esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Ti-B Grain Refiners on Wear and Corrosion of the A332 Alloy with Sr Modification
###### Abstract  
Grain refiners play a critical role in changing characteristics and properties of casting aluminum alloys. The Al-Si alloy (A332) is one of the most popular hypoautectic alloys with a large range of industrial applications. It has a varied phase and morphology; however, it features problems with acicular-shaped eutectic phase, and generally exhibits dendritic cast grain type. To change this situation, the Sr element acts as a modifier of eutectic, which, along with a grain refiner may increase mechanical properties. In this work, two different grain refiners (Al5 Ti1B, Al5 Ti2B) were applied to the A332 alloy modified with Sr, and analyzed in relation to grain size, hardness, corrosion resistance, and wear behavior. Corrosion tests in 3.5 wt.% NaCl solution, nanoindentations, and [PERSON]'s method to analyze grain size and microhardness as optical and SEM images were made to examine the changes caused by grain refiners. A reduction in grain size was achieved, and the influence in size and hardness of the \(\beta\)-Fe phase was verified in the wear and corrosion analyses.  
Al-Si cast alloys; AlTiB grain refiners; iron-containing intermetallics; strontium modifier; microstructure; nanoindentation; corrosion rate; hardness; wear resistance Article  
Effect of Ti-B Grain Refiners on Wear and Corrosion of the A332 Alloy with Sr Modification  
[PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Effect of Ti-B Grain Refiners on Wear and Corrosion of the A332 Alloy with Sr  
Modification. Approf. Sci. 2023, 13, 430. [[https://doi.org/10.3390/app13010430](https://doi.org/10.3390/app13010430)]([https://doi.org/10.3390/app13010430](https://doi.org/10.3390/app13010430))
## 4 Solvents for CO\({}_{2}\) Capture
### Chemical Solvents
#### 4.2.5 Other Solvents  
Aside from amines and ionic liquids, a few other solvents have been used for capturing CO\({}_{2}\) from flue gas streams. Salt solutions have been widely used thus far due to their abundance in the chemical industry and their relatively low cost compared to amines or ILs. These systems are mainly based on sodium or potassium carbonates which show low degradation as well as high stability and absorption capacity [103]. Carbonate systems can be classified as conventional or precipitating depending on the chemistry of the system, usually employing promoters due to the slow kinetics of the reaction with CO\({}_{2}\)[104]. However, aside from their slow kinetics, these solvents have low selectivity and promote deposition and corrosion of the equipment [105]. [PERSON] et al. [106] present a comprehensive review on potassium carbonate solutions. Amino acids can also be used as CO\({}_{2}\) absorbents. They are most commonly used in their salt form, resulting from the reaction of an amino acid with a base group such as KOH [107] or NH\({}_{3}\)[108]. Potassium salts of taurine and glycine [109] and sarcosine [110] are deemed to be promising solvents for CO\({}_{2}\) capture. [PERSON] et al. [111] numerically tested the absorption performance of three absorbents, namely potassium glycinate (PG), potassium arginate (PA), and sodium hydroxide (NaOH), concluding that PA was the best candidate absorbent, followed by PG and NaOH. Ammonia systems offer advantages compared to amine ones, including high cyclic capacity, high efficiency, lower rate of solvent circulation, lower regeneration demands, and no corrosion issues [112]. Such solutions also offer the benefit of simultaneous removal of NO\({}_{x}\) and SO\({}_{x}\) in addition to CO\({}_{2}\)[113]. They are, however, much more volatile, resulting in high solvent losses and often linked with irritations in the human body and toxicity [114]. Finally, the capture of CO\({}_{2}\) using seawater of variable salinity was studied by [PERSON] et al. [115]. They showed that salinity and CO\({}_{2}\) solubility are inversely related and that the addition of inorganic substances that promote precipitation, such as CaO, can enhance the solubility by up to 79%. One such solution can be the addition of steel slag which has been proven to be quite effective [116]. Usually, such systems operate as full capture, utilization, and sequestration systems and may seem favorable for use in maritime systems; however, further studies are needed in order to determine if seawater can be a viable solution for CO\({}_{2}\) capture. Table 2 summarizes the main identified advantages and drawbacks of each class of solvent addressed in this work. An upward (downward) facing arrow signifies a high (low) value for the corresponding property; very high (low) values merit a double arrow.
