esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Objective Real-Time Planning of Evacuation Routes for Underground Mine Fires
###### Abstract
Based on the mathematical model of the resource-constrained shortest path, with the rated protection time of the self-rescuer as the constraint and with the shortest equivalent length of the roadway and the shortest time for personnel to walk in the smoke flow as the optimization goal, a mathematical model for escape route planning during mine fire was established. Then, the Pareto optimal solution set of the evacuation path from underground workers to the surface safety exit and the underground refuge chamber was solved based on the labeling algorithm of dynamic programming. The Pareto optimal solutions of all safety exits were re-ranked based on the safety exit priority and the time to escape from the dangerous area. An example verification was carried out in a metal mine. The results showed that the algorithm proposed in this paper can quickly obtain disaster avoidance routes that meet actual safety needs, thereby assisting and guiding emergency risk avoidance decisions in underground mines. The rest of the paper is organized as follows. Section 2 introduces the related work on emergency escape systems and emergency escape route planning. Section 3 presents an algorithm for assigning equivalent lengths to underground roadways. Section 4 develops a mathematical model for evacuation routes and provides a solution algorithm along with a strategy for optimizing the solutions. Section 5 simulates disasters, solves evacuation paths, and discusses the obtained results. Finally, Section 6 draws a conclusion of the study.
## 4 Results and Discussion
### Aerodynamic Noise Characteristics  
#### 4.2.1 Aerodynamic Noise Source  
In this section, sources of aerodynamic noise are discussed as it is expected that the flow field of the different base configurations of the pantograph may have significant effects on noise spectrum characteristics and on the noise directivity, which could lead to a change in the perceived noise along the train track. Therefore, the aerodynamic pressure amplitude distributions on the pantograph at 200 Hz and 1000 Hz are compared. As shown in Figure 15, it can be observed that along the direction of the flow velocity, the aerodynamic pressure amplitude in the front regions of the pantograph is lower than that in the rear regions of the pantograph. This may be explained by the fact that the front parts always collide with the incoming flow sharply, forming a windward stagnation area. As a result, the fluctuating pressure is relatively small. Flow with higher levels of vorticity, as shown in Figure 13, passes over the rear parts of the pantograph. As a result, the amplitude of aerodynamic pressure fluctuation in the rear region is larger. Meanwhile, the amplitude of the aerodynamic pressure decreases when the frequency increases from 200 Hz to 1000 Hz. Where the flush configuration is used under the down-pantograph condition, the maximum aerodynamic pressure fluctuation at 1000 Hz is smaller than that at 200 Hz by as much as 18 dB. The phenomenon that the maximum aerodynamic pressure fluctuation decreases with the increasing frequency also can be observed by [PERSON] et al. [15]. At the low frequency, the SPL distribution of the aerodynamic pressure on the surface of the pantograph is non-uniform. The largest amplitude aerodynamic pressure mainly features on the supporting slide, the rotating shaft, the lower arm, and the leeward surface of the installation base. At 1000 Hz, the aerodynamic pressure amplitude distribution is more uniform than that at 200 Hz. It seems that, although the aerodynamic pressure amplitude distribution over the pantograph surfaces are different at these two frequencies, the largest amplitudes are always located on the downstream base frame, the connection between the upper and lower arms and the downstream position of the double skateboard bow. These aerodynamic pressure fluctuations act as dipole noise sources.  
Figure 15: _Cont._
# A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting
## 2 The Impact Factor of Solar Power Forecasting
### Model Performance Metrics
The forecast skill score is represented by FS, while \(\text{RMSE}_{\text{model}}\) and \(\text{RMSE}_{\text{simple model}}\) represent the RMSE values of the recommended forecasting model and benchmark or persistence model, respectively.
# Application of Molecularly Imprinted Polymers in the Analysis of Waters and Wastewaters
## 5 Application of MIPs for the Detection of Different Classes of Contaminants
### Pesticides  
The use of pesticides all over the world for protection against different plants hazardous infections with insects, viruses, invasive plants, fungus, and weeds has drastically increased in the past decades. Despite their protective effects, they are seriously accumulated in soils and can be transmitted to the surface water [85]. Thus, their presence and allowed WHO levels should be monitored, especially the use of forbidden types of reagents due to their toxic activity. Their hazards are not only limited to target organisms; yet, due to their chemical properties and persistence, they can negatively affect the ecosystem [86].  
