esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### ICMLD-Cfar
Eliminate the noise \(IMF\)s based on the discrimination criteria listed below \[\beta(q)= \begin{cases}\begin{aligned} 0,&q=1\\ \frac{\sum_{j=1}^{q-1}\tau(f)}{(q-1)\tau(q)}-1,&q=2,3,\ldots,\ Q_{min} \end{aligned}^{\prime},\end{cases} \] (19) \[\tau(q)=\frac{\sum_{gl}^{g_{r}}D_{as}(q,g)^{2}}{\sum_{g}D_{as}(q,g)^{2}},\] (20) where \(\beta(q)\) denotes the energy concentration of the \(q\)-order IMF, \(D_{as}(q,g)\) denotes the discrete autocorrelation sequence of the \(q\)-order \(IMF\), \(g=1,2,\ldots,g_{l},g_{r},\ldots,G\) denotes the time sequence, and \([g_{l},g_{r}]\) denotes the two middle points, namely, \(g_{l}=G/2\). Moreover, \(\tau(q)\) represents the energy ratio of \(D_{as}(q,g)\) in the interval \([g_{l},g_{r}]\). If \(\beta(q)\leq 2\), the \(IMF(q)\) is used to denote unwanted noise \(IMF\)s.
5. Reconstruct the radar signal using \[\widehat{R}_{AF}(x,\ n)=\sum_{q}^{Q_{min}}IMF(q),\ \ s.t.\ \beta(q)>2,\] (21) where \"\(s.t.\)\" means subject to. 6. Calculate the energy ratio on the reference frequency band of human respiration and digging motion of the shovel of the wheel loader using \[E_{r}(x)=\frac{E_{rfe}(x)}{E_{total}(x)},\] (22)where \(E_{total}(x)\) represents the total energy of \(\widehat{R}_{AF}(x)\) and is calculated by \(E_{total}(x)=\sum_{n}\widehat{R}_{AF}(x,\ n)^{2}\), and \(E_{refe}(x)\) represents the energy in the reference frequency band. Herein, the reference frequency band is set to 0.2-0.4 Hz. The \(E_{r}(x)\) value for human targets is usually higher than 40%, whereas that of the noise is less than 12% [31]. Our experimental results show that the \(E_{r}(x)\) value for mechanical vibrations is mostly larger than 40%. Thus, the value of \((1.4-E_{r}(x))\) of the human targets and mechanical vibrations are smaller than one, and that of noise is larger than one. Therefore, the difference between targets and noise can be enlarged by the multiplication operation. As shown in Figure 3, whether a range point is a target can be determined by  
\[C(x)=(1.4-E_{r}(x))\times T_{s}\times B_{e}(x), \tag{23}\]  
\[\left\{ \begin{array}{l}if\quad(0.6+E_{r}(x))\times\widehat{W}\widehat{E}^{ \prime\prime}(x)\geq C,\\ it\text{ is determined as target}\text{ preliminary}.\\ if\quad(0.6+E_{r}(x))\times\widehat{W}\widehat{E}^{\prime\prime}(x)<C,\\ it\text{ is determined as noise}\text{ preliminary}.\end{array} \right. \tag{24}\]  
where \(C(x)\) denotes a threshold as the criterion for detecting targets preliminarily. As it was mentioned, the trailing effect can cause the value of range points adjacent to the real target position to be similar. Thus, some adjacent range points, which are actually from the same target, are usually determined preliminarily as targets at the same time. To prevent interferences in this situation, only the range point of the maximum value of \(\widehat{W}\widehat{E}\) within the adjacent range points is regarded as the target position. Simultaneously, the interval between two adjacent real targets should not be smaller than two range points (approximately equal to the anterior-posterior dimension of an average human adult body).
## 3 Preparation of the Experiment
### Vibration Monitoring Sensors Description and Positioning  
During the experiment, we simulated the release of the robot fastening by loosening the M8\(\times\)20 screws, which secure the robot and the clamping plate to the workbench. We used Baluff BCM0002 (Balluff GmbH, Neuhausen, Germany) condition monitoring sensors for the experiment. These sensors can detect not only vibrations, but also other operational data related to the equipment or its environment, such as surface temperature, humidity, and atmospheric pressure. The essential technical specifications of the sensor used can be found in Table 2.  
