esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### First Query: Use of AI on CFD
#### 4.1.2 Analysis of Institutions  
This query also included an analysis of the institutions affiliated with each publication. As shown in Table 2, the ten institutions with the highest number of publications were all located in China. The first institution outside of China appears at rank eleven: Imperial College London in the United Kingdom, with a total of 78 publications.  
Considering this criterion, the countries with the highest number of affiliated institutions are China (59), the United States (28), the United Kingdom (9), and Iran (9). These figures once again highlight China's significant efforts in research and innovation, particularly in the field of artificial intelligence.  
A key observation from Table 2 is the dominance of China with two leading research institutions: the Ministry of Education of the People's Republic of China (MOE) [68] and the Chinese Academy of Sciences [69]. These public institutions represent the most significant entities for education and scientific research in China. Both were established in 1949, with the MOE focused on overseeing education at all levels and the Chinese Academy of Sciences dedicated to advancing scientific research.  
As a governmental institution, the MOE is primarily funded by the Chinese central government. In addition to managing research centers and universities, it is responsible for all aspects of education nationwide. Meanwhile, the Chinese Academy of Sciences, although also a public institution, receives a higher proportion of private funding compared to the MOE. It is more research-oriented than its counterpart and operates over 100 research centers across China [70; 71; 72].
# Factors Influencing the Effects of Triticale on Laying Hens' Performance: A Meta-Analysis
## 2 Materials and Methods
### Inclusion and Exclusion Criteria  
A study was deemed eligible for inclusion in this meta-analysis if it fulfilled the following criteria: (1) conducted as an in vivo study in English; (2) focused on examining the impact of dietary triticale inclusion levels as treatment variable; (3) compared the effects of one or more triticale inclusion levels to those of other cereals (such as maize, wheat, etc.); (4) utilized laying hens as the experimental animal model; (5) reported at least one response related to hens' production parameters (including egg production (EP); egg weight (EW); feed intake (FI); feed conversion ratio (FCR); and egg yolk color (EYC)), along with the respective variance (standard deviation (SD) or standard error (SE)). Conversely, review articles, studies where triticale served as the sole basal diet, and experiments involving animals other than laying hens were excluded from consideration.
## 3 Estimation of Stray Capacitance of Single-Phase CMIs  
### Turn-to-Turn Capacitance  
Figure 6 illustrates that the parallel stray capacitance, \(C_{\text{p}}\), is modeled as a ladder circuit consisting of the turn-to-turn capacitance, \(C_{\text{t-t}}\), and the turn-to-core capacitance, \(C_{\text{t-c}}\)[29]. Initially, the turn-to-turn capacitance is determined using Equations (10) and (11), which are based on the assumption of an electric force line existing between adjacent windings, as depicted in Figure 7[30].  
\[C_{\text{t-t}}=\frac{2\varepsilon_{\text{o}}l_{\text{w}}}{\sqrt{\beta^{2}\ -1}}\cdot\text{tan}^{-1}\sqrt{1+\frac{2}{\beta-1}} \tag{10}\]  
\[\beta=\frac{1}{\varepsilon_{\text{ri}}}\text{ln}\,\frac{d_{\text{o}}}{d_{ \text{w}}}+\frac{p}{d_{\text{o}}} \tag{11}\]  
where \(l_{\text{w}}\) is the total winding length, \(\varepsilon_{\text{ri}}\) is the permittivity of the insulation coating, and \(p\) is the distance between the center of the conductors.  
In toroidal inductors, the distance between the centers of conductors varies from the inside to the outside of the toroidal core. For this reason, the distance between the centers of conductors is calculated in three regions: inside, outside, and lateral of the core \(p_{\text{t}}\), \(p_{\text{o}}\)  
Figure 6: Capacitance network for calculating the total stray capacitance of the winding with \(N\) turns.  
Figure 7: Electric flux line between adjacent turns.  
and \(p_{1}\). Based on Figure 8, \(p_{\rm i}\) and \(p_{\rm o}\) are obtained as Equations (12) and (13), respectively, as a function of the angle \(\varphi\), at which the winding covers the core [20].  
\[p_{\rm i}=(ID-d_{\rm o})\cdot\sin\frac{\varphi}{2(N-1)} \tag{12}\]  
\[p_{\rm o}=(OD+d_{\rm o})\cdot\sin\frac{\varphi}{2(N-1)} \tag{13}\]  
Moreover, \(p_{1}\) is obtained as the average of \(p_{\rm i}\) and \(p_{\rm o}\) (\(p_{1}=(p_{\rm i}+p_{\rm o})/2\)).  
