esa-sceva/satcom-chunk-collection huggingface dataset:
# Correlation of Microstructure and Mechanical Properties of Metal Big Area Additive Manufacturing
## 4 Discussion
### Correlation of Tensile Properties to the Microstructure
Figure 11 shows the Continuous Cooling Transformation (CCT) diagram for the ER70s-6 nominal composition with a 0.1 wt% \({}^{\circ}\)C and a starting austenite grain size of 50 \(\upmu\)m. The CCT diagram is useful for predicting or rationalizing the existing microstructure. The cooling rates in most of the build may be close to 1 \({}^{\circ}\)C/sec. The cooling rate is estimated using previous analytical calculations based on wall thickness [24]. Obtaining accurate thermal profiles of welds should be addressed in future efforts. The cooling rate by thermal imaging is challenging due to saturation of the camera as the arc passes in front of the camera, along with emissivity transients and others. The diagram shows that mainly ferrite and pearlite should be the dominant phases. As Tweed and Knott have shown, with repeated thermal cycles and a HAZ peak temperature of 900 \({}^{\circ}\)C, the microstructure should be ferrite and pearlite [18]. Within the wall, with continued building, the 900 \({}^{\circ}\)C isotherm will move up the wall, slow the cooling rate, and produce the observed polygonal ferrite and pearlite microstructure. The larger grain size of polygonal ferrite is expected to reduce the yield strength; this hypothesis is confirmed by the tensile curves of the middle section. The total elongation can also be rationalized with microstructural features. As mentioned above, the mixed microstructure produces a higher yield strength but lower ductility. Although the polygonal ferrite produces a lower yield strength, the ductility increases as expected. The yield strength, UTS, and total elongation are consistent between samples and different orientations. This consistency shows the isotropic nature, with respect to build height and the consistent properties of the MBAAM built walls.
# Removal of Phosphate from Aqueous Solution by
## 4 Conclusions  
In this study, the zeolite-biochar composite material was prepared at different ratios to examine the adsorption of phosphate from water. In addition, the physicochemical features of the zeolite-biochar composite have been evaluated, in which the impacts and processes of phosphate adsorption have been investigated. The findings revealed that the optimal phosphate adsorption effect was observed when the biochar composition accounted for 30% and the composite material was prepared with the co-pyrolysis method. The maximum adsorption capacities of zeolite, biochar, and PZC 7:3 were 0.69, 3.60, and 2.41 mg/g. The main mechanism of phosphate removal by PZC 7:3 was the formation of thin-film amorphous calcium-magnesium phosphate compounds by ligand exchange. The adsorption process was also influenced by electrostatic attraction and ion exchange.  
In summary, considering the disadvantages of using zeolite and biochar alone, the C/H of the zeolite-biochar composite was lower than that of biochar, indicating that the combination of zeolite enhanced the stability of the composite. The oxygen-containing functional groups of biochar were added during the co-pyrolysis process. Compared to the adsorption of phosphate by zeolite, the adsorption capacity of phosphate by the  
Figure 12: Desorption rate of phosphate by zeolite, biochar, and PZC 7:3.  
zeolite-biochar composite was greatly enhanced. Experiments with desorption showed that the phosphate adsorption of the zeolite-biochar composite was more stable than the phosphate adsorption of either zeolite or biochar by themselves. Therefore, this study indicates that PZC 7:3 could be used to remove phosphate from water because it is a stable and effective adsorbent.  
Z.D.: conceptualization, methodology, writing--original draft, data curation; S.G.: conceptualization, writing--review and editing; H.C.: conceptualization, funding acquisition, writing--review and editing; D.X.: resources, methodology; G.T.: improvement of English language; X.W.: methodology, formal analysis, visualization; W.N.: data curation, formal analysis; M.M.: methodology, visualization. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (2018 YFC1802601), the \"Light of West China\" Program, and the Opening Fund of the State Key Laboratory of Environmental Geochemistry (SKLEG 2022216). Above fundings were from Institute of Geochemistry, Chinese Academy of Sciences.  
