esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Ferry Node Ranking Clustering Algorithm (FNRCA)
### Stopping Time of the Ferry at Each Checkpoint  
The stopping time (ST) is the time period the ferry will stay at each checkpoint, j, to allow the associated cluster heads to send gathered data to the ferry. This time period depends on the number of associated cluster heads, their buffer sizes and the transmission time of a bit based on the assumed medium physical characteristics:  
\[\text{ST (j) = BuffSize(CHj) \times NumberOfAttachedCHs \times TimeToTransmitAbit+T} \tag{5}\]  
where \(BufSize\) is the cluster head memory size in bits, the \(NumberOfAttachedCHs\) is the number of cluster heads associated with checkpoint j, \(TimeToTransmitAbit\) is the time needed to transmit a bit of information to the checkpoint and T is an assumed constant delay added to account for propagation delay.
## 6 Simulations Results and Discussions
### Total Network Energy Consumption over the Rounds  
Figure 10 depicts the network energy consumption over the number of rounds. In the benchmark protocols, data packets are transmitted from SNs to the CH in each cluster and then from each CH to the BS, which means that more transmissions occur for a single data packet in the Fol between the SNs, resulting in more network energy consumption, whereas in EEUCH, once the data packet is transmitted from SNs to UAV, no other transmission occurs between the SNs or in WSN. As a result, our proposed EEUCH uses the lowest amount of energy in each round.  
Figure 11 shows the amount of total network energy consumption when the first SN dies, and ten, fifty, and one hundred percent of SNs die. The number mentioned above a bar represents the amount of energy consumed in joules with the respective network condition along the x-axis. When the first SN dies, 50 joules out of 100 joules energy is consumed in EEUCH, which means almost half of the network energy is consumed. TEEN performs better, for which the 37 joules energy is consumed out of 100 joules. However, when fifty percent of SNs die, the consumed energy of EEUCH and IEE-LEACH is equal while the remaining protocols have consumed much more energy, which means that the SNs die approximately linearly as the number of rounds increases.  
Figure 10: Total energy consumption over the number of rounds.  
Figure 9: Reduction in the total energy of the network over the number of rounds.
## 3 Results and Discussion
### FTIR Characterization of Nanolubricant Stability  
The FTIR spectra for the SiO\({}_{2}\) nanoparticles coated with the KH570 silane coupling agent, the reference lubricant (Mistral 15W40) and its nanolubricants, as well as the reference biolubricant (PLANTO MOT SAE 10W40) and its nanobiolubricants, are presented in the following figures. A detailed comparison of the peaks of both lubricants and biolubricants is shown in Figure 5, and Table 7 compares them in detail. The biolubricant shows strong O-H stretching peaks (3649.1 cm\({}^{-1}\) and 3458.9 cm\({}^{-1}\)), indicating the presence of hydroxyl groups, which are typical in natural esters and bio-based additives. Both lubricants exhibit strong C=O stretching peaks, but the biolubricant has a single intense peak (1740.0 cm\({}^{-1}\)), reflecting its ester-rich formulation. The biolubricant has a notable peak at 1605.6 cm\({}^{-1}\), suggesting the presence of unsaturated components (C=C stretching). The biolubricant shows a peak at 1515.9 cm\({}^{-1}\), which could derive from certain additives (N-O asymmetric stretching). Both lubricants have CH\({}_{2}\) and CH\({}_{3}\) bending vibrations, but the conventional lubricant has a broader set of hydrocarbon-related peaks. The biolubricant displays peaks indicative of aromatic or unsaturated components, which are less pronounced in the conventional lubricant. These differences reflect the distinct nature of their formulations, as conventional lubricants rely heavily on refined hydrocarbon oils and synthetic additives, while biolubricants incorporate natural esters and bio-based components, thus leading to distinct spectral features [54; 55; 56]. Figure 6 highlights several distinct peaks at wavenumbers 1089.69 cm\({}^{-1}\), 800 cm\({}^{-1}\), 463.66 cm\({}^{-1}\), and a smaller peak at 1636 cm\({}^{-1}\). These peaks correspond to specific vibrational frequencies of molecular bonds within the samples. The peak at 1089.69 cm\({}^{-1}\) is characteristic of Si-O-Si asymmetric stretching, the peak at 800 cm\({}^{-1}\) is attributed to Si-O-Si symmetric bending, the peak at 463.66 cm\({}^{-1}\) is due to Si-Si-O bending vibrations, and the peak at 1636 cm\({}^{-1}\) corresponds to the C=O stretching of the ester group in KH570. These findings confirm the presence of both SiO\({}_{2}\) and the KH570 silane coupling agent in the samples [57; 58]. To analyse the chemical compositions of the nanolubricants, Figure 7a,b present the FTIR spectra for the reference lubricant (Mistral 15W40) and its nanolubricants at four different concentrations (0.25, 0.50, 0.75, and 1.00 wt%), as well as for the reference biolubricant (PLANTO MOT SAE 10W40) and its nanolubricants at the same concentrations. The absence of chemical by-products in the spectra confirms that no chemical reactions have occurred between the dispersed SiO\({}_{2}\) nanoparticles and the lubricants or biolubricants. These findings demonstrate that the basic functional structures of the lubricants and biolubricants remain unchanged, indicating excellent chemical stability [25; 50].
