esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Plant Material  
The tested materials were cultivated in the greenhouse of the Faculty of Biotechnology and Horticulture of the Agricultural University of Krakow. The seeds of three uncommon oilseed plants, nigella (_Nigella sativa_ L.), safflower (_Carthamus tinctorius_ L.), and camelina (_Camelina sativa_ L.), were collected from a horticultural industry. The seeds were sown thickly into seed boxes filled with ready-to-use, standard substrate TS 2, with universal use. TS2 growing medium is made of light peat and water-soluble fertilizer containing basic plant nutrients: 19% sulphur (SO\({}_{3}\)); 16% phosphorus (P\({}_{2}\)O\({}_{5}\)); 18% potassium (K\({}_{2}\)O); 14% nitrogen (which includes 5.5% N-NO\({}_{3}\) and 8.5% N-NH\({}_{4}\)); 0.8% magnesium (MgO); 0.03% boron (B); 0.12% copper; 0.09% iron (Fe); 0.16% manganese (Mn); 0.20% molybdenum (Mo); and 0.04% zinc (Zn). The growing medium had a pH level of 6. Temperature and humidity conditions in the greenhouse were 21 \({}^{\circ}\)C and 50-63% during the day and 18 \({}^{\circ}\)C and 78-88% at night.  
The plants of each species were collected at two harvesting periods. The first harvest was carried out when the plants had cotyledons and the first true leaf (BBCH (Biologische Bundesanstalt, Bundessortenamt, und CHemische Industrie) 11). Camelina and safflower were characterized by a similar development and reached subsequent phases at the same time. Nigella developed more slowly and entered the development phases approximately 12 days later. For safflower and camelina, the subsequent phases were reached after 11 days from sowing; for nigella, the subsequent phases were reached after 24 days. The second harvest took place when plants had 1-2 true leaves each (BBCH 12); for safflower and camelina, this was after 18 days from sowing; for nigella, this was after 30 days. The BBCH scale was calculated, as defined by [PERSON] et al. [23]. Photographs of young shoots are included in the Supplementary Materials. The above-ground parts of the young plants were cut by sterile scissors, washed under tap water, cut into very small pieces (1-2 cm wide), and combined to prepare the representative samples (at least four samples for each analysis carried out on fresh material). The other parts of the raw material were rinsed, dehydrated with filter paper, mechanically crushed, frozen at \(-\)22 \({}^{\circ}\)C, and finally lyophilized (Christ Alpha 1-4, Osterode am Harz, Germany). The material was then ground (Knifetec 1095 Sample Mill, Tecator, Hoganas, Sweden) to obtain a homogeneous sample with particles of the lowest diameter. The freeze-dried material was then analyzed for the basic composition and mineral content. The analyses were performed in triplicate.
# Temperature Dependence of Density and Viscosity of Biobutanol-Gasoline Blends
## 3 Results and Discussion
### Dynamic Viscosity  
Tables A3 and A4 show the mean values of the dynamic viscosity of the biobutanol-gasoline blends at the selected temperature range and concentration.  
It is evident that the dynamic viscosity of the blends decreases with an increasing temperature. Furthermore, the blends with a higher concentration of biobutanol reach a higher dynamic viscosity value and the RON 98 gasoline also has a lower dynamic viscosity value than the RON 95 gasoline, similar to the viscosity values of the bioethanol-gasoline blends [18; 32].
## 3 Optical Properties Measurement  
### Description of the Mesh Specim  
Table 1 shows five different mesh specimens for optical property measurements, classified by knitting pattern, OPI, and number of yarn ply. The knitting patterns include Atlas, Atlas-Atlas, Double-Atlas, and TUCH. Specifically, the knitting pattern for Case 3 is Double-Atlas, which refers to a fabric where either or both of the warp and weft threads overlap in two layers, unlike typical fabrics that consist of a single layer. Consequently, Double-Atlas exhibits greater durability than the Atlas pattern. Except for Case 5, all specimens are composed of a single yarn type. The OPI values of the specimens were set at 10, 12, 18, and 36.  
