esa-sceva/satcom-chunk-collection huggingface dataset:
# Systematic Analysis of the Hydrogen Value Chain from Production to Utilization
## 5 Hydrogen Applications in End-Use Sectors
### Decarbonizing Industry  
Hydrogen produced from renewable energy plays a crucial role in decarbonizing industry. Three separate hydrogen applications can be identified in this end-use sector [106]. First, hydrogen can be used as a chemical feedstock for the synthesis of other products where hydrogen is a molecular component. Examples of this application are the production of ammonia and methanol. Second, hydrogen can be used as a raw material for chemicals or fuel production in a chemical reaction that results in a final component that does not possess hydrogen in its chemical composition. One example of this application is the production of steel, where oxygen is reduced by hydrogen in the iron ore, replacing the cooking coal carbon source largely used in steel making. The development trends and technical challenges of hydrogen-based direct reduced iron production as an important decarbonization strategy for the iron and steel industry were explored by [PERSON] et al. [107]. [PERSON] et al. [108] presented a strategic policy framework for China that highlights the decentralized adoption of hydrogen in the steel industry. Third, hydrogen can be used as a heating source, replacing natural gas and other fossil fuels, to produce high-grade heat (temperatures above 650 \({}^{\circ}\)C). Hydrogen combustion in hydrogen-specific burners can be used in industries that require significant amounts of high-temperature process heat, like cement, iron, glass, and ceramic production [106].  
The utilisation of fossil fuel produced hydrogen as a feedstock to produce ammonia, methanol and other chemicals and the usage of hydrogen as a reducing agent to produce direct reduced iron constitute, together with the usage of hydrogen in the refining industry, the traditional hydrogen applications [8]. Recently, research has focused on proving the benefits of using hydrogen as a decarbonization path in a wide variety of industry sectors [109; 110; 111].
## 3 Results
### Effect of Gait Speed on Reaction Parameters  
The mean value, standard deviation, \(p\)-value, and Hedge's \(g\) of each parameter are presented in Table 2. Certain parameters vary significantly at different gait speeds. In addition, the correlation matrix of the reaction parameters is presented in Table 3. This information was used to perform PCA to determine the parameters that effectively represent reaction patterns. In the correlation matrix and PCA, the results of the trials at both speeds are included. The factor loadings of the four major components are calculated as shown in Figure 5. The total contribution ratio of these components was 63.8%, which consisted of the 4 components contributing 25.1%, 17.6%, 11.4%, and 9.6%. The results of the \(t\)-test showed that all four principal components did not differ significantly at different gait speeds.