Winding lengths inside, outside, and lateral of the core \(l_{\rm wri}\), \(l_{\rm wo}\), and \(l_{\rm wl}\) are calculated as  
\[l_{\rm wri}=l_{\rm wo}=HT+2d_{\rm o} \tag{14}\]  
\[l_{\rm wl}=\frac{OD-ID}{2} \tag{15}\]  
By substituting the distance between the centers of conductors and winding lengths in each part of the toroidal core calculated from Equations (12)-(15) into Equations (10) and (11), the turn-to-turn capacitances inside, outside, and lateral of the core \(C_{\rm t-t\perp}\), \(C_{\rm t-t\circ}\), and \(C_{\rm t-t\perp}\) can be calculated, respectively. The turn-to-turn capacitance \(C_{\rm t-t}\) is obtained as the sum of calculated results in each part of the core (\(C_{\rm t-t}=C_{\rm t-t\perp}+C_{\rm t-t\circ}+2C_{\rm t-t\perp}\)).
# Radiometric Comparison within the Sentinel-1 SAR Constellation over a Wide Backscatter Range
## 2 Materials and Methods
### Reference Targets  
The Microwave and Radar Institute of the German Aerospace Center (DLR) has established a SAR calibration center for preparing and executing calibration of spaceborne SAR systems, like TerraSAR-X [21], TanDEM-X [22] or S-1A [12], and S-1B [13]. Part of this center is a calibration site consisting of six automatically operated reference targets: three trihedral corner reflectors and three C-band transponders [14]. These targets are remotely controlled and permanently monitored for their health status. Each target is aligned by schedule in the expected line-of-sight direction for an upcoming satellite overpass for both ascending and descending orbits. The corner reflectors have a leg length of 2.8 m which corresponds in C-band to a RCS of 49.2 dB m\({}^{2}\). The high mechanical tolerance of 1 mm ensures an absolute radiometric accuracy for these targets of 0.2 dB (1\(\sigma\)). The C-band transponders have a center frequency of 5.405 GHz and a bandwidth higher than 100 MHz corresponding to the characteristics of the Sentinel-1 SAR instrument. The transponders are temperature controlled and automatically adjusted by their own internal calibration facility before each overpass to ensure a radiometric stability below 0.1 dB (1\(\sigma\)). An absolute radiometric accuracy of 0.2 dB (1\(\sigma\)) was achieved by executing and analyzing different calibration methods [23].  
The transponders are also able to operate as ground receivers. Using this feature, the transmitted SAR antenna pattern can be recorded at ground in order to analyze the relative power of the SAR instrument and to derive the related azimuth antenna pointing [24]. Each transponder consists of two antennae: one to receive (RX) and one to retransmit (TX) the electromagnetic pulses. Both antennae are rotated by 45\({}^{\circ}\) along the line-of-sight axis. This allows receiving both polarization signals (H and V) transmitted by the SAR instrument with the transponder but also receiving both signals (H and V) at the SAR instrument retransmitted by the transponder. This enables radiometric evaluation also for crosspolarized products (HV and VH). In contrast to this, trihedral corner reflectors show impulse responses for copolarized SAR images only (HH, VV) as the polarization of an incidence wave is not rotated by a trihedral corner reflector.
# A Compact Dual-Band Microstrip Patch Antenna for C- and X- and Ku-Band Applications +
## 2 Design of Antenna
### Step 3 Antenna  
Now, we have a pentagonal-shaped patch with a partial ground configuration. In this stage, four circles are attached to each corner of the pentagonal-shaped patch while the fifth one is attached to the feedline. The design of the antenna is shown in Figure 1c.
# RL-Based Sim2 Real Enhancements for Autonomous Beach-Cleaning Agents
## 5 Environment Project Setup
### Gym Environments  
Two environments have been developed to bridge the gap between theoretical models and practical application. The first, a simulated environment crafted within CoppeliaSim, mirrors the intricate dynamics of robot-environment interactions with high fidelity. The second, its physical counterpart, is established in a laboratory setting, ensuring that RL agents receive consistent training experiences across both settings.  
