esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### In-Situ Acidizing Micro-Model and Acid-Rock Interaction
During the injection under different temperature conditions, the initial porous media was filled by another  
Figure 6: Microphotographs illustrate (**a**) fine-coarse crystalline dolomite with numerous vuggs floating within dominant dolostone microfacies under normal conditions (t = 0), (**b**) the same dolostone microfacies in (**a**) but after in-situ acid injection (when t = 15 min). The alteration influenced the calcite crystals (close-up, the dark circle) more than the dolomite crystals; the final result is an enhancement of the porous media due to in-situ acid injection. mineral phase after acid injection; this probably indicated a new precipitation phase that would positively impact the damage of reservoir rock owing to the injection of the combined acid. Figure 7a,b show the change of pore area before and after 15 min of the acidizing process, respectively. Figure 7c illustrates the pore area calculated values based on the outline of each pore area for the enhanced porous media as a result of the matrix acidizing. On another hand, the effect of acidizing temperature was investigated by injecting the thin sections with identical concentrations of the combined acid in-situ using the micro-model design. Figure 8 shows the variation of the increase in pore area % as a function of acidizing temperature (from ambient to 50 \({}^{\circ}\)C) resulting from injection of the optimum concentration of the combined acids (12% HCl: 3.2% Oxalic acid). The results were estimated from RSM analysis after an acidizing of 15 min. Using this acid combination, the fluid-rock interaction enhanced the pore system to 31.52% pore area when acidizing temperature increases from ambient to 50 \({}^{\circ}\)C.  
An interesting correlation is also highlighted in this study with a relatively high regression coefficient of 91.39%. The estimated mathematical model is shown in Equation (8). \[\text{Y}=0.5926\text{x}+38.514 \tag{8}\]  
where Y is the increase in pore area (%) and x is the acidizing temperature (\({}^{\circ}\)C). Figure 7: Photomicrographs of thin sections under optical microscope show: (**a**) calculation of porous area under natural condition (i.e., t = 0), (**b**) calculation of porous area under altered condition when the same sample was subject to injection with acid combination (i.e., t = 15 min), (**c**) calculation of the same sample in b after its reaction with the acid combination. The numerical values calculated for the pore area were estimated by ImageJ software.
## 2 Materials and Methods
### Test Setup and Data Collection  
The equipment used for data collection includes a signal amplifier-splitter, a programmable attenuator [22], a radio front-end, a professional grade GNSS receiver, two hand-held jammers, a computer, and auxiliary components. The equipment was taken on-board the MS Megastar. A mounting position for the antenna was chosen at the top mast as shown in Figure 2 (right) in order to receive the best possible GNSS signals.  
Figure 2: MS Megastar [23] cruise ship (**left**), GNSS antenna installed on the top mast of the vessel marked within green ellipsoid (**right**).  
The hardware configuration for data collection is shown in Figure 3. An amplifier-splitter is used to amplify and split GNSS signals. A commercial-grade GNSS receiver is connected to one of the splitter ports, which remains unaffected by jamming and is used to compute reference trajectory. A mixer is used to combine jamming signal with clean GNSS signals. A programmable attenuator is connected between the jammers and the mixer, allowing the jamming power to be fine-tuned over time. The mixed signal is fed to the radio front-end that captures the raw GNSS signal. The radio front-end is linked via USB to a dedicated hosting computer that manages the data collection process and stores raw GNSS data.  
Table 2 presents the configuration of the radio front-end used in the experiments. The GNSS signals were recorded using a TeleOrbit GTEC(r) dual-band RF front-end developed by Fraunhofer Institute for Integrated Circuits IIS [24]. The front-end contains two RF channels: one was configured to receive L1/E1/B1 signals at 1574.89 MHz with a bandwidth of 38 MHz, and the other one was configured to receive L5/E5a/E5b signals at 1192.50 MHz with a bandwidth of 54 MHz as mentioned in Table 2. The L1/E1/B1 signals were sampled at 40.5 MHz, while the L5/E5a/E5b signals were sampled at 81 MHz. The received samples for all signals were complex and synchronized with an internal Temperature Compensated Crystal Oscillator (TCXO) reference clock.
