esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Saturation Index  
Because the groundwater system is affected by diverse solutes resulting from the interaction with the surrounding sediments, SI aids in the realization of reactive minerals in the groundwater system [39, 40]. SI values indicated that the bulk of groundwater samples  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Ion-Indices**} & \multicolumn{4}{c}{**Concentrations (ppm)**} & \multicolumn{4}{c}{**Concentrations (ppm)**} \\ \cline{2-7}  & **Min** & **Max** & **Aver** & **Min** & **Max** & **Aver** \\ \hline pH & 6.4 & 7.0 & 6.7 & - & - & - \\ \hline TDS & 156 & 1069 & 552 & - & - & - \\ \hline T (\({}^{\circ}\)C) & 23 & 33 & 26 & - & - & - \\ \hline Ca\({}^{2+}\) & 30 & 240 & 89 & 1.5 & 12 & 4.4 \\ \hline Mg\({}^{2+}\) & 21 & 170 & 63 & 1.7 & 14 & 5 \\ \hline Na\({}^{+}\) & 23 & 575 & 139 & 1 & 25 & 6 \\ \hline K\({}^{+}\) & 5 & 23 & 11 & 0.14 & 0.6 & 0.3 \\ \hline CO\({}_{3}\)\({}^{2-}\) & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline HCO\({}_{3}\)\({}^{-}\) & 189 & 915 & 439 & 3.1 & 15 & 7 \\ \hline Cl\({}^{-}\) & 50 & 886 & 186 & 1.4 & 25 & 5 \\ \hline SO\({}_{4}\)\({}^{2-}\) & 7 & 912 & 171 & 0.15 & 19 & 3.6 \\ \hline SI & \(-\)7.6 & 4.2 & \(-\)2.6 & - & - & - \\ \hline CAI\({}_{1}\) & - & - & - & \(-\)2.7 & 24 & 3.1 \\ \hline CAI\({}_{2}\) & - & - & - & 1.05 & 24 & 4.2 \\ \hline GC & - & - & - & 0.13 & 0.88 & 0.50 \\ \hline GA & - & - & - & 0.16 & 0.86 & 0.34 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The ion concentrations and physicochemical values in the studied groundwater samples.  
are undersaturated in these minerals (Figure 4), except five samples: No. G7, G19, G21, G25, and G26. Sample No. G7 is supersaturated with anhydrite, aragonite, calcite, dolomite, gypsum, halite, and sylvite minerals. Sample No. G19 is supersaturated with aragonite, calcite, and dolomite while samples No. G21 and G25 are supersaturated with dolomite only. Moreover, sample No. G26 is supersaturated with dolomite and aragonite. This supported by [51], who proved the presence of carbonate minerals in Assuit soils, which leached to the groundwater, using XRD, XRF, and SEM techniques.  
The SI values for the five surface water samples, on the other hand, are undersaturated with the previously mentioned minerals (Figure 4). This indicates that the groundwater in the study area is primarily recharged by surface water [23]. The evaporite and carbonate minerals are dissolutions based on the SI results, except in the supersaturated samples. These minerals are prone to precipitation [23; 39; 40]. The very strong correlation between TDS and Ca\({}^{2+}\), Mg\({}^{2+}\), Na\({}^{+}\), Cl\({}^{-}\), and SO\({}_{4}\)\({}^{2-}\), where r = 0.940, 0.935, 939, 0.962, and 0.848, respectively, supports the presence of carbonate and evaporite minerals in the groundwater (Table 2). This proved that the motioned ions (Ca\({}^{2+}\), Mg\({}^{2+}\), Na\({}^{+}\), Cl\({}^{-}\), and SO\({}_{4}\)\({}^{2-}\)), which were indicated on both carbonate and evaporite minerals, are the main control in TDS.
# Estimating the Optimal Overall Slope Angle of Open-Pit Mines with Probabilistic Analysis
## 3 Results  
As stated before, this study applies a shear strength reduction technique (SSRT) to demonstrate a hybrid method to determine the optimal ultimate slope angle of an open pit that maintains slope stability and allows high productivity. The analysis was conducted by constructing a series of models at different overall slope angles and observing the critical strength reduction factor (SRF) or its analogy (e.g., a factor of safety, **FOS**). The results of the deterministic analysis will be presented and compared with probabilistic analysis  
Figure 3: Mesh sensitivity analysis depicts the number of nodes on the external boundary versus vertical (**top**) and horizontal (**bottom**) displacements.  
in terms of factor of safety (**FOS**), maximum shear strain and incremental horizontal and vertical displacements with respect to overall slope angles.
