esa-sceva/satcom-chunk-collection huggingface dataset:
# Free Vibration Analysis of Curvilinearly Tapered Axially Functionally Graded Material Beams
## 2 AFGM Beams with Variable Cross-Section  
### Geometry of the Beams  
Focusing on the closed-form solution of the free vibration problem, we aim to describe various non-uniform beam geometries in a unified analytical manner. One effective way to achieve this is by using rational Bezier curves [42].  
We consider planar beams of length of \(L\) that are symmetrical about their midlines, with the left ends positioned at the origin of the Cartesian coordinate system as shown in Figure 1. The top curve of these beams is a conic arc, which can be represented by an algebraic quadratic equation in \(\mathbb{R}^{2}\) as follows:  
\[Ax^{2}+Bxy+Cx^{2}+Dx+Ey+F=0. \tag{1}\]  
The coefficients of (1) can be derived from the quadratic rational Bezier curve, which involves the control points \(P_{0}(x_{0};y_{0})\), \(P_{1}(x_{1};y_{1})\), and \(P_{2}(x_{2};y_{2})\) and the weight \(w_{1}\), utilizing the Grobner basis as presented in [43]:\[A =y_{0}^{2}-4w_{1}^{2}y_{0}y_{1}+4w_{1}^{2}y_{1}^{2}-2y_{0}y_{2}+4w_{1} ^{2}y_{0}y_{2}-4w_{1}^{2}y_{1}y_{2}+y_{2}^{2},\] \[B =-2x_{0}y_{0}+4w_{1}^{2}x_{1}y_{0}+2x_{2}y_{0}-4w_{1}^{2}x_{2}y_{0} +4w_{1}^{2}x_{0}y_{1}-8w_{1}^{2}x_{1}y_{1}+4w_{1}^{2}x_{2}y_{1}+2x_{0}y_{2}-4w_ {1}^{2}x_{0}y_{2}\] \[C =x_{0}^{2}-4w_{1}^{2}x_{0}x_{1}+4w_{1}^{2}x_{1}^{2}-2x_{0}x_{2}+4w _{1}^{2}x_{0}x_{2}-4w_{1}^{2}x_{1}x_{2}+x_{2}^{2},\] \[D =-2x_{2}y_{0}^{2}+4w_{1}^{2}x_{1}y_{0}y_{1}+4w_{1}^{2}x_{2}y_{0}y _{1}-4w_{1}^{2}x_{0}y_{1}^{2}-4w_{1}^{2}x_{2}y_{1}^{2}+2x_{0}y_{0}y_{2}-8w_{1}^ {2}x_{1}y_{0}y_{2}+2x_{2}y_{0}y_{2} \tag{2}\] \[+4w_{1}^{2}x_{0}y_{1}y_{2}+4w_{1}^{2}x_{1}y_{1}y_{2}-2x_{0}y_{2}^{2},\] \[E =-4w_{1}^{2}x_{1}^{2}y_{0}+2x_{0}x_{2}y_{0}+4w_{1}^{2}x_{1}x_{2}y _{0}-2x_{2}^{2}y_{0}+4w_{1}^{2}x_{0}x_{1}y_{1}-8w_{1}^{2}x_{0}x_{2}y_{1}+4w_{1} ^{2}x_{1}x_{2}y_{1}-2x_{0}^{2}y_{2}\] \[+4w_{1}^{2}x_{0}x_{1}y_{2}-4w_{1}^{2}x_{1}^{2}y_{2}+2x_{0}x_{2}y_{2},\] \[F =x_{2}^{2}y_{0}^{2}-4w_{1}^{2}x_{1}x_{2}y_{0}y_{1}+4w_{1}^{2}x_{0 }x_{2}y_{1}^{2}+4w_{1}^{2}x_{1}^{2}y_{0}y_{2}-2x_{0}x_{2}y_{0}y_{2}-4w_{1}^{2}x _{0}x_{1}y_{1}y_{2}+x_{0}^{2}y_{2}^{2}.\]  
For convenience, let \(l_{f}\) and \(l_{e}\) denote the length of lines \(P_{1}P_{m}\) and \(P_{s}P_{m}\), respectively, where \(P_{S}=(1-s)P_{m}+sP_{1}\) is the shoulder point, \(P_{m}\) is the midpoint of line \(P_{0}P_{2}\), and \(s=\frac{k}{l_{f}}\) is a shape parameter related to \(w_{1}=\frac{s}{1-s}\). By specifying the coordinates of the points \(P_{0}\), \(P_{1}\), and \(P_{2}\) and the parameter \(s\), the implicit equation of a conic curve (1) can be constructed. The curve will have a convex upward shape if the vertex \(P_{1}\) is above the line \(P_{0}P_{2}\) and a concave downward shape if \(P_{1}\) below the line \(P_{0}P_{2}\) as illustrated in Figure 1a,b, respectively.
