esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Objective Optimization of Production Objectives Based on Surrogate Model
## 2 Background of Multi-Objective Optimization Based on a Surrogate Model
### Procedure of Surrogate-Based MOOP Solving  
The scheme in Figure 1 illustrates the procedure of a metamodel implementation to the MOOP solving in a production environment. Vector \(\overset{\rightarrow}{X}\) represents system inputs that will be transformedto outputs \(\overset{\rightarrow}{Y}\). The model structure and its configuration affect the system performance. Thus, mapping input parameters (production system loading) to outputs-production objectives, such as costs, flow time, or product quantities can be observed. Due to the fact that the direct analytical expression of this mapping is unknown, we consider the simulation model of a production system as a black box and the mapping of inputs to outputs as a black-box transformation. All the information about it can be gained from the simulation. The simulation model of a production system (production system black-box in Figure 1) serves as a data source for building the surrogate model instead of a black-box function of the underlying simulation model. Simulation experiments need to consider the design of the experimental layout originated from DoE when evaluating the production objectives as outputs of the simulation runs. Once the production objectives have been evaluated, DoE adopts the observed production goals as responses \(\overset{\rightarrow}{Y}\) according to the suggested design of the planned experiment. The derived regression models (production system metamodel in Figure 1) estimate the values of production goals \(\overset{\rightarrow}{\hat{Y}}\). These regression models by means of individual objective functions represent metamodels as analytically expressed transformations of inputs \(\overset{\rightarrow}{X}\rightarrow\overset{\rightarrow}{Y}\) for the selected simulation model of a production system.  
The optimization of production means to find the effective configuration of input parameters for the black-box function to ensure the desired production performance. Therefore, metamodels for production objectives are incorporated into the scalar multi-objective function in the next step. It is subsequently minimized under specified constraints with respect to a priori defined preferences required by the selected MOO methods.
# Design of CubeSat-Based Multi-Regional Positioning Navigation and Timing System in Low Earth Orbit
## 3 Results
### Space Segment  
The S/C Bus consists of the following subsystems: Command and Data Handling Subsystem (CDHS), Electrical Power Subsystem (EPS), Attitude Determination and Control Subsystem (ADCS), Telemetry and Telecommand (TTC) Subsystem, Propulsion Subsystem (PRO), and Structure Subsystem (STR). The S/C's baselined configuration can be seen in Table 5, while its CAD is shown in Figure 10.  
Figure 8: Orbital decay analysis—passive disposal after deorbit maneuver.  
Figure 9: SARA re-entry simulation—altitude vs. time.  
As was already mentioned, the PNT Payload is based on AIOTY-CUBE's ATOMIC payload and consists of the following components:  
1. PNT Solution Generator:  
1. Tallysman GNSS Antenna;
2. NovAtel OEM719 GNSS receiver;
3. Microchip SA.45s CSAC;
4. Payload OBC.
2. NANOlink-boost Gen2 Transceiver.
3. GOMspace NanoCom ANT2150-DUP.
4. Laser Retroreflector Array (LRA).  
Overall, the PNT Payload is considered to have a mass of about 1.5 kg and take up a volume of 1U. Its maximum power consumption is 31.25 W.  
