esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Method of Local Anode Effect Prediction for Large Aluminum Reduction Cell
## 2 Aluminum Electrolysis Process and Anode Effect Analysis
### Analysis of Anode Effect  
Anode effect is a special phenomenon when carbon anode is used in molten salt electrolysis. When the concentration of locally dissolved aluminum oxide in the aluminum reduction cell is insufficient (\(<\)1%) or the local current density of the anode increases to its critical current density, the anode effect starts on several separate anodes locally and gradually spreads to the whole cell over time [16].  
With the development of large-scale aluminum reduction cells, the problem of uneven distribution of parameters gradually appears in aluminum reduction cells. Therefore, the concentration of aluminum oxide is too low in local areas. Then the current of one or two local anodes fluctuates and slowly affects the nearby anodes, after which the anode effect occurs in one or two local anodes, which is called local anode effect. The generation of fluorinated gas makes the contact between several local anodes and the electrolyte worse, thus seriously increasing the local anode resistance. The current is transferred to other anodes of the electrolytic cell, resulting in the increase of current density on other anodes. When the critical current density is exceeded, the anode effect also occurs on other anodes, and the anode effect spreads in the electrolytic cell, eventually leading to the global anode effect [17; 18]. Therefore, the local anode effect often precedes the global anode effect. So effective prediction of the local anode effect is conducive to preventing the global anode effect.
## 2 Samples and Measurement Techniques
The contact resistance can be expressed as:  
\[R_{\text{c}}=R_{\text{c2D}}L_{\text{T}}/W \tag{2}\]  
where \(R_{\text{c2D}}\) is the sheet resistance of the 2 DEG channel directly under the electrodes and \(L_{\text{T}}\) is a transfer length [50], which is assumed to be \(L_{\text{T}}\ll D\) in our work. Measured total resistance of different length TLM samples (symbols in Figure 2) was fitted by Equation (1) (lines) and both resistances, \(R_{\text{2D}}\) and \(R_{\text{c}}\), were extracted. The interception of lines with the abscissa axis gives \(2R_{\text{c}}\) and the interception with ordinate axis is \(2L_{\text{x}}\)[50]:  
\[L_{\text{x}}=(R_{\text{c}}/R_{\text{2D}})\times W=R_{\text{c2D}}L_{\text{T}}/ R_{\text{2D}} \tag{3}\]The total resistance dependencies on the current are shown in Figure 3a. At the current region up to \(\sim\)50-100 mA, \(R\) weakly depends on current, which corresponds to the [PERSON]'s law region. The corresponding electric field range (i.e., low-field range) is up to 1-2 kV/cm. This also means that the contact resistance and channel resistance are both ohmic at this region. A deviation from [PERSON]'s law (an increase in \(R\)) is observed when the current and electric field become higher than \(\sim\)100 mA and \(\sim\)2 kV/cm, respectively. Maximal applied voltages for each length channel are indicated in Figure 3a. Those voltages correspond to an electric field in the channels in excess of 10 kV/cm. This nonlinear electron transport phenomena in the AlGaN/GaN channels is explained by the increase in intensity of electron-phonon scattering at higher fields [25]. This is described as a decrease in field-dependent electron drift mobility \(\mu(E)\), which enters a simple relation \(R_{\text{2D}}\propto 1/(n_{\text{2D}}\mu(E))\) where \(n_{\text{2D}}\) is the sheet electron density. Unexpectedly, at high currents the total resistance for the shorter channels increases more steeply and thus, the ratio \(R(L_{\text{2}})/R(L_{\text{1}})\) decreases with current when channel lengths \(L_{\text{2}}>L_{\text{1}}\) (Figure 3a). Particularly, near [PERSON]'s region, the ratio for longest to shortest channel is \(\sim\)6.5, while at 270 mA it is \(\sim\)4.8. According to Equation (1), the change (decrease) in the ratio \(R(L_{\text{2}})/R(L_{\text{1}})=(L_{\text{2}}-L_{\text{1}})/(2 WR_{\text{c}}/ R_{\text{2D}}+L_{\text{1}})+1\) with current means that the ratio \(R_{\text{c}}/R_{\text{2D}}\) depends (increases) on current. It is worth noting that in most of the experiments, an independent contact resistance on the current density (with a constant value of \(R_{\text{c0}}\)) is assumed. However, a contact resistance vs. current dependence, \(R_{\text{c}}(I)\), should be considered by using a procedure that is illustrated in Figure 3.  
