esa-sceva/satcom-chunk-collection huggingface dataset:
# The Contribution of Near-Surface Geophysics for the Site Characterization of Seismological Stations
## 2 Methodology
### Density Determination  
The determination of the density of geological samples based on laboratory measurements is proposed by several authors [61; 62; 63; 64; 65; 66]. A typical procedure to achieve this task involves the measurement of three different types of weight for each specimen, i.e., (i) the dry weight of the specimen measured in air (\(W_{1}\)), (ii) the saturated weight of the specimen measured in air (\(W_{2}\)), and, finally, (iii) the saturated weight of the specimen measured while submerged in water. From this procedure, the dry bulk density \(\rho_{d}\), the saturated bulk density \(\rho_{s}\), and the granular density \(\rho_{g}\) can be obtained. In the context of this study, the physical properties of the subsurface are determined above the assumed level of the water table and, therefore, the use of the dry bulk density was considered to be more appropriate.  
For the determination of the measurement error, the weights of several samples of each geological formation were measured (Table 2), taking into consideration their median characteristic value. In total, laboratory density measurements on 60 geological specimens, collected from the locations of the geophysical measurements, were performed. For more reliable results, a _Kern EMB 100-3_ precision scale was used with an accuracy of 0.001 gr. In Table 2, the calculated densities of the geological formations from the three seismological stations (_MDRA_, _VILL_, and _ATAL_) are provided. Due to the existence of loose geological formations at the other sites of the seismological stations, i.e., Loutraki, Thalero, and Epidavros (_LOUT_, _THAL_, and _EPID_), the collection of a solid specimen and consequently the determination of the density was not possible. Therefore, the required density values for the ongoing calculations of the elastic moduli for those sites are based on literature sources [62; 63; 67].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ground Type** & **Description of Stratigraphic Profile** & \(\boldsymbol{V_{\text{S30}}}\)**(m/s)** \\ \hline A & Rock or other rock-like geological formations, including & \multirow{2}{*}{\(>\)800} \\  & at most 5 m of weaker material at the surface. & \\  & Deposits of very dense sand, gravel, or very stiff clay, at & \\ B & least several tens of meters in thickness, characterized by & \\  & a gradual increase of mechanical properties with depth. & \\  & Deep deposits of dense or medium-dense sand, gravel, & \\ C & or stiff clay with thickness from several tens to many & \\  & hundreds of meters. & \\  & Deposits of loose-to-medium cohesionless soil (with or & \\ D & without some soft cohesive layers), or of predominantly & \textless{}180 \\  & soft-to-firm cohesive soil. & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ground-type classification based on [57].
## 1 Introduction  
Because of their potential to provide high Q factors than those of microwave clocks, optical atomic clocks offer a tremendous advantage in both short-term and long-term stability in comparison with conventional microwave clocks [1; 2]. In the last few decades, optical clocks employing neutral atoms [3; 4; 5; 6] and single ions [7; 8; 9] have progressed remarkable regarding instability and achieving uncertainty levels of \(10^{-18}\), surpassing the cesium primary frequency standard as the definition for the second of the International System of Units. The great progress in optical clocks over the past decade has immensely influenced developments in science and technology areas such as topological dark matter hunting [10], gravitational wave detection [11], fundamental physics measurement [12; 13; 14], quantum simulators and geodetic applications [15; 16; 17].  
Alkaline earth(-like) atoms are the most promising candidates for optical lattice clock among possible atomic candidates because of their abundant and special energy levels. A large number of strontium optical lattice clocks [18] are currently under development world-wide with uncertainties of up to a few parts in 1 \(\times\) 10\({}^{-18}\)[3; 5; 6]. Recently, the team at the Joint Institute for Laboratory Astrophysics (JILA) obtained using an \({}^{87}\)Sr optical lattice clock [19] a measurement precision of \(5\times 10^{-19}\) in 1 h of averaging time by taking advantage of the high, correlated density of a degenerate Fermi gas in a three-dimensional optical lattice to guard against on-site interaction shifts.  
In this article, we report recent experimental progress concerning our \({}^{87}\)Sr optical lattice clock at the National Time Service Center. The organization of this paper is as follows. First, recent progress with the clock is described. Second, the experimental determination of the Lande \(g\)-factors for (5s\({}^{2}\))\({}^{1}S_{0}\) and (5s5\(p\))\({}^{3}P_{0}\) states of \({}^{87}\)Sr is presented. Finally, we mention work on the miniaturization of strontium optical lattice clock for future space applications.