# A Study on the Normalized Delineation of Airspace Sectors Based on Flight Conflict Dynamics
## 4 Sector-Delineation Method Based on Mean Shift Clustering Algorithm
### Sector-Delineation Method Based on Voronoi Diagrams  
The clustering centers are obtained based on the improved mean shift algorithm, and the clustering centers are used as generating elements using the Voronoi diagram approach to obtain a new sector-planning scheme. A Voronoi diagram [24] is a segmentation of the plane according to the known point set. Given the set of \(n\) points on the plane \(S=\left\{p_{1},p_{2},\cdots,p_{n}\right\}(i=1,2,\cdots,n)\), the segmentation of the plane given by \(V(p_{i})=\underset{j\
eq i}{\cap}\left\{p\middle|d(p,p_{i})<d(p,p_{j})\right\}\) is called a Voronoi diagram, with points \(p_{i}(i=1,2,\cdots,n)\) as generators (or parent points), where \(d(p,p_{i})\) is the Euclid distance between points \(p\) and \(p_{i}\), and the region \(V(p_{i})\) is called the Voronoi region of points \(p_{i}\). Obviously, this region is a set of points on the plane whose distance to the point \(p_{i}\) is smaller than that to other points in the \(S\).  
The Voronoi diagram is a Voronoi cell-division generation boundary for each generator, that is, the node. The distance from any position in the boundary of the generator to the generator is closer than that to other generators. Figure 5 shows an example of a Voronoi diagram segmentation plane, where the dots represent the generating elements, which in the context of control-area delineation can be the navigation points or mandatory reporting points of an airport, and the fold lines are the Voronoi edges, i.e., the control sector boundaries.
## 3 System Model
### Overview  
We plotted the framework diagram of FedNow, as shown in Figure 1, where we consider a typical FEL system with three major components: a data collecting network, a set of edge servers (\(\mathcal{I}=\{1,\cdots,I\}\)), and one cloud platform. The platform incentivizes edge servers to participate in FEL by compensating for their training and data collection costs. Considering the fact that the costs of edge servers represent private information that is unknown to the platform, the platform will send a set of contract items to edge servers. Each edge server will independently decide whether to participate in training and, if so, which training strategy to adopt, i.e., selecting a contract item and determining the number of times they complete contracts in each global round. Then, each edge server downloads the initial model from the cloud platform and conducts model training using its local data samples. At the beginning of each global round, each edge server first receives a batch of data samples from its associated data-collecting network and then updates its local model using this  
where \(f_{d}(\omega)\) is the loss function for the data sample. FEL aims to find the optimal global model \(\omega^{*}\) that minimizes the global loss function \(F(\omega)\), which is a weighted average of all clients' loss functions [31]:  
\[\omega^{*}=\underset{\omega}{\text{argmin}}\ F(\omega)=\underset{\omega}{\text{ argmin}}\sum_{j=1}^{J}\frac{s_{j}}{s}F_{j}(\omega), \tag{2}\]  
where \(s\) is the total data size of all edge servers.  
In this paper, we propose FedNow, which considers personal decision-making in local training rounds and the self-interest of strategic edge servers, aiming to alleviate the performance bottlenecks associated with synchronous incentive schemes in FEL (Compared to asynchronous updates, a synchronous update scheme has provable convergence [14] and stronger anti-attack capability [32]). FedNow allows edge servers to determine their local training rounds in each global iteration independently, thereby reducing useless waiting times. However, conducting more local iterations will also lead to higher training costs. Moreover, heterogeneous edge servers usually have different capabilities in data collecting and processing. In the following, we model the cost of edge servers and the platform, and we formulate contracts to incentivize the truthful contributions of the edge servers. Table 1 summarizes the key notations used in this article.
## 4 Resource Allocation Mechanism
### Interference Clustering + Improved Genetic Algorithm
#### 4.2.1 Interference Clustering  
To address these issues, we propose interference clustering based on the antenna model. More specifically, after determining the \(K\) active beams in current time slot, the off-axis angle between any two illuminated cells relative to the satellite can be calculated based on the cell center and the satellite position, and the corresponding antenna gain can be further obtained according to the gain curve. Set the interference threshold according to the gain and divide the \(K\) illuminated cells into independent clusters, and we consider that there is no CCI between clusters. Figure 6 shows the clustering of illuminated cells in a certain time slot. Within each cluster, if there are multiple cells, we adopt the improved genetic algorithm (IGA) to complete the joint allocation of bandwidth and power, and the search space becomes smaller due to the reduction of the number of beams. If the cluster only contains one single cell, we find the scheme with the largest capacity according to its Constraint Map as mentioned in Section 3.4.2.
