esa-sceva/satcom-chunk-collection huggingface dataset:
# Sensors and Communication Simulation for Unmanned Traffic Management
## 4 Agent-Based Simulation in a Distributed Architecture
### Simulation Implementation: A Microservice-Based Architecture  
The simulator is implemented within a microservice-based architecture that allows encapsulating the behavior of each type of agent in different microservices, so that the behavior of one agent can be modified without affecting the rest. In addition, the architecture is easily extendable as new agents could be added later simply by adding new microservices to the system. Microservices can be grouped in five different group based on their functionality: modeling services, pre-simulation services, simulation services, support services, and evaluation services. Thus, the operation of the simulation platform at high level is as follows:  
In scenario definition time, by the simulator user through the simulator HMI (human machine interface):  
1. Define a series of models for each actor.
2. Generate a scenario that determines all the actors involved in it and their configuration or model. In simulation time, by the simulator platform itself:
3. Generate instances that simulate those agents from the selected model.
4. Agents' instances would interact autonomously among them sending the needed information to the UTM system and receiving information from it.  
Additionally, an interface with evaluated UTM service microservice is available which will enable networked communications between the evaluated UTM platform and the simulator. Both platforms are expected to be deployed within the same network so that delays are minimized. This service acts as a proxy between both platforms allowing messages exchange by translating data formats and communication protocols. UTM-bound messages will include authorization requests, track information from sensors, or telemetry information from drones whereas simulation-bound messages consist of UTM outgoing tracks with alerts and contextual information.  
It is expected that each UTM system will require developing a compatible interface service. However, this interface decouples the simulator platform and the UTM platforms and therefore, there is no need to modify them synchronously. This microservice also collects UTM output information so that it can be analyzed within the multiple evaluation services, which in turn can also access the actual information from the simulation services. The complete simulator architecture is depicted in Figure 2.  
This architecture was implemented using state of the art technologies for microservices architectures. Node.js was used as the runtime environment for all microservices, which were coded using Typescript. On top, Express web framework was used to implement REST (representational state transfer) communication between microservices, whereas Redis allowed implementing both communications buses. Persistent information is stored using MongoDB, a document-oriented database. Furthermore, microservices deployment were orchestrated using Docker virtualization containers.
## 2 Foundation of NMM  
This section only introduces the basic concepts of NMM, and more details can be found in reference [30]. In order to solve continuous and discontinuous problems in a unified framework, NMM applies two covers, namely the mathematical cover (MC) and the physical cover (PC) [30]. In this manuscript, MC will be constructed by mathematical mesh of a regular segment.  
In NMM, MC is composed of a series of simply connected small domains, each of which is named as a mathematical patch (MP), consisting of all the elements or intervals sharing the same node. For example, in Figure 1, the one-dimensional problem domain \(\Omega\) is divided into five intervals, each of which has two nodes. M1 (the red circle) and M3 (the purple circle) are MPs. It is noteworthy that in NMM, MC does not have to match the problem domain but needs to cover it, which means that NMM does not have to deploy a mesh that matches the shock waves, making it very suitable for dealing with the discontinuities in problems. For simplicity in presentation, here let nodes M1 and M6 coincide with the problem domain ends  
The physical cover is formed by a combination of all the physical patches, which are obtained by cutting all mathematical patches with the problem region components. Here, a problem region component might be boundary segment, a discontinuity or a material interface. Cutting a MP, at least one PP can be generated. In Figure 1, for example, cutting M2 with the discontinuity marked by the blue dot, leads to the generation of \(P2-1\) and \(P2-2\), marked by yellow segments. Each PP  
Figure 1: Schematic diagram of 1D NMM with discontinuity.  
corresponds to a \"star point\" (also called \"NMM node\" or \"generalized node\") as exhibited with red circles in Figure 1.  
In NMM, the local approximation is defined on PPs. That is to say, the degrees of freedom are attached at NMM node. For convenience, the \"NMM nodes\" in the rest of this article will be referred to as \"nodes\". There are two types of _PPs_ in NMM as given in Figure 1. The one type is called non-singular patch which does not contain a crack tip in its domain, such as _PP2_. Another type is singular patch which contains a crack tip in its domain, such as _PP3_ and _PP5_. Different local approximations will be constructed according to different types of physical patches.  