Not applicable.  
The authors have no relevant financial or non-financial interests to declare.
# A Review of DSP-Based Enabling Technologies for Cloud Access Networks
## 2 Cloud Access Network Concept
### Digital Orthogonal Filtering-Based Channel Multiplexing
Figure 1: Digital orthogonal filtering-based channel multiplexing: SF, shaping filter; DAC, digital to analogue converter; IM, intensity modulator; ADC, analogue to digital converter; MF, matching filter; M\(\uparrow\), up-sampling by factor M; M\(\downarrow\), down-sampling by factor M.  
A vitally important characteristic of the aforementioned channel multiplexing technique is that channels can be routed and switched completely in the optical domain without the need for optical-electrical-optical (OEO) conversion or optical bandpass filters (OBPFs), thus allowing the realisation of low-cost network switching devices which are crucial for the cost-sensitive CAN scenarios and moreover, the optical domain channel switching devices can achieve negligible latency which is a key advantage when considering the ultra-low latency requirements of 5G. Optical domain channel switching devices for CAN applications are discussed in detail in Sections 3.2 and 3.3. Furthermore, the technique can allow the implementation of highly flexible multiple access PONs [9] based on channel multiplexing in the optical domain, where multiple independently generated optical channels or groups of channels are passively combined in the optical domain with a simple passive coupler [6].
## 1 Introduction and Motivation
In this particular paper the physical quantity we want to predict is the total harmonic flux in a predefined spectral region of the high-harmonic spectrum, which we define as yield:  
\[Y=\int\limits_{\omega_{1}}^{\omega_{2}}\int\limits_{0}^{R}[E_{h}(\omega,r)]^{2 }rdrd\omega, \tag{1}\]  
where \([\omega_{1};\omega_{2}]\) is the spectral domain of interest, \(R\) is the radial extent of the interaction region, \(E_{h}(\omega,r)\) is the harmonic field in spectral representation. Alternatively, one can identify different physical quantities which we want to predict using ANN, for example the existence and bandwidth/duration of isolated attosecond pulses. One can search to maximize the brightness of a single harmonic as the function of different experimental parameters, as it has already been done experimentally [42]. We propose here a combined method to predict the photon flux in the 20-40 eV spectral range while varying four experimental parameters: laser pulse energy, gas pressure, gas cell position and medium length. For this prediction an artificial neural network is designed, trained and tested. The input data are the results obtained with the 3D non-adiabatic model (see next section) from a 4-dimensional parameter scan [43]. The paper has the following structure: in Section 2.1 we briefly describe the 3D non-adiabatic model for laser pulse propagation and high-harmonic generation, while in Section 2.2 the artificial neural network method is presented. In Section 3 we present and discuss the results obtained with the ANN, we test its robustness and reliability. Possible applications are suggested as well since the major role of this prediction procedure is to provide a quick help for HHG laboratories: predict the expected outcome of HHG experiments to be performed with previously unexplored experimental parameter combinations. Theoretical Models and Numerical Methods for High-Harmonic Generation and Artificial Neural Networks
# Automatic Clustering for Improved Radio Environment Maps in Distributed Applications
## 7 Conclusions  
In this work, we presented an approach to improve the prediction process for REMs. The objective of this work was to improve wireless connectivity in densely distributed applications, such as smart cities. In our approach, we first improve the prediction accuracy of RSRP through applying the K-means clustering technique. The aim of this clustering approach is to enhance the estimation of the path loss exponent. The proposed clustering technique contributes to distinguishing the different wireless environments, which enhances the prediction process. We then categorize each resulting cluster. For each cluster in our model, we use the Kriging technique to construct the accumulated results. This is performed in order to improve the overall prediction accuracy for the entire dataset. To test the viability of the approach, we carried out a set of comparative studies. In each test, we compared the prediction accuracy with clustering and then retested it without clustering. We observed the RMSE in each test to measure the progress of the prediction accuracy. The results showed that the proposed approach provided accurate predictions with a gain of about 3.3 dB in terms of RMSE compared to the case without clustering. In future work, we plan to test the approach with other trending models, such as the multiple-trend path loss model. We also plan to generalize the clustering approach using other techniques to test the performance of different algorithms. In fact, we target test the approach with a large number of heterogeneous clusters to cope with communication demands in several distributed applications.  