## 3 Research Findings
### Initial Analysis  
The initial validation of the proposed model demonstrates its ability to address dynamic demand patterns effectively. Table 8 summarizes the results, showcasing the improvements in demand coverage, cost reduction, and environmental sustainability compared to baseline configurations.  
Table 8 summarizes the initial findings of the proposed model compared to the baseline configuration. The results highlight significant improvements in demand coverage, operational costs, and environmental sustainability achieved through the optimization process. These metrics demonstrate the effectiveness of the proposed model in addressing telecommunication network challenges while balancing cost efficiency and environmental goals.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Metric**} & **Baseline** & **Proposed** & **Improvement** \\  & **Configuration** & **Model** & **(\%)** \\ \hline Demand Coverage (\%) & 65 & 85 & 20 \\ Operational Costs (USD) & 500,000 & 425,000 & 15 \\ Environmental Sustainability Index & 0.6 & 0.8 & 0.33 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Summary of initial analysis.  
Figure 6: Pareto front obtained from solving the small-sized problem using NSGA II and MOPSO algorithms.
## 2 Status of Research and Objectives in Radiant Tube Lifetime Modeling
### Early Macroscopic Studies  
The macroscopic studies of radiant tubes, on the other hand, usually followed a one-dimensional approach in the early investigations. For instance, [PERSON] et al. [17] built numerical models based on the finite volume method (FVM) for a single-ended radiant tube operating with a 25 kW self-recuperative burner. These models were validated against the flue gas compositions and the corresponding temperature measurements on the tube. [PERSON] et al. [18] performed similar investigations and focussed on finding the optimum operating condition by determining the relationship between the burner air-ratio and the combustion efficiency of the radiant tube burner. [PERSON] et al. [19] carried out simulations in the same way to study the effects of air staging on thermal and prompt NOx formation using validated FVM models. These and some recent studies [20; 21; 22] followed the same trend of ignoring the thermomechanical behavior of the radiant tube under operation. In contrast, [PERSON] and [PERSON] [23; 24] investigated the thermomechanical stresses occurring in W-type radiant tubes using the finite element method (FEM) models. The tube temperature distributions were derived from experimental measurements instead of an FVM model. The results showed that the radial temperature gradients and local hot spots were major sources of the stresses in the tube. Also, the tube bends showed higher magnitudes of stress than the straight sections. Similar investigations were carried out by [PERSON] et al. [25] on P-type radiant tubes of a CGL furnace. However, it is not practical to obtain experimental measurements for each radiant tube application. This makes the use of FVM models indispensable.
## 2 Materials and Methods
### Error Analysis of the Prediction Models  
The predicted models' mean, minimum, maximum, and root mean square error (RMSE) were analyzed. The RMSE was the main factor used to determine the performance evaluation of the four models investigated. This is because it has been statistically determined to be a good metric to measure predicted performance with measured values. RMSEs of 0-7 dB and 7-15 dB are deemed acceptable for urban and suburban environments, respectively [38]. The prediction error (\(P_{err}\)), is the difference between the measured path loss (\(P_{MD}\)) and the model's predicted path loss (\(P_{PD}\)) [9; 36]. It is presented in Equation (16), while the RMSE is presented in (17); \(n\) is the number of observations.  
