esa-sceva/satcom-chunk-collection huggingface dataset:
_Applied_  
_sciences_  
_Review_  
**Review on In Situ Acoustic Emission Monitoring in the Context of Structural Health Monitoring in Mines**  
**[PERSON] 1,* and [PERSON] 2()**  
1 Department of Mechanical and Energy Engineering, THM University of Applied Sciences,  
D-35390 Giessen, Germany  
2 GMuG mpH (Gesellschaft fur Materialprufung und Geophysik mbH), D-61231 Bad Nauheim, Germany; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +49-152-0154-2896 check for updates  
Received: 31 July 2018; Accepted: 24 August 2018; Published: 9 September 2018  
**Featurered Application: This article describes various applications of in situ acoustic emission (AE) monitoring in the context of structural health monitoring (SHM) in mines and extensively presents the state of the art in this field.**  
A major task in mines and even more in underground repositories for nuclear waste is to investigate crack formation for evaluation of rock mass integrity of the host rock. Therefore, in situ acoustic emission (AE) monitoring are carried out in mines as part of geomechanical investigations regarding the stability of underground cavities and the integrity of the rock mass. In this work, the capability of in situ AE monitoring in the context of structural health monitoring (SHM) in mines and in various geological settings will be reported. SHM pointed out, that the AE network is able to monitoring AE activity in rock with a volume up to 10\({}^{6}\) cubicmeter and distances up to 200 m (e.g., 100 m \(\times\) 100 m \(\times\) 100 m) in the frequency range of 1 kHz to 150 kHz. Very small AE events with source size in approximately centimeter to millimeter scale are detected. The results show that AE activity monitors rock deformation in geological boundaries due to convergence of the rock. In addition, high AE activity occurs in zones of dilatancy stress in homogenous rock. In conclusion in situ AE monitoring is a useful tool to observe instabilities in rock long before any damage becomes visible.  
in situ acoustic emission (AE) monitoring; structural health monitoring; mines; host rock
# Exhaust Gas Characteristics According to the Injection Conditions in Diesel and DME Engines
## 3 Results and Investigations
### Effects of Needle Lift on Exhaust Gas According to Injection Timing in DME Engines  
Figures 15-17 show the exhaust gas rates (HC, NO\({}_{\rm x}\), and CO) for the engine performance tests. In this experiment, the exhaust gas characteristics were examined by applying the DLL-AP type needle lift of 0.14, 0.21, 0.24, and 0.36 mm nozzle (DL-AP type), which was one of the injector improvements, different from the examination result by the condition change of the ECU program.  
1. NO\({}_{\rm x}\) emission characteristics of DME engines depending on the injection timing and needle lift  
Figure 15 shows the effect of needle lift on NO\({}_{\rm x}\) emissions with injection timing at a loading rate of 75% at an injection pressure of 50 MPa. In this case, the effect of the needle lift on the NO\({}_{\rm x}\) emission was found to increase as the injection timing was delayed and decreased as the injection angle was advanced. Further, the needle lifts showed low values at 0.36 and 0.14 mm, while at 0.21 and 0.24 mm showed an increasing trend. First, the increase in the injection timing was caused by the temperature rise of the combustion chamber because of diffusion combustion from the delay of the injection period. The decrease of 0.14 and 0.36 mm of needle lift at 22\({}^{\circ}\) BTDC caused incomplete combustion at 0.14 mm due to the long supply period because of the common rail characteristics for the required fuel supply when injected with the required injection period.  
2. HC emission characteristics of DME engines depending on the injection timing and needle lift  
Figure 16 shows the effect of needle lift on HC emissions with injection timing at a loading rate of 75% at an injection pressure of 50 MPa. In this case, the HC emission fluctuation was not large with different injection timing delays. The 0.36 mm needle lift showed a HC emission concentration lower than 0.21 mm, suggesting that the DME supply per unit time was oversupplied to the engine. Thus, the needle lift of 0.21 mm was useful for engine gas; however, it was not good in terms of exhaust gas.  
Figure 15: NO\({}_{\rm x}\) emission characteristics of DME engines with nozzle needle lift as a function of injection timing degree.  
3. CO emission characteristics of DME engines depending on the injection timing and needle lift Figure 17 shows the effect of needle lift on CO emissions with injection timing at a loading rate of 75% at an injection pressure of 50 MPa. The CO emission slightly fluctuated at 0.36 mm because of the injection timing delay; however, there was no large fluctuation. This was expected to be incomplete combustion because of the longer injection period caused by the injection timing delay, as in the case of HC.  
