esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Test-Bed
After the metro-access convergence characterization in the urban test-bed, we address an experimental verification using two pre-commercial transceivers with the same modulation format used before. The purpose remains the same: to conduct acquisitions while varying the OSNR and the received optical power. To achieve this, we implement the set-up illustrated in Figure 3.  
This time, the experiment is conducted within a laboratory environment. The pre-commercial transceivers transmit 4 dBm optical power. Then, an optical filter shapes the signal emulating distortions at the transmitter side, through a three-order super-Gaussian filter and 95 GHz optical bandwidth (comparable to the previous 100 GHz case). At this point, variable optical noise is added to the signal. The noise is generated using an Amplified Spontaneous Emission (ASE) generator and then filtered with a 500 GHz flat bandwidth optical filter. This noise is then attenuated (green VOA in Figure 3) to set different OSNR values and finally added to the optical signal by a 90:10 optical coupler, generating the optical signal for the metro network (green in Figure 3). Subsequently, the signal is split by a 95:5 optical splitter to monitor the OSNR by an Optical Spectrum Analyzer (OSA). The following VOA is used to emulate the access network optical loss due to PON optical splitters and fiber link loss (blue in Figure 3); its characterization versus OSNR defines the metro network characteristics' impact on the access network power budget performances. The VOA settings, indeed, change the optical received powers, exactly as in the previous urban-fiber set-up. Finally, the signal is sent to the optical receiver of the pre-commercial transceiver for data acquisitions. The purpose of both configurations is to produce different OSNR signal levels (affected by the metro network) and detect them at different optical received powers (access network optical receiver side). Figure 3: Test-bed used for experimental convergence demonstration using pre-commercial transceivers, analyzing performance versus the metro signal OSNR and the access optical received power.
## 2 Convert Manifold Mesh into Layered Image in Printing Coordinate System (PCS)  
### Printing Coordinate System (PCS)  
For a polyhedron manifold model \(M\) to be printed in Euclidean space \(R^{3}\), let \(A_{i}\), \(i=0,\cdots,N-1\) be the N triangular faces, with vertices \((a_{i},b_{i},c_{i})\), which are assumed to be ordered counter clockwise (CCW) on \(A_{i}\). The Axis-aligned Bounding Boxes (AABB) of \(M\) is calculated to define the scale of \(M\). Meanwhile, the stroke length \(x_{b},y_{b},z_{b}\) of the AABB along \(x,y,z\) direction can be reckoned. The maximum print space of a printer along \(x,y,z\) direction are, respectively, \(x_{p},y_{p},z_{p}\).  
For any 3D component to be fabricated, the object is sliced into a number of 2D layers of defined layer thickness. In a printing coordinate system (PCS), the layer height sequence \(\mathbf{Z}\) about \(n\) layers can be expressed as  
\[\mathbf{Z}=\{z_{1},z_{2},z_{i},\cdots,z_{n}\}\ Z_{i}\in R\ \&\ i\in[1,n], \tag{1}\]  
where \(z_{i}<z_{j}\), for \(\forall\ i<j\).  
The layer thickness sequence \(\mathbf{d}\) can be obtained:  
\[\mathbf{d}=diff(\mathbf{Z})=\{d_{1},d_{2},d_{i},\cdots,d_{n-1}\}\ d_{i}\in R\ \&\ i\in[1,n-1], \tag{2}\]  
where \(diff\) means 1 st order forward difference, \(\forall\ d_{i}=z_{i+1}-z_{i}\), \(i\in[1,n-1]\).  
For uniform slicing, layer thickness is equal to each other, \(\forall\ diff(\mathbf{d})\ =0\). For adaptive slicing, layer thickness varies, \(\exists\ diff(\mathbf{d})\
eq 0\).  
In PCS, the normalized height \(h_{n}\) of i-th layer can be calculated hereby to represent relative height of the slicing plane:  
\[h_{n}=\frac{z_{i}}{z_{b}}\ h_{n}\in(0,1]. \tag{3}\]  
There exists the inherent staircase effect (SE) in 3 DP. The SE can be manifested by various geometric parameters, for instance, cusp height \(\delta\) and volume error \(V_{e}\). The cusp height is defined as  
\[\delta=d\times|cos\alpha|, \tag{4}\]  
\[\alpha=cos^{-1}\frac{\mathbf{n}_{z}\times\mathbf{n}_{f}}{\|\mathbf{n}_{z}\|\cdot\|\mathbf{n}_ {f}\|}\ \alpha\in[0,\pi], \tag{5}\]  
where \(d\) is layer thickness, and \(\alpha\) is the included angle between the z normal vector \(\mathbf{n}_{\mathbf{Z}}\) and facet normal vector \(\mathbf{n}_{f}\).  
