esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model of the SDF Cooperation  
A typical wireless multi-relay system at a flat Rayleigh fading channel is shown in Figure 1. It consists of a source node S, several relay nodes R\({}_{i}\) (\(i=1,2,\ldots,N\)), and a destination node D. The ideal CSI is obtained through channel training. For independent links, e.g., links S-D, S-R\({}_{i}\), and R\({}_{i}\)-D, their channel gains are \(|h_{sd}|^{2}\), \(|h_{sr_{i}}|^{2}\), and \(|h_{r,d}|^{2}\), respectively. The noise is additive white Gaussian noise (AWGN) with zero mean and variance \(N_{0}\).  
In an SDF cooperative system of poor direct S-D channel quality, the whole transmission is usually divided into two phases, i.e., Phase I and Phase II. In Phase I, the source S broadcasts messages to all relays \(\{\text{R}_{i}\}\) and the destination D; in Phase II, either the source node or the selected relay nodes send the messages to destination node, mainly depending on the status of relay channels. Thus, the messages received at the \(i\)-th relay and the destination are separately expressed as  
\[y_{sr_{i}}=\sqrt{P_{s}}h_{sr_{i}}x_{s}+n_{sr_{i}}, \tag{1}\]  
Figure 1: A typical wireless multi-relay system.  
\[y_{sd}=\sqrt{P_{s}}h_{sd}x_{s}+n_{sd}, \tag{2}\]  
where \(y_{sr_{i}}\) and \(y_{sd}\) are the received signals at the \(i\)-th relay and the destination, respectively; \(x_{s}\) and \(P_{s}\) are the transmitted signals and the power at source node in Phase I, respectively; \(h_{sr_{i}}\) and \(h_{sd}\) are the channel fading coefficients of link S-R\({}_{i}\) and link S-D, respectively; and \(n_{sr_{i}}\) and \(n_{sd}\) are the noise of links S-R\({}_{i}\) and S-D, respectively.  
In Phase II, if the received messages are decoded correctly at the \(i\)-th relay R\({}_{i}\), the decoded signals are re-encoded and re-transmitted to the destination D. Then, the signals received at the destination are expressed as  
\[y_{r_{i}d}=\sqrt{P_{r_{i}}}h_{r_{i}d}x_{r_{i}}+n_{r_{i}d}, \tag{3}\]  
where \(x_{r_{i}}\) and \(P_{r_{i}}\) are the transmitted signals and power at the \(i\)-th relay and \(n_{r_{i}d}\) and \(h_{r_{i}d}\) are the noises and channel coefficients of link R\({}_{i}\)-D, respectively. Finally, using the maximum ratio combining (MRC) [29] at the destination D, the combined messages are represented as  
\[y{=}ay_{sd}+\sum_{i\in relay\ set}b_{i}y_{r_{i}d}, \tag{4}\]  
where \(a\) and \(b_{i}\) are combining coefficients, denoted as \(a=\sqrt{P_{s}}h_{sd}^{*}/N_{0}\) and \(b_{i}=\sqrt{P_{i}}h_{r_{i}d}^{*}/N_{0}\), respectively. Here, the superscript \"\({}^{*}\)\" denotes the conjugate operation.
## 3 Methodology
### FD-TOPSIS Approach  
The FD technique combines the benefits of \"fuzzy and the Delphi method\". The results obtained using the Delphi technique exhibited objectivity in instances in which a limited sample size was employed. As a result, the utilization of the hybrid method resulted  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Fuzzy Score** & **Linguistic Scale** \\ \hline \(\left(0,0,1\right)\) & VPMG \\ \(\left(0,1,3\right)\) & P \\ \(\left(1,3,5\right)\) & MP \\ \(\left(3,5,7\right)\) & M \\ \(\left(5,7,9\right)\) & MG \\ \(\left(7,9,10\right)\) & G \\ \(\left(9,10,10\right)\) & VG \\ \hline \hline \end{tabular}
\end{table}
Table 3: Linguistic variables affecting each alternative’s preference.  
