esa-sceva/satcom-chunk-collection huggingface dataset:
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## 2 Theoretical Background of TD-NIRS
### Analytical Solutions for TD-DE  
Analytical solutions for TD-DE for a point and impulse source are very useful because they are [PERSON]'s functions, and they have been obtained for simple geometries such as infinite media, semi-infinite media, slabs, cylinders, and spheres with homogeneous optical properties. The most fundamental analytical solution for TD-DE is for infinite homogeneous media given by Equation (13),  
\[\phi(r,t)=\frac{c}{\left(4\pi Dct\right)^{3/2}}\exp(-\frac{r^{2}}{4 Dct}-\mu_{a }ct), \tag{13}\]  
where \(r\) is the distance from a point impulse source located at the origin.  
[PERSON] et al. [18] first derived analytical solutions for TD-DE for semi-infinite and slab media by use of the mirror image source method under a zero-boundary condition. In the extrapolated boundary condition, the fluence rate is zero at an extrapolated surface with a distance of \(2 AD\) from the physical boundary, as shown in Figure 3, and the fluence rate for semi-infinite homogeneous media is given by Equation (14),  
\[\phi(r,t)=\frac{c}{\left(4\pi Dct\right)^{3/2}}\exp(-\mu_{a}ct)\left[\exp(- \frac{r_{1}^{2}}{4 Dct})-\exp(-\frac{r_{2}^{2}}{4 Dct})\right], \tag{14}\]  
where \(r_{1}=[(z-z_{0})^{2}+\rho^{2}]^{1/2}\) and \(r_{2}=[(z+z_{0}+2z_{b})^{2}+\rho^{2}]^{1/2}\) are the distances from the position of interest, \(P(\rho,z)\), to the positive and negative point impulse sources, respectively, The reflectance at the surface, \(P(\rho,0)\) with the source-detector (SD) distance of \(\rho\), is given by [PERSON]'s law as Equation (15),  
\[R(\rho,t)=\frac{1}{16(\pi Dc)^{3/2}t^{5/2}}\exp(-\mu_{a}ct)\left[z_{0}\exp(- \frac{r_{10}^{2}}{4 Dct})+(z_{0}+2z_{b})\exp(-\frac{r_{20}^{2}}{4 Dct})\right], \tag{15}\]  
where \(r_{10}=(z_{0}{}^{2}+\rho^{2})^{1/2}\) and \(r_{20}=[(z_{0}{}^{2}+2z_{b})^{2}+\rho^{2}]^{1/2}\). Equation (15) is the Green's function for semi-infinite homogeneous media and is often used in the derivation of analytical solutions using the perturbation theory.  
Analytical solutions, or the Green's functions, for TD-DE for other simple geometries are comprehensively summarized in Reference [19].  
Figure 3: Concept of the extrapolated boundary and mirror image source for a semi-infinite medium.  
When the medium scatters light non-isotropically but anisotropically, the diffusion coefficient is dependent on the direction and is expressed by a diffusion tensor. The analytical solution for TD-DE for the anisotropic diffusion in a slab medium is given by [PERSON] et al. [20] using a diffusion tensor for the diffusion coefficient.
## 2 Experimental Details  
The commercial x-cut yttria-stabilized zirconia crystals grown by the arc melting method are provided by the Daheng Optics and Fine Mechannics Co., Ltd., Shanghai, China. Before irradiation, the crystals, with a dimension of 5 mm \(\times\) 10 mm \(\times\) 0.5 mm, are precisely polished and ultrasonically cleaned. The crystals are irradiated by 4.5 MeV oxygen ions at a fluence of 5 \(\times\) 10\({}^{14}\) ions/cm\({}^{2}\) and 1.5 \(\times\) 10\({}^{15}\) ions/cm\({}^{2}\) at room temperature. The oxygen ion's beam is scanned to ensure uniform irradiation with current of 100 nA, and beam direction is tilted by 7\({}^{\circ}\) to prevent channeling effects. The irradiation experiments are carried out by a 1.7 MV tandem accelerator at Peking University.  