# The Wettability and Numerical Model of Different Silicon Microstructural Surfaces
## 3 Numerical Methods and Setup
### VOF Model  
The VOF model is a surface tracing method in a fixed Eulerian grid, and can handle arbitrary-free interfaces between different kinds of immiscible fluids. The VOF model recorded the position and shape of the free surface using the fluid volume fraction (F), wherein the volume of the fluid was estimated by the volume of the grid itself. The present study employed the gas-liquid two-phase flow of water droplet moving on the solid wall as the simulated object, wherein air was the main phase and water was the secondary phase. In this paper, the value of the fluid volume fraction was shown in Table 3.  
The momentum equation of the two phases was defined as follows.  
\[\frac{\partial}{\partial t}(F_{a}\rho_{a}V_{a})+\
abla\cdot\left(F_{a}\rho_{a }\cdot\overset{\rightarrow}{\
u}\cdot V_{a}\right)=S \tag{4}\]  
\[\frac{\partial}{\partial t}(F_{w}\rho_{w}V_{w})+\
abla\cdot\left(F_{w}\rho_{w }\cdot\overset{\rightarrow}{\
u}\cdot V_{w}\right)=S \tag{5}\]  
where script \(w\) is water and \(a\) is air, \(F_{a}+F_{w}=1\), and \(S\) is the source term. The contact angle at any point on the wall boundary was assumed be equal to the contact angle of sample 1 (54\({}^{\circ}\)). The surface tension was the source term in the momentum equation, \(S=\sigma/A\). The surface tension coefficient \(\sigma\) was determined to be 0.07275 N/m, the acceleration of gravity was 9.81 m/s\({}^{2}\), and the direction was downward.
# Classification and Causes Identification of Chinese Civil Aviation Incident Reports
## 3 Report Classification
### Vectorization  
Before classification, each pre-processed report needed to be represented using a vector. Bag-of-words (BoW) representations and word embeddings are the two common models. The BoW representation regards each document as a bag of words, neglecting their order, grammar, and syntax. In effect, each document can be represented as a vocabulary-length vector, in which the values are equal to the number of occurrences of the corresponding words. However, BoW ignores the order of words and cannot reflect their importance. The employment of term frequency-inverse document frequency (TF-IDF) can be a complement by providing the importance of each word in a document [21], as shown in Equation (5).  
\[x_{ik}=f_{ik}\times log\left(\frac{N}{n_{i}}\right) \tag{5}\]  
where \(f_{ik}\) is the occurrence time of word \(i\) in document \(k\), \(N\) is the number of documents in the dataset, and \(n_{i}\) is the number of documents in which word \(i\) appears. Similarly, each word can be represented as a fixed-length vector with word embeddings.  
[PERSON] et al. [23] adopted word embedding techniques to derive a dense vector representation of a document. Since aviation accident reports are very different from texts in other domains, pre-training on a larger dataset may allow the model to capture the meaning of words more accurately. Therefore, the word-embedding model was trained on the entire CASTC database instead of only on the labeled reports. In implementing the word-to-vector (Word2 Vec) method, Gensim [30] was used to learn 128-dimensional word vectors in the database, with a window size of five and the skip-gram variant.  
Additionally, considering that a long and sparse feature vector of the labelled dataset may result in overfitting, we proposed a BoW method called occurrence position (OC-POS), which originates from the occurrence of keywords and their position in the report. The value of the vector can be calculated using Equation (6).  
\[M_{i}=\sum_{k=1}^{n}log\left(2-\frac{p_{i}^{k}}{N}\right),1\leq i\leq L \tag{6}\]  
where \(L\) is the length of the bag of words, \(N\) is the total number of words appearing in the report, and \(p_{i}^{k}\) is the relative position of the \(k\)th occurrence of word \(k\). Keywords were collected from the reports with human intervention.