For the measurements, three sensors, which were placed on the mounting plate via a Balluff BAM03 FA magnetic bracket (see Figure 3a) were used. This type of magnetic bracket is used specifically for this sensor type and the installation has been carried out according to the sensor manufacturer's instructions. The positioning of the sensors is shown in Figure 3b. The position of the sensors was determined with a view to install them as close as possible to the object to be traced while ensuring that the accelerometer axes were aligned with the robot's World Coordinate System (WCS).  
\begin{table}
\begin{tabular}{c c} \hline
**Specs** & **Balluff BCM0002** \\ \hline Evaluation values time domain & RMS, Peak to Peak, Max, Crest Factor, Skewness, Kurtosis \\  & 2...1800 Hz (\(\pm\)10\%) \\ Frequency range & 2...2500 Hz (\(\pm\)3 dB) \\ Measuring principle & MEMS \\ Measuring range & \(-16\ldots 16\) g \\ Number of measuring axes & 3 \\ Interface & IO-Link 1.1 \\ \hline \end{tabular}
\end{table}
Table 2: Technical characteristics for vibrations of condition monitoring sensor.  
Figure 2: Clamping method used for industrial robot.
## 3 Results and Discussion
### Micro-Hardness Measurements  
Ten Vickers micro-hardness measurements with 100 g weight along 1 mm line were taken at the interface line between the MIM preform and the L-PBF section of the three sets of samples and are presented in Figure 4. In the case of as-built bars, there was an increase of the hardness across the MIM/L-PBF interface, which is mainly due to the lack of porosity at the interface and the high dislocation density at the sub-grains boundaries which is typical for the as-built 316 L material [56]. Concurrently, the heat treatment reduced the hardness of the samples and this was in line with the obtained results on air-quenched samples. The relatively low hardness values for the air-quenched samples, can be associated with a residual stress relief and their porosity. Which in the case of the MIM preform, the hardness was even lower due to the porosity, which was introduced in the preform after the HT [64; 65]. Furthermore, the conventional HT led to a homogenisation of the hardness over the interface of hybrid samples and this could mainly be due to the refined microstructure at the fusion line (interface), and the recovery (reduction in dislocation density induced during the specific HT regime) [48]. Conversely, the hardness increased marginally for the HIP hybrid samples. This increase can be explained with the strain hardening that is particularly high on plastically deformed sections of the L-PBF built at the first few L-PBF layers, in combination with the elevated temperature and pressure during the post treatment that minimised porosity through the hybrid sample. In particular, the hardness increase there, was significant compared to the MIM preforms and this could be attributed to the applied heat load during the L-PBF process, the redistribution of dislocation density within the microstructure, and also the deformation and recrystallisation which is facilitated by higher pressure and temperature induced during the HIP process [60]. Overall, the small alternations of hardness at the interface indicates a strong bonding between the preform and L-PBF sections [64]. This conforms well to the results from the tensile tests, as all samples showed a ductile fracture at the L-PBF sections and no samples demonstrated a fracture at the interface or near the interface area.
## 1 Introduction
[PERSON] and [PERSON] [27] presented a techno-economic assessment of a biomass-based combined power and cooling plant suitable for off-grid rural areas. Integration of cold storage provided effective utilization of waste heat and saved additional electricity for conventional vapor compression refrigeration based cooling system. The estimated payback period of the plant, without subsidy, was estimated to be 14.4 years, and with 50% capital subsidy it was reduced to 6.6 years. [PERSON] et al. [28] investigated waste gasification based off-grid electricity generation in developing countries, such as Pakistan, utilizing mixed biomass composets. Although the estimated electricity price was higher in all studied scenarios as compared to the average governmental electricity tariff, a large potential of gasification for off-grid electricity generation was reported. [PERSON] et al. [29] performed an economic analysis on the utilization of rice residues for decentralized electricity generation in Ghana. They found that husk gasification mini-grids can be a suitable electrification solution, as their cost was lower than the average cost for grid extension of diesel mini-grids and off-grid solar systems in remote communities of Ghana. Finally, [PERSON] et al. [30] presented a model for the financial viability of biomass gasifier power projects for enhancing electricity access in India and other developing countries. They concluded that large-scale rural electrification of remote rural areas requires an alignment of financial incentives and institutional structures to implement, operate, and sustain the projects. However, no previous work has been found that focuses on the techno-economic viability of biomass gasification CHP plantsfor the electrification of olive mills located in remote areas using their own by-products as feedstock. Accordingly, the main aim of the present work is to demonstrate that biomass gasification CHP plants can be a feasible technology for electrification of off-grid olive mills in remote areas. The outline of the remainder of this work is described as follows. Section 2 introduces the case study and describes the simulation approach, as well as the experimental procedure. The data from the measurement campaign along with the results from the simulation are described in Section 3, in addition to a mass and energy balance overview. Subsequently, a detailed techno-economic assessment of the proposed biomass gasification CHP plant with a sensitivity analysis is included in Section 4 to determine the feasibility of gasification CHP plants as an alternative to diesel generators for electrification of off-grid olive mills. Finally, conclusions are drawn in Section 5.