Adaptive slicing is generally realized via variable thickness using iteration. Therefore, the calculation process of layer thicknesses can be expressed in the following mathematical model.  
\[\left\{\begin{array}{c}find\;\mathbf{d}\\ min\;card(\mathbf{d})\\ Subject\;to:\;f(\mathbf{d})\leq\delta_{max}\\ d\in[d_{min}\;,d_{max}]\end{array}\right., \tag{6}\]  
where \(card(\mathbf{d})\) is the cardinality of the \(\mathbf{d},\;d\in[d_{min}\;,d_{max}]\). \(f(\mathbf{d})\) is the constraint function which presents the accuracy requirement, and \(\delta_{max}\) can be the maximum cusp height of each layer.  
The Printing Coordinate System (PCS) of 3D reconstructed manifold model (right human knee distal femur) is shown in Figure 1.
# Design of an In-Process Quality Monitoring Strategy for FDM-Type 3D Printer Using Deep Learning
## 2 Development of In-Process Monitoring System
### Multi-Variate Data  
In this section, the input data and the structure of multi-head encoder-decoder temporal neural network is discussed. The data are acquired using the experimental setup illustrated in Figure 1. To rigorously search for the perfect machine learning algorithm to use, the nature of the data used is defined. The input data to the system shall include a multi-variate time series data, presented in Equation (1).  
\[D=[D^{1},D^{2},D^{3},\ldots,D^{T}] \tag{1}\]  
where:  
\(D\): the combination of various sensor readings that the acquisition device has collected;  
\(x\): the global epoch time in seconds;  
\(D^{x}\): the data matrix which contains the collection of unit data.  
For each data matrix \(D^{x}\), there is a set of data \(d^{k}_{a,b}\) where \(a\in\mathbb{R}\) and ranges from 1 to the number of parameters used for classification within the system and \(b\) refers to the measurement count. The data fed into the system are from thermistor sensor to record the temperature of the nozzle, as well as the humidity sensor in order to record the ambient temperature and humidity, and the data from spectrometer that are used to detect the filament's temperature. In each second, a sample of multiple logs is collected where the value of the measurement count depends on the capability of the data acquisition device to sample data per second. In this paper, the data acquisition rate is set to 2047 Hz.  
\[\mathbf{D^{1}}=\begin{bmatrix}d^{1}_{1,1}&d^{1}_{1,2}&\ldots&d^{1}_{1,b}\\ d^{1}_{2,1}&d^{1}_{2,2}&\ldots&d^{1}_{2,b}\\ \vdots&\vdots&\ddots&\vdots\\ d^{1}_{a,1}&d^{1}_{a,2}&\ldots&d^{1}_{a,b}\end{bmatrix} \tag{2}\]  
To conform to the training's predetermined level of unfolding, the data is first spliced using the sliding window approach, and the values are selected taking into account the sampling capability of the experiment's acquisition module. By adopting a sliding step of (\(\Delta=250\)) and a time window of (\(\delta=50\)), the elements in \(D^{k}\) are divided into fixed-length sequences of \(x^{(\Delta)}\). It may be expressed mathematically as \(x^{(\Delta)}=(x\delta-\Delta+1,x\delta-\Delta+2,\ldots,x\delta)\). The new data sequence consists of a matrix of dimension \((a,Delta)\), where \(x^{(\Delta)}\in\mathbb{R}^{(a,\Delta)}\).
## 1 Introduction
Here, the mapping problem of incoming service requests to the BBUs is formulated as a multi-objective non-linear programming (MONLP) optimization problem. Focusing on the QoE maximization of mobile operators as well as the subscribers and the service provider's profit, requests are selected in a scheduling slot and BBUs are allocated for the selected incoming requests. The main contributions of this paper can be summarized as:  
* An integrated priority metric is developed so that the priority of an incoming request to a suitable BBU can be identified. * Computational resource allocation problem for incoming requests is formulated as multi-objective non-linear programming optimization problem focusing on maximization of end-user QoE as well as service provider profit. * Tradeoff between profit and customer satisfaction while selecting the BBUs for service provisioning in CRAN is made by two scheduling algorithms which are computationally viable to be deployed. * To enhance system performance and resource utilization, the duration of the scheduling interval is determined dynamically according to the incoming requests and available resources. * The results of our extensive simulation experiments, carried out on CloudSimSDN [11], depict that significant performance improvements in terms of user QoE, QoS satisfaction, average waiting time, and service provider profit have been achieved by the proposed system compared to the state-of-the-art works. The rest of the paper is organized as follows. Section 2 describes some related works in our topic of interest. In Section 3, the system model and assumptions for execution environment of different requests are presented. The functional components of the proposed system architecture are described in detail in Section 4. In Section 5, the performance of our proposed scheduling system is analyzed, and Section 6 concludes the paper.