in a reduction of time and money necessary to attain acceptance while maintaining the integrity of the outcomes. Moreover, the TOPSIS method is utilized to resolve \"multi-criteria decision-making problems\" due to its efficacy in determining significance among several choices and its computational simplicity. Therefore, the fuzzy TOPSIS technique has been developed to enhance alternative performances' comprehensive and logical evaluation. This is done by taking into consideration uncertain and ambiguous judgments, as well as linguistic assessments.  
The FD-TOPSIS architecture methodology is presented in Figure 1. The methodology involves formulating criteria derived from the existing literature and expert evaluations using the Delphi technique. The application of the FD method determines the criteria weights. Finally, the suggested altered locations for the LC are ranked using the fuzzy TOPSIS approach.
# A Study on the Applicability of Bio-Sulfur as a Cement Admixture
## 4 Materials and Methods
### Evaluation of Sulfuric Acid Resistance  
To evaluate the sulfuric acid resistance outcomes, the test was conducted according to JSTM C 7401 (test method for chemical resistance of cement by solution deposition) (JSTM: Japanese standard test method). The water/cement ratio of the specimen was set to 0.4, and the bio-sulfur addition rates used here were 2.5%, 5%, and 7.5%, based on the amount of solid content used to prepare the specimens (Table 3b). Each produced specimen was subjected to standard water curing at a temperature of 20 \(\pm\) 2 \({}^{\circ}\)C until 28 days of age. At 28 days of standard water curing, the specimen was immersed in 5% sulfuric acid solution for 7 days and 14 days, and the sulfuric acid resistance was evaluated by measuring the weight loss rate and compressive strength.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**(a) Compressive strength (Unit: wt \%)**} \\ \hline \multirow{2}{*}{**Division**} & **Ordinary Portland** & **Bio-Sulfur** & \multirow{2}{*}{**ISO**} & **Water** \\  & **Cement (OPC)** & **(Dry Based)** & & \\ \hline Plain & 100 & 0 & 300 & 50 \\ \hline \multirow{4}{*}{Binder} & 97.5 & 2.5 & 300 & 48.75 \\  & 95 & 5.0 & 300 & 47.5 \\  & 92.5 & 7.5 & 300 & 46.25 \\  & 90 & 10 & 300 & 45.0 \\ \hline \multirow{4}{*}{Aggregate} & 100 & 2.5 & 300 & 48.75 \\  & 100 & 5.0 & 300 & 47.5 \\ \cline{1-1}  & 100 & 7.5 & 300 & 46.25 \\ \cline{1-1}  & 100 & 10 & 300 & 45.0 \\ \hline \hline \multicolumn{5}{c}{**(b) Sulfuric acid resistance(Unit: wt \%)**} \\ \hline \multirow{2}{*}{**Division**} & **Ordinary Portland** & **Bio-Sulfur** & \multirow{2}{*}{**Water**} \\  & **Cement (OPC)** & **(Dry Based)** & \\ \hline Plain & 100 & 0 & 50.0 \\ \hline \multirow{4}{*}{Aggregate} & 100 & 2.5 & 47.5 \\  & 100 & 5.0 & 45.0 \\ \cline{1-1}  & 100 & 7.5 & 42.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mixing ratios of the test samples. Indicates the composition ratio of the sample prepared to test the compressive strength (a) and sulfuric acid resistance (b).
## 4 Simulation Analysis of Different Tunneling Parameters  
### Effect of Disc-Cutter Diameter on Rock Breaking  
The 17-inch disc cutter is the most commonly used in TBMs. However, in challenging geological conditions, such as fault fracture zones, deep burial, high geostress, water-rich areas, larger disc cutters--19-inch or 21-inch--are sometimes employed to improve rock-breaking efficiency and reduce cutter wear. Larger disc cutters can sustain higher loads, resulting in greater forces during rock breaking. Although larger diameters generate higher forces per rotation, they have fewer rotations to achieve the same amount of rock fragmentation, leading to reduced wear over time.  