## 3 Research Model and Hypotheses
### Visual, Auditory, and Kinaesthetic (VAK) Learning Styles  
Learning style refers to the behavioral preferences of students in the process of acquiring knowledge. [PERSON] et al. [38] confirmed, through an experimental approach, that the same instructional strategies have different effects on learners with different learning styles. It is clear that most studies on game-based teaching did not take into account contextual factors, and that differences in students' cultural backgrounds, learning goals, and learning styles may influence their willingness to participate in game-based classrooms [30; 39]. In conjunction with the overall research, in this study, we will focus on the impact of differences in learning styles on behavioral intentions. Since classrooms with gamified activities are more interactive and practical, this study limits the variable \"learning style\" to \"visual, auditory, and kinesthetic (VAK) learning style,\" namely, it focuses on the relationship between the extent to which students have a VAK learning style and their intention to participate in online gamified classrooms. VAK learning styles refer to learners' preference for visual, auditory, or kinesthetic acquisition of information when engaging in learning activities, their expertise in listening, and their experience with acquiring knowledge [40; 41]. A common means of gamification in education is through the use of game-based websites, whose user interfaces usually consist of colorful and exaggerated visual images. Some websites also feature cheerful background music. From an objective point of view, these elements are a highly attractive new learning mode for students who are good at perceiving learning content through visual, auditory, and kinesthetic stimuli, and we hypothesize that students with VAK learning styles have a strong behavioral intention. On the other hand, in the TAM, learning style is an external antecedent variable which may have an impact on engagement intention through mediating variables such as usefulness, interest, and learning task arrangement. Therefore, four hypotheses are proposed:  
**Hypothesis 5a (H5a):**_VAK learning style has a significant positive influence on students' intention to participate in an online gamified classroom._  
**Hypothesis 5b (H5b):**_Perceived learning task has a mediation effect on the effect of VAK learning style on students' intention to participate in an online gamified classroom._  
**Hypothesis 5c (H5c):**_Perceived usefulness has a mediation effect on the effect of VAK learning style on students' intention to participate in an online gamified classroom._  
**Hypothesis 5d (H5d):**_Perceived enjoyment has a mediation effect on the effect of VAK learning style on students' intention to participate in an online gamified classroom._
###### Abstract  
This paper presents a two-stage DC/DC converter with high efficiency utilized in an electronic power conditioner (EPC), which is widely applicable in satellite communications, etc. The galvanically isolated converter contains two cascaded converters: a buck converter, which is a pre-regulator operating under a closed-loop condition, and a push-pull converter, which is intended to boost the input voltage, operating under an open-loop condition. In the push-pull converter, the power switches, including the main switches and the rectifier diodes, operate under zero-voltage switching (ZVS) and zero-current switching (ZCS) at both switch off and switch on, which minimizes the switching loss. Furthermore, all of the parasitic parameters, such as the parasitic capacitance, leakage inductance, and magnetizing inductance of the main transformer, are fully utilized. Therefore, the presented topology benefits from fewer semiconductors but higher efficiency. The proposed topology produces less EMI noise because of ZVS and ZCS processes whose fundamental switching frequency interference is relatively low. The presented converter achieves a wide bus voltage regulation range in a satellite because of the pre-regulation of the buck cell. The theoretical analysis is validated by a prototype and its experimental results. The maximum efficiency of the converter can be up to 94.5%, and the high-voltage output is 7000 V.  
two-stage DC/DC converter; zero-voltage switching (ZVS); zero-current switching (ZCS); high efficiency 2022156378[[https://doi.org/10.3390/en15176378](https://doi.org/10.3390/en15176378)]([https://doi.org/10.3390/en15176378](https://doi.org/10.3390/en15176378))
## 4 Experiment
### Experimental Results and Comparison
The results of this comparison are presented in Table 8, which shows the maximum completion time (\(C_{max}\)) across different scheduling methods for the 10 problem instances. The bolded values indicate the optimal makespan performance. Our model demonstrated competitive performance, achieving the lowest \(C_{max}\) in five out of the ten instances (MK05, MK06, MK07, MK09, and MK10). On average, our method achieved a \(C_{max}\) of 197.1 h, outperforming the existing DRL-based methods, such as [PERSON] et al. [46] and [PERSON] et al. [47], by 7.8% and 3.1%, respectively. Additionally, our method showed significant improvements when compared to rule-based heuristic methods, with an average optimization of 12.7%. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **S1** & **S2** & **S3** & **S4** & **S5** \\ \hline Proposed Method & 0.32 & 0.63 & 0.99 & 1.27 & 1.9 \\ DQN & 0.85 & 1.12 & 1.45 & 1.98 & 2.35 \\ SPT + NINQ & 0.15 & 0.29 & 0.42 & 0.58 & 0.87 \\ SPT + WINQ & 0.15 & 0.31 & 0.44 & 0.6 & 0.9 \\ MWKR + NINQ & 0.16 & 0.31 & 0.45 & 0.6 & 0.93 \\ MWKR + WINQ & 0.16 & 0.33 & 0.48 & 0.65 & 0.98 \\ EDD + NINQ & 0.17 & 0.35 & 0.5 & 0.68 & 1.05 \\ EDD + WINQ & 0.18 & 0.36 & 0.52 & 0.7 & 1.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of response times across instances (unit: s). Figure 6: Comparative visualization of scheduling results. Despite the overall strong performance, it is worth noting that in the MK08 instance, our method exhibited a slight drop in performance compared to [PERSON] et al. 's DRL-based method [46]. This discrepancy is likely due to the specific load balancing challenges in this instance, as MK08 requires careful balancing of machine loads--a factor that our current reward function does not explicitly account for. Future improvements could include incorporating load balancing considerations into the reward structure, which would enhance the performance in such cases.