Conceptualization: H.B.C. and A.A.; methodology: H.B.C. and A.A.; validation: A.A. and H.B.C.; formal analysis: A.A. and H.B.C.; writing--original draft preparation: H.B.C. and A.A.; writing--review and editing: A.A. and H.B.C.; supervision: H.B.C. and A.A. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Deanship of Scientific Research at Jouf University under grant No. (39/826).  
Not applicable.  
The authors extend their appreciation to the Deanship of Scientific Research at Jouf University for funding this work through research grant No. (39/826).  
The authors declare that there were no disclosed possible conflict of interest relevant to the research.
## 2 Materials and Methods
### FTIR Spectrometer Unit
A bandpass filter (Figure 3 position 8) installed in front of the photodetector (Figure 3 position 9) passes radiation in the spectral range of 2.0-2.3 \(\upmu\)m. The radiation reflected from the filter (Figure 3 position 7) passes through the bandpass filter (Figure 3 position 11) and is focused by the lens (Figure 3 position 12) on the photodetector PIN-040A (OSI Optoelectronics) (Figure 3 position 13). This channel is a reference channel used for registration of oxygen absorption bands. O\({}_{2}\) absorption band registration is carried out by a Si photodetector (Figure 3, position 13) with a linear size of \(\mathrm{w}=1\) mm. The noise equivalent power (NEP) value for the Si detector is \(6.2\times 10^{-15}\) W/Hz\({}^{1/2}\). To cut off visible radiation, a band-pass filter (Figure 3, position 11) is also installed in front of the receiver for the spectral range of 0.75-0.80 \(\upmu\)m.  
The bandpass filter in the signal channel (Figure 3, position 8) is optional and can be excluded from the optical subsystem. Exclusion of this filter would allow expanding the operating spectral range to the limits of the sensitivity of the InGaAs photodetector  
Figure 3: The compact low-resolution FTIR spectrometer optical scheme: 1, 2—mirror lens; 3—mirror, 4—ZnSe beam splitter; 5, 6—retroreflectors; 7—dichroic filter; 8—band-pass filter 2.0-2.4 \(\upmu\)m; 9, 12—focusing lens; 10—photodetector 2.0–2.4 \(\upmu\)m; 11—band-pass filter for a wavelength of 0.75-0.80 \(\upmu\)m; 13—photodetector 0.75–0.80 \(\upmu\)m; 14—reference radiation source; 15—632 nm laser; 16—reference channel beam splitter; 17—dihedron;18, 19—flat mirrors; 20—reference channel photodetector. (1.0-2.5 \(\upmu\)m). The extended range would allow simultaneous recording of spectral lines of CO\({}_{2}\) (1.6 and 2.05 \(\upmu\)m), CH\({}_{4}\) (1.66 and 2.36 \(\upmu\)m), O\({}_{2}\) (0.76 and 1.27 \(\upmu\)m) and CO (2.34 \(\upmu\)m). In order to stabilize the speed of the mirror and determine the moments to read the interferogram, a reference channel with a fourfold difference in the optical path is used. The reference channel includes a dihedron (Figure 3, position 17) and a flat mirror (Figure 3, position 18) [42]. A He-Ne laser with a wavelength of 632 nm is used as a reference source. To take into account the instrumental function of the spectrometer [43; 44] and adjust the recorded IR absorption spectra, a reference radiation source (small filament lamp MH 1.25-0.25) (Figure 3, position 14) is installed. The described FTIR spectrometer can be used for trace gas analysis [44] and remote sensing [34]. Mathematical methods of spectral analysis are described in [45; 46]. The main technical characteristics of the compact, low-resolution spacecraft-based spectrometer are given in Table 2.
