esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
demonstrated the design, analysis and manufacturing of composite (CFRP) struts both computationally and experimentally [10]. In the aviation industry today, the design of lightweight landing gear struts for a given aerial vehicle is one of the most challenging and highly demanding tasks. In this context, [PERSON] et al. described the importance of landing gear struts and a selection methodology for given aircraft in connection with participation in the 'Aero Design Series' competition organized by the Society of Automotive Engineers (SAE) [11]. Among the available landing gear systems, a fiber-reinforced composite material landing gear system was selected considering the design constraints and landing load conditions. [PERSON] et al. published work related to the design and structural analysis of a fixed composite strut for a lightweight aircraft [12]. Different cross-sectional shapes, including rectangular, circular and elliptical, were modeled in ANSYS ACP(r) and analyzed. The least total deformation, along with least equivalent stresses and aerodynamic drag, were considered for the selection of a landing gear strut shape configuration. Based on the results of this research, an elliptical cross-sectional shape was recommended due to it having the least stress distribution and deformation values. The application of fiber-reinforced composites in landing gear systems is very limited. Some of the landing gear assemblies, and associated components composed of fiber-reinforced composite materials, described in the literature are presented in Table 1.  
As with other design processes, the design of aircraft landing gear is a multi-step process that begins with the design requirements and constraints. According to these design requirements, a preliminary design is produced which is then further developed into a detailed design. After the detailed design stage, a prototype is fabricated and tested [13]. The design landing loads based on which the preliminary design is developed are also considered as key design constraints. For a UAV and other lightweight aircraft, the design must be according to USAR certification standards [17; 18]. The landing gear of an aircraft consists of multiple components. In the detailed design phase, the structure of all the landing gear components and their sizing is undertaken. In-depth design of the landing gear, including stress analysis, takes place during the detailed design phase. In the production planning phase, all the necessary steps and procedures for the fabrication are defined. The production planning phase finalizes the jigs, fixtures, fabrication procedures, tools, conditions and materials to produce the landing gear strut. After fabrication of the prototype, it undergoes various tests, including an actual drop test [19; 20]. [PERSON] et al. used the energy conservation principle to determine the height required for the drop test for any landing gear system [21].
# Multipath Mitigation for BOC Signals Based on Prompt-Assisted-Offset Correlator
## Abbreviations  
The following abbreviations are used in this manuscript:  
ACF Auto-Correlation Function  
BOC Binary Offset Carrier  
BPSK Binary Phase-Shift Keying  
DBT Dual BPSK Tracking  
DLL Delay Lock Loop  
EML Early-Minus-Late  
GNSS Global Navigation Satellite System  
LSB Lower Sideband  
MEE Multipath Error Envelope  
NC Narrow Correlation  
NCO Numerically Controlled Oscillator  
OC Offset Correlator  
PAOC Prompt-Assisted-Offset Correlator  
PLL Phase Lock Loop  
PSD Power Spectral Density  
RMSE Root-Mean-Square Error  
SNR Signal-to-Noise Ratio  
SPLL Subcarrier Phase Lock Loop  
USB Upper Sideband
###### Abstract  
This study applied high-resolution satellite data to address the siting and evaluation challenges for potential cage aquaculture areas of large yellow croaker in Zhejiang Province. A typical template of water environmental factors for large yellow croaker cage aquaculture was developed, incorporating various environmental parameters and considerations that were not fully addressed in previous studies. This paper established the Site Selection Method for Large Yellow Croaker potential aquaculture (SSM-LYC) based on the template. Site selection and grading evaluation of potential cage aquaculture areas were performed using SSM-LYC. The findings include the following: (1) Potential aquaculture sites for large yellow croaker include 11 areas with water depths of 15-60 m along the coast of Zhejiang Province from 27\({}^{\circ}\) to 31\({}^{\circ}\) north latitude, of which 7 are in water depths of less than 40 m, and 4 are in water depths of 40-60 m. (2) Assessment and scoring for potential aquaculture sites were performed, pinpointing 4 central locations of first-level aquaculture areas offering scientific evidence for the feasibility of deep-sea aquaculture of large yellow croaker along the Zhejiang coast. (3) The conclusions drawn from this research provide significant guidance for future aquaculture strategies and regional planning. Moreover, SSM-LYC can be applied to other coastal waters in the world.  
