esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Construction of an LED-Based Excitation Source for Lock-In Thermography
## 2 Hardware Design
### LEDs and Optics  
Placing the LEDs on a 14 mm grid gives space for 36 LEDs inside the housing in a 6 \(\times\) 6 layout. For the LEDs, infrared LEDs of the type OSLON(r) Black SFH4715 AS from OSRAM Opto Semiconductors are used [24]. At a rated forward current of 1 A, this single-emitter LED emits an optical power of 1.53 W at a nominal wavelength of 850 nm. Using 36 LEDs, the resulting optical power amounts to 55 W. The spectral bandwidth (full width  
Figure 2: Cross-section of the infrared LED excitation source, view from the side with the front side facing left.  
half maximum) of the LEDs is approximately 30 nm. The half angle of its emission cone (without additional optics) is 40\({}^{\circ}\).  
The LED board is divided into four smaller PCBs, each containing 9 LEDs. Electrically, the LEDs are split into 3 strings, each consisting of 12 LEDs. The resulting LED forward current and forward voltage are important specifications for the LED driver circuit (Table 1). This partitioning of the LED strings allows voltage matching of the LED strings. By swapping individual sub-strings between the total strings, the differences in forward voltage between the strings are minimized.  
The OSLON(r) Black SFH4715 AS provides a good compromise between price per unit and optical output power. Alternatively, chip-on-board LEDs could be used as optical emitters. While they offer a higher optical output, typically their emission cones are wider, the use of additional optics is more difficult, and the thermal load is more demanding.  
A 14 mm spacing allows the use of conventional LED lenses to narrow the beam angle of the infrared radiation and to increase the radiant intensity. To allow an unobstructed field of view when a sample is investigated from the excitation side, the excitation source has to be at a distance of approximately 30 cm from the sample. Therefore, the beam has to be narrowed down and ideally have a focus point at this distance. For this, LED optics of the type LEDiL LISA2-RS-PIN are added, which have a beam angle (full width half maximum) of 19\({}^{\circ}\). In Figure 3a, the LED lenses are visible. For some applications, it is useful to concentrate the optical power on a surface area that is smaller than the cross-section of the excitation source. For these cases, a parabolic, 3D-printed reflector is designed, which is attachable to the excitation source, see Figure 3b. The inside of the reflector is covered with an reflective aluminium adhesive tape.
# Synthesis of Hydrogel-Based Microgels and Nanogels Toward Therapeutic and Biomedical Applications
## 4 Paradigm Shift: Bulk Hydrogels to Hydrogel Micro/Nanoparticles
### Hydrogel Nanoparticle Synthesis
#### Microfluidic Mixing  
Microfluidic mixing is another microfluidic approach that induces precipitation and self-assembly of nanoparticles. The fundamental principle of this method is based on a continuous mixing between two or more miscible phases that occurs within the microfluidic channel, whereas emulsification requires a combination of immiscible fluids. In a reduced size scale, mixing in a straight flow path can be confined to the diffusion at the interfaces of miscible fluids with minimal contribution of convective flow [239; 240; 241]. This process requires a continuous flow of fluids, and the degree of mixing of two fluids becomes independent of time, but it depends more on the distance from the junction where two fluids conjoin, which is called \"controlled diffusive mixing\" [182]. The degree of mixing can be controlled by the operational parameters including flow rates of each fluid, as well as design parameters such as microchannel dimensions, angle of the junction, etc. The precipitation of the solute can be controlled in this environment [242; 243]. If a certain compound was dissolved in one solvent but is insoluble to another one, the continuous mixing between two solvents comprises a dynamic environment for the solute where the solvent composition constantly changes as bulk flow progresses. Thereby, the solute experiences a gradual decrease in solubility as mixing occurs, inducing precipitationsof nanoparticles. Benefiting from the controllable manner of the diffusion process, the resulting particle size can be controlled by manipulating the same parameters.  
Controlled self-assembly of the block copolymer or amphiphilic molecules within the microfluidic mixer is another spontaneous process to synthesize nanoparticles such as micelles or nanogels driven by non-covalent interactions. Self-assembly is a unique thermodynamic phenomenon where small building blocks spontaneously construct ordered, higher-scale supramolecular structures, largely driven by enthalpic gain [182; 187; 243]. A common example of self-assembly is the formation of bubbles in the presence of surfactants, or vesicle formation from lipid molecules. The underlying principle is similar to the precipitation of nanoparticles, except the dynamic change in solvent composition within a mixer allows us to control the self-assembly process to a certain extent.  