# Interaction of Machining Parameters on MRR of Sintered NdFeB Processed by EDM-Milling
## 4 Interaction of Machining Parameters on the MRR
### Interaction of Pulse on Time and Pulse off Time on the MRR
#### 4.2.1 Significance of Pulse off Time on the MRR  
It is obvious in Figure 5 that the significance of pulse off time on MRR gradually decreases with the increase of pulse on time. When the value of pulse on time is 30 us, \(\triangle_{\text{Ton}}(T_{\text{off}})\) reaches a maximum of 32.86 mg/min. Namely, the significance of pulse off time on MRR is the strongest. When the value of pulse on time is 90 us, \(\triangle_{\text{Ton}}(T_{\text{off}})\) is at its minimum of 1.50 mg/min, indicating that the significance of pulse off time on MRR is weakest. The \(\triangle_{\text{Ton}\,T_{\text{off}}}\) is 31.36 mg/min, as listed in Table 2.  
The above phenomenon can be explained by the significance of the effect of pulse off time on the pulse discharge frequency. It is generally known that the period of pulse discharge is equal to the sum of the pulse on time and the pulse off time. The variation  
Figure 4: Waveform of discharge voltage and discharge current under different current and voltage conditions.  
Figure 5: Interaction curve of pulse on time and pulse off time on MRR.  
in pulse off time affects the pulse discharge frequency. In the range of parameters in this study, when the pulse on time is 30 \(\upmu\)s, the variation interval of the pulse off time is [80 \(\upmu\)s, 190 \(\upmu\)s], which is about 2.7 times to 6.3 times the value of the pulse on time. Namely, the effect of the pulse off time on the pulse discharge frequency is amplified by about 2.7 to 6.3 times. However, when the pulse on time is 90 \(\upmu\)s, the value of pulse off time varies from about 0.9 times to 2.1 times of the pulse on time. Namely, the effect of the pulse off time on the pulse discharge frequency is amplified by about 0.9 to 2.1 times. It is clear that the influence of pulse off time on MRR is more significant when the pulse on time is low. Thus, it appears that the significance of the effect of pulse off time on MRR decreases as pulse on time increases.  
Figure 6 shows the diameters of craters under different pulse on time and pulse off times. Table 4 shows the mean and differences of the crater diameters corresponding to Figure 6. According to Table 4, the difference in crater diameter is 37.65 \(\upmu\)m between pulse off times of 80 \(\upmu\)s and 190 \(\upmu\)s when the pulse on time is 30 \(\upmu\)s, and the difference is 9.31 \(\upmu\)m between the same pulse off times when the pulse on time is 90 \(\upmu\)s. It is clear that the variation in crater diameter caused by variation in pulse off time is larger when the pulse on time is 30 \(\upmu\)s. The effect of
# Computational and Communication Infrastructure Challenges for Resilient Cloud Services
## 2 Background
### Computational Infrastructure for Resilience
#### 2.2.2 Container Orchestration  
Container orchestration allows clusters to be formed, which enables management functionalities, resource planning, scalability, load balancing, monitoring, and service discovery. The most widely used container orchestration platforms are Docker Swarm [29], Apache Mesos [30], Nomad [31], and Kubernetes [32]. Kubernetes has become the orchestrator par excellence, and the most important cloud providers have integrated it into their platforms through solutions such as Google Kubernetes Engine (GKE), Amazon Elastic Kubernetes Service (EKS), and Azure Kubernetes Services (AKS). Kubernetes allows for managing Linux containers in private, public, and hybrid cloud environments and is used to manage microservice architectures by most cloud service providers [33].