# A Low-Power Read-Decoupled Radiation-Hardened 16T SRAM for Space Applications
## 1 Introduction
Nevertheless, it fails to reliably recover from SEU at the '0'-storing node and exhibits only slight improvements in read stability. Overall, these designs offer limited SEU tolerance. The RHBD10T cell demonstrates improved read and write delay characteristics; however, its SEU immunity remains insufficient [13]. While SIS10T can recover from SEMNU affecting a single node pair, it is unable to tolerate high levels of deposited charge [14]. The SEA14T cell is designed to recover lost data from the primary storage nodes through two secondary nodes [15]. This architecture enables recovery from soft errors at all sensitive nodes; however, it has the drawback of increased read access time due to reduced read current. Additionally, cells such as EDP12T and SAW16T employ NMOS transistors in the pull-up path, which significantly degrades read stability [16; 17]. Our previous work, RHHS16T, demonstrated improved read stability; however, it suffers from the drawback of excessive hold power consumption [18]. In summary, while these designs provide partial performance improvements, they tend to lack comprehensive attention to SEU protection and other critical metrics. As a result, they often suffer from degraded read/write stability and increased leakage power. Therefore, it is essential to propose an SRAM cell that not only exhibits strong radiation tolerance but also ensures reliable memory operation across key performance metrics. This need becomes even more critical in space environments, where power efficiency directly impacts mission longevity and overall system reliability. To overcome the aforementioned limitations, this paper proposes a low-power read-decoupled radiation-hardened 16T (LDRH16T) SRAM cell. The LDRH16T employs a polarity-based architecture with four internal nodes, of which only two are sensitive to soft errors. It demonstrates full recovery capability from single-event upset (SEU) at all sensitive nodes. Furthermore, to avoid single-event multi-node upsets (SEMNU) caused by charge sharing, the layout ensures that the spacing between critical transistors exceeds the minimum threshold [19]. Compared to several state-of-the-art hardened SRAM cells, the proposed design exhibits superior radiation tolerance. LDRH16T achieves the highest read stability among the reference designs and offers enhanced write stability, ensuring reliable data retention. It also consumes lower leakage power than most other designs. In terms of overall performance, LDRH16T achieves the highest electrical quality metric (EQM), making it a highly robust and power-efficient solution for long-term operation in aerospace applications. This paper is organized as follows. Section 2 describes the basic operation of the proposed cell and analyzes its SEU recovery capability. Section 3 presents the simulation setup and comparative evaluation of key design metrics between LDRH16T and the reference cells. Finally, Section 4 concludes the paper.
## 2 Proposed TSP Readout System
### Proposed Second-Generation Current Conveyor (CCII)  
The CCII at the sensing front-end serves two purposes in the readout system. First, it converts the capacitive variation at the TSP into proportional current. Second, it utilizes the parasitic capacitance of the TSP (C\({}_{\rm{S}}\)[n]) to create a high-frequency low-pass pole, mitigating external noises. At the same time, the large C\({}_{\rm{S}}\)[n], of the order of 100-200 pF should not limit the system's bandwidth, aided by the small input resistance (R\({}_{\rm{IN}}\)) of the CCII. Previously, various methods have been proposed to implement the CCII [17; 18; 19; 20]. However, in this design, a negative-feedback closed-loop configuration is employed to effectively reduce the R\({}_{\rm{IN}}\) of the CCII by the loop gain.  
