esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Basic Models and Theory  
### General Motion Equations  
In FAST [27], a multi-body dynamics formulation is used to obtain the results. The users can give the blades and tower mode shapes as input in terms. Based on [PERSON]'s dynamics [32], the equations of motion are derived and implemented. By [PERSON]'s laws of motion, [PERSON]'s equations of motion for a holonomic system with \(n\) DOFs can be stated as follows [33; 34]:  
\[F_{k}+F_{k}^{*}=0\ \ (k=1,2,\ldots,n) \tag{1}\]  
The generalized active forces \(F_{k}\) and generalized inertia forces \(F_{k}^{*}\) can be expressed in Equations (2) and (3), respectively, for a set of \(p\) rigid bodies characterized by reference frame \(N_{i}\) and center of mass point \(X_{i}\):  
\[F_{k}=\sum_{i=1}^{p}{}^{E}\mathbf{v}_{k}^{X_{i}}\cdot\mathbf{F}^{X_{i}}+{}^{ E}\boldsymbol{\omega}_{N_{i}}^{k}\cdot\mathbf{M}^{N_{i}}\ \ (k=1,2,\ldots,n) \tag{2}\]  
\[F_{k}^{*}=\sum_{i=1}^{p}{}^{E}\mathbf{v}_{k}^{X_{i}}\cdot\left(-m_{X_{i}}{}^{ E}\mathbf{a}^{X_{i}}\right)+{}^{E}\boldsymbol{\omega}_{k}^{N_{i}}\cdot\left(-{}^{ E}\mathbf{H}^{N_{i}}\right)\ \ (k=1,2,\ldots,n) \tag{3}\]  
where \(\mathbf{F}^{X_{i}}\) and \(\mathbf{M}^{N_{i}}\) are the active forces on the rigid body \(N_{i}\), respectively; \({}^{E}\mathbf{a}^{X_{i}}\) is the acceleration; \(m_{X_{i}}\) is the mass of particle \(X_{i}\); \({}^{E}\mathbf{H}^{N_{i}}\) is the time derivative of the angular momentum of rigid body \(N_{i}\) about \(X_{i}\) in the inertial frame \(E\); \(E\
u_{k}^{X_{i}}\) is the partial linear velocity; and \({}^{E}\omega_{k}^{N_{i}}\) is the partial angular velocity.  
The multi-body formulation for Equation (1) can be written in Equation (4) [27; 34]:  
\[\mathbf{M}(\mathbf{q},\mathbf{u},t)\ddot{\mathbf{q}}+\mathbf{f}\big{(}\mathbf{ q},\dot{\mathbf{q}},\mathbf{u},\mathbf{u}_{d},t\big{)}=0 \tag{4}\]  
where \(\mathbf{M}\) is the mass matrix; \(\mathbf{f}\) is the nonlinear forcing function; \(\mathbf{q}\), \(\dot{\mathbf{q}}\) and \(\ddot{\mathbf{q}}\) are the vectors of DOF displacements, velocities and accelerations, respectively; \(\mathbf{u}\) is the control inputs; \(\mathbf{u}_{d}\) is the wind inputs; and \(t\) is time.  
FAST numerically linearizes the aeroelastic equations of motion by perturbing (represented by a \(\Delta\)) each of the system variables about their respective operating point (op) values [27]:  
\[\begin{array}{c}\mathbf{q}=\mathbf{q}_{\text{op}}+\Delta\mathbf{q},\ \dot{\mathbf{q}}=\dot{\mathbf{q}}_{\text{op}}+\Delta\dot{\mathbf{q}},\ \ \ddot{\mathbf{q}}=\ddot{\mathbf{q}}_{  
We are interested in the aerodynamic forces normal to and tangential to the rotor plane. In Figure 1, the local flapwise and edgewise forces, \(F_{\text{N}}(r,t)\) and \(F_{\text{T}}(r,t)\) are shown as follows:  
\[F_{\text{N}}(r,t)=L(r,t)\cos(\phi)+D(r,t)\sin(\phi) \tag{7}\]  
\[F_{\text{T}}(r,t)=L(r,t)\sin(\phi)-D(r,t)\cos(\phi) \tag{8}\]  
where \(\phi\) is the flow angle (detailed in [3; 35]), \(r\) is the distance between hub and element, \(D(r,t)\) is the drag force, and \(L(r,t)\) is the lift force. The approach of measuring the aerodynamic loads on the blade is in [3].
