esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Adsorption Kinetics and Adsorption Isotherms  
Table 4 lists the adsorption kinetics parameters where the Avrami and Bangham models exhibited the best fit to the data (r\({}^{2}>\) 0.9942) (Figure 3a,b). In contrast, other kinetic models did not show a good fit to the experimental data. CM and CAC showed the lowest and largest magnitude of adsorption, respectively, as shown by their \(Q_{e}\) and \(n\) values. Likewise, the predicted \(Q_{e}\) values were very close to the experimental data as seen by the Avrami model. Further, CAC and CM revealed the largest and lowest magnitude of CV interaction with the sorption sites as resulted from the \"\(n\)\" values. Although this model was initially developed to follow the progress of chemical reactions and crystallization processes, this study proves that could be applied in adsorption processes taking place with multiple mechanisms. Likewise, the Bangham model indicates that pore diffusion mechanism is one of those steps taking place during the adsorption process. Figure 2: Fourier-transform infrared (FT-IR) spectra showing the effect of pyrolysis and acidic activation on shrimp waste. On the other hand, the adsorption isotherms parameters at equilibrium are listed in Table 5 and Figure 3c,d proved that CAC, AC2, and CM had a good fit to the Freundlich model, whereas AC1 and SW, presented a good fit to the Langmuir and combined models. Thus, it is expected in SW a combination of homogeneous and heterogeneous areas on the surface with multilayer formation. For this reason, data fitted those hybrid models (i.e., Sips, Evolich and Redlich-Peterson) that combine the assumptions of the [PERSON] and [PERSON] models. Conversely, the pyrolysis process made the surface mainly heterogeneous and thus CAC, CM, and AC2 samples had a better fit to the Freundlich model. Interestingly, AC1 showed the best fit to the Langmuir-like model and thus, this material follows the assumptions (i) the active adsorption sites are energetically homogeneous, (ii) there is no interaction of CV adhered to the adjacent adsorption sites, (iii) there is a monolayer formation of CV on the AC1 surface, and (iv) the formation of non-polar interactions between the non-polar CV sites and the AC1 surface, resulting in loss of water structure around the non-polar CV sites, generating also hydrophobic bonds with AC1 (\(K_{hb}\)). The essential characteristics of the Langmuir isotherm can be expressed by a dimensionless equilibrium parameter, also known as the separation factor (\(R_{L}\)) which rendered values lower than 0.21 indicating a favorable adsorption process. However, this result is more significant for AC1 and SW which rendered a Langmuir and combined behavior. Conversely, it is expected in CAC, AC2, and CM the presence of energetically heterogeneous adsorption sites with the formation of several layers of CV on their surface. Thus, sites having a higher energy are occupied first, as well as the possible formation of CV multilayers adsorbed on the surface of these adsorbents.
### Motivational Statement
#### 1.1.1 Zero-Touch Network and Service Management  
Standard telecommunication entities suggest self-organizing networks (SON) with AI-assisted automation control by releasing technical specifications, testbeds, and application programming interfaces (APIs). Specifically, the 3 rd Generation Partnership Project (3 GPP) presents an enabler for autonomous networks by presenting network data analytics function to associate with service-based architecture and expose data gathering/analysis capability for applying data-driven AI models in 5G applications [8].  
From active phases of generic autonomic networking architecture (GANA) and experiential networked intelligence (ENI) using closed-loop AI architecture for SON, the European Telecommunications Standards Institute (ETSI) keeps emphasizing the ZSM aspect to describe the use cases in network functions virtualization (NFV), edge computing (EC), autonomous policy configuration, and service automation [9; 10; 11]. The Internet Engineering Task Force (IETF) has developed an autonomic networking integrated model and approach (ANIMA) for enabling the applicability of self-functions in configuration, optimization, protection, and healing [12]. ANIMA motivates AI-assisted approaches for autonomic orchestration in networking infrastructure, control plane, and slice management. Moreover, The International Telecommunication Union (ITU) organized a focus group on machine learning (ML) for future networks, including 5G, to study and plan the execution and evaluation of data handling, APIs, system architecture, standard protocols, and intelligence-level future network management [13].  
