esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 IFC Interoperability, a Literature Review
These comments were applied to SAP2000, Tekla Structures, and SCIA Engineering, the packages used as a reference. [PERSON] et al. [33] paid attention to the interoperability assessment of the BIM system in the structural domain, considering in particular cast-in-place concrete structures. Experiments with structural elements imported and exported through the IFC format were conducted twice, with a five-year gap between them. The results showed that in this period, an evolution of the performance in terms of interoperability occurred, but most of the major problems, such as the overlapping of structural parts, still persisted; such results applied particularly to Revit and TQS packages. A bidirectional-interoperability data exchange using IFC was conducted by [PERSON] and [PERSON] [34], which included architectural, structural, and MEP models to analyse issues in the process of data import and re-export between heterogeneous software such as ArchiCAD, Tekla Structures, and MagiCAD for Revit. The authors pointed out the different interpretations of objects from domains. The interoperability between architectural design and structural analysis was also investigated by [PERSON] et al. [12]. Their objective consisted of exploring BIM-based structural analysis through data transfer between different software, such as Revit, Robot Structural Analysis, SAP2000, and ETABS, via a direct link using a native file or API interface and a neutral link such as IFC. The authors found that IFC was unable to ensure a comprehensive transfer of the relevant data, such as the loads. [PERSON] [35] investigated the implementation of IFC schemas for the dynamic analysis of structures using structural-specific MVDs for both IFC 2x3 and IFC 4 and documented the loss of information in the CAD/CAE data exchange. [PERSON] and [PERSON] [13] provided a comprehensive interoperability assessment report testing IFC 2x3 for both architectural design and structural analysis involving CAD systems such as ArchiCAD, Revit, and AllPlan, and CAE systems such as RFEM and SCIA. After highlighting the issues already mentioned in the study previously recalled [30], they directed attention to the need to (i) focus the data exchange on multiple domain-specific building data schemas instead of on the integrated schema, and (ii) develop a new certification process based on the domain-specific building data schema. The above studies showed the issues related to the CAE environment as a receiving platform. In these cases, the interoperability of MVDs closely related to the structural design environment were tested, such as the SAV for the IFC 2x3 format. However, none of the studies reported in the relevant technical literature paid attention to the IFC 4 interoperability workflow, even though it is a standard dating back to 2013 and updated in 2018. Nevertheless, many software vendors claim the ability to also interoperate with the IFC 4 format.
# A Comprehensive Review of Mechanisms, Predictive Techniques, and Control Strategies of Rockburst
## 3 Technologies and Methods for Rockburst Research
### In-Situ Monitoring  
In-situ monitoring can accurately measure rockburst parameters in real-time, allowing for quick evaluation and prediction of rockburst danger. The most common monitoring methods include microseismic monitoring [86], microgravity [87], infrared thermal imaging [88], electromagnetic radiation [89], acoustic emission (AE) [90], and the photoelastic method [91]. Acoustic emission and microseismic monitoring can detect failure in the early stages, before total collapse and damage. Table 5 summarizes four non-destructive methods used in rockburst trials, including high-speed photography, which records dynamic information on rock ejection. Each method has unique properties and data processing mechanisms.  
Figure 3: (a) Rockburst intensity divisions based on different strength criteria (modified from ref. [71]). (b) Energy model of strainburst (modified from ref. [54]).  
Considering its advantages, such as real-time monitoring, wide detection range, big data scale, and no harm to production, microseismic monitoring is best for rockburst event monitoring. Figure 4a shows a typical mine microseismic monitoring system. It is mainly used for rockburst early warning. The time and location of microseismic events are analyzed from microfracture signals, and then the number of events, energy, apparent volume, and apparent stress are determined for rockburst prediction (Figure 4b). The precision of microseismic location meets mine requirements, but obtaining usable rules from huge amounts of data remains a challenge in microseismic monitoring [92].
# Handover Management for Drones in Future Mobile Networks--A Survey
## 5 Handover Challenges in Drone Networks
### High Mobility Speeds  
Controlling the movement of drones while in flight is one of the most challenging aspects of drone operation. Drone movement in the atmosphere is extremely complicated and difficult to control. The high mobility and arbitrary acceleration of a drone, for instance, causes sudden and instantaneous variations in the obtained signal frequency. The rapid variation in the received signal also increases the probability of handover and other mobility-related issues. Therefore, current handover techniques may not be sufficient for drones.
# QRLIT: Quantum Reinforcement Learning for Database Index Tuning
## 3 Related Work
### Classical Index Tuning Algorithms Using Reinforcement Learning  
Nowadays, there has been a significant contribution in the domain of index tuning, which plays a fundamental role in the efficacy of database searches.  
