esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Functionality Verification Results
#### 4.3.3. Elapsed Time of Calculation Comparison: 2D FE Model vs. 3D FE Model  
One of the advantages of the present methodology is to reduce the time of calculation during geometry optimization, where the effects of the variation in several geometric parameters are investigated. In previously published works on foot prostheses, geometry optimization has been carried out through 3D FE models [12; 13]. The 2D FE model aims to provide a design tool to study the influence of the geometry on the foot prosthesis behavior, giving the result in a shorter time than performing 3D FEAs. The time elapsed to carry out a structural FEA depends on the computing power of the calculator used. However, in similar conditions (same computing power), the time necessary to perform the FEAs depends on several factors, such as the number of degrees of freedom of the FE models. In Table 10, the number of nodes and number of degrees of freedom from the 2D FE model (Section 3.1.1) and the 3D FE model (Section 3.1.2) are listed. In the present specific application, the 2D static FEA that simulated the dorsiflexion test required a time of calculation of 45 s, while the equivalent 3D FEA required a time of calculation of 4 h and 19 min.
# Development of a Radio-Frequency Quadrupole Accelerator for the HL-2A/2M Tokamak Diagnostic System
## 3 RF Conditioning
### Inter-Vane Voltage Measurement  
The inter-vane voltage of RFQ is one of the most important parameters of the cavity. Measurement of the bremsstrahlung X-ray is a useful non-intrusive and precise technique for inter-vane voltage calibration. The bremsstrahlung spectrum originates from the stopping in electrodes of field emission electrons across voltage gaps. X-ray spectra were measured by using a CdTe spectrometer. The spectrometer was calibrated with Am-241 sources, as shown in Figure 9.  
Figure 8: The frequency of the RFQ as a function of inlet water temperature.  
Figure 7: The log of a 6-h long operation without a trip.  
The quartz window was located in the middle of the cavity. Because the spectrometer cannot directly face the gap between adjacent electrodes in the quadrilateral cavity, the spectrometer and the quartz window were at a 45-degree angle to obtain as many counts as possible, as shown in Figure 10.  
In the measurement, we used a 1.0 mm thick copper absorber plate to filter out low-energy peaks and backgrounds. A typical 1X-ray spectrum corresponding to the RFQ inter-vane voltage is shown in Figure 11. There are different ways of quantifying the cutoff energy [16; 17; 18]. We adopted linear fitting with a beveled edge for the high energy region and took the horizontal intercept as the cutoff energy. The specific shunt impedance is defined as follows:  
\[R_{s}=\frac{V^{2}}{P/l} \tag{4}\]  
where \(V\) is the inter-vane voltage, \(P\) the power loss, and \(l\) the RFQ length.  
Figure 10: The quartz window and spectrometer.  
Figure 9: Spectrometer calibration with Am-241.  
We measured the inter-vane voltage under different cavity powers, as shown in Figure 12. The specific shunt impedance of the RFQ can be deduced from the slope of the linear fitting line (\(y=100.086x\) from Figure 12). The specific shunt impedance is 221 k\(\Omega\cdot\)m, which is 92% of the simulated value. According to the calculated specific shunt impedance, an inter-vane voltage of 70 kV can be achieved at a cavity power of 48.6 kW.  
Figure 11: X-ray spectrum corresponding to the 74.8 kV inter-vane voltage (@ 55 kW).  