As shown in Figure 5, the OTA consists of two gain stages, with an additional resistor R\({}_{\rm{F}}\) in the first stage to further enhance the overall gain. Since R\({}_{\rm{F}}\) is significantly smaller compared to the channel resistances of the PMOS input pairs (M\({}_{\rm{P1}}\), M\({}_{\rm{P2}}\)) and NMOS current source pairs (M\({}_{\rm{N1}}\), M\({}_{\rm{N2}}\)), the gain of the first stage (A\({}_{\rm{V1}}\)) is determined by the product of the input transconductance of M\({}_{\rm{P1}}\) and M\({}_{\rm{P2}}\) (G\({}_{\rm{m,CC}}\)) and R\({}_{\rm{F}}\)[7]. The second gain stage (A\({}_{\rm{V2}}\)) is determined by the transconductance G\({}_{\rm{m,CC}}\) of M\({}_{\rm{N3}}\) and the parallel combination of the PMOS and NMOS cascode resistances (R\({}_{\rm{op,CCII}}\)//R\({}_{\rm{on,CCII}}\)). When unity-gain feedback is applied, the effective cascode resistance is reduced by the product of A\({}_{\rm{V1}}\) and A\({}_{\rm{V2}}\). With R\({}_{\rm{F}}\) tunable between 20 k\(\Omega\) to 37.5 k\(\Omega\) using 3b control and G\({}_{\rm{m,CS}}\) designed to be 150 u\(S\), the reduction factor in R\({}_{\rm{IN}}\) by the additional gain stage ranges from 3 to 5.625, depending on the actual panel loading. The current amplification ratio (from I\({}_{\rm{x}}\) to I\({}_{\rm{Z}}\)) is adjusted from 1/8 to 15/8 to prevent saturation of the subsequent C2V and Gaussian-shaped S/H circuit.  
Figure 5: Circuit diagram of the second-generation current conveyor (CCII).
## 5 Experiments and Analyses
### Performance on Different Data Rates  
The third experiment redid the second given different data rates, including 0.5, 1, 5, 10 and 20 Mbps, rather than 11 Mbps listed in Table 11, for each node. The experimental time was 100 s. In other words, like those in the second experiment, each node delivered 10 requests and a total of 60 requests were submitted. The experimental results are illustrated in Figure 12, in which Figure 12a shows the drop rates, which are higher when data rates increase. When data rate \(=1\) Mbps, six nodes totally send 6 Mbps which do not congest the AP. When data rate \(=5\) Mbps, packet drop rates sharply increase. The reasons are also packet congestion and long channel contention time since the total data rate \(=30\) Mbps, which congests the wireless channel. When data rates are higher, congestion and contention are serious.  
The throughputs of these four algorithms are shown in Figure 12b, in which we can see that the difference among them is significant when data rate \(\geq 5\) Mbps. Basically, a higher data rate means that higher numbers of requests are submitted in a second, thus generating a huge amount of data packets  
Figure 12: Cont.  
by the resource nodes and leading to higher packet collision and channel contention at the AP. A better scheme will raise the throughputs. Figure 12c illustrates the turnaround times for the four algorithms. When the data rates increase, the bandwidth of a link is more fully used, resulting in shorter turnaround times. The turnaround times of SPA-WP and SPA-Multipath are shorter than those of Random and FCFS. The turnaround times on 10 and 20 Mbps are very similar because the bandwidth is almost or already saturated. The transmission time on data rate = 0.5 Mbps is longer than those on other data rates. Theoretically, the turnaround time on the 0.5 Mbps data rate is \((\sum\limits_{i=1}^{60}\text{Mbits}/0.5\text{ Mbps}^{\times}i)/6=6100\text{ s}\). Others can be calculated by a similar method.
# Design of a Baseband Signal for the 406 MHz Satellite Emergency Radio Transmitter Based on STM32
## 3 Baseband Signal Generation METHOD  
### Structure of the Cospas-SARSAT Transmitter  
The structure of the Cospas-SARSAT transmitter is shown in Figure 1, which consists of an acquisition module, a pre-processing module and a processing module. The acquisition module is used to receive the GPS signal; the pre-processing module is conducted in the STM32 MCU, which mainly extracts the position information received from the GPS and generates the baseband signal of the 406 MHz transmitter and transmits it to the next stage circuit. At last, the processing module sends out the 406 MHz distress signal.
