266 & 1 \\ \hline \end{tabular}
\end{table}
Table 3: Starting conditions (in Watts and percentages) of the resources included in the IMG. \(L_{P}\) = Load power, \(P_{1}\) = Diesel power, \(P_{2}\) = ESS, \(P_{3}\) = Solar power and \(P_{4}\) = Wind power.
## 3 Results and Discussion
### Mechanical Properties of EP/BCI/BSEA Composites  
#### 3.2.1 Bending Properties  
The bending properties of EP/BCI/BSEA composites are exhibited in Figure 8, and the relevant data are presented in Table S1. As exhibited in Figure 8a and Table S1, the bending strength of EP/BCI/BSEA-2 achieved peak values of 163.10 MPa at RT and 251.64 MPa at 77 K, being 8.04% and 113.67% higher than the peak values of 150.96 MPa at RT and 117.77 MPa at 77 K of pure EP, respectively. The bending strength of BSEA-modified EP was decreased compared with the bending strength of EP/BCI-25, while the bending strengths of EP/BCI/BSEA-2 and EP/BCI/BSEA-3 were still higher than the bending strength of pure EP at RT. This phenomenon was mainly attributed to two main reasons. On one hand, the free volume of BSEA-modified EP crosslinking networks increased with the addition  
Figure 7: SEM images of section morphology of (**a**) pure EP, (**b**) EP/BCI/BSEA-2 and (**c**) EP/BCI/BSEA-5; surface morphology of (**d**) pure EP, (**e**) EP/BCI/BSEA-2 and (**f**) EP/BCI/BSEA-5 with no reaction; morphology of (**g**) explosion samples, (**h**) flash sample of pure EP and (**i**) burning sample of EP/BCI/BSEA-2 after liquid oxygen compatibility impact test.  
of BSEA content, which led to a decline in crosslinking density [37], thus resulting in the reduction in the bending strength of modified EP. On the other hand, the concentration of reactive functional groups (-OH, -NH) increased with the addition of BSEA in the modified EP system, resulting in an increase in volume shrinkage after curing, which led to increased internal stress in the EP system, and ultimately led to the decrease in bending strength. At an ultra-low temperature, the free volume of EP/BCI/PBAH composites at 77 K was smaller due to thermal shrinking, which led to increased intermolecular force and enhanced bending strength compared with that at RT. The bending strength of the modified EP decreased with the further addition of BSEA, which is similar to the scenario at RT and will not be discussed in detail.  
As shown in Table S1, the bending modulus in the cryogenic environment was improved compared with that at RT, which was attributed to the fact that the atomic binding force and intermolecular force in cured EP chains under cryogenic conditions were stronger than those at RT because of thermal shrinking. As summarized in Figure 8a and Table S1, the bending deflection reached the maximum value of 17.67 mm (EP/BCI/BSEA-5) at RT and 5.50 mm (EP/BCI/BSEA-2) at 77 K, achieving an enhancement of 283.30% and 107.55% compared with that of pure EP (4.61 mm at RT and 2.65 mm at 77 K, respectively). The free volume of BSEA-modified EP crosslinking networks increased with the increase in BSEA content, which provided space for segments' movement, thus resulting in enhanced bending deflection. Moreover, the deflection in bending at 77 K was decreased compared with that at RT for all specimens. This was mostly due to the fact that the free volume at 77 K became smaller than at RT, resulting in the poor mobility of chain segments, and macromolecules were partially frozen at 77 K.
# Dynamics Test Research of Bounce of Carrier-Based Aircraft Arresting Hook
## 4 Test Verification
### Verification of Horizontal Velocity  
The verification of horizontal velocity is mainly based on the comparison between the velocity input by the main control computer and the velocity displayed on the display screen. The velocity input by the main control computer is the target velocity, and the velocity displayed on the display screen is the actual velocity, as shown in Figure 15.  