## 6 Results and Discussion

Table 9: Optimal solution with values of the auxiliary function \(Z\) computed by CSMRO for non-zero decision variables (\(x=1\)) in the example set. The CSMRO was able to handle such a large amount of data without any problems and generated a tabular solution (similar to the one presented in Table 9) with a calculation time of less than 1 s. Even if, for the vast number of people affected, the calculation time is 1 min, this will still be very short for the DES personnel to make appropriate decisions based on the calculation results obtained from the CSMRO. It is worth emphasizing that in this short solution time, the CSMRO generates a ready-made solution for direct handling the mass casualty incident. Thus, the CSMRO can be used to:  
* detailed statistical analysis of actual mass incidents to identify the relationship between the various post-accident conditions of the injured and the expected values of death, disability and long-term medical costs;
* dynamic analysis of the current situation at the scene of a mass casualty incident by taking into account the time factor and
* analysis of very large-scale tasks. In summary, the proposed model for medical rescue operations in MCIs is briefly compared with the models described in the literature (Table 10). From Table 10, it can be seen that many approaches are based on a linear programming approach. Almost none of them include a multi-criteria formulation of MCI problems. They are all primarily based on the START algorithm. This means that the casualties are assigned to priority groups, but are not examined individually, i.e., the exact details of the  
Figure 2: The optimal solution was obtained using the CSMRO with the weighted sum criteria method and the B and C algorithm for the studied example. condition of the injured are not used. In the proposed model, a novel method of assigning each casualty to a specific emergency department and to a specific mode of transport is introduced. The current health status of the injured is classified and included in the considered optimization problem. The allocation of emergency medical services forces and resources in the MCI is formulated as the multi-criteria optimization problem.
###### Abstract
However, the communication waveform is random in nature, and the number of empty subcarriers in the communication depends on the number of users served by the networks. The data transmitted by the users exhibit bursts and discontinuity. [PERSON]. [29] proposed a novel ISAC scheme that constructs three adjacent BSs as a cooperative sensing system. This scheme stipulates that only one of the three adjacent BSs can be working in downlink mode at any time. However, this scheme would reduce the BS's duration in downlink mode by one-third, significantly reducing the communication data rate. Therefore, reusing the hardware of existing, widely deployed 5G BSs to achieve ISAC, while minimizing the impact on the existing communication data rate and avoiding the impact of the random autocorrelation property of communication signals on sensing performance, poses a complex issue, which is addressed in our proposed waveform design method. In this paper, we propose an inner-frame time division multiplexing waveform design method of ISAC in 5G NR systems, which achieves communication and sensing by reusing the hardware of existing 5G BSs, characterized by its low cost, rapid deployment, and minimal impact on the communication data rate. We transform the sensing waveform design problem under 5G communication constraints into an optimization problem by combining the advantages of time division multiplexed waveforms and communication-centric waveforms. We use the simulated annealing algorithm to solve the optimization problem and obtain the optimized sensing waveform. The designed waveform takes into account the constraints on the BS communication signal in 5G systems and is fully compatible with existing hardware devices for 5G. For the designed waveform, we also employ the corresponding signal detection and estimation methods to extract the target information. The results of the theoretical analysis and numerous simulation experiments show the effectiveness of the designed waveform. The remainder of this paper is organized as follows. In Section 2, the signal model is introduced in the 5G NR system. In Section 3, the sensing waveform is designed and the corresponding signal processing methods are detailed. In Section 4, numerical examples are presented to illustrate the performance of the designed waveform. In Section 5, the conclusions are drawn. Hereafter, \((\cdot)^{-1}\), \((\cdot)^{\mathrm{T}}\), \((\cdot)^{*}\), and \((\cdot)^{\mathrm{H}}\) stand for the inverse, transpose, conjugate, and conjugate transpose of the argument, respectively; \(diag(\mathbf{A})\) and \(tr(\mathbf{A})\) represent the vector consisting of the diagonal elements of matrix \(\mathbf{A}\) and the trace of matrix \(\mathbf{A}\), respectively; \([\cdot]\) represents rounding down, and the \(\mathrm{mod}(a,b)\) represents the remainder of \(a\) divided by \(b\).
## 4 Simulation Experiments and Discussion
### Experimental Data  
The design parameters of the antenna system used in conducting the experiments mentioned above were acquired from the instrument manufacturer and were set as follows:  
1. For the MR, the aperture was \(D_{0}=5.0\) m, the focal length was \(f=3.996\) m, and the offset height was \(h_{0}=3.899\) m.
2. For the FSR, the aperture was \(D_{1}=0.96\) m, the inter-focal distance was \(2c=5.725\) m, the major axis length was \(2a=4.453\) m, and the offset height was \(h_{1}=0.682\) m.
3. For the SSR, the aperture was \(D_{2}=1.4\) m.
4. The output of the ECF was set to a standard Gaussian beam, the taper angle was \(16.5^{\circ}\), the taper was \(-20\) dB, and the initial scan azimuth was \(\phi_{q0}=305^{\circ}\).  
Finally, it should be noted that all experiments in this study were conducted on a Windows 10 computer with an Intel i7-9700K/3.6 GHz CPU and 32 GB of memory. The ABP determination model was implemented as C++ code in the Microsoft Visual Studio 2019 programming environment. GRASP software version 10.3 was used.