Figure 9 shows the rolling forces for disc cutters of varying diameters under different penetration levels. The rolling force increased with the cutter diameter, although the increase was not substantial. Figure 10 illustrates the relationship between cutter diameter and rock-breaking specific energy, showing that specific energy initially decreased and then increased as diameter grew. The 19-inch disc cutter achieved lower specific energy compared to the 17-inch and 21-inch cutters. This is because, while the 19-inch cutter generated higher rolling force than the 17-inch cutter, it also produced a larger rock fragmentation volume, thereby reducing specific energy.  
Overall, the 19-inch disc cutter demonstrates a slightly higher rock-breaking efficiency, making it suitable for conventional tunneling operations. In hard rock formations (defined as rocks with a uniaxial compressive strength exceeding 70 MPa), the 21-inch disc cutter may be preferable due to its ability to reduce cutter replacement frequency, minimizing downtime caused by wear.  
The studies by [PERSON] et al. [5] and [PERSON] et al. [6] focused on geological adaptability development, while [PERSON] et al. [7], [PERSON] et al. [9], and [PERSON] et al. [10] concentrated on the rock-breaking mechanisms. In contrast, our study provides more specific guidance for engineering applications by systematically analyzing the impact of varying disc-cutter diameters on rock-breaking efficiency, a topic less explored in previous research. Through  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Penetration (mm)**} & \multicolumn{2}{c}{**Rolling Force (kN)**} & \multicolumn{2}{c}{**Specific Energy of Rock Breaking (J/cm\({}^{3}\))**} \\ \cline{2-5}  & **Test** & **FEM** & **Test** & **FEM** \\ \hline
2 & 28.02 & 30.13 & 260.758 & 298.19 \\
2.5 & 27.35 & 32.58 & 200.572 & 188.92 \\
3 & 34.00 & 36.03 & 161.927 & 180.64 \\
3.5 & 34.67 & 39.58 & 151.039 & 157.31 \\
4 & 36.94 & 43.98 & 124.835 & 154.23 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test and FEM data in reference [20].  
experimental and simulation validations, our findings contribute valuable insights for the design optimization of TBM disc cutters and tunneling parameters.
## 2 Materials and Methods
### Test Methods  
For each combination of moisture content, confining pressure, and FT cycle number, three parallel specimens were tested to ensure repeatability. The average values were reported and analyzed in this study.  
1. FT Cycle Test  
The FT cycles were conducted in an AH-1200A constant temperature and humidity chamber. The freezing temperature was set to \(-20\)\({}^{\circ}\)C, while the thawing temperature was maintained at 20 \({}^{\circ}\)C. Each specimen underwent 12 h of freezing followed by 12 h of thawing, forming complete 24 h FT cycles. This process was repeated according to the designated number of FT cycles. All specimens were compacted into cylindrical molds before the FT treatment and underwent full freeze-thaw cycles under sealed conditions. After thawing, the specimens were tested in their intact post-cycled state without remolding.  
2. Triaxial Shear Test  
After completing the predetermined number of FT cycles, the thawed specimens were subjected to unconsolidated undrained (UU) triaxial shear tests using a strain-controlled triaxial apparatus. The axial strain rate was set to 1.5 mm/min, and data acquisition was performed automatically at 2 s intervals. The equipment used in this test is shown in Figure 3.
# UV Light Application as a Mean for Disinfection Applied in the Dairy Industry
## 5 Application Areas
### Water Disinfection  
UV-C radiation has been utilized over many years in the disinfection of water and was effective in killing various kinds of micro-organisms. This is a suitable technology in comparison to chlorine treatment [10]. Effluent from dairy processing plants may be processed without the use of toxic substances that are harmful to the environment [26]. UV technology can be used to sterilize drinking, processing, effluent, and saltwater [10]. As process water can be filtered and recycled using UV, the volume of wastewater released is significantly reduced which results, all discharges comply with municipal environmental laws [26].
