esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Algorithmic Procedures
### Hamiltonian  
We can determine whether a graph is Hamiltonian using the following algorithmic procedure. Additionally, this approach identifies the Hamiltonian cycle or path if the graph is semi-Hamiltonian. The implemented procedure, hamiltonianGraph, takes a graph \(H\) as its input. The output is the Hamiltonian cycle (if the graph is Hamiltonian), the semi-Hamiltonian path (if the graph is semi-Hamiltonian), or a message indicating that the graph does not satisfy the required conditions. Regarding the implementation, it first verifies if the graph is Hamiltonian, using the function _isHamiltonian_. In such a case, it prints a message and the trail \(T\), which is the Hamiltonian cycle. In the case that the graph is not Hamiltonian, the procedure tries to find the path starting from the first vertex. In that process, it looks at the neighbors of the vertex and sees if they have been visited or if they are articulation points. If both conditions are not fulfilled, it adds the first neighbor in the path and repeats the process based on it. In the case that the vertex is an articulation point, it is only added if it is the only one that has not been visited. The procedure only stops when there are not new vertices to add to the path. Then, it tries the same process starting with the next vertex of the graph. If, after repeating the process starting with each vertex, the procedure can not find a path as long as the number of vertices, the procedure prints a message indicating that the graph is not Hamiltonian or semi-Hamiltonian. > hamiltonianGraph := proc(H) > local count, vertices, v, v1, w, i, j, path, N, artP, indexap, indexnp; > if IsHamiltonian(H, 'C') then > print(The graph is Hamiltonian); > print(The Hamiltonian cycle is C); > else > vertices := Vertices(H); > artP := ArticulationPoints(H); > for v in vertices do > path := [v]; > w := v; > while nops(path) < nops(vertices) do > N := Neighbors(H, w); > indexap := []; > indexnp := []; > for i from 1 to nops(N) do > if N[i] in artP then > indexap := [op(indexap), i]; > fi; > if not N[i] in path then > indexnp := [op(indexmp), i]; > fi; od; > if nops(indexmp) = 0 then > break; > fi; > if nops(indexmp) = 1 and indexmp = indexap then > path := [op(path), N[op(indexap)]]; > w := N[op(indexap)]; > fi; > for i from 1 to nops(N) do > if not N[i] in path and not i in indexap then > path := [op(path), N[i]]; > w := N[i]; > break; > fi; od; od; > if nops(path) = nops(vertices) then > print(\"The graph is semihamiltonian\"'); > print(\"Semihamiltonian Path::\", path); > break; > fi; od; fi; > if nops(path) < nops(vertices) then > print(\"The graph isn't hamiltonian or semihamiltonian\"'); > fi; > end proc:  
In the next algorithm, called hamiltonianGraph, we have added the Rahman-Kaykobad Theorem (see [28]):  
**Theorem 1**. : _Let \(G=(V,E)\) be a connected graph with \(n\) vertices such that for all pairs of distinct nonadjacent vertices \(u,v\in V\) one has \(d_{u}+d_{v}+\delta(u,v)\geq n\). Then, \(G\) has a Hamiltonian path._This implies that the semi-Hamiltonian path is not going to be calculated unless the condition is fulfilled.
## 2 Materials and Methods
### Process Conditions for Ultraviolet Radiation (IV)  
This study used a laboratory setup for UV radiation exposure of samples, equipped with a reactor, a cooling system, and a temperature sensor. A Heraeus TQ 150 UV lamp (type ZULRS-2) with an emitter power of 150 W was used. Samples with a volume of 50 mL were irradiated with ultraviolet light at an intensity of 10 mW/cm\({}^{2}\) (measured using a UV Light Meter PCE-UV34). The catalyst used was nanopowder Titanium (IV) oxide (21 nm primary particle size, \(>\)99.5% trace metals basis) with a density of 4.26 g/mL. Two doses of catalyst were used, 0.25 g/L and 1 g/L, to check the effect of the catalyst amount on the  
Figure 1: The chemical structures of the studied dyes: (**a**) Indigo Carmine; (**b**) Tartrazine; (**c**) Allura Red AC.  
- dye removal efficiency. In parallel, tests were carried out without the presence of a catalyst. The process was conducted over a time range of 0-30 min. Each analyzed parameter was tested in triplicate.  
The % of dye degradation was determined using Equation (1):  
\[\%\text{Degradation}=\frac{C_{0}-C_{t}}{C_{0}}\cdot 100\% \tag{1}\]  
where \(C_{0}\) is the initial concentration of dye; and \(C_{t}\) is the concentration of dye at time '\(t\)'.  