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Unit** & \(p\)**-Value (* \(p<0.05\))** & **Hedgis-\(g\)** & **Slow (3.5 km/h) \(n=60\)** & **Fast (4.0 km/h) \(n=69\)** \\ \hline _FS Stlength_ & & 0.22 & 0.22 & 0.44 (\(\pm\)0.08) & 0.42 (\(\pm\)0.08) \\ \hline _FS Sttime_ & s & * 0.02 & 0.42 & 0.55 (\(\pm\)0.06) & 0.53 (\(\pm\)0.05) \\ \hline _FS Kang_ & deg & 0.45 & 0.13 & 99.7 (\(\pm\)9.2) & 100.9 (\(\pm\)8.1) \\ \hline _FS Hang_ & deg & 0.56 & 0.10 & 38.3 (\(\pm\)6.6) & 39.0 (\(\pm\)7.5) \\ \hline _FS CoMVt_ & m/s & 0.30 & 0.18 & 1.26 (\(\pm\)0.10) & 1.28 (\(\pm\)0.11) \\ \hline _FS CoMVv_ & m/s & 0.15 & 0.25 & \(-\)0.29 (\(\pm\) 0.16) & \(-\)0.34 (\(\pm\)0.20) \\ \hline _FS MoSt_ & & * 0.01 & 0.49 & \(-\)0.06 (\(\pm\)0.07) & \(-\)0.09 (\(\pm\)0.07) \\ \hline _FS GRFt_ & & 0.52 & 0.11 & 0.25 (\(\pm\)0.11) & 0.25 (\(\pm\)0.09) \\ \hline _FS GRFv_ & & * 0.00 & 0.51 & 1.11 (\(\pm\)0.13) & 1.19 (\(\pm\)0.17) \\ \hline _SS Stlength_ & & 0.96 & 0.01 & 0.41 (\(\pm\)0.04) & 0.41 (\(\pm\)0.05) \\ \hline _SS Sttime_ & s & * 0.01 & 0.48 & 0.54 (\(\pm\)0.05) & 0.52 (\(\pm\)0.05) \\ \hline _SS Kang_ & deg & 0.16 & 0.25 & 69.7 (\(\pm\)4.3) & 71.1 (\(\pm\)6.6) \\ \hline _SS Hang_ & deg & 0.76 & 0.05 & 28.8 (\(\pm\)5.4) & 29.1 (\(\pm\)6.1) \\ \hline _SS CoMVt_ & m/s & 0.15 & 0.26 & 1.24 (\(\pm\)0.08) & 1.26 (\(\pm\)0.09) \\ \hline _SS CoMVv_ & m/s & * 0.01 & 0.46 & \(-\)0.14 (\(\pm\)0.05) & \(-\)0.18 (\(\pm\)0.08) \\ \hline _SS MoSt_ & & 0.10 & 0.29 & \(-\)0.02 (\(\pm\)0.07) & \(-\)0.04 (\(\pm\)0.07) \\ \hline _SS GRFv_ & & 0.08 & 0.32 & 0.23 (\(\pm\)0.06) & 0.25 (\(\pm\)0.07) \\ \hline _SS GRFd_ & & 0.10 & 0.29 & 1.37 (\(\pm\)0.35) & 1.47 (\(\pm\)0.32) \\ \hline \hline \end{tabular}  
* FS = first recovery step, SS = second recovery step.  
\end{table}
Table 2: Recovery motion parameters between different gait speeds, mean (SD).
# The Effect of Selected Herbal Extracts on Lactic Acid Bacteria Activity
## 2 Materials and Methods
### Determination of Antioxidant Capacities of Plant Extracts  
Before the analysis of antioxidant activity, each extract (3-6 mg) was diluted with distilled water (2-4 mL). The plant extract solutions were thus prepared in triplicate, and their average values of antioxidant capacity were determined as mmol Trolox equivalents per gram of extract (mmol TE/g extract).  
DPPH (2,2-diphenyl-1-picrylhydrazyl) radical scavenging activity was performed according to a procedure described by [PERSON] and [PERSON] [29] with minor modification. Briefly, 1 mL of 0.3 mmol/L freshly prepared DPPH methanol solution was mixed with 0.2 mL of the plant extract solution and 3.8 mL of methanol. The samples were vortex-mixed at high speed for 10 s and incubated for 10 min in the dark at room temperature. Then, their absorbance was measured at 517 nm using a UV/Vis spectrophotometer. A standard curve was obtained using the Trolox standard in the range of 8-40 umol/L.  
ABTS (2,2'-azinobis(3-ethylbenthiazoline-6-sulfonic acid)) radical scavenging activity of the extracts was determined according to the method described by [PERSON] et al. [30]. First, the ABTS\(\bullet\)+ solution was prepared by mixing ABTS aqueous solution (14 mmol/L) with potassium persulfate aqueous solution (4.9 mmol/L). The prepared solution was kept for 12-16 h in the dark at room temperature. Before the analysis, the ABTS\(\bullet\)+ solution was diluted with phosphate-buffered saline (0.01 mol/L, pH 7.4) to achieve an absorbance value of 0.7 \(\pm\) 0.05 at 734 nm. Then, 40 \(\upmu\)L of the plant extract solution or Trolox solution was mixed with 4 mL of ABTS\(\bullet\)+ working solution. The reactive mixture was incubated at room temperature in the dark, and after exactly 6 min, its absorbance was recorded at 734 nm. A series of Trolox solutions (final concentrations 0-15 \(\upmu\)M) were used for calibration.  
