esa-sceva/satcom-chunk-collection huggingface dataset:
# Improvement of Position Repeatability of a Linear Stage with Yaw Minimization
## 3 Factors Affecting the Yaw Motion
### Uncontrollable Parameters: Given Conditions  
#### 3.1.1 Aspect Ratio of Platforms  
The effects of platform aspect ratio and force offset have been investigated, as shown in Table 2 and Figure 8. Four platforms with different sizes were selected. The length (\(l\)) in the moving direction was fixed at 0.5 m, and the width (\(w\)) was varied from 0.3-1.0 m, resulting in the \(l\)/\(w\) ratio of 1.7 to 0.5. The LM Guide model HSR20 was selected with blocks on the four corners of the platform. The driving motion profile is trapezoidal with an acceleration of 1 g (9.8 m/s2) and an acceleration time of 0.1 s.  
The friction force and damping coefficient of LM Guides vary depending on the operating environment, maintenance, and driving conditions [20; 21]. Even for LM blocks of the same model number, the friction coefficients and damping coefficients may vary. To proceed, the friction coefficient (\(\mu\)) and damping coefficient (c) were fixed to 0.02 and 10 Nsec/mm, respectively. The effects of their variations will be discussed later.
## 3 Analysis of Original Research Articles
### Time Series Models
RMSE (root mean squared error) is the square root of the average of squared prediction errors; it gives a greater weight to larger errors. R\({}^{2}\) (coefficient of determination) is a statistical measure indicating how well predictions approximate actual outcomes; values closer to 1 indicate better model performance. MAD (mean absolute deviation) is the average of the absolute deviations between actual and predicted values; it is like MAE but sometimes used in different contexts. While additional studies were reviewed, some did not provide consistent, comparable performance data, or they focused on qualitative aspects, specific components of hybrid frameworks, or regional insights without clear model benchmarking. Therefore, to maintain clarity and comparability, only representative studies with well-documented metrics and outcomes are included in this summary.
# Assessing the Cloud-RAN in the Linux Kernel: Sharing Computing and Network Resources
## 5 Assessing Resource Sharing in the Cloud-RAN Architecture
### Results and Discussion  
Two scenarios of resource sharing have been considered for evaluation: (i) noA, where no CPU affinity is defined. This scenario provides a baseline for the evaluation, where the RT-Kernel Scheduler decides to which CPUs mapping the resources assigned to a LXC. Such a decision could vary over time depending on the system load, resource availability, and process priorities; (ii) IA-WA, which adopts the CPU affinity approach described above for all LXCs and vNICs. Here, the CPU mapping is predefined according to CPU affinities.  
Conducted experiments consist of measuring end-to-end throughput at each UE connected to a given vBBU. In this case, the end-to-end throughput is derived from a TCP downstream flow generated by an Iperf3 server, which is located right outside the mobile network. The destination of the TCP flow is an Iperf3 client instantiated at the UE. A set of six experiments of 10 min each is conducted per UE. In total, two downstream TCP flows, one per UE, are generated with an initial rate of 10 Mbps. While conducting an experiment for a given UE, the TCP downstream flow for the second UE runs as background traffic. In addition, the BT from the Anritsu device to the A service at the CU runs during the whole experimentation. Not only does the BT allow for evaluating traffic aggregation and  
Figure 10: CPU affinity allocation based on _IA-WA_ to the vBBU and to the vNIC VF’s RX/TX queue Hard-IRQ.  
network sharing in the Midhaul, but the BT's destination service A also allows evaluating resource sharing at the CU.  
Figure 11 shows the results after computing the end-to-end throughput as measured by the UE's 1 perf3 client.  