To summarize the two approaches, nano-precipitation produces polymeric nanoparticles and is suitable for encapsulating small molecules that exhibit similar properties or high affinity to the polymer material. On the other hand, controlled self-assembly can form nanogels, micelles, or vesicles through non-covalent interactions, and often utilizes amphiphilic or block copolymers as basic building blocks. Both techniques require precise manipulation of fluid dynamic conditions to acquire the controllability of synthesis, which can be achieved by taking advantage of microfluidic systems.
# Network Diffusion Algorithms and Simulators in IoT and Space IoT: A Systematic Review
## 4 Analysis
### Analysis of Research Question 3 and 5: Network Diffusion Algorithms and Performance Evaluation
#### 4.2.6 Conclusions  
In IoT and Space IoT configurations, the choice of diffusion algorithm--whether it be epidemic, cascading, or threshold--impacts the performance of the network in different ways. Each model has its strengths and weaknesses depending on the network topology, size, and dynamicity. Evaluating these algorithms against key performance metrics such as propagation delay, network load, diffusion reachability, resource utilization, and robustness allows researchers to select the most suitable model for their specific IoT environment.  
Epidemic models offer high reachability but at the cost of increased resource utilization. Cascading models provide more efficient propagation but face challenges in computational complexity. Threshold models are ideal for coordinated diffusion in clustered networks but may be limited by their binary activation approach.  
By understanding the characteristics and performance of these algorithms, researchers can optimize network diffusion for specific IoT applications, ensuring efficient and scalable diffusion processes in diverse network configurations.  
Figure 3 shows the distribution of the algorithm categories used, highlighting the dominance of epidemic models and other algorithms.
# Underwater Electromagnetic Sensor Networks--Part I: Link Characterization +
## 3 Results
### Simulated Impact of Loop Antenna Radius  
In this section, the influence of increasing loop antenna radius is assessed. Theoretically, as antennas cover a larger area, more magnetic flux is captured and more voltage is induced to antennas. Therefore, by designing loop antennas with 22 and 40 cm radius, almost double the size, performance should improve considerably.  
In Figure 8, the effect of doubling the loop radius in the lower band studied (10-100 KHz) is shown. An attenuation reduction of about 15 dB at 3 m is obtained and even more improvement is achieved at longer distances. This nonlinear behaviour could be explained due to the effect of the seabed. For the upper band (Figure 9), there is also an improvement, but not as dramatic as for the lower band, observing a constant gain of 7 dB approximately.  
To sum up, bigger loop radius enables communications over longer distances and 40 cm was found to be a good compromise between distance requirements and network node size. As attenuation in 10-100 KHz band is lower in all experiments performed, it was chosen as the band of interest in this project.  
Figure 8: Influence of loop radius (10 KHz–100 KHz).
## 2 Results
### Detection of Point and Frameshift Mutations in Ribosome-Related Genes  
To detect point mutations, four target genes-related to known SmR-involved genes, _rsmG_ (_dgeo_2335_), _rpsL_ (_dgeo_1873_), and two _mthA_ (_dgeo_0447_ and _dgeo_0776_), were selected for DNA sequence analysis. All genes were sequenced, and point mutations were identified in the SmR strains. A summary of all mutations in SmR mutants is shown in Table 1 and Figure 4. In _rpsL_ of the four SmR mutants of the wild type, a point mutation was found at the 272 nd nucleotide in which 'C' was replaced with 'A', resulting in a predicted amino acid change from proline to histidine at the 91 st residue. Mutant S2 of the wild type had an additional mutation at _dgeo_0776_, which was a nucleotide deletion at the 84 th nucleotide, resulting in a frameshift mutation. The _\(\Delta\)dgeo_0257_ S1 strain exhibited a mutation at the 262 nd nucleotide with an A-to-G substitution in _rpsL_, resulting in a predicted lysine-to-glutamic acid change at the 88 th amino acid residue and an additional deletion mutation at _dgeo_2335_. The _\(\Delta\)dgeo_0281_ S1 strain had the same mutations in _rpsL_  
Figure 4: Detection of insertion sequence (IS) transposition and loci of point mutations in the Sm resistance-related genes _rsmG_ (_dgeo_2335_) (**A**), _mthA_ (_dgeo_0776_) (**B**), and _rpsL_ (_dgeo_1873_) (**C**). The _ISDge_ element of the IS5 family was integrated at the 58 th nucleotide of _rsmG_ (**A**). Streptomycin resistance mutants from wild-type, _dgeo_0257_, _dgeo_0281_, and _\(\Delta\)dgeo_2840 mutant strains have frameshift or point mutations in three Sm resistance-related genes. All mutations are also summarized in Table 1.  
