esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Centralized Learning vs. FL Based on Edge-Fog-Cloud Architecture
#### 4.2.5 FL Based on Edge-Fog-Cloud Architecture (Training Time)  
This next analysis focuses on the length of training times across the FL system, as depicted in Figure 11. Upon examining the training times across the fog and edge devices, notable variations and patterns emerge. Fog Device 1 exhibits a range in training times, with Edge Device 4 recording the highest average training time (159.00 s) and Edge Device 1 the lowest (150.22 s), suggesting efficiency discrepancies in the learning process. Fog Device 2, conversely, demonstrates relatively consistent training times, with Edge Device 5 exhibiting slightly lower averages (151.67 s), indicating a well-distributed computational load. Fog Device 3 showcases consistent training times among its devices, with Edge Device 4 having a marginally higher average (155.84 s), suggesting minor variations in processing efficiency. In contrast, Fog Device 4 highlights Edge Device 1 as having the highest average training time (158.64 s), potentially indicating more intricate computations or data processing requirements. Fog Device 5 illustrates closely aligned training times,  
Figure 10: Loss, accuracy, precision, recall, and F1-score across cloud layer or global model.  
with Edge Device 5 recording the highest average (157.03 s), reflecting a balanced workload distribution among the devices.  
The analysis of training times across the fog and edge devices reveals intriguing insights into the efficiency and workload distribution within the system. While Fog Device 1 shows variability in training times, suggesting differing efficiencies in the learning process among its edge devices, Fog Device 2 demonstrates a more uniform distribution of computational load. Fog Device 3 maintains consistency in training times with minor variations, indicating stable processing efficiency. In contrast, Fog Device 4 exhibits disparities, with Edge Device 1 requiring more time, potentially due to complex computations. Fog Device 5 showcases balanced training times, implying an even workload distribution. These findings underscore the importance of optimizing computational resources and workload allocation to enhance overall system performance and efficiency.
# Retransmission Avoidance for Reliable Data Delivery in Underwater WSNs
## 3 FLMPC: Proposed Protocols
### Network Configuration and Data Transmission
#### 3.6.2 Tree Establishment  
When a sensor node sends a data packet, one of the given conditions is true. The current forwarder is a cross node, a source or a relay node.  
\[Y=\begin{cases}\text{L};&\text{if }N=S_{\text{n}}\text{ or }N=N_{n}\\ \text{T};&\text{if }N=C_{\text{n}}\end{cases} \tag{17}\]  
where \(L\), \(T\), \(N\), \(S_{n}\), \(N_{n}\) and \(C_{n}\) represent the linear transmission, tree, current forwarder, source node, relay node and cross node, respectively.  
If \(N\) is \(S_{n}\) or \(N_{n}\), it unicasts the data packet and follows a linear path. However, incase \(N\) is \(C_{n}\), it multicasts data packet and generates binary tree. A tree consists of a root node and subtrees. In case of a binary tree, every parent node has utmost two subtrees. In our schemes, only \(C_{n}\) generates binary tree. The establishment of binary tree can be expressed as [5]:  
\[T=G(B_{1},P_{0},B_{2}) \tag{18}\]  
Figure 6: Forwarder Node Selection in FLMPC-Two. (**a**) Cross Node PFN; (**b**) Relay Node PFN.  
where  
\[B_{i}=\left\{\begin{aligned} & G(B_{2i+1},P_{i},B_{2i+2});\\ &\text{if }B_{2i+1}\
eq\phi\text{ and }B_{2i+2}\
eq\phi\\ & P_{i};\\ &\text{if }B_{2i+1}=\phi\text{ and }B_{2i+2}=\phi\end{aligned}\right. \tag{19}\]  
where, the \(\phi\) is used to represent the leaf node. If, \(B_{2i+1}\) equals to \(\phi\), it means no further nodes are present to traverse. If, \(B_{2i+1}\) not equals to \(\phi\), then the current node is not a leaf node. \(P_{0}\) in Equation (18) is the root node, \(B_{1}\) and \(B_{2}\) are the left and right children which can be leaf nodes or trees. If \(B_{1}\) or \(B_{2}\) does not have children (low depth nodes), they are leaf nodes. In existing and proposed schemes, leaf nodes are the surface gateways as shown in Figure 1. \(P_{i}\) in Equation (19) is the parent of \(i\)th subtree. Left and right children of \(i\)th subtree can be found from \(B_{2i+1}\) and \(B_{2i+2}\).
