esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Study on Foam Acoustic Parameters
### Effect of Foam Thermal Characteristic Length on Acoustic Radiation Performance  
The thermal characteristic length is a key parameter that defines the microstructure of porous materials and their ability to exchange heat internally with fluids. Its physical essence is the mean pore radius in porous materials, with a unit of mm. Its standard value is generally approximately double the viscous characteristic length of the identical material. In order to investigate the influence of the thermal characteristic length of foam filled on the acoustic radiation performance of the reinforced sandwich panel structure, while keeping other parameters unchanged, the foam thermal characteristic length is set to its original value, twice the original value, and half the original value, namely, 0.119 mm, 0.238 mm, and 0.0595 mm. By inserting these values into the equation, the acoustic radiation power can be determined for various thermal characteristic lengths, as illustrated in Figure 12.  
According to Table 5, at 854 Hz, the sound power corresponding to a decreasing thermal characteristic length is 40.33 dB, 40.45 dB, and 40.69 dB, respectively. At 1467 Hz, the sound power corresponding to a decreasing thermal characteristic length is 17.45 dB, 17.75 dB, and 17.95 dB, respectively. Lastly, at 1588 Hz, the sound power corresponding to a decreasing thermal characteristic length is 41.96 dB, 42.43 dB, and 42.67 dB, respectively. It can be deduced that at 854 Hz, the sound power level attenuation is roughly proportional to the decrease in thermal characteristic length. However, for the last two peak frequencies, there is a clear occurrence that the suppression effect on sound power radiation is stronger with a smaller thermal characteristic length. This is because the foam structure can more efficiently convert high-frequency sound waves into thermal energy, and heat exchange efficiency is vital for suppressing sound radiation in the higher frequency range. Therefore, selecting fine foam materials, under the same porosity, is advantageous for reducing the sound radiation of the reinforced sandwich panel.  
In conclusion, it is apparent that alterations in thermal characteristic length do not markedly impact the peak frequencies of the curves. Additionally, there is a uniform growth in sound radiation power for all peak frequencies with an increase in thermal characteristic length. This arises from the circumstance that, under constant volume and porosity, the doubling of the size of individual pores leads to a halving of the total heat exchange area.
## 6 Research Deficiencies and Prospects
### Prospects  
In forthcoming research endeavors, we can refine our technological innovations by building upon existing methodologies. Incorporating real-time monitoring systems, augmented by artificial intelligence, satellite remote sensing technology, and Internet of Things (IoT) devices, can enable comprehensive monitoring, thereby enhancing both data precision and real-time capabilities. Methodologically, our approach encompasses the utilization of multi-scale atmospheric models, Positive Matrix Factorization (PMF), and other avant-garde source attribution techniques. By harnessing advanced data analytics and machine learning methodologies, we aim to conduct a comprehensive analysis of diverse datasets, thereby deepening our understanding of the intricate factors influencing air quality.  
Future inquiries should probe the latent hazards of atmospheric contamination, elucidating the spatial distribution and contributions of potential source regions. Such under-takings will shed light on primary pollution reservoirs and contributory zones, providing a foundational framework for tailored pollution mitigation and control strategies. Acknowledging the pervasive nature of air pollution as a transboundary challenge, it becomes imperative to champion strengthened inter-regional collaboration and seamless information dissemination, fostering a cohesive alliance against the multifaceted challenges posed by airborne pollution.  
In conclusion, the atmospheric pollution study conducted in Chenzhou City has conferred upon us invaluable insights and inspiration. Nonetheless, the translation of these research findings to diverse regions necessitates a nuanced understanding of regional idiosyncrasies, the formulation of bespoke interventions, and the enhancement of cross-regional collaborations, culminating in a unified endeavor to combat the menace of air pollution.  
**Author Contributions:** Conceptualization, X.C., H.Z., F.L. and C.U.I.W.; Data curation, X.C.; Formal analysis, X.C., H.Z. and F.L.; Methodology, X.C., H.Z. and C.U.I.W.; Software, X.C., F.L.; Writing--original draft, X.C., H.Z. and C.U.I.W.; Writing--review and editing, X.C., H.Z. and C.U.I.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding authors.  
