esa-sceva/satcom-chunk-collection huggingface dataset:
# Evolution of Hybrid LiFi-WiFi Networks: A Survey
## 2 Framework for HWLNet
### Cell Deployment  
In contrast to WiFi technology, wherein signal can reach indoor users up to 50 m [7] and a few hundred metres [7] in an open area, LiFi suffers from a short-range coverage due to light propagation properties as well as the currently available LED technology level. Therefore, it is vital for LiFi AP to be placed in a way that optimizes its deployment to achieve high-quality network performance. Usually, LiFi APs are integrated into ceiling lamps, which are susceptible to different constraints such as room shape and size,  
Figure 2: SDN-enabled HWLNet.  
number of windows, height from floor to ceiling, and other factors. There are three main cell deployment models: hexagon, matrix, and Poisson point process (PPP), described as follows:  
* hexagon deployment is borrowed from cellular networks, and it provides the highest signal to interference-plus-noise ratio (SINR) for LiFi [8]. This type of lamp arrangement is not common in real world applications, so it is used as an ideal model for testing;
* matrix deployment is the most common model used in research because of its simplicity. Research shows that it comes very close to hexagon in terms of SINR;
* PPP is primarily used for simulation of random distribution of APs. It is based on the Poisson equation and has the worst SINR [8].  
The distance between APs in LiFi networks is a trade-off between the handover rate and coverage. There were several studies on matrix deployment [9], which concluded that handover with an average speed of users is best at a three-metre distance between APs. Another study [8] used gradient projection to find the optimized distribution of APs. These distribution types are shown in Figure 3 and show that optimized types can improve the system throughput by up to 70% [10]. It must be taken into account that WiFi APs affect the placement of users, therefore influence the performance of HWLNets. Research has shown that matrix deployment of WiFi APs can increase the system throughput by 20% compared to that of traditional deployment [10].
## 3 Proposed Approach
```
0: Number of sub-problems \(N\), population size \(M\), neighborhood size \(T\), maximum generations \(G_{\text{max}}\), weight vectors \(\lambda_{1},\lambda_{2},\ldots,\lambda_{N}\)
0: Approximate Pareto front \(Y\)  Initialize population \(P=\{x_{1},x_{2},\ldots,x_{N}\}\);  Generate weight vectors and compute neighborhoods; for\(g=1\)to\(G_{\text{max}}\)do for\(i=1\)to\(N\)do  Randomly select two parents from the neighborhood of subproblem \(i\);  Apply crossover and mutation to generate offspring \(y\);  Evaluate the objective function values for \(y\);  Update the population using the offspring \(y\) by replacing solutions in the neighborhood of \(i\) if better under corresponding sub-problems;  end for  end for return final population \(Y\);
```  
**Algorithm 1** Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D)
## 2 A Near Isotropic Planar Antenna  
_Design Methodology_  
The basic design of the proposed isotropic antenna follows the same concept, as presented in [15], which states that it is possible to achieve isotropy if electric and magnetic dipoles with equal magnitude of electric field over the spherical surface are combined. Therefore, two orthogonal polarizations E\({}_{\theta}\) (component of electric dipole) and E\({}_{\phi}\) (component of magnetic dipole) are required to obtain an equal magnitude of electric field over the spherical surface around the antenna, thereby resulting in isotropic radiation pattern. For a vertically placed dipole (current flowing along \(z\)-axis), polarization in the far-field is also in vertical direction i.e., E\({}_{\theta}\) parallel to the direction of current. Therefore, in order to achieve isotropic pattern, an approximately equal magnitude of current is required in the orthogonal direction to produce E\({}_{\phi}\) component. The magnitude and location of each current source is then to be adjusted in such a way that the nulls and the points of low antenna gain are filled up gradually and eventually result in an approximate isotropic pattern. Theoretically, this can be achieved by placing various small dipoles at arbitrary locations with the same phase reference point, so that their combined radiation pattern achieve maximum isotropy. However, in theoretical implementation, each small dipole would require an independent feed, which is practically not possible in a single feed antenna. The field of a short dipole segment with arbitrary location but with single phase reference point is given by Equation [26]. \[G_{d}=C_{k}\eta I_{o}[\hat{\mathbf{I}}-(\hat{\mathbf{1}}.\hat{\mathbf{r}})\hat {\mathbf{r}}]\hat{J}(k\hat{\mathbf{I}}.\hat{\mathbf{r}})e^{jk\hat{\mathbf{r}}_{ \circ}.\hat{\mathbf{r}}} \tag{1}\]  
where \(G_{d}\) is the radiation pattern of the dipole segment at an arbitrary location \(\mathbf{r}_{\circ}\), \(C_{k}\) is the incremental source constant, while \(\bar{J}(k\hat{\mathbf{I}}.\hat{\mathbf{r}})\) is the radiation constant. \(\eta\) is the intrinsic impedance, while \(I_{o}\) is the excitation current at the feed gap. At first, Equation (1) was implemented in MATLAB to obtain an initial idea of achievable isotropy. The design was started with three segments: a horizontal segment (along \(x\)-axis), a vertical segment (along \(z\)-axis), and a ramp shaped linear segment. The magnitude of the electric field of a particular polarization can then be increased or decreased by varying the length of the current segment that is oriented in that direction. Figure 1 shows the initial design of arbitrary shaped antenna and the result, where radiation patterns of three segments are presented independently.