## 3 Beam Layout and Frequency Allocation Optimization Method Based on User Distribution  
To meet the explosive growth in the number of users accessing the S-IoT, we optimize the frequency resources allocated to the central and edge areas and frequency assignment  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notations** & **Definitions** \\ \hline \(K\) & Number of beams \\ \(N_{slot}\) & Number of time slots \\ \(L\) & Message length \\ \(V_{t}\) & Transmission rate \\ \(V_{code}\) & Code rate \\ \(T\) & Satellite ground coverage time \\ \(U_{i}^{i}\) & Number of users in the center of the \(i\)th beam \\ \(U_{i}^{s}\) & Number of users in the edge area of the \(i\)th beam \\ \(B\) & Bandwidth \\ \(B_{c}^{i}\) & Bandwidth resources allocated to the center area of the \(i\)th beam \\ \(B_{s}^{i}\) & Bandwidth resources allocated to the edge area of the \(i\)th beam \\ \(B_{\alpha}\) & Bandwidth after spread spectrum \\ \(B_{m}\) & Bandwidth per user \\ \(C_{i}\) & User throughput of the \(i\)th beam \\ \(C_{i}^{i}\) & User throughput in the edge area of the \(i\)th beam \\ \(C_{c}^{i}\) & User throughput in the center of the \(i\)th beam \\ \(R_{0}\) & Beam radius \\ \(r_{i}\) & Radius from the center of a sub-beam in the \(i\)th beam \\ \(R_{i}\) & Radius of the central area of the frequency allocation of the \(i\)th beam \\ \(p_{i}\) & User distribution density in the \(i\)th beam within \(r_{i}\) \\ \(q_{i}\) & User distribution density in the \(i\)th beam out \(r_{i}\) \\ \(N_{i}\) & Number of users that can be connected to the \(i\)th beam \\ \(N_{user}\) & Maximum number of users that can be connected to a single satellite \\ \(N_{sum}\) & Maximum number of users actually connected to a single satellite \\ \hline \(PL\) & Path loss \\ \(P_{i}\) & User terminal signal transmission power \\ \(G_{r}\) & Receive gain \\ \(G_{t}\) & Transmit gain \\ \(N_{0}\) & Noise power spectral density \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations and definitions.  
Figure 4: Ground user distribution model: (**I**) Users are uniformly distributed. (**II-a**) Users are randomly distributed, and the density of user distribution is higher within \(r_{i}\). (**II-b**) Users are randomly distributed, and the density of user distribution is higher outside \(r_{i}\).  
center area radius \(R_{i}\) through the genetic algorithm. When the number of users requesting access on the ground far exceeds the maximum number of satellite accesses, it can obtain the maximum number of user accesses as much as possible.
# Assessment of Interventions in Fuel Management Zones Using Remote Sensing
## 3 Results
### Temporal Analysis
#### 3.2.3 Both Satellites: Sentinel-1 and Sentinel-2  
The information from Sentinel-2 and Sentinel-1 was merged to analyze if it had impact in the results. Figure 13 presents the F1-score values for the different algorithms and data sets, note that the data sets are sorted by performance and by model. Globally these values maintained a behavior similar to the values observed using only Sentinel-2 data. For the SVM, the best F1-score values did not change in comparison to using the Sentinel-2 data. For the RF, the F1-score slightly increased in all data sets. This data combination had the biggest impact in the XGBoost results, raising the F1-score more than 1% in comparison to using just Sentinel-2 data. In this experiment, the XGBoost was the  
Figure 11: F1-score value for all algorithms and data sets using just Sentinel-2 data.  
