esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Technology Management in the Field of Study
[14] addressed the need for an effective recycling plan for solar PV modules. They introduced an optimization framework for reverse logistics to collect End-of-Life (EoL) solar PV modules in Delhi. Their study formulated a Convex Mix Integer model with a quadratic objective and constraints, using the McCormick envelope and CPLEX solver for optimization. Results suggested that centralized and optimally decentralized collection plans were more profitable than other approaches. Focusing on addressing sustainability concerns in the supply chain network design of photovoltaic systems, [PERSON] et al. [15] deployed a multi-objective, mixed-integer non-linear optimization model. The aim was to design a supply chain network that minimizes costs, environmental impacts, and social values while also incorporating reverse flow to address environmental and social issues. The augmented \(e\)-constraint method was used to optimize the model and ensure Pareto optimal solutions. The effectiveness of the study was validated through a real-world case study in Iran. A decision-making framework for sustainable supply chain development in the renewable energy field, with a PV focus, was developed by [PERSON] et al. [16]. The proposed framework included the whole energy production supply chain, from raw materials suppliers to disposal, and it was applied to assess the photovoltaic sectors of seven European countries, providing insights for sustainable investment decisions. [PERSON] et al. [17] explored the integration of renewable energy into supply chain network design to enhance sustainability. The authors introduced a two-stage robust stochastic model to locate facilities and determine flow quantities within the supply chain. Using a GAMS-CPLEX solver, this study analyzed the effects of conservative coefficients and demand variations on cost functions. [PERSON] et al. [18] addressed supplier selection problems in renewable supply chain management by using an integrated Fuzzy Best-Worst Method (FBWM) along with two techniques, which are the Complex Proportional Assessment of Alternatives (CORRAS) and the Weighted Aggregated Sum-Product Assessment (WASPAS). Nine strategic supplier selection criteria were identified, and a real-world example from Iran's renewable energy supply chain was studied to demonstrate the effectiveness of the proposed framework.
# Perpendicular Electrical Conductivity in the Topside Ionosphere Derived from Swarm Measurements
## 5 Summary and Conclusions
This approach will refine our understanding of ionospheric processes and their implications for practical applications. Finally, it might be interesting to build a perpendicular conductivity model at a specific altitude or within a range of altitudes (those satisfying the assumptions described in Section 2.2). This could be achieved by using the IRI model for estimating electron density and temperature at different QD magnetic latitudes, MLTs, and seasons and taking into account geomagnetic activity, together with a geomagnetic field model (empirical or based on expansion in spherical harmonics, or a mix of the two) for estimating the magnetic field strength and applying Equations (15) and (16). Conceptualisation, F.G.; methodology, F.G., A.P. and R.T.; formal analysis, F.G.; investigation, all authors; resources, F.G.; data curation, A.P., R.T. and I.C. ; writing--original draft preparation, F.G.; writing--review and editing, all authors; funding acquisition, F.G. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. This research has been carried out in the framework of the CAESAR (Comprehensive spAce wEather Studies for the ASPIS prototype Realization) project, supported by the Italian Space Agency and the National Institute of Astrophysics through the ASI-INAF n. 2020-35-HH.0 agreement for the development of the ASPIS (ASI Space Weather InfraStructure) prototype of scientific data centre for Space Weather. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} ASM & Absolute Scalar Magnetometer \\ CDF & Cumulative Distribution Function \\ EEJ & Equatorial Electrojet \\ EIA & Equatorial Ionisation Anomaly \\ ESA & European Space Agency \\ EUV & Extreme Ultra Violet \\ FAC & Field-Aligned Current \\ GOL & Generalised Ohm Law \\ IHFAC & Inter-Hemispheric Field-Aligned Current \\ LP & Langmuir Probe \\ MLT & Magnetic Local Time \\ QD & Quasi Dipole \\ RHS & Right Hand Side \\ UTC & Universal Time Coordinated \\ \end{tabular}
# An Improved Weighted and Location-Based Clustering Scheme for Flying Ad Hoc Networks
## 3 Preliminaries
### HELLO Message  
In the system, UAV nodes exchange information by sending HELLO messages. The structure of the HELLO message is presented in Figure 2. The HELLO message contains the following fields:  
* UAV ID \(ID\) and Cluster ID \(CID\) refer to the identification of the UAV and cluster.
