esa-sceva/satcom-chunk-collection huggingface dataset:
# Benefits of Advanced Air Mobility for Society and Environment: A Case Study of Ohio
## 5 Cost Factors  
Our cost analysis is based on the findings of the CCO report [31]. This study has utilized over 100 sources of information in estimating capital investments and operating expenditures. They, first, categorized the cost factors into three groups: (i) Ground infrastructure, (ii) Systems for Providers of Services for Unmanned Air Vehicles (PSU), and (iii) Vehicles.  
Then, they estimated the corresponding Capital Expenditure (CAPEX) and Operating Expenditure (OPEX) for the first two categories and the acquisition and maintenance cost for the third category. CAPEX will be used for the feasibility study of the planning, ground site, environment analysis, power grid, communication networks, and others.  
The initial CAPEX is needed for land acquisition, site construction, building infrastructure, passenger amenities, safety facilities, and many more. In addition, upgrading existing facilities (e.g., helipads, helipords, and airports) to support eVTOLs' landing, refueling, recharging, and parking is also considered as a part of CAPEX. Some PSU facilities go under the CAPEX section; for example, purchasing different kinds of sensors, navigation beacons, radars, computers, and other types of equipment are included as a part of CAPEX.  
As we are conducting our analysis from the perspective of the state government of Ohio, we excluded the costs associated with the vehicle category since these costs will be incurred by the AAM operators, not by the government. Thus, in our analysis, we included the first two types of costs, which are presented in Figure 3. Though the CAPEX and OPEX costs could be funded by state initiatives, private sector investment, or a combination through public-private partnership models, we analyzed the worst-case scenario in our study, assuming the government will fund all these costs. If other entities fund these costs in a partnership with the government, the profit will eventually go up.
## 2 Zone-Based Registration Scheme
### Type I: Serial Shape
#### 2.1.1 Case 1: Moving inside the Three Zones after Forming a Serial-Shaped RA  
Four states can be defined, as shown in Figure 2 when the UE travels without a separate registration process within the RA after configuring a serial-shaped RA. Figure 5 shows the transition probability to a feasible state, the zone number to be paged, and the number of paging procedures needed when an incoming call arises after changing to each state. To illustrate this point, the transition case from state \(E_{21}^{I}\) to other states is described as follows.  
(_i_) \(I_{21}^{I}\): The UE moves from 3\(\rightarrow\)2\(\rightarrow\)1 and Network_DB has \(\left|\frac{1|2|3}{2}\right|\). When the implicit registration is processed after the UE moves from zone 2 to zone 1, the Network_DB will not change. If an incoming call arrives before moving to another zone with a probability of \(1-m\), one paging procedure is performed to find the UE in zone 1.  
(_ii_) \(E_{12}^{I}\): UE can move to state \(E_{12}^{I}\) if it returns to zone 2 with a probability of \(\theta m\). When an incoming call arrives while the UE is staying in zone 2, the first zone 1 is selected from the Network_DB = \(\left|\frac{1|2|3}{2}\right|\) As a result, there is no response. Thus, an extra paging procedure is required to find the UE in zone 2.  
(_iii_) \(E_{21}^{I}\): The probability that the UE will move to another zone and then transit to state \(E_{21}^{I}\) constituting a new serial-shaped RA (Type I) is \(3(1-\theta)m/5\). To connect the incoming call after shifting to \(E_{21}^{I}\), one paging is processed in the new zone where the UE has moved.  
(_iv_) \(E_{21}^{II}\): The probability that the UE can transit to state \(E_{21}^{II}\) is \(2(1-\theta)m/5\), thus constituting a new cluster-shaped RA (Type II). One paging is needed to find the UE while remaining in the new zone.  
Similarly, for states defined in Type I, changeable states, transition probabilities between states, paging zones, and the number of paging procedures can be obtained, as shown in Figure 5.  
Figure 4: Moving inside \(3Z\) after call occurs in zone 2 for Type I.