Figure 2: The dependence of the low-field total resistance \(R\) on the channel length \(L\). Data for group of TLM samples 1, fabricated of AlGaN/GaN (bottom inset), are shown by rectangles, meanwhile, the triangles as well as circles demonstrate results for other two groups of TLM samples 2, made of AlGaN/AlN/GaN (top inset).
## 3 Molecular Characterization of Coat Color Affecting Genes in Rabbits
### A Structural Mutation in the High-Mobility Group AT-hook 2 (HMGA2) Gene Determines a Dwarf Locus  
Several dwarf breeds and strains, mainly used as pets, have been developed in rabbits. A few loci determining dwarfism have been described in this species in the pre-genomic era [30]. It is however not completely clear if they can be considered distinctive loci, or if they are determined by different alleles at the same locus and if modifying genes could be involved in further reducing body size.  
The first form of dwarfism was described in a progeny of a Polish buck raised in US [120; 121]. The condition was attributed to an incompletely recessive allele that was designated as _dw_. Homozygous _dw_/_dw_ rabbits, called peanuts, are usually born inviable with about one third of the weight of the normal litter mates, with swollen head and other malformations. The heterozygous _Dw_/_dw_ rabbits are viable and in adulthood can reach about two thirds of the size of homozygous _Dw_/_Dw_. Heterozygous _Dw_/_dw_ rabbits also have a short snout determined by a modified craniofacial development, small ears, and a large disproportionate head in comparison to the compact and rounded body [30]. [PERSON] et al. [122] sequenced the whole genome of rabbits with the three genotypes and identified the causal mutation of the _dw_ allele that is determined by a 12.1 kb deletion affecting the high mobility AT-hook 2 (_HMGA2_) gene. _HMGA2_ encodes for an architectural factor that is a component of the enhancesome, which plays regulator roles during embryo development. This large deletion eliminates the promoter and the first three exons of this gene, resulting in its inactivation by suppressing the transcriptional regulation [122]. As a cascade effect, insulin-like growth factor 2 mRNA-binding protein 2 (_IGF2 BP2_) expression is inhibited together with altered expression of several other genes that might help to disentangle the altered processes _and_ related pathways leading to reduced size of the animals [122]. Based on these results, HMGA2 has been proposed as a major regulator of body size in mammals [122].
