esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 QoS Self-Provisioning and Interference Management Concept
### A Note on Parameter Initialization versus Adaptation  
As mentioned earlier, we are focused in this paper on the initialization on the HNB parameters. The above method will typically be repeated when the HNB is idle or every time interval which commonly is configured by the HNB vendor or its management system (could be e.g., every few hours). It should be noted that the initialization will be the best network configuration possible given information we have at the HNB location. However, as users connect to the system, the imperfections of the modeling will become visible. For instance, while an HNB may see good isolation to a macrocell, a connected HNB user may be standing in a window opening with different coupling to the nearest macrocell. Also, load conditions which are assumed for the provided algorithm will vary over time which may lead to HUEs getting excessive or inadequate throughput compared to the targets.  
Hence, a method as devised here will be combined with adaptation, e.g., using observed throughput levels for its connected users as well as collecting neighbor interference reports (e.g., handover triggered measurements) to fine-tune the parameters compared to the initial settings. In this paper, we focus only on the initialization but we are to some extent including dynamic imperfections as we add users to the system. We have e.g., shadowing models as well as indoor modeling with wall counts which mimic the situation that some HNB users have a different path loss to its neighbors compared to the value predicted by the NLM method. Given our user distribution method, we also have varying network load conditions. As shall be seen by the simulation results, our method is robust against these variations. Further work will integrate and clarify the value of further dynamic adaptation.
# Dual-IoTID: A Session-Based Dual IoT Device Identification Model
## 4 Model Selection
### Algorithm Selection
#### 4.3.2 Second Classification Model Selection  
After the initial classification model, we obtained a confidence matrix for each session. Next, we combined the confidence matrix of the train set with the extracted session communication features (as described in Section 3.1.2). These merged data, together with the corresponding training set labels, were then fed into the second-layer classification model. For the sake of identification effectiveness, we only attempted DT, XGB, and RF for the second-layer model. Table 4 presents the IoT device identification results on the test set using different classifiers. Similarly, we employed a random search to find the optimal parameters for each model and utilized a 10-fold cross-validation to assess the effectiveness of the classification.  
From Table 4, it can be seen that in the second-layer classification model, regardless of the first-layer model, RF, XGB, and DT perform well, achieving identification accuracies of 99% and above. Among them, the XGB + XGB model exhibits the best performance, achieving an accuracy of 99.48% and a recall of 99%. Additionally, XGB + XGB also provides relatively fast identification efficiency. Based on these considerations, we selected the highest-scoring combination, XGB + XGB, for device classification.
## 3 Weather Component Analysis during the \"Oriental Star\" Accident
### Surface Wind Analysis  
During the \"Oriental Star\" accident, the nearby observatory site observed a maximum 10 min average surface wind speed as high as 6.8 m/s, far below the 32-38 m/s identified in the national investigation report [3]. This large gap was not surprising for two reasons: First, it was a small-scale event with a diameter of 2-4 km. Second, as [PERSON] [5] pointed out, wind gusts in deep-convective events could be as high as 2.9 times stronger than sustained wind.  
How well did the numerical prediction compare with observation? Figure 2 illustrates the near-surface wind simulations 92 min in advance (Figure 2a), and 268 min after the \"Oriental Star\" overturning time (Figure 2b), produced from the ECMWF half-degree-resolution product. Given that the grid-based dataset would reduce the wind strength due to a smoothing effect, the fact that sustained surface wind speeds are close to minimum around the shipw excision was surprising. So, at least in this case, real strong wind gusts introduced by deep convection cannot be simulated from raw numerical-mesoscale-model output and thus is hardly suitable for marine safety warning.  
Figure 2: Depiction of 10 m wind at UTC: (**a**) 1200 Z on 1 June 2015; (**b**) 1800 Z on 1 June 2015. Black thick line and red star mark the last route and shipw position of “Oriental Star”.
