esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Fast Protection System with Xilinx ZYNQ SoC for RAON Heavy-Ion Accelerator
###### Abstract  
The development of the fast protection system (_FPS_) was driven by the critical need to safeguard internal components of the accelerator from beam damage and minimize operational downtime. During accelerator operation, various faults can occur, posing a significant risk. The _FPS_ acts as a rapid response system, initiating a shutdown signal to a reliable chopper system to prevent beam damage and ensure the operational availability of the accelerator. To meet the stringent shut off time requirements specific to critical faults, the _FPS_ was designed to respond within 50 \(\upmu\)s, while the total _FPS_ time, including acquisition, redundancy, and processing, needed to be less than 20 \(\upmu\)s. In order to achieve these goals, a customized _FPS_ was developed for the RAON heavy-ion accelerator, utilizing the Xilinx ZYNQ system-on-chip (_SoC_). The _FPS_ system comprised seven acquisition modules, one mitigation module with an embedded _SoC_, and employed optical fiber connections for efficient data transmission. This article provides a comprehensive account of the design, development, and testing of the _FPS_ system. Experimental tests were conducted to validate its performance. These tests included verifying the accuracy of cyclic redundancy checks, acquiring interlock signals in short pulses, and measuring the delay time during abnormal signal occurrences. Of particular significance is the measurement of the total signal processing time for a 1 km optical cable in the RAON system, which was determined to be 9.8 \(\upmu\)s. This result successfully met the stringent requirement of 20 \(\upmu\)s for the _FPS_ time. The ability of the _FPS_ to operate within the desired time frame demonstrates its effectiveness in protecting the accelerator's components from beam damage and minimizing downtime. Consequently, the _FPS_ ensures the operational availability of the accelerator while maintaining the safety and integrity of its internal systems. By providing a detailed account of the _FPS_'s design, development, and testing, this article contributes valuable insights into the capabilities of the _FPS_ in real-world accelerator scenarios. The successful implementation of the RAON-optimized _FPS_ with the Xilinx ZYNQ _SoC_ reaffirms its effectiveness as a fast and reliable protection system, thus enhancing the overall operational performance of the accelerator.  
fast protection system; accelerator control system; Xilinx ZYNQ; experimental physics and industrial control system; RAON heavy-ion accelerator
# Development of the User Requirements for the Canadian WildFireSat Satellite Mission
## 3 Definition of the User Requirements
### Measurement Requirements  
In this section we define the measurements required from the payload to provide sufficient data to meet the FMFs. Specifically, this section explores instrument band requirements, as well as their associated spatial resolutions and dynamic ranges (Table 5). Information pertaining to band sensitivity and performance is presented in Section 3.3 (Precision Requirements).  
In order to meet FMF-3 and -4 the instrument payload must meet all the requirements for active-fire monitoring. This necessitates the ability to detect new wildfires, as well as map their active wildfire areas and behavior, thereby supporting smoke and air quality modeling. This requires the instrument to collect thermal observations to conduct wildfire detection and characterization analysis (e.g., [32; 73; 74]). It also requires multi-spectral data from a suite of Mid-, and Long-Wave infrared (MWIR and LWIR) combined with Near-infrared (NIR) and Visible (VIS) bands at a minimum (e.g., [47; 74; 75]). The IR  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **URD Parent Reference** & **Requirement** \\ \hline Coverage \& Temporal Resolution & CWFM-URD-0020 & The mission SHALL provide the capability to observe at minimum 97\% of all points within the AoI at least once per any 48-h period, and all points within the AoI at least once per 72-h period \\  & & The mission SHALL provide the capability to observe on average 85\% of all points within the AoI at least once per any 24-h period \\  & & As a goal, the mission SHOULD provide the capability to observe all points within the AoI at least once per any 24-h period \\  & & As a goal, the mission SHOULD provide the capability to observe all points within the AoI at least once per any 24-h period \\ \hline Peak Burn & CWFM-URD-0030/40 & The design SHALL provide observation during each peak burning period \\ \hline Data Latency & CWFM-URD-0050 & Time lag between data acquisition and delivery to user SHALL not exceed 30 min for the MWIR, LWIR, NIR, and VIS (red) for 90\% of observations and 24 h for all data Time lag between data acquisition and delivery to user SHOULD not exceed 30 min for all data \\ \hline Downlink Priority & CWFM-URD-0070 & Priority downlink SHALL be given to MWIR, LWIR, NIR, and VIS (red) bands \\ \hline Swath Width & CWFM-URD-0110/0120 & Swath width for all spectral bands SHALL be no less than 200 km \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of key Observational (coverage and latency) Requirements as outlined in the User Requirements Document (URD), and refined in the Mission Requirements for WildFireSat. AoI refers to the Area of Interest (Figure 2). See Table 5 for spectral band definitions.  