Figure 16: HC emission characteristic of DME engines with nozzle needle lift as a function of injection timing degree.  
Figure 17: CO emission characteristics of DME engines with nozzle needle lift as a function of injection timing degree.
###### Abstract  
This study sought to establish machine learning models for forecasting in-hospital mortality in non-ST-segment elevation myocardial infarction (NSTEMI) patients, and focused on model interpretability using Shapley additive explanations (SHAP). Data were gathered from the Medical Information Mart for Intensive Care--IV database. The synthetic minority over-sampling technique and Edited Nearest Neighbors were used to address class imbalance. Four machine learning algorithms were employed, including Adaptive Boosting (AdaBoost), Random Forest (RF), Gradient Boosting Decision Trees (GBDT), and eXtreme Gradient Boosting (XGBoost). SHAP was utilized to improve transparency and credibility. The all-features RF model demonstrated optimal performance, with an accuracy of 0.8513, precision of 0.9016, and AUC of 0.8903. The SHAP summary plot for the RF model revealed that Acute Physiology Score III, lactate dehydrogenase, and lactate were the three most crucial characteristics, with higher values indicating a greater risk. The study demonstrates the applicability of machine learning, particularly RF, in predicting in-hospital mortality for NSTEMI patients, with the use of SHAP enhancing model interpretability and providing clinicians with clearer insights into feature contributions.  
on-ST-elevation myocardial infarction; prediction model; machine learning; in-hospital mortality; Shapley additive explanations 2020
## 3 Results and Discussion
### Changes in FAA Composition during Fermentation of BBP-Meju with Different Salt Concentrations  
FAAs are crucial flavor compounds in fermented foods, closely associated with BBP-meju fermentation [42]. The changes in FAAs during BBP-meju fermentation with different salt concentrations are shown in Figure 2. It can be seen that the total amount of FAAs increased with fermentation at all three salt concentrations (Figure 2b). Meanwhile, the total amount of FAAs decreased slightly with increasing salt concentration, indicating that salt-reduced fermentation could further promote the accumulation of amino acids in BBP-meju. Studies have shown that salt reduction can enhance protease activity, resulting in higher levels of FAAs [43]. This is in agreement with the results of this study. Specifically, the levels of umami amino acids (glutamate and aspartate) decreased with salt reduction, with a total reduction of 16.04% (Figure 2a). On average, the glutamate (Glu) level decreased from 90.00 \(\pm\) 1.88 mg/100 g in the high-salt sample to 80.43 \(\pm\) 1.44 mg/100 g in the low-salt sample, while the aspartate (Asp) level decreased from 45.20 \(\pm\) 1.71 mg/100 g in the high-salt sample to 36.09 \(\pm\) 1.57 mg/100 g in the low-salt sample (Figure 2b). Similarly, the sweet amino acid threonine (Thr) level decreased with salt reduction. However, the levels of alanine (Ala), glycine (Gly), and proline (Pro) increased with salt reduction by 31.76%, 11.25%, and 24.35%, respectively (Figure 2c). Consequently, the total amount of sweet amino acids increased by 14.06% with salt reduction (Figure 2a). Likewise, the total amount of bitter amino acids showed an increasing trend, with an increase of 16.30% (Figure 2a). Thus, the salt reduction during BBP-meju fermentation led to a decrease in umami amino acids and an increase in bitter amino acids, resulting in flavor dissonance.  
Figure 1: Physicochemical properties during fermentation of BBP-meju with different salt concentrations: (**a**) moisture contents; (**b**) pH; (**c**) salinity; (**d**) total acid contents; (**e**) AAN contents; (**f**) reducing sugar contents.
# Point-Graph Neural Network Based Novel Visual Positioning System for Indoor Navigation
## 3 Proposed Method
### Voxel Labeling  
#### 3.2.1. Voxel Labeling in the Point Cloud Database  
We divided the point cloud of indoor space into voxel units of a certain size and constructed the pose labeling of the object in the divided voxel database space. The voxel database scans the indoor space using a high-performance 3D sensor and calculates the total size of the X, Y, and Z axes of the point cloud using the maximum and minimum values of the scanned point cloud coordinates. After dividing the calculated X, Y, and Z axes of the indoor space based on the set voxel size, we performed numbering to generate the voxel address that determines the user's spatial location.  