## 5 Conclusions  
1. The diffusion pattern of penetration grouting in gravel soil: in the beginning stage, most of the cement slurry penetrates and diffuses rapidly in the gravel soil from the grouting hole along the downward (especially vertical downward due to gravity) direction; in the middle stage, the cement mainly penetrates and diffuses horizontally and laterally around the grouting hole; in the end stage, most of the cement penetrates and diffuses along the upward direction.
2. The actual permeate diffusion size of cement in gravel soil is different from the theoretical diffusion value, and it shows a three-dimensional spatial diffusion morphology of nearly ellipsoid; its trajectory equation is quantitatively characterized, which can be used as a computational model for three-dimensional spatial diffusion size parameters, and thus can be used to predict the three-dimensional spatial diffusion size of cement permeate grouting in gravel soil in order to provide guidance for engineering practice.
3. The deviation rate of three-dimensional spatial diffusion pattern of cement penetration grouting in gravel soil is defined, and the effect law of coupling effect between gravel soil porosity and cement grout weight on morphologies of penetration grouting is discussed. That is, for Bingham type cement slurry, when the porosity exceeds 0.5, the coupling effect has a significant effect on the three-dimensional spatial diffusion pattern of gravel soil penetration grouting; for power-law type cement slurry, when the porosity exceeds 0.4, the coupling effect has a significant effect, while less than 0.4 is showing insignificant characteristics; for Newton type cement slurry, the coupling effect has a comparable significant effect.  
The research results of this paper can provide not only theoretical support for the actual penetration grouting engineering practice of gravelly soil layer, but also provide technical reference for the prevention and mitigation of geological disasters such as landslides that occur in gravelly soil.  
Conceptualization, B.W.; Data curation, M.C. and X.W.; Formal analysis, W.C., C.L. and J.W.; Resources, Y.Y.; Software, X.L.; Validation, C.Y.; Visualization, Z.Y.; Writing-original draft, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 41861134008), the [PERSON] academician workstation of Yunnan Province (Grant No. 202105 AF150076), the Key R&D Program of Yunnan Province (Grant No. 202003 AC100002), and the General Program of basic research plan of Yunnan Province (Grant No. 202001 AT070043).  
Data are contained within the article.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Energy Ratio Variation-Based Structural Damage Detection Using Convolutional Neural Network
## 4 Comparison of CNN and SVM Training Effect  
### Damage Identification by CNN  
The acceleration signal of the node in the Z direction of the finite element model of the steel beam is used as the original data, and the damage data set is obtained by WPD. Nine acceleration sensors are arranged on the beam, and six sets of data are collected at each sensor position. Then, the sample sets of eight types of damage states under five damage degrees of 1%, 2%, 5%, 10%, and 15% are 8 \(\times\) 6 \(\times\) 9 \(\times\) 5 = 2160, and each damage state corresponds to 270 data. Among the 2160 samples, 1800 of the data are selected as training samples, and 360 of the data are selected as validation samples.  
The training process of CNN is shown in Figure 8 below. The upper and lower layouts of the training process represent the training accuracy and loss function, respectively. The blue and red lines represent the training set, and the black dotted lines represent the verification set (the verification set is extracted from the original training set). The test set is input into the trained neural network model to obtain the recognition result of the damage scenario. The configuration and super parameter settings for the neural network training are shown in Tables 6 and 7. From the Figure 8, it can be seen that the accuracy of damage identification based on acceleration signal increases rapidly from about 20% to about 85%, and then, the change tends to be gentle, and the fluctuation range is small. The final accuracy rate can reach 95.14%.