To assess the sensitivity of optical properties to initial tension and the changes in OPI due to tension direction, a special jig was designed. This jig enables measurements under tension. The specimens were prepared as detailed in Table 2. Using specimen 5 (12 OPI) from Table 1, the mesh was fixed to the jig with appropriate tension applied in the Wale, Course, and Wale and Course directions.
# Heterogeneous Distributed Big Data Clustering on Sparse Grids
## 7 Discussion and Future Work  
Sparse grid clustering, as implemented in the open source library SG++, is one the few clustering methods available for the clustering of large datasets on HPC machines. In contrast to other density estimation approaches, our approach based on sparse grids does not depend on assumptions about the underlying densities and which can treat correlated densities [30; 31]. In particular, it enables the detection of clusters with non-convex shapes and without a predetermined number of clusters; see [21] for the comparison to other clustering algorithms. Due to the sparse grid discretization of the underlying feature space, the grid or discretization points are chosen independently of the data points. This is key to the linear complexity with respect size of the data of all sparse-grid-related algorithms. This rare property is highly useful for addressing big data challenges. Note that there is little work on distributed clustering on large scale for big data scenarios, see the discussion in Section 1.  
With our optimized implementation, we have demonstrated performance portability across three hardware platforms. Due to the use of OpenCL, careful and highly tuned performance optimization, and algorithms that map very well to the capabilities of modern hardware platforms, we expect similar performance on related platforms. Our strong scaling experiments show that even on 128 nodes of Piz Daint, scalability is mainly limited by the available work per node.  
Our method achieves a significant fraction of the peak performance on all devices tested. This shows that OpenCL is a good choice for developing performance-portable software. Furthermore, our GPU results illustrate how the higher raw performance of GPUs in contrast to CPUs translates to similarly improved time-to-solution.  
As our next steps, we plan to further improve the performance of our approach by addressing two key issues: First, the \(k\)-nearest-neighbor graph creation currently uses an \(\mathcal{O}(m^{2})\) algorithm and thus represents the bottle-neck. We already have an early implementation of a GPU-enabled variant of the locality-sensitive hashing algorithm. The locality-sensitive hashing algorithm can calculate an approximate \(k\)-nearest-neighbor graph in sub-quadratic complexity [32]. Adopting this algorithm, sparse grid clustering can be performed in sub-quadratic complexity as well.  
Our implementation supports the use of spatially adaptive sparse grids [22; 30]. They enable the placement of grid points only where they significantly contribute to the overall solution. An adaptive approach will significantly increase the dimensionality of the datasets that can be clustered as it has been demonstrated for standard learning tasks before. Currently, creating an adaptively refined sparse grid is itself expensive as it requires the system of linear equations of the density estimation to be solved repeatedly after each refinement. Thus, a priori refinement strategies that create well-adapted sparse grids with less effort are another important direction of future research.
## 5 Challenges and Open Issues
### Technology Demands
#### 5.1.1 Access to the Metaverse  
At present, the metaverse is mostly accessed through display applications while the current AR/VR/XR sets are immature and heavy. Entering and interacting in the metaverse will require immersive equipment that is flexible and convenient. In addition, the existing interactivity technologies are still limited to reflect minimum body movements while the need is for more accurate on- and off-body interface peripherals (i.e., sensors and haptics) to capture user input and output feedback cues.
# Measurement of Circadian Effectiveness in Lighting for Office Applications
### Light Measurements
**(A)** Luminance imaging photometer LMK-5 color with fisheye lens (right) and CSS-45 radiometer sensor (left) mounted both to the same camera tripod; **(B)** alignment of the measurement setup for assessing the NIF impact of the illumination of a typical display workplace and its surroundings. Figure 3: Room layout and luminaire positions (yellow-shaded rectangles) of the 2 nd floor offices of the Laboratory of Lighting Technology at TU Darmstadt. Nine different workplaces were selected to perform the spatially resolved light measurements as proposed in this work. Workplaces #1–8 were measured on a cloudy day between 12:00 p.m. and 1:00 p.m., whereas workplace #9 was measured in the evening of the same day at 6:00 p.m.  
