esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 MVO Algorithm and Its Application to Solve the RNP-WMN Problem
### MVO Algorithm
In addition, there are four outliers of 55.1, 63.5, 68.8 and 69.1 dB. Meanwhile, the PL of algorithms WOA, GA and PSO range from 70.8 to 89.7 dB, 72 to 92 dB and 72 to 94.7 dB, respectively. There are also some outliers between 60.5 and 71 dB. Thus, the PL of the MVO algorithm is better than that of the WOA, GA and PSO algorithms. ### Impact of the Number of Mesh Clients

In this section, we analyze the effect of the number of mesh clients on the performance of the mesh routers placement algorithms in WMN. The charts in Figure 8 show the CCR when executing INS-3 and INS-4 with 30 and 45 mesh routers, respectively. The number of mesh clients is varied from 50 to 400. We can observe that for GA and PSO algorithms, the CCR decreases with the increase in the number of mesh clients. For MVO and WOA algorithms, as the number of mesh clients increases, the CCR changes only slightly. Comparing four algorithms, the MVO gives the best CCR for both INS-3 and INS-4. Consider an example in INS-3 (Figure 8a) with 300 mesh clients, the CCRs when using

Figure 6: Performance comparison of the path loss versus the number of routers. (**a**) INS-1. (**b**) INS-2. Figure 7: PL comparison of the algorithms MVO, WOA, GA and PSO. (**a**) INS-1 with 40 mesh routers. (**b**) INS-2 with 40 mesh routers. algorithms MVO, WOA, GA and PSO are 89.5%, 74.4%, 78.0% and 83.6%, respectively. Thus, the CCR of algorithm MVO is greater than that of algorithms WOA, GA and PSO by 15.1%, 11.5% and 5.9%, respectively. For the INS-4 (Figure 8b), the CCR is greater than INS-3 due to the larger number of mesh routers (45 routers for this instance). Specifically, algorithm MVO yields higher CCR than other algorithms. The details of the CCR values when executing INS-3 and INS-4 are shown in Table 7.

For the impact of the number of mesh clients on the PL, the simulation results are shown in Figure 9, where we plot the average PL as a function of the number of mesh clients. These results are obtained when executing INS-3 and INS-4. We can observe that the average PL does not change much according to the change in the number of mesh clients for both INS-3 and INS-4. Comparing INS-3 (Figure 9a) and INS-4 (Figure 9b), the average PL of INS-4 is better than that of INS-3. This is because the number of mesh routers of INS-4 is larger than that of INS-3 (40 routers for INS-4 and 30 routers for INS-3), resulting in the CCR of INS-4 being larger than that of INS-3. As a result, INS-4 outperforms INS-3 in terms of average PL. Among the algorithms MVO, WOA, GA and PSO, MVO gives the highest efficiency.
## 5 Economic Opportunities and Benefits
### Grid Optimization, Efficiency Gains, and Cost Savings
Rather than over-engineering networks to accommodate worst-case projections, planners can assess specific combinations of non-wire alternatives such as energy storage, demand response, and reactive power control alongside traditional infrastructure reinforcements [20]. When the digital twin demonstrates that certain investments can be postponed or adjusted in scale, capital can be redirected to higher-value projects. Given the European Commission's estimate that approximately EUR 600 billion will be needed for grid upgrades during this decade, even modest improvements in how projects are sequenced and sized could lead to savings of tens of billions of euros, ultimately reducing tariffs and easing the financial demands of the energy transition [2]. Efficiency gains also apply to day-to-day operations and customer interactions. Automating routine analyses, such as evaluating connection requests for rooftop photovoltaic systems or heat pumps, reduces the need for engineering labor and accelerates customer service. This is especially important as millions of new devices seek access to the grid [24]. Loss reduction provides another substantial benefit. Transmission and distribution losses account for several percent of all electricity generated. Smarter management of voltage and phase, supported by digital twin analytics, can reduce these losses and save terawatt-hours of energy along with significant amounts of CO2 emissions each year [20]. Finally, enhanced renewables integration directly suppresses system costs. By forecasting congestion more accurately and coordinating remedial actions in advance, the twin minimizes renewable curtailment, avoiding the expenditure associated with compensating curtailed generators and replacing their output with more expensive thermal units [16]. The avoided curtailment during peak wind events in northern Europe alone represents hundreds of millions of euros each year, and the benefit will grow as renewable penetration rises [2]. The combined effects of higher asset utilization, deferred capital investment, reduced operation and maintenance expenditure, lower losses, and curtailed redispatch payments present a strong economic case for the digital twin. Preliminary cost-benefit assessments from EU-funded pilots indicate favorable benefit-to-cost ratios even when only a fraction of these savings is realized. From a broader macroeconomic perspective, the digital twin serves not only as a technological enhancement but also as a strategic efficiency instrument that can lower the overall cost of Europe's clean energy transition, improve reliability, and accelerate customer connections.