Figure 12: Square of inter-vane voltage as a function of cavity power.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameters** (mg. L\({}^{-1}\))} & \multirow{2}{*}{**Raw LL**} & \multirow{2}{*}{**Treated LL**} & \multicolumn{4}{c}{**Standard Limits for Treated LL (mg. L\({}^{-1}\))**} \\ \cline{4-7}  & & & & **Brazil** & \multicolumn{1}{c}{**WHO/FAO 2007 \({}^{b}\)**} & \multicolumn{1}{c}{**India \({}^{b}\)**} & \multicolumn{1}{c}{**Oman \({}^{c}\)**} \\  & & & & **[22]** & \multicolumn{1}{c}{**[23]**} & \multicolumn{1}{c}{**[23]**} & \multicolumn{1}{c}{**[24]**} \\ \hline As & 0.06636 & 0.02767 & 59 & 0.5 & \(\ast\) & \(\ast\) & 0.1 \\ Ba & 0.6363 & 0.0498 & 92 & 5.0 & \(\ast\) & \(\ast\) & 1.0 \\ Cd & 0.00412 & 0.00119 & 71 & 0.2 & 0.01 & 1.0 & 0.01 \\ Cr & 0.21614 & 0.10494 & 51 & 0.1 & \(\ast\) & 2.0 & 0.05 \\ Fe & 6.7248 & 1.5082 & 77 & 15.0 & \(\ast\) & \(\ast\) & 1.0 \\ Pb & 0.0547 & \textless{}DL & \textgreater{}99 & 0.5 & 5.0 & 1.0 & 0.1 \\ Ni & 10.66 & 0.5030 & 95 & 2.0 & \(\ast\) & 3.0 & 0.1 \\ Zn & 0.3054 & 0.08313 & 72 & 5.0 & 2.0 & \(\ast\) & 5.0 \\ \hline \hline \end{tabular}  
* LL: landfill leachate; DL: less than the detection limit; \({}^{\ast}\) Not mentioned; \({}^{\text{a}}\) discharging into water bodies; \({}^{\text{b}}\) irrigation water; \({}^{\text{c}}\) discharging in municipal sewers.  
\end{table} 
Table 1: Heavy metals concentration in the raw and treated landfill leachate (LL). water. The exposure of a biological cell to an electric field can produce a wide variety of biochemical and physiological responses, and most of these responses are based on the modification of the membrane potential by the influence of the external electric field. If the strength of this electric field exceeds a certain limit (0.2-1 V), it results in the formation of pores in the plasma membrane, a phenomenon called electro-permeabilization or electroporation [29; 30]. Another fact that contributes to the elimination of these microorganisms in an indirect way is the contact between bacteria and oxidants generated during the process and/or the adhesion of these microorganisms to the generated coagulants and consequent removal of the effluent by flotation or sedimentation. This phenomenon has been explained by other authors by observing live bacteria found in solids decanted throughout the EC process [29]. The results suggest that modifying parameters such as applied current, electrode size and treatment time the removal efficiency can be improved. Therefore, taking into account the great efficiency of the EC process with SfE in the removal of heavy metals and coliforms from LL, as well as the need in valorizing metallic  
Figure 4: Microbial load of LL: (**A**) Raw LL; (**B**) after 4 h of treatment and (**C**) after 8 h of treatment. Figure 3: Microbial load after electro coagulation (EC) experiments using electrodes of steel swarf (SfE). residues, the proposed Sfe it arises as promising alternative for the treatment of such wastewaters. This solution could also result in lower operation costs in LL treatment plants in comparison with chemical coagulation along with UF and RO technologies that present highly operation costs.
## 2 Literature Review
### Common Data Environment for Collaboration  
A Common Data Environment (CDE) serves as a single source of information for storing, sharing and managing documents in graphical, structured and unstructured form (BSI, 2013) [36]. CDE encourages parties to use the same/related (interoperable) systems and platform to work collaboratively together. Project data in different locations and formats can be connected into a single source and federated. The organization can therefore interact across project teams and collaborate more efficiently, avoiding confusions, omissions, repetitions and errors Such an interaction can be achieved using a myriad of devices including mobile platforms such phones and tablets. CDE is largely classed as a BIM Level 2 maturity tool where project design outputs are produced in 3D models and are managed by different professional platforms. The concept of CDE is not new in the software development domain. Early definitions of CDE include 'a virtual space where in all the stakeholders of a project, even if distributed by time and space, may negotiate, brainstorm, discuss, share knowledge and labour together to carry out some task, most often to create an executable deliverable and its supporting artefacts' [37]. CDE can be used across various domains. Examples of such domains include (i) non-software domains, (ii) asset management, (iii) information services, (iv) infrastructure, (v) community and (vi) software development (vii) construction, manufacturing and (viii) electronics industries [37].  
