esa-sceva/satcom-chunk-collection huggingface dataset:
## Results and Discussion
### Volatile Compounds (VOCs) and Sensory Attributes
#### 2.2.2 GC\(\times\)GC-TOF-MS and Sensory Attributes
Such a result proved that the two techniques have complementary outputs and that their use in combination has serious advantages. Interestingly, several compounds that were most characteristic (the highest \(F\)-values) for monovarietal PDO EVOOs were the minor ones, such as curcuminene, octanal, 2-hexanol, ocimene, protoamenonine, valencene, etc. (Table 2). On the other hand, GC\(\times\)GC-TOF-MS in a major part confirmed the GC-MS results, among other findings that higher amounts of standardly reported major LOX-derived C6 and C5 aldehydes, ketones, and alcohols, such as (Z)-2-hexenal, (\(E\))-2-hexenal, 1-penten-3-one, and (\(Z\))-2-penten-1-ol, are characteristic for this group of EVOO. The commercial-blended EVOO group was distinguished by a much larger number of volatile markers, including many of those often co-occurring with negative, defective sensory notes, such as 2-phenyl ethanol, isoamyl alcohol, isoamyl acetate, ethyl-2-methyl benzene, ethyl hexanoate, 3-methyl-2-butten-1-ol, 3-hydroxy-2-butanone, etc. [19; 21; 22; 28], accompanied by a large array of minor and unbeknown compounds (Table 2). The most characteristic compounds, such as acetic acid, 3-methyl-3-butten-1-ol, 3-hydroxy-2-butanone, 1-octen-3-ol, and others, did not coincide with those that were extracted as the most important by GC-MS (e.g., acetates), showing once again the synergistic potential of the two techniques used. Again, the group of commercial-blended EVOO was seriously deficient in the LOX volatiles that were known to be carriers of positive green and olive fruity flavor notes. Such results were confirmed by PCA: monovarietal PDO EVOOs were sharply separated from the commercial-blended group, owing to the higher amounts of LOX volatiles and more intense positive odor sensory notes (Figure 3). Although produced from different olive cultivars grown in different geographical areas in Italy, monovarietal PDO EVOOs exhibited a relatively high level of homogeneity, with the exception of the three samples that belonged to the same cultivar/area located in the fourth quadrant of Cartesian plane with high absolute values of the coordinates on both PC1 and PC2 axes, which were even more discriminated from the commercial-blended EVOOs (Figure 3a). It is probable that, in this particular case, the cultivar and/or the geographical area had a greater impact. The commercial-blended EVOOs were mostly grouped by the non-LOX compounds and the occurrence of sensory defects, which were probably in a causal relationship (Figure 3). Indeed, the defects that were observed during the sensory analysis of the commercial EVOOs put these oils in a lower quality category that usually have a lower price than EVOO. The results that were obtained in this study for the particular major volatile compounds were mostly in agreement with the findings from [PERSON] et al. (2018)[1], who also utilized their amounts, among other compounds, to differentiate high from low-priced EVOO.
# Visualising the Relevance of Climate Change for Spatial Planning by the Example of Serbia
## 2 Data and Methods
### Research Design
Only then was it possible to proceed with the quantification of the appearance of each effect for each planning document (Figure 2). This was performed by summing up the number of keywords addressed in each plan per each climate change effect, as shown in Figure 2. The result is a matrix that shows how many aspects of dealing with climate change were addressed in each analysed plan in relation to each climate change effect separately. Additionally, the matrix allows for comparisons between plans and between extents to which climate change effects are addressed (\"Total per plan\" and \"Total per effect\" in Figure 2). Parallel to the matrix, we applied the spider visualisation method that targets the presentation of climate change effects in spatial planning, i.e., the spatial planning of Serbia (Figure 2). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **Anti-Hail** & **Hail** & **Bank Revetments** & \\  & **“Protivgradni”** & **“Grad”** & **“Obaloutvra”** & \(\ldots\) \\ \hline \multirow{3}{*}{1} & Spatial Plan of the & & & \\  & Republic of Serbia & 1 & 1 & 1 & \(\ldots\) \\  & 2010–2020 & & & & \\ \hline \multirow{3}{*}{2} & Strategic & & & & \\  & Environmental & & & & \\  & Impact Assessment & 0 & 0 & 0 & \(\ldots\) \\  & for the SPRS & & & & \\  & 2010–2020 & & & & \\ \hline \multirow{3}{*}{3} & Implementation & & & & \\  & Program for the & 0 & 0 & 0 & \(\ldots\) \\  & SPRS 2010–2020 & & & & \\ \hline \multirow{3}{*}{2} & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) \\ \cline{2-5}  & TOTAL & 1 & 2 & 7 & \(\ldots\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Measures addressing climate change—example of the matrix for binary analysis and frequency grading. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Climate Change Effect**} & \multicolumn{1}{c}{**Keywords (Measures) (Ordinal Numbers in Figure 1)**} \\ \hline Drought & 2, 5, 6, 7, 8, 9, 11, 12, 15, 16, 17, 18, 19, 20, 22, 23, 24, 26, 27, 28, 29, 31, 32, 35 \\ \hline Floods & 1, 2, 7, 8, 9, 10, 11, 12, 14, 13, 15, 16, 17, 18, 19, 20, 22, 23, 24, 26, 27, 28, 29, 32, 36, 37,As maps are a consistent part of spatial plans where measures related to climate change can be potentially presented, we added a map analysis to the analysis of spatial plans. The focus was on infrastructural, conceptual, and territorial elements (river dams, errosian endangered areas, protection zones, etc.). In addition to the fact that such an analysis indicates the involvment of specific plans in climate change, these examples contribute to the general interperetation of mapping as a standard visualisation method in spatial planning. Therefore, this study proposes a methodology for the visual evaluation of the extent to which climate change is tackled in spatial-planning documents using the example of Serbia, while noting its applicability in any other international, national, regional, or local context.
# Load Forecasting Based on LVMD-DBFCM Load Curve Clustering and the CNN-IVIA-BLSTM Model
## 4 Case Analysis
### Analysis of Data Processing Results  
#### 4.1.1 Data Denoising and Decomposition  
The original load data are decomposed and denoised using VMD, as shown in Figure 13, and the original load data sequence is decomposed into 8 IMF components. From the figure, we can see that the IMF7-IMF8 values are small and contain more noisy data, which are removed from the original data, then the first six IMF components are used to reconstruct the data to achieve data denoising.  
The second step of LVMD is to redecompose the denoised data with LMD. The denoised data are decomposed into a total of six PF components, and the decomposition results are shown in Figure 14. The values of PF1 are larger and maintain the same trend as the original data, containing the main valid information of the data. The values of PF2-PF6 are smaller, among which the periodic changes of PF2-PF4 are more obvious and PF6 is monotonically increasing, which is convenient for the prediction of each component. The randomness of the F5 sequence volatility is strong and the prediction accuracy of the F5 component is not high when making predictions.  
Figure 13: Result of VMD decomposition.
## 2 Related Works
### Data Orientation  
As a result of the performed data registration process, exterior orientation elements are obtained for each scan, i.e., determination of the location of the scanner system central point in the assumed reference system together with rotation angles, which are successively used to transform the point cloud. For this purpose, the 3D affine transformation is generally applied. In cases where scale differences occur between the scanner system and the external reference system, it is recommended to apply the 3D transformation by similarity, which is performed based on a minimum of three points of matching distributed within the entire analysed area. When the number of these points is increased, surplus observations are created; therefore, the accuracy of data registration is also increased. This also allows for the elimination of gross errors.  
The relation between the local instrument system and the global reference system is expressed in Equations (1) and (2) [20; 21; 22].  
\[\mathrm{M}_{\mathrm{ext}}=\mathrm{R}_{\omega\cdot\varphi\mathrm{k}}\ast\mathrm{ M}_{\mathrm{int}}\ +\mathrm{T} \tag{1}\]  
\[\mathrm{R}_{\omega\cdot\varphi\mathrm{k}}=\mathrm{R}_{1}(\omega)\mathrm{R}_{2 }(\varphi)\mathrm{R}_{3}(\mathrm{k}) \tag{2}\]  
where \(\mathrm{M}_{\mathrm{ext}}\) is the vector of the coordinates of points in the global system, \(\mathrm{M}_{\mathrm{int}}\) is the vector of coordinates of points in the local (scanner) system, \(\mathrm{T}\) is the translation matrix, \(\mathrm{R}_{\omega\cdot\varphi\mathrm{k}}\) is the rotation matrix, \(\mathrm{R}_{3}(\mathrm{k})\) is the rotation matrix in relation to the z-axis by an angle k, \(\mathrm{R}_{2}(\varphi)\) is the rotation matrix in relation to the y-axis by an angle \(\varphi\), and \(\mathrm{R}_{1}(\omega)\) is the rotation matrix in relation to the x-axis by angle \(\omega\).  