# Defect Detection on LED Chips Based on Position Pre-Estimation and Feature Enhancement
## 5 Conclusions and Suggestions  
A chip segmentation method based on position pre-estimation and a modified NCC algorithm, as well as feature enhancement methods are proposed in this study. The segmentation method effectively avoids the interference of chip adhesion, disorderly arrangement, and large area missing. By developing modified NCC matching, the precise location result was not affected by brightness change in acquired pictures. Feature enhancement schemes were designed to separate low-contrast defects quickly and accurately.  
The results of the three experiments show that the average accuracy reaches 99.54% while inspection missing rate is 0.03%. In addition, detection time of a single chip is about 1.098 ms, and the smallest defect that can be detected is 2 \(\upmu\)m. After performing and comparing to the other five mainstream detection methods, it can be concluded that the proposed method is far more superior in terms of efficiency and accuracy. The work in this study can be used as the algorithm part of the Automated Optical Inspection (AOI) equipment to replace manual surface defect detection of LED chips.  
However, template pictures and chip sizes were obtained manually, which can be further improved. At present, the authors' laboratory has realized the automatic acquisition of chip size and interval. To address this limitation, combining these two programs and exploring how to achieve the automatic generation of templates will be left for future work.  
Conceptualization, L.X. and X.H.; methodology, L.X.; software, L.X. and J.Z.; validation, L.X.; formal analysis, L.X.; investigation, L.X. and J.Z.; resources, L.X.; data curation, L.X. and T.H.; writing--original draft preparation, L.X.; writing--review and editing, L.X. and K.H.; visualization, L.X. and K.H.; supervision, L.X. and X.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Characteristic Innovation Projects of Colleges and Universities of Guangdong Province, China, grant number 2021 KTSCX111; Shenzhen Pingshan District Science and Technology Innovation Project, Guangdong, China, grant number PSKG202006; Self-made experimental instruments and equipment project of Shenzhen Technology University in 2021 Guangdong, China, grant number 202101577701059.  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Application of Fiber Bragg Grating Sensing Technology for Bol Force Status Monitoring in Roadways
###### Abstract  
Bolts support have become a major active support method in coal mine roadways to control roadway roof failure and improve surrounding rock structure stability. The traditional bolt force status monitoring (BFSM) method has poor anti-interference performance, is easily affected by harsh downhole environments, and cannot support remote real-time monitoring. This paper presents a fiber Bragg grating (FBG) bolt force sensor that monitors the force of roadway bolts. This sensor uses a cantilever and a diaphragm as elastic elements and two FBGs bonded on the top and bottom surfaces of the cantilever as sensing elements. The experimental results indicate that the measuring sensitivity is improved by using the center wavelength difference between the two FBGs. The sensitivity is 38.79 pm/kN within the range from 0 to 150 kN, and the correlation coefficient reaches 99.98%. The engineering applications show that the FBG sensing technology can automatically acquire, and monitoring results are of great significance in roadway anchorage engineering safety and bolt support quality evaluation. Furthermore, such a sensor is also widely used in quasi-distributed measurement and long-term online monitoring of bolt force status in such fields as geotechnical engineering, tunnel engineering, and slope engineering.  
fiber Bragg grating (FBG); diaphragm-cantilever; bolt force; monitoring system; roadway
## 2 Materials and Methods
### Deep Learning Methods
#### 2.3.1 LSTM Architectures  
Recurrent Neural Networks (RNN) are specifically designed for processing sequential data. In the RNN model, the output of the previous step is fed into the current step as input. Mathematically, an RNN model is a nonlinear function that takes \(t\)-steps of variables in a time series as input and outputs a vector value at time \(t+1\)[31]. The output of an RNN unit is determined using a tanh function by Equation (3):  
\[h_{t}=\tanh(W_{x}x_{t}+W_{h}h_{t-1}+b_{t}) \tag{3}\]  
where \(h_{t-1}\) is the recurrent output from the previous step, \(x_{t}\) is the input at the current time, and \(W_{x},W_{h},b_{t}\) are the weights and bias of the network to be trained during the training. The main problem with RNN is the vanishing gradient, i.e., the gradient of the loss function approaches zero [31]. LSTM [32] is a special type of RNN, created as a solution to the vanishing problem in RNN. A single-layer LSTM is shown in Figure 3, where there is one unit for each time step.  
