esa-sceva/satcom-chunk-collection huggingface dataset:
# Fabrication and Analysis of a Ti6 Al4V Implant for Cranial Restoration
## 2 Methodology
### Image Processing  
A cranial defect case from the University Hospital was selected to employ the proposed approach. To begin with, CT scanning of a thirty-year-old patient suffering from a sizable cranial deformity in the left parieto-temporal area was carried out. The CT data were captured by using a ProMax 3D Cone Beam CT machine (Planmeca, Helsinki, Finland). The gathered images were stored as DICOM files. These DICOM files consisted of 2D layers from all-around (front, side and back ends) the patient's cranium. A well-known medical image processing software which is known as Mimics 17.0  
Figure 1: Methodology to obtain a customized cranial implant.  
(Materialize NV, Belgium), was utilized to transform the acquired DICOM files in the quintessential 3D model.  
The 3D model is useful because it includes information about the patient's bones, skin, soft tissues, etc. To access the area of interest as well as to divide the hard and soft tissues, segmentation and region growing techniques were exploited in Mimics(r). Figure 2 depicts the various steps of surface modeling from a fully scanned skull model to the region of interest. The segmentation was accomplished through the identification of image intensities in Hounsfield units (HU). The HU was used because it could quantify the attenuation coefficient of different regions in CT scan images and provided the right type of tissues. For example, bone exhibits a higher HU value in contrast to skin and soft tissues owing to its ability to absorb a greater amount of radiation. The region growing algorithm was required to minimize or remove any noise, outlier or gap in the image data, thus providing a continuous set of pixels as shown in Figure 2a. A 3D rendered model of the patient's cranium was obtained as shown in Figure 2b by exploiting the reconstruction tool. The 3D surface modeling is a tiresome and tardy procedure because it has to be reworked numerous times until the desired region of interest (affected area) is realized. The finally obtained region of interest, a 3D skull model without the neck region (Figure 2c\({}^{\prime}\)), was stored in the STL format for the subsequent implant modeling process.
## 3 Numerical Modelling and Analysis of Sound Absorption Properties
Normal incidence sound absorption properties for mid-porosity range modulated microstructures (A + B)--DL2 in both Darcy and inertial fluid flow regimes overlap completely, underscoring the importance of permeability changes in cellular materials for their vibroacoustic behaviour. Furthermore, it is important to demonstrate the direct relationship between formulated key pore-structure-related properties and acoustic properties. Figure 6d shows such a plot--categorising how these structural properties affect acoustic properties. Structures classified as Z have relatively poor sound absorption properties, with pore volume fractions (\(\epsilon\)) between 0.63 and 0.68, and pore size to opening ratios (\(r_{p}/r_{w}\)) between 2.0 and 4.0. Further, structures classed as X are the best for low frequency (\(f\leq 1.5\) kHz) absorption, while optimum absorption performance was obtained for structures classed as Y typified by pore volume fractions (\(\epsilon\)) between 0.68 and 0.76, and pore size to pore opening ratios (\(r_{p}/r_{w}\)) between 4.0 and 6.0. Table 2 demonstrates that the highest sound absorption was achieved for 50% volume S-S structures when created at 10 \(\upmu\)m and characterised by pore volume fraction (\(\epsilon\sim\) 0.73) and pore size to pore opening ratio (\(r_{p}/r_{w}\sim\) 4.8) that are within the specified range. In excess of these 50% volume packing conditions, key structural properties and resulting acoustic properties were below optimum levels. For instance, for 62.5 and 75.00% volume S-S packing conditions created at 10 \(\upmu\)m capillary radius, their computed pore volume fractions were 0.68 and 0.65, respectively, and their overall pore size to pore opening ratio and characteristic sound absorption properties fell within structures categorised as type Z (See Figure 6d). As a result, a higher number of smaller pores are in contact--resulting in a reduced mean pore connectivity that is closer to the 100% volume S-S packing condition. Thus, it is pertinent to recommend that a reduction in the number of design iterations can be achieved by working within the specified limiting range of these key pore structure parameters to satisfy application requirements for the processing of enhanced soundproofing metallic components.