## 3 Numerical Results
### \(\lambda\) Effects with Different Thicknesses of the Liners  
To provide more insights about the effect of the anisotropic initial stresses (i.e., anisotropic ratio \(\lambda\)), the failure probabilities of the tunnel supports calculated with different cases are compared: isotropic stress with constant value \(\lambda=1\) (case 1), anisotropic stress with constant value \(\lambda=0.66\) (case 2), and anisotropic stress with random variable \(\lambda\) as shown in Table 2 (case 3). In Figure 8, these failure probabilities at the springline of the concrete liner and at the crown of the outer flexible liner are calculated versus different liners' thickness. The results are consistent when the failure probability in each liner decreases with respect to its higher thickness. In comparison with the isotropic stress state, the anisotropic cases (i.e., cases 2 and 3) reveal the higher failure probability in the concrete liner for the whole range of considered thickness. Regarding the previous deterministic analysis, this result can be expected when the anisotropy of initial stresses induces in this concrete support element higher equivalent stress with respect to the case of hydrostatic stress state. Concerning the \(Pf\) of the outer liner, as a function of the thickness, the trend is not clear. More precisely, the lower failure probability in the anisotropic cases at small thickness can become higher than the one of the isotropic cases when the thickness of this flexible support element overpass a value of about 25 cm. Between the two anisotropic cases, no significant difference of failure probability is observed for both liners. To reinforce this statement, in the last numerical investigations, \(Pf\) was calculated as a function of the mean value and CoV of the anisotropic parameter \(\lambda\) (Figure 9), keeping the same liners thickness and other random parameters as in Tables 1 and 2. Whilst the lower mean value of \(\lambda\) induces a significant increase of the \(Pf\) concrete liner, the failure probability of the outer liner is slightly reduced (Figure 9a). The results also reveal the negligible effect of the uncertainty of \(\lambda\) on the failure probability of tunnel supports (Figure 9b), especially in the range CoV \(\in\) [5-25%].  
Figure 7: Sobol indices of the six random input parameters with respect to the two Qols.
## 3 Results and Discussion
### Application of [PERSON]'s Equation
Because of tight relationships \(\overline{\mu}\) vs. \(\overline{\mu}_{d}\) (Figure 10a) and \(K_{E}\) vs. \(K_{d}\) (Figure 13b), it is natural to expect a good relationship between the products \(K_{E}\cdot\overline{\mu}\) and \(K_{d}\cdot\overline{\mu}_{d}\). This is indeed the case as shown for our data combining the three wavelengths from the blue-green spectral region (440, 490, and 550 nm) and all examined discrete depths at all stations (Figure 14d). The best-fit linear fit to the data is:  
\[K_{E}\cdot\overline{\mu}\ =\ 0.980\ K_{d}\cdot\overline{\mu}_{d}-0.0017\Big{(}r^{2 }=\ 0.999,RMSE\ =\ 0.003\ m^{-1},N=\ 36\Big{)}. \tag{13}\]  
By indicating that \(K_{E}\cdot\overline{\mu}\) can be quite accurately estimated from \(K_{d}\cdot\overline{\mu}_{d}\), this relationship has important practical ramifications. Specifically, the measurements of three (\(E_{d}\), \(E_{u}\), \(E_{o}\)) or four (\(E_{d}\), \(E_{u}\), \(E_{od}\)) irradiances required for determining the absorption coefficient from [PERSON]'s equation can be replaced with just two downwelling irradiance measurements (\(E_{d}\) and \(E_{od}\)), thus significantly simplifying the experimental requirements. We note that the use of two AOPs based on the downwelling light field, \(K_{d}\) and \(\overline{\mu}_{d}\), has been previously introduced in the context of estimation of absorption by phytoplankton and CDOM, as well as chlorophyll-\(a\) concentration from measurements of \(E_{d}\) alone [96; 115]. The modeling formalism based on \(E_{d}\) alone required assumptions about \(\overline{\mu}_{d}\) and the backscattering coefficient of seawater.