\begin{table}
\begin{tabular}{c c} \hline
**Subsystem** & **Component** \\ \hline \multirow{4}{*}{CDHS} & GOMSpace NanoMind A3200 \\  & GOMSpace DMC-3 \\  & GOMSpace GPS Kit \\ \hline \multirow{4}{*}{EPS} & GOMSpace NanoPower P80 System \\  & GOMSpace NanoPower BP8 \(\times\)2 \\  & Body Mount Solar Array GOMSpace NanoPower MSP-B-4 \(\times\)4 \\  & Body Mount Solar Array GOMSpace NanoPower MSP-B-4 \(\times\)1 \\  & Deployable Solar Array GOMSpace NanoPower DSP-90 deg \(\times\)2 \\ \hline \multirow{4}{*}{ADCS} & CubeSpace CubeADCS Core \\  & CubeSpace CubeTorquer CR0008 \(\times\)3 \\  & CubeSpace CubeWheel CW0057 \(\times\)4 \\  & CubeSpace CubeSense Fine Sun Sensor \(\times\)1 \\  & CubeSpace CubeSense Coarse Sun Sensors \(\times\)6 \\  & CubeSpace CubeSense Earth \\  & CubeSpace CubeMag Compact \\  & CubeSpace CubeMag Deployable \\ \hline \multirow{4}{*}{TTC} & GOMSpace NanoCom AX100 \\  & GOMSpace NanoCom ANT-6F (UHF) \\ \cline{1-1}  & GOMSpace NanoCom Link-S \\ \cline{1-1}  & GOMSpace NanoCom ANT2150-DUP \\ \hline PRO & Enpulsion NANO \\ \hline \multirow{4}{*}{STR} & GOMSpace 8U Structure \\  & GOMSpace 6U Side Panels \\ \cline{1-1}  & GOMSpace NanoUtil MSP-FPP Flight Preparation Panel \\ \cline{1-1}  & Custom mounting components \\ \hline \multirow{4}{*}{PNT Payload} & PNT Solution Generator \\  & NANOlink-boost Gen2 Transceiver \\ \cline{1-1}  & GOMSpace NanoCom ANT2150-DUP \\ \cline{1-1}  & LRA \\ \hline \end{tabular}
\end{table}
Table 5: S/C baseline configuration.
## 5 Biological Activities of Zerumbone (ZER)
### Anti-Inflammatory Activity
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**In Vitro/In Vivo** & **Models** & **Activity** & **References** \\ \hline In vitro & Macrophages differentiated & ZER inhibited the secretion of pro-inflammatory cytokines \\  & from human monocyte & in lipopolysaccharide-saccharide (LPS)-activated inflammation in & [138] \\  & (THP-1) & THP-1 cell-derived macrophages. & \\ In vivo & Mice (endotoxin-treated mice & ZER reduced leukocytes infiltration into the alveolar space and inhibited lung edema in LPS-induced acute lung injury. & [139] \\ In vivo & Rats using Paw edema model & ZER reduced both \(\lambda\)-carrageenanan- and prostaglandin & [140] \\ In vitro & RAW264.7 murine & ZER induced proteo-stress leading to activition of HSF1 & [141] \\  & macrophages & resulting into anti-inflammatory activity. & \\ In vivo & Wild-type C57 BL/6 & ZER decreased ETBF-induced colitis through inhibition of & [142] \\  & mice & Nr-kB signaling pathway. & \\ In vitro & U937 monocytes & ZER supressed the activation of inflammatory markers in & \\  & & the macrophages via MyD88-dependent & [143] \\  & & NF-\(\kappa\)B/MAPK/PI3K-Akt signaling pathways. & \\  & & ZER showed protective effect on acute lung injury induced & by LPS via suppression of intracellular adhesion \\ In vivo & Adult male pathogen-free ICR & molecules-1, IL-1B, macrophage inflammatory protein -2, & \\  & mice & downregulation of Akt, p38 MAPK/JNK, and IxB/NF-kB & \\  & & pathways. & \\ In vivo & Adult male pathogen-free ICR & ZER showed protective effect on acute lung injury induced & \\  & mice & by LPS- via upregulation of antioxidative enzymes and & \\  & mice & Nrf2/HO-1 pathway. & \\  & & ZER inhibited proinflammatory gene inducible nitric oxide & \\ In vitro & RAW 264.7 cells & (iNOS) and COX2 expression by attenuating IkB & \\  & & degradation. & \\ In vitro & RAW264.7 cells & ZER significantly accelerated spontaneous COX-2 & \\ In vitro & Murine macrophage & mRNA decay. & \\ In vitro & RAW264.7 cells & ZER stimulated HO-1. & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Anti-inflammatory activity of ZER.