The authors declare no conflicts of interest.
## 3 Results and Discussion
### Optimization of Single Extraction Condition
#### 3.3.1 Effect of Liquid-Solid Ratio  
The liquid-solid ratio has an important effect on the yields of the target compounds. Excess solvent can complicate the experiment and produce unnecessary waste, but insufficient solvent results in incomplete extraction of the targets. Figure 5a shows that the yields of CPT, HCPT, VCS-LT, and EOs significantly increased with increasing liquid-solid ratio up to 12 mL/g. Within a certain range, increasing the liquid-solid ratio resulted in full immersion of the sample in the solvent; this increased  
Figure 5: Effects of liquid–solid ratio (**a**); extraction time (**b**); and microwave power (**c**) on yields of CPT, HCPT, VCS-LT, and EOs.  
mass transfer and higher yields of the target compounds were obtained. However, the yields of the target compounds did not improve greatly when the liquid-solid ratio was further increased. A liquid-solid ratio of 12 mL/g was therefore used for further optimization studies.
## 2 Materials and Methods
### Encapsulation Protection  
The most common protection method for EPC is encapsulation. However, this process may introduce reliability risks associated with encapsulation, such as bubbles, interface contamination or delamination, and deterioration of potting materials in the space environment. After overcoming these difficulties, we obtained an effective potting solution. We only discuss the results of potting, and the detailed potting scheme and process are not discussed. Potting was carried out on the uncontaminated sample surface. Figure 17 shows the potting sample. According to the requirements of MIL-STD-202 and 2.4.2, humidity and temperature-related tests were carried out. The test requirements and applied voltage values are shown in Table 4. The test voltages were 4, 7, and 10 kV. Figure 18 shows that the sampling values after encapsulation were stable and unchanged. For unencapsulated samples, even if the surface was contamination-free, the sampling values increased by 0.03 at 85 \({}^{\circ}\)C/90% RH and by 0.44 under alternating hot and humid conditions. Table 5 shows the comparison of sampling values for encapsulated and unencapsulated samples.  
Figure 16: Change of sampling value during rapid alternating damp-heat test. (**a**) Change in sampling value, and (**b**) change in SIR.
# A Novel Ranging Technique Based on Optical Camera Communications and Time Difference of Arrival
## 1 Introduction
This work also explores a novel error-mitigation strategy based on the shape of the probability density function associated with the ranging procedure. This paper is organized as follows. First, a thorough analysis of the current context in VLP is provided in Section 2. In Section 3, a description of the proposed method for obtaining the distance is presented. Section 4 analyzes the proposed method concerning performance. Section 5 discusses some relevant statistical aspects of the measurement technique. In Section 6, some relevant results are presented. Finally, Section 7 provides a thorough discussion of the results and their potential impact on the field.
# Research on Optical System of Dim Target Simulator Based on Polarization Stray Light Suppression
###### Abstract  
In view of the lack of high-precision optical simulation equipment for dim space targets at present, in this study, a simulation method for dim space targets based on polarization stray light suppression is proposed, the overall optical system architecture of the optical engine for depolarization stray light suppression is constructed, the mechanism of stray light generation is explored, and the dark state light leakage suppression method is presented by compensating the phase of LCOS reflected light with wave plate; a high-image quality collimating optical system with large field of view and flat aberration is designed based on the illumination system optimization method of the critical angle matching of the spectroscopic film; and the polarization stray light suppression effect and star position simulation error of the dim target simulator are tested. The test results show that the illumination of the simulated dim target is \(\lesssim\)10\({}^{-10}\) lx, the contrast is 6.96, the non-uniformity of the bright state is only 5.88%, and the simulation error of the star position is 9.9\({}^{\prime\prime}\). This research can make some contributions to enhancing the observability of detecting dim targets, breaking through the detection technology of extremely dark targets in space, developing advanced deep space detection capabilities, and improving the engineering technology system of deep space exploration.  