The primary task in these environments involves guiding the Khepera IV robot to reach a visually marked Target Point (TP) while navigating through scattered obstacles within a 2 m \(\times\) 1 m area. Training sessions initiate with the robot in a central position, concluding upon successful TP navigation, a collision, or when exceeding a predefined step limit (Figure 3).  
The observation space of the environments, which provides the current state to the agent, consists of a list that includes the distance between the robot and the target (\(d\)), the angular difference between the robot's orientation and the target position (\(O_{c}\)), ranging from \(\pi\) to \(-\pi\), and a set of normalized measurements (between 0 and 1) from the robot's eight infrared sensors (as illustrated in Figure 4). These values are either derived from the simulation or calculated through requests made via the simulation API. In actual environments, the absolute position and orientation of the robot are determined through a tracking platform equipped with a camera.  
Figure 2: Comparison of Simulated Khepera IV with IR Sensors (**a**) and Real Khepera IV [44] (**b**).  
The action space of the environments is defined by the linear (\(V\)) and angular (\(\omega\)) velocities of the mobile robot, reflecting the fundamental kinematic model of a differential drive robot. This framework allows for precise control over the robot's movements, as supported by the well-established kinematic equations cited in [45]. The linear velocity facilitates forward motion, while the angular velocity governs the rotation around the robot's central axis. The control signals for both linear and angular velocities are normalized to ranges of \(0\) to \(1\) and \(-1\) to \(1\), respectively, ensuring compatibility with the simulation environment.  
The reward function, depicted in Equation (4), is structured to reward proximity to the TP (distance \(d\)) and penalize collisions, thereby promoting a strategic and cautious approach. The reward increases as the robot nears the TP and adjusts negatively if sensor readings indicate proximity to obstacles. Parameters \(R_{collision}\) and \(R_{arrival}\) were empirically calibrated to enhance the learning and operational efficacy of the robot, with assigned values of \(-10\) and \(10\), respectively.  
\[Reward=\left\{\begin{array}{cc}R_{arrival}&\text{if the robot reaches the TP}\\ R_{collision}&\text{if the robot collides}\\ -d^{2}-\sum sensors&\text{in another case}\end{array}\right. \tag{4}\]
### Overview of Main Contributions  
Concerning network EE, implementing any type of network slicing other than E2E NS is not an overall solution for satisfying the ambitious goals dedicated to improving the EE of 5G networks. It can rather be treated as an improvisation in a specific use case or a transitional solution until the establishment of E2E NS in 5G networks. Examples of such slicing concepts are presented in Figure 2 as slice 1 (RAN slicing), slice 2 (TN slicing), slice 3 (CN slicing), etc. The increased cost of energy and ecological awareness has led to the development of the Energy-Aware Virtual Network Embedding (EA-VNE) concept [6; 7; 8] in which the goal is to optimize the network's energy consumption through the allocation of virtual network demands to physical network resources. Since the implementation of E2E network slicing and EA-VNE involves establishing numerous virtual networks on a shared 5G network physical infrastructure, technologies such as network function virtualization (NFV) and software-defined networking (SDN) become key enablers of 5G network slicing [9]. Apart from these two technologies, important enablers of network slicing include edge and cloud computing [10]. All of these concepts will be analyzed in the rest of the paper as means for improving 5G network energy efficiency through the implementation of network slicing.  
Thus, the contribution of this paper is to highlight the advantages of employing network slicing as a means of enhancing the EE of 5G networks. Based on a review of the published literature, it will be shown that the topic of improving 5G network EE through network slicing has not been thoroughly addressed at the time of writing this paper, and this paper aims to fill this gap. Key performance indicators (KPI) for determining the EE of 5G network slices will be presented, along with research related to standardization led by the third-generation partnership project (3 GPP) in the field of defining the energy efficiency of NSs. The paper also discusses energy efficiency in 5G network slicing lifecycle management. To show how efficient network slicing can optimize the energy consumption of 5G networks, versatile 5G network slicing use case scenarios, approaches, and resource allocation concepts in the space, time, and frequency domains are discussed, including artificial intelligence-based implementations of network slicing. The paper also discusses ideas for further research in the field of improving 5G network EE through the implementation of network slicing.  