# Decision Support System for Medical Diagnosis Utilizing Imbalanced Clinical Data
## 3 Proposed Methodology
### COCOA Integrated with a Regularized Boosting Approach for Multi-Class Classification  
Class imbalance still exists in \(D_{jk}^{tri}\) when the number of examples with label \(j\) or the number of examples with label \(k\) is too small. Therefore, it is necessary to apply a multi-class classifier that is able to handle multi-class imbalanced data sets in \(D_{jk}^{tri}\). In this study, a regularized boosting approach introduced in Section 3.3 was integrated into the process of multi-class classification in COCOA (named as COCOA-RE) to achieve better performance.  
Table 1 presents the COCOA-RE method. For each label, a binary-class classifier and K coupling multi-class classifiers were performed to train the multi-label data set. Instead of using a single multi-class classifier, a regularized boosting approach was applied to produce an ensemble classifier for the training data set of each coupling labels. The regularization parameter was initialized to be equal at 1, and the weight of each example was initialized with \(1/M\). Two indicator functions were used in the COCOA-RE approach, namely Function \(\mathbf{1}_{0}^{1}\) and Function \(\mathbf{1}_{-1}^{1}\). Function \(\mathbf{1}_{0}^{1}\) was equal at 1 if true, 0 otherwise, and it was used in calculation of the weighted error. Function \(\mathbf{1}_{-1}^{1}\) was equal at 1 if true, \(-1\) otherwise, and it was used to update the weight of examples. After training the multi-label data set, the predictive value for label \(y_{j}\) was integrated by the predictive confidences calculated by the binary-class classifier and multi-class classifiers. Eventually, the predictive models of all labels were performed to produce the predicted label set for the testing example.  
\begin{table}
\begin{tabular}{l} \hline \hline
**Algorithm: COCOA-RE** \\ \hline
**Inputs:** \\ \(D\): the multi-label training set \(T=\{(X_{1},Y_{1}),(X_{2},Y_{2}),\ldots,(X_{m},Y_{n})\}\) \\ \(B\): the binary-class classifier \\ \(M\): the multi-class classifier \\ \(K\): the number of coupling labels \\ \(x\): the testing example \((x\in X)\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The pseudo-code of (COCOA-RE). COCOA-RE: a regularized ensemble approach integrated into multi-class classification process of COCOA; COCOA: Cross-Coupling Aggregation.
# A Hybrid Genetic Algorithm for Ground Station Scheduling Problems
## 4 ITGA Design
### Tsa  
The TSA can avoid repeatedly visiting already searched chromosomes by introducing a tabu list during the local search process, thus helping the GA avoid getting trapped in local optimal solutions [19].  
In this paper, the TSA is used to improve the crossover and mutation operators of the GA: a local tabu list is established in the crossover operator, and this list, combined with a global tabu list that stores the optimal solutions generated in each iteration, can avoid repeated searches by the crossover and mutation operators. The execution process of the tabu crossover (TC) operator is shown in Algorithm 3.  
```
Input: Pop\({}_{1}\) and Pop\({}_{2}\)  population 1 waiting for TC operation and population 2 waiting for TC operation. Output: Pop\({}_{\text{Crossover}}\) :The new population generated by TC operation.
1: Initialize Flag = True
2:for\(i=1\): Pop\({}_{1}\).Len do\({}^{\ast}\)Len indicates the size of the population.
3: Flag\(\leftarrow\)True
4:while Flag!= False do
5: S\({}_{1}\).g\({}_{2}\)\({}^{\ast}\)-Crossover (Pop\({}_{1}\),Pop\({}_{2}\)) \(\cdot\)Pop\({}_{1}\) and Pop\({}_{2}\) generate new chromosomes \(\text{g}_{1}\) and \(\text{g}_{2}\) through the crossover operation described in Section 4.5.
6:ifg\({}_{1}\).g\({}_{2}\) do not exist in the local and global tabu lists, as well as in Pop\({}_{\text{Crossover}}\)then
7: Pop\({}_{\text{Crossover}}\)\({}^{\ast}\)-S\({}_{1}\).g\({}_{2}\)
8: Flag\(\leftarrow\)False
9:endif
10:endwhile
11:endfor
12:returnPop\({}_{\text{Crossover}}\)
```  
**Algorithm 3**TC operator  
Furthermore, the concept of a neighborhood solution set is introduced into the mutation operator to enhance the local search capability of the genetic algorithm. The definitions related to the tabu mutation (TM) operator are as follows:  
1. Initial solution (IS): represents the chromosome individual initially involved in mutation.
2. Neighborhood solution (NS) set: a collection of solutions generated in some manner based on the initial solution.
3. Candidate solution (CS): the optimal solution with the highest fitness value within the neighborhood solution set is compared with the initial solution to determine whether it should replace the initial solution.  