Keywords:large yellow croaker; environmental template; aquaculture site selection; remote sensing Article
# Area of Operation Planning for Free-Floating Car Sharing Systems
## 4 Results and Discussion
### Extending ELOOP's Current Area of Operation
#### 4.4.1 Approximating ELOOP's Current Area of Operation  
Since our optimization approach operates on the basis of sub-districts, but the ELOOP's current home zone also contains partial sub-districts along its boundary, we have to approximate the AoO before extending it. For this purpose, we define a parameterized set \(\mathcal{H}_{\theta}\) for any \(\theta\in[0,1]\) as containing all those sub-districts that both touch the current AoO and share at least \(\theta\) of its area with it. Choosing \(\theta=1\) results in an under-approximation of the current AoO (with a size of approximately \(0.64\cdot H\)) consisting only of those sub-districts that are entirely contained within it, whereas \(\theta=0\) results in an over-approximation (of size \(\approx 1.58\cdot H\)) containing any sub-district sharing even the slightest sliver with it.  
For our experiments, we chose \(\theta=0.5\), which results in an approximation that is visually quite close to the current AoO and includes approximately \(93.5\%\) of its original area. Figure 11 shows these approximations for the three previously mentioned values of \(\theta\).
# The Design of an Energy Harvesting Wireless Sensor Node for Tracking Pink Iguanas
## 6 Related Work
### Energy Management and Energy Harvesting  
The energy management architecture is a key component in the design of a wireless node that should work for a long time.  
When the node is battery-powered the only solution is to use techniques aimed at limiting power consumption. In this case, a simple but effective technique is to limit the working intervals (duty cycle) especially for the high consuming activities, i.e., GPS positioning, data transmission and wireless channel listening. Many energy preserving solutions for opportunistic network nodes have been investigated for a long time together with innovative methodologies aimed to find the optimum duty cycle for battery-equipped devices [15; 30]. More specifically, techniques such as erasure codes and probabilistic network coding have proven to maximize the probability of collecting data in networks where predictable and stable paths may never exist [31]. In this way the the number of contacts needed to collect the data is reduced [32].  
There are also many cases in which biological requirements calls for energy harvesting techniques. In these cases several solution can be adopted according to the type of harvesting technique selected. An extensive overview of is presented in [33] that compares sensor systems--architecture, energy sources and storage technologies. A recent overview on energy harvesting solutions is presented in [34]. They discuss how the various harvesting solutions are associated to the different operating scenarios. Authors also compare research and commercial solutions that also include an harvester component similar to the one we included in our design.
# Comparison of the Sentinel-3A and B SLSTR Tandem Phase Data Using Metrological Principles
## 5 Discussion
### Measurement Analysis
In particular, the data are showing that in a small but significant number of cases the GHRSST quality level can change between the SLSTR-A and SLSTR-B observation after only a 30 s time gap. This then means that the concept of a GHRSST quality level is not necessarily fixed for a given location but may be more fluid. For example, from the analysis above, it seems that cloud proximity has a role to play in determining the observed quality level giving rise to more variability in assigning a given quality level near cloudy regions. Secondly, there is evidence for variations in quality level directly related to measurement uncertainty which then means that there is a small but real uncertainty in the quality level itself. This is the first time that any sort of analysis on the possible variable nature of the GHRSST quality level has been done and again is only possible with tandem data where two measurements of essentially the same location at close to the same time are taken. Lastly, the tandem data are providing information of the impact of variable amounts of detected cloud in the SST estimates. In particular, the tandem data are showing evidence of a small amounts of cold bias caused by cloud in the data within the same quality level. Therefore, for the first time, it should be possible to formulate a model of SST bias as a function of the cloud probability once the SLSTR-B SSTs are fully formulated. Again, this sort of work is only really possible using tandem data as the changes seen are related to changes in the local environment (e.g., clouds) rather than in changes in the underlying SST itself and so processes such as cloud contamination can be studied in a way not possible with other techniques.