###### Abstract  
The goal of this research is to assess and guide the development of next-generation synthetic aperture radar (SAR) satellites, optimize their performance, and present the requirements related to the design parameters. In the current era, characterized by the rapid advancement of SAR technologies, the challenge of designing compact and efficient satellites is more relevant than ever. The present research provides a comprehensive analysis of design parameters for microsatellite applications, including altitude, incidence angle, operating frequency, antenna sizing, and transmitting power. The complex relationships between these parameters and their combined impact on SAR system performance and satellite dimensions are demonstrated through various scenarios. Special attention is given to balancing the SAR antenna area and the transmitting power needs, which are primary constraints in SAR microsatellite design. A detailed comparative study is presented, highlighting how each design decision affects the overall functionality and performance. Modern SAR satellites with masses under 150 kg can operate with approximately 1 kW of transmitting power and a 10 m\({}^{2}\) SAR antenna area. The present results analyze and validate the key parameters related to these satellites, coping with the challenging trade-offs through optimization. Furthermore, this study aims to guide future innovative spaceborne SAR system design, highlighting the potential of optimization techniques in advancing spaceborne SAR technology.  
synthetic aperture radar (SAR); microsatellite; small satellite; SAR antenna; SAR system design; SAR system optimization
## 2 System Model
### Secrecy Outage Probability  
Secrecy outage probability is a metric used in communication systems to quantify the likelihood of unauthorized information disclosure. It represents the probability that the confidential information transmitted between parties becomes susceptible to interception or eavesdropping. A lower secrecy outage probability indicates a higher level of security, where the confidential information is less likely to be compromised during transmission.  
The SOP is the probability that the instantaneous secrecy capacity, \(C\), is less or equal to a given capacity threshold, \(\ln\left(1+\gamma_{th}\right)\), which is expressed as  
\[\text{SOP}=Pr\left[\ln\frac{\left(1+\gamma_{D}\right)}{\left(1+\gamma_{E} \right)}\leq\ln\left(1+\gamma_{th}\right)\right]=\int_{0}^{\infty}\int_{0}^{ \left(1+\gamma_{E}\right)\left(1+\gamma_{D}\right)-1}f_{\gamma_{E}}(w)f_{ \gamma_{D}}(u)dudw, \tag{32}\]  
the instantaneous secrecy capacity can be written as  
\[C=\begin{cases}\ln\left(1+\gamma_{D}\right)-\ln\left(1+\gamma_{E}\right)& \gamma_{D}>\gamma_{E}\\ 0&\gamma_{D}\leq\gamma_{E}\end{cases}, \tag{33}\]  
where \(\gamma_{E}\) is the SINR of the channel between the source and the eavesdropper and \(\gamma_{D}\) is the SINR of the channel between the source and the correct destination (the user).  
[PERSON] et al. [15] derived the exact formula of the SOP for a Nakagami-\(m\) distributed eavesdropper channel as (34).  
\[\begin{array}{l}SOP=\sum\limits_{k=0}^{\infty}\frac{(-1)^{k}\beta^{\alpha+k} \gamma_{th}^{\alpha+k}}{\Gamma(\alpha)\Gamma(k+1)}\\ \times\big{(}\frac{\pi m^{m^{2}-\alpha-k}v^{-2m}\Omega^{-m}\Gamma(m+\frac{1}{2} )\csc(\pi(\alpha+k+2m))\emph{2}\emph{F}_{2}(m,m+\frac{1}{2};\frac{1}{2}(k+2m+ \alpha+1),\frac{1}{2}(k+2m+\alpha+2);-\frac{m}{2\alpha})}{\Gamma(-k-\alpha+1)} \\ +\frac{\pi^{3/2}\Omega^{\frac{k+1}{2}}\frac{1}{m^{2}(-\alpha-k)}v^{\alpha+k}}{ 2\Gamma(m)}\big{(}\frac{2\csc(\frac{1}{2}\pi(\alpha+k+2m))\emph{2}\emph{F}_{2} \left(\frac{1}{2}(-k-\alpha),\frac{1}{2}(-k-\alpha+1);\frac{1}{2},\frac{1}{2}( -k-2m-\alpha+2);-\frac{m}{2\alpha}\right)}{v\sqrt{\Omega}}\big{)}\\ -\frac{\sqrt{m}\sec(\frac{1}{2}\pi(\alpha+k+2m))\emph{2}\emph{F}_{2}\left( \frac{1}{2}(-k-\alpha+1),\frac{1}{2}(-k-\alpha+2);\frac{1}{2}(-k-2m-\alpha+3); -\frac{m}{2\alpha}\right)}{v\sqrt{\Omega}}\big{)},\end{array} \tag{34}\]  
where \(v=\frac{1+\gamma_{th}}{\gamma_{th}}\).