FRAP (ferric reducing antioxidant power) assay was performed as described by [PERSON] and [PERSON] [31] with some modification. Before the assay, the FRAP reagent was freshly prepared by mixing 300 mmol/L acetate buffer (pH 3.6), 20 mmol/L FeCl3 solution, and 10 mmol/L TPTZ (2,4,6-Tris(2-pyridyl)-s-triazine) in 40 mmol/L HCl in a 10:1:1 (v/v/v) proportion and stored away from light. Then, 100 \(\upmu\)L of the appropriately diluted sample extract was mixed with 0.3 mL distilled water and 3 mL FRAP reagent. The absorbance of the reaction mixture was measured spectrophotometrically at 593 nm after incubation at 37 \({}^{\circ}\)C for 10 min. The blank solution was obtained by mixing 0.3 mL distilled water with 3 mL of FRAP reagent. A standard curve was prepared using Trolox in the range of 80-500 umol/L. All determinations were carried out in triplicate.
## 3 Materials and Methods
### Service Prototype Design  
In [13], a proof of concept of the service was developed; this work presents a prototype, which is an incremental contribution to the development of the system. Considering the selected ITS architecture, a prototype of the system was built using LoRa between the transit vehicle OBE and ITS roadway equipment modules. Communication through LoRa is adequate between the mentioned modules, because several devices (one device per vehicle) transmit data packets (approximately 4-10 packets per minute) to a gateway. It was estimated that in a medium-sized city each gateway can receive packets of approximately 12-30 vehicles at a time, which can be easily handled by LoRa. Communication between the ITS roadway equipment module and the transit management center is done through the Internet, because the volume of data sent is greater and the distances may be longer, depending on the location of the transit management center (which is commonly located on the cloud). Figure 2 shows the prototype. The devices used in the transit vehicle OBE were an ESP32 LoRa Heltec card and an Ublox 6M GPS module. The selected gateway was the Dragino LG02, which allows the reception of LoRa messages. The recent versions of the Dragino gateways are mainly focused on the use of the LoRaWAN network protocol.
# Kinematics Model Optimization Algorithm for Six Degrees of Freedom Parallel Platform
###### Abstract  
The attitude closed-loop control of the parallel platform in the working space needs to determine the relationship between the pose of the top moving platform and the length of each mechanical arm, that is, the kinematics problem of the parallel platform. In this study, the kinematics model of the six-degree-of-freedom parallel platform was established. The kinematics forward solution algorithm based on Newton-Raphson iteration was studied. The kinematics forward solution method usually adopts a numerical solution, which often needs multiple iterations, and the algorithm has a poor real-time performance. In order to improve the real-time performance of the parallel platform control system, a multivariate polynomial regression kinematics forward solution algorithm is proposed in this paper. Moreover, by combining the multivariate polynomial regression with the Newton iterative method, we obtained an efficient solution algorithm with controllable solution accuracy. The effectiveness of the proposed method was verified by simulation tests and physical tests.  
parallel platform; multivariate polynomial regression; Newton iterative; kinematics Article
## 2 Materials and Methods
### Optimizing BPNN Using GA  
The GA is an optimization algorithm that simulates Darwinian evolutionary mechanisms to find the optimal solution. It features adaptability, randomness, and high parallelism. GA obeys the principle of survival of the fittest, repeats the operation of selection, crossover, and mutation with individual fitness as the evaluation standard, eliminates chromosomes with poor adaptability, retains the fitness of individuals, and forms a new population. This algorithm's procedures are usually demonstrated as follows.  
**Step 1: Population initialization**  
Within a certain range, a random initial population with the number of \(N\) is generated, and each individual in the population becomes a chromosome.  
**Step 2: Code the population**  
The initial population is coded according to binary rules, which are made up of Numbers 0 and 1.  