In the preceding sections, the rationale for adopting collaborative working approaches has been examined. The examination culminated in discussions about the use of BIM, cloud BIM, mobile BIM and CDE for fostering collaboration in the delivery of construction projects. However, the key questions yet to be answered are: What are the emerging mobile/cloud BIM technologies currently being used in the construction industry? How are they being used? What are their benefits and impacts on construction projects? What are the challenges associated with their implementation?
# 400 GbE Technology Demonstration Using CFP8 Pluggable Modules
## 1 Introduction
The following variants are defined in 803.bs: 400 GBASE-DR4, 50 gigabit per second (Gb/s) 4 parallel single mode fiber transmission (PSM4) up to 500 m; 400 GBASE-FR8, 25 Gb/s 8-\(\lambda\) wavelength division multiplexing (WDM) transmission up to 2 km; and, 400 GBASE-LR8, 25 Gb/s 8-\(\lambda\) coarse WDM transmission up to 10 km. All interfaces use a 1300 nm window with minimum chromatic dispersion and a low-cost transceiver. For 8-\(\lambda\) WDM transmission, the assignment of the optical wavelength on the coarse wavelength division multiplexing (CWDM) grid removes the requirement of precise temperature control for the laser diode. To improve sensitivity and increase the transmission distance, forward error correction (FEC) is widely used in today's client-side optics interface. IEEE has approved two FEC coding schemes, KR4 and KP4. KR4-FEC utilizes Reed-Solomon coding (528, 514). The net coding gain is 5.3 dB and the bit error rate (BER) threshold for the uncorrected code word (UCW) is 2.1 \(\times\) 10\({}^{-5}\). This is widely used in the non-return-to-zero (NRZ) OOK modulation format. However, with the same symbol rate, the sensitivity penalty of PAM4 over NRZ OOK is 10 \(\times\) log\({}_{10}\) (1/3) = 4.77 dB. In practice, there is further performance degradation due to the system nonlinearity. In order to close the link budget, KP4-FEC defined in IEEE 802.3 bs clause 91, is widely used in PAM4 transmission [17]. KP4-FEC utilizes Reed-Solomon coding (544, 514). The net coding gain is 6.4 dB and the BER threshold for the UCW is 2.2 \(\times\) 10\({}^{-4}\). Figure 3 illustrates PAM4 eye diagrams without and with pre-compensation. As one can see, especially for the high baud rate PAM4 signal, its eye can easily be closed due to system bandwidth limitation. Consequently, it is critically important to implement pre-compensation techniques to improve the signal quality and open up the PAM4 eye. A continuous time-domain linear equalizer (CTLE) has been widely used to equalize the frequency-dependent loss for the electrical interface between the packet forwarding engine (PFE) and the pluggable optical module. CTLE is essentially a high-pass filter which inverts the frequency response of the trace on the printed circuit board (PCB). The typical response of CTLE is plotted in Figure 4. In Reference [4], the emphasis of the CTLE is defined in 1 dB steps. However, 0.5 dB steps may be required in large-scale deployment to improve granularity. Figure 3: (a) Closed 4-level pulse amplitude modulation (PAM4) eye without pre-compensation, (b) open PAM4 eye with pre-compensation.
# SLMAS: A Secure and Light Weight Mutual Authentication Scheme for the Smart Wheelchair
## 3 Proposed Scheme
### Sensor Node Registration Process  
The steps taken to enrol a sensor node with the system presented in Table 4 include the following:  