The rest of the paper is organized as follows. Section 2 overviews scientific papers published on the topic of the impact of network slicing on 5G network EE. Section 3 deals with a detailed overview of network slicing and standardized types of NS use cases. Section 4 discusses elements of 3 GPP standardization dedicated to the implementation of NS on network EE. Section 5 provides a general description of the impact of network slicing on 5G network EE, as well as ideas for possible further research on improving the EE of 5G networks through the implementation of NS. Section 6 concludes the paper.
## 5 Parameter Analysis
### Tunnel-Surcharge Angle \(\alpha\)  
In the following analysis, the tunnel model in the previous analysis remains the same, and only the tunnel-surcharge included angle \(\alpha\) is changed. Figure 15 shows the relationship between the tunnel-surcharge angle \(\alpha\) and the maximum longitudinal displacement \(w_{\text{max}}\) of the existing tunnel at different tunnel burial depths, where the tunnel-surcharge angle \(\alpha\) ranges from 0\({}^{\circ}\) to 90\({}^{\circ}\). As shown in Figure 15, for different tunnel burial depths, the maximum longitudinal displacement \(w_{\text{max}}\) of the existing tunnel follows an \"S-shaped curve\" trend that decreases as the tunnel-surcharge angle \(\alpha\) increases. For the same tunnel-surcharge angle \(\alpha\), as the tunnel burial depth increases, the maximum longitudinal displacement of the existing tunnel decreases proportionally, indicating that the influence of the surcharge on the tunnel is linearly related to the burial depth of the existing tunnel. Furthermore, when the tunnel depth \(z_{0}=9\) m, the maximum displacement of the existing tunnel caused by overloading is close to the standard allowable value of 20 mm. When the buried depth of the tunnel \(z_{0}=24\) m, the maximum longitudinal displacement of the tunnel is approximately 10 mm, indicating that the tunnel is still greatly affected by the overload. Therefore, changing the depth of the tunnel cannot completely remove the effect of the overload on the existing tunnel.
## 3 Results and Discussion
### XRD, SERS and Surface Morphology of Annealed ZnO NRs  
The quality of seed layers affects the preferred orientation of ZnO NRs. Previous studies have proved that surface morphology plays a critical role in the semiconducting nanostructure of the SERS substrate [4]. Therefore, we annealed seed layers at various temperatures to achieve superior ZnO NRs SERS substrates. Figure 3a illustrates the XRD patterns of ZnO NRs fabricated with annealed seed layers at various annealing temperatures. The polar-terminated (100) and nonpolar low-symmetry (101) faces disappeared in the ZnO NRs fabricated using seed layers annealed at temperatures higher than 150 \({}^{\circ}\)C. Figure 3b depicts the corresponding Raman signal for 10\({}^{-3}\) M R6G, which indicated the enhanced Raman sensitivity of ZnO NRs grown using annealed seed layers. Figure S1 shows the SERS behaviors of ZnO NRs grown using annealed seed layers at the R6G concentration of 10\({}^{-4}\) M and 10\({}^{-5}\) M. Notably, the sample fabricated using ZnO seed layers annealed at 150 \({}^{\circ}\)C exhibited the highest sensitivity for the SERS signal. High annealing temperature was favorable for the growth of the c-axis (002) orientation and contributed to the increased SERS signal; however, the SERS signal decreased when the annealing temperature was higher than 150 \({}^{\circ}\)C. Figure 4 depicts the surface morphology of ZnO NRs fabricated using different seed layers annealed at various temperatures. The sample annealed at 100 \({}^{\circ}\)C exhibited a larger diameter (500 nm) of ZnO NRs than that of the unannealed sample (Figure 2a). However, the diameter of ZnO NRs decreased to 100 nm at the annealing temperature of 150 \({}^{\circ}\)C when using as-prepared seed layers. The diameter of ZnO NRs slightly increased at the  
Figure 2: (**a**) SEM image of ZnO NRs fabricated using the sol–gel method on a glass substrate; (**b**) XRD patterns of ZnO NRs fabricated by the unannealed seed layer, indicating both vertical (002) and horizontal (100) and (101) growth direction; (**c**) Raman signal of rhodamine 6G (R6G) using bare glass and ZnO NR-coated glass as surface-enhanced Raman scattering (SERS) substrates.  
annealing temperature of 250 \({}^{\circ}\)C. Compared with the corresponding SERS results in Figure 3b, applying the annealing technique when preparing seed layers decreased the diameter of ZnO NRs and further increased the SERS signal because of the increase in the surface-to-volume ratio of the SERS substrate. However, when the annealing temperature was higher than 150 \({}^{\circ}\)C, the diameter of ZnO NRs slightly increased, which was not favorable for the Raman signal enhancement of the ZnO NR SERS substrate.  