To drive the adoption between 5G end-to-end (E2E) architecture and AI-enabled applicability, next-generation mobile networks (NGMN) alliance has presented documentation and capabilities on emerging use cases, requirements, and enabler specifications as an autonomic networking framework [14]. Furthermore, deep learning (DL)-based network slicing approaches are further referenced by NGMN documentation for assisting E2E procedures of various 5G usage services, including enhanced mobile broadband, ultra-reliable and low-latency communications, and massive machine-type communications [15]. Additionally, a joint testbed federation between ITU, ETSI, and the Institute of Electrical and Electronics Engineers (IEEE) is expected to be established for 5G and beyond networks with applied AI models in the system architecture, interoperability, and reference APIs [16].
# Study on Temperature Correction of Asphalt Pavement Deflection Based on the Deflection Change Rate
## 5 Deflection Temperature Modelling
### Temperature Correction Model for the Deflection Basin  
In addition to correcting the load center deflection, temperature corrections for deflections are required for other points in the deflection basin that are affected by temperature. This section finds the optimal temperature correction method for deflection at other radial distances by comparing three different temperature correction methods.
# Fusion of Land-Based and Satellite-Based Localization Using Constrained Weighted Least Squares
## 5 Proposed Method
### Proposed Method for Land-Based Station
#### 5.1.1 Localized Method for Land-Based Station
Thereby, the optimal solution of (60) is [17; 22]:  
\[\mathbf{\tilde{\eta}}_{a}^{k}=\left(\mathbf{D}_{a}^{k}\right)^{\dagger}\mathbf{d}_{a}^{k} +\mathbf{P}_{a}^{k}\mathbf{\xi}_{a}^{k} \tag{61}\]  
where \(\mathbf{\xi}_{a}^{k}=\left(\mathbf{P}_{a}^{k}\mathbf{\tilde{\sigma}}_{a}^{k}\mathbf{P}_{a}^{k} \right)^{\dagger}\mathbf{P}_{a}^{k}\left(\mathbf{\tilde{\sigma}}_{a}^{k}-\mathbf{\tilde{ \sigma}}_{a}^{k}\mathbf{\left(}\mathbf{D}_{a}^{k}\right)^{\dagger}\mathbf{d}_{a}^{k}\right)\) and \(\mathbf{P}_{a}^{k}=\mathbf{I}-\left(\mathbf{D}_{a}^{k}\right)^{\dagger}\mathbf{D}_{a}^{k}\). Therefore, the iteration results can be updated by Equation (62):  
\[\mathbf{\tilde{\eta}}_{a}^{k+1}=\kappa_{a}\mathbf{\tilde{\eta}}_{a}^{k}+(1-\kappa_{a}) \mathbf{\tilde{\eta}}_{a}^{k} \tag{62}\]  
\(0<\kappa_{a}<1\) denotes the weighting factor. The initial estimation can usually be obtained by making \(\mathbf{W}_{a}=\mathbf{Q}_{z}^{-1}\) and then obtaining an initial estimation based on \(\mathbf{\tilde{\eta}}_{a}^{0}=\left(\mathbf{G}_{a}^{\mathsf{T}}\mathbf{W}_{a}\mathbf{G}_{a} \right)^{-1}\mathbf{G}_{a}^{\mathsf{T}}\mathbf{W}_{a}\mathbf{y}_{a}\). The DOA localization algorithm for land-based stations is summarized in Table 2.
## 2 Materials and Methods
### Data Processing Methods
#### 2.3.5 Methodology for Categorizing Evaluation Factors  
In the evaluation of collapse hazard susceptibility, the factor classification method constitutes a critical component of model construction. In this study, the natural breaks method (NBM) is employed to classify evaluation factors. This method leverages the natural clustering characteristics of the data, grouping them to minimize within-group variance while maximizing between-group variance [35]. The natural breaks method effectively highlights significant differences among factors, making it particularly suitable for classifying factors with non-uniform distribution characteristics in geohazard risk evaluation. To ensure the comprehensiveness and scientific rigor of the classification approach, this study also examines the equidistant classification method and the empirical classification method for comparison. The equidistant classification method, which divides the data range into equal intervals, is well-suited for uniformly distributed data and offers simplicity and ease of operation. In contrast, the empirical classification method categorizes data based on the experience of domain experts or existing research, making it applicable to specific regions or conditions, though it may introduce subjectivity [36]. Ultimately, the natural breaks method is selected as the factor classification method, not only because it more accurately reflects the natural variation characteristics of the factors but also because it facilitates the extraction of distinct characteristics of control factors within the region. This enables the formulation of a classification framework applicable to evaluating collapse hazard susceptibility, significantly enhancing the overall effectiveness and accuracy of the prediction model.
