esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Chemical Composition
#### 3.2.2 Crystalline Structure
The amount of carbonate in the resultant powders was around 2.5% for samples thermally treated at 750 \({}^{\circ}\)C and it decreased by increasing the temperature to 950 \({}^{\circ}\)C. This loss of carbonate also explains the low intensity peaks associated with carbonate groups obtained from the FTIR analysis of the samples treated at 950 \({}^{\circ}\)C. Furthermore, the amount of carbonate in powders treated at 750 \({}^{\circ}\)C is closer to the carbonate content in human bones, which is around 5% [42]. Thus, the obtained results demonstrate that, both in terms of crystallite size and carbonate content, a calcination temperature of 750 \({}^{\circ}\)C leads to a calcium phosphate with greater resemblance to that of an average human bone. The poor mechanical properties of calcium phosphates are known to be the main drawback preventing use of these materials for load-bearing bone replacements [43]. Early works in the 1990s described the limited properties of calcium phosphates if a direct replacement of large bones was the objective [44]. Regardless of whether the origin is synthetic or natural, calcium phosphates are brittle and their fatigue resistance is quite limited [45]. Therefore, the main application of this biogenic biphasic material could be as bone defect filler, provided all compulsory in-vitro, in-vivo, and clinical studies were successfully accomplished. The powder obtained could also be used as precursor material for nanoparticle production [46] that could also be incorporated into sunscreen creams [47], or directly used in filters for the treatment of polluted water [48]. This biphasic material could be converted into coatings for implants by means of the pulsed laser deposition technique [49] that improves the osseointegration of dental and orthopaedic implants.
## 2 Materials and Methods  
### The System Architecture  
In the absence of any country-specific emission factors for ammonia emissions from pig farming in Hungary, the work relied on the international literature. Although the primary focus was on estimating ammonia emissions at the farm level, it was important to develop a model that could be improved in the future and also complemented with modules that support greenhouse gas (GHG) emission estimation. In addition, a pig technology survey carried out by our research group in previous years provided a representative database on the housing, storage and application methods used in Hungarian pig farming.  
The most significant ammonia and GHG models [12]--Manure Model [13], DYNAMO [14], NARSES [15; 16], FARMSCOOPER [17], NEMA [18], FEM [19], REPRO [20; 21; 22; 23], EFEM [24], MAM/MAMBO [25], AGRAMON [26], DiaryGEM [27; 28], and DNDC Maure [29]--were analysed on the basis of the following considerations: adaptability, taking into account greenhouse gas emissions, calculation of field application, applicability or adaptability at the economic (farm) level, and that its data requirements should stay at a tolerable level for Hungarian farmers. Based on these aspects, the AGRAMON model was selected [30; 31] for adapting the domestic model.  
AGEM-S is a TAN-based material flow model for calculating the amount of ammonia emitted from the TAN presented at each module (Nutrition, Housing, Manure storage, Manure application) using emission rates (ERs) and considering the applied techniques and the correction factors (CFs) that influence ammonia emissions. For each technological stage, ERs indicate what percentage of the TAN content of the manure is emitted in the form of ammonia in the stable, storage, or application. The applied CFs take into account the (production) technological parameters that influence the rate of N excretion and ammonia emissions. The efficiency of emission-reducing procedures is indicated in each case in relation to a reference system.  
AGEM-S as a model includes strategies, technologies, and ammonia emission abatement techniques that are typical of domestic production practices and have a scientifically proven, i.e., quantifiable, ammonia emission reduction effect, based on literature data. During the development of AGEM-S, a main aim was to ensure that the system could make calculations from input data used daily by practicing farmers without compromising the information content of the output data, so that the input parameters could be entered as simply as possible in the shortest possible time. The calculated results in the form of numerical values are available as farm evaluations.
## 2 Materials and Methods
### Image Preprocessing  
During the image preprocessing, MRI images underwent several processing steps. First, the FSL v7.0 SynthStrip module was used for skull stripping of both T1- and T2-weighted images [27]. To standardize inter-image variability and enhance model robustness and computational efficiency, images were normalized to a grayscale range of 0-1 and resized to \(128\times 128\) pixels. From each T1 and T2 image modality, 6 consecutive slices surrounding the GP were selected as the 3D input for the model, resulting in a 3D tensor of intensity values with a size of 128 \(\times\) 128 \(\times\) 6. All preprocessing tasks were performed using Python 3.8 and FSL 6.0.6 software on an Ubuntu 20.04 system.  