Many of the compounds belonging to such class are either UV- or electro-inactive, which limits the number of applications using traditional HPLC-UV detection methods and restricts the solid phase extraction techniques to Gas Chromatography (GC)- or mass detection (MS)-based chromatographic methods. On the other hand, electrochemical detection, where an electroactive probe, such as ferrocyanide, can be used when inactive analytes are used, which were widely applied in the past years using molecularly imprinted polymers. The active probe can be used to detect the interaction between the target template and its specific binding sites in the modified sensors, which affects the electrochemical signal of the probe. Thus, MIPs have found many applications for the detection of herbicides, insecticides, and fungicides, representing the most-often used classes of pesticides, as shown in Table 3.
# Energy Consumption Model for Sensor Nodes Based on LoRa and LoRaWAN
## 6 Numerical Results and Discussions
### Full Communicating Sensor-Energy Consumption Results
#### 6.3.2 Consumed Energy: Scenario 2  
In this scenario, we suppose that the sensor node transmits data to the gateway, then it receives the first acknowledgment RX1 to confirm data transmission (in this case, the sensor node will not demodulate the second acknowledgment RX2). The energy consumption by the communicating sensor is given in Figure 17. As can be seen, the difference from Scenario 1 are the dissipated energy by the LoRa receiver (\(E_{R}\) = 0.27 mJ) and the consumed energy by the MCU unit.  
Figure 16: Sensor node autonomy: Scenario 1.  
Figure 15: Energy consumption of sensor node: Scenario 1.  
Figure 18 presents the sensor node lifetime using the same battery characteristics (capacity equals 950 mAh and supply voltage of 3.3 V). The sensor node autonomy is estimated at 4 years, 6 months and 12 days (54.4 months) for the same measurement period (30 s), which is 7.4 months shorter than Scenario 1 (i.e., there is a loss equal to 12% of the battery life compared to Scenario 1).
## 2 Materials and Methods
### Soil Sampling  
The study was conducted using 59 research plots located in the Babia Gora National Park. Soil samples for laboratory analyses were collected from each research plot. Soil samples were taken from the soil horizons that were distinguished from the profiles during fieldwork. Samples for laboratory testing were collected from the organic horizon (Oh, Ofh, Ot, OM), the first mineral horizon (AE, A, AB, or AG), and the subsequent mineral horizon (horizons B, G, and C). Soil samples were taken from horizons according to the depth at which they occurred. On average, soil samples were taken to a depth of 100 cm. Organic horizons had an average depth of 20 cm, a minimum depth of 2 cm, and a maximum depth of 92 cm. The humus forms were determined according to Classification of Forest Soils in Poland (2000). Four subsamples were collected from each plot and thoroughly mixed to obtain a composite soil sample, and the samples from each horizon were put into plastic containers. Research plots were grouped on the basis of species composition, soil type, and altitude. The studied plots were divided into four groups according to the species composition of the stand (Alder: the share of alder (_Alnus incana_) in the stand is more than 80%; Beech: the share of beech (_Fagus sylvatica_) in the stand is more than 80%; Spruce: the share of spruce (_Picea abies_) in the stand is more than 80%; Mixed: stands that contain beech and spruce). Seven soil groups were defined: dystric cambisols (CD), eutric cambisols (CE), fluvisols (FL), gleysols (GL), histosols (HI), podzols (PO), and stagnosols (ST). Additionally, the research plots were grouped according to parent material (I: terrace sediment accumulation; II: fluvial sediments; III: hieroglyphic sandstone; IV: Magura sandstone; V: osielecki sandstone; VI: peat sediments). Three groups of altitude were defined: \(<\)900 m a.s.l, 900-1100 m a.s.l., and \(>\)1100 m a.s.l.
# An Integrated Algorithm for Extracting Terrain Feature-Point Clusters Based on DEM Data
###### Abstract  
Terrain feature points, such as the peaks and saddles, are the basic framework of surface topography and its undulations, which significantly affect the spatial distribution of surface topography. In the past, terrain feature points were extracted separately for each type, while the internal connections between the terrain feature points were ignored. Therefore, this work proposes an integrated algorithm for extracting terrain feature-point clusters, including the peaks, saddles and runoff nodes, based on the DEM data. This method includes two main processes: positive terrain-constrained ridgeline extraction and terrain feature-point cluster extraction. Firstly, a threshold determination method of flow accumulation in the hydrological analysis is proposed by combining morphological characteristics with runoff simulation, and the ridgelines are extracted based on this threshold. Subsequently, the peaks and their control areas are extracted by space segmentation. Meanwhile, the saddles and runoff nodes are obtained by spatial intersection. Finally, the integrated terrain feature-point clusters are obtained by merging the three extracted terrain feature points. This method was experimented with in the six typical sample areas in Shaanxi Province and verified its results by contour lines and optical images. It shows that the spatial positions of the extracted terrain feature clusters are accurate, and the coupling relationships are great. Finally, the experiments show that the statistical attributes of point clusters and their spatial distribution trends have an obvious correlation with geometric types and geomorphic zoning, which can provide an important reference for geomorphic zoning and mapping.  