The advantages of implementing the proposed CPU affinity are evident in the distribution spread of the data illustrated in Figure 11. Particularly notable is the substantial benefit for the UE connected to vBBU1. In the IA-WA scenario, the median UE's received throughput reaches 6.7 Mbps, outperforming the noA scenario where the median throughput is 5.7 Mbps. Moreover, the Interquartile Range (IQR), representing the range in which 50% of the sample data fall, is reduced from 4.2 in noA to 2.5 in IA-WA. This indicates a 40% reduction in throughput variability achieved by allocating CPU affinity based on IA-WA in comparison with scenarios without CPU affinity.  
Although the impact of adopting CPU affinity is not as pronounced for vBBU2 with split 2 as it is for vBBU1, there remains a noticeable difference in the distribution spread compared to the noA scenario. As depicted in Figure 11b, for the UE connected to vBBU2, the IQR is 1.9 in the IA-WA scenario, whereas it is 2.3 in the noA scenario. In other words, by allocating CPU affinity based on IA-WA, the throughput variability is reduced by 21% in comparison with scenarios without CPU affinity.
# COSMIC-2 Mission Summary at Three Years in Orbit
## 3 Requirements, Products, and Performance
### Future Products  
All but one of the mission's baseline set of data products are already in production and published. The final remaining product to be released is IVM in-situ drift. This is undergoing analysis to determine the cause of measurement biases for some instruments and timeframes. Product release is pending resolution of this issue.  
The program is also developing and validating the following additional TGRS ionosphere products:  
* UHF scintillation \"all clear.\" Utilizes 1 Hz POD signal to noise ratio observations to identify regions where scintillation if unlikely to be present. This product will be generated at rapid cadence (<30 min) to support space weather monitoring.
* Scintillation geolocation. This product applies a back propagation technique to high-rate phase observations collected from the POD antennas when scintillation is detected to estimate where along the ray path the scintillation occurred [36; 37].
* Ionosphere \"bubble map.\" Aggregates scintillation geolocations over time to identify regions and times with the presence of ionospheric irregularities.  
For the neutral atmosphere, improved algorithms for the detection of super-refraction for COSMIC-2 data are under development and expected to provide additional information to data users.  
These new products are expected to be released in late 2022 and 2023. For up-to-date product status, please refer to [29].
# Erythrocytes and Nanoparticles: New Therapeutic Systems
## 3 NPs and RBC  
### RBC and Solid Lipid NPs (SLN)/Nanostructured Lipid Carriers (NLC)  
The last decade has witnessed a rising interest in lipid-based nano-systems such as solid lipid NPs (SLNs) for drug delivery purposes due to their lipid nature and submicron size [43; 44]. Because SLNs are made of natural lipids (triglycerides, fatty acids, sterols (usually cholesterol) and waves), they are less toxic in comparison to polymeric NPs [45]. However, SLNs are characterized by a low drug loading efficiency with hydrophilic compounds such as peptides and proteins [46]. A new generation of lipid NPs, namely nanostructured lipid carriers (NLCs), has been developed to overcome the limitations of SLNs. Thus, an improvement in loading efficiency can be obtained using NLCs composed of a mixture of solid and liquid (e.g., short-chain triglycerides, vitamin E, lecithin) lipids that can ensure a less structured solid lipid matrix [45]. Another noteworthy aspect of these lipid NPs is that they can protect the drug from degradation and promote its transport across mucosal barriers [47].  
To the best of our knowledge, no carrier in carrier system exploiting the potential of lipid carriers such as SLN and NLC in biological vectors (i.e., red blood cells) has been reported. However, some articles focusing on the interactions between lipid carriers and red cells are of interest, in view of the formulation of a vehicle where chemical advantages (i.e., from SLNs and NLC) and biological compatibility (as from red cells) can be combined.  
In vitro experiments have been conducted on chloroquine-loaded and heparin surface-functionalized solid lipid NPs [48], brought to formulated SLNs with the goal of specifically targeting parasitized red blood cells--as afforded by heparin. The investigated SLN NPs exhibited bigger in vitro activities than chloroquine (CQ) on CQ-sensitive parasites, showing antimalarial activity against chloroquine sensitive strains of _P. falciparum_. Moreover, lipid NPs of Precirol or Compritol were tested together with Precirol/squalene NLCs for application as delivery systems aiming at achieving parenteral camptothecin administration. Both carriers showed sustained drug release and strong cytotoxicity; also, limited hemolysis was provided (around 5%), suggesting a good tolerance to lipid NPs after injection [49].  
