esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Study of Concrete: A Mesoscopic Scale Simulation Methodology
## 2 Materials and Methods
### Stage 2: Numerical FEM Model
#### 2.2.2 Meshing Strategy and Utilized Finite Elements  
Meshing is conducted independently, utilizing tetrahedral finite elements for the aggregates and hexahedral elements for the mortar and the ITZ. Introducing the ITZ into the model adds complexity due to coupling and assembly issues at the boundary separating the hexahedral elements used in the mortar phase and the ITZ. Initially, an ITZ thickness of 100 \(\upmu\)m was established.  
During the meshing process, the number of finite elements (FE) was restricted to 1.5 million to ensure manageable computations. To prevent exceeding these limits, a prismatic Representative Volume Element (RVE) was utilized, representing one-quarter of a cubic specimen with a 50 mm edge tested in the laboratory. Boundary conditions include symmetry on the ZX and YZ faces to simulate a cubic specimen. Furthermore, this approach allows for managing smaller sizes in the ITZ finite elements. Within this volume, the integrity of all included aggregates (spheres) is maintained (Figure 3).  
In terms of meshing, three different methods have been utilized: a body-fitted Cartesian algorithm [19] for the mortar and ITZ components, free meshing with specific sizing parameters for coarse aggregate, and our own code with APDL programming for ITZ components.  
Specifically, the geometric model was meshed with linear Finite Element Method (FEM) elements using a body-fitted Cartesian algorithm [19], with an element size ranging from 6 \(\times\) 10\({}^{-4}\) m for coarse aggregate to 3 \(\times\) 10\({}^{-4}\) m for the ITZ. Considering the overall behavior, including crack patterns, a compromise between ITZ thickness and computational effort justifies the use of a thickness of up to 0.3 mm to replicate a real ITZ thickness of 100 \(\upmu\)m [20].  
The following element types were used [21; 22]:  
1. The mortar and ITZ were modeled using a solid type of tetrahedral finite element called SOLID65, suitable for simulating the nonlinear structural behavior of concrete. It features eight nodes with three degrees of freedom per node: translations in the X, Y, and Z directions.
2. The coarse aggregate was modeled by the SOLID185 element, representing a homogeneous structural solid defined by eight nodes with three degrees of freedom per node.
3. Contact between ITZ and coarse aggregate is modeled by the CONTA174 element. This element has eight nodes and three degrees of freedom per node. The contact element is used to simulate contact and sliding between both materials using a pure penalty algorithm between deformable surfaces.
# On the Application of Machine Learning to the Design of UAV-Based 5G Radio Access Networks
## 3 Technical Challenges and Requirements
### Payload and Flight-Time Constraints  
Size and weight limitations of UAVs induce severe restrictions on the embarked payload (including cameras, sensors, and transceiver machinery), which motivates the need for a careful choice of the most efficient type of UAV, especially in multi-tier UAV cells. Moreover, UAVs have limited on-board batteries and are subject to strict flight regulation rules and weather conditions. Therefore, minimizing the flight time, while meeting the demands needed to optimize the service performance,  
Figure 3: UAV-based cloud computing for IoT applications utilizing services provided by different collectors and clouds.  
is another challenge in U-RANs [50; 51; 52; 53]. For instance, in U-RANs supporting different applications, the choice of the type of UAV depends on payload, data generating period, delay sensitivity, and required amount of data processing. As another example, the usage of heavier UAVs is more suitable in windy weather, since they are more robust. However, their hovering time is limited and their maneuvering flexibility is low.