In order to determine the elements of point cloud registration, two main methods are applied: The point-based method and the feature-based method. The latter utilizes features detected in a point cloud.  
Point-based methods may be divided into two main groups: Target-based methods based on matching point clouds on the basis of signalled control points and ICP (Iterative Closest Point) and SLAM (Simultaneous Localization and Mapping) methods based on matching groups of points to reference planes, point clouds, or shapes [21; 23; 24; 25; 10]. The feature-based process for matching point clouds utilizes features detected in point clouds, such as curvature, edges, planes, etc.  
The feature-based registration methods are based on the SfM approach: (i) Detection of key points using a detector, (ii) key-point description, (iii) matching points, and (iv) triangulation and bundle adjustment [13; 14; 15; 16; 17].
# Disentangled Feature Learning for Noise-Invariant Speech Enhancement
## 4 Experiments and Results
### Feature Extraction  
The input and target features of the networks were extracted in the following way. First, we extracted the magnitude spectra from the noisy speech, the corresponding clean speech, and noise. A 512-point Hamming window with 50% overlap was applied to the audio signals, sampled at 16 kHz, and then short-time Fourier transform (STFT) was applied. 512 points STFT magnitudes were reduced to 257 points by removing the symmetric half. \(F\) and \(\tau\) were fixed to 257 and 5, respectively. Thus, input feature vectors, extracted from 11 consecutive frames, were concatenated in a similar manner as in [19].
## 2 Preliminaries
### Parallel Prefix Adders  
In order to compute the sum \(s\) of two \(n\)-bit inputs over \(\mathbb{Z}_{n}\), an addition circuit needs to compute a sum bit \(s_{i}\) for every pair of input bits \((a_{i},b_{i})\) using carry bits \(c_{i}\) as:  
\[s_{i}=a_{i}\oplus b_{i}\oplus c_{i}\ \ \text{with}\]  
\[c_{i}=c_{i-1}\wedge(a_{i-1}\oplus b_{i-1})\vee(a_{i-1}\wedge b_{i-1})\ \ \forall i>0\,\text{else}\ 0\]  
A direct realization of these equations leads to a carry-ripple adder with a circuit depth of \(n\) due to the dependency of \(c_{i}\) from \(c_{i-1}\). When masking this architecture in hardware where glitches occur, the required registers in each stage lead to a latency of \(n\) cycles.  
Parallel-prefix adders can reduce this latency by restating the computation of the carry bits using (group-) generate and propagate terms and computing them in parallel. Intuitively, the generate term \(p_{\{i,j\}}\) determines if the groups of input bits \(a_{\{ij\}}\) and \(b_{\{i,j\}}\) will generate a carry output \(c_{j+1}\), independently of the carry input \(c_{i}\). The propagate term determines if an input carry \(c_{i}\) will affect the output carry \(c_{j+1}\).  
For single-bit inputs \((a_{i},b_{i})\), the generate and propagate terms are computed as \(g_{i}=a_{i}\wedge b_{i}\) and \(p_{i}=a_{i}\oplus b_{i}\), respectively. We call this initial step, which is the same for all parallel-prefix adders, the preprocessing step. Given \(i>k\geq j\), the group-generate term is calculated as  
\[g_{\{i,j\}}=g_{\{k,j\}}\oplus\left(p_{\{k,j\}}\wedge g_{\{i,k+1\}}\right) \tag{1}\]  
and the group-propagate term as  
\[p_{\{i,j\}}=p_{\{i,k+1\}}\wedge p_{\{k,j\}}. \tag{2}\]  
The final sum bits can then be computed as \(s_{i}=g_{\{i-1,0\}}\oplus p_{i}\), where \(g_{\{-1,0\}}=0\). The costs in XOR and AND operations for implementing each of the functions mentioned above are itemized in Table 1.  