1. Sensor Node (\(SN_{n}\)) catches an identity \(SN_{ID_{n}}\), and sends it through a protected route to the \((TA)\).
2. \(TA\) is contacted by the registration request from \(SN_{n}\), decides a random values \(RND_{2}\in\mathcal{Z}_{p}^{*}\), a temporary identity \(PR_{ID_{n}}\in\mathcal{Z}_{p}^{*}\), and computes \(X_{n}=H(SN_{ID_{n}}||RND_{2})\)  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Ssymbols** & **Representations** \\ \hline \(SN_{n},SN_{ID_{s}},PR_{ID_{n}}\) & \(n\)th sensor node, its personal identity, pseudo-random identity \\ \(S_{s},S_{ID_{s}},PR_{ID_{s}}\) & \(\text{sth server}\), its personal identity, pseudo-random identity \\ \(TA,MSK\) & Trusted Authority and its secret master-key \\ \(\delta T,TC\) & Maximum admissible transmission-delay and present-time \\ \(SK_{ns}(=SK_{sn})\) & Shared session key between \(SN_{n}\) and \(S_{s}\) \\ \(SKEY_{TA_{s}H}\) & Shared-secret-key among the \(TA\) and \(SN\) \\ \(SKEY_{TA_{s}A}\) & Shared-secret-key among the \(TA\) and server \\ \(T_{TA_{s}},T_{n},T_{s}\) & Current timestamps of \(TA\), \(SN_{n}\), and \(S_{s}\) \\ \(RND_{i}\) & \(i\)th random value of 160 bits \\ \(i\geq i\) & Verify it \(i\) equals to \(j\) \\ \(H(.)\) & Cryptographic one way hash function \\ \(\mathcal{A},U_{\mathcal{A}}\) & An adversary and privileged insider \\ \(\oplus,||\) & Bitwise exclusive or and concatenation-operators \\ \hline \hline \end{tabular}
\end{table}
Table 2: Notation Guide.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Server (\(S_{s}\))** & **Trusted Authority (\(TA\))** \\ \hline Select an identity \(S_{ID_{s}}\) & \\ \(\frac{S_{ID_{s}}}{(S_{s}\to TA)}\) & \\  & Picks arbitrary number \(RND_{1}\in\mathcal{Z}_{p}\) \\  & and Pseudo-random identity \(PR_{ID_{s}}\) \\  & COMPUTE: \\  & \(X_{s}=H(S_{ID_{s}}||RND_{1})\) \\  & \(SKEY_{TA_{s}}=H(X_{s}||MSK)\) \\  & Store \(\{PR_{ID_{s}},ENC_{MSK}[S_{ID_{s}},X_{s}]\}\) \\  & tuple in the database \\  & \(\underset{(S_{s}\gets TA)}{\underbrace{\langle PR_{ID_{s}},SKEY_{TA_{s}} \rangle}}\) \\ Save \(\{PR_{ID_{s}},S_{ID_{s}},SKEY_{TA_{s}}\}\) & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Proposed server registration process.  
\(SKEY_{TA,n}=H(X_{n}||MSK)\). \(TA\) communicates the message that contains \(\{PR_{ID_{n}}\), \(SKEY_{TA,n}\}\) to \(SN_{n}\) over the protected medium. \(TA\) also saves the value \(\{PR_{ID_{n}}\), \(ENC_{MSK}[SN_{ID_{n}},X_{n}]\}\) in the database.
3. Upon receiving the response from \(TA\), \(SN_{n}\) saves the parameters \(\{PR_{ID_{n}},SN_{ID_{n}}\), \(SKEY_{TA,n}\}\), as presented in Table 7.
# Improving Decodability of Polar Codes by Adding Noise
## 4 Proposed Decoding Algorithms
### SNE Decoding Algorithm  
In order to further simplify the PDNE decoder structure and reduce the processing delay of the decoder, a sole neural-evolutionary (SNE) decoding algorithm of polar codes is proposed, in which only pre-trained offline directed neuro-evolutionary noise sequences are added to the received sequence for re-decoding. When the SSC decoding fails, the pre-trained neural-evolutionary noise is directly called, which greatly reduces the decoding delay. The details of the SNE decoding algorithm are given in Algorithm 3.