###### Abstract  
Complex illumination, solar flares and heavy smog on the sea surface have caused difficulties to accurately obtain high-quality imaging and multi-dimensional information of marine monitoring targets, such as oil spill, red tide and underwater vehicle wake. The principle of existing imaging mechanism is complex, and thus it is not practical to capture high-resolution infrared images efficiently. To combat this challenge by utilizing new infrared optical materials and single point diamond-turning technology, we designed and processed a simple, light and strong condensing ability medium and long wavelength infrared imaging optical system with large relative aperture, which can obtain high-quality infrared images. On top of this, with the training from a combination of infrared and visible light images, we also proposed a super-resolution network model, which is composed of a feature extraction layer, an information extraction block and a reconstruction block. The initial features of the input images are recognized in feature extraction layer. Next, to supply missing feature information and recover more details on infrared image extracted from a dense connection block, a feature mapping attention mechanism is introduced. Its main function is to transfer the important feature information of the visible light images in the information extraction block. Finally, the global feature information is integrated in the reconstruction block to reconstruct the high-resolution infrared image. We experimented our algorithm on both of the public Kaist datasets and self-collected datasets, and then compared it with several relevant algorithms. The results showed that our algorithm can significantly improve the reconstruction performance and reveal more detail information, and enhance the visual effect. Therefore, it brings excellent potential in dealing with the problem of low resolution of optical infrared imaging in complex marine environment.  
infrared imaging; super-resolution; attention mechanism; marine monitoring +
Footnote †: journal: 2022, 12, 10871. [[https://doi.org/10.3390/app122110871](https://doi.org/10.3390/app122110871)]([https://doi.org/10.3390/app122110871](https://doi.org/10.3390/app122110871))
# Improved Food-Processing Techniques to Reduce Isoflavones in Soy-Based Foodstuffs
## 3 Results
### Treatment-Water Analysis  
The mixture made of the drenching and washing waters of dehulled beans contained 2158 mg/L of suspended matter with a turbidity of 4195 FNU (Formazin Nephelemetric Unit). Laser diffraction analysis highlighted particle sizes ranging from 0.1 \(\upmu\)m to 300 \(\upmu\)m [d\({}_{10}\): 6.35 \(\pm\) 2.40 \(\upmu\)m; d\({}_{50}\): 106 \(\pm\) 8.91 \(\upmu\)m; d\({}_{90}\): 192 \(\pm\) 24.1 \(\upmu\)m; d\({}_{\max}\): 400 \(\pm\) 71 \(\upmu\)m.], considering that the few large particles present masked the smallest ones, and most agglomerates were dissociable under mechanical stress. The results of the first centrifugations (200 mL samples) are presented in Figure S3 in the Supplementary Materials. The different suspended matter concentrations obtained at different speeds ranged from 309 mg/L to 217 mg/L in the supernatant. Therefore, the centrifugation step allowed the clarification of the aqueous phase. The pellets obtained after the second centrifugation (3.4 L sample) had a volume of 60 mL. The pellets contained soybean fragments, and therefore proteins and other insoluble soy particles. The percentage of IFs in the dried extract was found to be 13.02 \(\pm\) 2.27%.  
The quality of the drenching and washing water mixture was significantly improved by membrane filtration on a PREcart PP 2 cartridge. This test was performed on raw water without centrifugation pre-treatment. The initial suspended matter concentration was 2158 mg/L and was reduced by a factor of 100 to reach a residual value of 21 \(\pm\) 11 mg/L after cartridge filtration. Considering the water sample tested, the cartridge filtration, for which the maximum pressure was set on 5 bars, started at a flow rate of 300 kg/h. The filtration flow was decreased progressively to 18 kg/h after 33 min. The amount of water that was filtrated during this period was 22.22 kg. Two filtrates were collected, one (filtrate 1) before pressure stabilization at 5 bars and the other (filtrate 2) when the pressure was kept at its maximum (Figure S4A in Supplementary Materials). The flow was maintained at a constant level of 300 kg/h for 6 min. A mass balance based on measurements performed before and after filtration was carried out for IF concentrations. The data seen in Figure S4B show that 83.33% of all IFs were trapped on the cartridge filter, representing 83.42% for DAI and 82.56% for GEN, specifically. In total, the 22.22 L of treatment water left 45.8 mg of IFs on the cartridge. In parallel, the suspended matter was reduced from 2158 mg/L to 25 mg/L, leaving a mass of 47,300 mg of solids on the filter. Considering these figures, the cartridge extract contained only 0.01% of all IFs.