dim target; optical engine; depolarization; stray light suppression +
Footnote †: journal: Applied
# Correlation between Coda Wave and Stresses in Uni-Axial Compression Concrete
## 3 Experimental Investigation
### Experimental System and Sensors  
The experimental system was mainly composed of a pulse generator, transmitter and receiver, data acquisition system, and an electro-hydraulic pressure testing machine. The signal generator used in the experiment was a 14-bit digital-to-analog conversion signal card, which can generate 100 M sample points of the ultrasound signal. The output signal of the pulse generator was 10 volts and the frequency ranged up to 15 MHz. A resonant piezoelectric ceramic transducer was selected to be the sensor for the wave's transmitter and receiver. In this experiment, three kinds of sensors with frequencies of 300 kHz, 150 kHz, and 50 kHz were used, the specific parameters of which are listed in Table 1. A signal amplifier was used in the experiment to guarantee that the received signal could be identified. A 40 db-60 db transmission gain could be supported by the amplifier. The sampling rate of the data acquisition system was set as 40 MHz and the maximum length of a single data signal could be recorded at 15 kB. The time interval to record each test point was 1 s. About 100 waveforms were recorded in the experiment, and the signal to noise ratio was improved by the superposition. During the experiment, the temperature and humidity of the environment were strictly monitored and controlled to eliminate their effects on the test results. An electro-hydraulic pressure testing machine was adopted in the experiment to provide compression on the specimen to analyze the influence of stress in concrete on the coda waves. The equipment is shown in Figure 4.
## 5 Modularization Case Study Based on the DIIS
### Design of the Generic Case Study
#### 5.2.2 Key Performance Indicators  
The results were calculated from the simulation for all models to evaluate the response according to the following key performance indicators:  
* Cumulated demand (# thous. products): market demand for the product;
* Cumulated real demand (# thous. products): real demand based on the market demand minus the demand loss due to long customer order lead times;
* Cumulated production (# thous. products): the cumulative sum of all units produced over the 500 simulated production weeks;
* \(\O\) Availability of the production plant (%);
* \(\O\) Performance at the final production step (# thous. products/week);
* Development Time (# weeks): time required for the innovation to be implemented;
* Absenteeism rate (%): percentage of employees that do not assist their workplace because of various motives, one being the medical conditions generated by non-ergonomic workplaces;
* Labor productivity (products/employeex week);
* \(\O\) Production lead time (# weeks): the number of weeks between the placement of the order and the delivery of the product for its distribution;
* \(\O\) Suppliers' quality rate (%);
* Cumulated Service level (%): the quantity of units delivered on time divided by the total number of delivered units;
* Sales (million USD): multiplication of the number of produced units by the sales price of each produced unit type;
* Cumulated Operational Costs (million euros): all costs related to production system operations. It is the sum of procurement, production and distribution costs considering raw materials costs, transportation activities, working capital, labor costs, working shifts, and maintenance costs. The running costs of the project initiative are also included;
* Cumulated Investment (million USD): the amount of the investment made to improve the production system;  
Figure 9: Structure of the simulation case study [61].  
* Profits (million USD): the result of the sales minus operational costs;
* Return on investment (ROI) (%): the difference in profits minus the investment value divided by the investment value.
## 3 CPN Models
### Basic Models
#### 3.1.1 Operators  
Due to their similar structures, operators such as arithmetic (+, -, \(\times\) /, and the modulus operator %.), relational (\(>\), \(>\)=, \(<\), \(<\)=, \(==\),!=), logical (&& and \(||\)), increment (++), decrement (--), bitwise (&, \(|\), \(\wedge\), \(<<\), \(>>\),-), and assignment (=) have been modelled through a generic CPN model as shown in Figure 3. The transition _op_1_ represents the operator and has associated the power consumption calculations.  
An action (_action_) is executed when _op_1_ is triggered and consists of three calculations performed by the functions addEnergy(), calcTime() and addResourceEnergy(). The function addEnergy() calculates a random value following the normal distribution of the instruction's power consumption at that time using its input parameters (_energyMean_ and _energyVariance_). Next, this function updates the power consumption (global variable) of the entire application. By generating a random value, it is possible to define a confidence interval that must contain it. Function calcTime() calculates the time  
Figure 2: Schematic view of the CPN models definition.  
consumed by the operation. Similarly to the previous function, calcTime() also uses the mean and variance to generate a random value to the time. Finally, the function addResourceEnergy() calculates the power consumed by the resources of the more (radio and LEDs) that may be active while the operation executes (see more details in Section 3.4). It is worth observing that the aforementioned mean and variance of time/power consumption are particular to each operator and are obtained by measurement.  