## 5 Conclusions  
In this paper, we have investigated the joint optimization problem for source and relay beamforming and source receiving in a MIMO FD BWSN SWIPT system. In terms of the problem, two iterative algorithms based on FPP-SCA and low-complexity diagonalizing designs which minimize the total-MSE subjected to the relay-and-source-transmitted power and energy-harvested constraints are proposed. The simulation results demonstrate that the low-complexity scheme always converges faster than the FPP-SCA based one, while the FPP-SCA-based scheme achieves a lower BER compared with the work of the low-complexity scheme. Moreover, when \(N=M\), the performance of the low-complexity scheme yields better than that of \(N>M\). In further works, we will analyze the system performance for multiple users and the interference suppression in the FD network scenario, where a large number of nodes are involved, and a discussion on the optimization scheme under the imperfect SCI will be developed.  
[PERSON], [PERSON], and [PERSON] conceived the main proposal of the system modeling and derived the analysis and numerical simulation of the proposed schemes. [PERSON] and [PERSON] wrote the manuscript. [PERSON] provided considerable comments and the technique review of the proposed scheme.  
This research was funded by the Beijing Municipal Natural Science Foundation (4172024).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} WSN & Wireless sensor network \\ MIMO & Multiple-input multiple-output \\ FD & Full-duplex \\ HD & Half-duplex \\ BWSN & Bidirectional wireless sensor network \\ BRN & Bidirectional relay network \\ OWRN & One-way relay network \\ OWSN & One-way wireless sensor network \\ SWIPT & Simultaneous wireless information and power transfer \\ TS & Time switching \\ PS & Power splitting \\ \end{tabular}  
Figure 6: The comparison between the proposed network and existing BWSN \(N=M=2\).
# Digital Twin Conceptual Model within the Context of Internet of Things
## 5 Virtual Space Layer  
The virtual space layer (VSL) consists of two sublayers: Data aggregation and data modeling. The data aggregation layer collects in-network sensor data from the underlying sensors communicated via the CNL layer for further storage and processing. The data modeling sublayer creates dynamic 3D models of the physical object or system, based on the present data available in the data aggregation sublayer.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **2G** & **3G** & **4G** & **5G** \\ \hline Latency & 500–1000 ms & 200 ms & 100 ms & \textless{}1 ms \\ Bandwidth & 200 KHz & 20 MHz & 20 MHZ & 1 GHz \\ Data Rates & 64 Kbps & 2 Mbps & 1 Gbps & 10 Gbps \\ Coverage Range & 1–10 km & 1–10 km & 31 km & 200–300 m \\ Security & 64-bit A5 & 128-bit A5 SUMI & 128-bit AES, 168-bit & D2D and continuous \\  & & Cipher & DES & authentication \\ Scalable Capacity & - & 10 bits/s/Hz/km\({}^{2}\) & 50–500 & 50,000 bits/s/Hz/km\({}^{2}\) \\ Network Capacity & - & - & 1000 nodes & 65,000 nodes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of cellular communication networks.
# Research on Key Parameters and Engineering Experiments of Coal Gangue Slurry Filling Technology
## 4 Industrial-Grade Circular Pipe Conveying Test of Gangue Slurry
### Design of Long-Distance Pipeline Transportation Test  
#### 4.2.1 Test Parameters of Long-Distance Pipeline Transportation  
Based on the above primary experimental results, the mass fraction of the gangue slurry used in the long-distance pipeline transportation test was 71%. The crushed gangue particle size ranged from 1.25 to 5 mm, accounting for 32.33%, 0.075 to 1.25 mm, accounting for 40.67%, and less than 0.075 mm, accounting for 27.0%. In this experiment, the inner diameter of the pipeline was about 201 mm, and this was determined by changing the  
Figure 5: Scheme and process of long-distance pipeline transportation test.  
pumping flow rate of the gangue slurry at different periods, monitoring the pressure at different positions of the pipeline, analyzing the relationship between the transportation resistance and flow rate of the gangue slurry at specific concentrations, and determining the stability and feasibility of the long-distance transportation of the gangue slurry.