The data analysis was conducted using Statistica 13 and MS Excel 2013. Since the datasets exhibited unequal variances, non-parametric statistical tests were utilized on the raw data. The homogeneity of variances was evaluated using the Levene and Brown-Forsythe tests. Hypothesis testing aimed at identifying significant differences, with a significance level set at \(\alpha\leq 0.05\). For multiple comparisons, including pairwise contrasts, the Kruskal-Wallis ANOVA was applied with subsequent post hoc tests, and this method was specifically used to examine concentration variations.
## 2 Background
### Power Line Histograms
#### 2.2.3 Residual Histogram  
Residual histogram generation begins by subtracting an ideal cycle from every time-synchronized cycle. The residual histogram highlights the imperfections imposed by (i) nominal line behavior, (ii) power disturbance events, and (iii) behavior that may occur before an event or fault. The residual histogram is calculated over a smaller voltage range than the cyclic histogram, which presents abnormalities with greater clarity. The ideal cycle is one cycle of the sine function oscillating at the power system's fundamental frequency, which in the United States is 60 Hz. The ideal cycle's magnitude is the same as the operational voltage and is determined using the bus RMS voltage  
\[v_{max}=v_{RMS}\times\sqrt{\frac{2}{3}}, \tag{4}\]  
where \(v_{RMS}\) is the voltage RMS value. For example, a 161 kV bus will have an operational voltage of \(v_{max}=131.45\)kV. After subtracting the ideal cycle, the per-sample PMFs are generated. The numbers of histogram bins and voltage range are conditionally set to normalize the voltage steps across different residual histograms. If the observed voltage range is \(\pm\)5 kV, the global voltage range is set to \(\pm\)5 kV, and the number of histogram bins is set to 1024. If the global voltage range is less than \(\pm\)10 kV but greater than \(\pm\)5 kV, the global voltage range is set to \(\pm\)10 kV and the number of histogram bins is set to 2048. Otherwise, the global voltage range is set to \(\pm\)20 kV with 4098 histogram bins. This ensures that all residual histograms have the same voltage step size. The per-sample PMFs are generated using the selected global voltage range and number of histogram bins to create the residual histogram. A representative illustration of a residual histogram is shown in Figure 3b.
## 3 Numerical Results and Discussions
### Effect of Random Phase Offset and Modified Threshold  
PQD feature detection is much sensitive to the start or end edge positions of PQD signals. Hence, the detection scheme must be robust to the random phase offset of the PQD signals, as shown in Figure 4. For this purpose, the proposed scheme uses a modified threshold \(TH_{M}\) rather than a universal threshold \(TH_{U}\), which results in a lower FER (\(<\)10% for Gaussian noise) while maintaining suitable DR, even though it is still low at \(\varphi_{m}\approx\pi/2\) or \(3\pi/2\) (Figure 4).  
Table 2 compares the performance (in terms of DR and FER) of the proposed scheme (herein, assuming double edge detection) for a sag signal of 80% \(pu\) with a fixed phase offset (assuming \(\varphi_{m}=\pi\), i.e., \"fixed PQD\") or random phase offset (assuming \(\varphi_{m}\sim U[0,\;2\pi)\), i.e., \"random PQD\"). In this simulation, MV is used as the detection metric and a Gaussian noise channel with SNR = 40 dB is assumed. Table 2 shows that for both fixed and random PQDs, the presented MV metric with \(TH_{M}\) dominates the same metric with \(TH_{U}\)[7] in terms of FER. Hereinafter, unless stated otherwise, \(TH_{M}\) with \(k\) factor (Table 3) is used for the proposed scheme.
## 3 Investigation of Scalability and Impact of Virtualization on Performance of the System
### Results Analysis
#### 3.2.2 Influence of Containerization on Scalability  
It is expected that launching a container with same core affinity as same configuration on bare-metal results in quite similar results, based on performance overheads identified in the literature described in Section 2. In Figure 6 we presented normalized difference of successfully processed requests \(N\) on bare-metal and same metric on the container. The value is calculated as \(\frac{N_{processed\_Pysical-N_{processed\_Pysical}}}{N_{processed\_Pysical}}\). These results and also the observation from previous charts in Figures 4 and 5 also Figures 2 and 3 may indicate that system has very similar characteristics with and without containerization. That clearly shows that containerization has no visible impact on scalability achieved with different server core counts.