# CoAR: Congestion-Aware Routing Protocol for Low Power and Lossy Networks for IoT Applications
###### Abstract  
The IPv6 routing protocol for low power and lossy networks (RPL) was designed to satisfy the requirements of a wide range of Internet of Things (IoT) applications, including industrial and environmental monitoring. In most scenarios, different from an ordinary environment, the industrial monitoring system under emergency scenarios needs to not only periodically collect the information from the sensing region, but also respond rapidly to some unusual situations. In the monitoring system, particularly when an event occurs in the sensing region, a surge of data generated by the sensors may lead to congestion at parent node as data packets converge towards the root. Congestion problem degrades the network performance that has an impact on quality of service. To resolve this problem, we propose a congestion-aware routing protocol (CoAR) which utilizes the selection of an alternative parent to alleviate the congestion in the network. The proposed mechanism uses a multi-criteria decision-making approach to select the best alternative parent node within the congestion by combining the multiple routing metrics. Moreover, the neighborhood index is used as the tie-breaking metric during the parent selection process when the routing score is equal. In order to determine the congestion, CoAR adopts the adaptive congestion detection mechanism based on the current queue occupancy and observation of present and past traffic trends. The proposed protocol has been tested and evaluated in different scenarios in comparison with ECRM and RPL. The simulation results show that CoAR is capable of dealing successfully with congestion in LLNs while preserving the required characteristics of the IoT applications.  
Internet of Things; LLNs; 6 LoWPAN; RPL; objective function; routing metrics Article
# A Novel Adaptive Sparrow Search Algorithm Based on Chaotic Mapping and T-Distribution Mutation
## 3 A Novel Adaptive Sparrow Search Algorithm
### T-Distribution Mutation Strategy  
In probability theory and statistics, t-distribution is also called student distribution. It is used to estimate the mean of a normally distributed population with unknown variance based on a small sample. The shape of the t-distribution curve is related to the degree of freedom n. Compared with the standard normal distribution, if the degree of freedom is smaller, the curve is flatter. If the middle of the curve is lower, the tails on both sides of the curve are higher. If the degree of freedom n is greater, the t-distribution is closer to the normal distribution. The Cauchy distribution and Gaussian distribution are two special case distributions at the boundary of the t-distribution, and the three function distributions are shown in Figure 1.  
It can be seen from Figure 1 that the shape of the two ends of the t-distribution is long and flat, and the process of approaching 0 is relatively gentle. The speed is slower than the Cauchy and Gaussian distributions, and the peak is similar to the Cauchy and Gaussian. The distribution is smaller than those, such that the t-distribution variation is stronger than the Cauchy variation and Gaussian variation in perturbation ability. Therefore, the t-distribution mutation is introduced into the target position update formula, and the disturbance ability of the t-distribution mutation is exerted to improve the global optimization performance. The adaptive t-distribution mutation strategy is used to update the sparrow position, which is described as follows.  
\[x_{i}^{t}=x_{i}+x_{i}\cdot t(iter) \tag{11}\]where, \(x_{i}^{t}\) represents the individual position of the sparrow after mutation, \(x_{i}\) is the position of the sparrow individual, and \(t(iter)\) denotes the t-distribution with the number of iterations, which is regarded as the parameter degree of freedom. On the basis of this definition, the t-distribution random interference term is added to make full use of the information interference of the current population, and the number of iterations t is used as the degree of freedom parameter. At the beginning of the algorithm, the value of the number of iterations is small, the t-distribution mutation is similar to that of Cauchy distribution mutation, and the algorithm has good global exploration ability. In the later period of the algorithm, the t-distribution mutation is similar to the Gaussian distribution mutation, and the algorithm has good local development capabilities. In the middle of the algorithm, the t-distribution mutation is between Cauchy mutation and Gaussian mutation. The mutation operator of the t-distribution combines the advantages of the Gaussian operator and the Cauchy operator, and the global exploration and local exploration of the algorithm are improved at the same time.