The radius of the large disc is 1 m. Considering that the hook head has a certain width, the impact point is 0.95 m away from the center of the disc, so the rotation radius of the impact point is 0.95 m. The calculation formula of the linear velocity at the collision point is  
\[V_{E}=n_{dn}\pi R/30. \tag{14}\]  
It can be seen from Formula (3) and Formula (4) that the average on-board joint velocity of the aircraft \(V_{E}\) is 169.8 km/h, and its standard tolerance of probability distribution \(\sigma_{V_{V}}\) is 9.26 km/h [20]. Therefore, the maximum joint velocity of the aircraft \(V_{E_{max}}\) is 198.4 km/h, and the minimum \(V_{E_{min}}\) is 142 km/h. In the experiment, the average joint velocity, the maximum joint velocity, and the minimum joint velocity are taken as the horizontal velocity for comparative test, and the collision rebound performance of the arresting hook under different horizontal velocity is analyzed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{3}{c}{**Sinking Velocity**} \\ \cline{2-4}  & **Theoretical Value** & & **Test Result (m-s\({}^{-1}\))** & **Error (\%)** \\ \hline
1 & 3.6 & 3.65 & 1.4 \\
2 & 5.0 & 5.08 & 1.6 \\
3 & 6.4 & 6.45 & 0.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of theoretical value and test result of hanging basket sinking velocity.  
Figure 14: Curves of time history of hanging basket sinking velocity.  
The target value and measured value of the linear velocity at the collision point are shown in Table 3. From the data in the table, it can be seen that the error between the test value and the target value of the horizontal velocity is within 1%. The slight difference is due to the motor speed control, but the results are within the acceptable range, thus the motor can accurately simulate the horizontal velocity of the aircraft and meet the test requirements.
# Amphishea: A Novel Persistent Buffer Management Strategy to Improve SMR Disk Performance
## 5 Amphisbaena Design
### Two-Phase Data Block Classification  
5.1.1. The Role Type of the Data Block  
The data classifier is responsible for capturing and reserving frequently updated blocks for the HFB. The data classifier process of classifying blocks consists of two phases. In the first phase, the data classifier predicts the attributes of the blocks it contains based on the explicit characteristics (i.e., write size) of the current write request. This is because smaller requests present better temporal locality, in other words, are more likely to be accessed frequently in the future [41]. The prediction of block attributes is done by comparing the size of the current write request with the \(Hot\_Write\_threshold\), where \(WINDOW\_LENGTH\) is the length of the observation window containing the last \(N\) write requests. The Equation (1) is introduced to dynamically determine the size of the \(Hot\_Write\_threshold\). If the size of the current write request is less than the current hot block threshold, then the write request contains blocks that are labeled as \"hot\" and vice versa. Write requests labeled as \"hot\" are considered to be potentially hot blocks, and all of them are reserved in the HFB. Instead, only partially qualified blocks from write requests labeled as \"cold\" can be reserved in the HFB. The blocks that are allowed to be loaded into the HFB are then handled further by the data classifier.  
\[Hot\_Write\_threshold=\frac{\sum\limits_{i=1}^{N}write\_request\_size_{i}}{ WINDOW\_LENGTH} \tag{1}\]  
In the second phase, the write request is split into multiple 4 KB blocks and the data classifier further refines the classification of the blocks based on their implicit characteristics (i.e., update period). To accurately capture the hot data, we designed four roles based on the access characteristics of the blocks, as shown in Table 1, namely long update period (LUP) blocks, short update period (SUP) blocks, ghost blocks, and shadow blocks. Specifically, the first two types are resident blocks with data (4 KB) in the HFB, and these blocks have significantly different update periods; while the latter two types are non-resident blocks that are much smaller than the former and only record the block number (4 B). Typically, updates to SUP blocks and LUP blocks generate write hits, especially the former, which can be updated frequently due to their short update period. In contrast, ghost and shadow block hits do not yield immediate benefits, i.e., acquisition of the target data, but their  
Figure 6: The workflow of Amphisbaena for processing write requests.  
presence is beneficial for the data classifier to identify blocks with long and frequent update periods, which can indirectly improve the write hit rate.
## 4 Antenna Broadcast Signal Propagation Model  
According to the atmospheric noise data in the book, _VLF Radio Engineering_ of the United States, in Tibet, the maximum atmospheric noise affecting the 100-kHz frequency band occurs during summer, and the maximum noise spectral density is approximately \(-120\)\(\mathrm{\,\,\,\mathrm{d}BV\cdot m^{-1}\cdot Hz1/2}\). The receiver bandwidth is 30 kHz (referring to the Loran-C receiver), which corresponds to a noise field strength of 44.7\(\mathrm{\,\,\,\mathrm{dB\cdot\,\,\upmu V\cdot m^{-1}}}\). Based on the operation experience of the long-running Loran-C navigation and BPL time service systems, a minimum signal field strength of 45\(\mathrm{\,\,\,\mathrm{dB\cdot\,\upmu V\cdot m^{-1}}}\) should be maintained to ensure reliable reception [12; 13].  