and VIS-NIR bands which observe primarily emitted and reflected energy, respectively, are treated as two complimentary data sets, and are described separately here.
# Analysis, Design, and Experimental Research of a Novel Wheelchair-Stretcher Assistive Robot
## 2 Mechanical Structure Design of the Wheelchair-Stretcher Assistive Robot
### Control of the Lifting Mechanism  
The control block diagram of the lifting mechanism is shown in Figure 21. This part adopts the position closed-loop control, and the modified _PWM_ value can be obtained through the transformation of real-time monitoring position information.  
The process is as follows: firstly, we assume that the same _PWM_ values of the two linear actuators are given as,  
\[PWM_{1}=PWM_{2} \tag{30}\]  
where \(PWM_{1}\) is the value of the linear actuator \(L_{1}\), \(PWM_{2}\) is the value of the linear actuator, \(L_{2}\).  
At time \(t\), the output voltage values of the two electronic rulers are \(U_{1}\), \(U_{2}\). Since the output voltage of the linear displacement sensor is linearly related to the moving displacement, the moving displacement of the two electronic rulers can be described as,  
\[\begin{array}{l}S_{1}=U_{1}\text{:}K_{d},\\ S_{2}=U_{2}\text{:}K_{d}\end{array} \tag{31}\]  
where \(K_{d}\) is the proportional coefficient.  
The velocity difference of the two linear actuators in time \(t\) can be computed as,  
\[\Delta V=V_{1}-V_{2}=\frac{\Delta S}{t}=\frac{S_{1}-S_{2}}{t}. \tag{32}\]  
where \(V_{1}\) is the velocity of the linear actuator \(L_{1}\), \(V_{2}\) is the velocity of the linear actuator \(L_{2}\).  
The data of the linear actuator \(L_{1}\) is used as the reference value, and the velocity of the linear actuator \(L_{2}\) can be modified as:  
\[V_{2}=\Delta V+V_{1}. \tag{33}\]  
Since the velocity of the motor is directly proportional to the duty cycle, it can be described as,  
\[V=K_{\gamma}\gamma \tag{34}\]  
where \(K_{\gamma}\) is the assuming proportional coefficient, \(\gamma\) is the duty cycle.  
According to Equations (33) and (34), the duty cycle of the linear actuator \(L_{2}\) can be modified as:  
\[\gamma_{2}=\frac{V_{2}}{K_{\gamma}}=\frac{\Delta V}{K_{\gamma}}+\frac{V_{1}}{ K_{\gamma}}. \tag{35}\]  
Figure 21: Control block diagram of the lifting mechanism.  
The position closed-loop control for the lifting mechanism is conducted by the STM32 chip. So PWM pulse modulation mode and ADC mode of STM32 are used in the process of control program design, and duty cycle can be calculated by the following formula,  
\[\gamma=\frac{CCRx}{(ARR+1)}(x=1,2) \tag{36}\]  
where \(CCRx\) is comparison value, \(ARR\) is value of automatic reload register in the stm32 chip.  
So the modified duty cycle \(\gamma_{2}\) of the linear actuator \(L_{2}\) can be obtained as:  
\[\gamma_{2}=\frac{V_{2}}{K_{\gamma}}=\frac{CCR1}{(ARR+1)}+\frac{K_{d}(U_{1}-U_{ 2})}{K_{\gamma}t}. \tag{37}\]  
According to the above analysis, the coordination control of the lifting mechanism can be completed.