In [25], the authors synthesize the current state of the art of this subject, referring to various index optimization methods, including methods using reinforcement learning. The method COREIL [4] uses policy iteration as the algorithm, while SMARTIX [3] uses linear Q-learning. As an evolution of SMARTIX, the authors in [20] present an approach that corrects the implementation of the TPC-H benchmark [26], which involved the execution order of the queries being processed incorrectly, as it differed from that specified in the TPC-H documentation [27]. The methods NoDBA [5], [PERSON]'s DQN [6], DRLindex [7], MANFIS [8], and DRLISA [9] implement deep Q-networks (DQN) as algorithms; [PERSON]'s index advisor [10] uses the Sinkhorn Policy Gradient, while SWIRL [11] uses proximal policy optimization (PPO). BAIT [12] and AutoIndex [13] adopt Monte Carlo tree search (MCTS), and [PERSON]'s PPO-MC [14] uses proximal policy optimization-Monte Carlo (PPO-MC). Finally, DBABandit [15] and HMAB [16] use a technique called the multi-armed bandit (MAB) as an algorithm.  
The methods presented use a variety of approaches to solve the problem of automating indexes; however, they were designed for classical computers.
## 5 Design of Prediction Model
### CBR Submodel  
This technique is based on the selection of a subset of representative cases from the available history. As for the prediction of expected speed, the type of day and hour were identified as the two most important factors. From the available history of hourly aggregated values for the given _TMC segment_, such records are selected which concern the same hour for which the prediction is to be issued, which are also the hours of the same type of day.  
When selecting the same type of day, the model distinguishes between workdays and days off. As for workdays, it distinguishes each day between Monday to Friday, and as for the days off, it considers Saturday and Sunday. For less frequent types of days (Friday or Monday bank holidays... ), selection is made of a similar type of day, so that it is possible to find a sufficient number of examples in the history.  
Due to an insufficiently long history and the attempt to increase the number of representative cases, the original implementation considered workdays in the middle of the week (Tuesday, Wednesday, Thursday) as identical ones. However, this revealed a negative impact on the prediction accuracy as the development of traffic density is different even between these individual days.  
Prediction of speed upon passing a _TMC segment_ was subsequently calculated as the average value of hourly speeds out of all representative cases in the currently selected subset of historical data:  
\[P_{\text{CBR}}(hour)=\frac{\sum_{i=1}^{n_{h}}S_{i}\times\Phi(i,hour)}{\sum_{i= 1}^{n_{h}}\Phi(i,hour)} \tag{1}\]  
where  
\(P_{\text{CBR}}(hour)\) is prediction of average speed for a given segment at the \"hour\";  
\(N_{h}\) is number of records in history for the given _TMC segment_;  
\(S_{i}\) is average hourly speed from an i-_th_ record in the history;  
\(\Phi(i,hour)\) is Boolean function of similarity providing values of {0,1} depending on whether the i-_th_ record in the history is for the same hour and the day of the week as the \"hour\" the prediction is currently made for:  
\[\Phi(i,hour)=\left\langle\begin{array}{c}1\Longleftrightarrow(Hour(S_{i}) =hour)\wedge(Dayofweek(S_{i})=Dayofweek(hour))\\ 0\Longleftrightarrow otherwise\end{array}\right.\]  
where  
\(Hour(S_{i})\) is hour for which the speed \(S_{i}\) was measured,  
\(Dayofweek(S_{i})\) is type of day of the week for which the speed \(S_{i}\) was measured,  
\(Dayofweek(hour)\) is function providing the type of the day in the week for the hour for which the prediction is being calculated.  
The identification code for this submodel is _CBRBasic_. Besides the actual prediction, both the lowest and highest achieved speed in the current subset of all representative cases is provided in Prediction supplementary information, so that it is possible to infer how the actual speed can vary.
# Improving Spatial-Temporal Data Fusion by Choosing Optimal Input Image Pairs
## 3 Methods
### Pair Selection Strategies  
While STARFM can operate using either one or two Landsat-MODIS pair images, the \"two pair\" option requires both fine- and coarse-resolution high-quality imagery on two dates. For many cloudy areas, the two pair option criteria are hard to satisfy. The \"real-time\" forward prediction will not have the second pair until a bracketing clear Landsat image becomes available. Therefore, in an operational data fusion process, a \"one pair\" option is typically used [11; 23]. The question is how to select an appropriate image pair if multiple input pairs are available. Suppose we have two image pairs (\(t_{1}\) and \(t_{2}\)) available for the prediction date \(t_{p}\), and we can select either \(t_{1}\) or \(t_{2}\). Two automatic strategies are considered in the system to select the best image pair. One is to select the pair image that is closest to the prediction date (the \"nearest date\" strategy, or ND). The second is to select the date that has the higher correlation (HC) between the pair and the prediction date MODIS images.  