## 3 Monitoring Results and Analysis
### Internal Force in the Secondary Supports  
Figure 25 has shown the time history curves of the stresses in the concrete of the secondary supports.  
The monitoring data showed that internal stresses were rapidly generated in the concrete within 48 hours after the concrete had been poured. The internal stresses mainly manifested as compressive stress, e.g., the compressive stress of Monitoring Point G on the inner side and Monitoring Point E on the outer side reached \(-\)4.3 MPa and \(-\)8.5 MPa after 48 h, respectively; however, tensile stress also appeared at a few monitoring points. The stresses at this stage were mainly temperature stresses, which were caused by the large amount of hydration heat generated by the concrete solidifying. The temperature stresses peaked around the second day after the concrete had been poured and then gradually attenuated. With the continuous solidification of the concrete, the stiffness of the secondary support rapidly increased; therefore the load on the secondary support also rapidly increased. At this time, the stress state of the concrete either showed a rapid decrease in the tensile stresses or a rapid  
Figure 24: Final distribution of the contact pressures between the primary and secondary supports.  
Figure 25: Time history curves of the stresses in the concrete of the secondary supports: (**a**) inner side; (**b**) outer side.  
increase in the compressive stresses. This stage lasted between 3-5 days, at which point most of the monitoring points in the tensile state had been completely converted to a compressive state, and the temperature stresses had basically disappeared. After the model board trolley had been dismantled, the concrete stresses experienced a slow increase over 10-20 days. After the concrete stresses reached a stable state, except for the stress of Monitoring Point K on the inner side being a tensile stress, with a value of 0.7 MPa, the stresses of the other monitoring points were compressive stresses. In general, the stresses on the outer side of the concrete of the secondary supports were greater than that on the inner side of the concrete of the secondary supports. The compressive stress of Monitoring Point A on the outer side was the largest, with a value of 8.4 MPa, and this was far lower than the compressive strength of the concrete.  
The axial forces and bending moments of the secondary supports were calculated according to the internal stresses, as shown in Figures 26 and 27.  
As can be seen from Figure 26, the maximum value of the axial forces of the secondary supports was 3896.1 kN at the crown. The distribution of the axial forces along the tunnel's section was relatively larger at the top and relatively smaller at the bottom. It can be seen from Figure 27, that the maximum bending moment occurred at the left arch springing, with values of 164.9 kN-m; followed by the crown and left spandrel, with values of 114.3 kN-m and 91.7 kN-m, respectively. Through a comprehensive analysis of the axial forces and bending moments, the tunnel crown could be considered to be the most unfavorable position.
## 4 Discussion
### Horizontal Movements  
Corroborating previous research on EA white sharks, tracked individuals spent most of their time in coastal waters, underlining the importance of the NSW coast for immature white sharks [18; 22; 23; 24; 38; 39]. Contrary to previous studies suggesting on-shelf shark activity to be primarily showerd of the 120-m depth contour [22; 24], most positional estimates in our study were located further offshore (Figure 1 and map in Figure 2). Yet the large majority of these positions are likely an artefact resulting from the relatively high uncertainty exhibited by light-based geolocations (root mean square errors within \(\sim\)80-150 km [40]). These positions should hence be treated with caution. During the PSAT tracking period the frequency of long-distance migrations was low (8%) but similar to that reported in previous tracking studies on EA white sharks [22]. Only one shark (shark 180) traversed from eastern Australia to waters southwest off New Zealand's South Island, using one of the three north-south migratory corridors previously proposed for the EA white shark population [6; 20; 22]. While another shark (shark 234) also crossed the Tasman Sea, the transit occurred after the 180 d PSAT tracking period and is not presented here (but see [22]).  
Consistent with previous tracking studies [18; 22; 23; 24], seasonal movement patterns were evident, with peak abundances in NSW coastal areas from October through December (Figure S2). However, when interpreting these results, effects of tag deployment date and track duration must be considered. Deployment of tags was biased towards the months when white sharks were present in the tagging area; i.e., May-December (Table 1). Additionally, 57% of tags deployed in this study did not report. Having more than half of the total number of purchased tags as non-reporting was not only financially expensive but also resulted in significant loss of scientific opportunity, as has repeatedly been reported in PSAT studies e.g., [11; 41; 42]. In terms of seasonal analysis, it prevented us from attaining equal numbers of tracks per month, which would have been necessary to fully characterise seasonal patterns. The sharks that provided data were tracked for periods <1 year (93-180 d). The resulting variation in tracked individuals per month might have caused an upward bias on abundance for the months with the highest numbers of tracked individuals, i.e., October-December (11-12 individuals cf. 1 individual for the months February-May).