## 3 Results and Discussion
### Distribution of Residual Stress with Depth
[PERSON] (2015) presented an empirical relationship for the wavenumber \(w_{\text{Si-Si}}\), the Ge content \(x\) and the strain \(\varepsilon\), which is regarded as suitable for the germanium-silicon alloy with \(0<x<0.25\). Meanwhile, the elastic parameters of the germanium-silicon alloy followed the modulus-content linear model proposed by [PERSON] et al. [PERSON] _et al._ (2016). \[C_{ij}=xC_{ij,\text{Ge}}+(1-x)C_{ij,\text{Si}},\quad i,j=1-6, \tag{3}\]  
where \(C_{ij}\), \(C_{ij,\text{Ge}}\) and \(C_{ij,\text{Si}}\) were components of the elastic coefficient tensors of the Ge-Si alloy, germanium and silicon, respectively. Hence the Raman-mechanical relationship suitable for the stress analysis inside the GeSi layers on the (110) cross-section in this work is given by Equation (4) [11]. \[\sigma=(100x-435)(\Delta w+62x)\ \ (\text{MPa}). \tag{4}\]  
The residual stress distribution along the deepness direction of this multilayer structure is given as Figure 10, where the stress inside the C-Si substrate was achieved by using Equation (2), and that inside the germanium-silicon buffer layers was achieved by using Equation (4). Figure 10 shows that there exists serious residual stress inside the buffer layers. The residual stress increases rapidly in the beginning of the epitaxial growth. The mean stress gradient during the first 2 \(\upmu\)m of the graded buffer layer was 777.5 MPa/\(\upmu\)m. With further growth of the Ge\({}_{\text{x}}\)Si\({}_{1-\text{x}}\) layer, the residual stress stopped increasing and then began to decrease, which reduced the stress gradients and even turned them negative, until the beginning of the relaxed buffer layer. In the Ge\({}_{0.2}\)Si\({}_{0.8}\) layer, the residual stress was stable compared with that in the Ge\({}_{\text{x}}\)Si\({}_{1-\text{x}}\) layer. The mean residual stress in the relaxed buffer layer was \(\sim\)1.01 GPa. Figure 10: (**a**) Distribution image of residual stress on sample cross-section, (**b**) mean residual stress along the depth direction.
## 4 Application Scenarios and Superiority Analysis
### Superiority Analysis  
The advantages of the NRRS architecture based on satellite Internet can be summarized as follows:  
* Response in Real Time: The ICRS application system guarantees the task planning, command upload, data transmission and distribution immediately, which allows for immediate (minutes) and routine (hours) mission response for imaging of the target area.
* Ubiquitous and Efficient: Remote sensing satellites access the Internet as information nodes, and the commands and data are transmitted instantly via the satellite communication network with global coverage. The task uploading and data downloading no longer rely on the limited resources of TT&C and data-receiving stations on the ground, hence greatly improving the information transmission rate. With the help of mobile terminals, the requirements for remote sensing can be proposed, as well as the remote sensing images of the target area which can be received anytime and anywhere.
* Intelligent Fusion: The intelligent algorithms on board and on the ground facilitate completing the rapid interpretation and execution of remote sensing requirements, as well as the transmission and processing of remote sensing images. Through intelligent task planning of remote sensing, multi-satellite collaborative resource scheduling, comparison and pre-processing of massive remote sensing data, etc., rapid response to the users' demand and near real-time acquisition of remote sensing images can be guaranteed.
## 5 Characteristics of Feature Groups
### Sentence Length Frequency and Word Length Frequency Types of Feature Groups  
Only two feature groups consisting of features related to the sentence length frequency type were used. These feature groups differ according to the sentence lengths to be used and the number of these different sentence lengths, where sentence length means the number of words in a sentence. In one such feature group, there are frequencies of sentences with lengths of 5, 6,..., 14. We will denote this feature group by the name SL10 in the study, where SL stands for \"sentence length\", and 10 indicates the number of features in the feature group. Naming the groups of features used in the study with such short expressions is for the reader to understand the differences in those groups and also for use in the results of computer experiments if needed. Another feature group, SL25, consists of frequencies of sentences with lengths of 5, 6,..., 29.  
The number of feature groups consisting of only word length type features is also two. These feature groups differ according to the word lengths to be used and the number of these different word lengths, where word length means the number of letters in a word. In one such feature group, there are frequencies of words with lengths of 3, 4,..., 7. We will denote this feature group by the name WL5, where WL stands for \"word length\", and 5 indicates the number of features in the group. Another feature group, WL10, consists of frequencies of words with lengths of 3, 4, \(\ldots\), 12.  
