esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of Direct-Link Backhaul for UAV-Aided Emergency Networks
###### Contents  
* 1 Introduction
* 2.1 Introduction
* 2.2.1 The 2.2.2 The 2.2.3 The 2.3.1 The 2.3.2 The 2.3.3 The 2.3.4 The 2.3.5 The 2.3.6 The 2.3.7 The 2.3.8 The 2.3.9 The 2.3.1 The 2.3.1 The 2.3.2  
groups related to (i) the access network where the UABSs provide a connection to the users and (ii) the backhaul link between the UABSs and the core network (CN). Although several studies have addressed the access network [10; 11; 12; 13; 14; 15] as will be discussed in Section 2, limited work has been done on the backhauling.  
This paper proposes a backhauling architecture for a UABS network. Its performance is evaluated in a scenario of a real 3D model of Ghent city in Belgium. To this end, the capacity-based deployment tool described in [5] has been extended with the proposed backhaul architecture. To the best of the author's knowledge, a deployment tool for UAV-aided networks that accounts for backhaul connectivity in a realistic scenario does not exist. The novelty of this proposal is that the allocation of ground users is done simultaneously to the allocation of the backhaul resources, accounting for both capacity and power constraints.  
The remainder of this paper is organized as follows. In Section 2, a review of the state-of-art of UABS in emergency networks is presented for both the access and backhaul network. Section 3 outlines the role of the backhaul in these networks and proposes three scenarios that will be studied based on the methodology described in Section 4. Section 5 discusses the results of the simulation for a realistic scenario in the city of Ghent, Belgium. Finally, in Section 6, the paper is concluded and future work is presented.
# Modeling Volatility Characteristics of Epileptic EEGs using GARCH Models
## 4 Confidence Intervals for Half-Life (HL)  
To construct conventional two-sided confidence intervals, the standard errors for the half-life can be approximated using the delta method and using the large sample asymptotic properties of the estimated parameters. Using the delta method, the variance of a differentiable function of random variables, \(f(X)\), where \(X=(X_{1},X_{2},\ldots,X_{n})\), can be approximated as  
\[Var(f(X))=\sum\limits_{i=1}^{n}f_{i}\prime(\theta)^{2}Var(X_{i})+2\sum\limits _{i>j}f_{i}\prime(\theta)f_{j}\prime(\theta)Cov\Big{(}X_{i},X_{j}\Big{)}, \tag{5}\]where  
\[\theta=(E(X_{1}),E(X_{2}),\ldots,E(X_{n})),\]  
and  
\[f_{I^{\prime}}(\theta)=\frac{\partial}{\partial X_{i}}f(x)\bigg{|}_{x_{1}=\theta _{1,\ldots,x_{n}=\theta_{n}}}\]  
Using the equation for half-life, \(HL=\ln(0.5)/\ln(a_{1}+b_{1})\), for a GARCH (1,1) model, taking partial derivatives and substituting into the variance equation above yields the following equation for the standard error:  
\[\sigma(HL)=\frac{\ln(0.5)}{(a_{1}+b_{1})[\ln(a_{1}+b_{1})]^{2}}[Var(a_{1})+Var (b_{1})+2 Cov(a_{1},b_{1})]^{\frac{1}{2}}. \tag{6}\]  
Based on large sample asymptotics, the confidence interval for the half-life can be constructed as:  
\[HL_{est}\pm 1.96*\sigma(HL_{est}). \tag{7}\]  
Additionally, using the fact that the variance of the sum of random variables is the sum of the variances plus the sums of twice the respective covariances, confidence intervals can also be constructed for the sum of the coefficients. The 95% confidence intervals for large sample sizes can be constructed as:  
\[Z\pm 1.96*\sigma(Z). \tag{8}\]  
where \(Z\) is the sum of \(n\) random variables. The estimates for the upper and lower confidence limits can then be transformed using the half-life formula since it is a monotonic function in order to obtain a confidence interval for the half-life.