Development and evaluation of lipid NPs for camptothecin delivery has also been carried out [49]. In view of targeting specific districts of the human body, SLNs and NLCs, once delivered from a biological carrier, should possess excipients capable of increasing contact with the same district. In this regard, in order to prolong the residence time of NLC in the nasal cavity following intranasal administration, [PERSON] et al. [50] modified the surface charge of lipid nanocarriers with polycation chitosan, giving rise to chitosan-coated lipid NPs (CS-NLC), in which the neurotrophic factor human IGF-I was efficiently encapsulated in view of administration in anti-Parkinson therapy.
# Reducing Model Complexity in Neural Networks by Using Pyramid Training Approaches
## 3 Methodology
### Pyramid Training  
Pyramid training in this study refers to a novel approach that diverges from traditional multiresolution pyramids, commonly used in object detection and other tasks requiring scale variance [33; 34]. Our methodology involves iteratively training and combining smaller, less computationally intensive networks to progressively build up a model capable of achieving the accuracy of much larger networks. By initially training a small network (A) and systematically integrating it with additional networks (B, C, etc.), we construct a larger and more capable model without the substantial resource requirements typically associated with large-scale neural networks. The network (A) is designed to capture fundamental patterns and features with minimal computational resources. Once network A has achieved a satisfactory level of performance, we then introduce an additional network (B), which is trained to complement and enhance the capabilities of network A. During this phase, both networks A and B may be trained jointly or sequentially, depending on the specific requirements of the task. As the training progresses, additional networks (C, D, etc.) are incrementally integrated into the system. Each new network is trained to refine and build upon the features learned by the previous networks, effectively creating a hierarchical structure that grows in complexity and accuracy. A significant feature of this approach is the ability to freeze the initial blocks as we add more layers to the pyramid. Once a block has been trained sufficiently, it can be frozen, meaning its parameters are no longer updated during subsequent training iterations. This not only saves computational resources and memory space but also speeds up the training process, as only the new blocks require intensive computation. This hierarchical approach not only optimizes computational resources but also allows for fine-tuning of individual network segments before they are integrated, enhancing overall model performance and efficiency.
# How Soft Polymers Cope with Cracks and Notches
## 3 Discussion
### Application to Experimental Data  
The model is applied to account for the effect of blunting in the failure of the specimens tested during the experiments. The data collected in Figure 12 illustrate the ultimate condition, when the remote stretch is equal to \(\lambda_{0}=\lambda_{U}\), with respect to the initial length \(a\) of the defect. In this plot we have also added the predicted trend of LEFM, as obtained from Equation (15), where an average value of the fracture resistance for silicone rubbers is taken equal to \(G_{c}=1\) kJ/m\({}^{2}\).  
It should be noticed that the plot in Figure 12 is by no means a representation of the flaw sensitivity in the canonical form, primarily because the smallest size of the flaw in the tested specimens is larger than the critical flaw size (see [20]). The discriminant here seems to be the grade of crack blunting: the edge-cracked specimens, which fail at smaller stretches, are well approximated by the 'pure' LEFM model, where the ultimate stretch decreases with the square root of \(a\). On the contrary, the plates containing central cracks (specimens CC1-CC4) resist to larger stretches and do not agree well with the power law trends, suggesting that crack blunting plays a fundamental role.  