A mixed feature group consisting of features belonging to the types of sentence length frequency and word length frequency was also used. The values of the features in this feature group are the frequencies of sentences with lengths 5, 6, \(\ldots\), 14 and the frequencies of words with lengths 3, 4, \(\ldots\), 12. Let us denote this feature group by SL10&WL10, where the \"&\" symbol is used in the names of mixed feature groups.  
In the study, the number of feature groups used in the considered author recognition problem, which contains features of sentence length frequency and word length frequency types, is 5:  
sentence length frequency: [number of feature groups] = 2;  
word length frequency: [number of feature groups] = 2;  
mixed groups of sentence length frequencies and word length frequencies: [number of feature groups] = 1.
# Simultaneous SAXS/SANS Method at D22 of ILL: Instrument Upgrade
## 3 Results and Discussion
### Shielding the X-ray Source against External Magnetic Fields  
It has occasionally been observed that the X-ray beam intensity periodically varies during SAXS measurements. Such beam intensity fluctuations had not been observed before operating it at the D22 SANS instrument. Using a slit size of 0.5 \(\times\) 0.5 mm\({}^{2}\) in front of the sample, it was observed that the X-ray beam moves partially outside the slit's openings, leading to an intensity reduction of up to 60%. It was a challenge to identify a reason behind such occasional instability behavior of X-ray intensity during operation inside the experimental zone. Finally, the variation of the residual weak magnetic field (\(\sim\)2-5 Gauss) from the IN15 spin-echo spectrometer was found to cause these X-ray beam intensity fluctuations. A plate of soft ferromagnetic alloy was installed to shield the static and low-frequency magnetic field at the position of the X-ray source, which leads to a stable beam intensity (Figure 3) for all extreme magnetic fields employed at the IN15 instrument.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \begin{tabular}{c} **SDD\({}_{\text{X-ray}}\)** \\ **(m)** \\ \end{tabular} & \begin{tabular}{c} **Neutron** \\ **Collimation Length** \\ **(m)** \\ \end{tabular} & \begin{tabular}{c} **X-ray Energy Mode** \\ **(keV)** \\ \end{tabular} & \begin{tabular}{c} **Shielding** \\ **Suppression Factor** \\ \end{tabular} & \
\begin{tabular}{c} **Detector** \\ **Suppression Factor** \\ \end{tabular} &
\begin{tabular}{c} **Total Suppression Factor** \\ \end{tabular} \\ \hline \multirow{4}{*}{0.55} & \multirow{4}{*}{2.8} & 4 & 87 & - & 87 \\ \cline{3-6}  & & 4–10 & 89 & 5.5 & 505 \\ \cline{3-6}  & & 4 & 58 & - & 58 \\ \cline{3-6}  & & 4–10 & 54 & 5.0 & 275 \\ \hline \multirow{4}{*}{1.64} & \multirow{4}{*}{2.8} & 4 & 50 & - & 50 \\ \cline{3-6}  & & 4–10 & 49 & 5.1 & 248 \\ \cline{1-1} \cline{3-6}  & & 4 & 17 & - & 17 \\ \cline{1-1} \cline{3-6}  & & 4–10 & 19 & 6.1 & 116 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Background suppression factors (SFs) for all investigated experimental configurations in an optimized lead shielding setup with a double-energy threshold energy detector (total SF \(\approx\) lead shielding SF \(\times\) detector SF).
# Reliable Application Layer Routing Using Decentralized Identifiers
## 5 Implementation and Evaluation
### Performance-Security Trade-Offs  
As discussed in Section 4.4, whenever an edge node retrieves an advertisement, it has to request the ICN network to retrieve the corresponding DID documents. In order to decrease the number of such requests, as well decreasing the advertisement verification time, these documents can be cached. Nevertheless, this means that an edge node may not have the current version of a DID document. A DID document is updated whenever a new entry is added in the assertion list or whenever an existing entry is removed. In the former case, the edge node will not be able to locate the key used for signing the advertisement in the cached documents; hence, it may refresh its cache. However, in the latter case, the edge node will consider a signature generated with a _revoked_ key to be valid. Therefore, there isa time window between the moment that a key is revoked and the moment that a cache entry expires during which a revoked key can be used. A solution to this problem that can be explored in future work is to leverage the ICN functionality and each edge node to \"subscribe\" for all future versions of a DID document; this way, every time a DID document is created, it will be pushed to the edge nodes that have subscribed to its updates.