esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Mathematical Formulation of the Problem
### Production, Inventory, and Delivery Costs Formulation
So, the optimization model is composed of an objective function that presents the minimization of the total inventory, delivery, and production cost, \(CT\), on the finite horizon, \(H\cdot\Delta t\), defined as follows:  
\[\underset{U=\{u(k),Q(k),\,k\,1,\ldots,H\}}{Min}\,CT_{P}=Min\left( \begin{array}{ c}C_{p}\times\sum\limits_{k=1}^{H}u(k)\cdot\Delta t\\ +C_{h}\times\sum\limits_{k=1}^{H}\left(\begin{array}{c}\max(S(k-1),0)\cdot \Delta t+\frac{1}{2}\cdot u(k)\cdot\Delta t^{2}\\ +\sum\limits_{i=1}^{L}\max(w_{i}(k-1),0)\cdot\Delta t+\frac{1}{2}\cdot Q_{i} \big{(}k-\frac{\tau_{i}}{\Delta t}\big{)}\cdot\Delta t^{2}\\ +C_{l}\times C_{O}\times\sum\limits_{i=1}^{L}\left(\frac{Q_{i}(k)}{Q_{o}} \right)\\ +C_{d}\times\left(\sum\limits_{k=1}^{H}\left(\sum\limits_{i=1}^{L}\left(\frac{ \left|\min\left(w_{i}(k)\Delta t\right)\right|}{Q(k+1-\frac{\tau_{i}}{\Delta t })}\right)\right)\right)\end{array}\right)\end{array} \right)\]  
Under several constraints, such as:  
For each warehouse, the service level constraint at each period \(k\) is expressed by the following probabilistic constraint. For (\(k=1,\,\ldots,\,H-1\)) and (\(i=1,\,\ldots,\,L\)):  
\[\text{Prob}\left[w_{i}(k)\geq 0\right]\geq\theta_{i} \tag{20}\]  
The lower and upper bounds on the production level are defined by the following constraint at each period \(k\):  
\[U_{\min}\leq u(k)\leq U_{\max} \tag{21}\]
## 6 Discussion
This study highlights the importance of sensitivity analyses and optimization techniques, such as those using finite element methods, to optimize the motor topology and further reduce losses and torque ripple in PMSMs. These optimizations are essential in aligning motor characteristics with the demands of modern electric propulsion. The analysis also illustrates how PMSMs can be tailored through design iterations to meet specific operational and environmental constraints, underscoring the need for continued research into high-efficiency motor designs that minimize power losses. Although experimental data on 10 MW motors for ship propulsion applications are scarce, the simulation results presented in this paper align with general trends reported in the literature [49]. Specifically, simulations confirmed that PMSMs achieve higher efficiency compared to IMs, and thermal analyses indicated that PMSMs exhibit lower temperature rise due to reduced rotor losses, reaffirming their superiority in applications where thermal management is crucial. Additionally, excellent acoustic performance makes them suitable for a wide range of ships. Overall, a comparison of our results with those in the literature [50] supports the validity of simulations and provides a reliable basis for evaluating the advantages and disadvantages of PMSMs and IMs in high-power ship propulsion applications. The lack of extensive experimental data for motors of this scale underscores the need for further research and experimental verification to confirm theoretical and simulation-based findings and guide the industry in making informed decisions regarding propulsion motor selection. While the presented results were derived from validated finite element models, it is acknowledged that simulation-based findings may deviate from real-world performance due to factors such as manufacturing tolerances, thermal aging, and marine environmental variability. A next step in this research includes experimental validation through lab-scale prototypes or full-system tests aboard electric vessels. The fault-tolerant design of multi-phase PMSMs for ship propulsion is crucial to ensure operational reliability and safety in demanding maritime environments. Incorporating modular inverter systems and multi-phase stator coils enhances system resilience by allowing continued operation even in the event of partial inverter failures [51]. This approach minimizes downtime, reduces maintenance costs, and ensures uninterrupted propulsion, which is critical for mission-critical applications such as naval vessels and commercial shipping. The redundancy provided by a fault-tolerant design significantly enhances the overall robustness and lifecycle performance of ship propulsion systems. Future research could explore further improvements in PMSM design by integrating advanced materials and exploring new topologies, potentially increasing efficiency and reducing weight further. Additionally, addressing the economic aspects, including the cost of materials like permanent magnets, could make PMSMs more accessible for a wider range of maritime applications.