A common LSTM unit consists of a block input \(z_{t}\) with corresponding weight matrices \(W_{xx},W_{zh},b_{z}\), an input gate \(i_{t}\) with corresponding weight matrices \(W_{ix},W_{ih},b_{i}\), a forget gate \(f_{t}\) with corresponding weight matrices \(W_{fx},W_{fh},b_{f}\), an output gate \(o_{t}\) with corresponding weight matrices \(W_{ox},W_{oh},b_{o}\), and a memory cell \(c_{t}\) (see Figure 4).  
Forget gate \(f_{t}\) determines what information remains in the cell by outputting a number between 0 and 1 using Equation (4). The output 0 means \"forget all information completely\" and one means \"keep all information\". The input gate \(i_{t}\) protects the unit from irrelevant inputs and decides which values to update using Equation (5). The output gate \(o_{t}\) uses the sigmoid function given by Equation (6) to decide which information in the cell is used to calculate the output of the LSTM unit. The block input \(z_{t}\) creates a new vector that could be added to the new state using the tanh function given by Equation (7). The memory cell \(c_{t}\) decides whether to update the information obtained from the previous inputs and the current information provided by Equation (8).  
Figure 3: LSTM in loops and unfolded in sequence.  
\[f_{t} =\sigma(W_{fx}x_{t}+W_{fh}h_{t-1}+b_{f}) \tag{4}\] \[i_{t} =\sigma(W_{ix}x_{t}+W_{ih}h_{t-1}+b_{i})\] (5) \[o_{t} =\sigma(W_{ox}x_{t}+W_{oh}h_{t-1}+b_{o})\] (6) \[z_{t} =\tanh(W_{zz}x_{t}+W_{zh}h_{t-1}+b_{z})\] (7) \[c_{t} =f_{t}*c_{t-1}+i_{t}*z_{t} \tag{8}\]  
The output of the current block \(h_{t}\) is calculated using Equation (9).  
\[h_{t}=o_{t}*\tanh(c_{t}) \tag{9}\]
## 2 Materials and Methods
### Cost-Effective Data Acquisition System
#### 2.3.2 Acquisition Board and Firmware  
In accordance with the low-cost framework of this study, the chosen data acquisition board was an Arduino Mega 2560 R3 (Arduino Mega 2560 Rev3, Arduino, Turin, Italy). This board is notably prevalent and operates on the 8-bit, 16 MHz ATmega2560 microcontroller, featuring 16 analog-to-digital converter (ADC) inputs that directly operate at 5 V, thus eliminating the need for any intermediary interface with the pressure sensors. Moreover, the ADC resolution is adjustable from 10 to 12 bits, ensuring accurate reconstruction of the sinusoidal signals detected by the pressure sensors. This configuration aligns with the research's aim for cost-efficiency while maintaining the integrity and precision of data collection.  
This acquisition board is used to read the sensor signals and pass the values to the computer where they will be processed and analyzed. The connection with the pressure sensors is made directly through the ADC ports, which translate the sensors' voltage output into a digital number stored in the microcontroller's memory. Figure 10 illustrates the wiring diagram of the data acquisition systems specifically employed for this work. On the computer side, the communication is performed with a universal serial bus (USB) connection over a serial protocol. Arduino firmware provides its own serial function to send and receive data in a reliable manner. However, the transmission speed is low for high- or medium-frequency applications. In order to obtain higher speeds in data transfer, an ad-hoc communication protocol was developed and tested in a previous work. Such protocol manages data at byte level and applies different framing techniques to ensure data compliance, achieving frequencies of about 3 kHz for a single sensor with no data losses. In [19], a detailed description of the protocol and the statistic evaluation of its speed and robustness can be found.  
Figure 9: Illustration of the sensitivity curve of the differential pressure sensor (MPX5100 AP), as detailed in the sensor’s technical specification sheet [22].