## 3 Results
### Comparison of GPP Modeling Results based on Sentinel-2 Data and MODIS Products  
We compared our GPPVI with an LUE-based GPP product (MOD17A2H) in the footprints near the flux tower sites. Table 3 shows that GPPEVI exhibited a higher correlation with GPPEC than MOD17A2H at the EBF sites. Table 4 shows that the red-edge-based GPPClr had a lower rRMSE value of 0.07 with GPPEC than that of the MODIS product at the GRA sites. The non-red-edge GPPEVI based on the coefficients at each site had a higher correlation with GPPEC than did the MOD17A2H product. Both GPPClr and GPPEVI showed a higher correlation than MOD17A2H with GPPEC at the spatially heterogeneous CUM sites. However, at the sites with homogeneous vegetation distribution near the flux tower footprint region such as WOM and YNC (Figure 8), the estimation accuracy between MOD17A2H and GPPEC was similar to that for GPPVI and GPPEC (Tables 3 and 4).  
To evaluate the effectiveness for large-scale GPP estimation, we used the same series of coefficients for estimating GPP across different sites (Figure 11). Figure 11 shows that the Sentinel-2 red-edge-based GPPClr corresponded better to GPPEC than GPPEVI and MOD17A2H in grasslands, with the highest \(R^{2}\) and RMSE values as 0.2 gC\(\cdot\)m\({}^{-2}\)-day\({}^{-1}\) less than those obtained using other methods. At the EBF sites, MOD17A2H showed the highest \(R^{2}\) and the lowest RMSE. Unlike the similar coefficient values of a and b at the GRA sites, the key coefficient b in GPPClr exhibited large differences across the three EBF sites (5.01, 2.88, and \(-\)0.13 for TUM, WOM, and CUM, respectively, Table 3). In addition, the key  
Figure 10: Temporal trends in GPP spatial distribution at the YNC (a1–a8) and CUM (b1–b8) sites. The GPP estimation of each subfigure is based on the Clr-PARin-GPP relationship shown in Tables 3 and 4.  
coefficient a in \(\mathrm{GPP_{EVI}}\) exhibited large differences across the three EBF sites (2.48, 1.85, and 1.15 for TUM, WOM, and CUM, respectively, Table 3), which led to high systemic bias with \(\mathrm{GPP_{EC}}\).
## 3 Results and Discussion of CNN Models Classification
### Results for Resnet101 Model
#### 3.5.1 Comparison of VGG16, VGG19, and Resnet101 Models  
The evaluation of the VGG16, VGG19, and Resnet101 models focused on assessing their accuracy and significant error rates. A summary of the evaluation metrics is provided in Table 10.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Average Accuracy** & **Total Critical Errors** & **Consistency of Performance** \\ \hline VGG16 & 66.60\% & 159 & Moderate \\ VGG19 & 67.03\% & 107 & High \\ Resnet101 & 61.19\% & 333 & Low \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of VGG16, VGG19, and Resnet101 models.  
The VGG16 architecture reached a mean accuracy level of 66.60%, accompanied by 159 critical errors. Conversely, the VGG19 architecture demonstrated a modest improvement, attaining a mean accuracy of 67.03% while registering 107 critical errors, marking it as a more accurate and reliable choice than the VGG16. Meanwhile, the Resnet101 model recorded the least impressive performance, with an average accuracy rate of 61.19% and the greatest amount of critical errors, totaling 333.