# From the Martian Surface to Its Low Orbit in a Reusable Single-Stage Vehicle--[PERSON]
## 3 Results
### Design Analysis
#### 3.5.1 Budgets  
Firstly, the mass budget is critical to its design, as the mass is a key input to multiple subsystems. Because of this, Charon's mass has been evaluated by a Class I method [50], and multiple iterations have followed. In each iteration, the mass of the subsystems has been slightly altered. These iterations have been carried until Charon's total mass was within 3% of the previous iteration. The final mass of all subsystems can be found in Table 3. For all subsystems, the MGA was taken to be 20% [51]. This margin covers the assumptions and uncertainties that were present throughout the subsystems design.  
\begin{table}
\begin{tabular}{c c} \hline
**Process** & **Power [kW]** \\ \hline H\({}_{2}\)O electrolysis & 483 \\ Sabatier reaction & \(-\)27.8 \\ H\({}_{2}\)O\({}_{2}\) production & 0.03 \\ H\({}_{2}\)O\({}_{2}\) electrolysis & 0.60 \\ N\({}_{2}\)H\({}_{4}\) heating & 20.0 \\ H\({}_{2}\)O\({}_{2}\) heating & 13.3 \\ LOX plant & 300 \\ CO\({}_{2}\) capture & 190 \\ CO\({}_{2}\) heating & 3.80 \\ H\({}_{2}\) heating & 0.01 \\ H\({}_{2}\)O mining & 40.0 \\
**Total** & **1023** \\ \hline \end{tabular}
\end{table}
Table 2: Propellant manufacturing power budget Secondly, the Delta-V budget directly follows from the mission profile, dictating Charon's capabilities. The different velocity increments required trough out a flight of Charon can be seen in Table 4.  
Finally, the last budget that has been assessed is Charon's cost. To do so, the Project Cost Estimating Capability software from NASA [52] has been used. This software produced mass-based cost estimations for almost every subsystem and component of Charon, using reference data from a catalogue of similar NASA launch and crew vehicle projects. From it, Charon's production cost adds up to $548 M, the design and development cost to $2687 M, the test and hardware cost to $527 M, and the test operations to $508 M. The production cost is recurring for each vehicle, and as mentioned in the life cycle discussion, five vehicles are required for optimal conditions. The production cost should then be multiplied by five, resulting in $2740 M. In total, the complete cost of having a fleet of reusable SRVs on Mars, excluding the infrastructure in orbit and on the surface, is $6462 M. Including the cost of the five contracted SLS launches (alternative launchers do not have a set price at the moment), and with a standard 60:40 Beta Curve for preliminary phasing of cost estimates over the project life-cycle [53], the project cost was calculated to be $13.8 billion over 18 years, with a peak annual cost of just over $1 billion for the 15 th year.