# A Compact Quad-Element UWB-MIMO Antenna System with Parasitic Decoupling Mechanism
## 3 Isolation Mechanisms
### Decoupling Structure for Side-by-Side Radiators  
The dumbbell shaped stub attached to the ground plane provides isolation between side-by-side radiating elements. The circles are etched in the center of the dumbbell structure and semi-circles are subtracted from its sides in order to effectively introduce the desired capacitive and inductive loading of the ground plane. The decoupling structure is shown in Figure 1a. Replicas of these stubs are placed on the top side between antennas as well. These strips, on the top side, act as transmission line resonators and effectively reduce the mutual coupling between the side-by-side radiating elements. Decoupling performance better than 20 dB is achieved between side-by-side antennas.
## 4 Discussion
### Usefulness/Usability Metrics  
This subsection is centered on the measurements of usefulness/usability by the user. These parameters are used in user-centered interaction design to evaluate a product through testing with the users themselves.  
In designing any application or system, it is essential to reduce uncertainty and relying on quantifiable data obtained in research is advantageous. Therefore some metrics based on the user experience and the appreciation of the tool used to have this information are considered. These parameters are listed in the Table 16.  
Table 17 shows the responses of each person who tested the sensor system in their home. They were asked the four questions in the Table 16 related to usability metrics for a system or application. The results show that 90% think that the monitoring system has Effectiveness. 92.5% think that the system has Efficiency. 95% are satisfied with the system. Finally, 25% feel that the system has the Learnability feature. This last result is curious and maybe because older people are not interested in learning elaborate technology-related aspects. Therefore, they may think that the system is complex. However, they did not need to do any installation or configuration.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Metric** & **Question** \\ \hline Effectiveness & Can users achieve their goal with the application or the system? \\ Efficiency & Is low mental effort required to launch the application or system? \\ Satisfaction & Can you reach your goal with minimal effort? \\ Learnability & Can you use the application or system without instructions in an intuitive way? \\ \hline \hline \end{tabular}
\end{table}
Table 16: Basic usability and user experience metrics.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**ID Person** & **Effectiveness** & **Efficiency** & **Satisfaction** & **Learability** \\ \hline
1 & ✓ & ✓ & ✓ & \(\times\) \\
2 & ✓ & ✓ & ✓ & \(\times\) \\
3 & ✓ & ✓ & ✓ & ✓ \\
4 & ✓ & \(\times\) & ✓ & \(\times\) \\
5 & ✓ & ✓ & ✓ & \(\times\) \\
6 & ✓ & ✓ & ✓ & \(\times\) \\
7 & ✓ & ✓ & ✓ & ✓ \\
8 & ✓ & ✓ & ✓ & \(\times\) \\
9 & ✓ & ✓ & ✓ & \(\times\) \\
10 & \(\times\) & ✓ & ✓ & \(\times\) \\
11 & ✓ & ✓ & \(\times\) & \(\times\) \\
12 & ✓ & ✓ & ✓ & \(\times\) \\
13 & ✓ & ✓ & ✓ & \(\times\) \\
14 & ✓ & ✓ & ✓ & \(\times\) \\
15 & ✓ & ✓ & ✓ & \(\times\) \\ \hline \hline \end{tabular}
\end{table}
Table 17: Older people who made use of sensor system in their homes.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Sensor** & **Confidence** \\ \hline Level & 88\% \\ PTH & 95\% \\ Motion & 77\% \\ Noise & 50\% \\ Gyroscope & 20\% \\ Light & NA \\ Air & 2\% \\ \hline \hline \end{tabular}
\end{table}
Table 15: Confidence percentage of real alerts from sensor network.