Figure 2: Geolocation (**left**) and image representation (**right**) of the institutional building. The various office workplaces serving as examples to evaluate the proposed methodology for spatially-resolved light measurements are all located on the 2 nd floor given by the uppermost row of windows shown on the right. To perform the corresponding light measurements, the luminance imaging photometer is positioned in such a way that the image acquisition concurs with the typical viewing range of an average sized person sitting at the office desk and being engaged in display/computer work. Thus, as can be seen from Figure 1B, the tripod with the measurement devices is placed centrally in front of the office desk and adjusted to a height of 120 cm, which, according to DIN EN 12464-1 [99], corresponds to the average viewing height of a seated person. The lens aperture of the imaging photometer and the light diffuser of the radiometric sensor are aligned to the front edge of the office desk, both pointing perpendicularly towards the plane of the workplace's computer monitor so that the corresponding tilt angle \(\alpha\) of the tripod initially equals zero. In a second step, the impact of a non-zero tilt angle on the reported measurement results will be explored and compared to those obtained for the initial \(\alpha=0^{\circ}\) setting (see Section 3). In all cases, the average \(\pm\) standard deviation (SD) distance between the measurement devices and the respective computer monitor is 75 \(\pm\) 2 cm.
## 3 Results
### Site-Specific Molecular Docking  
Our analysis discovered that the four picked phytocompounds, together with the control drug, successfully collaborate alongside the target protein ER\(\alpha\) (estrogen receptor-\(\alpha\)) (PDB ID: 3 ERT), employing a molecular attaching approach executed via Maestro (Schrodinger Release 2021-2: Maestro, Schrodinger, LLC, New York, NY, USA, 2020-3). In contrast with the control drug olaparib (CID-23725625), the privilege of chosen biological activity substances 2,3-oxidosqualene (CID-5366020), 5,8,11-eicosatriynoic acid, trimethylsilyl ester (CID-91696396), and 1-monolinolein (CID-250006068) all demonstrated greater connecting affinities of \(-8.6\) kcal/mol, \(-8.2\) kcal/mol, and \(-8.2\) kcal/mol. Nevertheless, contrasted against the chosen reference drug, this molecule 2, 4-di-tert-butylphenol (CID-7311) showed a binding ability of \(-7.8\) kcal/mol (Table 4).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Molecules (Chair)** & \(\varepsilon\mathbf{HOMO}\) & \(\varepsilon\mathbf{LUMO}\) & **Gap** & **H (Hardness, Gap/2)** & **S (Softness, 1/Hardness)** \\ \hline
23725625 (Control) & \(-0.207\) & \(-0.002\) & \(0.205\) & \(0.102\) & \(9.803\) \\
7311 & \(-0.208\) & \(-0.005\) & \(0.203\) & \(0.101\) & \(9.900\) \\
5366020 & \(-0.215\) & \(-0.002\) & \(0.213\) & \(0.106\) & \(9.433\) \\
91696396 & \(-0.239\) & \(-0.008\) & \(0.231\) & \(0.115\) & \(8.695\) \\
250006068 & \(-0.314\) & \(-0.005\) & \(0.309\) & \(0.154\) & \(6.493\) \\ \hline \end{tabular}
\end{table}
Table 3: HOMO-LUMO, softness and hardness studies of the compounds. Higher occupied molecular orbital (\(\varepsilon\)HOMO); lower unoccupied molecular orbital (\(\varepsilon\)LUMO); difference between HOMO and LUMO (Gap); hardness of the compound (H); softness of the compound (S).