\[P_{err}=\left|P_{MD}-P_{PD}\right| \tag{16}\]  
\[\text{RMSE}=\sqrt{\frac{\sum\left(P_{MD}-P_{PD}\right)^{2}}{n}} \tag{17}\]
## 4 VoLTE Scenario and Simulation Report
### Dual eNB Simulation Scenario  
For the dual eNB scenario, we introduce a network deployment to enhance network capacity and coverage within the dense forest area. This setup involves two eNBs, each serving as a central communication hub, positioned approximately 700 m apart (Figure 3). The rationale behind this separation is to show the scalability of our system and to enhance network capacity. One eNB operates at the central frequency of 2.69 GHz while the other works at 2.71 GHz to avoid interference between the two, thereby ensuring seamless and reliable VoLTE communication for the rescuers deployed in the forest. Like in the single eNB scenario, the UEs move with a speed of 1.5 m/s (5 km/h) at 1.5 m height towards the forest, whereas the eNB is stable at 2 m height, placed in a stationary vehicle. The measured mean distance between UEs and their serving eNBs during the 60 s simulation is between 800 and 1100 m.
# Bounding the Likelihood of Exceeding Ward Capacity in Stochastic Surgery Scheduling
## 3 Experimental Study  
The purpose of the experimental study is to compare ward combination optimization (WCO) and robust ward optimization (RWO) with respect to computational time and solution quality. In addition, we compare our optimized solutions to actual scheduling data. Various parameter settings for the proposed models are also studied. For our analysis, we have selected data from one typical month for a single surgical specialty, General Surgery at Landspitali Hospital. The given month consists of \(|D|=28\) days, which corresponds to the length of the planning horizon. During that month, 103 patients were operated on, with around 30% requiring ward admission for one or more days. A total of 10 of these patients required ICU admission. Moreover, 10 patients had a priority of one week. For the experiments, we assume that the patients with a priority of one week must be scheduled within 14 days (based on historical data) and that other patients have equal priority. Semi-acute patients (32 of 103) arriving during the month are scheduled after their arrival. To be comparable with actual scheduling data, the actual operators' roster days are used.  
The experiments were performed on a Windows desktop machine with 32 GB Intel Core i7-7700 3.60 GHz with four cores. The ORDS and the ward combination generators were coded in C, whereas the MIP model was programmed using the AMPL mathematical programming language and solved using Gurobi 8.1.
# Aerospace
## 4 Single Small Satellite to Mars Orbit as Secondary Payload
### Effect of Atmospheric Uncertainties and Target EFPA Selection  
In addition to ensuring the target EFPA falls within the aerocapture corridor for a nominal Mars atmospheric profile found in Section 4.4, mission analysis must also account for the effect of atmospheric uncertainties on the aerocapture corridor. MarsGRAM provides the minimum (\(-3\sigma\)), average, and maximum (\(+3\sigma\)) density values for the atmospheric profile. The effect of these density variations on the aerocapture corridor must be accounted for when selecting the approach trajectory and the nominal target EFPA for the aerocapture entry system. Table 3 shows the effect of mean density variation on the corridor bounds. As the atmosphere density increases from minimum to maximum, both the overshoot and undershoot limits become shallower. The target EFPA must be selected such that when delivery errors are considered, the vehicle enters the aerocapture corridor both in the case of minimum- and maximum-density atmosphere.  
Figure 8 shows the effect of atmospheric density variations on the Mars aerocapture entry corridor. The blue, green, and red rectangles indicate the aerocapture corridor for the minimum, average, and maximum density atmospheric profiles. The solid blue line indicates the overshoot limit for the minimum density atmosphere. If the atmosphere density is minimum and the vehicle enters any shallower than this EFPA, there is not enough aerodynamic drag and the vehicle will overshoot its target orbit or may fail to be captured. The solid red line indicates the undershoot limit for the maximum density atmosphere. If the density is at a maximum and the vehicle enters any steeper, it will  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Min. Density** & **Average** & **Max. Density** \\ \hline Overshoot, deg. & \(-9.035\) & \(-8.839\) & \(-8.651\) \\ Undershoot, deg. & \(-10.082\) & \(-9.992\) & \(-9.903\) \\ TCW, deg. & \(1.014\) & \(1.018\) & \(1.022\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Effect of density variations on Mars aerocapture corridor.  