In Figure 1\(P1\) is generate by \(M1\) cut by left domain boundary, \(P2-1\) and \(P2-2\) are generated by \(M2\) cut by the discontinuity (blue dot). It should be noticed that the physical patches generated by same mathematical patch have different star points at the same position.
# Comparison of Lab vs. Backcalculated Moduli of Virgin Aggregate and Recycled Aggregate Base Layers
###### Abstract  
The resilient modulus (\(M_{R}\)) and the backcalculated modulus from the FWD testing (\(E_{FWD}\)) of the unbound layers are critical inputs in the analysis/design of pavements. Several studies have tried to develop a conversion factor between these two parameters, while the nonlinear stress dependency of unbound materials and the pavement strain response are mostly missing from the literature. This study aims to compare the laboratory-measured \(M_{R}\) of recycled aggregate base (RAB) materials and a virgin aggregate base using field-based \(E_{FWD}\) and tries to establish pavement's responses to loading using vertical strains from both the \(M_{R}\) and \(E_{FWD}\) values of the respective materials as comparability parameters between the two. For this purpose, a control virgin aggregate (VA, limestone) and three types of RAB materials were selected to construct four test sections. The test sections were modeled in layered elastic- and finite-element-based pavement response models to calculate the vertical strains at the mid-depth of the base and top of the subgrade layers. A comparison of the lab-calculated vertical strains using \(M_{R}\) with actual vertical strains in the field from \(E_{FWD}\) showed that there was no relationship between the two stiffness parameters in all tested RABs. The vertical strains, based on the lab \(M_{R}\), undermined the stiffness of the recycled aggregates in the field. In contrast, the values of \(E_{FWD}\) based on the vertical strains remained close to the \(M_{R}\) strains of limestone (VA) throughout the testing period, establishing an \(E_{FWD}\) vs. \(M_{R}\) relationship (\(M_{R}=0.87\)\(E_{FWD}\)). The results also show that fine RCA was a better-performing material over three years. This research not only explores how the hydration process in RABs limits the development of \(M_{R}\)-\(E_{FWD}\) correlations but also underscores the need to consider real-world conditions when assessing their performance.  
resilient modulus; backcalculated layer modulus; nonlinear stress dependency; recycled aggregate base; limestone; vertical strain; NCHRP model +
Footnote †: journal: Applied Sciences
# Investigating Master-Slave Architecture for Underwater Wireless Sensor Network
## 2 Related Work  
This section discusses several existing protocols in the domain of Underwater Sensor Networks (UWSNs). In addition, the differences in routing protocols used in UWSNs are also highlighted. Energy Efficiency in UWSNs is one of the major concerns highlighted in the literature.  
The existing UWSNs protocols can be divided into the following two categories: (i) localization-based protocols, (ii) localization-free protocols [3]. Table 1 lists the protocols in these categories along with their advantages and disadvantages. The detailed discussion of protocols in each category is given below:
# Development of Cybersecurity Technology and Algorithm Based on Quantum Computing
## 5 Enhancement of Security
### Quantum Random Walk  
Quantum random walks are the quantum analog of the classical random walks [6]. Quantum random walks [7] execute coin operator and shift operator. The coin operator works by performing an arbitrary unitary transformation in the coin space, which creates a rotation similar to the \"coin-flip\" in a classical random walk. We use the Hadamard gate (Equation (3)) to execute the coin function because it puts qubits in a state of superposition, allowing for the simulation of a coin-based probability.  
\[H=\frac{1}{\sqrt{2}}\left[\begin{array}{cc}1&1\\ 1&-1\end{array}\right] \tag{3}\]  
The shift operator acts with the coin operator, with unitary gates that combine the probability amplitudes with individual subnodes under each node.  
Quantum random walks are studied as continuous- and discrete-time quantum random walks. However, we use herein the discrete-time quantum random walk because we need values to randomize the Shift Row.  
