esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermoelectric Generator-Powered Long-Range Wireless Real-Time Steam Leak Detection in Steam Traps
## 2 Steam Trap Study
### Steam Leak Cost Estimation  
One of the major energy inefficiencies in manufacturing industries that use steam in their processes is the steam leaks in boilers, pipelines, and mainly steam traps [8].  
Estimation of Steam Losses:  
A steam trap leaking 0.5 kg of steam per minute can result in the following:  
1. Steam Loss:  
Per hour: 0.5 kg/min \(\times\) 60 min = 30 kg of steam/hour.  
Per day: 30 kg/h \(\times\) 24 h = 720 kg of steam/day.  
Per year: 720 kg/day \(\times\) 365 days = 262,800 kg of steam/year.  
Figure 2: Steam circuit composed of the boiler, steam pipeline, and steam trap.  
Energy Costs: The cost of producing steam generally ranges between EUR 3 and 10 per ton of steam (1000 kg), depending on fuel type and boiler efficiency. Assuming an average cost of EUR 5 per ton, 262.8 tons \(\times\) EUR 5/ ton = EUR 1314 per year for each leaking steam trap. Therefore, in a mid-sized oil refinery with 10,000 steam traps with a poor maintenance process, assuming an annual failing estimation of 25%, the overall energy loss reaches EUR 3.3 M yearly.  
This issue has several additional repercussions [9]. Steam losses are exacerbated in systems with higher pressure or larger steam traps, potentially doubling or tripling the economic impact. In industrial plants with dozens or hundreds of steam traps, these losses can accumulate rapidly, amounting to tens of thousands of euros annually if proper inspection and maintenance are neglected [10]. Furthermore, boilers must compensate for lost steam, increasing fuel consumption and contributing to higher greenhouse gas emissions, such as CO\({}_{2}\). Steam losses also reduce the overall efficiency of the system, negatively impacting industrial processes that depend on specific steam pressures and temperatures [11]. These energy and cost losses are highly influenced by the steam pressure and the orifice size of the steam trap, as demonstrated by [PERSON]'s equation in Figure 3.
# Biosynthesis of Silver Nanoparticles Using _Tabernaemontana ventricosa_ Extracts
## 2 Materials and Methods
### Characterization of AgNPs
#### 2.6.2 High-Resolution Transmission Electron Microscopy (HRTEM)  
High-resolution transmission electron microscopy was used to determine the morphology and size of the synthesized AgNPs. Previously sonicated samples (for even distribution of AgNPs) were used for analyses. Carbon-coated (Quorum Q150 TE, Laughton, UK) form-var grids (200 mesh) were dipped into each solution and placed under a mercury lamp to ensure sample evaporation. The samples were viewed using the HRTEM JEM 2100 (JOEL, Tokyo, Japan), equipped with Gatan software (version 3.5), at a voltage of 200 kV. The morphology and size of the AgNPs were determined using ImageJ software for analyses (Java 1.8.0) (\(n=5\)).
