esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
We presented a protocol for routing of queries in a region of interest. The protocol relies on a structure that consists of routing trees and convex hulls of tree branches and the full trees, which represent the respective coverage areas. This structure is used for an efficient routing of queries. This paper focuses on handling mobility where several sensors move in the sensor field. The mobility imposes high needs in the updating of the geocast structure. We proposed a local update mechanism, where updates are triggered by moving sensor nodes and are kept constrained only to the parts of the structure affected by those nodes. We compared the performance of local updates with global updates of the structure in terms of query execution ratio and the communication overhead caused by the two types of updates. The proposed local updating mechanism is designed for coping well with such dynamic environments, by using distributed local link reversal. The simulation results demonstrate that geocasting with local updating performs a better query dissemination in terms of execution ratio with relatively low communication overhead, compared to geocasting with global updating. Although increasing number and speed of mobile nodes results in increase in the overall communication overhead on the network, local updating still performs better than global updating in terms of execution ratio. This is due to the fact that local updating performs continuous updating of the tree branches which are affected by the mobile nodes. The most critical issue is the mobility rate (_i.e._, number of mobile nodes/total number of nodes) to decide which approach performs better. Indeed, if the network under consideration is highly mobile where most of the nodes are moving, tree based routing approaches become very costly for maintaining the routing structure. Although tree based routing protocols can tolerate topology changes in mostly static sensor network deployments, they cannot survive excessive topology changes in highly mobile deployments. For highly dynamic WSN applications, an alternative approach can be routing protocols that do not involve any pre-computation of distributed routing structures (e.g., trees) and give routing decision based on locally available information, e.g., [38].
## 5 Filter Validation
### Second Scheme  
As a consequence of the second scheme, Figure 11 plots changes in the measured and estimated capacities, comparing estimation accuracy across the number of cycles. Unlike the first scheme, the present capacity is estimated during battery charging, corresponding to one single load type. This load profile is simply based on constant power charging at 9 W until the SOC reaches 90%, reflecting the real-world operations of PHEVs. By decoupling capacity estimation from the parameter filter of the DEKF, an estimation error within 3% can be realized, as witnessed in [42].  
Figure 12 presents the SOC error, comparing estimation accuracy across the type of load profiles and the number of cycles. The resulting estimates are comparable to those in Figure 8, which is derived from the first scheme. Unfortunately, despite notable improvement in capacity accuracy, no corresponding enhancement is observed in the SOC. The SOC error is actually worse than in the first scheme, yielding RMSEs exceeding 5%  
Figure 11: Variations in capacity estimates over cycles in the second scheme based on the constant power charging profile.  
Figure 10: Variations in capacity estimates over cycles in the first scheme based on the (**a**) City, (**b**) Highway, and (**c**) High-speed profiles.  
after 600 cycles. Despite having capacities close to their true values, the decline is persistent. Compared to the cell model, its filter is much more responsible for this inaccuracy in SOC estimation. This is because, as demonstrated in Figure 5, the ESC 1 RC model can only retain accuracy within a certain range if its parameters are properly identified using the sequential estimator. Therefore, in the next scheme, all the model parameters except for capacity are updated intermittently with the sequential estimator.
## 4 Results and Discussion
### Analysis of the Computational Complexity  
In this section, we will analyze the computational complexity of the ACRLPC framework in comparison to the three benchmark methods (FDU-MAC, TCH-MAC, and UW-ALOHA-QM). The main motivation behind this comparison was to analyze the computational efficiency of the ACRLPC framework compared to these other methods. This paper provides both theoretical derivation of orders of computational complexity and empirical validation through views plots.  
Figure 6: Accuracy comparison of ACRLPC with benchmark techniques.