To address the challenges of limited training data and potential overfitting, we incorporated robust data augmentation techniques into our methodology. These involved random horizontal and vertical translations of images ranging from \(-\)10 to 10 pixels, rotations between \(-\)30 and \(+\)30 degrees, and scaling of images from 0.8 to 1.2 times their original size (Figure 1).  
Figure 1: Axial images of T1 and T2 modality MRI scans. (**a**–**d**) represent images from the ABE group, while (**e**–**h**) are from the non-ABE group. (**a**,**e**) are original T1 modality images, (**b**,**f**) are the corresponding T1 images after data augmentation, (**c**,**g**) are original T2 modality images, and (**d**,**h**) are the corresponding T2 images after data augmentation.
# Signal Diversity for Laser-Doppler Vibrometers with Raw-Signal Combination
## 3 Results
### Comparison of the Algorithms
#### 3.3.1 Further Examination of the Developed Algorithm through the Second Experiment  
The first experiment results in predictable signals that are helpful for the development of the algorithm. The signals are thus applicable to real world applications in a limited extent only. For a more representative comparison, the second experiment is more suitable. Using the experimental setup for the second experiment shown in Section 2, measurements are recorded and demodulated (Fs = 10 MHz, RBW = 1 Hz). In Figure 14, the demodulated velocity signals of the four channels as well as a combined signal derived from the four raw signals of one measurement are shown. By aiming the measuring beam at a poorly  
Figure 13: (**a**) Frequency spectra of the velocity signals with a detailed view of the vibration frequency at 100 Hz; (RBW = 2.5 Hz). (**b**) Smoothed frequency spectra for the combined and one individual velocity signal with the amplitude in dB (0 dB = 1 m/s); same scale as in (**a**).  
reflecting part of the speaker, the signal level is relatively low and signal dropouts can be seen in the demodulated signal.  
Compared to the measurement from the first experimental setup on the shaker and four active channels, the measurement on the speaker and only one active channel results in a considerably higher noise level in the velocity signals.  
For the active channel CH4 and the passive channels CH1-CH3, numerous signal dropouts can be seen. Since the signal dropouts are uncorrelated, it is likely that one channel detects the vibration of the speaker at any given time. The fundamental vibration of the speaker at 205 Hz is recognizable in the displacement signals derived from the velocity signals, shown in Figure 15.  
The disturbances of the individual channels are also visible in the displacement signals, resulting in a higher noise level of the frequency spectra, shown in Figure 16.  
Figure 14: Comparison of the demodulated velocity signals of the four channels CH1–CH4 and the combined signal from the raw signals (VeloComb); (RBW = 1 Hz).  
Figure 15: Comparison of the demodulated displacement signals of the four channels CH1–CH4 and the combined signal; (RBW = 1 Hz).  
Due to the lower noise level of combined signal, the functionality of the algorithm can be shown. The relatively large amplitude of the speaker's vibration frequency is detected almost identically by all signals. With these results, we can demonstrate that the algorithm yields good results for close to real-world applications. For this measurement, the mean noise level in the frequency range up to 5000 Hz of the combined signal (RawComb) was reduced by 10 dB compared to the mean noise level of the signals of the individual channels.