In the following we use \(p_{\mathcal{S}\{i,j\}}\) (or PG term where the indices are not relevant) as a short form for the tuple \((p_{\{i,j\}},\mathcal{S}\{i,j\})\). We define the function block that computes these terms by combining existing terms as \(PG(\{i,j\},\{k,l\})=(p(\{i,l\}),g(\{i,l\}))\) and call them PG blocks.  
The different variants of parallel-prefix adders, such as the ones discussed here, all apply this same principle but differ in the way PG terms of larger groups of bits are constructed from smaller ones.
###### Abstract  
In this paper, the extended finite element method (XFEM) is first applied to account for the weak discontinuity of the axisymmetric electrostatic field. Firstly, the interface between two materials in an element is described by the level set method. The enrichment function is used to modify the shape function of enrichment elements. Secondly, to illustrate the feature of the enrichment function, the distribution diagrams of enrichment functions in sub-elements are drawn. The 3D field can be simplified to an axisymmetric field, which can reduce the difficulty of calculation. Finally, models with bubbles in liquid nitrogen in the axisymmetric field are used to prove the reliability of XFEM. Compared with the conventional finite element method (CFEM), XFEM costs lower computing resources with almost the same computational accuracy.  
extended finite element method; axisymmetric field; enrichment function; liquid nitrogen; bubbles; electric field
## 2 Previous Works
### Translations between CUDA and OpenCL  
As the parallel programming platform of CUDA was announced, utilizing GPU as a parallel processor has become easier. Many programs are written in CUDA to utilize GPGPU functionalities. However, since CUDA has been exclusively developed by NVIDIA, it supports only NVIDIA chip-sets. On the other hand, OpenCL, which was announced by Khronos group, is an efficient way to support various types of chipsets. In some cases, we can convert CUDA source codes to OpenCL source codes, to support wider ranges of parallel processors.  
For the translation procedure between CUDA and OpenCL, we need to check the details of each parallel processing platform to launch their own kernels. Each parallel programming framework provides API bound to C/C++. They have distinguishable function names to launch kernel functions to control the execution flow, or to manipulate kernel data. For example, CUDA has _cuda_ prefix to their API, OpenCL has _cl_ prefix, and ROCm has _hip_ prefix. Although each platform provides different function names, the processes of using such API in a system are very similar to each other.  
First, the procedure will allocate resources in the parallel processor memory space. Second, the system will provide a kernel (which can be pre-compiled) that will be executed by each thread in parallel processors. Then, after launching the kernel, the system waits for all threads to finish their own works. Finally, the system copies back the final data, which contains results of thread execution.  
A most intuitive way of the code translation is to let programmers manually translate the code of each platform by substituting similar API functions. There are also many efforts to achieve automatic translations, as shown in [22; 23; 24]. Researches show that the automatically translated code also performs well, at least for the case of converting CUDA codes to OpenCL codes.  
However, translating codes written for one platform to another platform is still challenging. This is mainly due to the differences in the work allocation models, memory space architecture, and synchronization techniques. Although the single source heterogeneous computing concept allows compilers to generate compiler intermediate codes, generating another full source code for another platform has not yet been considered. To follow the concepts of single source heterogeneous parallel computing, SYCL was announced by Khronos group. We will show the details of SYCL in the next section.