## 3 Experimental Results and Discussion
### Mechanical Properties and the Permolecular Structure

Table 3: The physico-mechanical properties of neat ultrahigh molecular weight polyethylene UHMWPE and the UHMWPE-based composites silanized with the “Penta-1006” coupling agent. It can be concluded from the SEM micrographs presented in Figure 3 that the similarity of the permeicular structure (small spherulites with evenly distributed microfibers) was in the composites with wollastonite both untreated and functionalized with all three studied coupling agents. Moreover, the spherulitic nature of the permeicular structure was observed at high loading degrees as well (up to 23 wt. %, Figure 4). An analysis of the FT-IR spectra of the composites loaded with wollastonite (both untreated and silanized) showed that the functionalization with the studied coupling agents contributed to the appearance of additional relatively weak peaks reflecting the formation of a chemical bond between the coupling agents and wollastonite (inverse wavelength range of 1100-759 cm\({}^{-1}\), Figure 5). The pronounced absorption peak at 717 cm\({}^{-1}\) was a characteristic one of SiCH\({}_{2}\). The peak at 800 cm\({}^{-1}\) was characteristic of Si(CH\({}_{3}\))\({}_{2}\), while at 1028 and 1090 cm\({}^{-1}\) there were twin absorption peaks of Si-O [42; 45]. Figure 4: SEM micrographs of permeicular structure of the composites: UHMWPE + 23 wt. % wollastonite (**a**) and UHMWPE + 23 wt. % wollastonite + “KH-550” (**b**). Figure 3: SEM micrographs of the permeicular structure of neat UHMWPE (**a**) and the composites: UHMWPE + 15 wt. % wollastonite (**b**), UHMWPE + 15 wt. % wollastonite + “KH-550” (**c**), UHMWPE + 15 wt. % wollastonite + “Penta-1006” (**d**), and UHMWPE + 15 wt. % wollastonite + “OTS” (**e**). It follows from the analysis of the IR-spectra that the intensities of the characteristic peaks of SiCH\({}_{2}\) (717 cm\({}^{-1}\)) were slightly enhanced in the composites with functionalized wollastonite in comparison to those with the untreated one. The degree of increase in SiCH\({}_{2}\) peaks was governed by chemical bonding with the modifier. The peaks at 1090 and 1028 cm\({}^{-1}\) were due to the presence of Si-O bonds and remained almost unchanged after the wollastonite functionalization with the coupling agents. Thus, the improvement of the mechanical properties of the UHMWPE-based composites with untreated and silanized wollastonite (as opposed to the fiberglass functionalization with the same coupling agents) was mainly due to the \"mechanical\" adhesion of the complex shape filler with the polymer matrix [42]. By the term \"mechanical adhesion\" the authors had in mind the effective mechanical adhesion of complex-shaped fibers due to their developed surfaces, reinforced by the action of compressive stresses formed during hot pressing of the polymer tightly fitted with the filler.
## 2 Materials and Methods
### Experimental Setup for Transmon Characterization  
Full characterization of the 3D transmon qubit was performed at the INFN National Laboratory of Frascati. The experimental setup is shown in Figure 2.  
The dashed lines indicate the different temperature stages of the cryostat of a dilution refrigerator. The device is hosted in the 10 mK stage. Control and readout signals entering Line 1 are attenuated by \(-20\) dB at 4 K and by \(-30\) dB at 10 mK. Including the attenuation of the coaxial cable, the total attenuation is \(-68\) dB.  
Both input and output ports are filtered with IR and low-pass filters with a 10 GHz cutoff frequency, while an additional 4 GHz high-pass filter is mounted on the input port. The output signal passing through Line 5 is amplified with a high electron mobility transistor by 36 dB at 4 K and with two field effect transistors by 35 dB and 30 dB at 300 K. Two circulators are used to minimize the reflected noise and decouple the amplification stages.  
For time-domain measurements, the qubit control pulses are directly produced by a RF source (ROHDE-SCHWARZ SMA100B). The readout pulse is obtained by the vector modulation of a signal generated by a second RF source (ROHDE-SCHWARZ SGS100A) at the cavity frequency and controlled by a square-wave pulse a width of 10 \(\upmu\)s generated by a wave function generator (KEYSIGHT 33500B) triggered by the SMA100B.  
Both the control and readout pulses are transmitted to Line 1 through a combiner. After amplification, the readout pulse is down-converted and I and Q quadratures are acquired with a 16-bit ADC board at a rate of 1 GS/s and post-processed to determine the qubit state.  