Figure 3 illustrates these two automatic selection strategies. The split date (\(t_{s}\)) can be determined by the different methods. Predictions before \(t_{s}\) (\(t_{p}<t_{s}\)) use the input image pair on day \(t_{1}\) as the pair image, while predictions after \(t_{s}\) (\(t_{p}<t_{s}\)) use the image pair from date \(t_{2}\) as the pair image. When the ND strategy is selected, the middle date between \(t_{1}\) and \(t_{2}\) is used as the split date (\(t_{n}\)). For the HC strategy, the split date is determined based on the Pearson correlation coefficient (\(R\)) (refer to Equation (4)) computed between coarse resolution MODIS images on the prediction date and the two possible MODIS pair images. In general, the correlation will decrease as the prediction date moves away from the pair date due to landuse and landcover change (such as vegetation growth and cultivation). There exists a date when the two correlation coefficients from two pair dates are close. This date is used as the split date (\(t_{m}\)) in the HC selection strategy. If the selected image pair is the same from two strategies (i.e., for prediction dates between \(t_{1}\) and \(t_{m}\), and between \(t_{n}\) to \(t_{2}\) in Figure 3), the data fusion results will be the same. The differences will occur for prediction dates between \(t_{m}\) and \(t_{n}\).
## 3 Results and Discussion
### Hierarchical Cluster Analysis (HCA) and Volatile Profiles Development
#### 3.3.2 Changes in the Concentration Profiles of Heterocyclic Compounds
E-6,10-dimethyl-5,9-undecadien-2-one, which presents a floral odor was another ketone that had a relatively stable concentration from S-0 to S-210 and disappeared at S-240. Moreover, E-1-(2,6,6-cyclohexadien-1-yl)-2-butten-1-one, that has a low aroma threshold value, underwent a significant increase at the post stage (S-210 to S-240, 3.23 to 4.67 \(\mathrm{\SIUnitSymbolMicro g}\)/g) and could be acting as an aroma-active compound for NCS. Meanwhile, 2,6-bis(1,1-dimethyl)-4-hydroxy-4-methyl-2,5-cyclohexadien-1-one was identified in the initial stage and increased to the maximum value at S-120 (10.66 \(\mathrm{\SIUnitSymbolMicro g}\)/g), followed by sharp decrease to zero at S-150, and then formed in the final product in a very small amount (0.6 \(\mathrm{\SIUnitSymbolMicro g}\)/g). These fluctuations could be due to their roles in the Maillard reaction as they are derived from sugars through Amadori rearrangement, and continually participated in the reaction with amino compounds forming MRPs [30]. Finally, 2-nonen-4-one, cyclopent-4-ene-1,3-dione are the two ketones developed in the post dehydration stages, and their production has been reported to be related to glucose [31; 32].
# Temporal Instability Analysis of Injury Severities for Angle and Non-Angle Crashes at Roundabouts
## 6 Results and Discussion
### Predictive Comparisons (Prediction Simulations)  
The summary of marginal effects in Table 11 showed a significant shift in the impacts of explanatory variables on injury severity across periods. Furthermore, it would be interesting to understand the effect of observed changes on the probability of injury severity. For example, use the parameters estimated from the model based on 2012-2013 to estimate the probability of a crash injury in 2018-2019 and compare the differences between predicted results and actual observations. Out-of-sample prediction is an effective way to achieve the goal [63]. It is important to note that variance must be considered for random parameter models. Otherwise, it will lead to biased estimates [31]. This study used different periods as the base year for prediction. Table 7 presents the results of the comparative analysis. The outcomes indicated that the probabilities of minor and severe injuries were overestimated when using the angle collision parameters of 2012-2013 to predict the injury probabilities of 2014-2015 and 2016-2017. However, severe injuries were underestimated by 0.0232 in the 2018-2019 prediction model. Regarding the out-of-sample prediction based on the 2014-2015 model, the probability of severe injuries was overestimated, while no injuries were underestimated for target periods 2016-2017. The predicted results for 2018-2019 were just the opposite. As for the 2018-2019 prediction model based on 2016-2017, the probability of severe injury was underestimated by 0.0170, while the probability of no and minor injuries was overestimated by 0.0149 and 0.0021, respectively. That is, if the explanatory variables were the same for every crash that occurred in 2018-2019, the 2016-2017 estimated parameter predicted crash injury severity would be less severe than it actually was. Figure 3 exemplifies the distribution of predicted probability differences for non-angle collisions (2012-2013 model for 2014-2015 prediction and 2014-2015 model for 2016-2017 prediction). According to Table 7, the model based on 2012-2013 underestimated the severe injury and no injury while overestimating the minor injury for target periods 2014-2015 and 2016-2017. In contrast, the probability of severe injury was overestimated by 0.0040 in the predicted period of 2018-2019. The estimated results of the 2014-2015 model for the subsequent two target periods were different. Severe injuries were underestimated in the 2016-2017 period (\(-\)0.0162), while the opposite was true in the 2018-2019 period (0.0048). The 2016-2017 model underestimated no injuries and minor injuries in the target period prediction (\(-\)0.0098, \(-\)0.0090) but overestimated the likelihood of severe injuries by 0.0188. Overall, the results of the predictive simulations provide strong support for the temporal instability of angle and non-angle collisions.