After irradiation, the characteristics of crystal waveguide are measured by a prism coupling instrument (Model 2010, Metricon, Pennington, NJ, USA) and the end-face coupling arrangement [18] at Shandong University. In the prism coupling measurement, a laser beam irradiates the bottom of a special rutile prism and is reflected and collected by the silicon photodetector. The prism, crystal and detector are fixed on the rotating workable so that the incident angle of the laser can be changed to meet the coupling condition. In the end-face coupling measurement, a light beam is focused using the objective lens (\(\times\)40) to meet the numerical aperture of the crystal waveguide and is coupled into the crystal waveguide from a 5 mm \(\times\) 0.5 mm end-face. It is then outputted from another end-face. The objective lens and the crystal are placed on a three-dimensional platform, which can move along three axes (\(-\)x, \(-\)y \(-\)z). The output light signal of the waveguide is collected by a CCD camera.  
High-resolution X-ray diffraction techniques are conducted using an AXS HRXRD D5005 system (Bruker Inc., Billerica, MA, USA) to characterize the lattice modification and tensile strain induced by irradiation at Tsinghua University. The lattice damage of irradiated crystals is investigated by the Rutherford backscattering/channeling measurements using a 2.1 MeV He\({}^{+}\) beam generated by the tandem accelerator. The backscattering of He\({}^{+}\) particles was detected at a scattering angle of 165\({}^{\circ}\).
# Optimization of Active Distribution Network Operation with SOP Considering Reverse Power Flow
## 3 Actual Maximum Transfer Capacity of Reverse Power Flow in the Distribution Network
### Case Analysis
#### 5.2.3 Optimization Results of the Joint Operation of Distribution Networks A, B, and C  
In Scenario 3, after the coordinated optimization of distribution networks A, B, and C through the SOP, the results improved significantly. As shown in Figure 17, the curtailment of wind and solar power in distribution network A improved.  
Figure 17: Joint operation of distribution networks A, B, and C. (**a**) The OLTC tap changes of networks A, B, and C show that taps of networks A and B remain unchanged, while network C increases its taps to transfer the reverse power flow from network A. This decision considers that network B still needs to handle its own reverse power flow, and network C’s has the smallest, allowing more margin for transfer. (**b**) Depicts the curtailment of wind and solar in network A. After joint operation with networks B and C, there is a significant reduction in curtailment compared to Scenario 1 and 2. (**c**) Illustrates the SOP output at this time. Notably, during intervals like period 4, although OLTC taps in network B could be further increased to enhance reverse power flow capacity, the SOP has already reached its capacity limit. During intervals such as 1–3, while the SOP still has a margin, networks B and C also need to transfer reverse power flow to the main grid, leaving limited OLTC adjustment space, restricting the mitigation of reverse power flow for network A.
# Blockchain and Information Integration: Applications in New Zealand's Prefabrication Supply Chain
## 4 Results and Discussion
### Blockchain in the Prefabrication Supply Chain
A study by Building Research Association New Zealand (BRANZ) revealed that most construction products are often tested and certified overseas before being shipped to New Zealand, and problems with these products may take many years to emerge, and by the time that the issues arise, the suppliers of products may no longer exist in the market [76]. Therefore, prefabrication supply chain organisations rely on suppliers/manufacturers products specifications to satisfy the Building Consent Authorities (BCAs) as well as their customers that the products used in their projects comply with regulatory requirements and are fit for the purpose. Blockchain can play a significant role in the transmission of quality assurance and certification information. The traceability function of blockchain can allow New Zealand's prefabrication supply chain organisations to conduct their products testing and shipment tracking in a real-time and consensus-based manner. This, in return, increases the visibility and control over the history of products from their origin to their final location. Moreover, instead of having the source of technical information generated by the supplier company and uploaded to their company website for tracking, all partner organisations can simultaneously trace and verify the provenance of information in a flexible fashion. Enhanced visibility and control associated with this flexibility lead to better trust and transparency between organisations involved in the project. Nevertheless, there are some challenges towards the practical implementation of blockchain technology in the prefabrication supply chain. Energy consumption is deemed as one of the biggest concerns associated with blockchain's requirement of electricity usage for its computational process [16]. Legal matters such as ownership rights, standardisation, corruption and risk allocation are also other types of concerns attributed to blockchain where the intervention of centralised legal authorities is limited [44]. Readiness of industry experts, lack of skilled blockchain professionals, technological status-quo of the construction industry and the cost of change are some other challenges towards the adoption of blockchain technology [15; 44; 62]. As blockchain grows and matures, different aspects of using this technology will be discovered, and its potential opportunities/risks can be practically examined.