## 3 Anaerobic Digestion Plant Technologies
### Monitoring and Control of Plant Efficiency  
An optimal management system for a biogas plant should allow a certain flexibility in terms of the hydraulic and organic load of substrates and a certain diversification of the types of substrates; disposal of waste; maximization of organic conversion efficiency in biogas/biomethane; production of good quality digestate and biomethane; reduction of the size of the plant and operating costs; reduction of the environmental impact. Several parameters need to be monitored at full-scale level in order to promote the efficiency of the process through an adequate biogas production, being related to the improvement of biomass quality, best environmental conditions for microorganism's growth, and reduction of energy losses: operating parameters of the power supply system; digestion temperature; composition of the produced biogas; quantity of biogas introduced into the engine; pH of the anaerobic digestion process; operating parameters of the gate valves and pumps; operating parameters of the motor-generator group (voltage, current and power output, operating hours, emissions); global self-consumption of plant. The reference parameter commonly used by the biogas plant operator is either the annual production or, alternatively, the operating hours. A list of the main parameters is given in Table 3.  
Anaerobic digestion is highly sensitive to process disturbances. The biological process can reach instability due to a variety of perturbations such as: overloading of organic or hydraulic rates; presence of toxic or inhibitory compounds; lack of nutrients for the growth of microorganisms; deviation from the optimal operating temperature. In order to facilitate the management and to favor the stability of the process, full-scale plants usually work with expanded and larger than necessary hydraulic retention times (HRT), and low organic load rates (OLR), both causing a reduced methane yield [25]. In order to optimize the process, even at high organic loads, the on-line monitoring and automatic process control techniques should be used to efficiently operate the AD process. There is no standardized monitoring method, in terms of fundamental parameters and optimal frequency of
# Towards an Indoor Gunshot Detection and Notification System Using Deep Learning
## 3 Materials and Methods
### A Deep Learning Model for Gunshot Detection
#### 3.1.1 Dataset Generation
As our goal is to develop a gunshot detector embedded system hardware with a microphone, we would like to make the dataset that is generated from the same microphone that will be used in the embedded system. To do that the one-second gunshot sounds were merged into a single file, played from a computer, recorded in another computer using the same microphone that will be used in the embedded system, and then split the recorded file into one-second files. Data augmentation in deep learning for sound is a technique where we create new training examples by slightly modifying the existing ones. This helps the model learn more effectively from the data it has. For sound, it means changing aspects such as pitch and speed; or adding background noise to audio recordings. By doing this, we provide the model with a broader range of examples, making it better at recognizing different variations in sound in real-world scenarios, ultimately improving its performance in tasks such as speech recognition or sound classification. In this project, the audio data augmentation [34] library is used to generate more sound samples. For each sample, 20 additional augmented samples were generated by: slightly shifting the signal to the left and right in the time axis, changing the tone, and the speed. The empty places were filled with silence in those files. The collected 'other' files contain non-gunshot sounds such as silence, mild noise, clock ticking, the door opening and closing, toilet flushing, the siren of an emergency vehicle, rain, streetcar, people talking, baby crying, animal voices, washing machine, and vacuum cleaner. This dataset contains possible false alarms for gunshots such as fireworks, can opening, door knocking, glass breaking, clapping, drums, and thunderstorms [25, 26, 27, 28, 29, 30, 31, 32]. Gunshot sounds from some of these datasets were identified from the dataset metadata and automatically removed using Python scripts. These audio files had different durations. To make one-second files: all these files were first joined, and then the joined file was split into equal-sized one-second duration files. The last split file was deleted as the joined file was not evenly divisible by one second.