The unitary operations in the discrete-time quantum random walk are made of coin and shift operators \(U=SC\) that work in a state space. The total state of the system is defined by the Hilbert space as follows:  
\[H=H_{c}\otimes H_{p} \tag{4}\]  
where \(H_{c}\) is the coin Hilbert space, and \(H_{p}\) is the position Hilbert space. A coin-flip defines the direction in which the particle moves. A subsequent position shift operation moves the particle in the position space. We conducted a quantum random walk on a circular graph and implemented it on the quantum circuit [8].  
Figure 17 shows the whole process of the quantum random walk on a circular graph with \(2^{2}\) nodes (Figure 18). The gray rectangular frame means a set of coin and shift operators. In this circuit, q[0] and q[1] represent the state of the quantum walker, and q[2] represents the coin operator.  
Figure 17: Quantum random walk on a circular graph.  
Figure 18: Circuit with an implemented quantum random walk.  
The coin operator decides whether the walker proceeds clockwise or counterclockwise. The INC is the gate that increments the walker state, which is equal to a clockwise rotation in the cycle graph. The DEC is the gate that decrements the walker state, which is equal to a counterclockwise rotation in the cycle graph.  
After repeatedly executing the coin and shift(INC, DEC) operators, we can now know the probability of measuring a qubit and the walker position.  
Figure 19 shows the probability that the qubit will be measured according to each value. However, a high probability value does not necessarily result in a measurement (=not an output value), and a low probability value can also be an output value.  
Random numbers generated using quantum random walk have no repetitive cycles, so there is no correlation between the front and rear numbers, and are unpredictable because they depend on the probability. Therefore, we enhanced security by making estimation of decryption more difficult using quantum random walk.
## 5 Performance Evaluation
### Parameter Effect  
In Figures 6 and 7, we show the average MES energy consumption and an average queue length after the 140 th time slot with different \(V\)s. These two figures show the influence of \(V\), which trades off energy consumption and queue length. \(V\) determines the importance of the penalty term (i.e., a MES energy consumption) in the drift-plus-penalty function. Thus, as \(V\) increases, the amount of energy consumed by a MES dominates \(\Delta_{p}L(Z(t))\). As \(V\) increases, our method attempts to further reduce the energy consumption. That is, the service time allocated to each queue decreases as \(V\) increases. Thus, the average queue length increases, whereas the amount of energy consumed by a MES decreases with \(V\). Therefore, by controlling \(V\) during the service-time allocation process, a MES operator can balance the energy consumption and queuing delay. In these figures, we also observe that, compared to WoPred, our method both consumes less energy and achieves a smaller average queue length for all \(V\)s because our method considers the previous workload history reflected in \(Q_{i}(t)\) and the workload expected during the current time slot \(\hat{w}_{i}(t)\) when determining \(\alpha_{i}(t)\)s. In addition, the average energy consumption and average queue length obtained using our method are comparable to those achieved in an ideal case, in which \(w_{i}(t)\) is known in advance.
## 4 Experiment
### Evaluation Metrics  
Following previous studies, we use Recall@k and Mean Reciprocal Rank (MRR), which are common metrics for recommender systems, to measure the prediction performance of our framework. The definitions of Recall@k and MRR are as follows:  
\[\text{Recall@k}=\frac{1}{|U|}\sum_{u\in U}\frac{|\{p\in P_{u}^{\text{test}}\cap P _{u}^{\text{rec@k}}\}|}{|P_{u}^{\text{test}}|} \tag{7}\]  
\[\text{MRR}=\frac{1}{|U|}\sum_{u\in U}\frac{1}{\text{rank}_{u}} \tag{8}\]  
where \(|U|\) is the number of users, \(P_{u}^{\text{test}}\) represents the set of POIs that user \(u\) actually visited in the test set, \(P_{u}^{\text{rec@k}}\) denotes the top k POIs recommended to user \(u\), and \(\text{rank}_{u}\) is the rank position of the first relevant POI in the recommendation list for user \(u\).