This model has two properties (see Section 2.1): bounded, because place _Operator_ does not store tokens; and it has not dead transition, because transition _op_1_ will always run.
## 5 Computational Experiments and Discussion
As reported in Tables 6-8, all the C.V. values are low which supports the high predictiveness of the proposed algorithm. Tables 9-11 present the detailed results of all problem instances produced by the proposed hybrid HS-SA for LIRP. The total cost for each problem instance is highlighted in bold. These tables served as the benchmark for the LIRP. For instance, Gaskell 1 required two depots with a fixed cost of 50 each to serve all the 21 customers with four vehicles (see, Table 10). In depot 1, with the inventory cost of 573.05, two vehicles are deployed to serve the customers (19\(\rightarrow\)21\(\rightarrow\)20\(\rightarrow\)17) and (18\(\rightarrow\)15\(\rightarrow\)12\(\rightarrow\)14\(\rightarrow\)16) with costs of 59.45 and 86.90 respectively. While in depot 2, with the inventory cost of 577.74, two vehicles are used to serve the customers (9\(\rightarrow\)7\(\rightarrow\)5\(\rightarrow\)2\(\rightarrow\)1\(\rightarrow\)6) and (8\(\rightarrow\)3\(\rightarrow\)4\(\rightarrow\)11\(\rightarrow\)13\(\rightarrow\)10) with costs of 83.01 and 95.55 respectively. In this result, the best solution obtained suggest to close Depot 3-5. The overall minimum cost for Gaskell 1 is 1575.7. Figure 7 illustrate the topological layout of the Gaskell 1 produced by the proposed hybrid HS-SA for LIRP. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Instance** & **Algorithm** & **Mean** & **Std. Dev.** & **C.V.** & **Best** & \(p\)**-Value** \\ \hline \multirow{3}{*}{Christofides 1} & SHS & 848.40 & 2.8983 & 0.0034 & 845.4 & 0.005 \\  & PHS & 834.99 & 2.4324 & 0.0029 & 833.6 & 0.005 \\  & HS-SA & 819.76 & 2.2342 & 0.0027 & **817.9** & – \\ \hline \multirow{3}{*}{Christofides 2} & SHS & 1220.87 & 4.4823 & 0.0037 & 1214.3 & 0.005 \\  & PHS & 1151.87 & 2.3561 & 0.0020 & 1148.2 & 0.007 \\  & HS-SA & 1149.48 & 2.2828 & 0.0020 & **1146.1** & – \\ \hline \multirow{3}{*}{Christofides 3} & SHS & 1384.60 & 3.2345 & 0.0023 & 1381.0 & 0.005 \\  & PHS & 1349.85 & 2.9744 & 0.0022 & 1347.2 & 0.005 \\ \cline{1-1}  & HS-SA & 1312.49 & 2.6949 & 0.0020 & **1307.2** & – \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparative results of Gaskell dataset in LIRP for SHS, PHS, and HS-SA. Figure 6: Best result of Perl, Gaskell, and Christofides dataset in LIRP for SHS, PHS, and HS-SA.
## 5 Relationship between Cable Unstrained Length and Cable Tension
### Relationship between \(d\) and \(T_{x}\)  
#### 5.2.1 Select Model  
Experimentally, the rational model (40) was chosen in order to estimate \(\Psi\). The rational model (40) that has a constant numerator and 2 nd order demonstrator, has an adjustable vertical asymptote based on \(a_{d}\), and horizontal asymptote \(d=0\). Furthermore, this model is the fittest and simplest that we tried our best to figure out.  
\[d=\frac{a_{d}}{{T_{x}}^{2}+b_{d}T_{x}+c_{d}} \tag{40}\]  
Since the vertical asymptote of \(\Psi\) is \(T_{x}=0\), by the properties of (40), we have \(b_{d}=0\) and \(c_{d}=0\). Hence, the model is simplified to (41)  
\[d=\frac{a_{d}}{{T_{x}}^{2}} \tag{41}\]  
where \(a_{d}\) depends on the desired position \(\mathbf{B}^{d}\) and \(\rho\) that can be expressed as (42). The only constraint of \(a_{d}\) is \(a_{d}\geq 0\) since \(d\geq 0\).  