# Infrared and Visible Image Fusion: Methods, Datasets, Applications, and Prospects
## 2 Infrared and Visible Image Fusion Methods
### Methods Based on Multi-Scale Transformation
#### 2.1.1 Methods Based on Pyramid Transform
The contrast pyramid is a set of images formed by the difference between a series of Gaussian-filtered images and their corresponding low-frequency component images. The amount of information in detailed images increases with pixel size, with the majority of that information corresponding to the edges and area boundaries of the image. Therefore, the pixel value with a high absolute value should be chosen as the pixel value of the fused image on the basis of decomposing the source image into the Laplace pyramid representation. A contrast pyramid-based image fusion technique was introduced by [PERSON] [8] et al. in 1989 in light of the fact that the human visual system is sensitive to local contrast and other visual properties. The contrast1 pyramid is built similarly to the Laplacian pyramid, but each of its layers of images represents the ratio between the two levels that are next to each other in the image of the Gaussian pyramid. * Filter-Subtract-Extract pyramid (FSD) The Laplacian pyramid fusion approach and the filter-subtract-extract method are conceptually equivalent. Their primary distinction is in the procedures used to obtain the many photos needed to build the pyramid. In the Laplace pyramid, the differential image LK of level K is obtained by subtracting from the Gaussian image GK of level K the image that is up-sampled from level K+1 and then low-pass filtered, while in the FSD pyramid, this differential image can be obtained by directly subtracting from the Gaussian image GK of level K the low-pass filtered image of GK. Therefore, the FSD pyramid fusion method is algorithmically more effective than the Laplacian pyramid method since it skips the up-sampling stage. * Morphological pyramid By applying a morphological filter to each layer of the Gaussian pyramid and then determining the difference between the two adjacent levels, one can create the morphological pyramid [7]. Morphological filters are frequently employed to reduce noise and smooth the image, simulating the effects of low-pass filters, but they do not alter the position or shape of the objects in the image. Therefore, fusion using the Laplace pyramid method and fusion using morphological pyramids are essentially equivalent. The only difference is that morphological pyramids are used in place of Laplace pyramids.
# A Thorough Investigation of the Dynamic Properties of Granite under Cyclic Loading
## 5 Dynamic Response Model  
### Model Establishment  
The Duncan-Chang model [59], known for its capability to accurately describe the stress-strain behavior of rock, has been extensively utilized by researchers [60, 61] to analyze the variations in rock stress-strain. Although originally proposed for static loading conditions, we applied the Duncan-Chang model in this study, considering the frequency range of 1-20 Hz used in our tests, which can be considered as quasi-static conditions. As such, we have opted for the Duncan-Chang model to effectively characterize the relationship between dynamic stress and dynamic strain amplitudes of the specimens.  
The relationship between the dynamic stress and dynamic strain amplitude of the specimens is as follows:  
\[\sigma_{\mathrm{d}}=\frac{\Delta\varepsilon_{\mathrm{d}}}{a+b\Delta\varepsilon_ {\mathrm{d}}} \tag{3}\]  
where \(\sigma_{\mathrm{d}}\) is dynamic stress level, \(\Delta\varepsilon_{\mathrm{d}}\) is dynamic strain amplitude, and \(a\) and \(b\) are test parameters.  
Figure 17 depicts the relationship between the dynamic stress and dynamic strain amplitude of the specimens at various loading frequencies. A notable observation is the excellent fit of the curve, implying that the model accurately reflects the relationship between the dynamic stress and dynamic strain amplitude of the specimens.  
To obtain the significance of the model parameters \(a\) and \(b\), we transform Equation (3) into the following relationship:  
\[\frac{\Delta\varepsilon_{\mathrm{d}}}{\sigma_{\mathrm{d}}}=a+b\Delta \varepsilon_{\mathrm{d}} \tag{4}\]  
where \(\sigma_{\mathrm{d}}\) is dynamic stress level, \(\Delta\varepsilon_{\mathrm{d}}\) is dynamic strain amplitude, and \(a\) and \(b\) are test parameters.  