## 2 Materials and Methods
### Content of Total Phenolic Compounds (TPC) and Antioxidant Activity  
The content of TPC of licorice extract was determined following the method of Folin-Ciocalteu [30]. Initially, 10 \(\upmu\)L of extract was mixed with 600 \(\upmu\)L of milliQ water and 50 \(\upmu\)L of Folin-Ciocalteu reagent. The content was thoroughly mixed and after 3 min, 150 \(\upmu\)L of sodium carbonate solution (20% mass) and 190 \(\upmu\)L of milliQ water were added to the mixture. After 2 h at room temperature in darkness, the absorbance was measured at 760 nm using a Genesys 105 UV-Vis spectrophotometer (Thermo Fischer Scientific Inc., MA, Waltham, USA). The results were expressed as GAE (mg of gallic acid equivalents/g of extract).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **Solvent** & **Temperature** & **Yield** \\  & & \({}^{\circ}\)**C** & **\%** \\ \hline ELe25 & Ethanol & 25 & \(3.85\pm 0.46\)\({}^{\text{d}}\) \\ MLe25 & Methanol & 25 & \(9.08\pm 2.77\)\({}^{\text{c}}\) \\ EWLe25 & Ethanol:water (50:50 \(v\)/\(v\)) & 25 & \(25.14\pm 1.07\)\({}^{\text{a}}\) \\ MWLe25 & Methanol:water (50:50 \(v\)/\(v\)) & 25 & \(15.69\pm 2.93\)\({}^{\text{b}}\) \\ ELe50 & Ethanol & 50 & \(3.69\pm 0.14\)\({}^{\text{d}}\) \\ MLe50 & Methanol & 50 & \(9.94\pm 2.08\)\({}^{\text{c}}\) \\ EWLe50 & Ethanol:water (50:50 \(v\)/\(v\)) & 50 & \(24.75\pm 0.76\)\({}^{\text{a}}\) \\ MWLe50 & Methanol:water (50:50 \(v\)/\(v\)) & 50 & \(23.36\pm 0.18\)\({}^{\text{a}}\) \\ \hline \hline \end{tabular} Values with different letters (\({}^{\text{a-d}}\)) differ significantly (\(p<0.05\)).  
\end{table}
Table 1: Extraction yields (% mass extracted related to the mass of licorice root) of licorice root ultrasound-assisted extraction (UAE).  
The ability of licorice extracts to scavenge DPPH free radicals was determined according to the method described by [PERSON] et al. [31]. For the reaction, 25 \(\upmu\)L of samples were added to 975 \(\upmu\)L of DPPH radical in ethanol (6.1 \(\times\) 10\({}^{-5}\)), which was prepared daily. The reaction took place at room temperature, in the dark, until it reached a plateau. Then, the absorbance was measured at 515 nm in a Genesys 105 UV-Vis spectrophotometer (Thermo Fischer scientific, Waltham, MA, USA). The DPPH concentration in the reaction medium was calculated from a calibration curve (linear regression). A control sample, containing the same volume of solvent instead of the extract, was used to measure the maximum DPPH absorbance. Trolox was used as a reference standard, so results were expressed as TEAC values (\(\upmu\)mol Trolox/g extract). All analyses were done in triplicate.
## 2 Literature Review
### Effects of Time-Dependent Thermal Loads on Steel Bridge Structures
The thermal behavior of spatial structures under solar irradiation was investigated. It was revealed that a significant temperature difference occurs between the steel member under a glass roof and negative thermal load is the prominent factor for member stresses [28]. Using FE simulation, [PERSON] et al. [29] investigated the influence of summer thermal loads on steel girder buckling behavior. The configurations of the steel flange, such as the width and the thickness, were considered in studying the steel girders' buckling behavior [29]. [PERSON] et al. [30] showed that the beam's thickness significantly affects the vibration response of cable-beam structures induced by temperature changes. [PERSON] et al. [31] investigated the temperature variation and distribution of the steel box girder bridge during construction, considering the effect of the temperature distributions of the steel box girder segment during construction, which might cause unfavorable thermal stresses. Therefore, a numerical approach was recommended to be applied during the planning and designing phases to accurately predict the temperatures during construction [31]. [PERSON] et al. [32] conducted an FE thermal analysis to investigate the behavior of the temperatures across the steel girder section and along the girder depth, finding that the thermal gradient variations at the top and bottom surfaces were comparable. [PERSON] et al. [33] used experimental and numerical thermal analysis methods to investigate the nonuniform and time-dependent temperatures of corrugated web steel beams under environmental thermal loads. To study the effect of the location and its corresponding climatic conditions, a thermal analysis study was conducted for eight cities. The results indicated a complex and nonuniform action of the steel girder. The maximum temperature difference of approximately 47 \({}^{\circ}\)C was determined in the steel girder [33]. [PERSON] et al. [34] showed that the heat exchange between the different solar radiation components, heat convection and other factors and the boundaries of the steel structures resulted in a significant nonuniform distribution of the temperatures in steel structures. Due to the complexity of the heat exchange phenomena and not applying it accurately, understanding the steel structure thermal actions can be restrained, leading to jeopardizing the structure safety. The study provided a better understanding of the steel structure thermal behaviors and some recommendations to improve the temperature prediction and distribution in steel structures [34]. The continued exposure of steel structures to solar radiation and other thermal loads during construction can lead to nonlinear and complex temperature variations and distributions, which might impact the quality of engineering construction, the accuracy of structural closures [35], and consequently, the safety of the structure [36].