Figure 3: (**a**) XRD patterns of annealed ZnO NRs indicating the disappearance of horizontal (100) and (101) growth direction with the increase in the annealing temperature applied on the growth of seed layers; (**b**) Raman signal of R6G (10\({}^{-3}\) M) using ZnO NR-coated glass as the SERS substrate, in which ZnO NRs were fabricated using seed layers annealed at different temperatures.
## 2 Methods and Results  
### Literature Search  
PubMed and Web of Science databases were searched for eligible published articles until March 2021, using specific free words. Different combination of the following terms was used: osteoblast, osteocytes, proteasome, proteasome inhibitors, bone, bone regeneration, bone remodeling. Of the results obtained, only articles published in English were included. Other potential articles were identified from references within the selected articles or reviews related to the topic.  
Inclusion criteria are listed as: (a) human MSC; (b) human osteoblast; (c) human osteocytes; (d) proteasome; (e) proteasome inhibitors; (f) studies focused on the role of proteasome on bone remodeling; (g) studies focused on the role of proteasome on bone remodeling in MM and metastatic bone cancers; (h) in vitro studies; (i) in vivo studies; (l) case reports; (m) review; (n) conference abstracts. Exclusion criteria are listed as (a) article does not meet inclusion criteria, (b) studies focused on the role of PIs on osteoclast formation.  
The titles and abstracts were screened to assess the suitability of the results. Then, the full text of the selected studies were analyzed to assess whether or not they satisfied the inclusion criteria. Only articles published in English were included, which may leave out other eligible publications that were reported in other languages. Therefore, the results should be interpreted cautiously due to the limited data.
## 1 Introduction
[13] reported the pre-grouting application in two subsea tunnels with fault zones, and they observed that pre-grouting volume considerably varied in different fault-related sub-zones. The injected cement ratio among the fault core, inner damage zone, and outer damage zone was given as 1:2:1. [PERSON] et al. [14] reviewed the grouting project of the Namntall Tunnel, and they found the geologic and hydraulic conditions of rock mass greatly influenced the grouting performance. Then, they suggested a novel method to predict hydrogeological conditions and to optimize the grouting process. [PERSON] et al. [15] presented a practical curtain grouting case in the highly weathered rock mass of Xiang'an subsea tunnel, the process of double compaction grouting and double fracture grouting were elaborated. And the subsea tunnel safely passed through the fault zones with the ground treatment of CGPR. Moreover, [PERSON] et al. [16] proposed a convergence-confinement model [17] to theoretically study the reinforcing effect of CGPR in a circular tunnel, they found the plastic zone may initiate from the outer boundary of the reinforced zone, the inner boundary of the reinforced zone, or both the outer and the inner boundaries simultaneously. Six different configurations were deduced based on the distribution and extent of the plastic zone(s) and the grouting thickness. Seven critical unconfinement factors were solved to define the possible transition between two consecutive configurations. Previous studies primarily focused on the independent application of PRPS or CGPR in the single tunnel. However, for the closely spaced tunnels excavated in the fault zone or jointed zone, an independent pre-construction technique may fail to meet the high safety requirements. In some cases, the PRPS and the CGPR should be adopted jointly. Besides, due to the inevitably complicated interaction behavior between neighboring tunnels [18], the effects of pre-construction techniques are more challenging to evaluate. This paper reports the combined application of PRPS and CGPR in the excavation of large span closely spaced triple tunnels of the Badaling Great Wall Underground Station. In situ data, including surrounding rock pressure and crown settlement, were initially monitored to show the urgent need for pre-constructions. Reasonable design parameters of PRPS and CGPR were determined by numerical optimization. The working mechanism of the PRPS and the CGPR were investigated by comparative analysis. The mutual effects of PRPS and CGPR were evaluated and verified by the numerical analysis and the engineering application. This study may provide a practical reference for other similar tunneling projects.