With reference to the geographic information data in four directions around the newly-built transmitter, setting the Loran signal propagation model and then simulating the transmission field strength of the antenna with MATLAB tools allows valuable data related to the terrain and signal propagation around the station to be obtained, which can be used to determine the propagation capacity and radiation efficiency requirements of the antenna.  
By comparing the structure and electrical performance of single-tower antennas and four-tower antennas, it can be seen that single-tower transmitting antennas have certain performance advantages. In order to further analyze the transmission capacity of a single-tower antenna, the antenna performance can be more directly understood from the aspects of the single-tower transmitting antenna pattern, the field intensity distribution under the antenna, the antenna magnetic field distribution and the radiation efficiency curve. Figure 7 shows the direction diagram of a single-tower antenna, Figure 8 shows the distribution of antenna field strength in the quarter area, Figure 9 shows the distribution of antenna magnetic field in the quarter area and Figure 10 shows the radiation efficiency curve.  
Figure 8: Distribution diagram of ground electric field under antenna (quarter area).  
Figure 10: Antenna efficiency curve.  
Figure 9: Antenna magnetic field distribution (quarter area).  
With reference to the geographic information data in four directions around the newly-built transmitter, setting the Loran signal propagation model, and then simulating the transmission field strength of the antenna with MATLAB tools allows valuable data related to the terrain and signal propagation around the station to be obtained, which can be used to determine the propagation capacity and radiation efficiency requirements of the antenna. Therefore, an antenna selection database can be established. Simulating the four-way radio signal propagation around the transmitter in Naqu, Tibet is to finally determine the electrical parameters of the transmitting antenna and better deal with the very special geological and environmental conditions in Naqu, Tibet [14; 15].
## 2 Conventional Channel Estimation Methods
### Blind Channel Estimation Method
#### 2.2.1 Subspace-Based Blind Channel Estimation  
The subspace-based blind channel estimation does not require any known training sequence, making it useful for systems where the training sequence cannot be sent or takes up a significant amount of bandwidth. The core concept of this algorithm is to use the statistical characteristics of the signal to separate the signal and noise subspaces and then estimate the channel parameters [33]. In [34], [PERSON] et al. proposed a generalized subspace algorithm via a minimized cost function, which focuses on integrating the kernel matrix set from signals that share the target channel through a weighted sum of projection errors. Experiments demonstrate that the proposed algorithm has exceptional performance, which can be further enhanced by employing a particular weight set for the cost function and adding more kernel matrices. Most subspace-based blind algorithms primarily estimate channel coefficients by utilizing the orthogonality of the correlation matrix of the signal and the noise subspaces of the received signal. However, in practice, the correlation matrix is unknown and necessitates time-averaging estimation using multiple received samples. For this reason, [PERSON] et al. [35] proposed a scheme that leverages the frequency correlation between neighboring subcarriers in MIMO-OFDM systems to minimize the time averaging.
## 3 Methodology
### Domain Analysis Unit
#### 3.6.2 Linguistic  
The utility function that is employed by the system to transform linguistic values into numbers is the following:  
\[g(x;p)=|L|-abs(pos(x)-pos(p)) \tag{7}\]  
In this formula \(L\) is the set of linguistic terms used in that attribute, the function _pos_ returns the position of a particular value in this set, \(p\) is the term preferred by the user and \(x\) is the term appearing in a certain alternative. The utility function returns a numberbetween 1 and the number of terms; the higher this number, the higher the satisfaction of the user with that alternative.  
**Example**: Let us have the price feature represented as a linguistic variable. Let \(L=\{very\ low,low,high,very\ high\}\) be the linguistic values. Here, \(pos(low)\) is 2. Assuming that the user prefers \(p=low\) and the alternative value is \(x=very\ high\), then the utility value of this alternative given the user preference is \(g(very\ high;low)=4-abs(4-2)=4-2=2\).
## 3 Results
### Performance Analysis
In terms of actual performance, the average accuracy of Yolo Light is 0.750, the recognition rate on the test set is 92.6%, and the inference speed on NX reaches 28.65 FPS. This indicates that Yolo Light is more suitable for detecting smoke from straw burning compared to Yolov5s and Yolov8s. It is also a small, low-cost, accurate model for use in straw-burning smoke equipment that provides a possible solution. In practical applications, the monitoring camera and Jetson Xavier NX can be mounted in the air at 15 to 20 m, or they can be mounted on the UAV to monitor the straw-burning smoke within 5 km.