## 3 Materials and Methods
### Data Sources  
The main data sources were Landsat TM digital images (28 scenes) in the early 1990s and in 2004 and HJ-1 images (mini-satellite constellation for environment and disaster monitoring) (15 scenes) in 2012 covering the whole THR region (Appendix Table A1), which were used to acquire wetland spatial data. In order to reflect wetland vegetation growth, RS images acquired concentrating on July and August were used. A series of image processing steps were done in three time periods, such as single band extraction, false color composition, geometric correction, histogram equalization, images mosaic and segmentation. Images were geo-referenced using 1:50,000 topographic maps and a polynomial method. The root mean squared error (RMSE) of geometric rectification was less than 1.5 pixels (or 45 m). Finally, standard false color image data set was built based on county administration unit and using the false color band combination (_i.e._, RGB: 432 for all sensors) method.  
The other data source was meteorological data, including the monthly maximum air temperature, minimum air temperature, precipitation, relative humidity, wind speed at 10 m height and sunshine hours from 1970 to 2012. It was obtained from 43 national meteorological stations in China, including 18 stations located in the TRH region and 25 stations around the TRH region, all maintained by the Chinese Meteorological Administration (CMA) [20]. These measured monthly data were interpolated to individual 1 km resolution grid. Because of the rough terrain in the TRH region, the impact of topographic conditions in the interpolation of the meteorological data had to be considered. The ANUSPLIN version 4.2 software [21; 22; 23], proven to be appropriate for spatial interpolation of climate, was used to interpolate the meteorological data with the terrain elevation dataset. The terrain elevation dataset derived from the Shuttle Radar Topography Mission (SRTM) C-band is the world's first publicly available near-global, high resolution raster digital elevation model (DEM) [24]. Through using some meteorological station data, which are not used in the interpolation to assess the accuracy, the overall accuracy of the interpolation is more than 86% for all meteorological variables.
# Tensor-Based Approaches for Nonlinear and Multilinear Systems Modeling and Identification
## 5 Tensor-Based Approaches for Nonlinear Systems
### Block-Oriented Models
#### 5.5.2 Wiener Model  
It is the dual of the Hammerstein model, i.e., the FIR linear functional block \(l(.)\), with memory \(M_{h}\), comes before the nonlinear one, as illustrated on Figure 8. It allows taking sensor nonlinearities into account for instance.  
For this model, the output \(y(t)\) is given by  
\[y(t)= c(w(t))=\sum_{p=0}^{P}c_{p}w^{p}(t) \tag{54}\] \[=\sum_{p=0}^{P}c_{p}\big{(}\sum_{i=1}^{M_{h}}l_{i}u(t-i)\big{)}^{p}\] (55) \[=\sum_{p=0}^{P}c_{p}\sum_{i_{1}=1}^{M_{h}}\cdots\sum_{i_{p}=1}^{M _{h}}\prod_{j=1}^{p}l_{i_{j}}u(t-i_{j}). \tag{56}\]  
This model is equivalent to a Volterra model of order \(P\), whose the \(p\)th-order kernel is a rank one symmetric tensor defined as  
\[h^{(p)}_{i_{1},i_{2},\cdots,i_{p}}=c_{p}\prod_{j=1}^{p}l_{i_{j}}\,\ i_{j}\in \langle M_{h}\rangle\,\ j\in\langle p\rangle, \tag{57}\]  
or equivalently  
\[\mathcal{H}^{(p)}=c_{p}\underset{j=1}{\overset{p}{\circ}}\mathbf{1}, \tag{58}\]  
where \(\mathbf{1}=[l_{1},\cdots,l_{M_{h}}]^{T}\) is the vector of FIR coefficients.
## 4 GNSS-Based Location Context Profiling  
In this section, we present a specially designed pipeline to transform raw GNSS measurements into compact data structures that is adapted to deep learning models. Then we engineer an autoencoder model to perform location context profiling based on these data. We further evaluate the effectiveness of this profiling with a supervised task, i.e., localization accuracy estimation, and an unsupervised task, i.e., semantic analysis on location contexts.