## 5 Comparison of Two Fiber-to-Waveguide Couplers  
Tables 2 and 3 show the parameters, maximum coupling efficiency, 3 dB fabrication tolerance, 3 dB alignment tolerance, and polarization-dependent loss of the Y-branch coupler and grating coupler respectively. Following results can be obtained from two tables, firstly, maximum coupling efficiency of the grating coupler (\(\eta_{g}=0.35\)) is greater than Y-branch coupler (\(\eta_{y}=0.24\)). Secondly, in terms of the etching process, difficulty of fabricating the grating coupler is slightly less than that of the Y-branch coupler. When fabricating the Y-branch coupler, minimum size to be etched is 0.10 \(\upmu\)m (width \(W\)) and its fabrication tolerance is \(-\)0.10 \(\upmu\)m\(\rightarrow\)0.05 \(\upmu\)m. Minimum size of the grating coupler is 0.20 \(\upmu\)m (width \(p\)) and its fabrication tolerance is \(\pm\)0.05 \(\upmu\)m. Thirdly, in terms of the coating process, fabricating the Y-branch coupler is easier because thickness of the bottom cladding \(h\) should be strictly  
Figure 10: (**a**) Influence of the SMF position on the coupling efficiency \(\eta_{\text{g1}}\). (**b**) Influence of the bottom cladding thickness \(h\) on the coupling efficiency \(\eta_{\text{g1}}\).  
controlled at 2.52 \(\upmu\)m and its fabrication tolerance is \(\pm\)0.06 \(\upmu\)m. Fourthly, compared with the Y-branch coupler, it is easier to align the single-mode fiber (SMF) and grating coupler. Because Y-branch coupler must be located at the edge of the waveguide reference wavefront source (RWS), and edge of the waveguide RWS should be polished before using the Y-branch coupler. However, grating coupler does not have this problem. When aligning the SMF and Y-branch coupler, SMF center must be located in a circular area with the radius of 1.10 \(\upmu\)m, and center of this circle is Y-branch coupler center. Aligning the SMF with the grating coupler is relatively simpler because of the greater alignment tolerance (\(-1.8\)\(\upmu\)m\(+3.4\)\(\upmu\)m along the \(z\) direction, \(-1.2\)\(\upmu\)m\(-\)\(+12.0\)\(\upmu\)m along the \(y\) direction). Fifthly, polarization of the mode in the SMF has a great impact on the coupling efficiency no matter for the grating coupler or Y-branch coupler. When mode in the SMF changes from TE mode to TM mode, coupling efficiency of the grating coupler is even reduced by 0.3. Therefore, polarization of the mode in the SMF must be strictly controlled.
## 4 Results
### Classification Systems  
An initial approach to determine the behaviour of the classification systems shows a correlation between the landslide susceptibility index values of the treated maps. This is especially true for a comparison with ELSUS and GYouden, as their classifications are more similar. The kappa index will be used for this comparison.  
Kappa is considered more robust than simply calculating the observed proportion of agreement because it accounts for the proportion of agreement that would be expected by chance. Kappa ranges from \(-1\) to \(+1\), with larger values indicating a higher level of concordance. It can be expressed as follows [37]:  
\[\kappa=\frac{d-q}{N-q} \tag{5}\]  
where d (observed agreement) is the proportion of cells in agreement, q is the proportion of agreement expected by chance, and N the total number of observations. The current study employed the Kappa statistic with linear weighting to measure the level of agreement between maps in all cases; see [PERSON] et al. (2003) [38]. The Kappa value penalizes linear disagreement among maps on a scale from smallest to largest. This Kappa index was utilized to assess the extent of change in model classes when the FR type was varied. Table 9 compares the models against the RFR model as a basis.  
Figure 4: Behaviour of the calculation methods for each province. ROC plots are shown in the left column and pAUC evolution in the right column.  
As expected, the CFR method is the most correlated with RFR. The best fit is obtained in the province of Alicante because, in the high susceptibility sections (landslide susceptibility index), the value of FR for the slope is more stable. With the GY classification, the groupings with GFR and EFR show a similar correlation with the improvement obtained with CFR being evident (0.926). In contrast, if ELSUS and RFR are used as the standard, correlations rise in most cases when compared to GY. This would seem to mean that the different groupings according to RF for ELSUS are more homogeneous and, therefore, a possible improvement in the definition of classes for the factors will have less influence.  
It is interesting to analyse the correlation values obtained using the kappa Index among the classification methods. Table 9 shows a greater homogeneity in the ELSUS system with higher values than for GY. This seems to indicate that ELSUS is worse at differentiating between the calculation methods for FR. The GY classification is less correlated and more sensitive to the differences between the FR methods.