## 3 Results and Discussion
Figure 7: Influence of the porosity parameter \(K\) on the streamline patterns for \(\alpha=0.4,\ \beta=0.4,\ \varepsilon=0.1,\ Q=1.5,\lambda_{1}=1,\)\(M=1,\ Re=0.1.\)  
Figure 8: Influence of the fluid parameter \(\lambda_{1}\) on the streamline patterns for \(\alpha=0.4,\ \beta=0.4,\ \varepsilon=0.1,\ Q=1.5,M=1,\ K=0.1,\)\(Re=0.1.\)  
Comparisons of the velocity profiles for both symplectic and antiplectic metachronal waves are shown in Figure 10a,b. It is noteworthy that the same behaviour is observed for both symplectic and antiplectic waves: if the length of the cilium increases, the amplitude of the velocity profiles becomes larger. However, the most important conclusion is that the horizontal velocity component (Figure 10a) is much higher for the antiplectic waves. Therefore, antiplectic waves are more efficient to transport a second-grade fluid in the presence of a magnetic field than symplectic waves, which confirms former results in the literature for a Newtonian fluid obtained by more complex numerical simulations [13]. The larger magnitude of the vertical velocity profiles for the antiplectic waves (Figure 10b) is in line with the blowing-suction mechanism highlighted by [PERSON] et al. [34] using a lattice Boltzmann solver. The authors showed that this mechanism is responsible for the enhanced transport capability of antiplectic waves over symplectic ones. Figure 11 displays the skin friction coefficient as a function of the second-grade parameter \(\lambda_{1}\) for different values of the cilia length \(\varepsilon\). It shows the linear relation between the skin friction coefficient, which is a measure of the fluid resistance on the wall, and \(\lambda_{1}\) through the straight line when \(\varepsilon=0.1\), which becomes nonlinear when \(\varepsilon=0.2\) and \(0.3\). Numerical results of velocity, pressure, stream function and shear stress are calculated by the Homotopy perturbation method (HPM) and shown through graphs. The distributions of velocity, pressure and shear stress are smooth. They also satisfy the boundary conditions defined for the velocity profile with decreasing coefficients of higher-order solutions, which guarantees that the series solution obtained with the HPM converges in the region \(-h<y<h\) and provides the best results of the present study. Figure 10: Profiles of the (**a**) horizontal and (**b**) vertical velocity components for different values of \(\varepsilon\) and symplectic and antiplectic metachronal waves. Results obtained for \(\alpha=0.4,\ \beta=0.4,\ Re=0.1\), \(Q=1.5,M=1,\ K=0.1,\ \lambda_{1}=0.1\).
# Energy Trading with Electric Vehicles in Smart Campus Parking Lots
## 4 Simulation Results
### Standalone Parking Lot: Electric Vehicles as Sellers and the PLCC as a Buyer  
All vehicles in case (1) were considered to be selling vehicles. The energy bought from SEVs could be used to support the load demand of engineering building 2-7 or used to charge other electric vehicles. Considering a local demand of 50 kWh, the PLCC selected a set of electric vehicles to satisfy the local demand, as given in Algorithm 1. Tables 6 and 7 compare the proposed energy trading mechanism using the Knapsack Algorithm (KPA) with the first-come-first-serve (FCFS) scheme. In the FCFS scheme, the PLCC selects electric vehicles that request selling first. In KPA, the PLCC sorts the revenue in an ascending order in order to minimize the cost of buying energy from SEVs. The PLCC selects a set of electric vehicles with lower selling prices to trade with. The simulation results of Figure 7 show that the proposed energy trading mechanism based on KPA outperforms the FCFS scheme with a reduction in costs of about 47%. For a single parking lot with 10 electric vehicles as sellers, the average parking lot costs would be about 4334 KRW and 2508 KRW for FCFS and KPA, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{6}{c}{**Seller Vehicles**} & \multicolumn{6}{c}{**FCFS**} \\ \cline{2-7}  & **Amount** & **Price** & & & & & \\