Figure 12: Examples of false positives obtained using the SVM with the data set \(DS\_BAND\).  
algorithm with the best results reaching an F1-score of 0.94. Overall, using Sentinel-1 data together with Sentinel-2 data showed some improvements in the results of the algorithms.
## 3 Algorithm Model and Simulation of the EBA
### Procedure of the Algorithm Model
#### 3.1.2 Database Creation  
The nonlinearity of the damping \(\delta_{m}\) is usually difficult to predict accurately after assembly. To obtain an accurate theoretical calculation, the damping corresponding to the open circuit should be sampled for replacing the damping in Equation (2). The absorption coefficient at each frequency in any group within the boundary can be calculated in a nested cycle using Equations (2)-(5). Then, the absorption coefficients of each group are stored after taking the average values, and thus the database of the average absorption coefficients of the six-dimensional parameters of the SL is established.
###### Abstract
The ENA then classifies earthwork quantities of civil and construction projects using the topological connectivity algorithm by creating a closed polyline in cross-section construction drawings [10]. The ENA framework is trained using these models, with features such as cut, fill, and other earthwork-related parameters extracted from the extracted datasets. The comparative analysis of different models, including the MLP, LSTM, and Transformers, revealed that LLMs outperformed others in terms of accuracy and efficiency, especially for large-scale projects. This study demonstrates the effectiveness of the Earthwork Network Architecture (ENA) in automating and improving the accuracy of earthwork volume estimation through a case study. To develop the ENA model, the drawing vector dataset was converted into an LLM training data format. The performance of the proposed ENA model was compared with deep learning models with different architectures. In reference, this study highlights the challenges and limitations of traditional manual estimation processes and showcases how the ENA framework mitigates these issues by streamlining the extraction of features from CAD drawings. The key contributions of this research include the development of a novel ENA framework, the application of advanced deep learning architectures to earthwork quantity estimation, and the demonstration of how automated methods can significantly reduce manual labor and errors. This study offers practical insights for civil engineering professionals, paving the way for the future adoption of AI-driven tools in project planning and cost estimation. It also sets the groundwork for expanding the use of deep learning in other construction management tasks, contributing to the growing body of knowledge on AI applications in civil engineering.
## 4 Impact Modelling Tool  
The current tools employed to simulate spacecraft catastrophic impacts and fragmentation implement empirical or semi-empirical algorithms and are based on observations of impact events and experimental data from ground tests. To date, such tools present limitations related to the novel materials employed on modern satellites (e.g., CFRP and other composites) and the evolution of manufacturing solutions; in addition, impact scenarios including glancing impacts cannot be considered in their complexity. To overcome the limitations of such solutions, the University of Padova developed the Collision Simulation Tool Solver (CSTS) in the framework of a contract awarded by the European Space Agency to CISAS and the German enterprise Etamax Space GmbH [51]. In CSTS, colliding objects are modelled with a mesh of Macroscopic Elements (MEs), representing major satellite parts, connected by structural links to form a system-level net (see Figure 12). During an impact event, the involved MEs are subjected to fragmentation, and structural damage can be transmitted through the links net; the generated fragments can affect the other MEs, creating a cascade effect representative of the object fragmentation. Based on this modelling concept, the simulator core of the CSTS is divided into three main parts: the ME Breakup Algorithm, providing fragments size, velocity, and area-to-mass distributions for a variety of spacecraft building blocks; the Structural Response Algorithm, calculating momentum transfer, energy dissipation, structural deformation, and fracture; and the Fragments Tracking Algorithm, which follows the trajectories of new debris created in the early stages of the event and detects the resulting multiple secondary impacts on other satellite parts.  
This approach allows CSTS to simulate complex collision scenarios with design details included, producing statistically accurate results. In particular, CSTS allows the reduction of the complexity of the fragmentation analysis, since it is based on a set of ME fragmentation models that are easier to develop, tune, and update through testing. It was therefore possible to validate CSTS using sub-scale test results and data available in the literature.