## Materials and Methods
### Solvent Extraction Experiments  
#### Batch Extraction Experiments  
The organic phase was preliminarily equilibrated with an aqueous phase. The pre-equilibration was performed by contact (three times) with the aqueous phase of the same composition (concentration of nitric acid and calcium nitrate) as the aqueous phase in a subsequent batch or countercurrent experiment, except that the aqueous phase for pre-equilibration did not contain praseodymium and neodymium nitrates. The organic-to-aqueous phase (O/A) volume ratio was 1:1. This was necessary to prevent the extraction of water further in the experiment, which could lead to a change in the volume of the organic phase and the extraction of nitric acid, which could lead to the precipitation of REE hydroxides in the aqueous phase during the experiment due to a decrease of nitric acid concentration.  
In the batch extraction experiments, the pre-equilibrated organic phase was contacted three times with a praseodymium and neodymium aqueous solution every time a fresh portion of the aqueous phase was used. Composition of the aqueous phase after the third contact was identical to the initial solution. The O/A volume ratio was 15:15 mL. Then, rare earth elements were back-extracted three times by a pure 0.3 mol\(\cdot\)L\({}^{-1}\) HNO\({}_{3}\) aqueous solution in the case of TBP and Aliquat 336 or by a 3 mol\(\cdot\)L\({}^{-1}\) HNO\({}_{3}\) aqueous solution in the case of HDEHP. Tests with radioactive tracers have shown that such a procedure results in complete back-extraction of REEs. All back-extracts from certain experiment were mixed together. All experiments were carried out in separatory funnels.  
The distribution ratio, \(D\), for each element was calculated as a ratio of its content in a back-extract (accounting the dilution factor \"3\") to its content in the aqueous phase after phase separation (Equation (1)):  
\[D=\frac{3\mbox{-}C(Me)_{\mathrm{be}}}{C(Me)_{\mathrm{aq}}} \tag{1}\]  
where \(C(Me)_{\mathrm{be}}\) is concentration of a metal cation in the final back-extract; \(C(Me)_{\mathrm{aq}}\) is concentration of a metal cation in the aqueous phase after extraction and \"3\" is dilution factor.  
Separation factor was calculated according to Equation (2):  
\[\mathrm{SF}=\frac{D(Me_{1})}{D(Me_{2})} \tag{2}\]  
where \(D(Me_{1})\) and \(D(Me_{2})\) are distribution ratios for metal (1) and metal (2).  
All the experiments were performed three times. Corresponding \(D\) values were averaged, and the resulting \(D\) value was plotted in graphs along with relative errors.
## 1 Introduction
### Contributions  
This paper presents a new and improved method for computing optimal MHPP layouts considering cost, power, and flow constraints. As in [23], the 3D terrain is discretized based on the available topographic data, and civil work costs to deploy and install the penstock are taken into account. However, in the new developed method, the penstock is not assumed to be composed of straight lengths, which is an unrealistic constraint in real installations where the pipe length bends considerably in the horizontal plane to accommodate the penstock layout (see Figure 2).  
The new method is formulated based on three main components. First of all, the main equations characterizing the MHPP power, flows, costs, and constraints are derived. Secondly, the penstock profile is approximated and used suitable approximation cubic Hermite splines for the vertical dimension. This choice is made to preserve, on the one hand, the monotonicity of the curve that describes the penstock (required to avoid air entrapment) and, on the other hand, to provide an easy formulation for the bending radius of the penstock length, as it can be determined in terms of the derivatives of the cubic polynomials. This consideration makes it possible to account for an extra degree of freedom that can be limited according to the material and diameter of the penstock. Finally, a specifically designed genetic algorithm is applied to characterize the optimal MHPP layout, studying both single-objective and multiple-objective problems.  