digital elevation model; terrain feature-point cluster; feature extraction; digital terrain analysis 2022
###### Abstract  
The modal frequencies, model shapes or their derivatives are generally used as the characteristic quantities of the objective function for the finite element model (FEM) updating. However, the measurement accuracy of the model shapes is low due to the few numbers of measurement points for actual structures, which results in a large correction error. The antiresonant frequency reflects the local information of the structure more accurately than the mode shapes, which is a good complement to the resonance frequencies. In this paper, a FEM updating using resonance and antiresonant frequencies with radial basis function (RBF) neural network is proposed. The elastic modulus, added mass, tensile stiffness and torsional stiffness are selected as the updating parameters of FEM for a cantilever beam, which were grouped by the uniform design method. The resonance and antiresonant frequencies identified from the frequency response function (FRF) obtained from corresponding FEM at only one node are taken as the characteristic quantities. The RBF neural network is adopted to construct the mapping relationships between the characteristic quantities and the updating parameters. The updated parameters are substituted into the FEM, and the FRF is obtained to verify the validity of the method. The results show that the relative errors between all the updated parameters and the target values are less than 7%, and the relative errors of the characteristic quantities in the updating frequency band are less than 3%. The proposed method can accurately reproduce the dynamic characteristics of the cantilever beam. It can be applied to the damage detection and safety evaluation of large structures which are difficult to arrange more measuring points.  
Keywords:antiresonant frequency; resonance frequency; model updating; uniform design method; RBF neural network +
Footnote †: journal: Applied sciences
# Optimal Placement of Multiple Feeder Terminal Units Using Intelligent Algorithms
## 3 Mathematical Model  
### Decision Variables  
To configure FTUs for the switching elements of a given primary distribution network, the decision variable \(x\) is the FTU types of the switching elements which are possible and suitable to install FTUs. In order to ensure a certain reliability in the distribution network, the feeder outlet CB and the tie switch between two feeders are supposed to be equipped with \"three remote\" terminal units. It means that the FTU types of these switching elements do not belong to the decision variables of this optimization model. The value of decision variable \(x\) may be 0, 1, 2, or 3, corresponding to the installation of no FTUs, \"one remote\" terminal unit, \"two remote\" terminal unit and \"three remote\" terminal unit respectively.  
Figure 3: Classification for \(T_{ij}\) analysis.  
Each component of decision vector \(X\) is the decision variable \(x\) for each switch to be considered:  
\[X=[x_{1},x_{2},\ldots,x_{m}] \tag{6}\]  
where \(m\) is the number of switches to be considered for configuring FTU.
## 3 Communication and Channel Modeling
### Efficiency with Multiple-Access Technologies and Cognitive Radio Communication  
To meet the high-performance demands of future wireless networks, such as high capacity, low latency, and seamless connectivity for IoT sensors and IoVs, it is essential to optimize efficiency [71]. Alongside RISs and STAR-RISs, which enable the creation of smart radio environments, next-generation multiple-access technologies play a crucial role in maximizing connection density and enhancing spectral efficiency [72]. Cognitive radio communication allows smart devices to dynamically detect available channels within the same frequency band, improving spectrum utilization [73]. Common multiple-access technologies include non-orthogonal multiple access (NOMA), rate-splitting multiple access (RSMA), space division multiple access (SDMA), and time division multiple access (TDMA) [74].
# Shear-Actuation and Vibrometer Reception of Penetrating Ultrasonic Guided Wave Modes in Human Tibia
## 2 Methods
### Numerical Modeling  
Two distinct finite element simulations using a commercial finite element package [23] were conducted to help interpret the experimental results. These simulations were based on CT-scan data from the diaphysis of a human tibia and the mechanical properties representative of a human cortical bone given in Table 1, while the experiments were conducted on synthetic bone. Due care must be taken when comparing experimental results from synthetic bone with finite element simulations from bone having different dimensions and properties. The first simulation determines the dispersion curves and associated wave structures for a selected cross-section of the tibial bone. This simulation provides information about the guided wave propagation characteristics for that specific cross-section. For the limited length of the sensing region along the long axis on the medial surface, the cross-sectional geometry is fairly constant. However, we must keep in mind that the tibia cross-section does change along the entire length [11; 24]. Using the cross-section at the mid-span of the diaphysis where the reflective tape is located, a 2D semi-analytical finite element (SAFE) analysis was performed to extract the phase velocity dispersion curves for the cross-section at the mid-span of the human tibia shown in Figure 3. The weak form PDE module in COMSOL Multiphysics was used to solve the quadratic  
Figure 2: The experimental setup comprises a synthetic tibia, an omni-directional shear transducer, and reflective tape for laser Doppler vibrometer (LDV) reception.  
eigenvalue problem [25]. The dimensions of the cross-section geometry extracted from the human CT-scan are compared with those physically measured on the synthetic tibia in Table 2.  