## 3 Results
### Suggestions for Drought Risk Management in Agroecosystems: Insights from VMFDI and VPD Thresholds  
Based on the findings of this study, several strategies are recommended for enhancing drought risk management in agroecosystems. First, integrating the VMFDI into agricultural drought monitoring and assessment systems is crucial. The automated joint monitoring of VPD, SM, and SIF throughout the growing season could provide real-time insights into drought conditions. The VMFDI could serve as a key indicator in visual drought monitoring platforms for smart agriculture. This could improve the early detection of drought conditions and inform decision-making in real time. Second, sustainable irrigation schemes should be designed and implemented, considering the critical thresholds for  
Figure 12: Thresholds in the relationships between VPD and the NDVI, GPP, or LAI in various agroecosystems. The temporal ranges of the corresponding data in (**a–c**) were 12 months (January to December), 6 months (April to September, which is the maize growing season), and 4 months (March to June, in which wheat regreens to maturity) from 2003 to 2020, respectively.  
SM and VPD. Tailoring irrigation schedules to these thresholds is crucial for optimizing water use, reducing water wastage, and improving crop resilience to water scarcity. For instance, irrigation schedules could be adjusted dynamically based on real-time VPD and SM data, allowing farmers to respond proactively to changing environmental conditions and minimize the adverse effects of drought. Third, crop planting layouts should be optimized by considering the VPD and SM thresholds for different crops, alongside regional climate and geographical conditions. Optimizing crop selection and planting dates based on these thresholds can improve drought adaptation and resource utilization. For instance, drought-tolerant crops, such as maize, may be more suitable in regions with higher VPD thresholds, enhancing overall productivity and sustainability. Finally, integrating climate projections into long-term agricultural planning is essential. Forecasting changes in VMFDI patterns and future drought risks would enable the development of adaptive management practices, ensuring the long-term resilience of agroecosystems under climate change.
## 2 High Diffraction Efficiency and Damage-Resistance Threshold Gratings  
Diffraction efficiency is an essential technical indicator for evaluating the performance of a diffraction grating. In actual research, it is necessary to meet certain parameter requirements. For example, optimizing the grating groove type and duty cycle, along with other parameters, is required to obtain a diffraction grating with a high diffraction efficiency and anti-laser-damage threshold. An overview of metal-film reflection, multilayer-dielectric-film reflection, and multilayer-dielectric-film transmission gratings is provided below.
# Inter-Unit Consistency and Validity of 10-Hz GNSS Units in Straight-Line Sprint Running
## 4 Discussion
### Distance  
We originally expect that all GNSS units, when placed on the participant, would be consistent and accurate in measuring total distance traveled during 45.72-m sprint. However, there was only _slight_ reliability for inter-unit consistency among the eight GNSS units and two out of eight units (Units 1 and 4, Table 2) had significantly different values from the criterion distance. In addition, seven out of the eight GNSS units overestimated the values during the straight-line sprint. These results in the present study are somewhat in congruence with previous research which reported moderate errors when measuring total distance over very high-speed running (>5.56 m/s) [17]. Additionally, overestimation of the total distance measured using GNSS units has also been found when the sprinting distances were set as 15 m and 30 m [34; 35]. The reliability and accuracy may also be affected by rapid changes in speed during the acceleration phase of the sprint. A previous study revealed that distance measures over the post-acceleration phase of 20-40 m were more accurate than the acceleration phase of 0-20 m in a 40-m linear acceleration run [16], suggesting that smaller variations in speed may facilitate more accurate measures in distance. In the present study, participants started from a stationary position and were asked to sprint as fast as they could using maximal effort. Hence, phases with great variations in speed could have resulted in inconsistent and less accurate total distances measurement across different units. It is also possible that some participants did not sprint in a perfectly straight line hence covering a longer distance than the reference value of 45.72 m. Although the deviation from a straight line can be expected to be quite small, this could partly explain why seven out of eight GNSS units recorded a longer total distance than the reference value based on the distance between standard marked lines on the field. Finally, the GNSS units could miss data owing to the poor satellite connection [19]. This may have caused measurement errors in certain GNSS units, leading to inconsistency among the different units.