# Energy and Environment-Aware Path Planning in Wireless Sensor Networks with Mobile Sink
## 6 Performance Evaluations
### Simulation Results
#### 6.3.2 Packets Delivery Ratio (PDR) Evaluation
The earlier research in [18; 19; 22; 24] does not account for how the environment affects network performance; as a consequence, no action was taken to avoid danger zones, which had a detrimental effect on network dependability since data transmission would stop.
# Two-Stage Short-Term Power Load Forecasting Based on SSA-VMD and Feature Selection
## 3 Sparrow Search Algorithm Optimizes Variational Mode Decomposition
### SSA Optimizes the Parameters of the VMD  
#### 3.3.1 Decomposition Evaluation Criteria for VMD  
In this paper, the VMD algorithm is considered as a function \(f_{vmd}()\), and the effect of decomposition mainly depends on the number of decomposition modes \(K\) and the quadratic penalty factor \(\alpha\), so the decomposition and reconstruction process of VMD is represented in this paper as Equations (11) and (12).  
\[\{u_{k}(t)|k=1,2,\ldots,K;\ t=1,2,\ldots,T\}=f_{vmd}(f(t),K,\alpha) \tag{11}\]  
\[f^{\prime}(t)=\sum_{k=1}^{K}u_{k}(t) \tag{12}\]  
where \(f(t)\) is the original power load value; \(f^{\prime}(t)\) is the reconstructed load value; \(T\) is the length of time; \(K\) is the number of decompositions modes; and \(\alpha\) is the quadratic penalty factor.  
It is obvious from the basic principles of VMD that a certain amount of decomposition loss is usually generated in the decomposition process. The decomposition loss comes from the residual signal that does not conform to the definition of the intrinsic mode function. It is characterized by small amplitude and fast fluctuation, making the forecasting work difficult. Hence, if the decomposition residual is large, it directly affects the precision of power load prediction. Therefore, in order to minimize the decomposition residuals, this paper proposes a decomposition evaluation criterion _Loss_ for VMD, which can be applied to the signal decomposition quality problem in the field of time series forecasting. _Loss_ is expressed as Equation (13).  
\[Loss=\frac{\sum\limits_{t=1}^{T}|f(t)-f^{\prime}(t)|}{T} \tag{13}\]  
_Loss_ represents the mean absolute error between the VMD-reconstructed load values and the original load values. The smaller this value is, the smaller the decomposition loss is, so the more complete the information contained in each IMF is, and the more accurate the final forecasting value will be. At the same time, in order to minimize the loss of effective information, the decomposition residuals are also considered in the forecasting model in this paper, which is involved in the forecasting process as an IMF.
## 5 Numerical Analysis
### Impact of Dynamic Pricing Mechanism on Ride-Hailing Platform's Order Allocation Strategy  
#### 5.3.1 Impact of Ride-Hailing Unit Cost Changes on Platform Order Allocation Strategy  
In ride-hailing operations, differential pricing mechanisms are widely adopted, especially during periods of order overflow. When the ride-hailing fare, \(M_{C}\), is subject to differential pricing, the changes in passenger order cancellation probabilities are illustrated in Figure 7, while the changes in driver earnings are depicted in Figure 8.  
An increase in the ride-hailing unit cost, \(M_{C}\), narrows the price gap between ride-hailing vehicles and taxis, reducing the price advantage when orders are assigned to ride-hailing vehicles. Consequently, passengers are more likely to cancel their orders and switch to taxis. This is visually represented in Figure 7 by the expansion of zones 2 and 4 and the contraction of zones 3 and 5, accompanied by a slight increase in the upper limit of order cancellation probabilities.  
For drivers, a higher ride-hailing fare increases their overall earnings range. However, the rise in order cancellation probabilities also expands the area of lower earnings. Therefore, when drivers can quickly reach the passenger locations assigned by the platform, the earnings increase from a higher fare is more significant. Conversely, if the pick-up time is longer, due to factors such as deadhead costs, the earnings gain from a higher fare becomes less pronounced.  