Figure 7: Nominal Mars small satellite aerocapture trajectory for a vehicle with \(\beta_{1}=20\) kg/m\({}^{2}\), \(\beta_{2}/\beta_{1}=7.5\), \(R_{N}=0.235\) m entering at atm. relative EFPA \(=-9.25\) deg. Target apoapsis \(=2000\) km.  
undershoot the apoopsis even with the high ballistic configuration throughout the flight. However, jettisoning the drag skirt immediately after entry will subject the vehicle to a substantially higher peak heat rate and must be avoided. Thus, the target EFPA is selected to be the mid-point of the shallow half of the available corridor across the three rectangles, which ensures the vehicle enters the corridor both in the case of minimum and maximum density atmosphere. The selected target EFPA = \(-\)9.25 deg. is indicated by the black dash-dot line, along with the assumed \(\pm\)0.2 deg. 3\(\sigma\) delivery uncertainty indicated by the dotted lines.
# Beacon-Based Hybrid Routing Protocol for Large-Scale Unmanned Vehicle Ad Hoc Network
## 2 Beacon-Based Multi-Agent Network Hybrid Routing Algorithm  
In a beacon-based hybrid routing protocol for a large-scale unmanned vehicle ad hoc network (LUVAN), the beacon node was the control center of the whole network; other nodes in the network took the pulse message sent by the beacon node as the time origin of the routing operation and established the optimal path tree to the beacon node. According to the optimal path tree, path discovery and maintenance and data transmission services were then completed.
## 2 Materials and Methods
### Analytical Methods  
For each _off-line_ reference analysis time point, two aliquots of 10 mL were sampled in preweighed 15-mL polypropylene test tubes. The samples were centrifuged for 15 min at 6000\(\times\)_g_ and pellets were washed either with a mixture of 5 mL cold deionized (DI) water and 2 mL cold hexane or with 7 mL cold DI water to remove residual lipids. The washed pellets were resuspended in 2-4 mL ice cold DI water, frozen at -80 \({}^{\circ}\)C, and dried for 24 h by lyophilization (Gamma 1-20, Martin Christ Gefriertrocknungsanlagen GmbH, Germany). Then the CDW was determined by weighing the test tubes.  
The PHB content was determined _off-line_ by high-performance liquid chromatography with a diode array detector (HPLC-DAD 1200 series, Agilent Technologies, USA). The method was adapted from [PERSON] et al. [32]. Pure PHB (Sigma-Aldrich Corporation, USA) or 8-15 mg freeze-dried cells were depolymerized by boiling samples with 1 mL concentrated H\({}_{2}\)SO\({}_{4}\) to yield crotonic acid. Dilution series of the depolymerized PHB were prepared to yield standards in the range of 0.1-10 mg\(\cdot\)mL\({}^{-1}\). Samples were diluted with 4 mL 5 mM H\({}_{2}\)SO\({}_{4}\), filtered through a 0.2 \(\upmu\)m cellulose acetate syringe filter, and subsequently 100 \(\upmu\)L were transferred to a HPLC vial containing 900 \(\upmu\)L of 5 mM H\({}_{2}\)SO\({}_{4}\). HPLC analysis was performed with an injection volume of 20 \(\upmu\)L using 5 mM H\({}_{2}\)SO\({}_{4}\) as an eluent with an isocratic flow rate of 0.4 mL\(\cdot\)min\({}^{-1}\) for 60 min on a NUCLEOGEL(r) ION 300 OA column  
Figure 1: Schematic experimental set-up of a photon density wave (PDW) spectrometer [13].  
(Macherey-Nagel, Germany). Crotonic acid was detected at 210 nm. RCDW was determined by subtracting the PHB from the CDW concentration.  
The nitrogen content was indirectly determined _off-line_ by measuring ammonia, resulting from urea cleavage, in the supernatant using a pipetting robot (Cedex Bio HT Analyzer, Roche Diagnostics International AG, Switzerland) with the NH3 Bio HT test kit (Roche Diagnostics International AG, Switzerland).  
During the fed-batch cultivation the _in-line_ PDW spectroscopy signals were used for an _on-line_ determination of the CDW, RCDW, and PHB content (see Section 3.4).