# Modulation of Ricin Intoxication by the Autophagy Inhibitor EACC
## 2 Results
### EACC Inhibits Release of Ricin A-Chain  
To intoxate cells, it is not sufficient that ricin is able to reach the ER; the A-chain has to be released from the B-chain by reduction of the disulfide bond connecting the two chains, and it also has to be translocated to the cytosol. To investigate whether the release of A-chain is affected by EACC, we again used RS1, the ricin construct with a sulfation site in the A-chain. The cells were incubated with this construct in the presence of radioactive sulfate with and without EACC, and at the end of the incubation, N-ethylmaleimide was added to the cells to prevent further reduction which might otherwise happen during the subsequent lysis of the cells. Ricin A-chain and intact ricin were immunoprecipitated, and then run on an SDS-gel under non-reducing and reducing conditions. As shown in Figure 4A to the left (non-reducing gel), EACC inhibited formation of the lower band which represents the free A-chain. The figure also demonstrates that in this experiment, there was little difference in total sulfated A-chain when the gel was run under reducing conditions. Quantification of independent experiments is shown in Figure 4B. These experiments were performed in the presence of the proteasome inhibitor MG132 to prevent proteasomal degradation, otherwise the bands became extremely weak. Thus, the data indicate that EACC inhibits ER-mediated reduction of ricin. It has recently been shown that ER function and exit of proteins from the ER are strongly dependent on signaling, and both adenylyl cyclase and the leukocyte tyrosine kinase (LTK), which is an ER-resident tyrosine kinase, affect transport out of the ER [1; 13; 14]. However, neither the addition of the membrane permeable cAMP analogue 8-Br-cAMP nor the inhibition of LTK by addition of cricotinib (Figure S3) had any effect on the ability of EACC to protect the cells against ricin or on the sensitivity of the cells to the toxin alone.
## 2 Related Work
### Feature Pyramid Network Model  
The feature pyramid network [29] was initially designed to address multi-scale challenges in object detection. Due to its capability to effectively capture features at various granularities, it has become widely adopted in object detection tasks. Lately, researchers have been investigating how feature pyramid networks can be applied to sequential tasks like speech recognition and classification. [PERSON] et al. [30] introduced a contextual pyramid  
Figure 1: Functional diagram of SER system.  
generative adversarial network for speech enhancement. This design effectively captures speech details at various levels and removes audio noise in a structured way. [PERSON] et al. [31] explored sound event detection based on feature pyramid networks, and their experiments demonstrated that the model utilizing feature pyramid networks surpassed long short-term memory (LSTM) in performance. Furthermore, researchers [32; 33; 34] have utilized feature pyramid networks to enhance speech recognition capabilities by combining multi-level features.  
Previous studies have shown that feature pyramid networks can effectively capture multi-scale features in sequential tasks. Yet, there has been limited research on how these networks benefit extracting emotion features from speech. So, we delved deeper into using feature pyramid networks for speech emotion recognition. We enhanced the original feature pyramid network by introducing a more powerful convolutional attention network to extract stronger emotion features.
# Evaluating Packaging Design Relative Feature Importance Using an Artificial Neural Network (ANN)
## 3 Results
### Case Study 2: Relative Feature Importance of the Real Dataset  
The real dataset comprises industry data on 429 commonly used commercial boxes, detailing five BCS features: box perimeters, depths, ECTs, and flexural stiffness in both the machine and cross-machine directions. These BCS values were obtained through actual testing, accurately reflecting real-world industry conditions. Since real-world data captures fluctuations in BCS feature values due to measurement inaccuracies and variations in material parameters, studying a real dataset from the industry is very meaningful to the feature importance assessment.  
As with the first synthetic dataset, the theoretical relative importance of these five BCS features were calculated using the improved McKee formula (Equation (8)) [45], where the ECT has a weight of 0.500, the perimeter has a weight of 0.330, both stiffnesses \(\text{EI}_{\text{x}}\) and \(\text{EI}_{\text{y}}\) have a weight of 0.085, and depth has no importance (0), as shown in Figure 10.  
\[\text{BCS}=2.028\text{ECT}^{0.746}\left(\sqrt{\text{EI}_{\text{x}}\times\text{ EI}_{\text{y}}}\right)^{0.254}\text{p}^{0.492} \tag{8}\]  
where  
ECT--Edge crush strength (lb/in).  