as the wild type, with two mutations in both _mthA_ genes: one was a G-to-C substitution at position 102 in _dgeo_0776_ but with no predicted amino acid change, and the other was a nucleotide deletion at the 627 th nucleotide, resulting in a frameshift mutation in _dgeo_0447_. Both _\(\Delta\)dgeo_2840 S1 and S2 strains revealed a point mutation in _rpsL_ compared to the wild-type SmR mutant. However, at the 272 nd nucleotide, we found a C-to-T substitution, with a predicted amino acid change from proline to leucine at the 91 st residue and a phenotypic change to streptomycin resistance. Nevertheless, _\(\Delta\)dgeo_2840 S1 and S2 strains exhibited somewhat different growth patterns; at the moment, we have not found any additional mutations in the selected Sm resistance-related genes. Importantly, from the mutants identified in our analysis, we found that the K88E and P91H mutations of RpsL were responsible for the SmR phenotype, and the P91L mutation of RpsL caused the SmD phenotype.
## 2 Results  
### Composition of EOs  
GC-MS analyses enabled the detection of 32 volatile compounds at a level higher than 0.05% of total EO content (Table 1) in three EOs obtained from the same genotype of the hybrid _Origanum vulgar ssp. viridulum_\(\times\)_Origanum vulgar ssp. hirthum_ (CAR1, CAR2, CAR3) and in five EOs distilled from plants from the same genotype of the subspecies _Origanum heracleoticum_ L. (THY1, THY2, THY3, THY4, THY5). Within each group of EOs, the plants obtained from the same genotype were grown on different farms located in a restricted area of southern Sicily. Thirty-one of the detected compounds were identified as monoterpene hydrocarbons (thirteen), oxygenated monoterpenes (eight), sesquiterpene hydrocarbons (eight) and two other compounds. The main feature of both groups of EO samples was the occurrence of carvacrol or thymol as the predominant constituent: in the EOs from the hybrid _Origanum vulgar ssp. viridulum_\(\times\)_Origanum vulgar ssp. hirthum_, named the CAR group, carvacrol was the prominent compound, varying in amount from 81 to 85%, whereas in the EOs from the subspecies _Origanum heracleoticum_ L., named the THY group, the main constituent was thymol, ranging in amount from 47 to 65%. Globally, the group of monoterpene hydrocarbons accounted from a higher proportion of EO content in the THY group (24-38%) than in the CAR group (12-14%): in both groups, \(\gamma\)-terpinene and p-cymene were the main constituents of the monoterpene hydrocarbon chemical class, being present at a higher level in the THY group (13-22% and 4-5% in THY, and 5-7% and 3% in CAR, respectively). Similarly, the sesquiterpene hydrocarbon content was higher in the THY group (3.9-6.9%) than in the CAR group (2.2-2.6%), with \(\beta\)-caryophyllene occurring as the main constituent (0.9-1.8% in THY and 1.9-2.4% in CAR). Conversely, the global level of oxygenated monoterpenes showed a higher content in the CAR group (84-85%) than in the THY group (56-71%), this difference being dictated by the level of carvacrol/thymol. Interestingly, when 0.05% was established as the cut-off level for quantification, EOs of the THY group were characterized by a more complex chemical profile, formed by 30 compounds, whereas only 19 compounds were represented in the CAR group, in which a larger proportion of total content was formed by a single compound. Moreover, the chemical profiles of the EOs of the CAR group were quite similar to each other, whereas a higher level of variability was observed in the profile of the EOs of the THY group, suggesting a higher influence of the farm location on the chemical profile for this group (Figure 1). Diversification of chemical profiles in the THY group was mainly due to variability in the level of monoterrene hydrocarbons, such as \(\alpha\)- and \(\beta\)-pinene and \(\alpha\)-thujene (Figure 1).
## 1 Introduction  
Aortic diseases represent a clinical relevant problem characterised by a growing annual incidence [1]. Amongst these diseases, acute dissection, with tear formation in the inner lining of the aorta, is one of the worst cardiovascular emergencies, associated with considerable morbidity and mortality [2; 3; 4]. The concept of using thoracic endovascular aortic repair (TEVAR) for the management of acute type B dissection as an alternative treatment modality to open repair was introduced in the late nineties [5; 6].  