# Numerical Analysis of Cyclic Impact Damage Evolution of Rock Materials under Confining Pressure
## 5 Conclusions  
Numerical simulation of cyclic impact tests on rock materials under confining pressure has been carried out by LS-DYNA using dynamic relaxation and full restart analysis. This analyzes the cyclic impact damage evolution of rock materials under confining pressure by defining damage variables with the crack density method. The conclusions are drawn as follows.  
1. Cyclic impact tests of rock materials under confining pressure can be simulated first by applying static confining pressure with a dynamic relaxation process, then conducting cyclic impact tests with full restart analysis.
2. In numerical simulation, the crack generation and propagation result in the failure of elements in the finite element model. Thus, the damage variable defined by the crack density method can be characterized by volume reduction.
3. The number of cyclic impacts before macroscopic fracture increases with the increase of confining pressure and decreases with the increase of the amplitude of the incident stress wave.
4. The cumulative damage of rock under confining pressure progressively increases with the number of cyclic impacts, and damage evolution exhibits three distinct stages: rapid rising, steady development, and sharp rising. A high incident stress wave leads to serve damage after the first impact and a small number of cyclic impacts.  
Under confining pressure, the damage of rock is alleviated due to the constraint effect on crack propagation. Large confining pressure results in minor damage and a large number of cyclic impacts.  
Conceptualization, P.Y.; Software, A.L.; Validation, Q.Z.; Formal analysis, P.Y.; Data curation, A.L.; Writing--original draft, A.L.; Writing--review & editing, P.Y. All authors have read and agreed to the published version of the manuscript.  
This work was funded by Anhui Provincial Natural Science Foundation (No. 2108085 ME156 and No. 1808085 QE148), The University Synergy Innovation Program of Anhui Province (No. GXXT-2022-020), China Postdoctoral Science Foundation (No. 2018M642504), and Natural Science Research Project of Colleges and Universities in Anhui Province (No. KJ2017A097).  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.
# Coexistence of 5G/6G and Digital Terrestrial Television Networks
## 5 Conclusions  
This paper has outlined potential challenges to and solutions for the seamless integration of emerging IMT 5G/6G systems with existing DTT networks within the 470-694 MHz UHF frequency band. The proposed solutions are designed in order to protect existing DTT broadcasting while facilitating the evolution of DTT networks and concurrently expanding frequency resources for the IMT services. The effective implementation of these solutions would lead to a mutually beneficial scenario for both broadcasting and mobile telecommunications. The solution presented, drawing on a case study conducted in Poland, can be easily applied in other countries within the ITU GE06 Agreement area (Europe, Africa, the Middle East, Iran, and former USSR countries), since the same frequency management regulations, techniques, and practical DTT implementations are used. For countries with long-term DTT exploitation plans, both alternatives are appealing. The first requires more administrative effort and engagement from adjacent countries and costly DTT network modifications, but it allows achieving a full duplex (uplink-downlink) IMT mode in a relatively short time (within a few years). However, for countries that want to switch off DTT in the next years, the first solution may be deemed unworthy of significant effort; hence, the second option will be most appealing.  
The feasibility of these approaches will rely on the choices made by countries, considering the current outcomes of the ITU-R WRC23 conference [32], which validate the potential for secondary utilization of the IMT systems within the DTT UHF frequency range in numerous countries. Regardless of the countries' decisions, the technical examination outlined in the paper provides valuable perspectives for national regulators and interested parties to contemplate for ensuring future proof and effective frequency management and spectrum efficiency. This is crucial for fostering the coexistence of 5G/6G services with DTT networks in the UHF band, ensuring that spectrum allocation advancements keep pace with technological growth and societal needs.  
Conceptualization, D.W.; methodology, D.W., I.M. and M.M.; software, M.M.; validation, D.W.; formal analysis, D.W.; investigation, D.W., I.M. and M.M.; resources, D.W. and M.M.; data curation, M.M.; writing--original draft preparation, D.W.; writing--review and editing, D.W.; visualization--M.M.; project administration, D.W.; supervision, D.W. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Data Availability Statement: Data are contained within the article. The authors declare no conflicts of interest.