**Step 3: Calculate fitness**Mean square error (MSE) is selected as the objective function. According to the objective function, the fitness value of each individual in the population is calculated.  
\[f_{i}=\frac{1}{X}\sum_{i=1}^{X}\left(Y_{i}-\overline{Y_{i}}\right)^{2} \tag{9}\]  
where \(f_{i}\) represents individual fitness; \(Y_{i}\) = actual output value of the sample; \(\overline{Y_{i}}\) = excepted output value of sample; \(X\) = number of samples.  
**Step 4: Select operator**  
According to the fitness value of each individual, individuals with high fitness are selected to carry out to the next iteration operation, while those with low fitness are less likely to enter the next iteration operation or may even be eliminated. The probability of an individual being selected is proportional to its fitness by using the wheel type probability selection method. The probability selection is as follows.  
\[P_{k}=\frac{f_{k}}{\sum_{i=1}^{N}f_{i}} \tag{10}\]  
where, \(N\) = initial population number, \(f_{k}\) is the fitness value of individual \(k\) and \(P_{k}\) is the probability that individual \(k\) is selected.  
**Step 5: Crossover operator**  
The selected individuals pair with each other according to the principle of arithmetic crossover, exchange some genes, and form new individuals, which will have the characteristics of their parents. The arithmetic crossover operator is as follows [27].  
\[{x^{\prime}}_{1}=ax_{1}+(1-a)x_{2},\ a\in(0,1) \tag{11}\]  
\[{x^{\prime}}_{2}=ax_{2}+(1-a)x_{1},\ a\in(0,1) \tag{12}\]  
where, \(x_{1,2}\) represent two parent individuals, while \(x_{1,2}\) represent two offspring individuals, \(a\) is a random number between 0 and 1.  
**Step 6: Mutation operator**  
By replacing certain alleles on individual chromosomes with a certain probability of mutation, new individuals different from their parents can be created to expand the population size.  
Repeat Step 3 to Step 6. The algorithm converges by iteration. When the iteration number reaches the maximum iteration number \(T\), the individuals with the maximum fitness obtained in the evolutionary process are taken as the output of the optimal solution.
# Active Clamp Boost Converter with Blanking Time Tuning Considered
## 3 Design Considerations
### Design of Input Inductance \(L_{in}\)  
Because the converter operates in the CCM for any load, the minimum value of \(L_{in}\), called \(L_{in,min}\), can be represented by  
\[L_{in,min}=\frac{R_{max}D(1-D)^{2}T_{s}}{2} \tag{39}\]  
where \(R_{max}\) = 176 \(\Omega\), \(D\) = 0.43, and \(T_{s}\) = 10 \(\upmu\)s.  
Based on Equation (39), \(L_{in,min}\) = 123 \(\upmu\)H after some calculations and, eventually, the value of \(L_{in}\) is chosen to be 150 \(\upmu\)H by considering the inductance reduction owing to the temperature and load. After this, a T175-18 core, manufactured by Micrometals, Inc. (Anaheim, CA, USA) is utilized, which has an inductance coefficient \(A_{L1}\) of 82 nH/N\({}^{2}\), and hence the number of turns, called \(N_{in}\), is  
\[N_{in}=\sqrt{\frac{L_{in}\times 1000}{A_{L1}}}=\sqrt{\frac{150\times 1000}{82}}=42.76 \tag{40}\]  
Eventually, based on Equation (40), the value of \(N_{in}\) is chosen to be 43.
# Effect of Thermal via Design on Heat Dissipation of High-Lead QFN Packages Mounted on PCB
## 3 Simulation Results
### Effect of Different Thermal via Distributions on Heat Dissipation in High-Lead QFN Packages  
Based on the discussion in the previous section, we can deduce that the closer the thermal via are to the heat source, the more pronounced their effect on the thermal dissipation of the high-lead QFN package. In order to deeply study the heat dissipation effects of thermal via, this section explores two distinct thermal via distribution patterns: square distribution and circular distribution (Figure 8). Our focus will be on investigating the impact of thermal via quantity under these two distribution conditions on heat dissipation performance. Concurrently, we have controlled the diameter and the copper plating thickness of the thermal via at 0.3 mm and 0.02 mm, respectively.  