## Proposed EDOVE Scheme  
In this section, we present the detailed discussion of our proposed EDOVE scheme. EDOVE avoids the void region by distributively selecting the forwarder with large residual energy and having multiple neighbors that are largely distributed depth-wise. The proposed scheme is based on and compared with the WDFAD-DBR, which avoids the void hole and uses the holding time \((T_{h})\) to suppress the duplicate packet transmission. Void hole avoidance is achieved through prioritizing the forwarders that have one or more than one neighbors with the smaller depth. WDFAD-DBR uses a constant weighting factor, \(\alpha\in(0,1)\), to prioritize either first hop (sender \(S\) and receiver \(i\)'s depth difference, \(d_{i}=(z_{S}-z_{i})\) and second hop depth differences (receiver and its minimum depth next hop neighbor \(j\), \(d_{i}^{NF}=(z_{i}-min(z_{j}))\), \(H\).  
\[H=\alpha(d_{i})+(1-\alpha)(d_{j}^{NF})\]  
In addition to that, a maximum acoustic delay between all the receivers, \(\beta\), is multiplied with \(H\) to compute the holding time at receiver \(i\), \(T_{h_{i}}\):  
\[T_{h_{i}}=\beta(t_{r}^{S}-H)\]  
where \(\beta=\frac{2t_{r}^{S}/n_{sound}}{\rho}\), \(\rho\in(0,t_{r}^{S}]\), \(t_{r}^{S}\) is the transmission range of \(S\), and \(v_{sound}\) is the sound velocity in the aquatic environment.  
Opposite to WDFAD-DBR, the proposed EDOVE computes \(T_{h_{i}}\) using the weighted two hop depth differences \(H\), the normalized residual energy of node \(i\), \(e_{i}\) and the next hop depth difference variance, \(\delta_{i}\). Hence, the resultant \(T_{h_{i}}\) prioritizes the receiver that has more available energy, large depth difference to \(S\) and receiver with large depth difference variance to minimize further data collisions. As a result, energy efficiency is achieved by avoiding the packet collision with void avoidance and increasing the network lifetime.
## 2 Mission Description
### Relative Orbit Control Scenario  
Multiple formation flight techniques are required to achieve the ultimate goal of the CANYVAL-C mission and the IAH. The relative orbit control for this mission is chronologically shown in Figure 2. After the launch of the two satellites, during the Launch and Early Orbit Phase the satellites will be separated by thousands of kilometers owing to orbital perturbations during antenna deployment, hardware commissioning, angular velocity stabilization, data transmission and reception, and so on. When the relative distance between the two satellites is reduced to a few kilometers, their orbits can be described as a relative motion. Drift recovery and station-keeping (DRSK) regulates the satellite's orbit using Gauss' variational equation, enabling the deputy satellite to stop within 2 km of the relative distance from the chief satellite [36]. This study only deals with the relative orbit control algorithms immediately after DRSK.  
The deputy satellite is then placed in a stable periodic relative orbit using rendezvous (RDV) within 0.5 \(T\), where \(T\) is the orbital period, and the periodicity is maintained using differential air drag control (DADC) maneuvers. An IAH is performed if the satellite's state is sufficient for the mission and the starting conditions for the mission mode are met. With the occulter stowed, the mission mode is executed once (commissioning-IAH) and the spacecraft is charged for five orbital periods while pointing toward the sun. In sun-pointing mode, the trajectory is uncontrolled, resulting in a drift from the perturbations. Consequently, the orbit control mode of (1) RDV and (2) DADC from Figure 2 is repeated ((5) RDV, (6) DADC). The deployment of the occulter increases the differences in perturbations; therefore, the occulter is deployed one day prior to the IAH to mitigate the drift.  
Figure 1: (a) Definition of the relative motion in local vertical, local horizontal (LVLH) coordinate frame (\(\hat{X}^{l}\hat{Y}^{l}\hat{Z}^{l}\), Earth-centered inertial (ECI) coordinate frame; \(\mathbf{r}_{e}\), position vector of the chief satellite from the center of the Earth; \(\hat{x}\hat{y}\hat{z}\), LVLH coordinate frame; \(\mathbf{r}\), relative motion from chief to deputy). (b) The occulter and the propulsion system of the deputy satellite in the body frame.
## 3 Materials and Methods
### Data Preprocessing
#### 3.3.4 Stationarity Checks  
For models like ARIMA and ARIMA-LSTM, stationarity was assessed using the Augmented Dickey-Fuller test. Non-stationary series were differenced as required to satisfy model assumptions.  