## 4 Experiments  
### Experiment Setup  
We used four Tesla K20 and Tesla V100 GPUs to conduct experiments and make comparisons for the proposed BJPB-ISAI algorithm in this section. A comparison of the main parameters of the two generations of NVIDIA's GPUs are shown in Table 2. Compared to Tesla K20, Tesla V100 delivered major performance improvements and had nearly 6.7 times higher double precision floating-point peak performance.  
The host of a Tesla K20 GPU node, with CentOS 6.4 installed, was configured with Intel E5-2680 V2 CPUs having a clock rate of 2.8 GHz and a memory size of 128 GB (host-1), while the host of a Tesla V100 GPU node, with CentOS 7.5 installed, was configured with E5-2640 V4 CPUs having a clock rate of 2.4 GHz and a memory size of 128 GB (host-2). PETSc-3.10.2, CUDA-aware OpenMPI v3.1.4 [30] and CUDA 6.5.14 [31] were used on the K20 GPU cluster, while PETSc-3.14, OpenMPI-4.0 [26] and CUDA 10.0 [27] were employed on the V100 GPU cluster. The CUDA drivers for K20 and V100 GPUs were 340.29 and 440.64, respectively. The multi-GPU version of GMRES algorithm was developed and integrated into PETSc, so the two environments shared exactly the same codes except the shared libraries including cuSPARSE and MPI even though the PETSc version was different. On both clusters, PETSc was installed without the debugging option that is used as the reference for analyzing speedups. For all the linear systems solved in the test cases, the right-preconditioned GMRES with restart 30 was employed. All floating-point operations on CPUs and GPUs were performed in double precision.
# Regiosellective De Novo Synthesis of Phenolic Isoprenoids Grifolin and Neogrifolin
###### Abstract

Table 1: Reactants, reagents, number of steps, and yields of the grifolin synthesis in Figure 1.  
Figure 1: Retrosyntheses of grifolin **1** and neogrifolin **2**[12; 13; 14; 15; 16; 17; 18; 19; 20; 21]. Enhanced coupling with farnesyl bromide proved possible by the selective deprotonation of methyl-protected orcinol with _n_-BuLi, followed by conversion to the corresponding mixed cuprate (entry 7) [18]. Once again, the greatest challenge in these methods was finding mild deprotection conditions that would ensure high yields of terpenophenols. A unique approach for the regioselective synthesis of grifolin utilizing the thermal anulation of a cyclobutene derivative with a substituted acetylene was reported by [PERSON] in 1984 (entry 8) [19]. The regiochemical problem was solved by pre-installing a farnesyl unit on the acetylene moiety. Pure grifolin was obtained in 21-43% yield from (_E,E_)-farnesol. Regiochemical control in the coupling with farnesol unit would be more practical using 1,3-cyclohexanedione, where the doubly activated methylene carbon could be the preferred site of alkylation. [PERSON] et al. attempted an alkylation between 5-methylcyclohexane-1,3-dione (7a) and farnesyl bromide 4 (X = Br), but obtained a mixture of four products, with the _C,C_-dialkylated compound being the major product. The dialkylation problem of 1,3-dione 7a was overcome by condensation with the aldehyde farnesal 4 (C-X = C=O, entry 9), and the synthesis of grifolin was completed by conjugate reduction followed by halogenative aromatization [20]. The creative synthesis of meroterpenoids was demonstrated by [PERSON] et al. utilizing the aromatization of polyketide, which was prepared starting from 2,2,6-trimethyl-4_H_-1,3-dioxin-4-one (8). Farnesol was introduced into a specially designed intermediate, dioxane-4,6-dione-keto-dioxinone (entry 10) [21]. Acetylation followed by Pd-catalyzed decarboxylase allylic rearrangement generated the diketo dioxinone structure required for biomimetic aromatization. The above regioselective grifolin syntheses require highly functionalized intermediates, non-trivial reaction conditions, and even additional step sequences. We have found short and simple reaction steps to synthesize grifolin and neogrifolin in a highly regioselective manner. Herein, we report the regioselective de novo syntheses of terpenophenols utilizing only ethyl acetoacetate, ethyl crotonate, and farnesyl bromide as substrates. The sequence of their coupling reactions is sufficient to determine the regiochemistry of the products. Subsequent cyclization, decarboxylation, and aromatization complete the meroterpenoid syntheses.