# siEDM: An Efficient String Index and Search Algorithm for Edit Distance with Moves
## 3 Problem  
We first review the notion of EDM. The distance \(d(S,Q)\) between two strings \(S\) and \(Q\) is the minimum number of edit operations to transform \(S\) into \(Q\). The edit operations are defined as follows:  
1. Insertion: A character \(a\) is inserted at position \(i\) in \(S\), which generates \(S[1,i-1]aS[i,|S|]\),
2. Deletion: A character is deleted at position \(i\) in \(S\), which generates \(S[1,i-1]S[i+1,|S|]\),
3. Replacement: A character is replaced by \(a\) at position \(i\) in \(S\), which generates \(S[1,i-1]aS[i+1,|S|]\),
4. Substring move: A substring \(S[i,j]\) is deleted from the position \(i\), and inserted at the position \(k\) in \(S\), which generates \(S[1,i-1]S[j+1,k-1]S[i,j]S[k,|S|]\) for \(1\leq i\leq j\leq k\leq|S|\), and \(S[1,k-1]S[i,j]S[k,i-1]S[j+1,|S|]\) for \(1\leq k\leq i\leq j\leq|S|\).  
**Problem 1** (Query search for EDM).: For a string \(S\in\Sigma^{*}\), a query \(Q\in\Sigma^{*}\) and a distance threshold \(\tau\geq 0\), find all \(i\in[1,|S|]\) satisfying \(d(S[i,i+|Q|-1],Q)\leq\tau\).  
[PERSON] and [PERSON] [9] proved the NP-completeness of EDM and proposed a polynomial-time algorithm for a restricted EDM. [PERSON] and [PERSON] [8] presented an approximation algorithm named ESP for computing EDM. We present a string index and search algorithm by leveraging the idea behind ESP for solving Problem 1. Our method consists of two parts: (i) an efficient index structure for a given string \(S\) and (ii) a fast algorithm for searching query \(Q\) on the index structure of \(S\) with respect to EDM. Although our method is also an approximation algorithm, it guarantees upper and lower bounds for the exact EDM. We first review ESP in the next section and then discuss the two parts.
## 6 Discussion
### Managerial and Policy Implications  
* _Proactive capacity planning_: Daily re-forecasting enables planners to redeploy staff or transport assets ahead of demand spikes instead of after occupancy thresholds are breached.
* _Scenario analysis_: Because the RL layer re-weights objectives on-the-fly, stakeholders can stress-test policies (e.g., carbon-tax hikes, visa-waiver programs) by perturbing the corresponding channels and observing counterfactual trajectories without retraining the entire model.
* _Transparent communication_: SHAP-based explanations furnish a defensible rationale for interventions, aiding consensus building among public agencies and private operators.
## 4 Challenges and Opportunities in Marine Biotechnology
### Regulatory and Economic Challenges  
Regulatory frameworks remain a major obstacle to commercialisation. In many countries, microalgal products are classified as novel foods or new cosmetic ingredients, requiring rigorous safety and efficacy evaluations. The lack of harmonised international standards for labelling, quality control, and environmental safety poses barriers for global trade. One critical regulatory layer is the Nagoya Protocol and associated Access and Benefit-Sharing (ABS) frameworks. These can delay commercialisation by requiring time-consuming legal negotiations to clarify the origin and use of genetic resources, which can vary by country. For example, the commercialisation of _Porphyridium_-derived polysaccharides in the EU was delayed due to unresolved ABS documentation and country-of-origin compliance. Such regulatory inconsistencies contrast with more flexible ABS approaches in countries like the United States and Australia, where frameworks are either non-binding or interpreted more leniently. This regional variability adds uncertainty for product developers and may deter international investment [17].  
From an economic standpoint, the high cost of microalgal biomass production remains a limiting factor, especially when compared to terrestrial plants or synthetic alternatives. However, co-product strategies, government subsidies, and circular economy approaches (e.g., use of wastewater or flue gas as inputs) are helping to offset production costs. For example, cultivating microalgae on nutrient-rich industrial effluents can simultaneously produce biomass and mitigate environmental pollution, offering dual benefits [36].