## 3 Results and Discussion  
### Chemical Composition  
The extraction yield of AO and HERA is, respectively, 28 \(\pm\) 2% and 55 \(\pm\) 1% _w/w_. The high extraction yield of HERA is justified by the extraction mechanism itself: with every extraction cycle using Soxhlet apparatus, the roasted almonds are soaked in fresh hexane. On the other hand, the traditional AO extraction is based on the strength of women's arms. In their published works, [PERSON] et al. mentioned the impact of the extraction process on the extraction yield: the use of hydraulic presses decreased the amount of time needed to extract 1 L of AO by five times and increased the extraction yield from 29 to 50% [31]; while organic solvent extraction gives a yield ranging from 50 to 57% [32].  
The results of GC-MS analysis (Figure 1 and Table 1) reveal that linoleic acid (C18:2 n-6) is the predominant fatty acid in AO (60.55 \(\pm\) 0.16%) and HERA (32.26 \(\pm\) 0.33%). While the second and the third predominant fatty acids for AO are, respectively, stearic acid (C18:0) (20.24 \(\pm\) 0.17%) and palmitic acid (C16:0) (15.87 \(\pm\) 0.17%); its order is reversed for HERA, i.e., 15.37 \(\pm\) 0.41% for palmitic acid and 5.14 \(\pm\) 0.12% for stearic acid.
## 4 Results and Analysis
### LAI Retrieval in Saihanba National Forest Park
#### 4.2.3 Uncertainty Analysis for Ground Validation  
In the ground validation, there is a 10-year temporal difference between the ground measurement and the GLAS data, which will introduce uncertainties in the validation process. Despite this weakness, our validation results are still useful and informative for two major reasons. First, the main purpose of our ground-based validation is to assess how well GLAS can capture spatial patterns and heterogeneity in LAI. This spatial variability contributed to the majority of the variation in both the in situ and GLAS LAI data--a signature that should be independent of time. In other words, our validation should provide valid quantification of the GLAS accuracies over space. Second, to overcome the limitation associated with the temporal discrepancy, we used the MODIS LAI product (i.e., MCD15A2H.A2018169 vs. MCD15A2H.A2006169) as an intermediary to evaluate the variation of LAI in 10 years for GLAS footprints located in Saihanba National Forest Park. The result shows that the LAI of 2018 has increased compared with the LAI of 2006, but the overall change is not very large (Figure 10(a)). Besides, it can be noted that the LAI of 2018 has a moderate correlation with the LAI of 2006 (i.e., R\({}^{2}\) = 0.41) (Figure 10(b)); thus, the error caused by the time discrepancy in the validation could be taken as a systematic error. Therefore, we can reasonably consider the impact of this error on all results is almost the same. This does not necessarily affect the overall validity of the correlation between in situ and GLAS data.
## 4 Experiment and Results
### Connection Scenario 3: No Anchor  
In connection scenario 3, three anchor nodes and six target nodes are set up. Three of six target nodes connect with one anchor, while others only connect with neighbor target nodes. Figure 6 depicts the location distribution (a), communication relationship between nodes (a), positioning error of various algorithms (b-e), and overall positioning error to constrast (f). There was no efficacious message in iteration 1 transmitted to target nodes with no anchor neighbor, resulting in their first prediction near the center. After two iterations, all three algorithms except PVSPA improve prediction accuracy. PVSPA makes locating even worse.  
Figure 5: Location results of connection scenario 2. (**a**) exhibits the setting of scenario 2. Anchors are represented by cyan nodes, whereas yellow nodes represent targets. Blue dotted lines link two nodes with communication. PVSPA, SPAWN, NBP, and SBPVEM iterative positioning results and corresponding positioning errors are shown in (**b–e**). (**f**) comparing the prediction errors of various algorithms in the iterative process.