# Comparative Study of Different Approaches for Islanding Detection of Distributed Generation Systems
## 2 Literature Review
### Sandia Voltage Shift:
#### 2.2.3 Hybrid Method  
With the passive method being cheap and relatively unreliable and the active method being reliable but with the trade-off in power quality, both of the discussed methods have their own set of advantages and disadvantages. The hybrid method aims to utilize the perks of both methods discussed above while suppressing their disadvantages [60, 61]. The passive method is used to monitor the overall stability and occurrence of islanding, and, in the event of islanding, the active method is employed to confirm the islanding event [3]. Some techniques include:  
1. Technique Based on Voltage Unbalance and High-Frequency Impedance [3, 62]:  
This technique continuously monitors the voltage unbalance (passive method) as follows:  
\[\text{Voltage Unbalance}=(\text{Positive Cycle Voltage})/(\text{Negative Cycle Voltage}). \tag{10}\]  
Voltage unbalance can occur in cases like voltage spikes, switching actions and islanding. Thus, to confirm the occurrence of islanding, positive feedback (active method) is used, where the high-frequency voltage is injected into the DG control loop. Furthermore, the voltage and current at the point of common coupling will be measured to estimate the impedance at the injected frequency. In addition, if not within a threshold, the islanding event is then detected. The selected of high frequency should not be equal to any harmonics of any generating the inverter. Furthermore, to make the signal filtering easier, there must be a spectral separation between the injected frequency and the harmonics of the fundamental frequency. Any frequency meeting these two criteria can be selected. For a 60 Hz system, 350 Hz can be selected as it lies between the fifth (300 Hz) and seventh (420 Hz) harmonics.  
2. Technique Based on Voltage and Reactive Power Shift [3]:  
In this method, the voltage variation of the system is measured over time and a mean is calculated (passive method) and, when this value crosses a threshold, the Adaptive Reactive Power Shift (ARPS) algorithm is used as an active method. This method varies the output reactive power of the DG system to detect islanding and the results have shown this technique to be effective, while maintaining less amount of disturbances in the inverter's grid-connected system operation.  
3. Positive Feedback Frequency Shift (PFFS) and Reactive Power Variation (RPV) Method [62]:  
This method continuously monitors the system using PFFS and, when the frequency deviates by 0.1 Hz, RPV is triggered to detect the islanding. Being capable of synchronizing converters with each other, this method is thus capable of detecting islanding when multiple converters are paralleled in distributed generation.
## 3 System Model
### Communication Range Judgment Model
#### 3.2.3 Candidate Nodes  
If node \(j\) is a candidate node of node \(i\), node \(j\) must meet the following conditions:  
* Node \(j\) is a neighbor node of \(i\). That is, the distance from \(j\) to \(i\) is less than the maximum transmission distance.
* Node \(j\) is a forward transmission node of node \(i\).
* Node \(j\) can receive packets, and it is not a void node, i.e., \(Flag_{j}=1,VFlag_{j}=0\).  
In summary, the conditions that candidate nodes must fulfill:  
\[\left\{\begin{aligned} d_{ij}&=\sqrt{\left(x_{j}-x_{ i}\right)^{2}+\left(y_{j}-y_{i}\right)^{2}}<d_{0}\\ &\left(x_{j}-x\right)^{2}+\left(y_{j}-y\right)^{2}<d_{\text{iD}} \end{aligned}\right. \tag{13}\]  
**Definition 3**.: _The candidate nodes set for node \(i\) is:_  
\[CN(i)=\left\{j|j\in FN(i),Flag_{j}=1,VFlag_{j}=0\right\} \tag{14}\]
# Reliability Analysis of Multi-Process Machining Based on Information Entropy
## 2 Introduction of Information Entropy and Mutual Information  
### Information Entropy  
Information entropy is a concept in information theory, and it was originally used in the field of communication. Information entropy measures the average uncertainty of discrete random variables (the following discrete random variables are referred to as random variables). When the base is 2, the unit of information entropy is \"bit\". Information entropy is a function of the distribution of random variables, which does not depend on the specific value of random variables [21]. The more dispersed the probability distribution of a random variable, the higher the uncertainty of the random variable and the greater its information entropy.  
The reliability analysis model proposed in this paper utilizes information entropy to quantify the uncertainty of the quality of the process [22]. The lower the uncertainty of the machining quality of the process, the more reliable the process is in the statistical sense.