# Systematic Review on Individualized Versus Standardized Parental Nutrition in Preterm Infants
## 4 Discussion
### Recommendations  
4.5.1. Recommendation 1  
Standard PN Solutions (SPN) Should Generally Be Used over Individualized PN Solutions (IPN) in the Majority of Preterm Infants, Including VLBW Premature Infants (LoE 2-, GPP, Conditional Recommendation).  
4.5.2. Recommendation 2  
Individually Tailored PN Solution should Generally Be Used When the Nutritional Requirements Cannot Be Met by the Available Range of Standard PN Formulations (i.e., in Very Sick and Metabolically Unstable Patients Such as Those with Abnormal Fluid and Electrolyte Losses; and in Infants and Children Requiring PN for Prolonged Periods Such as Those with Short Bowel Syndrome (LoE 2, RG B, Strong Recommendation for) [3].  
It has been estimated that the majority of preterm infants receiving PN via central catheters may actually be treated by SPN, although, the optimum composition is not known [37; 38]. SPN can improve PN efficiency and compliance with guidelines. No significant harmful effects have been found by the present systematic review. Therefore, the present review reconfirms the 2018 ESPGHAN, ESPEN, ESPR, and CSPEN recommendation that SPN solutions should generally be used over IPN solutions in the majority of pediatric and newborn patients, including VLBW premature infants [3].  
4.5.3. Recommendation 3  
Adequately Powered Randomized Controlled Trials Based on Up-To-Date Parenteral Nutrition Recommendations Are Required to Evaluate the Real Clinical Benefits of SPN vs. IPN (LoE 1, GOR A, Strong Recommendation).
# Enabling Technologies for Urban Smart Mobility: Recent Trends, Opportunities and Challenges
## 5 Enabling Technologies to Support Smart Mobility
### Role of Enabling Technologies in Smart Mobility Services and Applications
#### 5.2.1 Role in Mobility-as-a-Service  
In MaaS systems, a central operator made available through an intermediate layer of the system becomes essential to manage all communication between transport companies, passengers and other stakeholders of the system. In [125], the authors propose a blockchain-based solution that eliminates this intermediate layer, promoting trust and transparency among all stakeholders and eliminating the need for commercial agreements between the various MaaS agents. In [126], easy, quick and trusted transactions are covered, using AI and blockchain-enabled smart contracts.  
The use of geospatial technologies has big applicability in MaaS solutions, bringing added value to the entire system and associated services. One example is the monitoring of commuters. In [127], the authors exploit Multi-access Edge Computing (MEC) to propose a MaaS solution that collects and processes data related to the mobility of commuters in order to be able to recommend optimized routes taking into account the needs and preferences of each commuter. Another study [128] also uses MEC, IoT messaging protocols and virtualization (i.e., digital twins) in the proposal of a framework that collects data about commuters in order to feed the monitoring of mobility by the transport planning solutions.  
The use of Big Data in MaaS systems is present in the aforementioned solutions that collect and process data from commuters [127; 128]. A specific big data architecture is presented in [129], motivated by the specific requirements of mobility analytics.  
With regard to clean energy, the entire concept of MaaS recommends the progressive transition from private to public and shared transport, including car-pooling and car-sharing. This paradigm shift will bring with it a consequent reduction in vehicles and the emission of CO2 into the atmosphere. In [130], the authors present a study of when the transition to MaaS could happen on a larger scale and what impact it will have on the choice of forms of mobility.
# Predicting Traffic Sign Retro-Reflectivity Degradation Using Deep Neural Networks
## 5 Results and Discussions
### Predicting Useful Life of Traffic Signs Based on the Degradation Model  
It is understood that using the system for predicting the useful life of traffic signs is a more practical use-case for the end users and practitioners. Accordingly, in this section we present an approach to predicting the useful life of traffic sign using the trained machine  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Study** & **Dataset Size** & **Model** & \(R^{2}\) \\ \hline [PERSON] et al. [6] & 5722 & Linear model & 0.500 \\ [PERSON] [12] & 1107 & Neural networks & 0.81 \\ [PERSON] et al. [15] & 1047 & Linear model & 0.481 \\ [PERSON] et al. [9] & 6000 & Linear model & 0.520 \\ [PERSON] et al. [24] & 525 & Linear model & 0.444 \\ [PERSON] et al. [1] & 487 & Linear model & 0.570 \\ [PERSON] et al. [25] & 1600 & Linear and non-linear models & 0.67 \\ \hline Present Study & 426 & Deep neural networks & 0.976 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Model performance with reference to previous studies.  