## Abbreviations  
The following abbreviations and symbols are used in this manuscript:  
Acronyms  
IKAROS Interplanetary Kite-craft Accelerated by Radiation Of the Sun  
JPL Jet Propulsion Laboratory  
NASA National Aeronautics and Space Administration  
NEA Scout Near-Earth Asteroid Scout  
PMP Pontryagin maximum principle  
RSS reflective solar sail  
sailcraft  
STEREO Solar TErrestrial RElations Observatory  
Symbols  
\(a\) propulsive acceleration vector [mm/s\({}^{2}\)]  
\(a_{c}\) sailcraft's characteristic acceleration [mm/s\({}^{2}\)]  
\(a_{0}\) reference elliptical orbit's semimajor axis [AU]  
\(e_{0}\) reference elliptical orbit's eccentricity  
\(\mathcal{H}\) dimensionless Hamiltonian function  
\(J\) dimensionless performance index to be maximized  
\(m\) positive integer  
\(p_{0}\) reference elliptical orbit's semilatus rectum [AU]  
\(\dot{p}\) radial unit vector  
\(r\) Sun-sailcraft distance [AU]  
\(r_{\oplus}\) reference distance [1 AU]  
\(\mathcal{T}\) heliocentric polar reference frame  
\(\dot{\mathbf{t}}\) transverse unit vector  
\(t\) time [days]  
\(v\) spacecraft heliocentric velocity vector [km/s]  
\(v_{r}\) radial component of \(v\) [km/s]  
\(v_{t}\) transverse component of \(v\) [km/s]  
\(\lambda_{i}\) dimensionless variable adjoint to \(i\)-th sailcraft's state  
\(\Delta t\) flight time [days]  
\(\Delta\dot{\varphi}\) phasing angle [deg]  
\(\mu_{\odot}\) Sun's gravitational parameter [km\({}^{3}\)/s\({}^{2}\)]  
\(\dot{\varphi}\) angle between the Sun-sailcraft line and the Sun-virtual point line [deg]  
\(\theta\) polar angle [deg]  
\(\theta_{pf}\) polar angle of the virtual point at the final time [deg]  
\(\
u\) true anomaly along the reference elliptical orbit [deg]  
\(\tau\) dimensionless control term  
Subscripts  
0 initial, reference elliptical orbit  
\(f\) final, end of the phasing maneuver  
Superscripts  
\({}^{\prime}\) derivative with respect to dimensionless time \(\widetilde{t}\)  
\(\sim\) dimensionless value
## 4 System Model Implementation
### Outdoor Temperature  
Electricity load is affected by different variables like outdoor temperature and electricity prices. There is a strong relationship between setting the set-point and outdoor temperature that directly determine the energy consumption. The importance of outdoor temperature becomes more valuable when the temperature fluctuates strongly in many countries like Canada. This situation results in making the HVAC a highly variable load among all the appliances. Generally, when the outdoor temperature is very cold or hot, the electricity demand is very high. As this system is an extension of an adaptive fuzzy learning model [17] and a worldwide adaptive thermostat model [28], two extreme weather details have been considered.  
Two cities considered for analyzing the cooling and heating power consumption in any residential building are the Wadi Halfa in Sudan and Yakutsk from Russia. Wadi Halfa is one of the hottest cities in the world and Yakutsk is the coldest city in the world. After that, one of the hottest and coldest days from the respective cities is taken into account. Wadi Halfa experiences the highest temperature during the month of June, whereas Yakutsk is coldest during January. The outdoor temperature for Wadi Halfa and Yakutsk was taken from a weather forecasting website [56; 57], respectively. The outdoor temperature is considered on an hourly basis. Figure 4 shows the membership functions of the outdoor temperature.
# Performance and Mechanism of Asphalt Modified by Butadiene-Rubber
## 4 Results and Discussion
### Low-Temperature Performance  
Figure 5 shows the creep curve slope (m-value) and creep stiffness of BRA and BRA-SBR modified asphalt at \(-6\)\({}^{\circ}\)C. Previous research showed that the addition of BRA could significantly weaken the low-temperature performance of asphalt binders. The asphaltene concentration of asphalt binder increased due to the high asphaltene content of BRA [19]. Hardening effect could adversely affect the low-temperature properties of asphalt. The experimental results of Figure 5 showed that the creep stiffness of the two compound modified asphalts were lower compared with the BRA modified asphalt, and the m-value upper. This meant that when the temperature dropped to a lower value, adding two types of SBR into BRA modified asphalt made it difficult to accumulate tensile stress due to temperature shrinkage. The possibility of low-temperature cracking was then reduced. Unlike the BRA, two types of SBR could soften the asphalt binder, and improve the low-temperature performance. The network structure of SBR could overcome drawbacks of the low-temperature performance of asphalt binders with BRA.  
Figure 5: Creep stiffness and m-value of specimens at \(-6\)\({}^{\circ}\)C. (**a**) Creep modulus; (**b**) m-value.  