The object pose label was generated to estimate the object pose of the improved point-GNN in the built voxel database. Object pose labels comprise the voxel database object box (class, x, y, z, length, height, width, pitch, yaw, roll) and voxel number address (No.n) of the object. The center point and eight corner vertices (corner[0]-corner[7]) of the 3D box were generated based on the position of the object box in 9-DoF. Figure 2(a) illustrates the interior space divided by voxel size and the pose label of the object in the divided space, and Figure 2(b) depicts the pose label of the object box.
## 2 Simulation of the High Energy \(\gamma\) Photon Production
### Simulation Results
Figure 3 illustrates the energy angular distribution of the produced \(\gamma\) photons for the different initial energies of the electron bunches. Here, with increasing electron bunch initial energy for a fixed laser intensity (\(a_{0}=192\)), the divergence angle of the photons decreases. As aforementioned, electrons with low energy could easily be maintained in the high field region for a long time but were scattered at large angles with higher than initial energy by the laser ponderomotive force. Therefore, the divergence angles of the photons increased for low electron energies. The photon divergence angles were approximately \(20^{\circ}\) and \(15^{\circ}\) for initial electron energies of \(1\) and \(2~{}\text{GeV}\), as shown in Figure 3a,b, respectively. However, for high-electron initial energies, the momentum of the electron was high and well-directed to the propagation direction. Parent electrons maintained their initial directions and had fewer chances to be scattered to other angles. Because the photons were mostly produced in the direction of electron motion, they had low divergence angles. For the \(5\) and \(10~{}\text{GeV}\) electron initial energies, the divergence angle of the \(\gamma\) photon is approximately \(5^{\circ}\), as shown in Figure 3c,d. Figure 2: (**a**) The energy spectrum of electrons with different initial energies after interacting with the laser, (**b**) produced \(\gamma\) photon energy spectrum for different electron bunch initial energy with a fixed laser intensity of \(a_{0}=192\). The other parameters are the same as in Figure 1.  
Several practical applications of \(\gamma\) photons require high energy and the high brightness of the photon source. We estimate the brightness of the high-energy photon source in our scheme. The angular divergence, total number, and source size of the photon beam are determined by the electron bunch. The \(\gamma\) photon spectrum (see Figure 2) shows that for a photon beam with an energy of 1 GeV, the total number of photons (approximately \(3\times 10^{6}\)) is approximately the same for different initial electron energies. Most of the photons are concentrated within a source size of approximately \(x\times y=(5\times 2.7)\) \"m\({}^{2}\) for all cases (see Figure 1). However, the divergence angle is different for different electron initial energies, as shown in Figure 3, accounting for the different photon beam brightness. The photon number in a 0.1% bandwidth (BW) at 1 GeV is obtained by averaging the total number of photons. We predict the brightness of the 2 GeV electron initial energy to be approximately \(1.1\times 10^{20}\) photons/(s mm\({}^{2}\) mrad\({}^{2}\) 0.1% BW) at 1 GeV, which is more than two orders of magnitude brighter than recently suggested high energy polarized photon sources [30]. If we consider a high initial electron energy of 10 GeV, which is similar to that of the electron source of the FACET II [42], the brightness would be approximately one order higher as \(1.8\times 10^{21}\) photons/(s mm\({}^{2}\) mrad\({}^{2}\) 0.1% BW) at 1 GeV.
# MCCM: An Approach for Connectivity and Coverage Maximization
## 4 Distributed Maintaining Connectivity and Coverage Maximization (MCCM)
### Bridging
#### 4.3.2 Distributed Bridging  
A large enough number of drones are deployed in the area of interest. The steps are as follows:  
* Drones start scanning the area of interest using coverage path planning algorithms to find any neighborhood or isolated devices.
* If a drone finds a neighborhood, the drone stack to the closest point between the neighborhood and the central station. The drone becomes part of this island and all the devices in the neighborhood knows about it. The chain of connected drones continues growing as straight line between the neighborhood and the central station. Drones either connect to a device at the edge of the island or to another drone. This connection is established using the \(\tau\) formula.
* The process of discovery continues till all the links are established between the central station and the neighborhoods or isolated devices if any.  
Both ways can be used to form a connected network between all the devices in the area of interest. However, the distributed approach requires greater deployed number of drones. We opt to use the centralized approach.