## 6 Security Solutions
### End Device Security
#### 6.1.4 Secure Debug Interface  
The debugging interface is primarily used during development and manufacturing of a device. This interface allows debugging of on-chip applications and loading system images into flash memory. It also enables system administrators or device manufacturers to find and fix errors that occur during the lifetime of a system. The JTAG [146] is one of the most widely used debugging interfaces for chips. This interface can be a potential attack surface for adversaries. Adversaries can exploit the JTAG interface to read on-chip registers and memories and reprogram the system image (overwrite the trusted system image with the malicious one). Secure JTAG implementation has been proposed in [111; 112; 113] to defend against such malicious activities.
# Coal Seam Thickness Prediction Based on Transition Probability of Structural Elements
## 3 TTP-GPR for Coal Seam Thickness Prediction
### Implementation of STTP-GPR  
The basic steps of the proposed STTP-GPR algorithm are summarized, as follows:  
* Step 1: acquire the coal seam thickness data, as \(H=\{z_{1},z_{2},\ldots,z_{n}|z_{i}\in R^{+},0<i\leq n\}\);
* Step 2: divide the acquired data into \(3\times 3\) structural elements \(St=\{st_{i-1},st_{i},st_{i+1}|1\leq i\leq n\}\) and the center of each structural element is the prediction point using the method described in Section 3.1;
* Step 3: the structural elements are divided into training sets and test sets according to the data ratio of 8:2, and attains the distribution of known coal seam thickness;
* Step 4: divide the state of each point in the structure element according the Formula (12);
* Step 5: calculate the state transition probability within structural element through the formula is \(P(St)=P\big{\{}st_{i,j-1}=s_{i}|st_{i,j}=s_{j}\big{\}}=P_{s_{i}s_{j}}(1)\);
* Step 6: calculate the transition probability between structural elements, and the transition probability is \(P(st_{i-1},st_{i})=P\big{\{}st(i-1)=s_{i}|st(i)=s_{j}\big{\}}=P_{s_{i}s_{j}}(1)\);
* Step 7: calculate the conditional probability between structural elements, where the conditional probability is \(P(st_{i}|st_{i-1})=P(st_{i-1},st_{i})/P(st_{i-1})\);
* Step 8: combine the structural element transition probabilities and joint distribution of known coal seam thickness to derive a posterior probability distribution of prediction model \(\text{z}^{\prime}=STTP\_GP(m(st),P(st_{i}|st_{i-1}))\), that uses the structural element transition probabilities to replace the covariance function in GP algorithm. where \(\text{z}^{\prime}\) represents predicted coal seam thickness value, and \(m(st)\) represents the mean value of structural elements; and,
* Step 9: predict the coal seam thickness at different positions.  
Especially, the structural elements contain nine coal seam points that each point belongs to a certain state, and z is a coal seam point value that is computed through \(\text{z}^{\prime}=STTP\_GP(m(st),P(st_{i}|st_{i-1}))\), which uses the structural element transition probabilities to replace the covariance function in GP algorithm.  
Figure 7 shows the flow chart of the proposed STTP-GPR algorithm.
## Materials and Methods
### Synthesis of Aerogels  
Aerogels were prepared from resorcinol and formaldehyde as fundamental reactants, with sodium carbonate as a catalyst. The pH level of the reaction medium was adapted at neutral level with diluted nitric acid and ammonium hydroxide solutions. The quantities of resorcinol, sodium carbonate, formaldehyde, and water that were utilized in the synthesis of the aerogel were 12.44 g, 0.0240 g, 17.40 mL, and 32.60 mL, accordingly. The temperature was kept constant at 70 \(\pm\) 1 \({}^{\circ}\)C. Resorcinol and Na\({}_{2}\)CO\({}_{3}\) were weighed and blended with ultrapure H\({}_{2}\)O in Erlemeyer flasks, and then stirred until all constituents are entirely dissolved. Afterwards, formaldehyde was combined with the previously dissolved species during the stirring process. After that, the solution acidity was adapted at (pH = 7) by utilizing diluted HNO\({}_{3}\) and NH\({}_{4}\)OH buffers. The resorcinol-formaldehyde mixture solution was poured into vials, sealed tightly and set in an oven at 70 \(\pm\) 1 \({}^{\circ}\)C. In order to prevent the dehydration of the gel, and to increase their cross-linking density, a dilute (2%) acetic acid solution is poured onto the gel after its solidification. After seven days, the samples were retrieved from the oven and left to cool down to ambient temperature.  