# Multi-Objective Order Scheduling via Reinforcement Learning
## 5 Experiments
### Results on Generated Instances
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Size**} & \multicolumn{2}{c}{**Zhang [12]**} & \multicolumn{3}{c}{**Ours**} \\ \cline{2-7}  & **Time** & **Cost** & **Distance** & **Time** & **Cost** & **Distance** \\ \hline \(10\times 10\) & 983.57 & 441.41 & 456.09 & 1138.67 & 464.38 & **396.67** \\ \(15\times 15\) & 1489.91 & 800.79 & 789.92 & 1633.5 & **655.72** & **643.99** \\ \(20\times 15\) & 1735.11 & 909.56 & 937.24 & 1971.35 & **727.28** & **879.7** \\ \(20\times 20\) & 2005.05 & 1031.73 & 1072.31 & 2369.32 & 873.75 & 1009.42 \\ \(30\times 15\) & 2216.51 & 1199.61 & 1183.06 & 2542.12 & **1097.57** & **969.33** \\ \(30\times 20\) & 2471.58 & 1325.24 & 1311.9 & 2856.38 & **1200.59** & **1053.54** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results on generated instances compared with Zhang [12]. Figure 5: (**a–c**) The respective training curves for time, cost, and distance.
# Effects of Precipitation and Fire on Land Surface Phenology in the Brazilian Savannas (Cerrado)
## 4 Discussion
According to the literature, vegetation types and plant species composition in the Cerrado biome are largely shaped by a combination of precipitation (i.e., water availability), soil composition, and fire regimes [59]. Soil moisture, in turn, is influenced by soil composition or texture, which affects both fertility and the rate of water infiltration with soil depth. These factors determine the availability of nutrients and water to shallow and deep roots across different vegetation physiognomies. In this context, temperature and topographic relief play a secondary role in the Brazilian savannas. In the Southwest Goias Microregion, where the ENP is located in Brazil and surrounded by agricultural land, [PERSON] et al. [60] identified long-term declines in MODIS NDVI, which can influence vegetation phenology. Approximately 50% of these declines, likely associated with land degradation, occurred in pastures converted from savanna vegetation and situated on sandy soils. An additional 25% were observed in native savanna vegetation and 14% in croplands. The ENP itself exhibited overall NDVI stability, with only a few long-term declines detected in the southern portion of the park [59]. This area is characterized by a notably high fire frequency, with over ten fire events recorded solely between 1973 and 2003 [35]. However, the relationships among fire frequency, NDVI declines, and the determination of vegetation phenological metrics require further investigation. Despite these limitations, the comparison and convergence of vegetation phenology results derived from both products (MOD13Q1 and HLS) at the ENP have provided confidence in the analysis and interpretation of our findings. Vegetation index time series with higher spatial resolution than MODIS are essential for LSP studies of most protected savanna areas in Brazil, given their small size and the challenge of mapping and analyzing them accurately using satellite images. Given the potential importance of phenological metrics in classification studies of savanna physiognomies in protected areas of the Brazilian Cerrado [30, 31, 32], it is recommended to select the seasonal cycles least affected by fire disturbance in order to reduce uncertainties in the data analysis and improve classification accuracy. It is also essential to consider fire as a significant factor influencing variability in long-term analyses of phenological metrics, particularly in relation to interannual changes in precipitation.
# Reconstruction of Isolated Moving Objects by Motion-Induced Phase Shift Based on PSP
###### Abstract  
The reconstruction of moving objects based on phase shifting profilometry has attracted intensive interests. Most of the methods introduce the phase shift by projecting multiple fringe patterns, which is undesirable in moving object reconstruction as the errors caused by the motion will be intensified when the number of the fringe pattern is increased. This paper proposes the reconstruction of the isolated moving object by projecting two fringe patterns with different frequencies. The phase shift required by the phase shifting profilometry is generated by the object motion, and the model describing the motion-induced phase shift is presented. Then, the phase information in different frequencies is retrieved by analyzing the influence introduced by movement. Finally, the mismatch on the phase information between the two frequencies is compensated and the isolated moving object is reconstructed. Experiments are presented to verify the effectiveness of the proposed method.  
phase shifting profilometry; phase retrieval; phase unwrapping +
Footnote †: journal: Applied sciences
# Seasonal Monitoring of Volatiles and Antioxidant Activity of Brown Alga _Cladosteplus spongiosus_
## 2 Results and Discussion
### Headspace Volatilome Variations of C. spongiosus
Carboxylic acids such as hexanoic and heptanoic acids contributed strongly to the increase in other compounds abundance, especially in the dry May sample.