The constraint relaxation based on spline interpolation not only involves a better approximation of the penstock layout, but also makes it possible, as it will be shown in the paper, to obtain designs that are better in terms of cost and attainable power.
## 5 Discussion  
As commented above, the implementation of the reduced order observer actually requires only \(r_{2}-1\) (i.e., for the case study with \(r_{2}=17\) this corresponds to only 16) variables, while the Kalman Filter requires the additional information about the error covariance matrix at each time step, yielding a total of \(r_{2}^{2}+r_{2}\) variables (i.e., for the case study 306). This implies a significant reduction (by a factor about 19 for the case study) in comparison to the Kalman Filter in terms of memory storage. In addition, as discussed in the previous section, in the performed simulation studies the core calculations of the reduced-order observer were about four times faster than the ones for the Kalman Filter. It should be mentioned anyway, that in the implementation in Matlab, when taking into account the time for the decoding, i.e., the last step in both observer schemes, this time advantage is basically lost and both approaches perform similarly.  
As it can be seen, the performance of the Kalman Filter and the reduced order observer are quite comparable for this particular case study. As commented in the introductory discussion for the reduced order observer, this is clearly to be attributed to an output approximation with low noise level. For systems with more sensor noise it is not recommendable to use the unfiltered output directly in the observer scheme.  
As a consequence of these results, as long as computation times are below system specific constraints in terms of time scales, both approaches can be employed, while in the case that the computation times should be held as low as possible and that the measurement noise is neglectable, the reduced order observer has clear advantages. This holds true in particular if in addition to the observer scheme some complex calculations or real-time parallel optimizations are performed.
## 3 Results
### Criterion Validity of the rGMFM-66 Compared to the GMFM-66  
The mean score obtained with the rGMFM-66 (52.2 points \(\pm\) 18.1) was slightly higher than the mean score of the GMFM-66 (50.7 points \(\pm\) 17.4), with the difference being statistically significant (_p_ = 0.008, Table 1, Figure 2). The agreement between the two  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{3}{c}{time saving} \\ \hline  & GMFM-66 & rGMFM-66 & _p_-value & effect size \\ implementation time, min & 38.5 (13.6) & 17.2 (5.2) & \(<\)0.001 & 1.9 \\ \hline  & & \multicolumn{3}{c}{score agreement} \\ \hline  & GMFM-66 & rGMFM-66 & _p_-value & effect size \\ Score, points & 50.7 (17.4) & 52.2 (18.1) & 0.008 & \(-\)0.09 \\ \hline ICC & & 0.970 (95\% KI 0.942; 0.983) & \\ \hline  & Upper limit (97.5\%) & Lower Limit (2.5\%) & \\ [PERSON] statistics & 6.5 (4.5; 8.5) & \(-\)9.7 (\(-\)11.6; \(-\)7.7) & \\ \hline ICC intraclass coefficient. Effect size was assessed using Cohen’s d for dependent groups. All data are given as mean (SD). & & & \\ \end{tabular}
\end{table}
Table 2: Results.  
Figure 1: Time Efficiency (**A**) and Scores (**B**) of GMFM-66 vs. rGMFM-66. The difference of the mean time for rGMFM-66 was significantly (* \(p\) < 0.001) lower than for GMFM-66 (Subfigure (**A**)). The subfigure (**B**) shows the agreement between the two scores.  
methods was evaluated using the Intraclass Correlation Coefficient (ICC), which yielded a value of 0.970 (95% CI: 0.942-0.983, Table 1). The scatter plot between the GMFM-66 and rGMFM-66 scores is given in Figure 1B. The results of the Bland-Altman statistics are given in Table 2 and Figure 2.