\(\text{EI}_{\text{x}}\), \(\text{EI}_{\text{y}}\)--Flexural stiffness in the machine direction and cross-machine directions of the corrugated board (lb\(\times\)in).  
P--Perimeter of the box (in).  
Figure 9: Comparison of the average feature importance assessed by the selected ANN-based methods and theoretical BCS feature importance calculated using the simplified McKee formula.
# Diagnosis of the Permian Fengcheng Formation in the Mahu Sag, Junggar Basin, China
###### Abstract  
The Fengcheng Formation in the Mahu sag of the Junggar Basin was primarily composed of detritus, pyroclastic material, carbonates, and evaporites. In order to establish the diagenesis pathways of the Fengcheng Formation, some methods of polarized light microscope, SEM, CL, EPMA, LR, and fluid inclusion analysis were applied to discuss the diagenesis process. The results showed the following: (a) The formation of an alkaline lake was the result of the influence of a high concentration of sodium-rich sources, and it led to the preservation of alkaline minerals in the stratum. (b) After the sediments were buried, three mineral assemblages were formed in the Fengcheng Formation, which are carbonate mineral assemblages (i.e., calcite + ferrous dolomite), reedmergnerite and carbonate mineral assemblages (i.e., redmergnerite + calcite + ferrous dolomite), and reedmergnerite and alkaline mineral assemblages (i.e., reedmergnerite + shortite + trona), respectively. (c) According to the homogenization temperature of reedmergnerite primary fluid inclusions, the alkaline diagenesis of Fengcheng Formation was divided into an early stage (\(\leq\)100 \({}^{\circ}\)C) and a middle stage (\(>\)100 \({}^{\circ}\)C), respectively. The earlier stage is marked by the formation of ferrous saddle dolomite, quartz dissolution, and the agglutination of laumontite. These processes occurred under normal burial conditions. The latter is marked by the reedmergnerite's appearance, which is correlated with the deep hydrothermal activity controlled by faults. (d) Based on sedimentary and diagenetic factors, including climate, provenance, diagenetic surroundings, and the action of subsurface fluid, the alkaline deposition-diagenesis model for shale series in four stages of the Fengcheng Formation was established.  
Keywords:diagenesis pathways; Fengcheng Formation; Mahu sag; northwest China +
Footnote †: journal:
## 2 Materials and Methods
### Hs- SPME-GC/MS Analytical Conditions  
VOCs were extracted using Head Space Solid Phase Microextraction on (HS-SPME-GC/MS) with 2-cm fibre coated with 50/30-\(\upmu\)m divinyl benzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS, Supelco, Bellefonte, PA, USA). The fibre was exposed to the headspace for 40 min. The compounds absorbed on the SPME fibre were desorbed at 250 \({}^{\circ}\)C in the GC/MS injection port. The mass detector operated in electron ionization mode (EI, internal ionization source; 70 eV) with a scan range from \(m/z\) 33 to 350. Analysis was carried out using Perkin Elmer Clarus 500 GC/MS equipped with an HP-INNOWax fused silica capillary column (30 m, 0.32-mm ID, 0.5-\(\upmu\)m film thickness; Agilent Technologies, Palo Alto, CA, USA). The oven temperature was initially set at 40 \({}^{\circ}\)C for 1 min, then increased to 220 \({}^{\circ}\)C at 4 \({}^{\circ}\)C/min, increased to 250 \({}^{\circ}\)C at 15 \({}^{\circ}\)C/min and maintained for 2 min. Helium was used as carrier gas with a flow rate of 1.5 mL/min. Compound identification was based on mass spectra matching with NIST14/Wiley98 libraries. Linear retention indices were calculated under the same chromatographic conditions after the injection of a C7-C30 \(n\)-alkane series (Supelco).
## 2 Food Security: A Global Challenge in View of Soil Pollutants and Heavy Metal Toxicants
### Benefits of Smart Farming  
In certain ways, technology and IoT have the ability to change agriculture. There are five ways in which IoT can boost agriculture:  
* Data:
* Loads of data gathered by sensors from the fields, i.e., temperature, soil quality, crop growth progress, and animal health can be used to monitor the condition of the farm along with the performance of field workers and efficiency of equipment.