The onset, generation and progression of aortic dissection can be influenced by mechanical factors including hemodynamic, vascular geometry and mechanical properties of the aortic wall [7; 8]. Hemodynamic parameters are believed to play a crucial role in the formation and the progression of the dissection. Blood mass flow rate and wall shear stress (WSS) are the main parameters that change after aortic repair (TEVAR) [7; 9]. The blood flow pattern within the dissected aorta is dominated by locally highly disturbed and possibly turbulent flow with strong recirculation [10]. Therefore, in this study we focus on the evaluation of hemodynamic parameters such as mass flow rate and wall stress pre- and post TEVAR.  
Computational cardiovascular mechanics has allowed scientists to create complex 3D models for the simulation of cardiovascular problems [11]. With the aid of computational fluid dynamics (CFD), simulations of blood flow in patient-specific aortic models were reported [12; 13; 14]. Application of computational methods, including the CFD technique in the topic of blood flow in vessels is widely described in literature [15; 16]. Previously, the CFD technique has been applied to assess blood flow hemodynamic in vessels after stent-graft implantation in aortic aneurysms [17]. The real three-dimensional geometry of vessels is usually reconstructed with the use of medical data acquired from CT or magnetic resonance imaging [18; 19]. Using this approach it is possible to measure hemodynamic parameters within 3D models that provide important information on hemodynamic changes within the true and false lumen after TEVAR for the treatment of acute type B dissection [20].  
Therefore, the aim of this study was to verify the accuracy of CFD-calculated changes in blood flow parameters within the aorta and aortic branches in patients with type B aortic dissection (TBAD) before and after TEVAR procedure. Further, we verified numerical results with USG-Doppler data and calculated accuracy by comparing numerical (CFD) and clinical (USG-Doppler) measurements.
# A Survey on the Application of WirelessHART for Industrial Process Monitoring and Control
## 4 Challenges and Design Requirements
### Autonomous and Predictive Characteristics  
Unexpected network/mote collapse results in catastrophic failures in the closed-loop process, which creates the need for the independent operation of motes as autonomous and self-organizing devices without any human intervention [128]. In this situation, the addition or removal of the deployed motes may lead to network partitioning. WirelessHART has the advantage of mesh networking, which enables it to form a self-organizing network coordination framework (SoNCF). The framework can independently create multiple packet time slots in a self-organized manner for better data prediction throughput [129]. The implementation of autonomous nodes comes with greater energy consumption and more extensive data aggregation due to their continuous monitoring and transmission. Most WHNCS challenges and design goals are interlinked with one another, causing them to evolve continuously with new technological advancements to acquire the same promising performance as the wired networks.
## 2 Materials and Methods
### Adsorption Process Using the PET-SiAMPy or WPET-SiAMPy Nanofibres  
Batch adsorption experiments were carried out to determine the optimum contact time, adsorbent dosage, Cu\({}^{2+}\) aqueous solution pH, and the initial concentration of the adsorbate (Cu\({}^{2+}\)). The batch experiments were performed by immersing 0.01 g of the adsorbent into the Cu\({}^{2+}\) aqueous solution of 100 mgL\({}^{-1}\) initial concentration. The Cu\({}^{2+}\) aqueous solution with the adsorbent was agitated at 190 rpm for 120 min. Thereafter, the Cu\({}^{2+}\) aqueous solution concentration was sampled at fixed intervals and determined using ICP-OES. The evolution of the Cu\({}^{2+}\) aqueous solution concentration was monitored to equilibrium. The adsorption capacity of the adsorbents was calculated using the following Equation:  
\[q_{e}=\frac{(C_{o}-C_{e})V}{W} \tag{1}\]  
where \(q_{e}\) is the equilibrium adsorption capacity per gram of dry weight of the adsorbent (mg/g), \(C_{o}\) is the initial concentration of Cu\({}^{2+}\), \(C_{e}\) is the final concentration of Cu\({}^{2+}\) (mgL\({}^{-1}\)), \(V\) is the volume of the solution (L) and \(W\) is the weight of the dry adsorbent (g).  