## 4 Conclusions  
This work evaluated the potential of dredged sediment from dam (DSD) for the elimination of the cationic dye crystal violet (CV) in a batch mode. The elimination of CV dye by DSD powder is slightly influenced by the solution pH (3.0-11.0) and ionic strength (0-1.0 M NaCl). The kinetic study showed that approximately 74% (from 30 mg/L) of the total CV dye content could be eliminated from the solution within the first 20 min of stirring. Kinetic data follows the pseudo-second-order and the Avrami models. The adsorption process of CV dye was attributed to ion exchange and pore filling mechanisms, with little contribution from electrostatic attraction forces. The adsorption process of CV dye takes place in three stages: (i) film diffusion mechanism, (ii) intraparticle diffusion, (iii) and equilibrium condition, respectively. The CV dye adsorption process was spontaneous (\(-\Delta\)G\({}^{\circ}\)), endothermic in nature (\(-\Delta\)H\({}^{\circ}\)), and increased the randomness at the adsorbent/liquid interface (\(+\Delta\)S\({}^{\circ}\)). The adsorption efficiency increased with the temperature, and isotherm data was better described by the Langmuir and Liu equations. The adsorption capacity of Langmuir model reached 243.6 mg/g (at 323 K). These results show that the studied sediment of the dam can become an alternative adsorbent because of its near-zero production cost coupled with excellent capacity and rapidity of dye removal.  
A.O.: Investigation, Data curation, Formal analysis, Writing--original draft. M.A.: Formal analysis, Methodology, Writing--review and editing. B.T.: Formal analysis, Methodology, Writing--review and editing. H.-O.C.: Formal analysis, Methodology, Writing--review and editing. G.H.: Formal analysis, Methodology, Writing--review and editing. N.H.: Formal analysis, Methodology, Writing--review and editing. A.S.: Conceptualization, Writing--review and editing. R.B.: Writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**T (K)** & **K\({}_{\text{C}}\)** &
\begin{tabular}{c} **Van ’t Hoff** \\ **Equation** \\ \end{tabular} & \(\Delta\)G\({}^{\circ}\) (kJ/mol)** & \(\Delta\)H\({}^{\circ}\) (kJ/mol) & \(\Delta\)S\({}^{\circ}\) (J/mol K) \\ \hline
293 & 106426.8 & y = 427.8 x + 10.12 & \(-28.18\) & \(-3.56\) & 84.14 \\
308 & 101898.0 & R\({}^{2}\) = 0.946 & \(-29.51\) & & \\
323 & 92840.4 & & \(-30.70\) & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Thermodynamic parameters for CV dye adsorption process onto DSD adsorbent.
# Web Unevenness Due to Thermal Deformation in the Roll-to-Roll Manufacturing Process
## 2 Finite-Element Analysis: Thermal-Structural Coupling Analysis
### Finite-Element Modeling  
A PET film (CD-901, KOLON Inc., Korea) was used in this study. Table 1 presents the mechanical and thermal properties of the web; boundary conditions defined for the model are shown in Table 2.  
Figure 1a indicates the schematics of the web transporting and drying sections in the R2R process. Figure 1b,c present enlarged views of the section and tension distribution, respectively, at the inlet of the dryer indicated by the black-dotted box in Figure 1a. In Figure 1b, x, y, and z directions were taken as the web-moving, transverse, and web-thickness directions, respectively. In Figure 1a, it was inferred that web temperature steadily increased and converged to the drying temperature due to conduction and convection heat from the dryer as the web was close to the inlet of the dryer. The temperature distribution of the web in the FEM was set considering the temperature distribution at this position. Considering the tension distribution shown in Figure 1c, boundary conditions at both edges were defined, and set tension was applied to both sides of the web [21]. The drying temperature range was set from 30 to 120 \({}^{\circ}\)C considering the temperature range of the actual dryer in the R2R process. Solid 226 was used as the element in the FEM to carry out multiphysics coupling analysis using a commercial finite-element analysis program (ANSYS Fluent, ANSYS Inc., Canonsburg, PA, USA). Equation (1) represents the governing equation to calculate the net thermal deformation in Solid 226 on the basis of thermal-structural coupling analysis.  