The remnant solution on top of the cured gel was decanted and disposed. The remaining solution was replaced with fresh acetone at ambient temperature by casting acetone on top of the sample surface and keeping it at ambient temperature for one day, then replacing the leftover acetone with fresh acetone everyday for three consecutive days. Following the 3 rd day of solvent replacement with acetone, the drying process was conducted by supercritical CO\({}_{2}\) extraction with the following procedure. Firstly, the gel was put underneath pressure of 20 MPa of liquid CO\({}_{2}\) at a temperature of 25 \({}^{\circ}\)C to dissolve the acetone with the liquid CO\({}_{2}\). The exit valve was opened and the extractor was depressurized to 12 MPa, allowing liquid CO\({}_{2}\) to flow through the gel at 25 \({}^{\circ}\)C for times ranging from 1 to 2.5 h. Then, the temperature was elevated to reach the supercritical state at 31 \({}^{\circ}\)C and 7.4 MPa, and maintained at this condition for 2 h. After that, the extractor was gradually depressurized to atmospheric pressure and the dried resorcinol-formaldehyde aerogel was attained [28; 29; 30; 31]. The supercritical drying was performed using a critical point dryer (E3100 Critical Point Dryer, Quorum Technologies--Preparation for Excellence, UK). The proposed reaction mechanism of resorcinol and formaldehyde to produce a gel is represented in Figure 1.  
Figure 1: Suggested reaction mechanism of resorcinol-formaldehyde gel.
# Niching-Driven Divide-and-Conquer Hill Exploration
## 2 Related Works
### Clustering with an Adaptive Number of Groups  
In existing niching methods, clustering with an adaptive number of groups is implemented in various ways. In niching covariance matrix adaptation evolution strategy with nearest-better clustering (NEA2) [15], locally best individuals are identified as they have higher nearest-better distances compared to other individuals. In DE based on distinguishing niche centers [16], locally best individuals are identified as they contribute to the maximum fitness-entropy measurement. In ESPDE [17], two individuals are assigned to the same group if the box space formed by them contains no individual inferior to both of them. In history-based topological speciation crowding DE [18], an enhanced version of the process in ESPDE is adopted, where a middle individual in the box space is determined first, and two individuals are assigned to the same group if one of them is inferior to the middle individual while both of them are in the same group with the middle individual. In an adaptive multi-population framework [19], a single linkage hierarchical clustering method is used to create nonoverlapping subpopulations. In the automatic niching DE [20], as the population converges towards a number of centers with the evolution, a density-based clustering method is adopted for the population. In evolutionary multi-objective optimization-based multimodal optimization [21], individuals with their fitness higher than a threshold are divided into groups by a density-based clustering method.  
Although the specific implementations are different, a common principle lays behind them, i.e., group centers are identified as they are far away from each other while both having good fitness. Since both \"far\" and \"good\" are relative terms defined in comparison to other individuals within the population, a larger population size is required to obtain group centers in smaller BoAs by these clustering methods. However, as the initial population is commonly uniformly distributed, the outcome of redundant individuals in large BoAs is inevitable.