The adsorption (\(R\)) is defined in Equation (2)  
\[R=1-\frac{C_{e}}{C_{o}} \tag{2}\]  
The percentage recovery is defined in Equation (3):  
\[R=\left(\frac{C_{o}-C_{e}}{C_{o}}\right)\times 100 \tag{3}\]  
Adsorption experiments were studied using the isotherm and kinetics modelling to know the details about the performance, mechanisms, practical design, and the operation of the adsorption systems.
## 4 Numerical Example
### Bridge System Reliability Estimation of the Single Failure Criterion
#### 4.2.2 Failure Criterion II
Then, based on the Gaussian copula function, the joint failure probability of each pair of failure modes was obtained according to Table 4 and Equation (7), as shown in Table 10. According to the binary Gaussian copula and Equation (10), the system failure probability \(P_{f_{\parallel}}\) of the case bridge under failure criterion II was \(1.049\times 10^{-5}\). For comparison, the system failure probability based on the other three copulas were also calculated, which are \(P_{f_{\parallel}-\text{[PERSON]}}=1.057\times 10^{-5}\), \(P_{f_{\parallel}-\text{[PERSON]}}=7.862\times 10^{-6}\), and \(P_{f_{\parallel}-\text{[PERSON]}}=2.736\times 10^{-8}\).
## 5 III-V-on-Silicon Devices for Optical Sensing Applications
### III-V-on-Silicon Spectrometers for Optical Sensing Applications
#### 5.2.3 InP-Based Photodetector Integration  
Compared with GaSb, the InP-based material system uses a cheaper substrate for epitaxial growth and the related heterogeneous integration technology is more mature. The cut-off wavelength of active device based on type-I lattice matched active regions is limited to about 1.75 \(\upmu\)m. By straining the active region, operation up to 2.3 \(\upmu\)m can be obtained [87]. In recent years, high performance electrically pumped lasers using type-II InP-based heterostructures were reported, lasing in a wavelength range from 2.2 \(\upmu\)m to 2.7 \(\upmu\)m [88]. Therefore, the heterogeneous integration of type-II active devices on SOI is attractive for spectroscopic sensors operating in the 2 \(\upmu\)m wavelength range. Type-II InP-based photodiodes were integrated on a silicon waveguide circuit using adhesive bonding technology [89]. The absorbing active region of the photodiodes consists of six periods of a \"W\"-shaped quantum well as shown in the inset of Figure 26a. Light is coupled from the silicon waveguide to the III-V waveguide using a taper structure. The dark current under reverse bias of 0.5 V is 12 nA as shown Figure 26a, which is much lower than the hybrid GaSb-based photodiodes (1.13 \(\upmu\)A at -0.1 V). Figure 26b shows the fiber-referred optical spectral responsivity of the photodiodes, together with the fiber-to-chip grating coupler efficiency. The waveguide-referred responsivity is higher than 0.5 A/W over the wavelength range of 2.2-2.42 \(\upmu\)m (the wavelength range limitation of our laser source). The peak fiber-referred responsivity is 0.12 A/W at 2.32 \(\upmu\)m, corresponding to a waveguide-referred responsivity of 1.2 A/W. The epitaxial layer stack used for the photodiode integration can also be used to realize edge emitting lasers or superluminescent LEDs, thereby enabling fully integrated spectroscopic sensor systems in the 2 \(\upmu\)m wavelength range.  
Figure 25: **(a)** Microscope image of a silicon AWG after integration of GaInAsSb photodetectors. **(b)** Measured photoresponse of this III-V-on-silicon spectrometer. Reproduced from [15].
# Design of Novel Laser Crosslink Systems Using Nanosatellites in Formation Flying: The VISION
## 2 Laser Crosslink Mission
### Orbit Scenarios
#### 2.2.1 Phase I--Launch and Early Orbit Phase (LEOP)  
Following separation from the launch vehicle, Altair and Vega deployed a UHF antenna and started transmitting the beacon signals. Subsequently, they stabilized their initial spins using three-axis reaction wheels (RWs). When the detumbling operation ended, they deployed solar panels to maximize battery charging. Commissioning operations were performed to calibrate the components, including RF devices and attitude sensors. Before the orbit maneuvers, the propulsion system was commissioned to evaluate and calibrate the firing logic conditions, enabling heaters on the propellant tank and each nozzle. In addition, the optical properties of the payload were calibrated by measuring the on-orbit background noise of the sensors for tracking and communication. During the LEOP, the inter-satellite distance gradually increased to over a few thousand kilometers owing to the perturbation forces.