* Improved internal process management and therefore lower output risks:
* The ability to forecast the production performance enables farmers to become prepared for a better delivery of goods. Wise decisions can be made if the estimated production of the crop is known.
* Cost savings and waste reduction by improved quality control:
* If any irregularities in crop growth and animal health are seen, then the risk of losing the yield can be minimized.
* Improved business productivity through process automation:
* Smart farming can automate several processes during the development cycle, i.e., irrigation, fertilization, or pest control with intelligent devices.
* Improved consistency and volume of the commodity:
* Greater control over production processes and retaining higher crop quality levels and growth potential by automation can be achieved.
* Helps to mitigate soil toxicity and monitor significant parameters, thereby providing sustainable agriculture:  
With the advent of low-cost sensors for monitoring agriculture, significant parameters in the agricultural ecosystem can be analyzed to increase the factor of sustainability, i.e., assessing soil quality and monitoring soil toxicity, water quality and its controlled usage, weather conditions, and optimized usage of fertilizers.  
All the aforementioned variables will contribute to higher revenues. However, the benefits of smart farming can only be exploited if the quality of internet is able to accommodate the IoT infrastructure. In this regard, the following section discusses internet access quality in developing countries.
## 2 Materials and Methods
### Molecular Docking  
To verify the network pharmacology predictions, molecular docking of QBA with anti-aging proteins was simulated using AutoDock to confirm interactions and evaluate binding. The crystal structure of the target protein was downloaded from the RCSB Protein Data Bank [18] and saved as a PDB file. PyMOL software (version 2.5, 2000, Schrodinger, New York, NY, USA) was used to remove water molecules. Subsequently, the AutoDock Tool (version 1.5.7, 1989, The Scripps Research Institute, San Diego, CA, USA) was used to add hydrogen atoms to the target protein and save it as a receptor file in PDBQT format, while QBA was saved as a ligand file in PDBQT format. The grid box was set to cover the entire receptor protein. Molecular docking simulation was performed using AutoDock Vina (version 1.1.2, 2010) with default parameters, measuring the binding free energy and selecting the lowest docking energy as the final docking conformation. The 2D and 3D docking results were displayed using Ligplot+ (version 2.2.7, 2011, University of Dundee, Dundee, Scotland) and PyMOL.
## 2 Related Research
### Multi-Intention Recognition Task  
For multi-intention recognition tasks, early on, [PERSON] et al. [2] used feature-based logarithmic linear models and perceptron training methods to utilize shared intention information between different intention combinations for multi-intention recognition tasks. However, when faced with a large number of intention combinations, the problem of sparse training data may arise. [PERSON] et al. [3] proposed a multi-intention recognition method based on single-intention training data, which divides the problem text into single-intention problems, two-intention problems with conjunctions, and multi-intention problems with two clauses without conjunctions. The method utilizes maximum entropy and conditional random field models to perform multi-intention recognition using a two-stage method. However, the model is based on the maximum number of user intentions being two intentions. Later, with the rapid development of deep learning technology, experts began to use this technology for multi-intention recognition tasks. [PERSON] et al. [4] first used a dependency syntax analysis to determine whether text was multi-intention, and calculated the distance between the words in the sentence and the keywords in the intention category through word frequency-inverse document frequency (TF-IDF) and trained word vectors to determine the number of intentions. Then, CNN (Convolutional Neural Networks) models were used for intention classification. Finally, the true intention of the user was obtained by determining the polarity of the positive and negative emotions of the intention. It relies too heavily on the results of dependency parsing, but the complexity of Chinese is high, and errors in dependency parsing can have a significant impact on the results. [PERSON] et al. [5] further extracted the deep semantics of text by adding convolutional capsule layers into the capsule network [6] to improve the performance of the multi-intention recognition. However, the capsule network is currently immature and only performs well on MNIST dataset. Some scholars believe that intention and semantic slots are interrelated, so they have proposed a model that combines intention detection and semantic slot filling [7; 8] to improve the accuracy of intention recognition and semantic slot filling. However, this method requires a lot of manual labeling, and the cost of manually implementing feature representation is high.