Figure 9 presents the maximum temperatures for the high-lead QFN package under square distribution and circular distribution conditions with different numbers of thermal via. The computational outcomes reveal that as the number of thermal via increases, their contribution to heat dissipation becomes progressively more substantial. Notably, when the thermal via quantity reaches 9, the maximum temperature of the high-lead QFN package with thermal via that are circularly distributed is 56.1 \({}^{\circ}\)C, which is slightly better than that  
Figure 7: Maximum temperatures at different distances from thermal via to the center of the heat source.  
of the square-distributed thermal vias. However, with a further increase in the number of thermal vias, the square distribution of these thermal vias demonstrates a remarkable enhancement in heat dissipation effectiveness. Beyond 13 thermal vias, the heat dissipation performance of the square distribution becomes even more significant. This observation can be attributed to the fact that with a higher number of thermal vias, the circular distribution results in a more closely packed arrangement, causing heat accumulation and hindering timely heat conduction. When the number of thermal vias is 25, the maximum temperature of the high-lead QFN package with square-distributed thermal vias is 54.2 \({}^{\circ}\)C, which is 1.8 \({}^{\circ}\)C lower relative to that for thermal vias of 9.  
Figure 8: Distribution of thermal vias (**a**) square distribution and (**b**) circular distribution.  
Figure 9: Maximum temperatures at different thermal via quantity.
## 5 Performance Evaluation
### Illustrative Scenarios  
In order to analyze the impact of the relationship among the data receiving, the computing and the data transmission processing powers of a computing worker, we illustrate the model by using 3 scenarios: (1) in the first scenario, the 3 processing powers are equal (\(\mu_{1,r}=\mu_{2,r}=\mu_{3,r}\)); (2) in the second scenario, the receiving processing power is less than the computing processing power (\(\mu_{1,r}<\mu_{2,r}=\mu_{3,r}\)); and (3) in the third scenario, the receiving processing power is greater than the computing processing power (\(\mu_{1,r}>\mu_{2,r}=\mu_{3,r}\)). In our experiments a load will be divided equally on the available computing workers. We consider a range of chunk sizes to analyze the system. In our experiments we vary the chunk sizes arriving at computing workers to explore several scenarios of data distribution and their impact on the system efficiency. To analyze the impact of an arrival rate to the system efficiency, we vary the Poisson rates, representing real world scenarios [26].  
\begin{table}
\begin{tabular}{l c} \hline
**Parameter** & **Values** \\ \hline Workload (\(\alpha_{r}\)) & \(0.01,0.05,0.1,0.2\) \\ Computing rate at a computing worker (unit/s) & \(\mu_{2,r}\!=\!1\) \\ Receiving rate at computing worker & \(\mu_{1,r}\!=\!1,0.8,1.2\) \\ Transmitting rate at a computing worker & \(\mu_{3,r}\!=\!1\) \\ Applications’ arrival rate at master worker (applications/min) & \(\lambda\!=\!1,2,3,4,5\) \\ \hline \end{tabular}
\end{table}
Table 2: Illustrative Parameter Values.