# Prioritization Based Task Offloading in UAV-Assisted Edge Networks +
## 5 Task Offloading Algorithms in Our Model
### Adaptive Offloading Algorithm (ADP)  
In ADP, during an offloading decision, the processors with the tasks being transmitted are skipped, avoiding multiple transfers for the same processor. It aims to decrease the pressure on the limited network resources and to improve prioritization since it leads to more tasks in the controller task pool \(TP\) and more processor-task combinations upon arrival of new processor nodes. Algorithm 2 shows the details of the algorithm.  
```
Wait for a system event \(ER\);  The event handling part is identical to AGG-1. whiletruedo  Calculate \(w_{i,P_{j}}\) for each \(K_{i}\) from \(TP\) and the available \(P_{j}\) which is not assigned  to any tasks that are marked as being transmitted; ifThere is no task-processor pair used for the calculationthen  break; \(X_{ij}=\) The task-processor pair with the lowest \(w_{i,P_{j}}\);  Assign \(K_{i}\) to \(P_{j}\) and mark \(K_{i}\) as being transmitted;  Remove \(K_{i}\) from \(TP\);  Decrease available \(S_{Q_{j}}\) by \(S_{K_{i}}\);  Go back to the first step.
```  
**Algorithm 2**Adaptive Offloading Algorithm (ADP)
## 4 Inorganic Fertilizers
### Secondary and Micromutrients  
Secondary nutrients are also needed in large amounts similarly to nitrogen, phosphorus and potassium: calcium (Ca), sulfur (S) and magnesium (Mg). Micronutrients on the other hand are needed in small quantities and necessary for crop development. Iron (Fe), boron (B), copper (Cu), zinc (Zn), manganese (Mn), molybdenum (Mo), nickel (Ni), chlorine (Cl) and molybdenum are important micronutrients. Their deficiency can impair crop yield, cause low absorption of other nutrients and structural problems. In as much as nitrogen, phosphorus and potassium are the principal nutrients for crop production, their excesses may lead to imbalance in the micronutrients. For instance, when nitrogen and potassium are used in excess, they lead to deficiency in magnesium; and when phosphorus is in excess, it causes imbalance in zinc content.  
These secondary and micronutrients can be blended with the primary plants nutrients (NPK), and applied to crops; and their quantities should be able to correct the soil deficiencies as well as be bio-available. These nutrients behave differently in soils; some of them may be in adequate forms but  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fertilizers** & **N** & **P\({}_{2}\)O\({}_{5}\)** & **K\({}_{2}\)O** \\ \hline \multicolumn{4}{c}{**Nitrogen fertilizers**} \\ Ammonium nitrate & 34 & 0 & 0 \\ Ammonium sulfate & 21 & 0 & 0 \\ Urea & 45–48 & 0 & 0 \\ Urea-ammonium nitrate & 28–33 & 0 & 0 \\ Anhydroous ammonia & 82 & 0 & 0 \\ Ammonium polyphosphate (a or b) & 10–11 & 34–37 & 0 \\ \hline \multicolumn{4}{c}{**Phosphorus fertilizers**} \\ Ammoniated super-phosphate & 3–6 & 48–53 & 0 \\ Ammoniated super-phosphate & 3–6 & 48–53 & 0 \\ Di-ammonium phosphate & 11–18 & 48 & 0 \\ Mono-ammonium phosphate & 11 & 48–55 & 0 \\ Super-phosphate & 0 & 18–50 & 0 \\ Triple super phosphate & 0 & 46 & 0 \\ Ammonium polyphosphate & 10–15 & 34–37 & 0 \\ Urea ammonium phosphate & 28 & 27 & 0 \\ \hline \multicolumn{4}{c}{**Potassium fertilizers**} \\ Potassium chloride (muratie of potash) & 0 & 0 & 60–63 \\ Protassium+\(\otimes\)(sulfate of potash) & 0 & 0 & 50 \\ Potassium nitrate & 13 & 0 & 44 \\ Potassium-magnesium sulfate & 0 & 0 & 22 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Some macronutrients fertilizers and their composition Data from [52].  
may not be available for plant absorption (e.g., Fe and Mn). Boron on the other hand can be difficult to accumulate, especially in sandy soils due to its high mobility [58].  