On the other hand, as presented in Figure 5, the two kinds of compound modified asphalts presented the more pronounced decline in creep stiffness and an increase in m-value in the wake of the increase of SBR content. That is to say that the higher the addition contents of SBR, the more noticeable the improvement effect on anti-crack capacity at low temperatures. However, there are still some differences in the improvement extent in low-temperature performance. The compound modification effect on low-temperature cracking was superior because of the existence of BRA and SBR B at the same time. When the content of SBR B was 2%, 4%, 6%, and 8%, the low-temperature flexibility of compound modified asphalt was 1.09, 1.11, 1.29, and 1.36 times of 15% BRA modified asphalt at \(-6\)\({}^{\circ}\)C, respectively. Under the same low-temperature condition, the addition of SBR B resulted in a more significant improvement in stress relaxation and flexibility for the compound asphalt than the introduction of SBR A.
# Joint Location-Allocation Model for Multi-Level Maintenance Service Network in Agriculture
## 4 Benders Decomposition Approach
### The Framework of Benders Decomposition  
Thus, the Benders decomposition approach is applied to obtain the location-allocation results in a multi-level maintenance service network. As shown in Figure 2, the detailed information of the developed solution algorithm is described as follows.  
Step 1. Input initial data.  
Step 2. \(\mathbf{MP}\) is solved to obtain a feasible solution (\(\overline{x},\overline{y},\overline{z},\overline{u}\)), and LB (Lower Bounded) is updated.  
Step 3. The obtained service station location results \(\overline{y}\) and service assignments plan \(\overline{z}\) are introduced to \(\mathbf{DSP}_{1}\). If the \(\mathbf{DSP}_{1}\) is infeasible, there are no results for the original problem. If the \(\mathbf{DSP}_{1}\) is unbounded, the generated Benders feasibility cuts (26) are added in \(\mathbf{MP}\), and a feasible solution is found to update UB (Upper Bounded). And if the \(\mathbf{DSP}_{1}\) is feasible and bounded, the generated optimality cuts (27) are added in \(\mathbf{MP}\), and UB is updated.  
Step 4. The obtained service station location results \(\overline{y}\) and service assignments plan \(\overline{z}\) are introduced to \(\mathbf{SP}_{2}\). If it returns a feasible solution, service units served by service stations are contiguous. Otherwise, the generated Benders cuts (28) are added in \(\mathbf{MP}\).  
Step 5. Calculate the difference between UB and LB, and determine if \(\mathbf{SP}_{2}\) is feasible. If \(\mathbf{SP}_{2}\) is feasible and the values of UB and LB are equal, the optimal results of MLFLAPs are obtained. Otherwise, repeat Step 2 to Step 4.
# A Dynamic Service Reconfiguration Method for Satellite-Terrestrial Integrated Networks
## 3 Problem Descriptions  
### Problem Statement of Service Migration in STINs  
Figure 1 shows the service migration and reconfiguration process of STINs. The basic architecture of a STIN is a three-layer network structure composed of a satellite network, a high-altitude platform (HAP), and a terrestrial network, as well as a two-layer control structure composed of a ground management control center (GMCC) and GEO satellite controller. Assuming that LEO satellite edge nodes and ground edge nodes have certain computing and storage resources, they can carry a variety of VNFs according to the deployment strategy, and can provide users with high-speed network access, edge computing service, data relay, and backhaul capabilities. At the same time, both UAV nodes and satellite nodes have communication relay and backhaul capabilities. Due to the limitations of energy and load, UAV nodes are not considered as edge nodes in this paper, but only work as data relay and forwarding functions. The satellite nodes in the satellite network layer are connected with each other by directional wireless communication links and can afford high-speed data transmission.  
The upper part of Figure 1 shows the basic process of a service request made by a STIN user. At present, the object of service provisioning is mobile vehicle end users. Vehicle users drive from core urban areas to remote areas. At present, the service function chains (SFCs) required by the vehicle users are sequential virtual network functions (VNFs), as an example, VNF-1, VNF-2, and VNF-3.  