For cavity and qubit spectroscopy, the generation and acquisition of the readout pulse is replaced by the S21 measurement with Vector Network Analyzer (VNA).  
Figure 1: **(Left)**: Al cavity hosting the transmon chip. **(Right)**: optical image of the transmon shunt capacitance pads acquired with a 100\(\times\) magnification. The JJ is not observable since it is roughly 200 \(\times\) 200 nm\({}^{2}\), but it is located between the pads, in proximity of the two observable metal extensions.
###### Abstract  
The trailing edge regions of high-temperature gas turbine blades are subjected to extremely high thermal loads and are affected by the external wake flow during operation, thus creating great challenges in internal cooling design. With the development of cooling technology, the dimple and protrusion have attracted wide attention for its excellent performance in heat transfer enhancement and flow resistance reduction. Based on the typical internal cooling structure of the turbine blade trailing edge, trapezoidal cooling channels with lateral extraction slots are modeled in this paper. Five channel outlet configurations, i.e., no second passage (OC1), radially inward flow second passage (OC2), radially outward flow second passage (OC3), top region outflow (OC4), both sides extractions (OC5), and three dimple/protrusion arrangements (all dimple, all protrusion, dimple-protrusion staggered arrangement) are considered. Numerical investigations are carried out, within the \(Re\) range of 10,000-100,000, to analyze the flow structures, heat transfer distributions, average heat transfer and friction characteristics and overall thermal performances in detail. The results show that the OC4 and OC5 cases have high heat transfer levels in general, while the heat transfer deterioration occurs in the OC1, OC2, and OC3 cases. For different dimple/protrusion arrangements, the protrusion case produces the best overall thermal performance. In conclusion, for the design of trailing edge cooling structures with lateral slots, the outlet configurations of top region outflow and both sides extractions, and the all protrusion arrangement, are recommended.  
t 24 May 2019; 9, 2000; doi:10.3390/app9142900
## 4 Results and Discussion
### Optimization Results for Four Degrees of Freedom
For the present study of the physical problem, the algorithm DE1 with \(NP=40\) and \(NG=112\) is highly adequate for the reproduction of the effect of degrees of freedom over the thermal performance and corresponding optimal configurations. As a consequence, it will be used in future studies associated with constructal design for optimization of a complex system with 6 or 7 degrees of freedom, significantly reducing the computational effort in comparison with exhaustive search (benchmark solution). Figure 17 shows the evolution of the optimal configuration for some values of \(H_{0}/L_{0}\), demonstrating the adaptation of the design for different heights of the main stem of a double Y-shaped cavity. Figure 17a-c shows that the optimal values of the \((S_{1}/H_{0})_{0}\), \((\alpha)_{20}\), and \((\beta)_{30}\) changed to assume the configurations that led to three times minimized dimensionless maximum excess of temperature \((\theta_{max})_{3m}\) in the solid domain for each value of \(H_{0}/L_{0}\). In Figure 17c it is possible to notice the configuration that led to the dimensionless maximum excess of temperature four times minimized, \((\theta_{max})_{4m}\), when \((H_{0}/L_{0})_{0}=24.5\). For lower magnitudes of the height of the main stem of the double Y-shaped cavity, Figure 17a, the optimal angles are positive to improve the thermal distribution in the upper region of the solid domain. As the stem height increases, Figure 17b,c, the angles of the branches and the position of the inferior branch are reallocated to redistribute the heat in the center and inferior areas of the solid domain, minimizing the thermal resistance in the solid domain. Figure 17 also demonstrates that the number of hot regions in the solid domain changed from five to six from cases of Figure 17a,b to case Figure 17c, showing that the most homogeneous distribution of temperature leads to the best thermal performance. Figure 16: Effect of \(H_{0}/L_{0}\) over overall \((\theta_{max})_{3m}\) and standard deviation for different DE versions and combinations of \(NP\times NG\): (**a**) \(NP=10\), \(NG=50\); (**b**) \(NP=20\), \(NG=100\); (**c**) \(NP=30\), \(NG=100\); (**d**) \(NP=40\), \(NG=112\). This behavior is in agreement with the constructal principle of \"optimal distribution of imperfections\".