Figure 6: The result from running the permutation feature importance test on the dataset.  
learning system. In order to achieve this objective, we predict the retro-reflectivity values of sign boards at different ages and look at the age that predicts the minimum acceptable retro-reflectivity value. This age is accepted to be the useful life of that traffic sign. In Figure 7a-c, we illustrate the idea with the help of plots from three different traffic sign instances from the test set, each with a different color. We plot the predicted retro-reflectivity values at various ages denoted by the green color. In addition, the actual values are also plotted using the violet color. We can observe from the plots that the predicted values from the system are closely aligned to the actual values, which demonstrates the effectiveness of the system in learning the retro-reflectivity degradation function. Furthermore, looking at the age where the traffic signs reach the minimum retro-reflectivity values, we can see that the system predicts useful life of 17.36, 14.58, and 15.06 years for the traffic sign represented by the plots (a), (b), and (c), respectively. These values are quite reasonable if we compare these values to the recommended useful life of traffic signs (cf. [33]). It should be noted that we use the minimum retro-reflectivity value for white = 30 (cd/lx/m\({}^{2}\)), green = 7 (cd/lx/m\({}^{2}\)), and yellow = 15 (cd/lx/m\({}^{2}\)) as suggested in literature (cf. [34; 35]).We also notice from the plots that the learned function is not entirely linear across all the age spectrum. For higher retro-reflectivity values, the degradation seems to be close to a linear function but later the rate of degradation slows down.  
Figure 7: _Cont._
# Support Vector Machine-Based Transmit Antenna Allocation for Multiuser Communication Systems
## 2 System Model and Conventional Optimization Approach
### Optimization-Driven Solutions  
To solve the formulated combinatorial optimization problem in Equation (4), a brute-force exhaustive search (or any other more sophisticated optimization-driven algorithms) with high computational complexity can be applied to find \(l^{*}\) that maximizes \(R_{\text{sum}}\). First, the data rates of \(U\) users, i.e., \(R_{i}(\mathbf{s}_{l})\), \(i\in\{1,\cdots,U\}\), are computed by using Equation (2) for all the \(L\) antenna allocation schemes. Then, the sum rate can be computed based on (3). Among the antenna allocation schemes, the optimal index \(l^{*}\) that maximizes the value of \(R_{\text{sum}}\) is determined. For each antenna allocation scheme, the exhaustive search algorithm traverses all \(U\) users to compute the data rate, where \((U-1)\) computations are required to calculate the total interference term. Thus, the computational complexity of this exhaustive search is given by \(\mathcal{O}(LU(U-1))=\mathcal{O}\left(\frac{U^{2}N_{l}!}{(N_{l}-U)!}\right)\). It can be seen that the complexity increases and become prohibitively large as \(N_{l}\) or \(U\) increases.  
For better readability, the main notations used here to describe communication systems are summarized in Table 1.
# FBG-Based Monitoring of Geohazards: Current Status and Trends
## 3 Development of FBG-Based Geohazard Monitoring Systems
### Development of FBG Pressure Sensors  
Besides displacement sensors, FBG can be pressure sensors for measuring earth pressure or pore water pressure. [PERSON] et al. developed innovative pressure transducers using FBG strain sensors [52]. They were used for performance evaluation of pavements and weigh-in-motion measurement. Three methods for bonding the FBG to the transducer diaphragm were considered. Owing to the disc shape of the bulkhead, the FBG sensor is designed to be installed circumferentially so that the optical spectrum of the sensor will show a clear peak. A self-temperature compensated chirped FBG pressure sensor was developed by [PERSON] et al. [53]. They demonstrate through laboratory calibrations that this sensor can perform timely and accurate measurement of pore water pressure over a wide range of temperatures. On the basis of indirect sensing using epoxy resin-embedded FBG sensors, [PERSON] et al. developed a pressure sensor with a resolution of 0.66 kPa over a measurement range of 300 kPa, which was suitable for measuring pore water pressures in soil [54]. A similar sensor, but instead encapsulated in a softer material of high Poisson's ratio, was devised by [PERSON] et al. with the advantage of enhancing the longitudinal strain induced in the fiber [4]. [PERSON] et al. measured the pore pressure profile in boreholes using FBG sensors and evaluated the stability of rainfall-induced deep landslides [55] (Figure 5). [PERSON] et al. introduced the application of FBG mini-osmometer for measuring the pore water pressure of the main pumping aquifer in the field [56]. Compared with piezometers, the FBG sensors can be serially connected to capture the distribution of pore water pressure with respect to depth.