## 3 Results
### Logistic Regression Analysis on Musculoskeletal Pain
#### 3.3.2 Logistic Regression Analysis on Upper Limb Pain  
Table 7 presents the model's results regarding upper limb pain. The model demonstrated satisfactory explanatory power with a 0.175 Nagelkerke value. Moreover, the result regarding model fit was considered suitable, with a chi-square value of 4.527 and a significance level of 0.807. The model's prediction accuracy was 65.9%. In Table 7, the factors influencing upper limb pain were found to be gender (\(p\) < 0.001), work experience (\(p\) < 0.001), vibration (\(p\) = 0.041), and handling heavy loads (\(p\) = 0.002). Female was 2.966 times more likely than male (\(p\) < 0.001). Additionally, workers with over 10 years of work experience were 2.621 times more likely to be affected than those under 3 years (\(p\) < 0.001). Workers exposed to vibration for 2-4 h were 1.816 times more likely to experience upper limb pain than those under 2 h (\(p\) = 0.031). Workers who handled heavy loads for 2-4 h were 1.769 times more likely than those under 2 h (\(p\) = 0.009), whereas workers for over 4 h were 2.345 times greater than those under 2 h (\(p\) = 0.001). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multirow{2}{*}{**N**} & \multirow{2}{*}{**Prevalence Rate (\%)**} & \multirow{2}{*}{**B**} & \multirow{2}{*}{\(p\)-Value**} & \multirow{2}{*}{**OR**} & \multicolumn{2}{c}{**95\% C.L. for OR**} \\ \cline{5-8}  & & & & & & **Lower** & **Upper** \\ \hline Age & & & & 0.012 & & & \\ \(\leq\)40s (ref) & 179 & 25.7\% & & & & & \\ \(\leq\)50s & 181 & 33.7\% & 0.430 & 0.085 & 1.537 & 0.943 & 2.505 \\ \(\geq\)60s & 192 & 43.2\% & 0.766 & 0.003 & 2.152 & 1.299 & 3.564 \\ Work experience & & & & 0.007 & & & \\ \(\leq\)5 years (ref) & 150 & 26.7\% & & & & & \\ \(3\)-9 years & 190 & 28.9\% & 0.102 & 0.689 & 1.108 & 0.671 & 1.828 \\ \(\geq\)10 years & 212 & 44.8\% & 0.725 & 0.006 & 2.064 & 1.228 & 3.470 \\ Industry type & & & & & & & \\ Manufacture of other & & & & & & & \\ food products (ref) & & & & & & & \\ Processing and storage & 219 & 37.4\% & 0.611 & 0.003 & 1.841 & 1.231 & 2.755 \\ Handling heavy loads & & & & & \(<\)0.001 & & & \\ \(\leq\)2h (ref) & 242 & 24.3\% & & & & & \\ \(2\)-4 h & 181 & 37.1\% & 0.713 & 0.001 & 2.041 & 1.317 & 3.163 \\ \(\geq\)4 h & 129 & 47.3\% & 1.261 & \(<\)0.001 & 3.529 & 2.163 & 5.758 \\ Constant & & & & \(-\)2.209 & \(<\)0.001 & 0.110 & & \\ \hline \hline \end{tabular} Note. * Significant at 0.05, ref = reference, OR = odds ratio, C.I. = confidence interval.  
\end{table} 
Table 6: Logistic regression analysis on back pain.
## 3 Methods
### Vegetation Photosynthesis Model  
The Vegetation Photosynthesis Model (VPM) proposed by [46] is a big-leaf light use efficiency model to simulate vegetation GPP. The underlying idea for VPM to improve the GPP modeling is to separate photosynthetically active vegetation (mostly green leaves) and non-photosynthetically active vegetation (mostly senescent leaves, stems, and branches). The formulas of VPM are as follows:  
\[\text{GPP}=\text{PAR}\times\text{FPAR}_{\text{PAV}}\times\epsilon_{\text{max}} \times\text{T}_{\text{scalar}}\times\text{W}_{\text{scalar}}\times\text{P}_{ \text{scalar}} \tag{6}\]  
where GPP denotes gross primary production; PAR denotes photosynthetically active radiation; FPAR\({}_{\text{PAV}}\) denotes the fraction of photosynthetically active radiation absorbed by photosynthetically active vegetation; \(\epsilon_{\text{max}}\) denotes the maximum light use efficiency; and T\({}_{\text{scalar}}\), W\({}_{\text{scalar}}\), and P\({}_{\text{scalar}}\) denote the down-regulation scalars that account for the effects of air temperature, water, and leaf phenology, respectively, on plant light use efficiency. Further,  
\[\text{FPAR}_{\text{PAV}}=\text{a}\times\text{EVI} \tag{7}\]  
where EVI denotes Enhanced Vegetation Index as derived from the surface reflectance from near-infrared, red, and blue bands; and a denotes the correlation coefficient between EVI and FPAR\({}_{\text{PAV}}\).  