## 3 Data Routing Approaches in WSNs
### Energy-Efficient Approaches
#### 3.1.1 Energy Balancing
In this area, although a lot of work has subsequently been put into the design and development of solar-powered sensor nodes, only a few makeshift topologies and routing protocols have been implemented. The paper in [59] presents a scheme which uses solar energy efficiently. In this approach, each node operates in an energy-saving (ES) mode, if the node is short of residual energy; otherwise, the node operates in an energy-rich (ER) mode, and tries to construct an ER-backbone network consisting of fellow ER-nodes. Moreover, to choose the best next-hop node, they proposed ER-backbone-based geographic routing (ERB-GR) scheme, which is designed to balance energy and achieve low energy consumption. In the ERB-GR scheme, all the ES-nodes try to route data in an energy-efficient manner, considering the remaining energy and the geographic location of their neighbors. Firstly, a node tries to route data directly to the ER-backbone by sending it to one of its ER-neighbors. However, if it has no ER-neighbors, it tries to find its most promising ES-neighbors. Additionally, this routing scheme is applied to some ER-nodes which have no ER-neighbors in the direction of the sink node. These ER-nodes should transmit data to one of their ES neighbors in an energy-efficient manner. In order to choose the best next-hop node in terms of energy-efficiency, ERB-GR calculates metrics called Progress and Cost Progress for each neighbor. From node _i's_ point of view, Progress (i, j) and CostProgress (i, j) for a neighbor node \(j\) are calculated as follows:  
\[\text{Progress (i, j)}=D_{l}^{sink}-D_{j}^{sink}\] \[\text{CostProgress (i, j)}=\frac{f(j)}{Progress\left(i,j\right)}\]  
Figure 2: Hotspot area in hybrid routing scheme [54]. Where \(D_{i}^{stnk}\) is the Euclidean distance from node \(i\) to the sink, \(D_{j}^{stnk}\) is the Euclidean distance from node \(j\) to the sink, and \(f(j)\) is proportional to the inverse of node \(j\)'s remaining energy, which expresses the degree of reluctance to forward a packet to node \(j\). To calculate\(f(j)\), every node periodically broadcasts its energy level to its neighbors. Based on the values that it calculates, ERB-GR selects the next-hop node which has the smallest CostProgress (i, j) among all its neighbors.
## 5 Results
### Effectiveness Analysis of SAVOA Path Planning  
5.5.1. Effectiveness Analysis of Mountainous Terrain  
Figure 12 shows the average of the four-performance metrics for GA, WOA, ABC, SSA, AVOA, and SAVOA in the mountainous environments. In mountainous 1 scenario, for optimal fitness, SAVOA is 15.48%, 9.86%, 4.73%, 17.19%, and 4.68% less than GA, WOA, ABC, SSA, and AVOA, respectively; on average fitness was reduced by 15.30%, 11.35%, 5.40%, 17.68%, and 6.16%, respectively; for flight time, SAVOA decreased by 23.89%, 22.36%, 19.70%, 31.55%, and 19.56%, respectively; in terms of smoothness, SAVOA is 86.98%, 92.39%, 72.71%, 94.23%, and 68.01% lower than the other five algorithms.  
In the mountainous 2 scenario, the optimal fitness of SAVOA was reduced by 16.87%, 19.67%, 5.79%, 20.88%, and 6.39% compared to GA, WOA, ABC, SSA, and AVOA; the average fitness was reduced by 16.67%, 20.33%, 6.66%, 21.74%, and 8.36%; in terms of flight time, SAVOA was reduced by 32.41%, 39.27%, 19.15%, 38.33%, and 23.20%, respectively, while smoothness was reduced by 92.42%, 96.34%, 90.72%, 97.76%, and 89.07%.
## 3. Results and Discussion
### SEM Analysis of FPs  
The surface microstructure of FPs by different drying methods was observed under scanning electron microscope, as shown in Figure 3. There is a significant difference in the surface of the FPs after treatment with different drying methods. FPF(A) has a smooth surface with no bumps or aggregates. The surface of the FPFI(B) shows a few bumps and aggregates. The surfaces of both FPH(C) and FPV(D) appeared to be densely raised and aggregated, which caused varying degrees of damage to the surface structure of the polysaccharides.  
Figure 2: (**A**) FT-IR spectra of FPs, (**B**) X-ray diffraction curves of FPs.  
The surface microstructure of FPs is related to temperature, humidity and other conditions during the drying process. The FD process removes water from the material by sublimation under vacuum and low-temperature conditions [23]. The low-temperature environment gives the FPF a stable structure and a smooth surface without agglomeration. FID process through far infrared radiation, moisture evaporates rapidly in a short time [22]. The rapid discharge of evaporated moisture from the drying box was hindered, and a high-humidity vapor film was formed above the material, leading to the induction of a small amount of polysaccharide aggregation. HD exchanges moist heat with figs by strong convection of hot air [20]. Prolonged high temperatures cause severe shrinkage and hardening of the surface structure of FPH. For VD under vacuum conditions, the fig's own water evaporates and boils at the same time, and the accumulation of bubbles in the boiling process causes the polysaccharides to accumulate and agglomerate together. According to the SEM results, our inference was also confirmed that the increase in temperature and evaporation of water during the drying of figs easily led to the surface structure shrinkage and surface hardening of FPs, resulting in the phenomenon of heat accumulation. FD treatment could keep the microstructure of FPs stable.