* Role distinguishes a UAV as a CH or a CM in a cluster. It is a binary field, where 1 refers to CH and 0 is for CM.
* Position, Speed and Energy of a UAV is represented by \((x,y,z)\), \(v\) and \(E\).
* \(dtCH\) and \(dtG\) means the distance between the UAV and its CH and the GCS, respectively.
* Direction \(\theta\) and \(\varphi\) is the flying direction of the UAV.
* Weight \(W\) details the weight of a CH candidate in the CH selection phase.
## 2 Theory  
### Cross-Correlation Seismic Interferometry  
This section mainly presents the basic formulas of SI derived from the reciprocity theorem by [PERSON] and [PERSON] (2006) [8], as shown in Figure 1. To fulfill the assumption of SI that sources enclose the receivers, receivers \(\mathrm{X_{A}}\) and \(\mathrm{X_{B}}\) are located within a closed region formed by \(\partial\mathrm{D_{1}}\) and \(\partial\mathrm{D_{2}}\), with multiple sources \(\mathrm{S_{n}}\) situated along \(\partial\mathrm{D_{2}}\) (when \(\partial\mathrm{D_{1}}\) represents a free surface, the assumption remains valid even if the receivers are located on \(\partial\mathrm{D_{1}}\), as long as sources are present along \(\partial\mathrm{D_{2}}\)).  
In this scenario, frequency-domain Green's functions between the source S and the receivers \(X_{A}\) and \(X_{B}\) are represented as \(\hat{G}(X_{A},S,\omega)\) and \(\hat{G}(X_{B},S,\omega)\) (Figure 1). The reconstruction of Green's functions between receivers \(X_{A}\) and \(X_{B}\) can then be expressed as  
\[\begin{split}&\hat{G}^{*}(X_{B},X_{A},\omega)+\hat{G}(X_{A},X_{B}, \omega)=2\mathrm{R}\big{\{}\hat{G}(X_{A},X_{B},\omega)\big{\}}\\ &=\hat{f}_{\partial D_{2}}\frac{-1}{j\omega\rho(x)}\bigg{\{} \begin{array}{l}\hat{G}^{*}(S,X_{A},\omega)\partial_{i}\hat{G}(S,X_{B}, \omega)-\\ \big{[}\hat{g}_{i}\hat{G}^{*}(S,X_{A},\omega)\big{]}\hat{G}(S,X_{B},\omega) \end{array}\bigg{\}}n_{i}d^{2}x\end{split} \tag{1}\]  
Figure 1: Diagram of the principle of seismic interferometry.  
Equation (1) represents the exact frequency-domain expression for any lossless, heterogeneous acoustic medium. The Green's function \(\hat{G}(X_{A},X_{B},\omega)\) reconstructed from Equation (1) includes multiple wave components between receivers \(X_{A}\) and \(X_{B}\). However, Equation (1) has certain limitations: the integral in Equation (1) contains two cross-correlation terms, which need to be calculated independently; the response generated by monopole and dipole sources at arbitrary positions on \(\partial D_{2}\) must be received; and the source is required to be a point impulse source. These conditions do not align with practical applications. Therefore, in practice, Equation (1) is often approximated as follows ([PERSON] and [PERSON], 2006) [8]:  
1. Approximate the medium outside the \(\partial D_{2}\) boundary as homogeneous;
2. Apply the far-field approximation, using a monopole to represent the dipole source;
3. The surrounding medium parameters are assumed to undergo a uniform transformation. The final equation for cross-correlation seismic interferometry is \[2\mathrm{R}\big{\{}\hat{G}(X_{A},X_{B},\omega)\big{\}}\approx\oint_{\partial D _{2}}\frac{2}{j\omega\rho(x)}\big{[}\partial_{i}\hat{G}^{*}(X_{A},S,\omega) \hat{G}(X_{B},S,\omega)\big{]}n_{i}d^{2}x\] (2)
# Upscaling Gross Primary Production in Corn-Soybean Rotation Systems in the Midwest
## 2 Material and Methods
### GPP Upscaling and Radiation Use Efficiency  
The 8-day GPPMODIS product from MODIS satellite imagery (MOD17A2H) is calculated as [6]:  
\[GPP_{MODIS}=RUE_{max}*Tmin_{s}*VPD_{s}*fPAR*IPAR \tag{3}\]  
where: GPPMODIS = MODIS gross primary production product (g C m\({}^{-2}\) 8-day\({}^{-1}\)), RUEmax = maximum radiation use efficiency (g C M\({}^{-1}\)), Tmin\({}_{s}\) = a temperature scalar (no units), VPD\({}_{s}\) = a vapor pressure deficit scalar (no units), fPAR = fraction of IPAR absorbed by the crop (no units), IPAR = incoming photosynthetic active radiation (MJ m\({}^{-2}\) d\({}^{-1}\)).  