## 3 Fast Handover Algorithm
### Process of the Fast Handover Algorithm
#### 3.4.3 Weight Calculation of the Handover  
According to a large number of simulation experiments and the statistics measured data, the value range of SS-RSRP acquired by UE in 5G-R wireless communications for HSRs is \(-156\) dBm \(\sim-31\) dBm, and the value of SS-RSRQ ranges from \(-43\) dB to \(20\) dB. SS-RSRP\({}_{i}\) and SS-RSRQ\({}_{i}\) are normalized into \(P_{i}\) and \(Q_{i}\), and SS-RSRP and SS-RSRQ of all sampling points of UE are normalized into sets \(\{P\}\) and \(\{Q\}\). The result \(d_{i}\) of the weight calculation of the sampling point \(i\) is shown in Equation (15), where \(W_{i}^{N}\) and \(W
## 3 Background
### Feature Selection Problem Formulation  
Feature selection is an optimization task aimed at reducing the dimensionality of datasets by identifying the most relevant features while eliminating redundant or irrelevant ones. This problem can be represented mathematically as a binary combinatorial optimization problem. Each candidate solution is encoded as a binary vector, where 1 represents the inclusion of a feature and 0 represents its exclusion [24]. Figure 2 illustrates this binary representation.  
Consider a dataset D comprising N features, represented as D = \(\{f_{1},f_{2},\ldots,f_{N}\}\). The goal of FS is to extract a subset S, where S has fewer features than N. The aim of the optimization process is to determine which subset S, which is frequently determined by a particular objective function, maximizes model performance. The FS process lowers computing complexity, enhances predictive performance, and reduces the possibility of overfitting in ML models [24].  
FS is an NP-hard problem due to the exponential growth of possible subsets 2\({}^{\circ}\)N with the number of features \(N\)[32,33]. So, exhaustive search is computationally impractical, making optimization algorithms indispensable for solving FS problems efficiently. This limitation underscores the value of metaheuristic algorithms, which leverage stochastic search strategies to efficiently explore large solution spaces and identify near-optimal solutions. Inspired by natural processes, these algorithms have demonstrated superior effectiveness  
Figure 2: The binary representation of the FS problem.  
in addressing the combinatorial challenges of FS, often surpassing the performance of exhaustive or greedy methods [34; 35; 36].  
According to the No-Free-Lunch (NFL) theorem, no single optimization algorithm is guaranteed to work best for all problems. This means that while metaheuristics have shown success in FS, there is always potential for better results with the development of new algorithms tailored to specific problems [37].
## 4 Energy-Aware Routing Protocol
### Clustering Algorithm
It is worth noticing that sometimes there may exist a gradient phenomenon as shown in Figure 3, where \(S_{1}\)_weight_\(>S_{2}\)_weight_\(>S_{3}\)_weight_. Consequently, S2 and S3 will give up competition and S1 is the only winner. In this case, after the clustering phase, some nodes will be neither cluster heads nor member nodes. However, because the time interval \(\Delta t\) is short, the probability that several nodes within the same cluster range broadcast the Compete_Msg in the same time interval (2*\(\Delta t\) ) is considerably small. In addition, through expanding the time duration T or decreasing the cluster radius, we can guarantee that there is only one head in a cluster range and each node will either be a cluster head or a plain node Whp (with high probability). Figure 2: Illustration of broadcasting Compete_Msg. In order to minimize the energy consumption in each round, we argue that the plain nodes should join the nearest head. Because the cluster heads always keep rotation in whole lifespan of network, we can maintain uniform energy consumption among all nodes. So minimizing energy consumption for each round can help to prolong the network lifetime. The pseudo code for cluster formation is shown in Figure 4.  
Figure 4: The Pseudo Clustering Algorithm. Figure 3: The gradient phenomenon.
## 4 Results and Discussion
### Comparison with MSC-30/R134a Pair  
#### 4.3.1 Adsorption Potential  
Based on the D-A mode method in Equation (17), we obtained the adsorption potentials \(\epsilon\) for several different refrigerant/activated carbon pairs. The adsorption potential dependence shown in Figure 8 expresses the chemical potential difference between the adsorbent and the adsorbate, and it can be expressed by the differential change of free energy in a reversible isotherm transition from the liquid phase to the adsorbate phase. We have observed the adsorption potential of both pairs is remarkably similar. Therefore, the evaporation enthalpies of R1234 yf and R134a make the main difference in the isosteric heat of adsorption. At the saturation point of 30 \({}^{\circ}\)C (at equilibrium), the specific enthalpy of evaporation is 141.24 (kJ/kg) in the case of R1234 yf [35] and 173.10 (kJ/kg) for R134a [34].  