These preprocessing steps were essential for maintaining data integrity and enabling fair comparison across diverse model architectures.
# Performance Analysis of Two-Hop mmWave Relay Nodes over the 5G NR Uplink Signal
## 4 Simulation Results and Discussion
### Average Throughput
Furthermore, in the case of the SISO technique, the average capacity of the uplink mmWave D&F PCE protocol at SNR = 20 dB was approximately 17.83 Mbps, where a throughput gain of about 4.5 Mbps was achieved in comparison with the uplink mmWave SISO A&F algorithm. Moreover, when the (2 \(\times\) 2 \(\times\) 2) antenna configuration was considered in each implemented algorithm, a 34.03 Mbps may be achieved at the SNR = 20 dB by the uplink mmWave D&F PCE protocol, which is associated with performance gains of 12.5 and 4.2 dB, respectively, respect to the uplink mmWave A&F and uplink mmWave D&F LS protocols. Additionally, it should be highlighted that the throughput maximum that could be obtained without any channel for the 5G NR-PUSCH 256-QAM system is 56.4 Mbps, which alone was achieved by the uplink mmWave (2 \(\times\) 4 \(\times\) 4) D&F PCE protocol at SNR = 20 dB. Nevertheless, when an uplink mmWave (2 \(\times\) 4 \(\times\) 4) D&F LS strategy was taken into account, the achievable throughput was near to the throughput maximum, approximately 53.5 Mbps, which had a loss of 2.9 Mbps; however, with respect to the uplink mmWave (2 \(\times\) 4 \(\times\) 4) A&F protocol, a loss 16.46 Mbps was reached. Figure 14: Throughput comparison for different uplink mmWave NR-RN co-operative schemes implemented with the 256-QAM signal.
## 1 Introduction
A review of the literature suggests that even though researchers are developing both DPIFs [35; 36] and IPIFs [33; 34; 37; 38; 39], more effort is going into IPIF, and thus there more published studies involving IPIFs. The second dimension splits PIFs into parametric and non-parametric forecasters. With parametric forecasters, researchers assume that the parameters involved can be described by PDFs such as Gaussian or Laplacian distributions [31; 38; 40], whereas with non-parametric forecasters they do not make this assumption [34; 35; 41]. The aim of this paper is to develop an IPIF to predict energy demand 15 min ahead. The IPIF relies on the mathematical analysis of the effect of each PPF parameter in the final output and through function linearization compute the energy demand's prediction interval. These study's key findings are as follows:  
A novel VST parametric indirect prediction interval energy demand forecaster, with a prediction horizon of the next 15 min, is proposed in order to reduce power systems' uncertainty level. The model developed is based on combining a PPF that predicts the energy demand's point value with mathematical concepts and a t-Student PDF that predicts the interval. Because studies in the literature propose different PPFs, the most popular models were examined to choose the most suitable one for our energy demand prediction. The root mean square error metric is calculated to compare the results provided by each programmed PPF. Prediction interval coverage percentage (PICP) and skill score error metrics are computed for different confidence levels (95, 90, and 85%) in order to analyze the accuracy of our developed PIF, as well as the width of the predicted intervals. In addition, the final forecaster achieves higher PICP values than the stated confidence levels in all analyzed situations. Hence, as noted in the literature [33; 34], when this condition is satisfied, the proposed model is acceptable for forecasting the desired parameter. The remaining sections of this paper are organized as follows. Section 2 explains the energy demand PPF developed and the PIF methodology proposed. Section 3 presents the error metric results for the programmed PPFs and the developed PIF. The conclusions drawn from this research are summarized in Section 4.