#### Article
###### Abstract  
An exponential increase in mobile video delivery will continue with the demand for higher resolution, multi-view and large-scale multicast video services. Novel fifth generation (5G) 3 GPP New Radio (NR) standard will bring a number of new opportunities for optimizing video delivery across both 5G core and radio access networks. One of the promising approaches for video quality adaptation, throughput enhancement and erasure protection is the use of packet-level random linear network coding (RLNC). In this review paper, we discuss the integration of RLNC into the 5G NR standard, building upon the ideas and opportunities identified in 4G LTE. We explicitly identify and discuss in detail novel 5G NR features that provide support for RLNC-based video delivery in 5G, thus pointing out to the promising avenues for future research.  
random linear network coding; mobile cellular networks; 4G long-term evolution (LTE); 5G New Radio (NR); mobile video delivery +
Footnote †: journal: Information
# The Standardization Method and Application of the BIM Model for Interchanges
## 4 Model Depth Grading of BIM Components for Interchanges
### Model Granularity of BIM Components  
In traditional highway design, the design information of interchanges is described in separate drawings for each type of interchange. Both China and the USA divide the construction phases of highway projects and specify the main work content for each phase, as shown in Table 2.  
Figure 3: Interchange BIM components coding. According to the analysis in Table 2, there are commonalities between China and the USA in the division of road construction stages. Although the designation of each engineering stage is different, the requirements and contents of each stage are the same. Compared with the work content and the need for project information, \"planning\" and \"project development\" are the same as the \"feasibility stage\" and \"preliminary design stage\" of Chinese engineering projects. \"Detailed Design\" is equivalent to the \"Construction Design Stage\", with the main work being the detailed design of the project. Depending on the stage of the project, the content and description focus of the drawings vary. As for the definition of the engineering stage in China, too complex information about BIM components in the engineering stage will bring a lot of ineffective work and reduce efficiency, while too simple information about BIM components in the construction drawing design stage will not meet the demand of construction application. Therefore, this paper proposes the correspondence between the model depth level of BIM components and the engineering stages of design, construction, operation, and maintenance, based on the Chinese design specifications and guided by the information requirements of the design stage. With the continuous progress of engineering stages, the information demand of the BIM model changes, and there may be a situation where the actual depth level of the BIM model does not match the evaluation level. To solve this problem, the number of information covered by the BIM model, geometric expression accuracy level, and information depth should be judged at each engineering stage change node, with the information demand of the engineering stage as the judgment standard. For example, when the project is in the linkage between the preliminary design stage and construction drawing design stage, the model depth level required in the construction drawing design stage should be used as the basis to supplement and improve the current information to ensure that the information content change is synchronized with the engineering stage. However, in different design stages, the appointing party may put forward too high information requirements, resulting in unnecessary labor for the designers. To avoid this situation, designers and managers should communicate several times to clarify the actual project information requirements to ensure the effectiveness of the BIM model depth level evaluation at each stage.
# Dispensing of Prescribed Medicines in Swiss Community Pharmacies-Observed Counselling Activities
## 4 Discussion
### Strengths and Limitations  
The approach of the study to describe the dispensing practice of prescribed medicines at the counter was a non-participant observation, which is a useful way to study the quality of services and consistency of care [56]. The full pattern of the real-life situation, with all of the activities and interventions triggered by a prescription at the time of dispensing could be described, which forms a basis to improve these processes. In general, observations allow the description of customers' behavior and practice in real daily life [57], avoiding consequently the biases of self-report methods [46]. The observation method used in this study was based on ad-hoc note-taking of exchanged information and subsequent transcribing into quantitative information. This demonstrated that observation is a feasible method to provide valuable insight into pharmacies' activities. The documentation of the observed PEs has been tested for consistency and plausibility. The data was collected in 18 randomly selected pharmacies; however, the study was restricted to one region in Switzerland. The principal limitation was the presence of an observer, which could positively influence the counselling performance of the pharmacy staff by triggering them to be more aware of their way of approaching customers (the Hawthorne effect) [58]. To minimize this effect, the observer became accustomed to the pharmacy staff prior data collection to make them feel comfortable. Moreover,the observation lasted a whole working day, which allowed observation of normal practice over time. Simulated client methods, such as mystery shopping, could minimize observation bias, but present limitations of their own. The extracted information corresponds to a small part (snapshot) of healthcare practice only, and is therefore hard to generalize to other healthcare situations [59]. The observations were not recorded and not reviewed by a second investigator, which might have limited the reliability of the results.