# Design and Validation of a U-Net-Based Algorithm for Star Sensor Image Segmentation
## 2 Materials and Methods  
To face the segmentation of star tracker images, an ML approach has been considered. This AI area is being extensively explored and applied for image processing due to the great improvements achieved in several tasks. In particular, CNNs have been developed for computer vision algorithms because of their capability of image data processing and information extraction. Actually, lots of them are available to be used as Vgg-16 and Vgg-19 [20], Inception-v3 [21] from Google, the U-Net [22], AlexNet [23] and many others. CNNs have shown great performances in achieving many tasks such as object detection and classification, semantic segmentation and pose problems involving both cooperative and non-cooperative targets.  
The problem faced in this work is semantic segmentation. Indeed, the scope is dividing the original image pixels in two groups: foreground pixels and background pixels belonging, respectively, to the white area and the black area of the segmented output. This result will be achieved through the application of the U-Net to obtain a final output which contains as white areas just the most salient objects in the sky, avoiding stray light noise, faintest stars and objects on the background and reducing the overall noise signal in the image. The reason why this is performed through an ML-based algorithm is due to the CNN capability to handle objectives and targets which are easily understandable for a human being but sometimes very difficult to formulate in a rigorous fashion; the problem of segmenting just stars and RSOs which stand out the most in a night sky image is one of them. This is the goal pursued in this work.
# 5G Cross-Border Operation for Connected and Automated Mobility: Challenges and Solutions
## 4 5 GCroCo Solutions
### ToD  
The telco requirements for tele-operated driving can only be met by the new 5G uplink capacity, latency and reliability capabilities. Furthermore, 5G solutions will find application in the deployment of the technology.  
For ToD, a vast amount of data is continuously transmitted between the vehicle and the vehicle control center. With the 5G solution of QoS prediction, it is enabled to predict the available bandwidth. In the first place, if predictions are provided minutes in advance, they can be used to select a route for the vehicle along which a sufficient bandwidth is expected. At prediction times of several seconds, a reaction of the tele-operator, if a lower available bandwidth is predicted, would be to slow down the vehicle. If the time horizon of the predictions is even shorter, it is assumed that an automation can take actions, e.g., adapting the video compression rates and/or the resolution of the images, to ensure a jitter-free video stream displayed in the vehicle control center.  
A QoS prediction scheme, as shown in Figure 4, targets the assessment of the experienced quality of service for each client and the timely notification. It monitors different parameters of the network  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Requirement** & **ToD** & **HD Mapping** & **ACCA** \\ \hline High data rate & X & X & \\ Functional safety & X & & \\ High reliability & X & & X \\ Low latency & X & & X \\ Seamless availability & & X & \\ QoS prediction & X & X & \\ Mobile Edge Computing & & & X \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the 5G requirements of 5 GCroCo use cases.  
and environment from present and past to obtain a prediction. Furthermore, prediction algorithms can learn from past decisions to improve their accuracy or be trained.  
A notification is provided to the system in case the minimum required QoS cannot be met, in terms of specific KPIs, such as data rate, packet loss and end-to-end (E2E) delay. Minimum required QoS--in this context--is the minimum acceptable quality of the provided vehicular network's resources in order to address the respective V2X service and application requirements sufficiently. A QoS prediction mechanism needs to identify the different parts, which comprise the end-to-end communication path. In the context of cross-border scenarios, the QoS prediction may be of crucial value as well. As coverage of multiple MNOs overlaps in border areas, the point at which the handover occurs can be optimized.  
A second 5G solution, namely network slicing, is applicable for ToD. Instantiating a virtual network for the data transmission, it facilitates sufficient and predictable bandwidth, and thus enabling improved safety during the tele-operation. Furthermore, it is part of ongoing research, if and which computations can be carried in MEC application servers in order to reduce the data volume transmitted from the vehicle to the vehicle control center.