# A Novel Method for Estimating the Dosage of Cold Atmospheric Plasmas in Plasma Medical Applications
## 2 Materials and Methods
### CAP Bio-Med Platform and Treatment of Cells
With an unchanged discharge voltage, the power input was also constant and should be measured, because it was an important factor for plasma dosage as discussed in Section 1 and would influence various interaction processes as shown in Figure 1. The CAP treatment times (\(t\)) were 0 (control), 2, 3, 3.5, 4, 4.5, 5, 7, and 10 min. All the experiments were repeated three times. Starting from the moment that the discharge was triggered, the gas temperatures of the CAP jet were measured and recorded every 30 s to estimate the thermal effects. To qualitatively analyze and quantitatively determine the value of LD\({}_{50}\), both the optical and electrical features of the CAP generator were measured. First, the reactive species in the CAP jet were detected by the spectrometer (Avaspec Multi-Channel Spectrometer, Avantes, Apeldoom, the Netherlands) with a fiber probe to show whether RONS exist in the CAP jet which are important for the plasma medical applications. The discharge power (\(P_{\text{in}}\)) was obtained by the voltage/charge Lissajous figure method [29] with the aid of a digital oscilloscope and two voltage probes. To quantitatively obtain the LD\({}_{50}\) value of the CAP jet to the HEK293 cells, the ratio of the discharge energy (\(W\)) to the number of cells (\(N\)) in each well, i.e., (\(W/N\)), was used when half of the cells in a well were killed. For a certain CAP treatment time \(t\), the value of (\(W/N\)) could be calculated by:  
\[W/N=P_{\text{in}}\times t/N. \tag{1}\]
## 3 Framework of Dynamic-DSO
### Direct Visual Odometry  
Dynamic-DSO is based on DSO [11], which is the state of the art in direct visual odometry. DSO adopts the direct sparse model to jointly optimize the camera pose, camera internal parameters, inverse depth of points by a sliding window. It includes a front-end and a back-end. The front-end is responsible for initialization, camera pose tracking, and the back-end is used for sliding window optimization. The details of the front-end and back-end are described as follow.  
Figure 1: Framework of Dynamic-DSO (Direct Sparse Odometry), color parts and the content in the dotted box show our contributions.
###### Abstract  
Vehicle Ad-Hoc Network (VANET) is a dynamic decentralized network that consists of various wireless mobile vehicles with no individual user management. Several routing protocols can be used for VANETs, for example, the Location-Aided Routing (LAR) protocol that utilizes location information provided by the Global Positioning System (GPS) sensors. It can help to reduce the search space for the desired route--limiting the search space results in fewer route discovery messages. However, two essential aspects are ignored while applying the LAR protocol in the VANET-based environment. Firstly, the LAR does not exploit the fact that nodes in VANET do not have pure random movement. In other words, nodes in LAR predict the position of destination node by ignoring the fact that the pre-defined constraint on the destination node navigation is met. Secondly, the nodes in the conventional LAR (or simply stated as LAR) protocol use the location information of the destination node before selecting the route location, which is most likely to expire because of the fast movement of the nodes in the VANET environment. This study presents an estimation based on a heuristic approach that was developed to reject weak GPS location data and accept accurate ones. The proposed routing protocol stated as Rectangle-Aided LAR (RALAR) is based on a moving rectangular zone according to the node's mobility model. Additionally, the proposed RALAR protocol was optimized by using the Genetic Algorithm (GA) by selecting the most suitable time-out variable. The results were compared with LAR and Kalman-Filter Aided-LAR (KALAR), the most commonly utilized protocols in VANET for performance metrics using Packet Delivery Ratio (PDR), average End-to-End Delay (E2E Delay), routing overhead and average energy consumption. The results showed that the proposed RALAR protocol achieved an improvement over the KALAR in terms of PDR of 4.7%, average E2E delay of 60%, routing overhead of 15.5%, and 10.7% of energy consumption. The results proved that the performance of the RALAR protocol had outperformed the KALAR and LAR protocol in terms of regular network performance measures in the VANET environment.  
vehicle ad-hoc network (VANET); location-aided routing (LAR); genetic algorithm (GA); rectangle-aided lar (RALAR); kalman-filter-aided lar (KALAR) +
Footnote †: journal: Applied Soft. 2020, 10, 5759; doi:10.3390/app10175759
## 3 Materials and Methods
### Data Acquisition  
To collect data from _StarCraft II_ matches, a simulation environment was implemented using PySC2 (a Python v3.11.7 environment for interacting with the game) and the official _StarCraft II_ API. One hundred matches were played on a symmetric map called Simple64. Each side was controlled by a finite-state machine (FSM)-based AI, and the Terran race was used to make sure that the conditions of the experiments were always the same. However, it is important to note that selecting a specific race limits the generalization of the results to other races in the game.  