**(kWh)** & **(KRW/kWh)** & \(\mathbf{x}_{i}^{t}\) & **Amount** & **EV’s Revenue** & **OPEX** & **COST\({}_{\text{PLCC}}\)** \\
**(kRW)** & **(KRW)** & **(KRW)** & **(KRW)** & **(KRW)** & **(KRW)** & **(KRW)** \\ \hline SEV1 & 12 & 81 & 1 & 12 & 972 & 516 & 456 \\ SEV2 & 12 & 60 & 1 & 12 & 720 & 516 & 204 \\ SEV3 & 9 & 171 & 1 & 9 & 1539 & 387 & 1152 \\ SEV4 & 8 & 146 & 1 & 8 & 1168 & 344 & 824 \\ SEV5 & 11 & 189 & 0.81 & 9 & 1701 & 387 & 1314 \\ SEV6 & 9 & 59 & 0 & 0 & 0 & 0 & 0 \\ SEV7 & 12 & 166 & 0 & 0 & 0 & 0 & 0 \\ SEV8 & 13.5 & 190 & 0 & 0 & 0 & 0 & 0 \\ SEV9 & 8 & 85 & 0 & 0 & 0 & 0 & 0 \\ SEV10 & 12 & 193 & 0 & 0 & 0 & 0 & 0 \\ \hline Total & 106.5 & & & 50 & 6100 & 2150 & 3950 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Case (1) with the first-come-first-serve (FCFS) scheme for electric vehicles as sellers and the PLCC as a buyer.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Number of Parking Lots** & **Case** & **Demand** \\ \hline \multirow{2}{*}{Scenario (1)} & Single PL & Case (1): SEVs & \\  & 10 Charging Stations & Case (2): BEVs & \\ \hline \multirow{2}{*}{Scenario (2)} & 2-PLs & Case (3): SEVs & \\  & 20 Charging Stations & Case (4): BEVs & \\ \hline \multirow{2}{*}{Scenario (3)} & 4-PLs & Case (5): SEVs & \\  & 40 Charging Stations & Case (6): BEVs & \\ \hline \multirow{2}{*}{Scenario (4)} & 8-PLs & Case (7): SEVs & \\  & 80 Charging Stations & Case (8): BEVs & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Simulation scenarios.
## Materials and Methods
### Antibacterial and Antifungal Activity Assay  
Antimicrobial activity and minimum inhibitory concentration for the proteins from seven different snails were assessed against 6 human pathogenic bacterial strains and 4 pathogenic fungal strains. The pathogenic bacteria were _Staphylococcus aureus_, _Microoccus luteus_, _Pseudomonas aeruginosa_, _Proteus vulgaris_, _Serratia marcescens_ and _Hafnia alvei_; the pathogenic fungal strains were _Candida albicans_, _Aspergillus fumigatus_, _Penicillium chrysogenum_ and _Mucor racemosus_. These pathogenic cultures were obtained from the King Institute of Preventive Medicine, Guindy, Chennai, Tamil Nadu, India, and were periodically subcultured and maintained in a respective medium at 4 \({}^{\circ}\)C. Active cultures for experiments were prepared by transferring a loop full of culture from the stock cultures to test tubes of Luria Bertani (LB) broth for bacteria, and Potato Dextrose (PD) broth for fungi, and were incubated for 24 h at 37 \({}^{\circ}\)C. Antibacterial and antifungal activity of the proteins from the seven different snails was determined by the agar well diffusion method [19]. The nutrient agar plates were swabed with the respective 24 h broth culture of organisms and kept for 15 min in a laminar chamber for absorption of cultures. Wells were made in agar plates using a sterile cork borer of 5 mm. The extracted protein of various concentrations, such as 25, 50, and 100 \(\upmu\)g/mL, were prepared and 20 \(\upmu\)L of respective concentrations were added to each well. Distilled water was used as the negative control and chloramphenicol (100 \(\upmu\)g/mL) used as positive control for bacteria and Amphotericin-B (100 \(\upmu\)g/mL) used as positive control for fungi. The plates were incubated at 37 \({}^{\circ}\)C for 24 h. The diameters of the zone of inhibition were measured in millimeter by using the antibiotic zone measuring scale.  