## 2 Related Work  
There is an abundance of proposed strategies for transforming raw text to a structured representation in order to populate a knowledge graph [1; 2; 3; 4]. However, especially in the case of OIE approaches and due to concerns on scaling, the use of syntactic or semantic relation extraction techniques has been relatively sparse, with the exception of a few recent examples aiming at domain-specific knowledge extraction [5; 6; 7; 8; 9]. Most domain-specific information extraction approaches are focused primarily on evaluating the efficiency of different triple extraction tools on raw data, not taking useful pre-processing and post-processing strategies into account, thus resulting in a large number of potentially uninformative triples [10; 11; 12]. There exist a few systems that go beyond triple extraction by implementing a more thorough preprocessing strategy, including coreference resolution or discourse analysis to improve the quality of the extracted triples; however, these do not address the scalability issues that arise from processing large corpora [13; 14]. By treating each sentence in the corpus equally, we run the risk of overflowing the graph database with unrelated information compared to the documents' true scope, seriously impeding data exploration tasks. On the other hand, by complying only with a strictly defined ontology schema, we are likely to lose all information that is not covered by the existing ontology properties [15]. Finally, the available bibliography lacks a clear triple representation strategy that would resolve duplication issues and would equip the user with a set of data enrichment processes for visualizing latent information such as the temporal dimension (continuity) of the extracted triples, connections to existing ontologies (entity linking), sentence polarity, and hidden interconnections between different corpora based on similar extracted entities. Our methodology encompasses a number of pre-processing (coreference resolution, text summarization) and post-processing (entity enrichment, graph representation) tasks coupled with a core parallel triple extraction process, combining different approaches to enhance the contextual connectivity of the extracted information.
The operating layer uses commercial solvers (such as Cplex, Gurobi) to handle large-scale linear or nonlinear programming problems with complex constraints, ensuring efficient and accurate optimization of operations, while the planning layer employs intelligent algorithms (such as Particle swarm, Whale, and Grey wolf) to optimize the entire lifecycle cost and find the best configuration. This hierarchical strategy balances efficiency and global optimality, enhancing the framework's practicality. Furthermore, an illustration of the configuration optimization and evaluation framework of the PEDF system is provided in Figure 1.
## 4 Phenolic Acids and Physcon as Inhibitors of the Pentose Phosphate Pathway (PPP)  
The PPP is an important pathway of glucose utilization in addition to glycolysis. This pathway is especially critical to cancer cells because it not only generates R5P to supply high rates of de novo nucleotide synthesis but also provides NADPH required for both fatty acid synthesis and cell survival [89]. Consistently, aberrant activation of PPP was frequently observed in various types of cancer cells [90; 91; 92].  
Phenolic acids are organic chemicals containing phenolic rings extracted from plants. Multiple phenolic acid compounds, including caffeic acid, ellagic acid, ferulic acid and sinapic acid, have been reported to inhibit glucose-6-phosphate dehydrogenase (G6 PD) and 6-phosphogluconate dehydrogenase (6 PGD), the latter of which is a rate-limiting enzyme of the PPP [23]. Caffeic acid could cause accumulation of the G6 PD mRNA in cultured rainbow trout gill cells [24], although the molecular mechanism underlying this inhibition has not been resolved.  