# Noiseless Attenuation for Continuous-Variable Quantum Key Distribution over Ground-Satellite Uplink
## Appendix B The Calculations of \(I_{AB}\) and \(\chi_{E}\)  
The Shanon mutual information \(I_{AB}\) can be explicitly obtained from the variances and the correlations between the modulation data and measurement data, which is given by [44]  
\[I_{AB} = \frac{1}{2}\log\frac{V_{A}}{V_{A|B}}, \tag{16}\]where \(V_{A|B}=V_{A}-C_{AB}^{2}/V_{B}\) is the conditional variance with the correlation \(C_{AB}^{2}\) and measured data variance \(V_{B}\). According to Equation (14), the mutual information between [PERSON] and [PERSON] in the homodyne detection case is given by  
\[I_{AB}=\frac{1}{2}\log_{2}\left(\frac{1}{1-\frac{\langle\sqrt{T}\rangle^{2} \gamma^{2}}{\langle T\rangle(X+\chi_{\text{line}})(X+1)}}\right),\] (A12)  
where the factors \(X=V\) and \(Y=\sqrt{V^{2}-1}\) for coherent-state protocol.  
The Holevo bound \(\chi_{E}\) for reverse reconciliation is identified as [45]  
\[\chi_{E}=G(\frac{\lambda_{1}-1}{2})+G(\frac{\lambda_{2}-1}{2})-G(\frac{ \lambda_{3}-1}{2}),\] (A13)  
where \(G(X)=(x+1)\log_{2}(x+1)-x\log_{2}x\), \(\lambda_{1,2,3}\) are the symplectic eigenvalues derived from the covariance matrices of the sharing quantum state [27]. In this paper, we consider the reverse reconciliation case so that \(\chi_{E}\) denotes the information available to [PERSON] on [PERSON]'s measurement results. According to Equation (14), the symplectic eigenvalues \(\lambda_{1,2}\) are given by  
\[\lambda_{1,2}^{2}=\frac{\Delta\pm\sqrt{\Delta^{2}-4D^{2}}}{2},\] (A14)  
with the notations  
\[\Delta=X^{2}+\langle T\rangle^{2}(X+\chi_{\text{line}})^{2}-2\langle\sqrt{T} \rangle^{2}Y^{2},\] (A15)  
and  
\[D=\langle T\rangle X(X+\chi_{\text{line}})-\langle\sqrt{T}\rangle^{2}Y^{2}.\] (A16)  
\(\lambda_{3}\) in homodyne detection case is given by  
\[\lambda_{3}^{2}=X(X-\frac{\langle\sqrt{T}\rangle^{2}\gamma^{2}}{\langle T \rangle(X+\chi_{\text{line}})}).\] (A17)
# Pico-Sat to Ground Control: Optimizing Download Link via Laser Communication
## 2 Physical Components and Methods  
In this section, we describe the physical components of the two types of parties in the described network--the ground station and the satellite. We then proceed to describe the required operations by the two sides to obtain reliable FSO communication.
## 4 Omnidirectional Images
### Methods for Omnidirectional Images
They also proposed working on a six-face cube projection, in order to mitigate the heavy distortions of equirectangular projection, and implemented so-called cube padding to hide the discontinuities of representation to the neural network processing. [PERSON] et al. [73] proposed an aggregation technique for the application of existing saliency estimation methods to different map projections. They mitigate the discontinuities introduced at the edge of 2D representations by performing a double cube projection, the results of which are eventually merged. They also proposed the automation of a navigation pattern that maximizes exposition to estimated salient areas. Despite the clear dominance of machine learning approaches to saliency estimation (and, specifically, deep learning approaches), a good deal of recent methods for omnidirectional saliency are based on hand-crafted design and combination of visual features [29; 33; 34; 62; 63; 64; 73]. [PERSON] et al. [62] defined a hand-crafted approach to saliency estimation for omnidirectional images. Their color-dictionary sparse representation (CDSR) is applied in conjunction with multi-patch analysis to simulate human color perception. Fixation bias is also taken into consideration for the specific characteristics of the domain. [PERSON] et al. [63] extended existing solutions to the estimation of saliency in omnidirectional images, namely Boolean Map Saliency (BMS) and Graph-Based Visual Saliency (GBVS). They then defined a novel framework, called Projected Saliency, to adapt existing estimation models with a simple mechanism, which allowed extensive analysis of features interaction in computational saliency models. [PERSON] et al. [64] developed a hand-crafted solution based on the fusion of feature contrast and boundary connectivity, leaning on the figure-ground law from Gestalt Theory. Boundary connectivity is designed to describe the spatial layout of the image region with an upper and a lower boundary. Feature contrast is based on luminance and color features from the CIE Lab color space. [PERSON] et al. [29] presented a hand-crafted approach based on low-level image descriptors, such as edges and texture features. They also exploit a depth description of the image itself, to produce a more robust estimation of image saliency, which is evaluated using metrics such as the Kullback-Leibler divergence, and the correlation coefficient. Methods based on deep learning [35; 36; 61] are built on the basis of existing Convolutional Neural Network (CNN) backbones, such as the VGG_CNN_M [74] and VGG-16 [75] architectures (from the Visual Geometry Group), and the residual-learning-based ResNet-50 [76] architecture.