Before the vehicle user moves, there is a nearby access point of a ground edge node N3, and it can provide sufficient edge computing capability and service support. The data flow initiates from the source node N3 and reaches the destination node N4 through the service function path (SPF) of the SPF1 (N3-N1-N2), while the VNF-1, VNF-2, and VNF-3 are deployed at nodes N3, N1, and N2.  
After the vehicle user moves to the remote area, it loses the access point and service support from the ground edge node. The management and control plane run the service migration and reconfiguration algorithm to obtain the optimal migration location and migration path according to the real-time network states and vehicle user service request. For example, VNF-1 on ground edge node N3 is migrated to satellite edge node N9, VNF-2 on N1 is migrated to satellite edge node N7, while the migration path is the shortest path. Furthermore, the data flow of the migrated user's vehicle reaches the destination node N4 through the new path SFP2 (N9-N7-N2), so as to satisfy the requirement of vehicle users and maintains the continuity and reliability of communication and service.  
As mentioned above, the key contribution of this paper is to find the appropriate migration location for VNF through service migration and reconfiguration algorithms, and solve the optimal migration strategy with specific objectives and constraints of the STIN system.
# Fundus Image Registration Technique Based on Local Feature of Retinal Vessels
## 3 Methodology
### Stage 2: Feature Extraction
#### 3.2.1 Feature Detection Module
Pixels with a dark green dot have higher intensity values than pixels with a pink dot. * STEP 3(b): Outer ring test A circumference of 16 pixels surrounding an extremum that passes the inner ring test forms the outer ring pixels (\(b_{l}\mid l\in[1,\dots,\ 16]\)) as shown in Figure 4a. These pixels are divided into groups of _low_, _middle_ and _high_ as defined below: Group low (red dot) : \(I_{b_{l}}<\beta-\epsilon\) Group middle (purple dot) : \(\beta-\epsilon\leq I_{b_{l}}\leq\beta+\epsilon\) Group high (green dot) : \(I_{b_{l}}>\beta+\epsilon\) where, \(I_{b_{l}}\) is the intensity of the outer ring pixels and \(\epsilon\) is the offset. The offset \(\epsilon\) is set to 0.0010 as the intensity value of the pixels is in the range of [0, 1][14]. Then, the extrema are tested for the outer ring patterns consisting of consecutive and alternating arcs from groups _low_ and _high_. The length of each arc can be between 2 to 8 pixels. These arcs can also be separated by pixels from group _middle_ up to two pixels. Examples of the outer ring patterns are depicted in Figure 4b-e. The extrema that pass the outer ring test are the extrema found within the curvature structure, as shown in Figure 5. These extrema are assigned as candidate feature points and included in the feature selection module.
# The Effect of Isomorphic Pitch Layouts on the Transfer of Musical Learning +
## 4 Discussion
### Limitations and Future Directions  
There were several limitations to this study. Firstly, the generalisability of these results is somewhat limited by the demographic factors of this study, such as the age and nationality of the participants. Secondly, given the nature of the way in which performances were scored, it was possible for performances in which, say, only two notes were played but played correctly, to achieve higher accuracy scores than performances where an attempt was made to play the entire sequence in question but many errors were made. Therefore, highly inaccurate scores were not necessarily a reliable representation of what a participant had learned. Thirdly, as already mentioned, it is hard to make general statements about high-level features such as isomorphism without having tested a greater variety of layouts. However, given the time-consuming nature of the experiments, there was a practical limitation as to the number of layouts that could be feasibly tested. Fourth, as in [15], only melody performances were tested; bearing in mind that melodies often comprise numerous major seconds, different results might be obtained when participants play bass lines (which often comprise numerous fourths and fifths) and harmonies (which often comprise numerous thirds and sixths as well as fourths and fifths). Lastly, while the instruments played by musicians were identified, they were not controlled for. Piano was the primary instrument played by a large proportion of musicians in this study, and it is possible that their results may be biased with respect to the broader population of musicians. As such, future research could aim to include equal numbers of participants playing a range of instruments so as to eliminate this potential bias.  