# Multi-Scale CNN-Transformer Dual Network for Hyperspectral Compressive Snapshot Reconstruction
## 5 Discussion
### Ablation Experiments  
In this section, we conduct ablation experiments to examine the impact of the complementary fusion structure and mask attention on the reconstruction performance. The main verification method is to remove specific modules from the DualCT-Net network and determine their impact on reconstruction performance. The ablation experiments are performed on an HSI dataset [26]. The model obtained by removing our mask guidance mechanism and parallel complementary fusion structure from DualCT-Net is regarded as the baseline model. The ablation experiment results are shown in Table 2. The reconstruction quality of the baseline model is 33.88 dB, and when we use the parallel complementary fusion structure and masked attention, the reconstruction performance is continuously improved by 0.87 dB and 1.05 dB. These experimental results fully demonstrate the effectiveness of the parallel complementary fusion structure and mask attention. Next, we further analyze the impact of different settings within the parallel complementary fusion structure and mask attention on the reconstruction results.
## 5 Conclusions  
In this study, the combination of L1-MCCA and PSO optimized SVM was proposed to improve the classification accuracy of the SSVEP-based BCI. Using the combination method, the concept of tensor to construct multiway EEG signals and sine and cosine signals to seek maximization is introduced. The combination uses L1 regularization to have a stronger learning ability when training data. The optimized reference signal is classified using the original CCA. Its essence is to optimize the reference signal and introduce some experimental information. The results show that the proposed L1-MCCA-PSO-SVM method further increased the classification accuracy of SSVEP-based BCI when compared with the traditional CCA and PSD ways. The proposed method has reached 96.36% accuracy under the 2 s TW and has reached 98.18% accuracy under the 6 s TW. In a further study, advanced methodologies and technologies will be used to further improve the performance and to promote the applications of SSVEP-based BCI.  
Conceptualization, [PERSON] and J.S.; methodology, [PERSON] and J.S.; software, [PERSON] and H.L.; validation, [PERSON] and J.C.; formal analysis, Y.G., J.S., W.L. and S.W.; investigation, [PERSON]; resources, Y.Z.; data curation, Y.G.; writing--original draft preparation, Y.G.; writing--review and editing, [PERSON] and J.S.; visualization, Y.G.; supervision, J.S. and H.L.; project administration, J.S., Y.Z. and Q.H.; funding acquisition, J.S. and Y.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Natural Science Foundation of Beijing, China (Grant No. 4214080), the National Natural Science Foundation of China (Grant No. 81871398), and the Beijing Municipal Education Commission Science and Technology Program (Grant Nos. KM202011232008 and KM201911232019).  
The experimental paradigms in this study were approved by the ethics committee of the Beijing Information Science and Technology University and the Institute of Automation, Chinese Academy of Sciences.  
Informed Consent Statement:Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from the patient(s) to publish this paper.  
The authors declare no conflict of interest.
## 3 Service Environment Clustering Method
### Fuzzy Cluster Center  
The essence of FCM is the process of optimizing the cluster centers by means of iteration; the cluster centers will change with iterations.  
Figure 4: General flowchart for altitude feature extraction.  
Assume \(\{S_{i}|i=1,2,\cdots,q\}\) is a sample set composed of \(q\) samples. Let \(d_{ij}\) be the Euclidean distance between the \(i\)th and \(j\)th samples. Then, the expression of \(d_{ij}\) is as follows:  
\[d_{ij}=\left\|S_{i}-S_{j}\right\|^{2}, \tag{5}\]  
To prevent a certain variable from significant impact on distances, the same variable in all samples should be normalized by its maximum before solving the distances. For example, a 3D set \(S=(S_{1},S_{2},S_{3},S_{4})^{\text{T}}\):  
\[S_{1}=(50,0.2,0.01),\;S_{2}=(120,0.5,0.004),\;S_{3}=(72,0.28,0.003),\;S_{4}=(11 4,0.45,0.008).\]  
If the sample set is normalized with the maximum values of each column, then the sample set becomes:  
\[S_{1}=(0.417,0.4,1),\;S_{2}=(1,1,0.4),\;S_{3}=(0.6,0.56,0.3),\;S_{4}=(0.95,0.9, 0.8).\]  
The original distance between the samples \(S_{1}\) and \(S_{2}\) is \(d_{12}=70\), and the normalized distance is \(d_{12}=1.03\). Normalization transforms the distances in the original sample set, of which the main component is the first column or first variable, into distances in the normalized set, of which the weight of each variable is basically the same, avoiding the clustering result that tends to come from one variable while the others are useless.  