In Figure 18, the relationship between the ratio of dynamic strain amplitude to dynamic stress and dynamic strain amplitude is displayed. However, it can be observed that Equation (4) inadequately captures the variations of the material property parameters of the specimens at lower frequencies. Consequently, we postulated that the relationship between the ratio of dynamic strain amplitude to dynamic stress and dynamic strain  
Figure 16: Dynamic damping ratio of specimens: with number of cycles (**a**) confining pressure; (**b**) loading frequency.  
amplitude may better conform to a quadratic polynomial curve. To address this, we enhanced the Duncan-Chang model with the following modified formula:  
\[\sigma_{\text{d}}=\frac{\Delta\varepsilon_{\text{d}}}{a+b\Delta\varepsilon_{ \text{d}}+c\Delta\varepsilon_{\text{d}}^{2}} \tag{5}\]  
\[\frac{\Delta\varepsilon_{\text{d}}}{\sigma_{\text{d}}}=a+b\Delta\varepsilon_{ \text{d}}+c\Delta\varepsilon_{\text{d}}^{2} \tag{6}\]  
where \(\sigma_{\text{d}}\) is dynamic stress level, \(\Delta\varepsilon_{\text{d}}\) is dynamic strain amplitude, and \(a\), \(b\), and \(c\) are test parameters.
# Efficient Multipath Clutter Cancellation for UAV Monitoring Using DAB Satellite-Based PBR
## 4 Performance Analysis
### Computational Cost
As can be seen, precompression can reduce the Flops by about five times. Since the range of \(B\) is far from reaching the boundary in (24), the average Flops of all ECA versions monotonically decreases in the figure, except for ECA-S with a notable increasing total Flops. Besides the Flops, precompression can significantly save memory usage when \(B\ll 2N/\left(2L+K\right)\), bringing about better cache efficiency, which is much more critical for modern processors with high frequency [39]. The above benefit is tenable in the focused scenarios, as the low RCS of UAV results in a long accumulation time and the effective monitoring area is relatively small. In other words, the processing parameters include large \(N\), and small \(K\) and \(L\), which guarantees efficiency improvement under the complete segmentation needed in ECA-C/LF-PC. Although cache efficiency is difficult to evaluate before implementation, (24) is still a rough criteria for algorithm selection. \begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Flops** \\ \hline ECA & \(4 NK(K+3)+\frac{4K^{3}+30K^{2}-10K}{3}\) \\ ECA-B & \(4 NK(K+3)+B\frac{4K^{3}+30K^{2}-10K}{3}\) \\ ECA-S & \(4 BN_{s}K(K+3)+B\frac{4K^{3}+50K^{2}-10K}{3}\) \\ ECA-ES & \(4 NK(K+3)+B\frac{4K^{3}+30K^{2}-4K}{3}\) \\ ECA-C/LF & \(4 NK(K+3)+B\frac{4K^{3}+30K^{2}+8(38-2)K}{3}\) \\ ECA-C/LF-PC & \(8N(K+L)+B\frac{8K^{3}+21K^{2}+4(8-5)K-6L}{3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Flops of different ECA versions within the evaluation stage.
## 6 Route Discovery by TW
### Operations in TW  
The TW algorithm starts from user \(i\) and searches the network as depicted in Figure 4. Suppose the TW algorithm finds users that are not in his territory and \((k-1)\)-hop away from user \(i\). Among these users, we assume that user \(j\) is \(k\)-hop away from user \(i\), and among these users, \(m\) are directly connected to \(j\). We label these \(m\) users as \(s_{1},s_{2},\cdots,s_{m}\).  