As a key enzyme of the PPP, 6 PGD plays a key role in the oncogenic process and has been recognized as an effective target in cancer therapies [42]. Physcon was identified as a 6 PGD inhibitor from a library of 2000 Food and Drug Administration (FDA)-approved small molecule compounds [42; 93]. A recent study by [PERSON] et al. showed that physcon could inhibit 6 PGD, but not G6 PD, leading to reduced proliferation of human lung cancer and breast cancer cells [43]. Based on the crystal structure of 6 PGD, physcon could fit in a pocket near the binding site of glucose-6-phosphate (G6P) and dampen lipogenesis, leading to reduced growth in xenograft tumors of nude mice without causing any detectable side effect [42]. Multiple recent studies verified physcon as a bona fide inhibitor of 6 PGD [94; 95; 96].
## 1 Introduction
In this research, our focus is on languages that already have lexical and inflective ambiguity. Hence, the omitted diacritics exacerbate this problem even more, and simple solutions are not enough. Virtually all the previous works (see Section 2) investigated the diacritics restoration problem in isolation, i.e., restoring diacritics in otherwise correct texts. This is, however, not realistic: if not enough care and attention is given to using proper diacritics, while typing a text, then, typically, the same is with using the correct spelling. A carefully-typed text without diacritics might be more common in the past, when Unicode was not widely supported for technical reasons, but this is no longer the case. Crucially, it is neither easy to correct typos before restoring diacritics, as those are not proper texts, nor after, as diacritics would not be restored on mistyped words. If, in addition to the missing diacritics, other typographical errors are introduced (as is common with fast, careless typing), specialized diacritics restoration algorithms break down. Considering these limitations and trends in the current state of the art in diacritic restoration and typo correction, we take an approach with these main contributions:  
* In contrast to the current state of the art, we use the latest universal sequence-to-sequence byte-level transformer model ByT5 [8] that has no task- or language-specific structure, vocabulary, or character set;
* We experimentally investigate the effectiveness of this universal method in restoring diacritics on a standard set of 12 + 1 languages, comparing it to the state of the art;* We experimentally investigate the effectiveness of this universal method in correcting typos while simultaneously restoring diacritics on the same set of \(12+1\) languages. The rest of this paper is organized as follows. We provide a review of related work in the literature on diacritics restoration and typo correction in Sections 2 and 3, respectively. In Section 4, we give a detailed background on our chosen approach and related transformer models in general. In Section 5, we describe the datasets used. In Section 6 we outline the experimental setting, and in Section 7, we present the results. Finally, we discuss the findings of this work in Section 8 and summarize them in Section 9.
## 3 Results and Discussion
### AHC Analysis of Aldehyde LOP Variables
Sub-cluster B1, which contains the _cis_-2-alkenal aldehyde classification alone, is statistically distinct from sub-cluster B2, which contains a composite of _trans_-2-alkenals and _n_-alkanals, that are both derived from the peroxidation of both MUFAs and PUFAs, as noted above. However, since all three of these aldehydes are found 'nesting' within a single major cluster, it remains a possibility that _cis_-2-alkenals may be generated from the thermally mediated isomerism  
Figure 4: AHC clustering analysis of a model dataset containing 7 aldehyde classification variables, the concentrations of which were those generated at all 7 LSSFE heating time-points. Two major clusters were isolated, the first containing structurally complex aldehydes arising from PUFA peroxidation (cluster A), the second structurally simpler ones generated from both MUFA and PUFA peroxidation (cluster B). Clusters A and B were both split into two sub-clusters, A1 and A2, and B1 and B2, respectively. However, from the dissimilarity truncation value (dotted horizontal line), only the B1/B2 sub-clustering split was found to be statistically significant. of their more predominant _trans_-isomers. Indeed, plots of its concentration versus LSSFE heating time-point confirmed that this minor aldehyde class was not generated until the later (\(\geq\)30 min) LSSFE time-points, and only for sunflower and extra-virgin olive oils at 30 min itself (data not shown). This suggests that _cis_-2-alkenals arise as tertiary LOPs from isomerism of their _trans_-adducts. For example, in principle _cis_-2-heptenal may be generated from isomerism of its corresponding _trans_-isomer, which is a \(\beta\)-homolysis product of linoleic-12-hydroperoxide [56].