Let the number of clusters be \(l\); each cluster center can be randomly initialized with different samples in set \(\{S_{i}\}\), and the initialized cluster center set can be defined as \(\{M_{k}|k=1,2,\cdots,l\}\).
# Using Artificial Intelligence for Space Challenges: A Survey
## 6 Conclusions  
AI is exploited in the contexts of many application to perform tasks in a human-like way, and it has an important position in the digital transformation of society. As always, the research community has an important role in developing new AI-based solutions for the most arduous engineering challenges, and survey papers have been published discussing such solutions in almost all fields except the field of space applications.  
In this paper, we filled this gap by presenting a framework that surveys all the recent AI-based solutions used in space applications and classifies them based on (1) the specific problem to which they are applied and (2) the typology of the AI approach used to solve the problem, thus developing a taxonomy of all recently proposed solutions. In particular, we organized and classified over a hundred papers based on two main challenges (mission execution and mission object) and thirteen sub-challenges.  
The presented classification allows the reader to identify the most recent solutions applied to solve specific challenges and the most important challenges of space applications that AI-based approaches can help face.  
Our analysis has highlighted some future directions to follow. A first direction concerns applying artificial intelligence techniques to the field of satellite communication with the purpose of making communication systems more efficient and secure. Another  
Figure 2: Artificial Intelligence techniques for space challenges.  
interesting direction regards the next frontiers of artificial intelligence for navigation in terms of the global navigation satellite systems.  
Conceptualization, A.R. and G.L.; methodology, A.R.; validation, A.R. and G.L.; investigation, A.R.; resources, A.R.; data curation, A.R.; writing--original draft preparation, A.R.; writing--review and editing, G.L.; visualization, A.R.; supervision, G.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
A1 Artificial Intelligence  
ES Expert System  
SVM Support Vector Machine  
ANN Artificial Neural Network  
CNN Convolutional Neural Network  
RNN Recurrent Neural Network  
DCNN Deep Convolutional Neural Network  
GAN Generative Adversarial Network  
NPL Natural Language Processing  
LSTM Long-Short Term Memory  
EO Earth Observation  
SSA Space Situational Awareness  
SST Space Surveillance and Tracking
# On the Use of Learnheuristics in Vehicle Routing Optimization Problems with Dynamic Inputs
## 5 Computational Experiments
### Description of Instances  
The set of instances that we employ to test our approach is a natural extension of the classical benchmark instances for the VRP, which are available at [[http://vrp.atd-lab.inf.puc-rio.br/index.php/en/](http://vrp.atd-lab.inf.puc-rio.br/index.php/en/)]([http://vrp.atd-lab.inf.puc-rio.br/index.php/en/](http://vrp.atd-lab.inf.puc-rio.br/index.php/en/)). This site provides a detailed description of each instance as well as a tool to visualize the distribution of the nodes. Each instance describes the capacity of the vehicle, the coordinates of each node and also its demand. Instances from different sets (A, B, E, M, F, and P) have been selected for the experiments. Instances with less than 24 nodes have been discarded because they can be easily solved by using exact methods. Instances from the sets A, E, and P have a homogeneously dispersed configuration of nodes, where the depot may be at the center or at any other location. The set F and some instances of E have a more heterogeneous density of nodes. Otherwise, sets B and M have several clusters of nodes and, in some cases, the depot belongs to one cluster. The name of the instances considered are listed along with the number of nodes and the capacity of the vehicles in the first columns of Table 1.  