###### Abstract  
An accurate estimation of the state of health (SOH) of lithium-ion batteries is essential for the safe and reliable operation of electric vehicles. As a single hidden-layer feedforward neural network, extreme learning machine (ELM) has the advantages of a fast learning speed and good generalization performance. The bat algorithm (BA) is a swarm intelligence optimization algorithm based on bat echolocation for foraging. In this study, BA was creatively applied to improve the ELM neural network, forming a BA-ELM model, and it was applied to SOH estimation for the first time. First, through Pearson and Spearman correlation analysis, six variables were determined as the input variables of the model. The actual remaining capacity of the battery was determined as the output variable. Second, BA was used to optimize the connection weights and bias in ELM to construct the BA-ELM model. Third, the battery data set was trained and tested with BA-ELM, ELM, Elman, back propagation (BP), radial basis function (RBF), and general regression neural network (GRNN) models. Five statistical error indicators, and the radar chart, scatter plot, and violin diagram were used to compare the estimation effects. The results show that the evaluation function of BA-ELM can converge quickly and effectively optimize the network model of ELM. The RMSE of the BA-ELM model was 0.5354%, and the MAE was 0.4326%, which is the smallest among the 6 models. The RMSE values of the other model were 2.27%, 3.53%, 3.07%, 3.86%, 3.24%, respectively, indicating the BA-ELM has good potential for future applications.  
state of health; extreme learning machine; bat algorithm; Pearson and Spearman correlation; actual remaining capacity; neural network +
Footnote †: journal: Applied  
Article
# Two-Dimensional Shape Analysis of Complex Geometry Based on Photogrammetric Models of Iconostases
## 4 Method for 2D Shape Analysis Based on Photogrammetric Models
The complete workflow consists of several main steps that contribute to the quality of the final 2D visualization:  
* Point cloud preparation from mesh, which implies converting the mesh back to a new point cloud, to produce more homogenous point data of the photogrammetrically reconstructed complex geometry. The mesh was obtained on the basis of the originally generated point cloud. * Adding ambient occlusion filters to the point clouds for the enhancement of depth information and better visualization of small details. * Generating height maps (DEMs) and converting object coordinates to the scalar field in order to maintain information on object depths when segmenting its parts. * Segmenting points by certain values of the verticality geometric feature to identify the boundaries between surfaces of different scales and extract sections. * Contour detection and working with point clouds in CAD environment. Such a method has allowed for the detection of exact points defining the contours of the main iconostasis' structural elements which were further used in the shape analysis. In the third phase, 2D shape analysis techniques were investigated to achieve quantitative data regarding the geometric characteristics of the particular iconostasis elements. The goal was to mathematically describe and classify the shape characteristics of the iconostasis' main architectural elements, concerning the artistic period to which it belongs to. The idea driving the experimentation was to design the appropriate shape measurements for the numerical evaluation of the considered shape features. For that purpose, the shape measurements were designed to reflect the main attributes of the real shapes. Furthermore, for numerical characterization of such shape attributes, the shape descriptors were applied to the particular 2D contours extracted from the digitized 3D models. A combination of 2D global shape descriptors was used to determine the relations between shape characteristics of the iconostases and their style origin, with the assumption that they will indicate the differences between iconostases from different periods of origin. The application of the shape descriptors was performed by retracing the detected contours inside a CAD environment and applying the shape measurements to them. This led to achieving quantitative information of the geometric characteristics of the particular iconostasis elements. The shape analysis method is divided into two main steps:  
* designing shape measurements to characterize the main attributes of the observed shapes and
* applying 2D shape descriptors to the retraced contours to numerically describe and classify the main structural elements of the iconostases. The main steps of the proposed methodology are explained in detail in Sections 4.1-4.3.