\[\{\varepsilon\}=[D]^{-1}\{\alpha\}+\{\alpha\}\Delta T \tag{1}\]  
where \(\{\varepsilon\}\) is the total strain vector, \([D]\) is the elastic stiffness matrix, \(\{\alpha\}\) is the vector of coefficients of thermal expansion, and \(\Delta T\) is the temperature variation. Although there was a web where permanent deformation occurred after the thermal cycle, such as slit-extruded PET foils, permanent deformation after the thermal cycle was ignored because this study considered the correlation between thermal deformation inside and outside the dryer, and coating performance.  
\begin{table}
\begin{tabular}{c c c} \hline
**Property** & **Unit** & **Value** \\ \hline Density & kg/m\({}^{3}\) & 1390 \\ Poisson’s ratio & - & 0.33 \\ Young’s modulus & MPa & 4800 \\ Specific heat & J/kg K & 1172 \\ Thermal-expansion coefficient & K\({}^{-1}\) & \(1.7\times 10^{-5}\) \\ \hline \end{tabular}
\end{table}
Table 1: Mechanical and thermal properties of polyethylene terephthalate (PET) flexible substrate.  
\begin{table}
\begin{tabular}{c c} \hline
**Physics** & **Boundary Condition** \\ \hline Mechanical & Fixed support \\ Mechanical & Tension \\ Thermal & Drying temperature \\ Thermal & Drying area expansion \\ \hline \end{tabular}
\end{table}
Table 2: Boundary conditions in thermal–structural coupling analysis.
## 4 Method Validations Using Simulations and Experiments
### Experiment Signal Analysis
Comparing with raw signal, the noises within the raw signal is effectively depressed, and unrelated frequencies are filtered out by TSMA filtering. Comparing Figure 21a,c, namely, CVS-MED signal under health and fault condition, five equally spaced impulses emerge in the CVS-MED signal when planet gear is under fault condition. The fault-induced impulses are stronger than other impulses within the signal. Moreover, amplitudes at fault characteristic order and its multiples increases significantly, which highly suggests the occurrence of planet gear fault. After processing, using the proposed CVS-MED method, planet gear fault could be diagnosed much more easily. The experimental signal processing result indicates that the CVS-MED-based method is effective to diagnose planet gear fault. For comparative study purpose, experimental signals are also processed using VS. Shown in Figure 22 is VS signal, as well as its envelope order spectrum. Comparing VS signal and raw signal, the noises within raw signal is effectively depressed. Comparing VS signal waveform with and without planet gear fault, it can be easily observed that VS signal with planet gear fault is more impulsive than health condition. A strong impulse is introduced in VS signal when planet gear is faulted. However, since VS signal is not periodic, the fault symptom could not be detected by envelope order spectrum. Energy of the fault-induced impulse spreads along the whole frequency axis instead of concentrating on fault characteristic orders. Figure 21: CVS-MED signal: (**a**) health condition waveform, (**b**) health condition envelope order spectrum, (**c**) fault condition waveform, and (**d**) fault condition envelope order spectrum. Figure 22: Vibration separation method (VS) signal: (**a**) health condition waveform, (**b**) health condition envelope order spectrum, (**c**) fault condition waveform, and (**d**) fault condition envelope order spectrum. The comparative study between proposed CVS-MED method and VS indicates that both CVS-MED-based method and VS are effective to detect planet gear fault when damage occurs on planet gear. However, the fault-induced impulse appears only once in VS signal since it contains only one cycle of dynamic responses of planet gear. In CVS-MED signal, the fault-induced impulse appears periodically, which helps to detect planet gear fault more easily. CVS improves the VS by keeping signal periodicity while overcoming modulation effect and depressing noises. In summary, experimental signal analysis indicates that planet gear fault diagnosis method based on CVS and MED is effective to detect planet gear fault under heavy noises. The proposed CVS-MED method improves VS by keeping signal periodicity while overcoming modulation effect and depressing noises.