# Open Bite Treatment with Combined Aligners and Myofunctional Appliances: A Case Report
## 2 Case Description and Results
### Treatment Progress  
Before beginning the use of clear aligners, intrusion of the posterior teeth with skeletal anchorage was planned. Two miniscrews were inserted in the left and right sides of the upper arch between the first and second molars; this area was chosen because the cortical bone has an adequate thickness that does not require predrilling [21] even though it is notconsidered a safe zone for miniscrew insertion [22]. After insertion of the miniscrews, a TPA with a distal loop and molar bands for the upper second molars were placed, along with palatal and vestibular segmented 0.019 \(\times\) 0.025 stainless steel wires connecting the first and second molars on each side (Figure 3). The intrusion force was applied by an elastomeric chain tied from the sectional arch to the miniscrew. At the end of the intrusion phase of the upper molars, new orthodontic records were collected, along with digital impressions of the dental arches taken with an intraoral scanner, to plan the treatment phase with clear aligners (Figure 4).  
The first phase of therapy involved 18 aligners in the upper and lower arch. The patient was instructed to wear the aligners for at least 22 h a day and to change the set of clear aligners every 15 days. Inter-proximal reduction (IPR) of 0.10 mm was performed between the upper and lower incisors before the third and the sixth aligner. The patient was also given the myofunctional Freedom device to be used daily while wearing the aligners, performing 3-s-long clenching exercises with closed lips for a total of 30 min per day (Figure 5). At the end of the first phase of 18 aligners, a better molar relationship was achieved, the upper incisors were slightly extruded, and the overbite improved.  
New digital impressions were taken and a set of six refinement aligners for the upper and lower arches was planned for the correction of the remaining crowding. The last treatment phase involved the use of class III elastics on the left side and vertical intercuspation elastics on the right side to optimize the molar and canine relationships and to correct the dental midlines (Figure 6). Once the sagittal relationship was fully corrected, intercuspation elastics were used on both sides to improve occlusal settling at the level of the canines and first premolars: silicone moulds were used to place composite buttons on the buccal face of the crowns of the upper cuspid, lower cuspid, and lower first bicuspid, and elastics were used full-time for 3 weeks without aligners.  
Figure 3: Intraoral photographs of the first treatment phase with TPA.  
Removable orthodontic upper and lower essay retainers were given to the patient at the end of treatment, and she was instructed to use them overnight every night.  
Figure 4: Dental casts before clear aligner treatment.  
Figure 5: (**A**) Intraoral photographs with clear aligners; (**B**) Intraoral photographs with Freedom appliance and clear aligners on.
## 3 System Design
### Design of DGRL Training Method, Controller Node Features and Reward Function
#### 3.4.2 Design of Node Characteristics and Reward Function  
In the process of deep reinforcement learning, the controller needs to observe the state of the system to obtain the feedback after executing the forwarding policy. In this paper, the state matrix \(X_{t}\in R^{N\times F}\) is composed of four features of all nodes, which are: number of connections per node, average transmission delay of the channel (link quality), packet loss rate of nodes, and occupancy of node buffer.  
The designed objective function can guide the controller to forward the data packet towards a high benefit goal. High benefit is defined as: shorter forwarding time, shorter forwarding path, lower buffer occupation. In this paper, the reward function is as follows:  
\[R(t)=\frac{1}{delay_{pre}\times distance\times buffer+1} \tag{14}\]  
where \(delay_{pre}\) represents the time taken by the data packet from the previous node to the current node and \(distance\in[0,1]\) represents the relative distance of the data packet to its destination node. The relative distance is calculated by the ratio of the total hops of the shortest path from the packet to its destination to the total number of nodes (in a weighted network, the sum of the shortest path weights and the weights of all edges is calculated). \(buffer\in(0,1]\) represents the occupancy of the node buffer after receiving the data packet. The buffer of each node stores the data packets to be sent, and the occupation of buffer determines the packet loss and congestion of the network. The reason for using these three items is to avoid the larger order of magnitude factor from occupying a dominant position in the optimization process. Therefore, the three objectives will be optimized at the same time. The design of \(R(t)\) ensures that \(R(t)\in[0,1]\), which is equivalent to a standardized operation and beneficial to the training of deep neural network. It is important to note that when the data packet has reached the destination node, \(distance=0,R(t)=1\); when the data packet is discarded because the buffer queue is full or TTL (Time to Live) arrives, set \(R(t)=0\).