# An SSI-Based Solution to Support Lawful Interception
## 3 Motivations and Goals
However, the scenario presented above represents a simplification of a real-life LI procedure. Indeed, when an employee of the agency asks the LEA for the credentials, the LEA has to be sure that such an employee has the right to obtain such credentials. Again, the SSI approach can solve the problem. Indeed, the right to obtain the credentials from the LEA can be granted if the employee provides the LEA with other credentials released by the agency. These new credentials attest that the employee is authorized by the agency to participate in this LI procedure. In other words, in this case, the roles of the parties are different in contrast with the previous case (the agency represents the issuer and the LEA represents the verifier). In our solution, we pursue the following goals:  
* To allow the LEA to start a new LI procedure by commissioning an entity for the role of LEMF. In this phase, the LEA also interacts with the NO according to the standard [10]. * To allow an employee of an external agency to obtain from the LEA the credentials to access an interception procedure already started on the LEMF. * To allow the LEA to revoke the credentials released to the employee and the LEMF. The design of the proposed solution is the objective of Section 4.
## 4 Stability Analysis of Slopes Under Freeze-Thaw Cycles  
### Construction of the Model  
To investigate the impacts of freeze-thaw (F-T) cycles on slope stability, a 3D model of the eastern mine slope was constructed by integrating geological data from the P2N, P4N, and P8N cross-sections, borehole logs, and contour maps of the mining area. The geometry of the slope was 476 m long, 490 m wide, and 502 m high generated by 3 Dmine (2020) software. Rock mass mechanical parameters incorporating F-T effects (for dacite, magnetite, and skarn) were subsequently imported into FLAC3D to analyze the slope stability under varying F-T cycles. As illustrated in Figure 6, the 3D geological model employs color-coded lithologies: magenta for dacite, blue for magnetite, and maroon for skarn. The points P1-P6 are six monitoring points, whose locations are essentially consistent with the six observation points in field monitoring. The model base was subjected to fixed constraints, while the surrounding boundaries were assigned normal constraints.  
Slope stability under 0, 30, 60, and 90 freeze-thaw (F-T) cycles was analyzed using the strength reduction method. The initial stress field of the model was defined as the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Lithology**} & **Number of Freeze-Thaw Cycles** & \begin{tabular}{c} **Uniaxial** \\ **Compressive** \\ **Strength** \\ **\(\sigma_{\text{cn}}\)/MPa** \\ \end{tabular} & \begin{tabular}{c} **Modulus of** \\ **Elasticity** \\ **E/GPa** \\ \end{tabular} & \begin{tabular}{c} **Poisson’s** \\ **Ratio** \\ **Ratio** \\ **\end{tabular} & \begin{tabular}{c} **Cohesive** \\ **Force** \\ **\(\sigma\)/MPa** \\ \end{tabular} & \
\begin{tabular}{c} **Angle of** \\ **Internal** \\ **Friction** \\ **\(\sigma\)/MPa** \\ \end{tabular} &
\begin{tabular}{c} **Tensile** \\ **Strength** \\ **\(\sigma\)/MPa** \\ \end{tabular} \\ \hline \multirow{4}{*}{Dacite} & 0 & 134.34 & 44.46 & 0.35 & 9.01 & 26.38 & 8.32 \\  & 30 & 111.71 & 31.62 & 0.28 & 5.60 & 21.01 & 4.56 \\  & 60 & 100.81 & 28.68 & 0.29 & 3.78 & 16.52 & 2.79 \\  & 90 & 94.39 & 22.04 & 0.22 & 2.55 & 12.53 & 1.72 \\ \hline \multirow{4}{*}{Magnetite} & 0 & 114.61 & 55.83 & 0.28 & 7.87 & 27.58 & 7.49 \\  & 30 & 85.77 & 51.08 & 0.21 & 4.55 & 22.12 & 3.80 \\  & 60 & 76.18 & 33.38 & 0.26 & 3.05 & 17.51 & 2.29 \\  & 90 & 62.10 & 31.97 & 0.20 & 1.80 & 13.36 & 1.23 \\ \hline \multirow{4}{*}{Skarn} & 0 & 130.62 & 64.09 & 0.25 & 9.25 & 26.99 & 8.80 \\  & 30 & 112.82 & 53.57 & 0.24 & 5.99 & 21.78 & 5.01 \\ \cline{1-1}  & 60 & 86.60 & 40.50 & 0.21 & 3.47 & 17.05 & 2.61 \\ \cline{1-1}  & 90 & 73.76 & 23.54 & 0.21 & 2.14 & 12.76 & 1.47 \\ \hline \end{tabular}
\end{table}
Table 5: Mechanical parameters of slope rock mass under different freeze-thaw times.  