# Classification of EEG Signals for Prediction of Epileptic Seizures
## 8 Conclusions and Future Work  
This research proposes a method for seizure prediction in epileptic patients using scalp EEG. Patients that have epilepsy can lead a risk-free life if timely and accurate seizure prediction is ensured. In comparison to existing methods, the proposed method uses a regression-based alternative to notch filtering to increase SNR, then combines automated feature extraction with CNN and handcrafted features and performs classification with an LSTM classifier to achieve better sensitivity and specificity. The model was trained and validated on the CHB-MIT scalp EEG dataset, and it achieved 94% accuracy, 93.8% sensitivity, and 91.2% specificity. In the future, intelligent algorithms such as CNN and GAN-based denoising methods [49] can be used to preprocess the data to further increase the SNR. From the deep learning aspect, large numbers of parameters need to be learned; research can be done to make the algorithms more efficient by reducing the number of operations and learnable parameters, which will lead to less-computationally intense models. This study proposes patient-specific seizure prediction; a lot of research can be done to develop a patient nonspecific seizure prediction method.  
Conceptualization, M.H.A., S.M.U., S.K., J.A. and R.A.; methodology, M.H.A., S.M.U., S.K., A.A. and J.A.; software, M.H.A., A.A. and A.Y.; validation, M.H.A. and A.Y.; formal analysis, S.M.U., S.K., A.A. and A.Y.; investigation, S.M.U., S.K., S.H., S.S.U., A.Y.; resources, R.A., S.H., J.A., S.S.U. and A.Y.; data curation, S.M.U.; writing--original draft preparation, M.H.A.; writing--review and editing, S.M.U., S.K., A.Y.; visualization, A.A., R.A., S.H. and S.S.U.; supervision, S.M.U. and S.K.; project administration, S.K., J.A. and R.A.; funding acquisition, R.A. and J.A. All authors have read and agreed to the published version of the manuscript.  
The authors are grateful to the Taif University Researchers Supporting Project number (TURSP-2020/36), Taif University, Taif, Saudi Arabia.  
Not Applicable.  
The authors declare no conflict of interest.
## 5 Methodology  
### Mathematical Models of the Evaluation System  
The comprehensive compaction quality evaluation includes the real-time hybrid evaluation and the real-time compaction quality heterogeneity analysis. The mathematical models are shown in Equations (5)-(7), comprised of three parts: the objective model, the method set model and the parameter set model. Equation (5) defines the objective model \(E\) of compaction quality evaluation,  
Figure 3: User interface of the 3D real-time compaction monitoring (RTCM) system.  
where \(E_{R}\) denotes the real-time hybrid evaluation model, \(E_{P}\) denotes the real-time compaction quality heterogeneity analysis model. Equation (6) defines the method set model, where \(f\) is the \(i\)-AHP method for the hybrid evaluation of material physical indices (\(I_{P}\)) and mechanical indices (\(I_{M}\)), \(y\) denotes the \(i\)-GAM, and \(h\) denotes the lag distance, a parameter in \(i\)-GAM. \(S\) and \(V\) are the rating matrix and the total weight vectors. \(k\) denotes the interval mathematics method, \(g\) denotes the prediction model, \(\bigotimes\) denotes the coupled algorithm. Equation (7) defines the parameter set model, where \(C\) and \(a\) represents the semivariogram model parameters in \(i\)-GAM. The \(i\)-AHP and \(i\)-GAM will be elaborated in the following parts of this section.  
\[E=E_{R}\bigcup E_{P}, \tag{5}\]  
\[\left\{\begin{array}{l}E_{R}=f(I_{P},I_{M})\\ E_{P}=y(h)\\ I_{P}=g\Big{(}N_{s},N_{p},N_{v},N_{t},M_{g},M_{m}\Big{)}\\ I_{M}=g(A_{0.5\Omega},A_{\Omega},A_{1.5\Omega},A_{2\Omega},A_{2.5\Omega},A_{3 \Omega})\\ f=k(S,V)\\ g=SVR\bigotimes FA\end{array}\right. \tag{6}\]  
\[\left\{\begin{array}{l}E_{R}=[HCl]\\ E_{P}=[C,a]\\ I_{P}=[K,n,\rho_{d}]\\ I_{M}=[E_{\text{n}d},E_{0},K_{30}]\end{array}\right.. \tag{7}\]