To the latter group of specimens, we have applied the'modified' model previously described. The results are summarized in Figure 13, where the quantity \(K_{t}(\lambda_{U}-1)\) is plotted as a function of the crack length \(a\). Note that the quantity \(\frac{1}{E}(K_{t}(\lambda_{U}-1)E)\) represents the normalized true stress at the notch root at incipient failure, and can be therefore considered a material property, independent of the presence of the flaw. From the observation of the results reported in Figure 13, it can be noticed that such a local stress seems to be an appropriate parameter for quantifying the material failure in the case of the centered-cracked plates CC1-CC4, but not in the others. In other words, where crack blunting allows rupture at larger stretches, failure can be predicted by a local stress quantity rather than by a fracture mechanics-related one.  
Figure 12: Ultimate remote stretches at failure \(\lambda_{U}\) as a function of the initial crack length \(a\). In the case of the elliptical notches, the upward arrows indicate that failure has not occurred within the considered range of applied stretch. The continuous line corresponds to the prediction of the ‘pure’ LEFM solution, Equation (15). The dashed lines correspond to best-fit curves with a \(\lambda_{U}\) vs. \(a\) power law dependence with exponent \(-0.5\). Results for EC1–EC4 for a strain rate \(\dot{\epsilon}_{0}=1.9\cdot 10^{-3}\) s\({}^{-1}\).
# A Simple Method to Convert Cellular Polymers into Auxetic Metamaterials
## 3 Results and Discussion
### Auxetic Foams
#### 3.2.3 Mechanical Properties  
Figure 7 presents the tensile stress-strain curves for the samples before and after the VMC treatment. For all the properties (tensile modulus, energy at break (\(E_{p}\)) and elongation at break), an improvement was observed compared with the original foams. This was similar to that reported in the literature, where the mechanical strength and energy absorption were much better for auxetic foams compared to conventional ones [18; 25; 32; 33]. The curves for the original foams (solid lines in Figure 7) showed a classical quasi-linear behavior until failure at about 60%, 90%, 92% and 78% tensile strains for D16, D21, D30 and D36, respectively. The auxetic foams showed higher tensile stresses at failure than the original foams due to their higher density (Table 4). The values of Young's modulus and energy absorption (\(E_{p}\)) were also improved at a higher final foam density (and crystallinity) because Young's modulus is related to the amount of material sustaining the stresses and its intrinsic rigidity [34]. The energy absorption, which is related to toughness, also increased with the density [35]. Figure 7: Tensile stress–strain plots of the auxetic and original foams based on **(A)** D16, **(B)** D21, **(C)** D30 and **(D)** D36. However, one tensile modulus ([PERSON]'s modulus) decreased after the VMC treatment. The value of [PERSON]'s modulus for the original D30 foam was approximately 2.39 kPa, while that of the PE-D30-P1.5 sample was 1.13 kPa. This was similar to [PERSON] et al. [30], who attributed the decrease to the foam unit cells buckling during processing, providing a rotational point between the cell ribs [34]. However, above a specific tensile deformation, the cells/ribs tended to align, generating a higher resistance to the applied stresses [34]. As such, only low stresses are needed at low strain (beginning of a test and below 5% deformation) [34]. Here, we proposed another explanation related with the OCP (Table 1): as some closed cells became open cells because their cell walls were broken during the VMC process, there was less material available to sustain the applied stresses, especially at lower strains (before cell buckling and collapsing). Quasi-static cyclic tensile tests (up to 3% engineering strains at a rate of 0.2 mm/min) were performed to generate hysteresis cycles. Figure 8 presents the hysteresis loops for the auxetic and initial samples. The strain energy (\(E_{p}\)) of PE-D16-O was the lowest compared to the VMC-treated samples, but PE-D21-O, PE-D30-O and PE-D36-O had higher Ep values
## 3 Results and Discussion
### ET Partitioning  
Figure 8 shows the variation in T/ET ratio for five different sites. Of the three natural vegetation sites, the average weekly T/ET ratio is highest for Sidaoqiao (0.59) and lowest for Xitaizi (0.36). In contrast, the averaged \(uW\mathit{UIE}_{a}\) of Sidaoqiao is the lowest, whereas that of Xitaizi is relatively high. It is also observed that the T/ET ratio is negatively related to precipitation for the three sites; for instance, a site with lower annual precipitation has a higher T/ET ratio. It is also evident from Figure 8 that the T/ET ratio of the three sites shows a different level of variation: Arou has a larger variation range from 0.04 to 0.93 compared with the other two sites, whereas Sidaoqiao shows the lowest level of variation in T/ET ratio, ranging from 0.22 to 0.88.  