The environmental scalars are derived as follows:  
\[\text{T}_{\text{scalar}}=\frac{(\text{T}-\text{T}_{\text{min}})(\text{T}- \text{T}_{\text{max}})}{(\text{T}-\text{T}_{\text{min}})(\text{T}-\text{T}_{ \text{max}})-\left(\text{T}-\text{T}_{\text{opt}}\right)^{2}} \tag{8}\]  
\[\text{W}_{\text{scalar}}=\frac{1+\text{LSWI}}{1+\text{LSWI}_{\text{max}}} \tag{9}\]  
\[\text{P}_{\text{scalar}}=\left\{\begin{array}{cc}\frac{1+\text{LSWI}}{2}& \text{from bud burst to leaf full expansion}\\ 1&\text{after leaf full expansion}\end{array}\right. \tag{10}\]  
where T denotes daily mean air temperature; T\({}_{\text{max}}\), T\({}_{\text{min}}\), and T\({}_{\text{opt}}\) denote the maximum, minimum, and optimal air temperature for vegetation photosynthesis, respectively; LSWI denotes the land surface water index; and LSWI\({}_{\text{max}}\) denotes the maximum land surface water index during the vegetative growing season.  
Based on previous studies [20; 46], the key parameters in VPM are set as follows: T\({}_{\text{min}}=-1\)\({}^{\circ}\)C, T\({}_{\text{max}}=40\)\({}^{\circ}\)C, T\({}_{\text{opt}}=20\)\({}^{\circ}\)C, and \(\epsilon_{\text{max}}=0.528\) g C mol\({}^{-1}\) PPFD.
## 6 Conclusions  
In this paper, we have presented the new 3-D channel model for 5G designed by 3 GPP in TR 38.901, providing a step-by-step tutorial of its implementation in MATLAB. The new channel model characterizes the rich environment in which signal propagation takes place, with the customization of a significant number of parameters. This allows us to run extensive simulations to test new systems before their actual implementation. Furthermore, we have deonstrated an OFDM MIMO system by simulating a trisectorial single cell 5G cellular network. The developed system uniquely resorts on SDMA techniques,  
Figure 14: Sum rate and average spectral efficiency of the UTs for different traffic density, \(N_{t}=72\) with \(N_{ty}=36\), \(N_{tz}=2\), \(N_{r}=1\), single polarization, \(P_{TX}=47\) dBm.  
Figure 13: CDF of UT rates for different cell radius lengths, \(N_{t}=72\) with \(N_{ty}=36\), \(N_{tz}=2\), \(N_{r}=1\), \(P_{TX}=47\) dBm, average number of UTs in the cell, \(\mu\frac{3\sqrt{3}}{2}R^{2}\approx 65\).  
based on _SLNR_ precoding and MMSE combining schemes, to establish communication between the BS and UTs, which are randomly generated and located in the cell. We have implemented our system with different configurations for antenna arrays on both the transmitter and receiver sides. We have shown that the number of the antenna elements and their arrangement can affect the efficiency of MIMO techniques, as well as the adoption of double cross-polarized antenna elements instead of single polarized ones. From the results obtained by a variety of simulations, we have investigated how the achievable rates and the outages of the users may change according to the chosen parameters, such as the array configuration, the number of layers and polarizations, the traffic density, the transmission power, the percentage of indoor UTs and the cell radius. _SLNR_ precoding shows great performance in efficiently multiplexing different UTs in a cell sector, but it shows some limitations when the users are spatially correlated, as it might be expected. This behavior becomes more and more evident as the density of users increases. Future works will aim at broadening the current channel generation to support the mobility of the users, but also at studying efficient scheduling algorithms to better exploit the benefits of MU-MIMO techniques, i.e., by grouping or clustering users in order to limit their spatial correlation and, consequently, their interference.  