# A Service-Constrained Positioning Strategy for an Autonomous Fleet of Airborne Base Stations
## 7 Conclusions  
The results show that the proposed strategy can reduce the differences in average rate that each user receives, obtaining a fairness index close to one and fulfilling all the proposed constraints on average, which outperforms considerably the state of the art. Furthermore, the results show that the proposed strategy is robust to changes in the scenario. Finally, regarding the energy consumption, results show that fast forward displacements consumes a lot of energy and that quadcopters and hexacopters with higher automonies are needed in the ABBS framework.  
As a future work, on the one hand, transmission power control is being considered as a way to increase the lowest average rate in addition to UAV movement, where the transmission power would be neither constant over the scenario nor fixed over the iterations. On the other hand, other practical restrictions are being studied, either to obtain more realistic results (e.g., differentiation between active and non-active users, minimum bitrate per user, or upper and lower bounds for flying altitude) or to limit the movement for other benefits (e.g., energy consumption reduction by penalizing the cost function if the obtained displacement requires a high forward speed). A more realistic simulator will also be developed as future work including real data (e.g., a detailed geographical and geometric description of a city, for example, real traffic profiles, etc.).  
**Author Contributions:** Formal analysis, A.P.-I. and J.V.; Funding acquisition, A.P.-I. and J.V.; Investigation, F.J.-T., A.P.-I. and J.V.; Methodology, F.J.-T., A.P.-I. and J.V.; Project administration, A.P.-I. and J.V.; Software, F.J.-T.; Supervision, A.P.-I. and J.V.; Validation, A.P.-I. and J.V.; Writing--original draft, F.J.-T.; Writing--review and editing, A.P.-I. and J.V.  
**Funding:** The research leading to these results received funding through the grant 2017 SGR 578 (funded by the Catalan Government--Secretaria d'Universitats i Recerca, Departament d'Empresa i Coneixement, Generalitat de Catalunya, AGAUR) and the project TEC2016-77148-C2-1-R (AEI/FEDER, UE): 5G&B-RUNNER-UPC (funded by the Agencia Estatal de Investigacion, AEI, and Fondo Europeo de Desarrollo Regional, FEDER).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Ground Subsidence Analysis in Tianjin (China) Based on Sentinel-1A Data Using MT-InSAR Methods
## 1 Introduction
[24] used PS and SBAS to estimate the surface deformation caused by an earthquake in Italy based on 14 ASAR images, indicating that both the methods successfully pick up measurement pixels and display a comparable displacement pattern. [PERSON] et al. [25] investigated the ground subsidence of Tongzhou district in Beijing using 21 COSMO-SkyMed images, showing that the PS and SBAS methods have high consistency in the pattern of land subsidence, i.e., in the time series of corresponding points with the same geographical coordinates. Tianjin is one of the four municipalities in China. Since the year of 2018, it has had a permanent population of about 15.6 million, with an urbanization rate of 83.15%. There is no doubt that human activities have increased the demand for geological and water resources. In the evolution of urbanization, Tianjin has experienced large scale groundwater extraction, inducing serious land subsidence [26]. Land subsidence has been affecting Tianjin for the past 60 years. From the year of 1959 to 2008, the maximum subsidence value reached 3.22 m, and the affected area was almost 8000 km\({}^{2}\)[27; 28; 29]. Nowadays, Tianjin is still the city with the most serious ground subsidence in China [12]. Since the 1980s, the ground subsidence rate in Tianjin's urban area has been controlled at a relatively low value (\(-\)10 mm/year) by importing the Luan River to Tianjin and limiting the extraction of groundwater. However, the region of groundwater exploitation has expanded to the periphery following the economic increase since the 1990s, forming new suburban subsidence centers [29]. Although it has attracted the attention of the local government, the subsidence problem in the suburbs has not been fundamentally solved due to various reasons such as insufficient control measures. In order to understand the subsidence evolution in Tianjin in recent years, the goal of the paper is to (1) obtain and compare the remote sensing monitoring results of Tianjin ground subsidence using PS and SBAS methods; (2) analyze the characteristics of the latest spatial distribution of ground subsidence in urban and suburban areas and (3) put forward suggestions for the prevention and control of suburbs subsidence. This paper is organized as follows: Section 2 describes the materials and methods. It includes the description of the study area and the used datasets; the detailed steps of the PS and SBAS implementation; and the cross-comparison analysis method between the two MT-InSAR techniques. Section 3 discusses the main results of this study. Finally, the conclusions of this research are presented in Section 4.