# Parameter Analysis on Seismic Response of Long Lined Tunnel by 2.5D Substructure Method
## 3 The 2.5D Finite Element Substructure Method
### 2.2.5D High-Precision Artificial Boundary Conditions  
The artificial boundary diagram of the 2.5D finite element model is shown in Figure 2a. In Figure 2b, a local coordinate system (\(r\), \(s\), \(t\)) is established along the outer surface of the tunnel. The red surface in Figure 2a and the red curve in Figure 2b represent artificial boundary conditions. The coordinate conversion relationship of partial derivatives between different coordinate systems is shown below. \[ \begin{Bmatrix}\frac{\partial}{\partial y}\\ \frac{\partial}{\partial z}\end{Bmatrix} = \begin{bmatrix}1&0\\ -n_{y}/n_{z}&1/n_{z}\end{bmatrix}\begin{Bmatrix}\frac{\partial}{\partial z}\\ \frac{\partial}{\partial t}\end{Bmatrix}  \tag{7}\]The coordinate conversion relationship of the partial derivative is introduced into the wave Equation (2) of solid medium, and the following equation is obtained:  
\[\mathbf{L}_{st}^{\mathrm{T}}\mathbf{D}\mathbf{L}_{st}\widetilde{\mathbf{u}}=- \omega^{2}\rho\widetilde{\mathbf{u}} \tag{8}\]  
where the symbol \(\sim\) indicates in the frequency domain wavenumber domain. The differential operator in local coordinate system can be written as  
\[\mathbf{L}_{st}=-ik_{x}\mathbf{L}_{r}+\mathbf{L}_{s}\frac{\partial}{\partial s }+\mathbf{L}_{t}\frac{\partial}{\partial t} \tag{9}\]  
By introducing the differential operator (9) into Equation (8), it is obtained that  
\[\mathbf{A}_{ss}\frac{\partial^{2}\widetilde{\mathbf{u}}}{\partial s^{2}}+ \left(\mathbf{A}_{st}+\mathbf{A}_{st}^{T}\right)\frac{\partial^{2}\widetilde{ \mathbf{u}}}{\partial s\partial t}+\mathbf{A}_{tt}\frac{\partial^{2}\widetilde {\mathbf{u}}}{\partial t^{2}}-ik_{x}\bigg{[}(\mathbf{A}_{rs}+\mathbf{A}_{rs}^{ T})\frac{\partial\widetilde{\mathbf{u}}}{\partial s}+(\mathbf{A}_{rt}+ \mathbf{A}_{rt}^{T})\frac{\partial\widetilde{\mathbf{u}}}{\partial t}\bigg{]} -k_{x}^{2}\mathbf{A}_{rr}\widetilde{\mathbf{u}}+\omega^{2}\rho\widetilde{ \mathbf{u}}=0 \tag{10}\]  
The coefficient matrices \(\mathbf{A}_{ss}\), \(\mathbf{A}_{st}\), \(\mathbf{A}_{tt}\), \(\mathbf{A}_{rs}\), \(\mathbf{A}_{rt}\) and \(\mathbf{A}_{rr}\) are as follows:  
\[ \begin{array}{cccc}\mathbf{A}_{ss}=\mathbf{L}_{r}^{T}\mathbf{D}\mathbf{L}_{ s}&\mathbf{A}_{st}=\mathbf{L}_{r}^{T}\mathbf{D}\mathbf{L}_{t}&\mathbf{A}_{tt}= \mathbf{L}_{r}^{T}\mathbf{D}\mathbf{L}_{t}\\ \mathbf{A}_{rs}=\mathbf{L}_{r}^{T}\mathbf{D}\mathbf{L}_{s}&\mathbf{A}_{rt}= \mathbf{L}_{r}^{T}\mathbf{D}\mathbf{L}_{t}&\mathbf{A}_{rr}=\mathbf{L}_{r}^{T} \mathbf{D}\mathbf{L}_{r}\end{array}  \tag{11}\]  
By discretizing Equation (10) along the \(s\) direction, the displacement vector of any thin layer element can be expressed  
\[\widetilde{\mathbf{u}}=\mathbf{N}_{e}\overline{\mathbf{u}} \tag{12}\]  
The nodal shape function matrix \(\mathbf{N}_{e}\) as  
\[\mathbf{N}_{e}= \begin{bmatrix}N_{1}&0&0&N_{2}&0&0\\ 0&N_{1}&0&0&N_{2}&0\\ 0&0&N_{1}&0&0&N_{2}\end{bmatrix}  \tag{13}\]  
where \(N_{1}\) and \(N_{2}\) are one-dimensional finite element shape functions.