## 1 Introduction
### Review Motivation  
The allure towards this technique is demonstrated by the variation in the surface area (SA), pore-size, pore-size distribution (PSD), pore-volume, and morphology. The SA is found to vary from \(\sim\)10\({}^{1}\)-10\({}^{3}\) m\({}^{2}\)/g, PSD ranges from microporous, mesoporous and macroporous with unimodal and bimodal distributions. Pore-volumes typically range from \(\sim\)0-3 cm\({}^{3}\)/g [1,2,4,5,7,11-16,21,22] where the higher SA values are due to solvating diluents. The average pore diameter and PSD may also vary, _i.e._, increase, decrease, or both. Moreover, the morphology coarsens in a less subtle fashion when a non-solvating diluent is used, and does not coarsen or becomes more refined in the case of a solvating diluent [2,25,26].  
Some common types of porogens investigated include organic solvents of variable molecular weight. Examples of low molecular weight solvent porogens include toluene, hexane, cyclohexanone, 2-ethylhexanol, \(p\)-xylene, \(n\)-heptane [2,4,15,16] and an example of a higher molecular weight solvent is poly(ethylene glycol) [15,23]. On the other hand, inorganic porogens include sodium chloride [24,25,26], silica [25], sodium hydrogen carbonate [27] and supercritical carbon dioxide (SCD) [25,26,27]. SCD is a suitable porogen substitute for other types of solvents that are otherwise challenging to remove from the polymer matrix at the purification stage and/or polymers which are sensitive to very small composition changes of the porogenic solvent mixtures. SCD offers fine control over average pore-sizes and PSD using temperature and pressure conditions to alter the density of SCD. The popular  
Figure 1: Schematic representation of pore formation in poly(DVB) resins: (**A**) using a “bad” solvent porogen; (**B**) using a thermodynamically “good” solvent porogen; and (**C**) using a mixture of “good” and “bad” solvent porogen as co-porogens. (Reprinted (adapted) with permission from [18]. Copyright 2004 American Chemical Society). NB: The pores are of different sizes and are not drawn according to scale, as shown below.  
use of organic porogens is traced back to the 1950s [9]; whereas, the first example of SCD as a porogen for modifying PCR materials was more recently reported by [PERSON] and [PERSON] [28, 29, 30].  
PCR tuning with porogens has not been extensively studied in a systematic manner and is the subject of this review. This paper describes the surface area, pore-size, PSD, pore-volume and changes in the morphological properties when polymers are tuned with porogenic \"inert\" solvents at various conditions. In addition, knowledge gaps related to this technique will be addressed and some recommendations for future research are suggested herein.
# Inter-Data Center RDMA: Challenges, Status, and Future Directions
## 5 Future Directions
### Network Device (Switch and NIC)  
The existing egress devices for DCs are generally traditional switch devices, which excel at packet switching but lack support for novel inter-DC RDMA congestion control and packet retransmission mechanisms. Inter-DC RDMA often requires these DCI switches to run corresponding functions to better assist its congestion control algorithms and packet retransmission mechanisms, thereby offering superior data transmission performance. For instance, congestion notification can be performed in advance to significantly shorten congestion feedback time, prompting the sender to quickly converge to a reasonable sending rate. Alternatively, the state of each flow, including congestion level and the number of in-flight packets, can be detected and recorded, and the flow state can be used to calculate and notify the sender for appropriate rate adjustment. Similarly, commercial NICs often struggle to meet the precise rate calculation and rate-limiting functions required by new congestion control algorithms, and they cannot support the functions required by new retransmission mechanisms either, such as the packet reordering function needed by selective retransmission schemes.  
In summary, due to the limitations of existing network devices (i.e., traditional switches and NICs) in leveraging the advantages of new congestion control and packet retransmission methods for inter-DC RDMA, there is a clear need to design new network devices that offer these essential capabilities.
# Hybrid Satellite-Terrestrial Networks toward 6G: Key Technologies and Open Issues
## 4 HSTN Applications
### Maritime Communication  
Although humans mainly live on land, the sea is also of great importance for transportation, marine resource exploitation, and tourism. Traditional terrestrial networks can only enable communication offshore, but satellite systems have the wide coverage capability to span the entire sea region. By extending traditional terrestrial-based services to the sea area for different consumers, the HSTN can link the remote sea area with the land. Users onboard a cruise ship can access the same terrestrial services as those available on the ground thanks to a HSTN.  
Additionally, gathering maritime data and maintaining maritime surveillance are crucial to ensure the safety and security of the maritime environment. An HSTN can provide efficient storage, transmission, and calculation of collected maritime information, improving the ability of continuous situational awareness of the sea [37].