# Proposed New Analytical Method of Tower Load in Large-Span Arch Bridge Cable Lifting Construction
## 3 Accuracy Verification of Calculation Method  
The correctness of the calculation method is verified by measuring the displacements of the tower tops. A representative bridge is selected as an engineering case for validation. The numerical calculation model of the tower is established and, using the calculation method proposed above, the cable system load on the tower when the main cable transports a rated load to mid-span is calculated and applied to the numerical model of the tower, from which the displacement of the tower can be calculated under the action of an only cable system load. The displacement of the tower top is monitored during the test operation of the cable lifting system. The measured displacements at the tower tops are compared with the values calculated by a finite element method. The size of discrepancy of the displacement at the tower top obtained by two methods can indicate the correctness of the calculation method and the accuracy of the calculation. The smaller the displacement discrepancy value, the higher the accuracy of the calculation method, and the larger the displacement discrepancy value, the lower the accuracy of the calculation method. The detailed method and procedures of verification are presented in the following using a specific engineering project.
## 3 Results and Discussion
### Imaging of an Abdominal Phantom  
The results of the simultaneous imaging of an abdominal phantom are described as follows. The cross section of an MRI image was estimated based on MR-visible fiducial markers in the corresponding cross section of an ultrasound image, as shown in Figure 6. The sound velocity was estimated to be 1500 m/s in the agar gel and 1430 m/s in the fat-mimicking region. Image degradation was compensated for by using a sound velocity of 1540 m/s for the soft-tissue-mimicking region. The resulting image was compared to an ultrasound image that was obtained by assuming a sound velocity with a uniform distribution as shown in Figure 7. Fine specks are evident in the lower layer of fat in the compensated image, as shown in Figure 7a, although the margins of darker regions are indistinct. Similar specks are evident in the horizontal direction in a conventional B-scan image as shown in Figure 7b. This image was obtained when a constant sound velocity of 1540 m/s was used in beamforming. The margins of the darker circular region are clearly evident compared to those in the compensated image. Image degradation due to a fluctuation in sound velocity was improved by correcting beamforming using the estimated sound velocity distribution. To improve the spatial resolution, the half-width of the autocorrelation function in the horizontal direction was calculated for the three red squares shown in Figure 7; the half-width of the autocorrelation function is defined as the distance equal to half of the maximum value of the autocorrelation function. The calculated half-widths are shown in Table 6. The average ratio of improvement was 0.43. The SNR is defined as the signal power divided by the noise power; the signal power is calculated as the square of
# Design and Dynamic Simulation Verification of an On-Orbit Operation-Based Modular Space Robot
## 2 Configuration Design of Reconfigurable Modular Space Robot
### Space Mission Description
#### 2.1.4 Module Design Principles  
Through the analysis of space missions and combined with the characteristics of reconstructed robots, the following design requirements are put forward, i.e.,  
1. Versatility: the system is composed of unit modules and assembled into systems with different configurations and functions to meet different mission requirements.
2. Interface standardization: to meet the connection and disconnection between any two modules, the interface must adopt an isomorphic design and have a simple structure.
3. Self-driving capability: each module is the joint of a robot system, and the module contains a motor and a mechanical transmission system.
4. Independence: in order to achieve rapid reconstruction of the entire system, each module must have independent data processing capabilities, data transmission capabilities, and independent power supplies.
5. Platform: the robot module must have the ability to carry payloads, reserve a payload cabin inside the module, and set up a connecting mechanism outside the module.
6. Miniaturization: modular reconfigurable robots can use a certain number of modules to complete space missions in a limited space area and have the advantages of flexibility and adaptability.  
The above design principles can ensure the fact that modular space robots can achieve flexible and changeable space missions, realize connection and disconnection between modules, as well as topological changes in the overall configuration, making the entire system highly adaptable to the environment.