Note that the GPPMODIS is calculated as 8-day sums, and daily values can only be calculated as daily means (Appendix B). The scalars Tmin\({}_{s}\) and VPD\({}_{s}\) are proxies for RUEmax limitations of temperature and vapor pressure deficit (VPD), respectively. The scalars Tmin\({}_{s}\) and VPD\({}_{s}\) are calculated as [6]:  
\[Tmin_{s}=\left\{\begin{array}{cl}0&Tmin<\;Tlim_{min}\\ \frac{Tmin-Tlim_{min}}{Tlim_{max}-Tlim_{min}}&Tlim_{min}\leq\;Tlim_{max}\\ 1&T_{min}>\;Tlim_{max}\end{array}\right. \tag{4}\]  
\[VPD_{s}\left\{\begin{array}{cl}0&VPD>VPD_{max}\\ 1-\frac{VPD-VPD_{min}}{VPD_{max}-VPD_{min}}&VPD_{min}\leq VPD\leq VPD_{max}\\ 1&VPD<VPD_{min}\end{array}\right. \tag{5}\]  
where: Tmin\({}_{s}\) = scalar for daily Tmin (no units), Tmin = daily minimum Ta (\({}^{\circ}\)C), Tlim\({}_{min}\) = lower Tmin limit (\({}^{\circ}\)C), Tlim\({}_{max}\) = upper Tmin limit (\({}^{\circ}\)C), VPDs = scalar for daytime VPD (no units), VPD = average daytime vapor pressure deficit (kPa), VPD\({}_{min}\) = lower VPD limit (kPa), VPD\({}_{max}\) = upper VPD limit (kPa).  
The Tlim\({}_{min}\) and Tlim\({}_{max}\) are set to \(-8\) and \(12.02^{\circ}\)C, while VPD\({}_{min}\) and VPD\({}_{max}\) are set to 0.65 and 4.3 kPa, respectively [6].  
The GPP was upscaled from field to county following the same concept in Equation (3) (referred to as GPPcalc). First, RUE\({}_{max}\) for corn and soybean was calculated using the EC station data and NDVI field measurements from the Story County site. Equation (3) was solved for RUE\({}_{max}\) and fPAR substituted with field-measured corn and soybean NDVI, assuming that fPAR \(\approx\) NDVI [6]. The VPD was calculated as the difference between saturated (e\({}_{s}\)) and actual vapor pressure (e\({}_{a}\)), of which the former was calculated as a function of Ta [40], and the latter as e\({}_{s}\) multiplied with rH. Then VPD\({}_{s}\) and Tlim\({}_{s}\) were calculated. RUE\({}_{max}\) was calculated as the slope of a linear regression (p \(<\) 0.05) with the product of Tlim\({}_{s}\), VPD\({}_{s}\), NDVI and IPAR as independent variable and EC-derived GPP as dependent variable [7]. At last, GPP\({}_{calc}\) was upscaled for the whole county using Equation (3), with MODIS fPAR products cropped for county-wide corn and soybean fields, respectively, and crop-specific RUE\({}_{max}\). GPPcalc was set to zero when fPAR \(<\) 0.4 (see Section 4).