## 5 Discussion, Limitations, and Future Perspectives  
Vibroarthrography (VAG) is a promising diagnostic method for assessing the condition of the knee joint, especially in the context of pathologies such as osteoarthritis. The recording of vibroartrographic (VAG) signals is a key element in the effective diagnosis of knee joint disorders. The main recommendations for recording VAG signals emphasise the importance of precise placement of sensors, such as piezoelectric and MEMS accelerometers, which have high sensitivity and wide bandwidth. The placement of sensors in strategic locations, such as the tibia, patella, or femoral and tibial condyles, has a significant impact on the quality of the signals obtained. Despite technological advances, anatomical variability and the lack of clear guidelines for sensor placement remain a challenge, requiring further research in this area.  
Processing VAG signals requires advanced noise reduction techniques such as wavelet transforms, principal component analysis (PCA), or ensemble empirical mode decomposition (EEMD). Time-frequency analyses, such as short-time Fourier transform (STFT) or Hilbert-Huang analysis, enable accurate representation of signal variability relevant to diagnostics. The cleaned data are then analysed using parameters such as sample entropy, autoregression coefficients, kurtosis, or asymmetry coefficient. The extracted features form the basis for effective signal classification, which, using machine learning algorithms such as neural networks, SVM, or logistic regression, achieves classification accuracy of more than 95%. Recent studies indicate the potential of techniques such as LS-SVM or k-NN in differentiating between healthy and ageing joints. The use of multichannel measurement systems and the selection of highly discriminating features increases classification accuracy, opening up new possibilities for the diagnosis of advanced pathologies as well as the detection of degenerative changes at an early stage of their development.  
The main limitations of the VAG method are the variability of results between patients and the lack of standardisation of measurement procedures. Research is also required on mechanisms for generating vibration signals and more intuitive sensor attachment systems, such as gloves with integrated accelerometers. The standardisation of sensor placement and the development of less invasive recording methods can increase the reproducibility and acceptance of the technique in clinical practice.  
Future research should focus on integrated diagnostic systems involving biomechanical modelling of the joint, expansion of sample sizes, and testing under realistic environmental conditions. Further development of measurement technologies, such as laser vibrometers and multichannel MEMS systems, could significantly increase the diagnostic value of VAG, enabling its wider application in medicine.
# Progressive Multiple Alignment of Graphs
## 2 Theory
### Progressive Alignments  
Consider an alignment \((X,\mathcal{S})\) and let \(\{S_{1},S_{2}\}\) be a bipartition of the set \(S\) of rows, i.e., \(S_{1}\cap S_{2}=\emptyset\), \(S_{1},S_{2}\
eq\emptyset\), and \(S_{1}\cup S_{2}=S\). Furthermore, let \(Y_{1}=\{x\in X|\exists i\in S_{1}:f_{i}(x)=1\}\) and \(Y_{2}=\{x\in X|\exists i\in S_{2}:f_{i}(x)=1\}\). Consider the restriction \((Y_{k},\mathcal{S}_{k})\) of the sub-object \((X,\mathcal{S})[Y_{k}]\) to the rows \(i\in S_{k}\) for \(k=1,2\). Then \((Y_{k},\mathcal{S}_{k})\), is an alignment of the input objects \((X_{i},\mathcal{S}_{i})\) with \(i\in S_{k}\) and \((X,\mathcal{S})\) can be seen as a pairwise alignment of \((Y_{1},\mathcal{S}_{1})\) and \((Y_{2},\mathcal{S}_{2})\). For the full formal details, we refer to [5]. As a consequence, every alignment can be recursively subdivided into alignments of bipartitions of its rows. Since every input object is itself a (trivial) alignment, this decomposition yields a rooted tree \(T\) whose leaves are input objects \((X_{i},\mathcal{S}_{i})\), whose root is the alignment \((X,\mathcal{S})\), and whose interior nodes correspond to alignments of certain subsets of input alignments.  
The idea of _progressive alignment algorithms_ is to construct an alignment \((X,\mathcal{S})\) from the input objects \((X_{i},\mathcal{S}_{i})\) along a binary guide tree \(T\) such that at each inner node, a _pairwise alignment_ of the alignments associated with its children are computed (Figure 2). The compositionality properties ensure this is always possible, irrespective of how the tree \(T\) is chosen and how the pairwise alignments are computed in practice [5]. Here, we construct a progressive alignment algorithm for graphs. Clearly, for each interior node of an arbitrary binary guide tree, it suffices to find a maximum common induced graph and use it to determine how to glue the two child-graphs together. Note that at this point, we make no statement on the optimality of the either \((X,\mathcal{S})\) or any of the intermediates. We merely state that every graph alignment \((X,\mathcal{S})\) can be obtained by reverting the process of its decomposition along any binary tree \(T\). Ambiguous edges only provide a moderate complication in progressive alignments, which we will consider below in conjunction of the scoring model. In fact, when extending a set of matches, one can choose whether an ambiguous edge is considered to be present or absent.