## 3 Results and Discussion
### Thermal Analysis of BPN with Different Ratios  
TG-DSC analysis of BPN samples with different ratios (heating rate 5 \({}^{\circ}\)C/min) were carried out, and the curves are shown in Figure 3. For BPN samples with different ratios, the thermal reaction process is similar to that of KNO\({}_{3}\) before 500 \({}^{\circ}\)C.  
As shown in Figure 3, the first endothermic peak (approx. 134 \({}^{\circ}\)C) corresponds to the conversion temperature of KNO\({}_{3}\), and the second endothermic peak (approx. 332 \({}^{\circ}\)C) corresponds to the melting point of KNO\({}_{3}\). Before 500 \({}^{\circ}\)C, the mass change of sample 1# and 2# is about 9%, while that of sample 3# is less than 5%. Between 500 \({}^{\circ}\)C and 650 \({}^{\circ}\)C, there is a drastic decomposition reaction, and the  
Figure 3: (a) TG–DSC curve of 1#BPN (15:85); (b) TG–DSC curve of 2#BPN (25:75); (c) TG–DSC curve of 3#BPN (33:67) (heating rate 5 \({}^{\circ}\)C/min).  
sample weight drops sharply. The temperature of the decomposition reaction is about 200 \({}^{\circ}\)C higher than the melting point. The main reaction at this stage is  
\[\text{B}+\text{KNO}_{3}\rightarrow\text{KBO}_{2}+\text{NO}. \tag{4}\]  
The weight ratio results of BPN samples with different proportions at typical temperatures are shown in Table 3. At 200 \({}^{\circ}\)C, the mass variation of BPN samples with three ratios are in the range 0.9%-1.7%, and at 500 \({}^{\circ}\)C, 5.8%-11.4%. At 650 \({}^{\circ}\)C, the 2# (25:75) BPN sample (zero oxygen balance ratio) has the least weight loss. Based on TG analysis of KNO\({}_{3}\), the initial reaction temperature of BPN (500 \({}^{\circ}\)C) is about 50 \({}^{\circ}\)C lower than that of KNO\({}_{3}\) (545 \({}^{\circ}\)C). However, when comparing the endothermic peak temperature of DSC curves of samples, the exothermic peak temperature showed a trend of rising with the increase of boron content, as shown in Table 4.  
The results show that with an increase of boron content, the exothermic peak is advanced, but the thermal stability of BPN samples are improved, with the 2#BPN sample (25:75) showing the best performance. Analysis at 180 \({}^{\circ}\)C shows that the DSC decomposition temperature of BPN is above 500 \({}^{\circ}\)C, and TG weight loss ranges from 5.8% to 11.4% at 500 \({}^{\circ}\)C. It can be qualitatively judged that BPN is stable at 180 \({}^{\circ}\)C.
# A Virtual Prototype for Fast Design and Visualization of Gerotor Pumps
## 2 Previous Works
### Fluid Mechanical Simulation  
Several approaches have been proposed to simulate the performance of gerotor pumps, depending on the level of detail required. Most previous work relies on two methods for the fluid simulation: (i) lumped parameters models (LP) and (ii) computational fluid dynamics models (CFD), with each one exhibiting different performances regarding time and memory complexity.  