# Improving Graph Neural Network Models in Link Prediction Task via A Policy-Based Training Method
## 3 Methodology
### The Outputs of the Negative Sample Selector  
The action for each randomly sampled unobserved edge is determined given its state vector. To achieve this, we design a policy network that is implemented as a fully connected neural network. The policy network is denoted as \(D_{p}(\cdot,\theta_{p})\), where \(\theta_{p}\) is its parameter set. For a randomly sampled unobserved edge \((v,v^{\prime})\), the policy network takes its state vector \(s(v,v^{\prime})\) as input and outputs the action probability \(p_{a}(v,v^{\prime})\) for it, which is expressed as follows:  
\[p_{a}\big{(}v,v^{\prime}\big{)}=D_{p}\big{(}s\big{(}v,v^{\prime}\big{)},\theta _{p}\big{)} \tag{2}\]  
The action probabilities \(p_{a}(\cdot,\cdot)\) output from the policy network determine the actions for the randomly sampled unobserved edges. For a randomly sampled unobserved edge \((v,v^{\prime})\), its action \(a(v,v^{\prime})\) is determined by the following policy function \(\pi_{\theta_{p}}(s(v,v^{\prime}),a(v,v^{\prime}))\):  
\[\begin{array}{c}\pi_{\theta_{p}}(s(v,v^{\prime}),a(v,v^{\prime}))\\ =\left\{\begin{array}{ll}p_{a}(v,v^{\prime})&if\ a(v,v^{\prime})=1\\ 1-p_{a}(v,v^{\prime})&if\ a(v,v^{\prime})=0\end{array}\right.\end{array} \tag{3}\]  
where \(a(v,v^{\prime})=1\) represents the action to select the unobserved edge \((v,v^{\prime})\) as a negative sample, and \(a(v,v^{\prime})=0\) represents to drop it. With the output actions, the randomly sampled unobserved edges \(E^{s}\) are filtrated by the negative sample selector, and high-quality negative samples \(E^{neg}_{train}\) are generated.
# Data Mining and Augmented Reality: An Application to the Fashion Industry
## 2 Materials and Methods
### Association Rule Mining  
The application of data mining techniques can provide valid support for the decision-making regarding quality control policies since they enable us to simultaneously take into consideration several attributes in the dataset. To this end, using ARM can support relating attributes in order to discover unknown patterns leading to useful knowledge.  
ARM involves the identification of pairs of conditions that co-occur frequently. Specific goals can be set for the analysis and the association rules, depending on the attributes in the dataset and on the available information provided [28], starting from a set of Boolean data named items A = {a1, a2,..., an} and a transaction set V = {v1, v2,..., vm} composed of an itemset (i.e., a set of items) belonging to A, the implication between two itemsets can be defined as an association rule: I \(\rightarrow\) J. Specifically, the following assumptions have to be verified:  
* I, J \(\subseteq\) A;
* I \(\cap\) J = \(\emptyset\).  
The quality and relevance of an association rule are measured through different metrics; the support (Supp) and confidence (Conf) are used in the current application: the former is determined as the joint probability of finding both the itemsets characterizing the rule in the same transaction, i.e., the number of transactions both containing the itemsets I and J over the cardinality of the transaction set.  
\[Supp\ (I\to J)=\frac{(\#\{I,J\})}{m}\]  
The latter, instead, is the conditional probability between the itemsets composing the rule (P(J! I)).  
\[Conf\ (I\to J)=\frac{Supp(I\to J)}{Supp(I)}\]  
Different algorithms can be used to mine the association rules: in the current application, the FP-Growth algorithm [29] is used to identify all the frequent itemsets included in the database, having support higher than a user-defined threshold. Then, item sets are combined to obtain the rules, excluding the ones beyond the minimum confidence threshold.