## 2 Various PPF Strategies
### PPF of Hydroxy Monoliths  
A novel poly(hydroxyethyl methacrylate (HEMA)-_co_-pentaerythnitol triacrylate (PETA)) monolithic capillary column was prepared and subjected to PPF with epoxy alkane, octadecyl isocyanate and epoxy biphenyl for reversed phase CEC [36; 37]. The template monolith referred to as hydroxy monolith (OHM) capillary column was synthesized via in situ thermal polymerization of a mixture containing HEMA as the functional monomer and PETA as cross-linker in the presence of cyclohexanol and 1-dodecanol as binary porogen to obtain neutral monolith. The obtained OHM capillary column was then grafted with epoxy alkanes at varying alkyl chain lengths to have epoxy OHM C-m, where m = 8, 12, 14 and 16, see Scheme 25. The same template monolith was also grafted with octadecyl isocyanate to obtain Isocyanate OHM C-18 (Scheme 25). The study demonstrated that changing the alkyl chain length in the monolith could change the column phase ratio and the solute distribution constant. Small solutes at different polarity have been tested with these capillary columns and showed successful separation performance.  
Moreover, the poly(HEMA-_co_-PETA) monolith was subjected to PPF with epoxy biphenyl yielding biphenyl OHM capillary column [37], see Scheme 26. The obtained monolithic column exhibited hydrophobic interactions and \(\pi\)-\(\pi\) interactions with various solutes. The Biphenyl OHM capillary has successfully separated different compounds, such as anilines derivatives, phenols and phenoxy acid herbicides with good reproducibility.  
The poly(HEMA-_co_-PETA) monolith, on the other hand, was activated with glycerol diglycidyl ether to have epoxy activated OHM for CEC, see Scheme 27 [38]. The activated monolithic capillary column was then subjected to PPF with either glycerol or polyamines to obtain polyol OHM and polyamine OHM columns, respectively. These resulted columns were used in HILIC. In addition, the epoxy activated OHM was also grafted with hydroxypropyl-\(\beta\)-CD (HP-\(\beta\)-CD) to yield HP-\(\beta\)-CD OHM for enantioseparation. These columns exhibited good separation performance and good reproducibility.
## 3 Results and Discussions
### Surface Morphology and Roughness
As the basic strategy behind creating a superhydrophobic surface is making a hierarchical structure, the roughness parameters Ra and Rq are not enough to explain the superhydrophobicity; we need other factors such as the skewness parameter to complete interpretation. In fact, the Sku parameter measurement showed that the method of roughening has a great effect on the peaks/valleys distribution [34]. These parameters are summarized in Table 2. The as-received (A_R) sample has a low roughness of Ra = 0.254 \(\upmu\)m and Rq = 0.301 \(\upmu\)m. Moreover, Sku (= 0.256) indicates that the height distribution on the A_R surface is symmetric. After treatment with the different roughening processes, the surface roughness clearly increased. In fact, the A_WS sample is characterized by a positive skewness value (1.063), indicating a large number of peaks and a few valleys on the surface. Thus, the silane layer is likely mainly deposited on the peaks of this surface. However, the negative skewness obtained on the A_NS surface indicates a greater percentage of the profile above the mean line and a number of valleys higher than the number of peaks. On the other hand, the Sku value of the A_FS sample is around zero (0.234), indicating a symmetric and regular distribution of peaks and valleys on the surface [35]. These different profiles are illustrated in Figure 2.
## 5 Discussion  
A demonstrator for a miniaturized optical encoder with a resolution of 10 bits was presented in this article. In comparison to available absolute optical encoders on the market with comparable small dimensions, this represents a significant fourfold improvement. The MAS 10 series from CUI with a housing diameter of 13 mm only achieves 8-bit resolution [2].  
Attention was paid to a relatively simple assembly concept. Once the optical module was built, the final assembly of the encoder was possible without any further alignment process. In the case of conventional optical encoders, an alignment process between the encoder disc and the optical module is essential. Therefore, it is expected that, in conjunction with the injection-molded encoder disc, the proposed approach for the new optical encoder is even suitable for low-cost applications. Currently, the opto-ASIC is the most expensive part of the encoder. That means further research projects should focus on smaller opto-ASICs with a smaller silicon surface area to reduce costs. For  
\begin{table}
\begin{tabular}{c c} \hline
**Feature** & \\ \hline Method & Incremental and absolute \\ Resolution & 1024 positions \\ Speed & 12,000 rpm \\ Interface & Digital and SPI \\ Temperature & \(-40\)... \(+85\)\({}^{\circ}\)C \\ Size & \(10\times 10\times 10\) mm\({}^{3}\) \\ \hline \end{tabular}
\end{table}
Table 2: The most important features of the encoder; SPI—Serial Peripheral Interface.  