# A Survey of Algorithms and Systems for Evacuating People in Confined Spaces
## 3 Evacuation Wayfinding Algorithms
### On-Line Evacuation Wayfinding Algorithms
#### Prediction-Based Algorithms
To deal with the uncertainties during an evacuation process in an unfamiliar built environment, the work in [103] proposed a dynamic Bayesian network (DBN)-based [159] spatio-temporal probabilistic model to capture the uncertain nature of the hazard and crowd dynamics and forecast the movement of evacuees; the integrated hazard and crowd evacuation DBN model was composed of a hazard model, a risk model, a behaviour model, a flow model and a crowd model; each model embedded a DBN and was subject to the Markov condition; by using the integrated hazard and crowd evacuation DBN model, the relations between the location of evacuees and the hazard status of each location (dormant, growing, developed, decaying and burnt-out) were tracked and predicted over adjacent time steps; hence, the probabilistic risk level of each location could be derived from the model; the egress paths were calculated by [PERSON]'s algorithm with respect to the estimated risk level of each location with the purpose of minimising the overall fatality rate. These algorithms were developed in recent years and are quite promising owing to the increasing popularity and tremendous computing power, elasticity and the ability of the cloud computing paradigm to execute tasks in an adaptive manner, which takes priorities and urgency into account [160]. Since the performance of many evacuation wayfinding algorithms is sensitive to various initial conditions (e.g., the initial distribution of evacuees, congestion level, type of disaster and initial disaster location) and the choice of certain parameters, the research in [104] presented a faster-than-real-time simulation-based routing algorithm to predict the future situation before guiding evacuees to exits when a disaster breaks out; instead of guiding evacuees in a real-time manner, this algorithm borrows the tremendous computational power of the cloud-based simulator to identify the potential death victims rapidly by predicting the future movements of evacuees and spreading of the hazard; then, an iterative-based algorithm was employed to search appropriate paths gradually for these potential death victims; finally, all the calculated paths were sent to evacuees for instruction, and evacuees could follow the paths in a source-routed manner (they did not need to switch paths during the evacuation process).
# Recent Progress on Vegetation Remote Sensing Using Spaceborne GNSS-Reflectometry
## 2 Research Status  
Vegetation parameter monitoring and biomass inversion is a new research field of GNSS-R remote sensing technology. This section is going to give a comprehensive review of the development of the GNSS-R in vegetation study. Therefore, we have presented the development of GNSS-R on vegetation monitoring according to the platforms. Of course, at first, researchers indeed have presented works on the ground-based experiments, from which they have manifested that the vegetation parameters can be detected by the GNSS-R remote sensing technique (Section 2.1). Then, several works related to the airborne-GNSS-R vegetation campaigns have been carried out (Section 2.2). The scientific objectives of these works are related to the fact that the vegetation information can be obtained from airborne experiments. From the ground-based and airborne experiments, the researchers have proven the potentialities of this technique for vegetation study. Then, after the pioneer works have been done, the spaceborne missions have launched and a lot of space-borne data have become available to the scientific community and works related to vegetation study using spaceborne data have emerged (Section 2.3). At the same time, the development of mechanism models is an important research tool to promote the development of GNSS-R technology; therefore, we give a review of the research status of the GNSS-R vegetation model in detail (Section 2.4).
## 2 Materials and Methods
### Two-Step Proposed EMS
#### 2.4.2 EMS Balancing Module  
Because the scheduling is performed using day-ahead PV output power forecasts, it is expected to have errors on the forecasted PV power output. The battery is an element that allows to compensate those forecasting errors to some extent, but due to its limited capacity, it could eventually happen that the scheduled grid profiles cannot be followed as expected. In this situation, modifications to the power profiles must be done in order to assure the balance between production and consumption. The balancing module (BAL) is based on rules that are meant to favor the grid commitment, as long as the physical constraints allow it. This means that, any unexpected PV surplus will be stored in the battery, or any lack of PV power required to supply the load, will be supplied by the battery, as long as possible. The BAL module, being a rule-based method, requires very few time (<10 s) to find a solution. A flow diagram of the working principle of the BAL module explained above is presented in Figure 5.  
Figure 5: Working principle of the balancing strategy \(GC_{max}\). The output of the scheduling module (\(p_{grid}^{scheduled}\)), the consumption (\(P_{load}\)) and the real PV output (\(P_{pv}^{mppt}\)) are the inputs of this module.  
As it is noted in the rules that are described above, this module favors the GC service, always assuring the proper balance between production and consumption. In this way, after applying the two-staged EMS, we will have favored two different objectives: GC during the BAL stage and a second objective (i.e., EC, CO\({}_{2}\) or GPP) assured during the SCH stage. It must be reminded that, during the scheduling stage (SCH), PV curtailment is not possible while it is possible during the balancing stage (BAL).  
Table 1 presents a summary of the EMS strategies proposed in this work, including the nomenclature, services favored, working/optimization objectives, as well as the possible forecasts to be used.