# Comparison of Radio Frequency Path Loss Models in Soil for Wireless Underground Sensor Networks
## 1 Introduction
However, the CRIM-Fresnel model itself was also not validated by a range of test trials. The modified-Friis model estimates the attenuation of EM signals mainly based on the effect of phase shifting on the signal, while CRIM-Fresnel considers the effect of the material absorption and signal reflection. Estimation of signal attenuation for a soil sample with real and imaginary parts of complex dielectric permittivity of \(\varepsilon^{\prime}=13.25\) and \(\varepsilon^{\prime\prime}=2.18\) at 434 MHz is shown in Figure 1 for the two different models. Results below 0.05 m are omitted as near field effects not accounted for in the models are present this close to the transmitter. The comparison of RF propagation models presented in Figure 1 shows that the estimated values for the attenuation of the signal by the modified-Friis and the CRIM-Fresnel models are significantly different from each other. As is shown, the modified-Friis estimates the signal attenuation in dB as a non-linear function while in contrast; the CRIM-Fresnel model estimates the attenuation of the EM signals as a linear function in dB. These differences suggest a need for further investigation into the accuracy of these models by comparing the predicted values from the models with measured values from laboratory or field trials. Hence, in this paper, the applicability of these two most commonly used signal attenuation models (modified-Friis and CRIM-Fresnel) are investigated and analysed for a variety of soil types and conditions, with specific EM propagation measurements being undertaken for comparison. The following section describes the methodology for EM attenuation measurement in soil, as well as extraction of soil permittivity parameters. This is followed by presentation and discussion of the comparison of experimental attenuation measurement with the prediction from the two models.
## 2 Optical Waveguide Cross-Section Design and Simulation
### Waveguide Taper for Mode Matching  
Waveguide tapers are required to achieve efficient coupling between the DLW waveguide and other attached waveguides with different mode field diameters. The coupling efficiency can be enhanced by adiabatically increasing the waveguide core diameter (i.e., the waveguide taper) and, subsequently, its fundamental mode size to match the target waveguide. The taper rate, or angle, should be sufficiently small, satisfying the adiabatic condition, so as to not excite higher-order modes. Due to the azimuthal symmetry of the waveguide (ignoring the negligible effects of nano-fin and the nearby substrate), the only higher-order mode that can become excited from a rapidly increasing LP\({}_{01}\) mode is the LP\({}_{02}\) mode. Our study investigated the taper performance, starting from a 2 \(\upmu\)m core diameter (single-mode waveguide) linearly transitioning to a 4 \(\upmu\)m core diameter (supporting modes LP\({}_{01}\), LP\({}_{11}\), and LP\({}_{02}\) when the nano-fin and substrate are not present, which were the simulation parameters). The taper analysis can be extended to any final diameter, e.g., matching to a single-mode fiber; in our case, we expanded to a 4 \(\upmu\)m diameter, which was related to a lensed fiber we used experimentally. For a long transition taper, satisfying the adiabatic transition regime, the launched fundamental mode expanded with the taper and remained in the fundamental (see Figure 5a). For short tapers, and in the extreme case with no taper (or direct butt-coupling), LP\({}_{01}\) mode coupled to LP\({}_{02}\). The power carried in these two modes as a function of taper length is shown in Figure 5b, and was evaluated using EME. The excitation of the higher-order modes was negligible for taper lengths above 30 \(\upmu\)m (1:15 taper rate).  
Figure 4: (**a**) Top view of the electric field modulus propagation through three 90\({}^{\circ}\) bent waveguides of 2 \(\upmu\)m diameter, with bend radii of 5, 10, and 15 \(\upmu\)m (composite view, separated by hatched pattern). (**b**) Bend loss as a function of bend radius for waveguide diameters of 1.5 and 2 \(\upmu\)m, with and without nano-fin support. Insets show intensity profiles in bent waveguides of radius 5, 15, and 25 \(\upmu\)m.