## 2 Materials and Methods
### Model Formulation
#### 2.2.1 Objective Function
\begin{array}{l}w^{\rm car}_{(i,j)},w^{\rm car}_{(i,j),(i+1)}, \overline{w}^{\rm car}_{(i,j),(i,j+1)},\overline{w}^{\rm car}_{(i,j+1)}\geq 0 \\ w^{\rm bus}_{(i,j)},w^{\rm bus}_{(i,j),(i,j+1)},\overline{w}^{\rm bus}_{(i,j +1)},\overline{w}^{\rm bus}_{(i,j+1)}\geq 0\\ w^{\rm car}_{(i,j),(i,j+1)}-\epsilon^{\rm car}_{(i,j+1)}\geq-\chi^{\rm car}_{(i,j),(i,j+1)}M\\ w^{\rm bus}_{(i,j),(i,j+1)}-\epsilon^{\rm bus}_{(i,j+1)}\geq-\chi^{\rm bus}_{( i,j),(i,j+1)}M\end{array} \right.,\] (34) where \(\epsilon^{\rm car}_{(i,j+1)}\) [\(\epsilon^{\rm bus}_{(i,j+1)}\)] denotes the initial queue clearance time corresponding to the car [bus] outbound bandwidth at \((i,j+1)\). Assume that a closed road network consists of \(m_{\rm WE}\) W-E arterials and \(n_{\rm SN}\) S-N arterials. In this road network, the number of road segments is \((n_{\rm SN}-1)\times m_{\rm WE}+(m_{\rm WE}-1)\times n_{\rm SN}\), the number of intersections is \(m_{\rm WE}\times n_{\rm SN}\), and the number of closed loops is \((n_{\rm SN}-1)\times(m_{\rm WE}-1)\). The number of constraints for the proposed model is shown in Table 2. The number of variables for the proposed model is shown in Table 3. \begin{table}
\begin{tabular}{l l} \hline \hline
**Type of Constraints** & **Number of Constraints** \\ \hline Constraints on the location of green wave bands & \([(n_{\rm SN}-1)\times m_{\rm WE}+(m_{\rm WE}-1)\times n_{\rm SN}]\times 8\) \\ Arterial-level loop integer constraints & \([(n_{\rm SN}-1)\times m_{\rm WE}+(m_{\rm WE}-1)\times n_{\rm SN}]\times 4\) \\ Network-level loop integer constraints & \((n_{\rm SN}-1)\times(m_{\rm WE}-1)\times 4\) \\ Bandwidth ratio constraints & \([(n_{\rm SN}-1)\times m_{\rm WE}+(m_{\rm WE}-1)\times n_{\rm SN}]\times 2\) \\ Mutual offset constraints & \([(n_{\rm SN}-1)\times m_{\rm WE}+(m_{\rm WE}-1)\times n_{\rm SN}]\times 2\) \\ Minimum bandwidth constraints & \([(n_{\rm SN}-1)\times m_{\rm WE}+(m_{\rm WE}-1)\times n_{\rm SN}]\times 4\) \\ Constraints that bandwidths are forced to be 0 & \([(n_{\rm SN}-1)\times m_{\rm WE}+(m_{\rm WE}-1)\times n_{\rm SN}]\times 8\) \\ Common cycle time constraints & \(1\) \\ Additional constraints on interference variables & \([(n_{\rm SN}-1)\times m_{\rm WE}+(m_{\rm WE}-1)\times n_{\rm SN}]\times 10\) \\ Constraints about \(u_{(p,q),(r,s)}\), \(v_{(p,q),(r,s)}\), \(y_{(p,q),(r,s)}\), & \(m_{\rm WE}\times n_{\rm SN}\times 3\) \\ and \(z_{(p,q),(r,s)}\) (Constraint 23) & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The number of constraints for the proposed model.