The minimum inhibitory concentration (MIC) was determined by the micro dilution method [20] by using liquid nutrient broth with different concentrations of proteins from seven different snails. A total of 3.6 mL of sterilized nutrient broth was poured into a sterilized test tube. Different concentrations of protein, such as 3.25, 6.5, 12.2, 25, 50 and 100 \(\upmu\)g/mL, were added to the nutrient broth, to all the test tubes, and 0.4 mL of bacterial suspension and fungal suspension was added separately; the tubes were then incubated at 37 \({}^{\circ}\)C overnight. In the negative control, the nutrient media was inoculated with microbes without a protein sample (3.6 mL nutrient broth added with 0.4 mL bacteria or fungal culture) and the positive control contains antibiotics by replacing the protein (3.6 mL nutrient broth + 0.4 mL bacterial culture + chloramphenicol or Amphotericin-B 25 \(\upmu\)g/mL). The growth was observed and the optical density was read at 595 nm spectrophotometrically for the development of turbidity. MIC was determined by the lowest concentration of sample that inhibited the development of turbidity.
# Advances in Biomaterials for Breast Reconstruction
### Breast Implant Associated Anaplastic Large Cell Lymphoma (BIA-ALCL) Issues  
As the evolution of the silicone gel implant proceeded, breast implant-associated anaplastic large cell lymphoma (BIA-ALCL) became a significant public concern of women with textured implants. The rising incidence of BIA-ALCL has increased awareness of the use of textured implants. The FDA has received approximately 359 adverse reports of ALCL in women with breast implants, which were reported to the Manufacturer and User Facility Device Experience (MAUDE) database as of February 2017. According to a previous study, BIA-ALCL only occurs in association with textured implants [60]. According to the study carried out by the US FDA, the disease risk of BIA-ALCL was 1:2943 with the Biocell textured implant [61]. The Australian Therapeutic Goods Administration reported that the disease risks were 1:3705 for the Biocell implant, 1:3894 for the Silimed polyurethane implant, and 1:60,631 for the Mentor Siltex implant [62].  
In 2019, the FDA issued a recall of the Allergan Biocell textured breast implant, which is classified as a macrotextured implant [63; 64]. However, the FDA did not recommend removing or replacing textured implants in asymptomatic patients [65]. Instead, all patients with breast implants were notified of the risk for developing BIA-ALCL, and patients with textured implants were counseled about risk management options. Two thirds of BIA-ALCL patients present with a malignant effusion between the fibrous capsule and the implant. The other one third of patients present with a palpable mass, which may indicate a more aggressive clinical course [66]. The National Comprehensive Cancer Network (NCCN) has suggested several guidelines for management [67].  
If BIA-ALCL is localized to the capsule only, it may be treated with surgery; however, in advanced cases, including lymph node involvement, surgery and chemotherapy or radiotherapy are indicated for treatment. To reduce the risk of developing BIA-ALCL, removing textured devices with subsequent closure, exchange for a smooth implant, or conversion to autologous reconstruction could be options [68]. Consequently, in clinics there is a tendency worldwide for surgeons and patients to alter their implant choices from textured to smooth-surface implants [69].
# NDN Content Store and Caching Policies: Performance Evaluation
## 3 In-Network Caching  
Caching in NDN is similar to the buffer's caching concept in TCP/IP. However, since this architecture is based on end-to-end communication and the identification of content is associated with its location (address), packets stored in the buffer are useless as soon as they are sent to their destination. NDN caching is more granular, ubiquitous and transparent to applications, unlike what happens, for instance, with _content delivery networks_ (CDN) [25] and web caching.  
Caching benefits the network in such aspects as (a) the higher probability of sharing content, saving bandwidth and decreasing the requesting delay; (b) the dissociation of the content from its producers, improving mobility for both consumers and producers; (c) the reduction in latency in content dissemination; and (d) the elimination of a single point of failure.  
Although it is desirable to keep the content cached for a long period to allow continuous sharing, the size of the CS is a limiting factor. Several studies have been performed in order to determine the best way to extend this period, or, when the replacement is unavoidable, ensure that it does not negatively affect the whole network's performance.  
Caching performance in CCN depends on factors such as (a) the content routing; (b) the decision for content placement (caching strategy); and (c) the decision for content replacement (caching policy) [3; 4]. It is usually measured based on metrics such as (a) the cache hit ratio; (b) the network traffic; (c) the average number of upstream hops; and (d) the delay for data delivery.  
Content forwarding is an intensively investigated area in NDN. Some reviews in this area can be found in [26; 27]. Some routing studies with particular attention on caching issues are presented in [4].