## 3 Results and Discussion
### Principal Component Analysis-PCA  
The analyses concerning the effect of the application of microbial preparations in winter wheat cultivation on the SPAD index, photosynthesis, grain yield, protein content in grains and the N-NO\({}_{3}\); N-NH\({}_{4}\); and P contents in the soil were supplemented by the determination of correlations between the above-mentioned factors (Figure 8). To this end, the principal component analysis (PCA) method was used to determine the links (the strength and direction of the correlation) between the measurement variables. The analysis showed a strong correlation between the leaf greenness index (SPAD) as well as photosynthesis and the protein content in wheat grains during both development stages (BBCH\({}_{31}\) and BBCH\({}_{51}\)). The N-NO\({}_{3}\), N-NH\({}_{4}\) and P contents in the soil were also strongly correlated with the grain yield.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Year** & **NPK** & **NPK + PA *** & **NPK + BM ** ** & **NPK + BS *** & **NPK + PA + BM + BS** \\ \hline
2017 & 7.78 \(\pm\) 1.34 b-e & 6.74 \(\pm\) 0.84 de & 8.73 \(\pm\) 1.24 bed & 7.99 \(\pm\) 1.52 b-e & 11.84 \(\pm\) 0.24 a \\
2018 & 5.91 \(\pm\) 0.18 e & 6.14 \(\pm\) 0.35 e & 8.54 \(\pm\) 1.62 bed & 7.26 \(\pm\) 0.18 b-e & 9.24 \(\pm\) 1.28 b \\
2019 & 6.89 \(\pm\) 0.09 cde & 7.39 \(\pm\) 0.46 b-e & 8.56 \(\pm\) 0.65 bed & 9.03 \(\pm\) 0.38 bc & 11.77 \(\pm\) 0.48 a \\ \hline \end{tabular}  
* PA—_Parenhacillus zaroofiuzs;_** BM—_Bacillus megaterium;_** BS—_Bacillus subtilis._ Values followed by the same letters do not differ significantly in [PERSON]’s (HSD) test (_p_ < 0.05).  
\end{table}
Table 8: P content in the soil after application of microbial preparations (mean value and standard deviation), interaction between the years and treatments.  
Figure 7: P content in the soil after application of microbial preparations (mean value and standard deviation), (**a**) average for the years, (**b**) average for treatment. Values followed by the same letters do not differ significantly in [PERSON]’s (HSD) test (_p_ < 0.05). PA—_Parenhobacillus azofioxins_; BM—_Bacillus megaterium;_ BS—_Bacillus subtilis._
## 3 System Model and Security Goals
### Security Goals
#### 3.3.1 Key Exchange and Mutual Authentication  
In the hospital network, very sensitive data are exchanged between several nodes and users. Users can access critical data remotely. Mutual authentication between remote users and servers is required for secure communication. Remote users exchange symmetric keys  
Figure 1: Smart hospital environment.  
and then perform authentication to avoid the anonymity of the users. The mutual remote user authentication process blocks unauthorized access to the network and data.
# Monitoring the Dynamics of Ephemeral Rivers from Space: An Example of the Kuiseb River in Namibia
## 3 Data Used for This Study
### Sentinel Missions
#### 3.1.2 Synthetic Aperture Radar (SAR) from Sentinel-1
The different pre-processing steps are shown in Figure 2.  
The various processing steps applied to the SLC, and therefore the phase (Figure 2), resulted in interferograms and coherence maps. An interferogram is obtained by the phase difference of two SLC images, a master image and a slave image, while coherence is a measure of the quality of the interferogram due to the complex correlation between a master image and its slave image. Coherence values are between 0 and 1, depending on the degree of correlation between the master and slave images, with a value of 1 implying perfect coherence between the two images and zero corresponding to a change in the state of the ground between the two acquisitions, thereby leading to a total loss of coherence.