Figure 22: Encoder with the segmented photodiodes. (**a**) Laser spot on the photo detector; (**b**) user interface for evaluation of the position of the laser spot.  
mass production of the presented encoder, an automatic assembly of lenses using active alignment has yet to be implemented into assembly lines.  
Another topic is the encoder signals. As shown in Figure 20, the signals were very stable; however, they can obviously be further improved. Slight fluctuations over one revolution could be observed. Firstly, the reasons for these fluctuations should be analyzed. A small misalignment of the lens can possibly cause such effects. Once the signals are improved, the resolution of the encoder can be increased using interpolation. The interpolation can be integrated into the opto-ASIC. Therefore, specific interpolation algorithms need to be developed, such as a correction of the analog sine and cosine signals. In this way, the usual conversion function arc tangent can be used, such that the phase angle can directly be determined from the sine and cosine voltage, thereby resulting in a linear correlation between angular position and the encoder signal. Thus, a discretization of the angular position is possible [23]. Initial assessments prove the expectation that a resolution of up to 15 bits is achievable.
# Mathematical Models for Named Data Networking Producer Mobility Techniques: A Review
###### Abstract  
One promising paradigm for content-centric communication is Named Data Networking (NDN), which revolutionizes data delivery and retrieval. A crucial component of NDN, producer mobility, presents new difficulties and opportunities for network optimization. This article reviews simulation strategies designed to improve NDN producer mobility. Producer mobility strategies have developed due to NDN data access needs, and these methods optimize data retrieval in dynamic networks. However, assessing their performance in different situations is difficult. Moreover, simulation approaches offer a cost-effective and controlled setting for experimentation, making them useful for testing these technologies. This review analyzes cutting-edge simulation methodologies for NDN producer mobility evaluation. These methodologies fall into three categories: simulation frameworks, mobility models, and performance metrics. Popular simulation platforms, including ns-3, OMNeT++, and ndnSIM, and mobility models that simulate producer movement are discussed. We also examine producer mobility performance indicators, such as handover data latency, signaling cost, and total packet loss. In conclusion, this comprehensive evaluation will help researchers, network engineers, and practitioners understand NDN producer mobility modeling approaches. By knowing these methodologies' strengths and weaknesses, network stakeholders may make informed NDN solution development and deployment decisions, improving content-centric communication in dynamic network environments.  
named data networking; producer mobility; simulation methods; performance optimization; mobility models; ns-3; OMNeT++; ndnSIM; machine learning; artificial intelligence; performance metrics  
68M10; 68M12; 68M20; 68R10; 90B10; 90B18
## 2 Millimeter-Wave Integrated Cellular Network  
### HetNet Control Plane and User Plane Segmentation  
CP/UP segmentation [17] is also referred to as a \"phantom unit\" or \"soft unit\" in which CP and UP are processed separately. In a CP/UP-segmented HetNet, a mobile station (MS), which is also known as user equipment (UE), is capable of maintaining a logical link with a macrocell and a plurality of junior units. This capability is referred to as dual connectivity (DC), and can handle CP and UP separately and flexibly. A single CP also supports efficient, centralized radio resource control (RRC). Since UE must start multiple RF devices at the same time, DC will affect the peak power consumption of UE. However, a higher throughput reduces the download time, allowing the device to shift to sleep status shortly after the download ends.  
To achieve CP/UP segmentation, macrocells and junior units are linked together through a backhaul line interface and controlled by the same RRC entity, as shown in Figure 1. The backhaul line, as the intermediate link between the unit and the centralized network, constitutes the core part of the CP/UP-segmented HetNet.  
Figure 1: Control plane/user plane (CP/UP) segmentation based on heterogeneous network (HetNet) architecture in a centralized radio frequency access network (C-RAN).  
DC and CP/UP segmentation features are introduced in Release 12 of the 3 GPP LTE standard. In Release 13, it is extended to support DC (dual connectivity) between LTE and IEEE 802.11 wireless local area networks (WLAN).