# Roto-Translational Control of Spacecraft in Low Earth Orbit Using Environmental Forces and Torques
###### Abstract  
In this paper, relative orbit and attitude adaptive controllers are integrated to perform roto-translational maneuvers for CubeSats equipped with a Drag Maneuvering Device (DMD). The DMD enables the host CubeSat with modulation of aerodynamic forces/torques and gravity gradient torque. Adaptive controllers for independent orbital and attitude maneuvers are revisited to account for traslational-attitude coupling while compensating for uncertainty in parameters such as atmospheric density, drag/lift coefficients, location of the Center of Mass (CoM) and inertia matrix. Uniformly ultimately bounded convergence of the attitude error and relative orbit states is guaranteed by Lyapunov-based stability analysis for the integrated roto-translational maneuver. A simulation example of an along-track formation maneuver between two CubeSats with simultaneous attitude control using only environmental forces and torques is presented to validate the controller.  
adaptive control; CubeSat; drag; lift; attitude; uncertainty; gravity gradient; low Earth orbit
# Evaluation of the Robustness for Integrated Production Scheduling and Maintenance Planning Problem
## 4 Robustness Measure for JSP with Preventive Maintenance
### Analytical Approximation Calculation
(**c**) The effect of the start time of \(O_{s,j}\). In chain 1, i.e., \(O_{1,1}\to O_{3,1}\to O_{3,2}\), when the failure occurs in the operation \(O_{1,1}\), it may first affect the completion time of operation \(O_{3,1}\). As shown in Figure 6b, the expected completion time of \(O_{3,1}\) is denoted as \(C_{3,1}^{I}=C_{3,1}+\max\left\{pr_{1,1}\left(tc_{1}-idle_{1,1}^{3,1}\right),0\right\}\). The failure will continue to propagate along the chain of constraints, and the operation \(O_{3,2}\) will be affected. The expected completion time of \(O_{3,2}\) is \(C_{3,2}^{I}=C_{3,2}+\max\left\{pr_{1,1}\left(tc_{1}-idle_{1,1}^{3,2}\right),0 \right\}\). When the failure occurs in the operation \(O_{3,1}\), the propagation process is shown in Figure 6c. The expected delay time of \(O_{3,2}\) affected by RMBs on \(O_{3,1}\) is denoted as \(\max\left\{pr_{3,1}\left(tc_{1}-idle_{3,1}^{3,2}\right),0\right\}\), where \(idle_{3,1}^{3,2}=C_{3,2}^{I}-C_{3,1}^{I}\). When the failure occurs in the operation \(O_{3,2}\), as shown in Figure 6d, the expected delay time of \(O_{3,2}\) is \(pr_{3,2}\cdot tc_{2}\). The expected completion time of \(O_{3,2}\) by affected by RMBs on the operation of chain 1 can be denoted as \(C_{3,2}^{cm,1}=C_{3,2}+\max\left\{pr_{1,1}\left(tc_{1}-idle_{1,1}^{3,2}\right),0\right\}+\max\left\{pr_{3,1}\left(tc_{1}-idle_{3,1}^{3,2}\right),0\right\}+ pr_{3,2}\cdot tc_{2}\). The expected completion time of \(O_{3,2}\) by chain 2 is denoted as \(C_{3,2}^{cm,2}=C_{3,2}+\max\left\{pr_{2,1}\left(tc_{2}-idle_{2,1}^{3,2}\right),0\right\}+pr_{3,2}\cdot tc_{2}\). The expected completion time of \(O_{3,2}\) caused by RMBs is denoted as \(E[C_{3,2}(\sigma_{r})]=\max\left\{C_{3,2}^{cm,1},C_{3,2}^{cm,2}\right\}\). At last, we propose the following surrogate measure to approximate the expected completion time. \[E\left[C_{i,j}(\sigma_{r})\right]=max\left\{C_{3,2}^{cm,1},\cdots,C_{3,2}^{cm, n}\right\} \tag{10}\]  
The detailed Algorithm1 is shown as follows:  
Figure 5: The Gantt chart for n = 3, m = 2. Figure 6: The calculation of the expected completion time of operation. (**a**) Process sequence without adding breakdowns (**b**) The influence of the failure in operation O\({}_{1,1}\) on the completion time of the subsequent operations. (**c**) The influence of the failure in operation O\({}_{3,1}\) on the completion time of the subsequent operations. (**d**) The influence of the failure in operation O\({}_{3,3}\) on its own completion time. For the robustness evaluation problem of scheduling scheme with \(nm\) operations, since each operation node has at most two successor nodes, the failure impact propagation of each operation only needs to be calculated twice. The total number of calculations is \(2 nm\), and its computational complexity is \(O(nm)\).