# High-Performance Digital Devices Design by the ASMD-FSMD Technique for Implementation in FPGA
## Appendix A  
This appendix presents an example of a Verilog code of the sequential multiplier in the form of the FSMD, which implements the multiplication algorithm c.  
module mult_c_Mealy_FSMD  
#(parameter N = 4) //operand size  
(input clk, reset, run, //control signals  
input [N - 1:0] a, b, //multiplicand a and multiplier b  
output reg [2*N-1:0] p, //product p  
output reg done); //multiplication termination flag  
reg [2*N - 1:0] rp; //register declaration  
reg [N - 1:0] ra, rb;  
reg [N:0] cnt; //counter  
localparam [0:0] s0 = 0,s1 = 1;//states of FSMD  
reg [0:0] state; //state variable  
//behavioral description of FSMD  
always @(posedge clk, negedge reset)  
if(!reset) state <= s0; //reset of FSMD  
else  
case (state)  
s0: if(run)  
begin //the first ASMD block  
rp = 0; cnt <= 0; done <= 0;  
ra <= a;  
rb <= b;  
state <= s1;  
end  
else state <= s0;  
s1: begin //the second ASMD block  
if(rb[N - 1])rp = rp + ra;  
cnt <= cnt + 1?b1;  
rb <= rb << 1;  
if (cnt == N - 1)  
begin  
done <= 1?b1;  
p <= rp;  
state <= s0;  
end  
elsebegin  
rp = rp << 1;  
state <= s1;  
end  
end  
default: state <= s0;  
endcase  
endmodule
# Chinese Story Generation Based on Style Control of Transformer Model and Content Evaluation Method
## 1 Introduction  
Previous research on Chinese story cycle generation models has demonstrated that Chinese sentences can be effectively generated based on part-of-speech structures constrained by the model. Building on this foundation, this paper aims to enhance the cycle generation process by incorporating a fuzzy matching method to identify the most suitable vocabulary within an FP-Tree. The relevance of generated sentences is then used as the final selection criterion, ensuring that the chosen sentence improves contextual coherence in story generation.  
Natural Language Processing (NLP) focuses on enabling computers to process and analyze large volumes of natural language data. Common applications, such as chatbots, automatic summarization, and reading comprehension, primarily generate shorter sentences by analyzing longer text. These applications are often statistical and analytical. Even in reading comprehension tasks, answers are typically extracted directly from a specific paragraph of the given text rather than being generated through sentence restructuring and deeper analysis. In past natural language research, evaluating generated text was often challenging. Standard evaluation metrics, such as BLEU [1] and ROUGE [2], primarily assess text quality by comparing generated outputs to predefined ground truth references. However, in natural language generation, there is no universally fixed evaluation method, particularly in the case of Chinese sentences, where word order and phrase structures exhibit significant variability. Existing NLP evaluation methods rely heavily on the original dataset, making it difficult to assess the performance of text generation tasks effectively.  
To address this issue, this study proposes an evaluation approach that includes the Bilingual Evaluation Understudy (BLEU) for simple sentence verification, along with two additional methods designed explicitly for PoS structure evaluation: (1) PoS matching degree and (2) PoS context ordering coherence. The PoS matching degree method determines whether the expected distribution of parts of speech in the generated text aligns with the overall structural patterns. In creative writing, different authors exhibit unique content styles, and the model-generated text is influenced by the writing style present in the training dataset. Thus, assessing whether the distribution of various parts of speech in the generated content aligns with that of the training data can indicate whether the generated sentences reflect the author's writing habits. By calculating the PoS matching degree, a smaller difference value signifies a higher degree of alignment, suggesting that the generated text more accurately follows the expected linguistic structure.