# Dynamic Multi-Task Graph Isomorphism Network for Classification of Alzheimer's Disease
## 2 Related Work
### Deep Learning-Based Alzheimer's Disease Classification Methods
#### 2.1.2 GNN-Based Methods  
GNN-based methods build on brain functional networks stored as binary matrices, with each brain region as a node, forming graph data input into GNNs. For example, [PERSON] et al. [19] proposed an interpretable GNN framework with a new regularization pooling layer. The pooling layer calculates the node pooling scores to identify Regions of Interest essential for AD classification. This method is characterized by encouraging reasonable Region of Interest selection. [PERSON] et al. [20] introduced a method called PSCR, which inputs transformed brain maps into a graph attention network for fMRI image classification. This method demonstrates good performance, with a maximum accuracy of 72.4%, a satisfactory sensitivity of 71.15%, and a specificity of 75.00%. [PERSON] et al. [21] proposed a structure and feature-based Graph U-Net model that integrates GNN into the high-performance U-Net structure, considering graph node structure and features in the pooling layer to enhance AD classification accuracy. Compared to previous methods, this approach excels at extracting both node-level (low-level) and structural (high-level) information from graphs. The low-level information involves direct attributes of individual nodes and connections, as derived from functional magnetic resonance imaging (fMRI). On the other hand, the high-level information encapsulates the overall structure and interconnected patterns within the graph, which are captured through the structure and feature-based Graph U-Net's adaptive pooling layer.These optimizations have allowed the model to reach an average classification accuracy of 83.69%. [PERSON] et al. [22] introduced a dual-modality fusion brain connectivity network that combines fMRI and diffusion tensor imaging, proposing three mechanisms in the current GCN to improve classifier performance. However, GNNs have limited representation capabilities and cannot distinguish graph isomorphic structures, leaving room for improvement in these methods.
# Sulfonation Treatment of Polyether-Ether-Ketone for Dental Implant Uses
## 4 Discussion
The authors of [23] showed that the surface morphology of sulphonated PEEK as well as the hydrophilic sulfonate functional groups play a major part in increasing the hydrophilicity of sulfonated PEEK. Despite the fact that many studies have reported various results, this research has shown that both of these factors have a vital impact. Within the scope of this research, a rise in surface roughness was documented as a probable concurrent impact. As a result, the adhesive qualities of the surface were shown to increase even more. It is feasible to develop oxygen-rich nanofilms on PEEK with a high surface energy, which can result in an increased cell performance [24; 25]. Along with the surface's chemistry, the surface's topography also has a significant impact on the behavior of cells when they are attached to the surface [26; 27]. The mechanical interlocking that occurs as a result of nanoscale surface roughness can lead to improved bone implant fixation. The surface roughness at the nanoscale, on the other hand, has been shown to dramatically alter hydrophilicity, which in turn has the potential to drastically affect the behavior of cells. When cells are cultivated on substrates of varying roughness, they frequently take on distinct forms. There is a wealth of data to suggest that the shapes of the cells are connected to their behaviors, such as proliferation and protein secretion [27; 28; 29].
## 4 Discussion
### How Do UAV and Sentinel-2 Chlorophyll Index Maps Compare across Multiple Growth Stages?  
When comparing between the CIred-edge values derived from the Sentinel-2 and UAV imagery, we found that the correlation between the values from the two platforms generally increased as crop growth stage advanced. During early phenological periods, it is likely that the influence of bare soil on the reflectance spectra would have been greater, with the wheat canopy being highly heterogeneous across the field. In particular, the earlier growth stages noted for both farms (i.e., growth stages 31 and 39) correspond to the start of significant periods of canopy development, including stem elongation and the development of yield-forming leaves [11]. During this stage, when both crop ground cover and fractional light interception increase linearly with LAI [55], farmers target their main applications of nitrogen fertiliser. When compared to the relatively coarse-scale (20 m) Sentinel-2 observations, the 5-cm spatial resolution of the UAV dataset was sufficient to resolve these small discontinuities in the canopy. The distribution of values across the field was also much narrower for Sentinel-2 when compared to that of the UAV, further suggesting that these variations in canopy cover were not detected by the satellite sensor.  
By later growth stages (i.e., growth stages 51 to 77), the leaf canopy is closed with the crop entering ear emergence, flowering and grain-filling phases. Correspondingly, at these growth stages, the spectral reflectance and spatial variability of the canopy across the field is generally more homogenous. Increased uniformity in the wheat canopy was also evident in the statistics where the variance and distribution of CIred-edge values estimated from the Sentinel-2 observations were comparable to those of the UAV. Studies in [PERSON] et al. [56] also demonstrated a similarly high agreement between UAV and Sentinel-2 CIred-edge values during the later growth stages for maize crops.