Conceptualization, D.G.R.; Investigation, D.G.R., F.D.S. and R.T.; Methodology, D.G.R.; Software, D.G.R., F.D.S. and R.T.; Supervision, D.G.R.; Writing--original draft, D.G.R., F.D.S. and R.T.; Writing--review and editing, D.G.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Acknowledgments: [PERSON] acknowledges support from TIM S.p.A. through the PhD scholarship.  
The authors declare no conflict of interest.
## 2 Experimental Program  
### Details of HPLW and LW Walls  
Six L-shaped short-limb shear walls with high-strength rebar and high-strength concrete and six L-shaped short-limb shear walls with high-strength horizontal rebars were constructed and loaded with cyclic loading. The vertical height of all walls was 1400 mm. The design parameters of the walls are shown in Table 1, where the steel ratio, \(\rho_{v}\), of the longitudinal rebars is defined as the ratio of the cross-sectional area of the longitudinal rebars to the total wall cross-sectional area; and the volumetric steel ratio, \(\rho_{v}\), at the free web boundary is defined as the ratio of the volume of the stirrups to that of the wall [21]. The section parameters and rebar details of the walls are shown in Figure 1. The design strength grade of concrete used in the specimens of HPLW and LW was, respectively, C60 (nominal cubic compressive strength \(f\)cu,k = 60 MPa) and C40, respectively, and the average cubic compressive strength fcu of the concrete measured on cubes of 150 mm size was 62.3 and 47.2 MPa, respectively. Table 2 shows the mechanical properties of the rebars.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Specimens** & \begin{tabular}{c} **Cross-Section** \\ **Height-to-Width Ratio** \\ \end{tabular} & \begin{tabular}{c} **Design Axial Load** \\ **Ratio** \\ \end{tabular} & \
\begin{tabular}{c} **Experimental Axial** \\ **Load Ratio** \\ \end{tabular} & \(\rho_{v}\) & \(\rho_{s}\) &
\begin{tabular}{c} **Aspect** \\ **Ratio** \\ \end{tabular} \\ \hline HPLW500-1 & 5.0 & 0.17 & 0.10 & 1.51\% & 2.51\% & 2.80 \\ HPLW500-2 & 5.0 & 0.50 & 0.30 & 3.02\% & 2.51\% & 2.80 \\ HPLW650-1 & 6.5 & 0.50 & 0.30 & 1.51\% & 2.26\% & 2.15 \\ HPLW650-2 & 6.5 & 0.50 & 0.30 & 3.02\% & 2.26\% & 2.15 \\ HPLW800-1 & 8.0 & 0.17 & 0.10 & 1.51\% & 2.41\% & 1.75 \\ HSLW800-2 & 8.0 & 0.34 & 0.20 & 3.02\% & 2.41\% & 1.75 \\ LW500-1 & 5.0 & 0.34 & 0.20 & 0.62\% & 1.95\% & 2.80 \\ LW500-2 & 5.0 & 0.50 & 0.30 & 1.25\% & 1.95\% & 2.80 \\ LW650-1 & 6.5 & 0.34 & 0.20 & 1.25\% & 1.71\% & 2.15 \\ LW650-2 & 6.5 & 0.17 & 0.10 & 1.25\% & 1.71\% & 2.15 \\ LW800-1 & 8.0 & 0.34 & 0.20 & 0.58\% & 1.74\% & 1.75 \\ LW800-2 & 8.0 & 0.17 & 0.10 & 1.16\% & 1.74\% & 1.75 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of HPLW and LW.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Steel Type** & \begin{tabular}{c} **Yield Stress** \\ **(\(u\)/mm\({}^{2}\))** \\ \end{tabular} & \begin{tabular}{c} **Ultimate Stress** \\ **(\(u\)/mm\({}^{2}\))** \\ \end{tabular} & \
\begin{tabular}{c} **Elongation (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Elastic Modulus** \\ **(\(u\)/mm\({}^{2}\))** \\ \end{tabular} \\ \hline D6 Rebar & 538 & 682 & 15 & 206,000 \\ D12 Rebar & 550 & 697 & 16 & 208,000 \\ \#4 Rebar & 730 & 985 & 8 & 205,000 \\ \#8 Rebar & 295 & 510 & 28 & 210,000 \\ \#12 Rebar & 345 & 600 & 31 & 216,000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mechanical properties of steel rebars of HPLW and LW walls.