Future research could also investigate whether learnability is better facilitated by pitch layouts congruent with the SMARC effect, something that was not tested here because all our layouts were congruent. Furthermore, there are still open questions regarding the individual importance of various interval directions in pitch layouts, such as the directions of the pitch axis, the octave axis, and the major second axis, the impacts of which have, to date, been only partially differentiated [15].
## 3 Double-Clad Raman Gain Fiber
The fiber was perpendicularly cleaved in both ends. However, because of the higher fiber loss, the resulting 4\(\%\) feedback in the pump launch end of the cavity was no longer sufficient for lasing to occur. Although not available, a longer fiber could perhaps help to reduce the threshold, but the 0.39 km effective length is already 67\(\%\) of the limiting case of an infinitely long  
Figure 5: Reflected and transmitted pump power after VBG with 60-W diodes _vs._ power incident on the VBG. fiber, due to the relatively high propagation loss of the DCRF. Therefore, we reduced the cavity losses by using an external output coupler. This reflects 60\(\%\) of the incident power back towards the DCRF via a dichroic mirror that splits the pump and Stokes beam paths. The output power and spectrum from the FRL are shown in Figure 7. The laser threshold is 33 W, and we obtained a maximum output power of 6 W for 65 W of launched pump power. The laser slope efficiency is 19\(\%\). Theoretical power characteristics calculated as for the MM fiber are shown, too. We set the effective area equal to the pump waveguide area (1130 \(\upmu\)m\({}^{2}\)). This corresponds to a uniform pump distribution across the pump waveguide. We then used the pump and Stokes re-launch efficiencies as fitting parameters in order to obtain good agreement with experimental threshold and output power. The fitted re-launch efficiencies become 64\(\%\) at the high reflectivity (HR) end and 58\(\%\) at the out-coupling end. Note that although we would like the Stokes wave to be confined to the core, also the inner cladding guides and amplifies light. Because the germanium content of the inner cladding is slightly smaller, both the Raman gain and propagation loss can be expected to be a little lower, for overall similar laser threshold for cladding-modes. Thus, also cladding-modes can last with a similar threshold, which means that it is the re-launch losses into the inner cladding rather than into the core that matter, for the Stokes as well the pump. Although there are uncertainties in the parameters also with the DCRF, the relatively flat refractive-index profile makes the assumption of a uniform pump distribution well motivated, at least in the absence of SRS-induced depletion. While the use of fitting parameters makes the simulations less robust, we still consider the model adequate for quantitative predictions of the influence of parameters such as fiber length. Figure 6: Experimental setup of the CW FRL with DCRF. VBG: volume Bragg grating; DM: dichroic mirror; HR: high reflection mirror; DCRF: double clad Raman gain fiber. The measured output spectrum is free from higher order Stokes light even at the highest output power. Again, the measured linewidth was resolution-limited (2 nm). The output beam quality became \(M^{2}\) = 1.9 and the radiance 1.6 W\(\cdot\)sr\({}^{-1}\cdot\)um\({}^{-2}\) at maximum power (6 W). The brightness enhancement in the case of DCRF was about 9.6 times, compared to the incident pump beam.
## 3. Experimental Results and Discussion
### Bench Test  
A comprehensive experimental investigation was conducted based on the factor level table, and the results are summarized in Table 8.  
Through a systematic investigation with varying inlet airflow velocities and seeding quantities, the pressure loss values for the optimized seed delivery system with pressurized tubes were determined through experimental tests and calculations, demonstrating a similar trend to the simulated values. Comparative analysis with simulation results revealed that pressure loss decreases initially and then increases with increasing inlet airflow velocity and seeding quantity. Discrepancies between experimental and simulated pressure loss values were observed, primarily attributed to issues related to the experimental setup's sealing and the material properties of the pressurized tubes' inner walls. Further analysis indicated that the optimized pressurized tubes effectively reduced pressure losses, enhanced seed distribution uniformity, and, consequently, reduced the coefficient of variation in seed quantities across rows.