###### Abstract  
Reliability and energy efficiency are two key considerations when designing a compressive sensing (CS)-based data-gathering scheme. Most researchers assume there is no packets loss, thus, they focus only on reducing the energy consumption in wireless sensor networks (WSNs) while setting reliability concerns aside. To balance the performance-energy trade-off in lossy WSNs, a distributed data storage (DDS) and gathering scheme based on CS (CS-DDSG) is introduced, which combines CS and DDS. CS-DDSG utilizes broadcast properties to resist the impact of packet loss rates. Neighboring nodes receive packets with process constraints imposed to decrease the volume of both transmissions and receptions. The mobile sink randomly queries nodes and constructs a measurement matrix based on received data with the purpose of avoiding measuring the lossy nodes. Additionally, we demonstrate how this measurement matrix satisfies the restricted isometry property. To analyze the efficiency of the proposed scheme, an expression that reflects the total number of transmissions and receptions is formulated via random geometric graph theory. Simulation results indicate that our scheme achieves high precision for unreliable links and reduces the number of transmissions, receptions and fusions. Thus, our proposed CS-DDSG approach effectively balances energy consumption and reconstruction accuracy.  
WSNs; CS; distributed data storage; packet loss rate; energy efficiency +
Footnote †: journal: Sensors
## 2 Related Work and Problems
### Motivating Example and Problems  
Following the links between cybersecurity databases, CVE-ID can be traced to CAPEC-IDs. In some cases, tracing back to CAPEC is not possible. An example is CVE-2020-10108, which is a vulnerability related to HTTP request splitting. The description of CVE-2020-10108 is as follows: In Twisted Web through 19.10.0, there was an HTTP request splitting vulnerability. When presented with two content-length headers, it ignored the first header. When the second content-length value was set to zero, the request body was interpreted as a pipelined request [38].  
There is an attack pattern identifier for HTTP request splitting in CAPEC-105. CVE-2020-10108 is linked to CWE-20, but CAPEC-105 is not. Therefore, CWE-20 cannot be traced from CVE-2020-10108 to CAPEC-105. The exact number of CVE-IDs that cannot be traced to CAPEC via CWE is unknown. Since the issue is the link between cybersecurity databases, it is preferable to directly trace from CVE to CAPEC. The motivation of this study is to create an approach to directly trace from CVE to CAPEC.
# Quantum Circuit Synthesis Using Fuzzy-Logic-Assisted Genetic Algorithms
## 3 Related Work
Variational quantum circuits provide an effective and resourceful method for utilizing error-prone NISQ devices to perform optimization, simulation, and machine-learning tasks. Various studies have explored the synthesis and tuning of variational circuits using GAs for different purposes. Some research works have focused on discovering effective ansatzes for use in variational algorithms [64; 65; 66]. Other studies have concentrated on tuning the free parameters of these circuits to optimize their performance [67]. Additionally, some works have aimed to compile noise-limiting circuits using GAs [68]. We now briefly discuss how our work differentiates itself from the existing literature. Our proposed method performs approximate circuit synthesis directly from an arbitrary unitary for a target quantum device, accounting for qubit connectivity. It incorporates and optimizes three compilation objectives simultaneously: approximation error, depth, and gate counts. Our approach leverages the NSGA-II algorithm with modifications, includes qubit mapping strategies and utilizes a nested numerical optimization step to tune parameterized gates. It further enhances the GA through runtime parameter adaptation using a fuzzy logic controller, and is, to our knowledge, the first work to combine fuzzy logic in GA-based circuit synthesis. We also provide parallelization strategies for acceleration of the implementation and perform experimental comparison with industry-standard quantum compilers, making it a comprehensive and unique contribution to the field of GA-based quantum circuit synthesis. Further, by using a GA, our proposed work is also able to avoid many of the limitations of gradient based techniques, such as local minima and unguaranteed convergence. Finally, while our proposed method is able to incorporate several objectives during compilation, and the NSGA technique makes it possible to add more.