# Reduction of Satellite Signature Effect in High-Accuracy Satellite Laser Ranging to Etalon
## 1 Introduction  
Ever since Etalon was launched in 1989, it has been dedicated to satellite laser ranging to support various scientific applications [1]. Observation data from Etalon are useful for analyzing the accuracy of terrestrial reference frames, modeling long-term disturbances, and conducting other geophysical studies [2]. As compared to LAGEOS satellite, Etalon satellite has a much higher orbit and is less affected by atmospheric drag. The variations in Earth's harmonic term at the orbital intersection are about one tenth of those observed by LAGEOS, which implies that Etalon might provide more reliable data for geodynamic research and analysis [3].  
KHz satellite laser ranging has led to the flourishing application of research for Etalon satellites [4]. The new set of measurements presents new challenges for the post-processing of range measurements, as well as providing additional insights into the sources of potential errors. Satellite signature effect is one of the main sources of error in satellite laser ranging, which results in biases ranging from millimeters to centimeters [5, 6]. Various pro-processing strategies have been developed to reduce the satellite signature effect on SLR products. In 2008, [PERSON] et al. selected only the leading range measurements of LAGEOS-1/2 to improve the accuracy of normal points less than one millimeter [7]. In 2015, [PERSON] and colleagues introduced the concept of the leading range filter, which retains only 10% of the overall range residuals as useful echo signals [8]. In 2017, [PERSON] proposed the use of tight clipping to improve the quality of LAGEOS observation data [9]. Prior algorithmsattempted to minimize the satellite signature effect by modifying the acceptable return level according to empirical formulas or mathematical analysis with fixed values for center of mass corrections. Unfortunately, these methods fail to take into account the variations in satellite signature effects on the various reflections. As a result, range bias due to variations in the center of mass corrections cannot be eliminated.  
In this paper, we simulate the reflected signals from Etalon and calculate the variations of the center of mass corrections at different incident angles on the basis of structural analysis. A quantitative assessment of the association between rejection criteria for returns and RMS of center of mass corrections is performed by taking into account the residual bias caused by variations in center of mass corrections. The normal point algorithm is then modified to incorporate a center of mass correction filter to minimize the effect of satellite signatures on the post-fitting range residual distributions. In this new algorithm, we intend to reduce the variability inherent in long arcs of observation data, and improve the stability and accuracy of the results obtained from these observations.
# Reconfigurable Formation Control of Multi-Agents Using Virtual Linkage Approach
## 4 Simulation and Results
### Parameters Analysis  
In this section, two simulations are presented to illustrate the effects of the number of agents and number of virtual links. In the first part, five agents are required to perform the same formation moving task as in Section 4.1. In the second parts, nine agents are designed as a virtual linkage which consists of three virtual links to perform the same simulation task as in Section 4.2.
## 3 Method
### Control Schemes  
The purpose of the control schemes is to arbitrate input from the user, received through the ITCI, with the information from the computer vision module, i.e., what object to grasp and how to grasp it, in order to actuate the upper limb exoskeleton. Parts of the control of the exoskeleton will hence be automated, which is why some of the control schemes are referred to as semi-autonomous control.  
Three different control schemes are implemented and tested against each other:  
* **Non-Autonomous Control** --The system offers no assistance at any point and the input from the computer vision module is ignored. The exoskeleton is manually controlled by the user at all times.
* **Fixed Semi-Autonomous Control--A** fixed level of autonomy is used where the system will take over control of the exoskeleton when the user presses and holds the \"auto grasp\" button in the ITCI layout (Figure 2c). While doing so, input from the computer vision module guides the hand of the exoskeleton towards the most likely object to grasp.
* **Adaptive Semi-Autonomous Control--The system will at all times assist the user in controlling the exoskeleton. The level of autonomy is adapted based on a confidence measure related to the certainty of the intent prediction from the computer vision module. A high certainty of the predicted intention being correct will result in a high confidence and the system will provide more assistance. In low-confidence scenarios, the opposite is true, and the system will provide little to no assistance. If the user does not activate the tongue interface the system does not move. The \"auto grasp\" button does nothing in this control scheme.