# Study on the Thermospheric Density Distribution Pattern during Geomagnetic Activity
###### Abstract  
The atmospheric density of the thermosphere is a fundamental parameter for spacecraft launch and orbit control. Under magnetic storm conditions, the thermospheric atmospheric density experiences significant fluctuations, which have a negative impact on spacecraft control. Exploring thermospheric density during geomagnetic storms can help to mitigate the effects of such events. Research on the inversion of accelerometer measurements for different satellites and the variations of atmospheric density under extreme conditions is still in its infancy. In this paper, the distribution of atmospheric density during three geomagnetic storms is investigated from the inversion results of the Swarm-C accelerometer. Three major geomagnetic storms and their recovery phases are selected as case studies. The thermospheric density obtained by Swarm-C is separated into day and night regions. The empirical orthogonal function analysis method is used to study the spatiotemporal distribution of thermospheric density during geomagnetic storms. The results indicate that storms have a more significant impact on nighttime thermospheric density. The impact of magnetic storms on the temporal distribution of thermospheric density is considerable. The first-order empirical orthogonal function (EOF) time coefficient value on the day after the storm is the largest, reaching 2-3 times that before the magnetic storm. The impact of magnetic storms on atmospheric density is mainly reflected in the time distribution. The spatial distribution of atmospheric density is less affected by magnetic storms and is relatively stable in the short term. The impact of magnetic storms on the spatial distribution of nighttime thermospheric density is more significant than that of daytime regions, and the response of daytime regions to magnetic storms is slower.  
Swarm-C; atmospheric density; geomagnetic storm; satellite accelerometer; remote sensing; empirical orthogonal function +
Footnote †: journal: Applied Sciences
# A Compact, Low-Profile, Broadband Quasi-Isotropic Antenna for Non-Line-of-Sight Communications
## 2 Broadband Quasi-Isotropic Antenna Design  
### Crossed Dipole Antenna  
The basic configuration of the proposed antenna is composed of two electric dipoles that are oriented perpendicularly along the \(x\)- and \(y\)-axes, as shown in Figure 2. In this antenna, the electric dipole oriented along the \(x\)-axis has maximum radiation in the \(yz\)-plane and null in the \(xz\)-plane, whereas the electric dipole oriented along the \(y\)-axis has maximum radiation along the \(xz\)-plane and null in the \(yz\)-plane. Therefore, a combination of the two dipoles can produce a quasi-isotropic radiation pattern [33]. This type of configuration is also widely used in the design of unidirectional CP antennas, where the directional radiation pattern is obtained with the reflectors [35; 36; 37; 38; 39].  