## 7 Conclusions  
To address the challenge of time-delay estimation between channels in multiantenna, wideband receivers, this paper proposes a polynomial inversion-based time-delay estimation method. The new algorithm improves the delay estimation accuracy while adding \(P+1\) multipliers compared to the convex parabolic interpolation method, as summarized in Table 2, which is a very competitive method in multiantenna real-time communication systems. To evaluate the performance of the new algorithm, we modify the expressions of CRLB and derive equations for bias, variance, and MSE. Then, the effectiveness of the algorithm is verified by simulation in conjunction with CRLB. The results show that the proposed time-delay estimator is asymptotically unbiased with its MSE performing on the CRLB. Finally, the algorithm is applied to the radar radiometer test system, named the wideband LSAS test-bed, and high-accuracy and high-stability parameter test results are achieved.  
Figure 8: Signal-processing block diagram.  
Figure 7: Wideband multichannel acquisition system. (**a**) Hardware physically wired diagram; (**b**) Application result.  
**Author Contributions:** Conceptualization, X.L. and G.R.; methodology, X.L.; software, X.L.; validation, X.Y., Y.W. and B.Z.; investigation, X.L.; writing--original draft preparation, X.L., B.Z. and X.Y.; writing--review and editing, X.L. and Y.W.; visualization, X.Y.; supervision, G.R.; project administration, X.L.; funding acquisition, G.R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** National Key Laboratory of Electromagnetic Environment, Grant No. 202103012.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Resilient Scheduling as a Response to Uncertainty in Construction Projects
## 2 Resilience Approach to Manage Uncertainty in Construction Projects
### Understanding Uncertainty  
During the execution phase, many undesirable events might affect project constraints, such as budget overrun or timely execution of significant project activities. Disruptions in a project schedule due to mechanical malfunctions or unfavorable meteorological conditions on site could cause delays in project makespan unless managed properly. Occurrence, as well as the impact of disruptive events on project objectives, is uncertain; therefore, it is important to understand sources of uncertainty in a construction project.  
Some authors consider uncertainty as various undesirable conditions or events, e.g., unpredictable resource unavailability, changing environmental conditions, various user needs, and system intrusions or faults [52]. Further examples of uncertain events include an increase in material prices, an accident during construction, a decision-maker's change of mind, etc. [53]. The corresponding perspective was adopted in this study, defining uncertainty as a high variety of possible events and conditions that could occur and affect project objectives.  
Previous research has revealed certain characteristics considering the uncertainty in construction projects. For example, [PERSON] et al. [54] reported that most of the uncertainty sources can be traced to conditions and events on site. Authors discovered that from 83 identified uncertainty factors that could delay construction performance, over 90% were traceable to the activities within the construction site. On the other hand, considering the time distribution of uncertainty through the project's life cycle, [PERSON] [55] concluded that in the initiation phase the majority of uncertain events and conditions could be expected. When it comes to the classification of uncertainty sources, various perspectives are emerging from different studies in the project management domain. Most of the research work categorizes uncertainties in two major categories: internal or external, while both of the categories could be further extended into subcategories, such as technical, commercial, environmental, socioeconomic, political, etc. [56]. Since most studies in the construction domain have only focused on certain aspects of uncertainty analysis, such as uncertainty related to the specific phase of a project [55], or uncertainty related to the particular objective of a construction project [56], this paper aims to discuss a conceptual method based on the comprehensive literature review to accumulate, process, and reuse the dispersed knowledge on uncertainty, considering different aspects of a construction project. The following section aims to explain the process of development for the structured and systematic knowledge base of uncertainties, considering construction projects.