However, their excesses are detrimental to the growth of crops, as too much calcium in the form of calcium sulfate or chloride can lead to higher pH from the anions (Cl\({}^{-}\) and SO\({}_{4}\)\({}^{2-}\)) and thereby result to some other nutritional problems. Higher levels of boron in starter fertilizers could also lead to toxicity in some sensitive crops such as beans and grains [59].
# Spectrum Demand Forecasting for IoT Services
## 3 A Market Study
### Market Size and Penetration  
After identifying a list of IoT services, it is necessary to establish the real size of each market and estimate the rate of penetration that these services will have.  
* _Market size:_ An indicator of the real market size should be the amount of the country's population with enough income to access the product or service. If the product or service is not a mass consumption one, then the population should be reduced according to the size of the economic sector or the number of clients of related products that already exist.
* _Technology penetration rate:_ A very well studied and related subject is found in the literature with the name of ICT adoption. A complete paper giving reviews of proposed methods, data from empirical results, and models can be found in [38]. Since applying one of those models should be carefully adapted to your country's situation, an alternative would be using two different indicators combined as proxies of IoT penetration: (i) IoT penetration forecasting; some examples can be found in [3; 4; 5; 39; 40; 41; 42]. (ii) The IDI (ICT Development Index), published every year by ITU in [43]. The IDI will most likely serve as a weight factor for the first indicator.
## 2 Conceptual Model and Test Method  
### Conceptual Model of Coupled Cooling System  
The heat exchange forms of the underground heading face are mainly heat conduction and heat convection. The heat transfer between a human body, mechanical equipment, and palisades is inevitable, but it has less impact on the ambient temperature than the heat dissipation of the high temperature of surrounding rocks. The main heat transfer processes include heat conduction \(\phi_{11}\) and natural convection heat transfer \(\phi_{12}\) of the air between the high-temperature rock and WF; forced convection heat transfer \(\phi_{2}\) between the high-temperature surrounding rock and airflow; and forced convection heat transfer \(\phi_{3}\) between cold airflow and the air in the work area. The environment temperature could be lowered by controlling heat damage. It is necessary to reduce the heat released by \(\phi_{11}\), \(\phi_{12}\), and \(\phi_{2}\) while increasing the heat exchanged by \(\phi_{3}\) to take full advantage of the cooling capacity of the cold airflow. In order to achieve this goal, a conceptual model of a coupled cooling system for ventilation and PTI in a mine heading roadway was proposed, as presented in Figure 1b.  
The coupled cooling concept model works as follows: The ventilation device delivers low-temperature airflow to the WF to realize the cooling function of \(\phi_{3}\). In the PTI part, the insulation layer using low-thermal conductivity materials is arranged between the VDO and the surrounding rock. The TIL can reduce \(\phi_{11}\) and \(\phi_{12}\). Simultaneously, the PTI layers prevent the airflow from coming into direct contact with the nearby hot rock, thereby weakening \(\phi_{2}\). The heat exchange layer with cold water delivery is attached to the interior and exterior surfaces of the PTI layers. On the one hand, the heat exchange layer can exchange heat with the high-temperature rock surface to weaken the effects of \(\phi_{11}\) and \(\phi_{12}\), and on the other hand, it can provide cooling capacity to the airflow and conduct forced convection heat transfer to promote the cooling effect of \(\phi_{3}\).
# Hg\({}^{2+}\) Optical Fiber Sensor Based on LSPR with PDDA-Templated AuNPs and CS/PAA Bilayers
## 4 Discussion  
### Specificity  
The specificity of heavy metal detection is important for the practical water quality monitoring in the real aqueous solution, which contains various heavy metal ions. In order to study the specificity of the heavy metal sensor, the proposed LSPR sensor was experimentally employed to test the spectral response to K\({}^{+}\), Na\({}^{+}\), Ca\({}^{2+}\), and Cu\({}^{2+}\) solutions besides the Hg\({}^{2+}\) solution. Figure 7a illustrates the wavelength shift of the above metal solution with a concentration of 30 uM. It is clear that the wavelength shift of Hg\({}^{2+}\) is much larger than those of other ions.  
Figure 6: Hg\({}^{2+}\) detection based on the proposed LSPR sensor. (**a**) Normalized LSPR spectra of different concentrations of Hg\({}^{2+}\) solution; (**b**) LSPR peak wavelength as a function of the concentrations of Hg\({}^{2+}\) solution.  
Figure 7: Specificity and stability of the proposed heavy metal sensor (**a**) wavelength shift of the LSPR sensor in different ion solution with a concentration of 30 uM; (**b**) absorption spectra of the LSPR sensor tested immediately versus the one exposed to air for two weeks.