## 4 Results
### Experimental Setup
To generate predictions, we feed the predicate and entity representations learned from each model version into a ConvKB model [27]. We then train this ConvKB model to act as a decoder, assigning scores to candidate head and tail entities based on the scores given by the ConvKB decoder for each triplet in the test set. Finally, we sort these entities based on these scores, calculate the corresponding metrics (Hit@K and MRR), and evaluate the performance of each model. All experiments were conducted on a machine equipped with 6 Nvidia Tesla V100 GPUs and 32 GB RAM (Beijing, China). We used the PyTorch library in Python for implementation. **Analysis of computational complexity in experiments. ** Let \(d\) be the dimension of embeddedings in the model, it can be analyzed that the spatial complexity of Relgraph is \(\mathcal{O}(2|\mathcal{E}|d+|\mathcal{R}|d)\) and the temporal complexity is \(\mathcal{O}(|\mathcal{K}|d^{2}+|\mathcal{K}|d+|\mathcal{R}\mathcal{K}|d^{2}+| \mathcal{R}\mathcal{K}|d)\). In contrast, the TransE model has a spatial complexity of \(\mathcal{O}(|\mathcal{E}|d+|\mathcal{R}|d)\) and a temporal complexity of \(\mathcal{O}(d)\), while the single relation GAT model has a spatial complexity of \(\mathcal{O}(|\mathcal{E}|d+|\mathcal{R}|d)\) and a temporal complexity of \(\mathcal{O}(|\mathcal{E}|d^{2}+|\mathcal{K}|d)\). Without considering the relation graph, the spatial complexity of Entity GAT (multi-relational GAT) is \(\mathcal{O}(|\mathcal{E}|d+|\mathcal{R}|d)\) and the temporal complexity is \(\mathcal{O}(|\mathcal{K}|d^{2}+|\mathcal{K}|d)\). It can be seen that compared to GAT and multi-relational GAT models, the complexity of Relgraph only increases linearly. In the KGC experiment on the FB15K-237 dataset, when \(d=200\), Relgraph can complete training in about 1 h.
###### Abstract  
MOD17A2 provides near real-time estimates of gross primary production (GPP) globally. In this study, MOD17A2 GPP was evaluated using eddy covariance (EC) flux measurements at eight sites in five various biome types across China. The sensitivity of MOD17A2 to meteorological data and leaf area index/fractional photosynthetically active radiation (LAI/FPAR) products were examined by introducing site meteorological measurements and improved Global Land Surface Satellite (GLASS) LAI products. We also assessed the potential error contributions from land cover and maximum light use efficiency (\(\epsilon_{\text{max}}\)). The results showed that MOD17A2 agreed well with flux measurements of annual GPP (R\({}^{2}\) = 0.76) when all biome types were considered as a whole. However, MOD17A2 was ineffective for estimating annual GPP at mixed forests, evergreen needleleaf forests and croplands, respectively. Moreover, MOD17A2 underestimated flux derived GPP during the summer (R\({}^{2}\) = 0.46). It was found that the meteorological data used in MOD17A2 failed to properly estimate the site measured vapor pressure deficits (VPD) (R\({}^{2}\) = 0.31). Replacing the existing LAI/FPAR data with GLASS LAI products reduced MOD17A2 GPP uncertainties. Though land cover presented the fewest errors, \(\epsilon_{\text{max}}\) prescribed in MOD17A2 were much lower than inferred \(\epsilon_{\text{max}}\) calculated from flux data. Thus, the qualities of meteorological data and LAI/FPAR products need to be improved, and \(\epsilon_{\text{max}}\) should be adjusted to provide better GPP estimates using MOD17A2 for Chinese ecosystems.  
MODIS; gross primary production (GPP); validation; eddy covariance; China +
Footnote †: journal: Remote Sensing
## 6 Recent Advancements in Analytical Methods
### Instrumental Analysis  
A selection of instrumental analysis of BPA from meat and meat products are listed in Table 5. The cleaned-up extract is used in the final step for analysis by either liquid chromatography (LC) or gas chromatography (GC). A variety of detection methods have been used in LC including ultraviolet (UV), fluorescence, and mass spectrometry (MS). In recent times, fluorescence and MS-based detectors are more frequently used because of their greater specificity as well as higher sensitivity [96]. The use of a traditional C18 column with 3.5-5 \(\upmu\)m particles and 4.6 mm internal diameter has been frequently reported for the HPLC analysis of BPA from food materials [78; 97; 98]. However, C18 columns with smaller particle sizes such as 1.7 \(\upmu\)m have also been used for the determination of BPA from canned fish, vegetables and sauces [99]. [PERSON], [PERSON] and [PERSON] reported that columns with smaller particle size (sub-2 \(\upmu\)m) improved chromatographic resolution and decreased analysis time [98]. While the traditional C18 column was the most popular choice (67%) [4], other stationary phases such as PentaFluoroPhenyl (PFP) bonded phase column have gained acceptance due to the alternative selectivity they provide. In comparison to the traditional alkyl phases, PFP bonded phases have been shown to provide enhanced dipole, \(\uppi\)-\(\uppi\), charge transfer, and ion-exchange interactions [100]. For instance, [PERSON] et al. reported enhanced selectivity for a PFP column (100 \(\times\) 3.0 mm, 3 \(\upmu\)m particle size) over the C18 liquid chromatography column (100 \(\times\) 2 mm i.d., 3.5 \(\upmu\)m particle size) and C8 liquid chromatography column (250 \(\times\) 4.6 mm i.d., 5 \(\upmu\)m) [101]. Similarly, C18 columns containing superficially porous particles (SPP) are gaining popularity over the frequently used fully porous particles (FPP). [PERSON], [PERSON], and [PERSON] reported achieving efficient chromatographic separation of bisphenols (BPA, BPF, BPE, BPB, BPS) with a rapid analysis time (less than 3 min) by using a SPP C18 column [102]. Better chromatographic efficiency through the use of a SPP C18 column as compared to a FPP column for the analysis of BPA from fruit drinks has been also reported [103].