Starting with this basic principle of the crossed dipoles, we design the proposed antenna, as shown in Figure 3. To achieve the quasi-isotropic radiation pattern using the crossed dipoles, the two electric dipoles necessarily have an equal amplitude and a 90\({}^{\circ}\) phase difference. Instead of using power dividers, feeding networks, or delay lines to meet the phase condition, we adopt phase delay curved lines, which are often referred to as vacant rings for the compact design [36]. CP performance can be achieved by choosing the proper dipole lengths [37]. Each half of the two perpendicular dipoles along the \(x\)- and \(y\)-axes are placed on the top and bottom of the substrate, Rogers RT/duroid\({}^{\circledR}\) 5880 (\(\varepsilon_{r}\) = 2.2 and \(tan\delta\) = 0.002), with a thickness (\(h\)) of 0.254 mm, respectively. The \(x\)- and \(y\)-oriented dipoles on the top layer are connected through the vacant ring, and those in the bottom are connected through another ring at the bottom, as shown in Figure 3a. In this case, the \(x\)- and \(y\)-oriented dipoles are connected perpendicularly. The antenna is fed at the center of the dipole by a coaxial cable through a hole in the dielectric substrate. To eliminate the current flowing over the outer jacket of the cable, it is surrounded by ferrite beads, as described in Figure 3b. The radius of the substrate close to the dipole arm length is selected.  
Figure 2: Basic configuration of the crossed dipole antenna. (**a**) \(x\)-oriented dipole and its radiation pattern. (**b**) \(y\)-oriented dipole and its radiation pattern.
# Anomaly Detection for Service-Oriented Business Processes Using Conformance Analysis
## 2 Literature Review
Our work can be seen as an extension of [14; 15] in which we presented the conformance in several depths for pinpoint identification of the fault, making it easier for business users to identify the root cause of the failure in process execution. In contrast to [13; 14], our work is focused on the generic implementation of anomaly detection using conformance analysis. The conformance lifeline technique allows for a further breakdown of process traces and allows analysis of the event log at any given stage of the process. [PERSON] et al. in [17] proposed a multi-stage method to identify anomalous traces and executions of event logs generated by process-aware information systems and a root cause analysis technique for anomalous traces. To detect deviations, the event log is converted into a sequential dataset and application of window-based and Markovian techniques are presented. The authors of [17] used decision tree classifiers to obtain the rules explaining the root causes of anomalous transactions, while in this work we used several model-related properties such as fitness score, misalignment and causality analysis to determine the cause of abnormality in the business process. Root cause analysis by enriching the process log and application of the classification techniques is presented in [18] by [PERSON] et al. Relevant information about root cause analysis is determined and the event log is enriched with this information. As for classification techniques, the J.48 classification tree is applied on an enriched log and data mining analysis is performed. In contrast, we solely used process mining techniques, such as conformance checking, to present RCA to business users which allowed the knowledge about the case abnormality to be extracted from within the event log. In another work [19], techniques based upon the mixture of process mining and machine learning are described. Security violations in the audit trail are identified using the \(\alpha\)-algorithm and any behavioural deviations from the identified model are classified as violations. The methodology focuses on the security domain in a business process environment, which differs from a services-related business environment, we also considered four process discovery algorithms to compete for the best model.
## 2 System Description and Problem Formulation
### Problem Formulation  
The aim of this study is to determine the VESS operation action required to manage WPG forecast uncertainty. Particularly, this study only deals with the WPG forecast error minimization problem. Therefore, the mean absolute error (MAE) is considered to be the performance metric of uncertainty management.  
The MAE is calculated as  
\[\mathcal{O}(\mathbf{a})=\frac{1}{T}\sum_{u\in\mathcal{U}}\sum_{t\in\mathcal{T}} \abs{e_{t}^{u}+q_{t}^{u}}=\frac{1}{T}\sum_{u\in\mathcal{U}}\sum_{t\in\mathcal{ T}}\abs{e_{t}^{u}} \tag{8}\]  
where \(\mathbf{a}=\left\{a_{1}^{1},\cdots,a_{t}^{u},\cdots,a_{T}^{U}\right\}\).  
Considering the VESS constraints, the VESS operation problem for managing WPG forecast uncertainty can be formulated as  
\[\begin{array}{ll}\min_{a}&\mathcal{O}(\mathbf{a})\\ \text{subject to}&(\mathbf{4})\text{ and }(\mathbf{7}).\end{array} \tag{9}\]  
If perfect information is known, such as the actual WPG properties about a future time, the problem in Equation (9) can be optimally solved using iteration-based search algorithms such as the gradient descent method and Newton method [32]. However, this non-causal assumption cannot be implemented in the real world. In this study, the solution of this problem using perfect information, including the future time, is used as the optimal solution that is compared to the performance of the proposed VESS operation strategy.