The second simulation was a transient dynamics analysis based on the loading function provided by the omni-directional shear transducer. It provides a reference for, and understanding of, the experimental results. A cylindrical coordinate system was used for the transducer, and the displacement loading was applied as a 10-cycle tone-burst in the circumferential direction, thus mimicking a real omni-directional shear transducer. The transducer contact region was modeled as a circular footprint on the medial surface. The imperfect mating of transducer and synthetic bone was not modelled.  
Material damping was included using the Rayleigh coefficients given in Table 1. A time-marching solution for the diaphysis of the tibia was implemented. The maximum element size was 0.8 mm, and the time-step was 100 ns.
# Random Access Using Deep Reinforcement Learning in Dense Mobile Networks
## 5 Evaluation
### Impact of Proposed Algorithm on RA KPIs
#### 5.4.2 RA Delay  
We also test the RA delay, measured from different positions. We find that the DQN-based algorithm has superior performance compared to algorithms for different positions, as shown in Figure 7. The same reasoning follows from that described for the reliability metrics. Generally, some performance degradation is observed for DQN at some locations. This is because of the dynamic environment, as expected. Although the \(Q\)-based algorithm shows a reasonable performance, DQN shows the most promising performance on average. Particularly, in comparison with the current 3 GPP's methodology of selection, The DQN-based algorithm reduced the RA delay by 58.89%.
## 4 Numerical Results and Discussion
### Dry Friction Parameter Study  
A subsequent analysis considers the effect of the presence of dry friction on the performance of agent \(A\). Case 10 of Table 3 proves the impact of this phenomenon to be high since the cumulative reward drops sharply, and the system has an unstable response.  
In Case 10 of Table 3, the swing-up has not been achieved; therefore, no swing-up time is reported. Apart from Figure 11, this is also shown in Figures 12 and 13, where the swing-up is performed only for short periods.  
Figure 11: Learning curve of agent \(A\) on Case 11 featuring dry friction. (**a**) Cumulative reward per episode of training. (**b**) Average reward per episode of training in a five episode window.  
Figure 10: Controller force and agent reward for the task of Cases 1, 8, and 9. (**a**) Controller force during the swing-up task. (**b**) Agent reward during the swing-up task.  
Additional training is performed on agent \(A\) in an environment with the properties of Case 11 in Table 3, and the corresponding learning curves are shown in (a) and (b) of Figure 11. The optimal policy is found after 39 training episodes, and the resulting agent is called agent \(B\), featuring optimal performance with or without the presence of dry friction, as well as with high increments of the dry friction coefficient, as shown in Cases 12 and 13 of Table 3.
## 2 Velocity Estimation
### Doppler-Based Velocity
In case of redundant measurements, a suitable estimation technique should be adopted; in this study, a weighted least squares (WLS) technique is used; the weighting matrix \(W\) is related to satellite elevation and C/N0 as specified below. The formula for estimating \(\underline{v_{U}}\), using the WLS technique is reported in (7):  
\[\underline{v_{U}}=\left(H_{D}^{T}\ WH_{D}\right)^{-1}H_{D}^{T}W\underline{y}_{D} \tag{7}\]  
The geometry contribution to the solution quality is quantified by the Dilution Of Precision (_DOP_) metrics. After the \(4\times 4\) matrix \(G_{D}=\left(H_{D}^{T}H_{D}\right)^{-1}\) is defined, the square root of the sum of the diagonal elements of the submatrix of \(G_{D}\) obtained excluding the 4 th row and 4 th column is the _3D-DOP_, which is considered in this study and is shown in Formula (8). \[3D-DOP=\sqrt{trace(G_{D}(1:3,1:3))} \tag{8}\]  
This particular _DOP_ quantifies the effect of geometry on the three components of the estimated velocity.
## 4 Results and Discussion
### Consensus on Benefits
#### 4.2.7 Night Use  
AR application for communication purposes is not only used during daylight; four reports mentioned that AR could be used during dark hours [11; 30]. This attribute is related to the surroundings of the practitioner. Therefore, this benefit is related to the environment of the user. The consensus shown among the papers and the low number of papers (four) reflecting this consensus is arguably a product of the small number of studies conducted in uncontrolled environments during dark hours using both head-mounted displays and handheld mobile devices.