## 3 Methodology
### LightGBM  
Light GBM, developed by Microsoft Research [37], is a variant of Gradient Boosting Decision Trees based on a decision-tree technique. LightGBM constructs trees leaf by leaf, employing a histogram-based technique to partition leaf nodes. This approach yields notable efficiency and memory savings [38]. Although the leaf-wise tree growth enhances model complexity, LightGBM can achieve greater accuracy improvements with each iteration of the algorithm. However, this method poses a significant risk of overfitting, which is addressed through regularization terms. To render LightGBM a swift, effective, and dependable ensemble method, two strategies are employed: gradient-based one-side sampling (GOSS) and exclusive feature bundling (EFB) [38]. GOSS selectively retains samples with higher gradients during the training phase, as they contribute more to information gain, discarding those with lower gradients. Consequently, compared to traditional methods like DT, RF, and gradient boosted decision trees (GBDT) that examine all data for information gain calculation, LightGBM with GOSS significantly reduces computation time. On the other hand, EFB clusters exclusive features together in a sparse feature space, reducing dimensionality and enhancing efficiency. Particularly in scenarios involving high feature dimensions, LightGBM combined with EFB substantially enhances model efficiency and scalability compared to standard approaches (e.g., RF, DT, and GBDT). Consequently, this adaptation markedly accelerates the computation process and minimizes memory usage while preserving prediction accuracy.  
To enhance the performance of the LightGBM in this study, grid search cross-validation was employed to explore various hyperparameter combinations for LightGBM, resulting in the selection of optimal values: n_estimators = 300, learning_rate = 0.3, max_depth = 10, num_leaves = 30, and min_child_samples = 20.
## 1 Introduction  
EIT is a non-invasive imaging technology that reconstructs the electrical impedance distribution inside an object by applying a small current to the object's surface and measuring the resulting voltage response [1]. Since the early 1980s, this technology has gained significant attention in various fields including medicine, industry, and geophysics. Its appeal lies in its non-radiation nature, cost-effectiveness, and real-time monitoring capabilities. In the medical field, EIT finds applications in breast tumor detection, monitoring of brain and abdominal bleeding, and measuring lung function [2].  
EIT is a technology that utilizes the surface potential change exhibited by the area being tested [3], along with an appropriate imaging algorithm, to obtain an image of the impedance change in that area. Conductivity distribution changes are associated with pathological changes and physiological activities such as tumors, hemorrhage, ischemia, inflammation, etc. This technology offers several advantages over other methods for monitoring lung injuries [4], including safety, the absence of radiation, real-time monitoring,and visual feedback. It can be effectively used for diagnosing lung diseases and monitoring physiological activities, thereby contributing to the advancement of precision medicine.  
The solution to the inverse problem is crucial for achieving the desired imaging effect in EIT technology. It is also a challenging and widely discussed topic in this field. The current difficulties in solving the EIT inverse problem can be summarized as follows: Linear algorithms are known for their fast computation, but they may not accurately reconstruct complex electrical impedance distributions [5]. On the other hand, nonlinear algorithms offer more-accurate reconstructions, although they are computationally expensive and often require good initial guesses [6]. Alternatively, deep-learning-based methods have the potential to provide higher-quality reconstructions and can handle more-complex scenarios [7]. However, these methods demand large amounts of training data and their models are less interpretable.
## 5 Outlook  
In the past decade, we have witnessed the impressive rise of graphene within silicon photonics. Silicon photonic modulators and photodetectors with outstanding performances have been reported by several groups, leveraging to the exceptional properties of graphene. However, there are several challenges that hinder further development of silicon/graphene hybrid devices.  