Similarly, [PERSON] et al. [59] presented the adsorption isotherm analysis and heat of adsorption using the common difluoromethane (R32) on activated carbon powder (ACP) of type MCS-30 and activated carbon fiber (ACF) of type A-20. As demonstrated in Figure 8, R32 shows a higher adsorption potential of the adsorption uptake than the two other refrigerants, which is later compensated by the actual uptake capacity. It is, however, necessary to mention that in comparison to R1234 yf, the R32 operates at significantly higher pressures, especially when used for heating, and poses a certainly non-negligible GWP.  
Figure 8: Adsorption potential \(\epsilon\) at 20 \({}^{\circ}\)C using the parameters of D–A model (\(\alpha=1/\)T[K]).  
Figure 7: Isotherm fitting results of the D–A model (\(\alpha=1/\)T[K]) for MSC-30/R1234 yf.
# Climate-Smart Agro-Hydroological Model for a Large Scale Rice Irrigation Scheme in Malaysia
## 3 Results and Discussion
### Hydraulic Modeling  
To evaluate the hydraulic model, HEC-RAS daily observed and simulated discharge data from the model were compared. R\({}^{2}\), NSE and PBIAS were 0.66, 0.64, and 0.94, and 0.65, 0.59, and 1.77, respectively, during calibration and validation periods. These show that the simulation is satisfactory since the values are greater than 0.5 [40]. However, the model compared to the calibration period underestimated some of the simulated discharge. Nevertheless, the model was able to optimally capture low and peak flows for most days.
## 4 Point Cloud Segmentation
### Graph-Based Segmentation
#### 4.3.1 Graph Construction  
As shown in Figure 6, there are four types of adjacency relationship between points: stair-like, convex, concave, and invalid. In real scenes, stair-like adjacency is common between objects located at various distances from the viewer, such as two books stacked together. Convex adjacency is commonly seen in object surfaces, such as the corner of a box or the surface of a cup. Concave adjacency is typically found where two objects intersect, such as between the surface of a table and an object on the table. Invalid adjacency generally only occurs when two objects of different height are placed together.  
Figure 5: Plane detection results. (**a**) Original image, (**b**) obtained planes.  
Figure 6: Types of adjacency between points. (**a**) Stair-like adjacency, (**b**) convex adjacency, (**c**) concave adjacency, (**d**) invalid adjacency.  
Suppose there are two supervoxels, \(v_{i}\) and \(v_{j}\). The centers of the supervoxels are \(\mathrm{p}_{i}\) and \(\mathrm{p}_{j}\), and the normal directions of the two points are \(\mathrm{n}_{i}\) and \(\mathrm{n}_{j}\). We determined whether the adjacency relation between the points was convex or concave as follows:  
\[Convex(v_{i},v_{j})=\left\{\begin{array}{ll}\mathrm{true}\ \mathrm{if}( \mathbf{n}_{j}-\mathbf{n}_{i})\cdot(\mathbf{p}_{j}-\mathbf{p}_{i})>0\\ \mathrm{false}\ \ \ \ \mathrm{otherwise}\end{array}\right. \tag{7}\]  
Given angles \(\alpha_{i}\) and \(\alpha_{j}\), we denoted the adjacency relation between the points as stair-like when the two angles were nearly parallel and below the threshold value \(\theta_{thresh}\), as follows:  
\[Step(v_{i},v_{j})=\left\{\begin{array}{ll}\mathrm{true}\ \mathrm{if}( \alpha_{i}-\theta_{thresh})\cdot(\alpha_{j}-\theta_{thresh})>0\\ \mathrm{false}\ \ \ \ \mathrm{otherwise}\end{array}\right. \tag{8}\]  
Using Equations (7) and (8), we considered the geometric relationships of most connecting edges. However, the real environment is extremely complex. Therefore, we must also take the rarer invalid adjacency into account. Using the external product of the normal directions \(\mathrm{s}=(\mathrm{n}_{i}\times\mathrm{n}_{j})\) and the vector of the two points \(\mathrm{d}=(\mathrm{p}_{j}-\mathrm{p}_{i})\), we defined angle \(\theta_{ij}\) as  
\[\theta_{ij}=\min(\angle(\mathrm{d},\mathbf{s}),\angle(\mathrm{d},-\mathbf{s })) \tag{9}\]  
where \(\angle(;.)\) represented the angle between the two vectors. If \(\theta_{ij}\) was less than the preset threshold value, then the two points were deemed to belong to different objects. The threshold value determined the weight value between two points.