## 3 Results and Discussion
### SERS Texture Analysis of Brain Tissue  
The excellent SERS activity of BP-AuNSs made the rapid and precise Raman mapping of brain tissue possible. In this issue, we carried out the Raman scanning (step length: 50 \(\upmu\)m) of the mouse brain section treated with BP-AuNSs in the Streamline mode for 0.1 s laser exposure. The scanning time of the entire tissue (28842 pixels) took about 27 min, i.e., 56 ms/pixel. The Raman data set was then processed in the WIRE software (version 5.1, Renishaw) with the empty modeling approach. As displayed in Figure 5, an accurate SERS image of the mouse brain section was presented. The interior texture of brain tissue (cereb cortex, hypothalamus, thalamus and hippocampus, etc.) could be obviously distinguished in this grayscale SERS image. More detailed texture was exhibited in the enlarged region (Figure 5b), including the medial habenular nucleus, corpus callosum and the internal structure of hippocampus ( dentate gyrus and Ammon's horns), which was well consistent with the data measured from the classical histological analysis (Figure 5c). It is well known that the H&E stained tissue section often suffers from multifarious and time-consuming sample preparation protocol. Technical and biological variation is another problem in the tissue processing procedure, leading to false-positive  
Figure 4: Mean Raman spectrum and BP-AuNSs or AuNPs-excited SERS spectra of mice brain tissues, shade areas represented the standard deviations.  
Figure 3: **(a)** Full-scale label-free SERS imaging of mouse brain section. **(b–d)** Raw Raman spectral lines collected from five pixel points in the brain sections treated without **(b)** or with nanostructures (AuNPs **(c)** and BP-AuNSs **(d)**).  
data. Additionally, we observed a part of tissue loss and displacement in the H&E stained section (Figure 5c). In much contrast, the Raman analysis can be directly performed without any sample pretreatment, which allows the gathering of first-hand data. Furthermore, Raman can also provide the chemical component information of biological tissue through the molecular vibrational spectrum. The bright and dark areas in the grayscale image (Figure 5b) indicated the discrepant distribution of molecular components throughout the entire brain tissue. It could be noticed that there was a certain degree of difference in chemical composition between the left and right hemisphere, though the two looked almost identical in the brightfield image (Figure 5a) and H&E stained section. This difference might be the molecular basis that resulted in various physiological and psychological functions of left and right hemispheres. However, there is still a long way from clinical practice: several issues should be considered, such as clinical sample validation, assessment criterion, the standard operation procedure of this method and so on.
## 1 Introduction
proposed the digital-twin-driven AGV scheduling and routing framework, aiming to deal with uncertainties in ACT [21]. Figure 1: The pulsating assembly flow shop workflow. The large volume of parts in pulsating assembly flow shops necessitates the use of AGVs for distribution. To reduce the number of required AGVs, multi-functional AGVs are employed, enabling each AGV to transport a variety of products. Efficient AGV scheduling is thus critical in pulsating assembly flow shops, playing a pivotal role in the production process and demanding careful attention. However, existing research on pulsating assembly flow shop scheduling primarily focuses on production scheduling, with limited attention given to AGV scheduling. For example, [PERSON] et al. proposed a three-stage integrated scheduling algorithm for AGV routing planning [22]. [PERSON] et al. designed a belt-guided AGV car with a lifting module according to the design requirements of the production line of a drip irrigation belt rolling AGV car, which improved the production efficiency of the drip irrigation belt [23]. [PERSON] et al. developed an independent AGV that can operate autonomously and make decisions based on changes in the environment [24]. [PERSON] et al. proposed a reinforcement learning multi-agent system for job scheduling and vehicle planning to optimize FJSP, such as AGV coordination [25]. [PERSON] et al. developed a set of AGV scheduling system architectures based on the already successfully developed outdoor heavy AGV [26]. Addressing the research gap identified above, this paper explores the pulsating assembly flow shop scheduling problem with multifunctional AGVs (PAFSP_AGV). To tackle this issue, a mathematical model is established. We propose three AGV selection strategies and three AGV standby strategies. Additionally, three metaheuristic algorithms are introduced to solve the problem. Finally, the effectiveness of the AGV scheduling strategy and algorithms is validated through test cases. The contributions of this paper are as follows:  
(1) We proposed the architecture for solving the studied PAFSP_AGV problem and modeled the problem by establishing MILP models. (2) We designed four AGV selection strategies and three AGV standby strategies for the studied PAFSP_AGV by considering specific problem characteristics. (3) We proposed a heuristic algorithm and three meta-heuristics to solve the studied PAFSP_AGV to provide scheduling schemes under different computing times. The remainder of this paper is organized as follows. Section 2 describes and models the problem under study. In Section 3, the AGV scheduling strategies are designed. Section 4 introduces the heuristic algorithms. Section 5 presents the metaheuristic algorithms. In Section 6, the experimental results are analyzed and discussed. Finally, Section 7 concludes the paper and outlines future work.