With fluctuations occurring in the ride-hailing unit price, \(M_{C}\), the optimal order allocation ratio, \(\alpha\), for maximizing social welfare is illustrated in Figure 9. For cases where the arrival time of cruising taxis, \(T_{X}\), precedes the penalty time, \(T_{P}\), for passenger order cancellation, it is generally observed that utility is maximized when \(\alpha\) is set to 1, and this range expands as the fare increases. This implies that, as the ride-hailing fare, \(M_{C}\), rises,  
Figure 8: Impact of ride-hailing unit cost, \(M_{C}\), changes on driver earnings.  
Figure 7: Impact of ride-hailing unit cost, \(M_{C}\), changes on passenger order cancellation probability.  
the platform needs to allocate more orders to taxis in order to mitigate the negative utility associated with passenger cancellations. Consequently, the platform will continuously adjust the order allocation ratio, \(\alpha\), towards 1 earlier, considering the arrival times of ride-hailing vehicles in the system. For situations where cruising taxis arrive after the penalty time for cancellation, as the ride-hailing fare, \(M_{C}\), increases, changes in taxi arrival times more frequently influence variations in the order allocation ratio, \(\alpha\), at the same ride-hailing vehicle arrival time level. Moreover, when the ride-hailing fare, \(M_{C}\), is either low or high, achieving optimal social welfare requires larger adjustments in the order allocation ratio, \(\alpha\), compared to when \(M_{C}\) is at realistic average prices. This underscores the rationality of ride-hailing platform pricing in practice.
# Modeling of Geophysical Flows through GPUFOW
## 2 Materials and Methods
### The Physical-Mathematical Model
#### 2.1.1 The Geometric Bias Resolution  
A well-known issue with cellular automata is the emplacement bias due to the cell and neighborhood geometry [17; 18], with the flow tending to follow the symmetry axes, leading to unnatural emplacements on a flat surface: where a circular shape would be expected, the final emplacement is a square either parallel to the cell axis when using a Moore neighborhood (i.e., including the diagonal neighbors) or rotated by half a right angle when using a von Neuman neighborhood (i.e., only including adjacent cells in the cardinal directions). Historically, these issues have been solved either by increasing the number of symmetry axes [40] or by computationally expensive and non-deterministic Monte-Carlo methods [41].  
Our proposed solution is to introduce a correction factor that scales the diagonal neighbor contributions, calibrated in order to achieve a circular emplacement on a flat DSM. Specifically, the optimal value for the diagonal correction factor \(\alpha\) was determined empirically by simulating a pillar collapsing on a flat plane and estimating the fitness between the final emplacement and a circular form, for values in the [0, 1] range, with 0 indicating no mass flux to the diagonal neighbors (thus effectively implementing a von Neumann neighborhood), and 1 indicating equal weight for all neighbors (standard Moore neighborhood). Bisection was used to refine the range, with the optimal results obtained for \(\alpha=2^{-6}\).
# Nucleobase-Containing Polymers: Structure, Synthesis, and Applications
## 3 Synthetic Nucleobase-Containing Polymers
### Synthesizing Nucleobase Polymers by ROMP
Like other polymerization strategies, ROMP provides another efficient method to access nucleobase-containing polymers; however, the driving force of ROMP is the relief of ring strain in cyclic monomers, which usually requires the additional step of preparing nucleobase-functionalized cyclic olefins before polymerization. The cost of the Ru catalyst is another concern, especially in large-scale polymer preparations. Most recently, [PERSON] et al. [99] presented a novel work that using a ring-opening polymerization method other than ROMP. In this work, thymidine-based cyclic carbonate incorporating CO\({}_{2}\) at 1 atm pressure and this monomer underwent ROP under mild reaction conditions with organocatalyst TBD and 4-MeBnOH alcohol initiator to yield novel thymidine-based polycarbonates. Similarly, [PERSON] [43]  
Figure 8: Preparation of poly(oligonucleotide): PNA homopolymer (**A**); Poly-PNA block copolymer (**B**) [42]. Reproduced with permission. Copyright American Chemical Society, 2014.  
et al. presented a synthetic strategy that employed organocatalytic ROP of a six-membered cyclic phosphoester monomer at ambient temperature to afford a new type of thymidine-derived 3',5'-linked polyphosphoester with a butenyl group located on each repeat unit. This is the first time such well-defined DNA-derived polyphosphoesters with 3',5'-linkages in the backbones have been prepared via ROP.