CFD modelscomputational fluid dynamics models use specialized software to solve the Navier-Stokes equations in a discretized domain. CFD models can be classified in two categories: (i) two-dimensional (2D) models and (ii) three-dimensional (3D) models. [PERSON] et al. [2] and [PERSON] et al. [3] presented 2D CFD models for the simulation of rotary pumps that present accurate results with respect to an experimental setup. Recently, 3D simulations of the pump have been performed in order to analyze specific aspects of the pumps design, such as: (a) profile geometry optimization [4; 5], (b) discharge coefficient calculation [6], and (c) fluid leakage due to clearances [7]. The main advantages of CFD based methods are: (i) detailed description of the fluid's behavior inside the cavity of the pump and (ii) very accurate prediction of the effect of cavitation and fluid-body interaction on performance. The main disadvantages of CFD methods are: (i) large simulation time and memory requirements, (ii) the requirement to remesh the entire domain in each step of the solution, and (iii) the difficulty to mesh appropriately the inter-teeth clearance domain [2].  
LP modelslumped parameter models discretize the pump in a number of control volumes, where each CV (control volume) corresponds to a cavity of the pump. The mass and energy conservation equations are used to integrate the pressure in each control volume. The pressure inside each control volume will depend on the instantaneous volume of the chamber and net flowrate of fluid through its surface. [PERSON] et al. [8; 9] presented a simple lumped parameter model that was mounted on AMESIM software, coupled with a geometric module that calculates the instantaneous areas and volumes of the chambers. The results show good agreement between predicted and measured data. [PERSON] et al. [10] presented a lumped parameter model in AMESIM software for the prediction of cavitation effects on the pump simulation; the results show that the model is accurate in predicting the effect of cavitation phenomena on the overall performance of the system. The main advantages of the lumped parameter approach are: (i) the low time and memory complexity and (ii) the flexibility to integrate with larger hydraulic circuits [1; 9]. The main disadvantages of the lumped parameter approaches are: (i) the results are coarse with respect to CFD methods and (ii) calibration of the model vs. experimental data is needed, which makes this approach unsuitable for detailed analysis of local behavior of fluid [11].
## 9 Conclusions  
In this paper, we investigated the robust joint user scheduling and hybrid beamforming design scheme for the massive MIMO LEO satellite multigroup multicast communication system. The scheme design considered the limited transmission power of the LEO satellite and the requirement of QoS and analyzed the influence of residual Doppler shift and phase disturbance on CSI errors. On this basis, taking the system EE as the optimization objective, we focused on the robust joint user scheduling and hybrid beamforming design. To reduce the complexity of the algorithm, we proposed the user preselection step, which can significantly reduce the system complexity while ensuring the system performance. For the nonconvex problem of the objective function, we adopted the CCCP framework after transforming the optimization problem into the DC programming problem. For the rank-one constraint, we proposed the penalty iterative algorithm. Finally, to obtain the digital and analog beamforming matrices, we adopted the MM-AltOpt algorithm.  
Numerical results indicated that the proposed algorithm can effectively improve the system EE. The EE performance gain of the proposed robust algorithm was improved by nearly 10% compared with the traditional nonrobust algorithm. Meanwhile, the EE performance gain of the proposed joint user scheduling and hybrid beamforming design algorithm was improved by nearly 40% compared with the traditional decoupling design algorithm. In conclusion, the robust joint user scheduling and hybrid beamforming design algorithm proposed in this paper can significantly improve the system EE performance.  
Conceptualization, Y.L. and C.L.; methodology, Y.L. and J.L.; software, Y.L. and L.F.; validation, Y.L., C.L. and J.L.; formal analysis, Y.L. and C.L.; investigation, Y.L.; writing--original draft preparation, Y.L. and L.F.; writing--review and editing, C.L. and J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 62001516.  
Not applicable.  
The data presented in this study are available in the manuscript.  
The authors declare no conflict of interest.