The execution process of the TM operator is outlined in Algorithm 4.  
Figure 8: Mutation operation. P1, P2, and different colors indicate chromosomal position changes.
# Marketing Insights from Reviews Using Topic Modeling with BERTopic and Deep Clustering Network
## 7 Discussion  
In this study, topic modeling was employed to extract product characteristics and related products from consumer reviews for potential utilization in marketing insights. During this process, the clustering module of BERTopic was replaced with a DCN, a recently created neural network-based algorithm, to examine the outcomes of the task. Furthermore, a performance comparison was conducted between the DCN-based model, the baseline BERTopic, and other alternative algorithms. Despite the various clustering algorithms that can be applied to BERTopic's clustering module, instances of utilizing recently proposed neural network-based clustering algorithms were limited. The endeavor to incorporate the DCN carries significance from the perspective of applying unexplored clustering algorithms to BERTopic and comparing their performances.  
The model employing the DCN exhibited a favorable performance; however, in comparison to the baseline BERTopic, it showed similar or slightly inferior results. This disparity might be attributed to the fundamental paradigm differences between the DCN and HDBSCAN. The DCN relies on the K-means algorithm, which utilizes distances between data points, while HDBSCAN employs data density for hierarchical clustering. The baseline BERTopic benefits from excluding reviews identified as noise during the clustering process, which is anticipated to positively impact its performance. Nevertheless, reviews categorized as noise could potentially be covered to some extent during data pre-processing, and there is a possibility of the loss of valuable information if informative reviews are mistakenly categorized as noise. Therefore, further investigation is warranted in this regard.  
The extracted product characteristics and related products obtained by utilizing the proposed method in this study can potentially aid in discovering marketing insights and devising strategies. Observing the extracted product characteristics can provide insights into consumer reactions and purchasing intentions, informing strategies for product improvement, advertising emphasis, and more. The insights gained can be utilized as reference materials for formulating marketing strategies. Concerning the extraction of related products, clustering products with similar content mentioned in reviews allows for the identification of products that consumers purchase with similar intentions or features, even if they do not belong to the same category. This information can aid in decisions about which additional products to offer alongside high-selling items or in exposing related products to consumers purchasing a specific item.
# A Low Sampling Rate Receiver Design for Multi-Antenna Multi-User OFDM Systems
## 2 OFDM Systems  
The new design presented in this paper is an upgrade of existing OFDM systems. We first introduce a system model of the existing system in this section. Then, in the next section, we elaborate on the details of our new design.  
Consider the downlink multi-user OFDM system shown in Figure 1. One base station (BS) is equipped with \(M\) antennas and \(K\) users are each equipped with a single antenna. All users share \(N\) subcarriers of the OFDM system. For simplicity, we assume that \(N\) is an integer multiple of \(K\) and that the share of each user is \(V=N/K\).
# An Admission Control Mechanism for 5G LWA
###### Abstract  
To alleviate the spectrum scarcity problem in fifth-generation (5G) networks, traditional mobile data offloading schemes from long term evolution (LTE) to wireless local area networks (WLANs) have been revised by the third-generation partnership project (3 GPP) in release 13, which is known as LTE-WLAN aggregation (LWA). With LWA, user equipment units (UEs) supporting both LTE and WLAN can utilize both LTE and WLAN links simultaneously. Thus, UEs under the coverage of an LWA network will be surrounded by multiple standards, such as LTE, WLAN, and LWA, along with cells of different sizes and coverage. Providing the LWA service to all UEs unconditionally may lead to serious intra-cell unfairness, degradation of system-level quality of service (QoS), and a reduction in system resource utilization. Hence, to resolve this issue, two important challenges need to be addressed: Which LTE UEs should be transferred, and how many LTE UEs need to be transferred. In this paper, we propose a user-offloading algorithm for evolved node B (eNB) hardware that smartly allocates the deprived LTE UEs and assigns the LWA service to an optimal number of UEs without degrading the QoS for existing WLAN UEs. With this proposed scheme, all LWA-preferred UEs with poor LTE performance and a good WLAN condition have the opportunity to access LWA service to improve performance. We show that the proposed scheme maximizes the throughput performance of the whole network.  