Figure 7: Frequency distribution of weekly \(uW\mathit{UIE}_{a}\).  
Xinier has a higher averaged weekly T/ET ratio (0.63) than the Daman site (0.39), mainly due to the difference in irrigation scheme for the two sites (Figure 8). At the Xinier site, much drip irrigation is applied, which delivers water precisely to the root zone of crops and consequently soil water is low in the inter-film zone, resulting in a high fraction of transpiration and low soil evaporation. This result is similar to the finding of [PERSON] et al. [57]. At the Daman site, flood irrigation is conducted and, as a result, there is no obvious difference in soil water under root zone and inter-film zone. Soil evaporation contributes a high portion of ET and consequently the T/ET ratio is lower at Daman.
## 4 Simulation Results
### Summary  
The simulations above compare some key network parameters obtained by the proposed algorithm and the typical algorithms using LEACH and AODV. LEACH and AODV are important algorithms in ad hoc networks and have been widely used in recent years. But the urban rail transit scenario is different from the traditional ad hoc network, due to the linear deployment of nodes and there being no need to consider energy consumption, and to our knowledge there are few works focused on the ad hoc network routing algorithm in this scenario. It can be seen that in an urban rail transit scenario, the proposed algorithm has better performance in terms of packet loss rate, end-to-end delay, and throughput. In addition, the proposed algorithm can guarantee different service requirements according to the service characteristics in urban rail transit, such as reduced delay and packet loss rate for strict security services, greater throughput for non-security services, and better anti-congestion capability. Overall, the proposed algorithm is suitable for the urban rail transit scenario, and can provide better performance than other typical algorithms.
# Seismic Resilience of CRC- vs. RC-Reinforced Buildings: A Long-Term Evaluation
## 4 Predictive Analysis of Seismic Capacity Resistance
### Pushover Analysis  
The nonlinear static pushover analysis (NSPA) was employed to compute the capacity curves for the tested structures using SAP-2000, Version 10.07, a commercially available software [28] based on guidelines from FEMA-273 and FEMA-356 [44,45]. The model of the tested frame included all relevant features of the existing structure, along with the properties of members and joints used in previous modal analysis as presented in the studies [46, 47, 48].  
The NSPA was conducted using the following steps: First, the structural model of the building was developed in SAP-2000, with the geometry, member properties, material characteristics, and support conditions defined, initially without incorporating pushover data. Next, we established the properties and acceptance criteria for pushover hinges based on the maximum stress occurring in either the compressive or tensile zones of the sections experiencing maximum bending. The yield strength of the RC sections, denoted as Fy, was determined and presented in Tables 1 and 2. Our approach assumed that plastic hinges could form only at the frame's joints, as no concentrated loads were applied.  
The analysis was initiated by applying gravity loads to establish the structure's initial conditions. Following this, lateral loads were assigned during the pushover loading phase, commencing from the final conditions set by the gravity load case. These lateral loads were incrementally increased and distributed in line with the structure's first mode of free vibration. This setup simulates earthquake conditions by applying loads in a triangular pattern, which effectively models the step-by-step lateral displacement of the frame in one direction. To ensure the analysis faithfully represented the building's expected seismic performance, maximum and minimum load steps, incremental event loads, and deformation tolerances were meticulously selected by the guidelines provided in [1].  
The model incorporated failure criteria for all joints and the structural members' moment and shear capacity. As the lateral loads increased incrementally, elements began to yield sequentially, altering the frame's stiffness as new plastic hinges formed. The displacement at the roof was chosen as the control point for the NSPA, and the resulting capacity curves were generated by plotting the base shear against the roof displacement.