## 4 Numerical Results
### Discussion and Comparison of the Four MLR Models
This (combined with the first conclusion) shows that the applied outlier detection algorithm, CODPA, produced conservative and consistent results under three different setups (Op2, Op3, and Op4). 6. The developed MLR models (51)-(54) are very similar which is demonstrated by the closeness of the four curves in each of the nine Figures 3-11. The fact proves that the MLR models produce robust and reliable predictions when constructed properly by testing and relaxing the classical assumption of the CNLRM. 7. Taken on face value, model (54) has the best performance measures, followed on almost equal distances by the models (53), (52), and (51). However, the first model rejected four observations, the second rejected three, the third rejected two, and the last rejected none of the observations. It seems that the marginal improvement in performance measures  
Figure 11: The four models for Group 9. The outliers for a model are denoted above the point with a cross in a respective color. under the different options are due to the slightly increased outlier rejection rate in Op4 compared to the other options. Another consideration in the classification of the four options is that Op4 is computationally most expensive, followed by Op3 and Op2 which are equally computationally expensive, and Op1 is the computationally cheapest options. All these considerations point out that there is no clear winner from the four models. Additional research activities are needed to determine the right place of the CODPA in the process of assumption's diagnostics in linear regression models. It is not impossible to consider that the answer is problem specific and/or more than one option should be used consistently to achieve robust outlier rejection.
## 5 Mission Technical Implementation
### Spacecraft Design: Mass and Power Budgets  
The overall size of the primary spacecraft will be in the range of 3.5 m \(\times\) 2.5 m \(\times\) 2.5 m and will be shaped by the limb and nadir telescopes, the optical benches, solar arrays and radiators. It will have  
Figure 10: MIR/TIR optical system design.  
a dry mass of roughly 3,000 kg. In this configuration it can be launched from a Soyuz launcher. The payload is estimated to consume an average of 1,500 W, with the spacecraft bus requiring around 700 W. To supply the energy for the subsystems and the payload, and to charge the batteries for the eclipse time, a solar array of 17 m\({}^{2}\) will be required. It will be panel-mounted to accommodate the large area and optimize the Sun incidence angle by two-axis Sun tracking.  
To maintain the payload and the primary spacecraft subsystems within their operating temperature ranges, two radiators with an area of 5.8 m\({}^{2}\) each are attached at two sides of the spacecraft to dissipate heat. Louvers ensure that one radiator will always face deep space, while they are closed when exposed to direct sunlight. Heat pipes will transport the heat to the radiators, and to the heaters for eclipse time, and in emergency cases will sustain a minimum temperature for the payload and the battery.  
The role of the retroreflector spacecraft is to reflect the laser pulses back to the primary spacecraft. The design performance directly determines the quality of the IPDALLS measurement. Several options for the design of the retroreflector spacecraft were studied.  
Initially, completely passive satellites with multiple retroreflective surfaces were considered, similar to those flown in the LAGEOS mission [72]. Several issues, such as orbit deterioration, lack of control and inability to de-orbit in a controlled manner, caused this idea to be rejected and an active retroreflector was deemed necessary. Active spherical retroreflector satellites, having multiple retroreflective surfaces but with added orbit control, were then considered. This idea was eventually dismissed because of launcher housing complexity and difficulty to manufacture. In addition, the multiple retroreflective surfaces may have caused speckle noise distortion of the beam signal and point-ahead-angle issues [55].  
The final retroreflector spacecraft consists of a microsatellite with attitude and orbit control (0.65 m \(\times\) 0.65 m \(\times\) 0.8 m) carrying a single corner cube retroreflector with a diameter of 0.5 m and an effective reflective area of 0.2 m\({}^{2}\). The mass and power budget of both the primary spacecraft and a retroreflector spacecraft are shown in Table 2.
## 4 Results and Discussion
### Deformation Behaviour of Twin Tunnel
#### 4.2.3 Deformation of Lined and Unlined Tunnels Samples Having 2.5D c/c Spacing  
In the case of 2.5D c/c spacing, the least deformation is computed. So it can be concluded that in the case of twin tunnel samples, as the distance between the tunnels increases, the deformation decreases. From the results, it can also be concluded that maximum deformation occurs in the case of a 3 cm unlined tunnel due to the smaller cover depth, and its value is 0.19 mm at L/2, whereas the minimum deformation value is noticed in the case of 5 cm lined samples and its value is 0.08 mm. The deformation profile of lined and unlined Tunnel for 2.5D Spacing is shown in Figure 14.  