During each match, key variables were recorded, including:  
* Resources: The amount of minerals and gas available to players.
* Military units and structures: The number of units and buildings controlled by each player.
* Strategy and outcomes: The attack timing of a player, their attack strategy, and the match outcome.
* Interaction-related variables: For instance, the reward and damage inflicted by one of the players.  
Regarding data preprocessing, the _strategy_ variable was numerically encoded as follows: 0 = _defense_ and 1 = _attack_. Additionally, square brackets enclosing the attack timing values were removed. All numerical variables were then scaled using StandardScaler from scikit-learn to ensure a mean of 0 and a standard deviation of 1. Finally, the data were stored in a structured CSV file with labeled columns and timestamps to ensure traceability.
## 4 Discussion  
### Particle Density  
The particle density of briquettes of all mixtures ranged from 0.92 to 1.01 g cm\({}^{-3}\), meeting the standard EN 17225-3 [35] and earning classification as \"acceptable\" according to [PERSON] and [PERSON] [36]. However, these values were lower than the 1.08 to 1.2 g cm\({}^{-3}\) of pine briquettes reported by [PERSON] and [PERSON] [37]. They were also lower than the 1.2 g cm\({}^{-3}\) of briquettes produced with 10% moisture content and at a temperature of 73 \({}^{\circ}\)C reported by [PERSON] et al. [26]. Briquettes made from 100% small particles (T3 (0:0:100)) were classified into class A1 (\(\geq\)1 g cm\({}^{-3}\)), and all the rest were placed in class A2 (\(\geq\)0.9 g cm\({}^{-3}\)). The values of particle density were consistent with those mentioned by [PERSON] and [PERSON] [38], who reported that the pressure exerted on compaction with hydraulic piston machines, such as used in this work, reached densities lower than 1.00 g cm\({}^{-3}\) because of limited pressure.  
On the other hand, the highest density was achieved when 100% small particles (T3 (0:0:100)) were used, which is consistent with the findings of [PERSON] [39], [PERSON] et al. [13], and [PERSON] et al. [40], who mentioned that the smaller the particle size, the lower the porosity and consequently the greater the density. In the same line, except for T6 (67:0:33), high percentages of small and medium particles, such as in mixtures T0 (12:71:17) (control), T2 (0:100:0), T4 (33:33:33), T8 (0:33:67), and T7 (0:67:33), produce briquettes with particle densities higher than 0.97 g cm\({}^{-3}\). According to [PERSON] et al. [12], small particle size yields better physical properties for briquettes, and [PERSON] and [PERSON] [41] have stated that a sufficient amount of small particles is necessary for embedding into the larger particles to produce desirable characteristics for briquettes. The observed effect can be attributed to the fact that the high pressure caused by external force causes an increase in the area of contact between the particles, making the molecular forces high enough and increasing the adhesion force between the particles [42]. A contrary effect was seen with a high percentage of large particles, as in T1 (100:0:0), or large particles plus medium particles, as in mixtures T5 (67:33:00), T9 (33:67:00), and T10 (33:0:67), where low particle density values (\(\leq\)0.96 g cm\({}^{-3}\)) were produced. As was shown by the response surface and contour graphs (Figure 4), briquettes with a density higher than 1 g cm\({}^{-3}\) are produced when  
Figure 8: Response surface graph (**a**) and contour graph (**b**) of gross calorific value from briquettes produced with different percentages of sawdust particle sizes.  
small particles in the mix are 100%; however to obtain higher values than 0.98 g cm\({}^{-3}\), the optimal mixture is achieved when the percentage of large particles is in the range 40-60%, medium particles are in the range 10-40%, and small particles are in the range 10-50%.
# Exploiting the Capture Effect to Enhance RACH Performance in Cellular-Based M2M Communications
## 4 Analysis Model
### RA Throughput  
In this sub-section, we provide an analysis model for the RA throughput. First, we provide a one-shot model when \(N\) and \(E[N_{T=t}^{k}]\) for \(\forall t,k\) are given. Then, we demonstrate a steady-state RA throughput using the one-shot model because \(E[N_{T=t}^{k}]\) is affected by the RA success probability, as determined by the RA throughput.