Keywords:coexistence; LTE; LWA; unlicensed band; WLAN; 5G +
Footnote †: journal: Nucl. Instr. Meth. A
## 3 Numerical Simulation Models and Methods  
### DEM Modeling  
In this study, commercial software EDEM 2018 was used for particle stacking and vertical compression processes. During the DEM packing process, spherical particles with a diameter of 7.24 mm were generated at the top of the container and fell by gravity to form a cylindrical packed structure with a diameter of \(R=100\) mm and a height of \(H=75\) mm. The wall effect has less effect on the packing rate when the diameter ratio of packed bed to particle is more than 10 [35; 36]. Based on the Hertz-Mindlin contact model, a total of 1815 particles were generated after considering the particle-particle and particle-wall interaction. The DEM packed structure modeling parameters are listed in Table 1[37]. After the pack was constructed, a circular loading sheet was created on its upper surface and compressed downward at a constant speed, stopping when the downward movement of the loading sheet reaches the test displacement. The information of DEM gravity packing and compressed particles was exported, and then the Gambit software was used to reconstruct the geometric model of particles and the outer cylindrical wall of the packing section, setting the inlet region \(H_{i}=30\) mm and the outlet region \(H_{j}=25\) mm in the packing section to reduce the inlet and outlet effects. The geometric model of soybean particle packing is shown in Figure 2.  
Figure 2: Soybean packing geometry model diagram.
## 1 Introduction
(2014) found that burned areas in Australia had a strong correlation with the average precipitation [22]. [PERSON] et al. (2014) found that climate was the primary factor compared with human activity [23]. To sum up the above, the mechanisms and interactions leading to forest fires in China are poorly understood. The causes of forest fires need further exploration. Forest ecosystem carbon cycling is an important component of the entire terrestrial ecosystem. The most significant impact of a forest fire can be seen in vegetation. Forest fire disturbances have a significant impact on ecosystem stability and sustainability [7; 24]. On the one hand, plants usually die instantly due to considerably severe forest fires. Frequent and high-intensity fires will lead to permanent changes in ecosystems and their components [25]. On the other hand, forest fires have been an important mechanism for generating ecological succession by acting as an environmental filter, selecting species, and shaping ecosystem communities [26]. Forest fire contributes significantly to climate change, consuming and transferring carbon to the atmosphere [27]. However, carbon change is usually ignored after a forest fire. Observational data suggest that vegetation growth and soil carbon content gradually recover over the following years [28]. If ecosystems can be restored to their pre-disturbance state, the terrestrial ecosystem carbon cycling remains in long-term dynamic equilibrium [29]. The effects of forest fire disturbance on ecosystems are not clear for a long time. The study proposes a new method to map burned areas using multisensor remote sensing data by taking advantage of the high temporal resolution of Moderate-Resolution Imaging Spectroradiometer (MODIS) and the medium spatial resolution of Thematic Mapper (TM). In this study, a new spectral index called Normalized Difference Fire Index (NDFI) was derived from MODIS surface reflectance data to timely and accurately acquire the forest fire distribution and its dynamic change. The objectives of this study are as follows: (1) to accurately obtain the spatiotemporal variations of fire distribution by NDFI; (2) to identify the causes of forest fires; and (3) to explore the impacts of forest fire on GPP.
## 3 Results
### Total Aboveground Biomass and its Distribution in Different Banners
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Banners**} & \multirow{2}{*}{**Grassland**} & \multicolumn{8}{c}{**Aboveground Biomass Density (g\(\cdot\)m\({}^{-2}\))**} \\ \cline{3-11}  & & **Area (km\({}^{2}\))** & **2005** & **2006** & **2007** & **2008** & **2009** & **2010** & **2011** & **2012** & **Average** \\ \hline East Ujimqin Banner & 45,060 & 127.04 & 84.20 & 55.26 & 86.54 & 90.03 & 92.13 & 91.20 & 111.03 & 92.18 \\ Abaga Banner & 27,492 & 65.19 & 55.07 & 50.18 & 72.66 & 62.93 & 60.09 & 56.88 & 87.03 & 63.75 \\ West Ujimqin Banner & 23,726 & 147.30 & 106.82 & 77.13 & 112.89 & 87.87 & 105.18 & 103.72 & 129.01 & 108.74 \\ Sonid Left Banner & 34,814 & 34.62 & 27.98 & 29.09 & 37.37 & 34.37 & 27.31 & 30.24 & 44.28 & 33.16 \\ Xilinhot City & 15,753 & 91.60 & 78.25 & 63.23 & 88.41 & 69.78 & 81.17 & 69.25 & 101.70 & 80.42 \\ Erenhot City & 186 & 15.54 & 10.85 & 9.40 & 14.06 & 10.80 & 7.88 & 9.13 & 13.48 & 11.39 \\ Sonid Right Banner & 25,212 & 35.53 & 27.50 & 23.60 & 31.80 & 26.38 & 21.84 & 25.90 & 34.89 & 28.43 \\ Zhenglan Banner & 10,281 & 105.60 & 102.60 & 81.82 & 111.67 & 95.95 & 84.87 & 101.70 & 111.58 & 99.47 \\ Zhengxiangbai Banner & 6256 & 76.18 & 76.76 & 57.60 & 79.26 & 66.34 & 55.31 & 73.53 & 83.30 & 71.03 \\ Xianghuang Banner & 5018 & 59.94 & 61.44 & 44.64 & 57.57 & 52.96 & 45.02 & 62.25 & 65.43 & 56.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Aboveground biomass in different banners.