\[a_{d}=a_{d}(D,\beta,\rho) \tag{42}\]  
Similar to constructing \(\pi\), Equation (42) will be constructed based on the model (41) when one variable is variated and the others are fixed. Hence, the recently constructed model may have parameters that depend on variables that have not been investigated. In our case, the order of investigating parameters will be \(D\), \(\beta\) and \(\rho\).
# Travel Time Prediction on Long-Distance Road Segments in Thailand
## 3 Materials and Methods
### Our Approach
#### 3.3.3 Self-Attention LSTM (SA-LSTM)  
Our main approach was to use the SA-LSTM to predict the travel time. We learned the TTR data, which were processed by a filter method, to create one model for one road, so we had 10 models for 10 selected road segments. In addition, we used a window size of 24 due to the observation of hourly patterns across different days. However, the window size can be configured according to the pattern of the data. In addition, the decision to use self-attention or operate without attention depends on the experiment. In our case, the comparison between the LSTM only and the SA-LSTM models guided us to choose the SA-LSTM.  
Finally, our approach is formulated as expressed in the following equation:  
\[\text{tt}^{\text{dh}}_{\text{rid}}=\text{(SA\_LSTM}\circ\text{BF)}\Big{(} \text{TTR}^{\text{(dh\_24\_4\_4\_1)}}_{\text{rt}^{\text{(dh\_1\_1)}}}\Big{)}, \tag{5}\]  
where \(\text{tt}^{\text{dh}}_{\text{rt}^{\text{dh}}}\) denotes a predicted travel time (tt) of a given road segment (rid) at a given date-hour (dh). The formula is the composite of the SA_LSTM (SA_LSTM) model and the Butterworth low-pass filter (BF), where the input \(\text{TTR}_{\text{rid}}^{[\text{dh}_{-24}:\text{dh}_{-1}]}\) is a set of travel time (TTR) of a given road segment (rid) between 24 h (\([\text{dh}_{-24}:\text{dh}_{-1}]\)) before the given dh.
# Hardware-Accelerated Data Readout Platform Using Heterogeneous Computing for DNA Data Storage
## 4 The Proposed DNA Data Storage Readout Method Based on Heterogeneous Computing
### Overall Design of Heterogeneous Acceleration Scheme  
We proposed a heterogeneous-computing-based readout method for DNA data storage. The overall architecture of the proposed acceleration scheme is illustrated in Figure 3, consisting of both software and hardware components. The software component runs on a general-purpose computing server with a CPU, developed in C++, and is responsible for processing sequencing data, managing data transmission to and from the off-chip DRAM, and controlling kernel execution. For the hardware implementation, we utilized the Alveo U200 data center accelerator, a high-performance FPGA featuring 64 GB of DDR4 memory and a PCIe 3.0 interface. The hardware acceleration kernel was developed using an RTL-based design approach, incorporating a standard AXI interface. It was integrated with an x86 CPU in the host PC via the PCIe interface, forming a heterogeneous computing system.  
Through the collaborative operation of the software and hardware components, the proposed method efficiently completes the entire process from sequencing data acquisition to original file reconstruction. Specifically, the workflow proceeds as follows. First, the software component converts the DNA sequencing data file (in FASTQ format) from a base sequence to a bit sequence. Then, it transfers the converted data to the off-chip DRAM  
Figure 2: Information encoding in DNA data storage.  
of the hardware component via the PCIe interface while sending an enable signal to the acceleration kernel. Upon receiving this signal, the kernel executes six key steps: primer sequence identification, index checking, majority voting, deinterleaving, valid-sequence filtering, and LDPC decoding. Next, the sequencing data are restored to their original information sequence and stored in a designated location within the off-chip DRAM. Finally, the software component retrieves the restored information bit sequence from the off-chip DRAM of the accelerator, reassembles it, and reconstructs the original file.