## 3 Innovation
### Innovative Mapping and Forecasting Methods  
#### 3.3.1 Empirical and Mechanistic Approaches  
Remote sensing inversion techniques primarily encompass two methodologies: empirical and mechanistic approaches [72]. Empirical inversion methods focus on establishing linear or nonlinear models that exhibit a strong correlation with the parameters to be inverted, utilizing ground truth data from corresponding areas. However, these physical models are burdened with numerous parameters and heavily depend on prior knowledge of these parameters. Consequently, the precision of inversion is contingent upon this prior knowledge, which limits the model's generalizability and hinders its direct application in inverting parameters associated with obscure remote sensing mechanisms [75].  
In recent years, ML has gained extensive interest and application in the field of remote sensing due to its remarkable capacity for managing intricate nonlinear issues. By training on sample data, ML uncovers the relationships between inversion parameters or remote sensing products and various satellite data, thereby constructing inversion models that are both universally applicable and highly precise [76]. This method, to some extent, circumvents the complex processing and computational demands inherent in physical models. Furthermore, the use of consistent auxiliary data sources implies that the precision of the outcomes is not strictly reliant on the absolute accuracy of the data in ML-based remote sensing inversion. Instead, it significantly depends on the model's ability to accurately simulate the interplay between the target and auxiliary variables. Thus, ML models can also mitigate uncertainty arising from incomplete preprocessing of remote sensing data, including geometric, radiometric, and atmospheric corrections. In the context of remote sensing inversion, ML methodologies are well-suited to address challenges stemming from unclear mechanisms or complex processes [77].
## 4 Case Study  
### Case Study Context and Analysis  
In this section, we propose an empirical case study to support our conceptualisation and to provide an example of how network analysis methods can be exploited to analyse the teamwork dynamics of health teams and to improve the decision-making process.  
We collected data from 75 breast surgeries in the Breast Unit of an Italian University Hospital. All cases underwent breast quadrantectomy or breast mastectomies related to cancer problems and were chosen randomly. These surgeries tend to present quite a high level of task routineness [53], with the procedures being well defined beforehand.  
Every breast surgery is carried out by two to four surgeons, assisted by two anaesthisologists, one scrub nurse, and one assistant nurse. The surgeons involved in each surgery are planned in a weekly unit-planning meeting and are defined based on their availability and, mostly, on who followed the patient in the diagnostic phases.  
To study the surgical team composition and its influence on performance, we collected all the data related to the surgeons taking part in each surgery under investigation. Inthe end, we collected and analysed 212 surgeon distinct recordings for about 110 h of surgery observed.  
In addition, we also gathered the main features of the surgeries such as the duration, the size of the operating team, and the number of people present in the operating room.  
Finally, the surgical performance was evaluated through the occurrence of surgical glitches as reported by the surgical register, given that providing a safe and effective operation is the main goal of surgery and that a surgical glitch may have a highly relevant repercussion on patient health and on hospital expenditure.  
The details on network building and analysis are provided in Appendix A, while the main results of the investigation are presented in the following section.  
To summarise, the data collected were used to build a network composed of 75 surgeries, which constitute the nodes, and 1236 links meaning that two nodes are connected if at least one surgeon took part in both surgeries. Such a network is depicted on the right side of Figure 1.  
As reported in the Appendix A section, we obtained both the network of the individuals' interactions and the network of teams. We decided to analyse the latter since the Breast Unit is composed of a limited number of surgeons; thus, the network is not fully significant, as such a network has only 12 nodes. In cases where the staff is more numerous, it is possible to extend the analysis to the individual network.