The first is to include graphene structures in optimized foundry processes for commercial, wafer-scale fabrication featuring silicon/graphene components. Up to now, most reports rely on the wet transfer of CVD-grown or exfoliated graphene onto the target substrate. This is suitable for prototype demonstrations, but the typical uniformity of the fabricated devices is lacking. Moreover, the wet transfer process can induce particles that significantly increase the transmission loss of the silicon chip. Fortunately, there is already exciting progress on this front [72, 73], and we expect wafer-scale silicon/graphene hybrid devices without the issue of uniformity and excess loss soon.  
The second is enhancing the light-graphene interaction. Although the atomic thickness of graphene has brought advantages in certain applications--especially the transparent microheater--it also causes low modulation depth and low responsivity in the photodetector. A lot of effort has been dedicated to enhancing the absorption of the graphene layer and significant progress has been made. However, the overall performance in terms of the modulation depth and responsivity so far only matches devices based on bulk materials and is achieved at the expense of sacrificing bandwidth. Therefore, further enhancing the light-graphene interaction while maintaining the advantages of the graphene-based devices remains a challenging but extremely valuable topic.  
Overall, both silicon photonics and graphene technology are still in rapid development, while the hybrid integration of these two cutting-edge platforms holds great potential. We believe that once the above-mentioned challenges are fully addressed, hybrid silicon/graphene optoelectronic devices will enable truly new technological capability, both in industry and for the scientific community.  
Conceptualization, S.Y. and Y.D.; methodology, S.Y. and Y.D.; writing--original draft preparation, S.Y.; writing--review and editing, S.Y., J.A. and Y.D. All authors have read and agreed to the published version of the manuscript.  
This work was funded by QUANTIC project sponsored by VILLUM FONDEN (No. 00025298).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Effect of Concentration of SLX on % Recovery  
To investigate the effect of the concentration of SLX on % recovery, polymers MIP1 and MIP3 were compared to NIP1 and NIP3. These polymers were chosen as being representative of the 1:6 ratio that showed better binding capacities. Ideally, extraction recovery should not rely on sample concentration so there should be no significant difference in recovery across all analyzed concentration ranges [57]. However, this research showed that the recovery of SLX in isopropanol was preferable at a high concentration. The phenomenon that arose upon loading using propanol was concentration dependent because the ability of SLX to produce SLX-SLX complexes is higher in high concentrations, both on the polymer surface and in solution, leading to an increase in the selectivity of SLX [58]. Figure 6 shows that the recovery of SLX with isopropanol as a loading solvent was reduced at low concentration (0.1 \(\mathrm{\SIUnitSymbolMicro g}\)/mL) compared to high concentration (4 \(\mathrm{\SIUnitSymbolMicro g}\)/mL). The opposite occurred with water as the loading solvent. The polymer was synthesized to extract SLX in trace concentration; based on the results, water was most suitable for the loading solvent, as it showed a higher recovery in low concentrations than isopropanol. The recovery of SLX at 0.1 mg/L was 87.99 \(\pm\) 4.57% from MIP3 and 27.10 \(\pm\) 4.58% from NIP3 with isopropanol as the loading solvent, and 98.75 \(\pm\) 6.15% from MIP3 and 15.52 \(\pm\) 3.72 from NIP3 with water as the loading solvent. The higher recovery of SLX on MIP compared with NIP showed that water is better as a loading solvent for the extraction of SLX in trace concentrations. In fact, the concentration of SLX in real plasma is 0.1-2 \(\mathrm{\SIUnitSymbolMicro g}\)/mL after a single dose (50-400 \(\mathrm{\SIUnitSymbolMicro g}\)) via inhalation [59]. We believe that loading in water will align with the trace concentrations of SLX in real plasma.
## 4 Discussion
### Future Directions  
Several approaches may be attempted to improve the performance of prediction models for readmission. First, high-dimensional information in administrative claims data may be used to supplement clinical information in the development of prediction models. Readmission models for patients with chronic pancreatitis using standardized billing codes and basic patient characteristics were found to perform reasonably with AUCs ranging from 0.65 to 0.73 [41]. Second, in addition to structured clinical information, significant determinants of readmission, such as social factors, can be extracted from clinical notes through natural language processing [42], and in turn, used to build prediction models. Third, other ML algorithms can be explored. For example, extreme gradient boosting models were shown to have higher AUCs than traditional statistical models in predicting 90-day readmissions due to recurrent ischemic events in patients with ischemic stroke [23].