# Geotechnical Study of Raspadalica Cliff Rockfall, Croatia
## 2 Site Description
### Raspadalica Cliff Rockfall History
\begin{table}
\begin{tabular}{c c c c c} \hline
**Date** & **Time** & **Rockfall Volume** & **Consequences** & **Note** \\ \hline
11 February 1963 & 21:48 & Unknown & Traffic interruption & - \\
6 September 1964 & 16:52 & Unknown & Traffic interruption & - \\
18 November 1975 & - & \(>\)500 m\({}^{3}\) & Facilities damage, human casualties & The train ran into rock blocks \\  & & & & human casualties & and slipped off the tracks \\
16 March 1976 & 20:30 & Unknown & Traffic interruption & - \\
28 May 1976 & 20:30 & Unknown & Traffic interruption & - \\
28 January 1977 & 00:25 & Unknown & Traffic interruption & - \\
2 March 1977 & 10:20 & Unknown & Traffic interruption & - \\
7 May 1987 & 17:30 & Unknown & Traffic interruption & Damage to train bumper \\
11 November 1987 & 01:45 & Unknown & Traffic interruption & The train stopped before the rock blocks \\
21 December 1990 & 03:45 & \(\approx\)20 m\({}^{3}\) & Traffic interruption & - \\
11 August 1992 & 14:15 & \(>\)500 m\({}^{3}\) & Traffic interruption & Caused by storm \\
21 October 1993 & - & \(\approx\)10 m\({}^{3}\) & Traffic interruption & Locomotive and first wagon \\
3 October 1994 & 04:47 & Block \(>\) 15 m\({}^{3}\) & Traffic interruption & slipped off the tracks \\  & & & & Blocks stopped over the railway \\ November 1998 & - & Six blocks \(\approx\) 0.7 m\({}^{3}\) each & Traffic closed & in the forest \\
10 February 1999 & 17:45 & \(>\)500 m\({}^{3}\) & Facilities damage & Caused by storm \\
2 November 1999 & 05:35 & Block \(\approx\) 2 m\({}^{3}\) & Traffic interruption & The train stopped before the rock block \\
27 November 1999 & - & Block \(\approx\) 0.8 m\({}^{3}\) & Traffic closed & - \\
16 December 2013 & - & \(\approx\)20 m\({}^{3}\) & Facilities damage & Traffic stopped for several months \\ \hline \end{tabular}
\end{table} 
Table 1: The rockfalls registered at the Raspadalica Cliff location by the Croatian Railways Technical Office. protection design [15] and was completed in 2012. The field investigations are based on traditional in situ surveys carried out by traditional engineering geological mapping of the cliff face's rock mass, limited to a narrow accessible zone at the foot of the cliff. The design of rockfall protection [15] using rockfall protection barriers was carried out based on assumptions about the rock blocks' distribution in the cliff as well as the estimation of unstable rock block positions at the cliff, rock block volumes, and possible rockfall trajectories. The applied protection measures and rockfall barrier installation were carried out in 2014. Details of the Raspadalica Cliff analyses carried out in recent years based on new remote sensing techniques are presented in this article.
## 4 Topology Control
### Post-Deployment  
After the nodes are deployed, topology control can be achieved by adjusting the transmission range (power) or scheduling the nodes' status to be active or inactive. This ability is achievable in most sensor platforms, such as Crossbow and Sunspot. However, increasing the transmission range also causes higher signal inference. Consequently, it has a negative influence on link reliability.
# Applications for Microwave Kinetic Induction Detectors in Advanced Instrumentation
## 4 MKIDs in Particle Physics
### MKIDs for Neutrino Physics Experiments
#### 4.1.1 Cuore  
CUORE (the Cryogenic Underground Laboratory for Rare Events) is an underground experiment based in LNGS (Laboratori Nationali del Gran Sasso), Italy whose main task is to detect neutrino-less double beta decay events in refrigerated tellurium dioxide (TeO\({}_{2}\)) crystals [120]. The experimental setup consists of 988 TeO\({}_{2}\) crystals for a total mass of 741 kg (206 kg of \({}^{130}\)Te). The detectors deployed on this experiment are NTD Germanium thermistors [121] whose task is to detect temperature changes induced by the radioactive decay of the \({}^{130}\)Te nuclei, which is a double-beta-decaying isotope of tellurium. For CUORE, just as for other rare event experiments, an optimal background rejection is crucial. It is expected that CUORE will have a background of \(10^{-2}\) counts/kg/keV/year in the energy range of the neutrino-less double beta decay; this is expected to be approximately 200 counts over the 5 years the experiment will run for [122].  
The successor of CUORE is currently being planned and will go under the name of CUPID (CUORE Upgrade with Particle IDentification). In addition to CUORE's current structure, CUPID intends to switch to Li\({}_{2}\)MoO\({}_{4}\) crystals and will have particle identification capabilities which arises from the coupling of photodetectors to CUORE's bolometers. These detectors will allow to distinguish events due to \(\alpha\)-particles from the natural background (mainly decays in Copper) and double-beta decays as in Li\({}_{2}\)MoO\({}_{4}\) alpha- and beta-particle events differ in light yield and time development of their scintillation light [123; 124; 125]. Although the photodetectors that will be deployed in CUPID are currently planned to be based on NTD Ge thermistors, multiple further options are being considered for possible future upgrades, one of which involves MKIDs as photo-detectors.