When the TW algorithm moves from the previous level to the next level, i.e., from the \((k-1)\)th level to the \(k\)th level, it updates the corresponding opinion of the user \(i\)'s on user \(j\) as follows:  
\[\begin{split}&\Omega_{ij}^{k}=\Theta\left(\triangle(\Omega_{is_ {1}}^{(K-1)},w_{s_{1j}}),...,\triangle(\Omega_{is_{m}}^{(K-1)},w_{s_{m}j}) \right)\\ &\Omega_{ij}^{k-1}=\Theta\left(\triangle(\Omega_{is_{1}}^{(K-2)}, w_{s_{1j}}),...,\triangle(\Omega_{is_{m}}^{(K-2)},w_{s_{m}j})\right),\end{split} \tag{10}\]  
The Equation (10) combines all \(m\) opinions that are computed as discounting \(w_{s_{1j}}\) by \(\Omega_{is_{i}}\), for all \(l=1,\ 2,\cdots,\ m\). If \(\Omega_{ij}^{k}\
eq u\), i.e., the TW algorithm already had user \(i\)'s opinion on user \(j\) from the previous interaction. In this case, user \(i\)'s opinion on user \(j\) is replaced with \(\Omega_{ij}^{k}\). In other words, only the opinions \(\Omega_{is}^{k-1}\) where \(w_{sj}\
eq u\) are used in individual opinion vector updating. Finally, the TW operation multiplies matrix and individual vector \(y_{i}^{k-1}\) to yield an updated vector \(\mathbf{y}_{i}^{k}\) as follows:  
\[\mathbf{y}_{i}^{k} = OM^{T}\mathbf{y}_{i}^{(k-1)} \tag{11}\] \[= \begin{bmatrix}\Theta\left(\triangle(\Omega_{i1}^{k-1},w_{11}), &\cdots,&\triangle(\Omega_{in}^{k-1},w_{n1})\right)\\ \Theta\left(\triangle(\Omega_{i1}^{k-1},w_{12}),&\cdots,&\triangle(\Omega_{ in}^{k-1},w_{n2})\right)\\ &\vdots&\\ \Theta\left(\triangle(\Omega_{i1}^{k-1},w_{1n}),&\cdots,&\triangle(\Omega_{ in}^{k-1},w_{m})\right)\end{bmatrix}\] (12) \[= [\Omega_{i1}^{k},\ \Omega_{i2}^{k},\cdots,\ \Omega_{ij}^{k},\ \Omega_{ in}^{k}]^{T}. \tag{13}\]In a matrix-vector multiplication, we use the operations defined in 3 VSL. Let us look at element \(\Omega_{ij}^{k}\) in \(\mathbf{y}_{i}^{k}\), where \(i\
eq j\). It is computed by multiplying vectors \([\Omega_{i1}^{k-1},\Omega_{i2}^{k-1},...,\Omega_{in}^{k-1}]^{T}\) and \([w_{1j},w_{2j},...,w_{nj}]\).
## 5 Conclusions and Future Work  
This paper proposed a sensor fusion framework for indoor localization using Wi-Fi RSSI signals and smartphone sensors. In the proposed sensor fusion framework model, we used a PDR algorithm which reduces the smartphone sensor errors and gives accurate position results for indoor localization. In the case of Wi-Fi localization systems, we proposed a Wi-Fi fusion algorithm for localization that achieves better performance than conventional Wi-Fi localization systems. To combine the position results from PDR and Wi-Fi systems, we proposed a sensor fusion framework model that shows better indoor localization accuracy than conventional Wi-Fi localization systems. The results from the rectangular motion experiment shows that the proposed sensor fusion framework has a maximum of 1.17 m error when compared to the predefined path. In the case of linear motion, the results show that the proposed sensor fusion framework has a maximum of 0.44 m errors when compared to predefined path. From the experiments and results, the proposed sensor fusion framework showed reasonable localization accuracy for indoor localization with fewer IMU sensor errors and solved the Wi-Fi RSSI signal fluctuation problems. However, in the future, we will mainly focus on multiple pedestrian localization. We will carry out experiments for multiple users in indoor scenarios.  
Writing--original draft, A.P. and J.K.; Writing--review & editing, D.S.H.  
This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (2016-0-00564, Development of Intelligent Interaction Technology Based on Context Awareness and Human Intention Understanding).  
The authors declare no conflict of interest.