## Title Evolution of the Modulus of Elasticity of Metakaolin-Based Geopolymer  
**[PERSON] \({}^{1,*}\)**, [PERSON] \({}^{2}\)** and [PERSON] \({}^{3}\)  
\({}^{1}\) INESC Coimbra, Department of Civil Engineering, University of Coimbra, 3030-788 Coimbra, Portugal  
\({}^{2}\) Centre for Mechanical Engineering, Materials and Processes, Department of Civil Engineering, University of Coimbra, 3030-788 Coimbra, Portugal  
\({}^{3}\) DCSAT Department of Health Sciences, Environment and Technologies, University of Santiago, Praia, Cape Verde  
\({}^{*}\) Correspondence: [EMAIL_ADDRESS]  
**Citation:** [PERSON], [PERSON]; [PERSON], [PERSON]; [PERSON], [PERSON] Time Evolution of the Modulus of Elasticity of Metakaolin-Based Geopolymer. _Appl. Sci._**2023**, 13, 2179. [[https://doi.org/10.3390/app13042179](https://doi.org/10.3390/app13042179)]([https://doi.org/10.3390/app13042179](https://doi.org/10.3390/app13042179))  
\({}^{1}\) INESC Coimbra, Department of Civil Engineering, University of Coimbra, 3030-788 Coimbra, Portugal  
\({}^{2}\) Centre for Mechanical Engineering, Materials and Processes, Department of Civil Engineering, University of Coimbra, 3030-788 Coimbra, Portugal  
\({}^{3}\) DCSAT Department of Health Sciences, Environment and Technologies, University of Santiago, Praia, Cape Verde  
\({}^{*}\) Correspondence: [EMAIL_ADDRESS]  
**Abstract:** The objective of the research is to develop a new family of geopolymeric materials and to use an experimental methodology to characterize the mechanical behavior of the materials obtained by alkaline activation of metakaolin using a compound activator. The researchers also intend to study the unknown time evolution of the modulus of elasticity and the influence of the composition of the aggregates on the strength of the material. Like the material's strength, the results have a direct influence on structural safety evaluations. For the analysis of the mechanical properties of the mixtures, different types of tests were carried out: Flexural and compression tests on parallelepipeds and compression tests on cylinders were performed to assess the main strength characteristics of metakaolin-based geopolymers. Regarding the aggregate composition, the results show that the correction of the aggregate particle size line did not improve the mechanical properties. From about 400 h of curing, at ambient temperatures, the mechanical properties of the geopolymeric material are almost invariable. The highest value of the elastic modulus of elasticity occurs around 420 h, at about 18 GPa. The modulus of elasticity is independent of test load rate as per standards, and 1.7% strain was observed during maximum compressive stresses in the rupture tests. Also, the secant modulus values at 60% and 80% of maximum stress are within 12% of the value at 40% of maximum stress.  
**Keywords:** experimental study; metakaolin; geopolymer; modulus of elasticity; strength; time evolution
# DK-SLAM: Monocular Visual SLAM with Deep Keypoint Learning, Tracking, and Loop Closing
## 3 Deep Keypoint-Based Monocular SLAM
### Deep Keypoint-Based Loop Closing
#### 3.3.3 Global Map Correction via Loop Closing  
Optimizing the similarity transformation matrix \(\mathbf{T}_{\text{sim}}\) refines the pose of the current keyframe and its co-view related keyframes. Concurrently, we update the positions of co-observed map points across these keyframes. Next, we align the map points of the matched keyframe \(K_{m}\) and its connected keyframes with those of the current keyframe \(K_{c}\) and its connected keyframes. Updating the visibility graph between keyframes based on matched map points follows. Utilizing \(K_{c}\), \(K_{m}\), global map points, and the visibility graph,  
Figure 3: An illustration of our proposed Online Learning-based Binary BoW. The BoW is constructed incrementally, with matched descriptors in the keyframes database stored within the same leaf node. In the presence of unmatched descriptors in the current keyframe, a new leaf node is created.  
we construct and optimize the essential graph. Finally, a global bundle adjustment refines all keyframes and map point positions within the global map.
# Application of Ultrasound Homogenization in Milk Ice Cream Mixes
## 1 Introduction
The appropriate stability of the emulsion, which was identified with a lack of changes in the sizes of particles, may in-fluence the obtaining of the desirable structure of ice cream and the control of the shelf-life of the product [13; 14]. The initial stability of ice cream mixes is created at the stage of homogenization, where the adsorption of proteins and emulsifiers occurs [15; 16; 17]. It is worth paying attention that, in ice cream mixes, the amount of destabilization should not be treated as decreasing the physical properties of ice cream mixes. The amount of destabilized fat in ice cream may contribute to the beneficial changes in melting time and smoother texture of ice cream [18; 19; 20]. The findings from the study by [PERSON] et al. [21; 22], using the example of vegan ice cream, showed that the lower stability of samples had a greater impact on the ice crystal structure, contributing to obtaining smaller ice crystals in comparison to traditional homogenization. The aim of this research is, therefore, to use ultrasound during homogenization and establish the influence of ultrasonic cavitation on the physical properties of milk ice cream mixes. This study not only focuses on the change during ice cream production, but also uses innovative stabilizers such as iota carrageenan hydrolyzates in the ice cream recipe, which are known for having the ability to be more flexible than the primary form of carrageenan. The obtained data and knowledge may shortly prove to be crucial information for producing milk and vegan ice cream.