In our experiments, these basic instances have been extended by incorporating different levels of dynamism in the traveling times. The specific level of dynamism employed is a design parameter. Hence, we should expect our results to converge to the best-known solutions for the original instances whenever the level of dynamism of travel times converges to zero. On the contrary, as this level grows, our results should diverge from the classical ones, and using the near-optimal solutions for the non-dynamic version in the dynamic scenario should lead to suboptimal results.
## 2 Materials and Methods
### Preparation and Characterization of PEI-MNPs  
The preparation PEI-MNPs was carried out based on our previous report [(29)]. Briefly, 1 mL of carboxylated MNPs (10 mg/mL) were washed with sterile PBS and then resuspended in 10 mL of sterile PBS containing 2.9 mg of EDC and 3.26 mg of NHSS for 1 h to activate the carboxyl groups. Excess activation reagents were removed by magnetic separation. Then, 7.5 mg PEI was added to the activated MNPs and coupled for 4 h. The PEI-MNPs were washed and re-suspended in 10 mL PBS after excess PEI was removed by an external magnetic field and stored at 4 \({}^{\circ}\)C for later use. Zeta potential of PEI  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Target Bacteria** & **Target Gene** & **Primer** & **Sequences (5’-3’)** & **Length (bp)** & **Reference** \\ \hline _L. monocytogenes_ & _LMO/2365\_2721_ & lm13-F & GTTGCGCGCGCGTGTA & 583 & [(26)] \\  & & lm13-R & TTGGCAAGCAAAGCAGTTCA & 311 & [(27)] \\ \hline _L. ivanovii_ & _iacta_ & _iacta_-F & T  
MNPs at different pH was measured by Zeta Sizer Nano ZS90 (Malvern Instruments Ltd., Malvern, UK). Lake Shore 7404 vibrating sample magnetometer (Lake Shore Cryotronics, Inc., Westerville, OH, USA) was used to measure hysteresis loops of MNPs. Scanning electronic microscopy (SEM) (JSM-6701F, JEOL Ltd., Tokyo, Japan) was used to observe the capture of _Listeria_ by PEI-MNPs.
## 1 Introduction  
Recently, interest in ceramic membranes has skyrocketed due to their superior thermal and chemical stability compared to polymer membranes [1]. They have a hydrophilic surface that makes them more durable and resistant to failure due to less membrane contamination [2]. In addition, ceramic membranes are attractive in industrial wastewater treatment because they are low-pressure-driven membranes that effectively remove colloidal and suspended particles and require a small footprint [3]. However, low-pressure-driven membranes are not effective at removing ionic contaminants such as heavy metals [4], and thus many physicochemical strategies have been proposed to increase their ionic removal efficiency. The deposition of nanoscale inorganic adsorbents such as iron oxide and zirconium dioxide on ceramic membrane pores is the most common modification method [5; 6; 7], with coating the top layer with different types of polymers such as chitosan and cellulose acetate over the ceramic support being an alternative [8; 9]. The development of hybrid processes combining ceramic membranes with ozone or ion-exchange processes has also been reported [10; 11; 12].  
Ion-exchange processes are well known for their effectiveness in removing ionic contaminants during water treatment. Recent studies have been conducted actively on ion-exchange processes using ion-exchange fibers due to the advantages of the fibrous form [13; 14; 15; 16]. The fibers have a faster adsorption/desorption rate than ion-exchange resins due to the short transit distance of the ions and a better absorption capacity due to the large number of functional groups present along the fibers. Furthermore, the compact configuration of the treatment module is easy to fabricate and relatively free from head loss. Most ion-exchange fiber contact systems for wastewater treatment processes involve the production of a fixed-bed columns or filters made by grafting on nonwoven fabric [17; 18; 19].  
To remove turbidity and ionic contaminants simultaneously, in this study, a hybrid system was prepared by combining the ceramic membrane and ion-exchange fabric in the reactor rather than separating each process. The performances of both the prepared ceramic membrane and the ion-exchange fabric were evaluated under various solution pH conditions, and the removal mechanisms were estimated using geochemical software. The removal efficiency of metal ions As(V) and Zn(II), and turbidity by the hybrid ion-exchange fabric/ceramic membrane system, were evaluated in a membrane module system including backwashing and regeneration processes.