## 1 Introduction  
Autogenous bone is still the gold standard for bone grafting because of its excellent biocompatibility, osteogenic potential owing to the living osteoblasts, osteoinductivity, and osteoconductivity [1]. Autogenous bone grafts can provide support and stability to dental implants and maintain a proper volume with the surrounding bone, which can accelerate bone healing. However, limitations of autografts, such as possible harvesting morbidity and unpredictable resorption, have been reported in cases of intraoral bone grafts. [PERSON] et al. [2] reported that up to 42% of patients presented vertically resorbed bone at 6 months after transplantation. Bone grafting with autogenous bone alone has been controversial because of its volumetric instability.  
Among autogenous bone grafts, intramembranous bone has been known to show minimal resorption and more rapid integration compared with that shown by endochondral bone [3]. In addition, when compared with the symphysis, the mandibular ascending ramus can allow a larger volume of graft material, resulting in a lower possibility of harvesting complications [4]. Harvesting complications, such as nerve damage, hemorrhage, hematoma, or severe pain, can be prevented if the surgeon can harvest the cortical ramus without marrow invasion.  
Considering tooth extraction, immediate implant placement with guided bone regeneration (GBR) can shorten the healing period and reduce the duration of the surgical procedure. However, in the case of multiple teeth with severe bone loss due to periodontitis or sinusitis, a conventional delayed protocol has been recommended to prevent the possibility of infection and unpredictable resorption of the grafted bone [5]. The conventional protocol requires a healing period of 3-6 months for the consolidation of the extraction socket [6]. Delayed implant placement is inevitable when provisional dentures are used. Provisional partial dentures apply a retentive force on the remaining teeth; there fore, strategic extraction of teeth in poor condition is needed before the application of a provisional denture.  
However, a common complication after bone grafting in patients with dentures is wound dehiscence [7]. If a patient requires a bone graft even after the consolidation of the extraction socket, immediate implant placement with early loading could reduce the overall rehabilitation time, increase patient satisfaction and function, and have a psychological benefit. The aim of this case report was to introduce a case of successful maxillary rehabilitation without the use of provisional partial dentures by immediate implant placement with extensive autogenous bone graft and early loading after 4 weeks of surgery for soft tissue healing.
## 2 Literature Review
### Using Data Acquisition Models and Data Warehouses for the Collection of Historical Data  
Numerous studies have applied data acquisition models and data warehousing to tackle scheduling, cost estimation, and resource planning in construction. For instance, [PERSON] et al. [24] presented a construction management decision support system that integrates with a project's material data warehouse, with a specific emphasis on inventory decision-making. [PERSON] et al. [25] developed a data warehouse system that determines the most suitable residential site. Similarly, [PERSON] et al. [26] proposed an equipment data warehouse integrated with a decision support system. [PERSON] and [PERSON] [27] introduced a project-oriented data warehouse (PDW) for contractors that combines WBS and OBS structures for detailed management insights. In 2013, [PERSON] and [PERSON] [28] developed a newer infrastructure decision support system based on a data warehouse capturing data from installation, inspection, and renewal processes, aiding in strategic decision-making. [PERSON] et al. [29] developed a labor resources data warehouse to predict activity delays and estimate the use of labor resources, respectively. [PERSON] and [PERSON] [30] developed a data acquisition model to collect cost data from completed projects to predict construction project cost overruns. [PERSON] et al. [31] designed a data acquisition model to track and store project data systematically. Their main goal is to create a labor resource forecasting model with data mining for future project use. Notably, it does not collect labor requirements at the work package level. [PERSON] and [PERSON] [32] created a data acquisition system with a tracking feature to gather labor resource needs for various work packages from multiple projects to forecast resource utilization rates throughout the project.  
As summarized in Table 1, existing data acquisition models primarily focus on isolated dimensions (e.g., labor, materials) and project-level data, with only [PERSON] and [PERSON] [27] and [PERSON] and [PERSON] [32] capturing work package (WP)-level details. Importantly, none incorporate emerging metrics such as GHG emissions or social impact, nor allow for dynamic updates for new dimensions. These gaps--limited scope, coarse granularity, and static frameworks--directly motivate the design of the CDH in Section 3, which standardizes WP-level data collection across 12 dimensions while facilitating flexible expansions (e.g., through modular SQLite architecture in Section 5).