# Horizontal Wind Effect on the Aerodynamic Performance of Coaxial Tri-Rotor MAV
## 4 Simulation Analysis
### Simulation Results
#### 4.2.2 Streamline Distribution  
The streamline distribution of the coaxial Tri-rotor with the horizontal airflow is shown in Figure 10.  
In Figure 10, it can be observed that compared with no-flow environment, the streamline deformed with more vortices around the rotor tip in the horizontal wind environment. The streamline is squeezed and deformed, resulting in the vortices under the rotor being deformed and the streamline inclined distribution. At the same time, with the increase of the horizontal wind speed, the streamline arrangement is more compact and the aerodynamic interference between rotors is more intense in this case. Therefore, it can be seen that the horizontal airflow will move the coupling interference between rotors and affect the overall aerodynamic performance. In addition, it can be also noted that  
Figure 10: The streamline distribution of the coaxial Tri-rotor (2200 r/min): (**a**) 0 m/s; (**b**) 2.5 m/s; (**c**) 4 m/s.  
compared with the horizontal wind at 4 m/s, the streamline distribution with the horizontal wind at 2.5 m/s is more uniform. The more uniform the streamline arrangement, the better the aerodynamic performance, which also verifies that the power loading of the horizontal wind at 2.5 m/s is greater than that of the horizontal wind at 4 m/s in Figure 6.
# Web Mapping for Farm Management Information Systems: A Review and Australian Orchard Case Study
## 4 Case Study Results
### Historical Imagery Misfit Vectors
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Farm/CP**} & \multirow{2}{*}{**Imagery Date**} & \multicolumn{2}{c}{**GEI Coordinates**} & \multicolumn{2}{c}{**Misfit Vectors**} \\ \cline{3-6}  & & **Latitude (\({}^{\circ}\))** & **Longitude (\({}^{\circ}\))** & **Magnitude (\({}^{\circ}\))** & **Bearing (\({}^{\circ}\))** \\ \hline \multirow{5}{*}{FNQ-2/O1} & 10 July 2009 & \(-17.109818\) & 145.086856 & 3.317411 & 241.4570 \\  & 19 August 2011 & \(-17.109811\) & 145.086824 & 2.445809 & 166.6781 \\  & 15 September 2015 & \(-17.109782\) & 145.086809 & 5.909510 & 158.1297 \\  & 14 July 2019 & \(-17.109822\) & 145.086829 & 1.087267 & 178.4557 \\  & 15 June 2021 & \(-17.109813\) & 145.086819 & 2.329465 & 153.7840 \\ \cline{2-6}  & \multicolumn{2}{c}{**Mean misfit vector**} & \multicolumn{2}{c}{**2.532**} & **175.8819** \\ \hline \multirow{5}{*}{FNQ-3/W4} & 8 December 2009 & \(-17.129002\) & 145.423675 & 1.158942 & 221.7447 \\  & 18 June 2011 & \(-17.128994\) & 145.423681 & 2.242521 & 220.8642 \\  & 28 June 2013 & \(-17.129004\) & 145.423674 & 0.952090 & 230.1685 \\  & 25 September 2016 & \(-17.128991\) & 145.423688 & 2.937458 & 226.3162 \\  & 14 July 2019 & \(-17.128977\) & 145.423668 & 3.568632 & 179.7362 \\  & 18 July 2021 & \(-17.128957\) & 145.423658 & 5.915459 & 169.8191 \\ \cline{2-6}  & \multicolumn{2}{c}{**Mean misfit vector**} & \multicolumn{2}{c}{**2.523**} & **195.4489** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._