# An Overview of Cube-Satellite Propulsion Technologies and Trends
## 3 Liquid Propulsion (LP) Systems  
### Operating Principle  
In a Liquid Propulsion System, thrust is generated by means of ejecting the gases formed during the process of combustion of liquid propellant(s). Depending on the mission requirements, a spacecraft can have LP systems with one (mono) or two (bi) propellants. Mono-propellant LP systems make use of a catalyst to decompose (ignite) the propellant and generate thrust. Decomposition process takes place when the propellant is injected into the combustion chamber through the catalyst bed [8]. Examples of mono-propellants are hydrazine and nitrous oxide [24] and examples of a catalyst are  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Companya/Initialization with Location** & **Engine** & **Threat (sn)** & **ly \& **op** & **Propellant** & **Henting** & **Remarks** & **Ref.** \\ \hline SSTL, Gaillard, UK & SNAP 1 & 50 & 43 & Liq, Ibate & & flown on Cove-A (600 kg) & [12,15,44] \\ UTAS-SP, Toronto, ON, Canada & CNAPS & 10-40 & -35 & \(Sf_{c}\) & Cars4-3 (kg\(\cdot\), Caill\(\cdot\), Caill\(\cdot\), Sca) (6 kg) & - & [77] \\ Microsuspake Rapid, Singapore & POTS/A4 HP1 & 1 & 43 & Argon & POTS/A4 HP1 (U/3.1 kg) & & [18] \\ COMSpace, Denmark & MEMs Cold Gas & 1 & 50-75 & Methane & TW-1 (one 3U and two 2U) & also flown on PIESMA (180 kg) & [9,19,20] \\ VACO Industries, II Monne, CA, USA & CYOD & 25 & 40 & R334a & CYOD (UU) & - & [21,22, 23] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of Cold Gas Propulsion Systems.  
liquid permanganates, solid manganese dioxide, platinum, and iron oxide [8]. A bi-propellant LP system, on the other hand, comprises of both oxidizer and fuel. Combination of liquid oxygen and kerosene, or of liquid oxygen and RP1 are examples of bi-propellants that are widely used [24]. The fuel in the bi-propellant system can sometimes be used in a mono-propellant context with the inclusion of a catalyst. Either LP systems have primarily been used on larger satellites for high \(\Delta v\) (orbit-raising) operations and a single propellant is typically used for low \(\Delta v\) operations (station keeping). The schematic of a bi-propellant LP system is shown in Figure 2 and it primarily consists of a combustion chamber, nozzle and propellant storage for both oxidizer and fuel. Note here that the for the case of a mono-propellant system, the major components are propellant storage (only fuel), catalyst bed and the nozzle [8].  
The thrust and specific impulse of an LP system can be obtained from the Equations (1) and (2) respectively. Exit velocity (shown in Equation (7)) of an LP system, like a CGP system is dependent on the exit-to-chamber-pressure-ratio \((P_{e}/P_{c})\) and combustion chamber temperature (\(T_{c}\)) [8].  
\[v_{e}=\sqrt{\frac{2\gamma}{\gamma-1}\Re T_{c}\left[1-\left(\frac{P_{e}}{P_{c} }\right)^{\frac{\gamma-1}{\gamma}}\right]}, \tag{7}\]  
where \(\gamma\) is the Poisson constant and \(\Re\) is the universal gas constant.
### Assessment of Crystallinity Development during
###### Abstract  
Although semi-crystalline polymers are associated with considerably better mechanical properties and thermal stability compared to their amorphous counterparts, using them as feedstock for Fused Filament Fabrication still poses some major challenges. Hence, the development of printed part crystallinity during printing should be fully understood in order to control the developed weld strength, as well as part shrinkage and warpage. Infrared thermography was utilized to record the thermal history of deposited layers while printing a single-layer wall geometry, employing two PA 6/66 copolymers with distinct molecular weights as feedstock. Print settings were varied to establish which settings are essential to effectively monitor final part crystallinity. The resulting temperature profiles were simulated in a Fast Scanning Chip Calorimetry device that allows for the rapid heating and cooling rates experienced by the printed polymer. Both liquefier temperature and print speed were found to have very little influence on the total attained crystallinity. It became apparent that the build plate, set at a temperature above the polymer's glass transition temperature, imposes a considerable annealing effect on the printed part. A reduced molecular weight was observed to enhance crystallinity even more strongly. The presented methodology proves that Fast Scanning Chip Calorimetry is an effective tool to assess the impact of both print parameters and feedstock characteristics on the crystallization behavior of semi-crystalline polymers over the course of printing.  
additive manufacturing; fused filament fabrication (FFF); 3D printing; polymer crystallization; infrared thermography; fast scanning chip calorimetry