Figure 6: Three-dimensional geological model map of the slope.  
gravitational field with g = 9.8 m/s\({}^{2}\). For the strength reduction analysis, the following procedure was applied to determine the cohesion (c) and internal friction angle (\(\varphi\)):  
\[c^{trial}=\frac{1}{F^{trial}}c \tag{12}\]  
\[\varphi^{trial}=tan^{-1}\left(\frac{1}{F^{trial}}tan\varphi\right) \tag{13}\]
# Hollow-Core Optical Fibers for Telecommunications and Data Transmission
## 11 Transmission Performance of HCFs
### Nanf  
The technical data of the fibers (Table 3) suggest that NANF/DNANFs can compete with SMFs in metro/core networks, especially in large-capacity, long-distance DWDM networks where the elimination of nonlinear effects and the extension of the bandwidth are desired.  
This has been confirmed by several DWDM transmission experiments summarized in Table 5. First, [PERSON] et al. built DWDM links with channels with a relatively low, 25 Gb/s, data rate in 2019-2021 [112; 113; 114]. In 2021, a successful transmission at 400 Gb/s and 800 Gb/s channel data rates was reported [54; 115] using off-the-shelf commercial transceivers. Maximum transmission distances at 400 Gb/s satisfy the requirements for national core networks in most European countries, approx. 500-1500 km. However, new active equipment and WDM filters will be needed for networks operating at wavelengths beyond the L Band and up to \(\approx\)2100 nm--see Sections 6.2, 12.3 and 13.1.  
Two important observations:  
* The transmission distance was, in some cases, limited by Inter-Modal Interference (IMI) resulting from the coupling of optical power between the fundamental mode and higher-order modes. While a five-tube NANF with high attenuation of higher-order modes [54; 114] improved the situation, the problem is not yet fully solved.
* The practical absence of Stimulated Raman Scattering (SRS) eliminated the transfer of optical power from short wavelength channels to long wavelength channels in wideband DWDM links, e.g., from C band to L band [113; 115].
# Assessment of Deep Learning Methodology for Self-Organizing 5G Networks
## 3 From Machine Learning to Deep Learning
### Applications of Deep Learning  
Some of the successful application areas of deep learning are listed:  
* Image--facial recognition, image search, machine vision
* Text--sentiment analysis, augmented search, natural language processing
* Time series--risk detection, weather prediction, economic analytics
* Sound--voice detection, speaker recognition, sentiment analysis
* Video--motion detection, real-time threat detection  
The number of areas of application of deep learning is growing everyday. It is now predicted that deep learning is going to transform many industries as well as society through developments in health care, education, and business. Deep learning has been applied in fields like bioinformatics [27],medicine and health care [28], space information and weather forecasting [29], education [30], traffic and transportation [31; 32], agriculture [33], robotics [34], and gaming [35].  
Deep learning has been extensively applied in mobile and wireless networking [36]. State-of-the-art deep learning methods have been applied for network prediction [37], traffic classification [38], Call Detail Record (CDR) mining [39], and Quality of Experience (QoE) driven by big data analysis [40]. Deep learning techniques have produced great results in network optimization [41], routing [9], scheduling [42], resource allocation in vehicle-to-vehicle communication (V2V) [43], radio control [44], and power control [45].  
From the future wireless network perspective, deep learning driven techniques will be having great impact on radio resource management (RRM), mobility management (MM), management and orchestration (MANO), service provisioning management (SPM), quality of service (QoS), and energy and spectrum efficiency for 5G and beyond.