# Deep Point Cloud Facet Segmentation and Applications in Downsampling and Crop Organ Extraction
## 4 Discussion
### 1.2.1. Experimental Results and Comparative Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Plant** & **Method** & **ITP** & **IFP** & **IFN** & \begin{tabular}{c} **Instance** \\ **Recall** \\ \end{tabular} & \
\begin{tabular}{c} **Instance** \\ **Precision** \\ \end{tabular} &
\begin{tabular}{c} **Instance** \\ **F-Measure** \\ \end{tabular} \\ \hline \multirow{3}{*}{Tobacco 1} & [23] & 5 & 1 & 1 & 83.34\% & 83.34\% & 83.34\% \\  & DBSCAN & 4 & 3 & 0 & 100\% & 57.14\% & 72.73\% \\  & Ours & 7 & 1 & 1 & 100\% & 100\% & 100\% \\ \hline \multirow{3}{*}{Tobacco 2} & [23] & 5 & 1 & 0 & 100\% & 83.34\% & 90.91\% \\  & DBSCAN & 5 & 0 & 1 & 83.34\% & 100\% & 90.91\% \\  & Ours & 6 & 0 & 0 & 100\% & 100\% \\ \hline \multirow{3}{*}{Tomato 1} & [23] & 1 & 1 & 1 & 50\% & 50\% & 50\% \\  & DBSCAN & 3 & 0 & 0 & 100\% & 100\% & 100\% \\  & Ours & 3 & 0 & 0 & 100\% & 100\% \\ \hline \multirow{3}{*}{Tomato 2} & [23] & 1 & 1 & 0 & 100\% & 50\% & 66.67\% \\  & DBSCAN & 2 & 0 & 0 & 100\% & 100\% & 100\% \\  & Ours & 2 & 0 & 0 & 100\% & 100\% & 100\% \\ \hline \multirow{3}{*}{Sorghum 1} & [23] & 3 & 1 & 1 & 75\% & 75\% & 75\% \\  & DBSCAN & 2 & 0 & 3 & 40\% & 100\% & 57.14\% \\  & Ours & 3 & 1 & 1 & 75\% & 75\% & 75\% \\ \hline \multirow{3}{*}{Sorghum 2} & [23] & 1 & 1 & 0 & 100\% & 50\% & 66.67\% \\  & DBSCAN & 1 & 0 & 1 & 50\% & 100\% & 66.67\% \\ \cline{1-1}  & Ours & 1 & 0 & 1 & 50\% & 100\% & 66.67\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Organ-level metric comparison of tobacco and tomato. Despite requiring a higher training time than unsupervised methods (SLIC/DBSCAN), the FFN achieves superior inference efficiency, especially for large point clouds (100k points), where its linear runtime growth (1120 ms) outperforms DBSCAN's quadratic scaling (4500 ms). The controlled memory usage (4.2 GB at inference) and reduced FLOPs highlight its scalability for large-scale point cloud processing, such as high-density crop phenotyping datasets with 100k+ points. This balance between accuracy and efficiency supports its practical deployment in resource-limited scenarios (e.g., field-deployed 3D scanners).