After conducting the experimental tests on twin tunnels samples of different spacing and cover depth, a combined deformation graph is obtained. A comparison of deformation profiles obtained from experimental testing results for twin tunnels is shown in Figure 15. The results show that the maximum deformation occurs in the case of unlined samples of 1.5D c/c spacing tunnels having a cover depth of 3 cm and its value is 0.25 mm at L/2 distance, whereas the minimum deformation occurs in the case of a lined sample of 2.5D c/c spacing tunnels having a cover depth of 5 cm and its value is 0.16 mm at L/2 distance.  
Figure 13: Deformation Profile of Lined and Unlined Tunnel for 2D Spacing.
## 2 Gaussian Pulse
### Photoelectron Angular Distribution  
The photoelectron angular distribution from 2 PI of H and He is given by [12],  
\[I(\theta)=\frac{\sigma}{4\pi}\left[1+\beta_{2}P_{2}(\cos\theta)+\beta_{4}P_{4}( \cos\theta)\right] \tag{5}\]  
where \(\sigma\) is the total cross section, \(\theta\) is the angle between the laser polarization and the electron velocity vector, and \(\beta_{2}\) and \(\beta_{4}\) are the anisotropy parameters associated with the second- and fourth-order Legendre polynomials, respectively. The interference of the \(S\) and \(D\) wave packets produces a photoelectron angular distribution,  
\[I(\theta)\propto\left|c_{S}Y_{00}-c_{D}Y_{20}\right|^{2}=\left|\tilde{c}_{S}e ^{i\delta_{0}}Y_{00}-\tilde{c}_{D}e^{i\delta_{2}}Y_{20}\right|^{2} \tag{6}\]  
where \(\tilde{c}_{S}\) and \(\tilde{c}_{D}\) are the real amplitudes that have absolute values of \(\left|c_{S}\right|\) and \(\left|c_{D}\right|\), respectively, and can be either positive or negative in principle, and \(\delta_{l}\) is the phase of the partial wave, or the _apparent_ phase shift. The apparent phase shift difference,  
\[\delta\equiv\delta_{0}-\delta_{2}=\delta_{sc}+\delta_{ex} \tag{7}\]  
consists of a part \(\delta_{sc}\) intrinsic to the continuum eigen wave-functions (scattering phase shift difference), which has previously been studied both theoretically [61; 62; 63] and experimentally [29], and the extra contribution \(\delta_{ex}=\arg c_{S}/c_{D}\) (if \(\tilde{c}_{S}/\tilde{c}_{D}>0\)), \(\pi-\arg c_{S}/c_{D}\) (if \(\tilde{c}_{S}/\tilde{c}_{D}<0\)) from the competition of the two paths. This situation presents a contrast to the case of the photo-ionization from photo-excited states [29], where the nonresonant path is absent and only \(\delta_{sc}\) is present (\(\delta=\delta_{sc}\)).  
The amplitude ratio \(W\equiv\tilde{c}_{S}/\tilde{c}_{D}\) and \(\delta\) are related with the anisotropy parameters as [64],  
\[\beta_{2}=\frac{10}{W^{2}+1}\left[\frac{1}{7}-\frac{W}{\sqrt{5}}\cos\delta \right],\quad\beta_{4}=\frac{18}{7(W^{2}+1)} \tag{8}\]  
Figure 1: Photon-energy dependence of (**a**) the relative phase (phase-shift difference) \(\delta\) and (**b**) the amplitude ratio \(W\) between the \(s\) and \(d\) wave packets for a pulse width of 7 fs (circles) and 14 fs (squares). The target atom is hydrogen. Thin sold and dashed lines in (**a**): \(\left|\delta_{sc}\right|\) and \(\pi-\left|\delta_{sc}\right|\), respectively. Vertical dotted lines: positions of \(2p\) to \(6p\) resonances.  
It should be noticed that, when one extracts \(W\) and \(\delta\) from \(\beta_{2}\) and \(\beta_{4}\), one cannot distinguish between \((W,\delta)\) and \((-W,\pi-\delta)\). One cannot distinguish between \(\delta\) and \(-\delta\), either. Hence, in what follows, let us take \(W\) as positive, _i.e._, \(W=|c_{S}/c_{D}|\) and define the value of \(\delta\) within the range \([0,\pi]\). Then, \(\delta_{sc}\) in Equation (7) should be replaced by \(|\delta_{sc}|\) or \(\pi-|\delta_{sc}|\) for appropriate interpretation in some cases (see the discussion on Figure 1 below).