esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Evaluation of IJA
### Performance Evaluation  
We first varied the join selectivity and the synopsis selectivity for synopsis strategy. Join selectivity \(\delta\) is defined as \(|R\ \infty\ S|\ /\ (|R|\ \times|S|)\). The join attribute values are uniformly distributed within the domain of the attribute.  
Figure 3 shows the total communication cost for different join selectivities while keeping the memory capacity and synopsis size fixed at 250 \(\times\) 40 bytes and 10 bytes respectively. As shown in the Figure, na we join performs worse than all others due to the high cost of routing \(S\) in region S to all nodes in \(\mathrm{N_{R}}\). In addition, sequential join performs worse than centroid join and synopsis as well. Therefore we exclude them from when join selectivity is greater than 0.01. Synopsis strategy is lower than others and outperforms because non-candidate pairs can be determined in the synopsis join state, and only a small portion of data are transmitted during the final join. However, IJA performs than all  
algorithms though not to be shown in the Figure 3. Therefore Figure 4(a) shows the comparison synopsis algorithm to incremental join algorithm.  
Figure 4: Comparison IJA to synopsis algorithm. **(a)** Comparison synopsis with IJA. **(b)** Magnification with join selectivity \(<=0.02\).  
Figure 3: Impact of selectivity.  
Figure 4(b) shows the enlargement of lower selectivity than 0.02 in the axis of Figure 4(a). For that case, synopsis has lower communication cost than IJA. This is because synopsis strategy has both join selectivity and synopsis selectivity parameters which have a strong effect on communication cost. For the experiment, the rate for synopsis in this case is fixed with 0.01. In case of synopsis rate variation, it is shown in Figure 5.  
We can see the result that the more replicated data are existed, the more the synopsis algorithm is efficient. In spite of that fact, the IJA is more efficient than synopsis methodology under 60% of replicated data. In the environment for above 60% of replicated data, it is unrealistic case. Therefore, the suggested IJA is appropriate for the algorithm in sensor network environment to integrate data.
# Power Efficient Random Access for Massive NB-IoT Connectivity
## 6 Conclusions  
Narrowband Internet of Things (NB-IoT) is expected to support a massive number of devices over a wider area of coverage. In NB-IoT, the concept of coverage enhancement (CE) level is included to support devices that operate at different path losses. CE levels are characterized by independent configurations for signal repetitions to achieve good signal quality at the desired coverage. The interaction among CE levels complicates the random access procedure (RAP) in NB-IoT. The RAP is expected to become even more challenging since NB-IoT devices would experience frequent collisions due to simultaneous access by an enormous number of devices. Enhancing power saving of the devices while performing RAP in such an environment is an important issue that is addressed in this article. We present power efficient RAP for reducing power consumption of the NB-IoT devices while they perform RAP in the congested environment where each RAP (re)attempt is configured with several repetitions. The proposed novel model avoids the indiscriminate power rampings and unnecessary CE level transitions. Analytical results of power saving gain are compared with the existing RAP and are also validated through simulation studies.  
Conceptualization, M.A., M.K.M. and H.J.; Methodology, M.A., M.K.M. and H.J.; Software, M.A., M.K.M. and H.J.; Validation, M.A., M.K.M. and H.J.; Formal Analysis, H.J. and M.A.; Investigation, M.A., H.J. and M.K.M.; Writing--Original Draft Preparation, M.A.; Writing--Review & Editing, M.A., M.K.M. and H.J.; Project Administration, H.J.; Funding Acquisition, H.J. This work was supported by the NRF grant funded by the Korea government (MSIT) (NRF-2018R1C1B6008126 and NRF-2017K1A3A1A19071179).  
The authors declare no conflict of interest.
## 5 Results and Discussion
### The Modelling Approximations Impact on the Cask Criticality  
One of the main modelling approximations here was the data averaging of 25 DYN3D nodes to six layers (Figure 5) within the fuel assembly. A series of calculations with a 25-layer FA model was performed to evaluate this approximation effect on the GBC-68 storage cask criticality. The criticality calculations were conducted for L68 and L5 NL loads of the least optimal burnup distribution SP_C with BWR GE14 fuel assembly with one-year cooling time. The six-layer model underestimated cask k\({}_{\text{eff}}\) by approximately 3800 pcm or 2.6% compared to 25-layer in case of L68 load and overestimated it by 4800 pcm or 3.6% in case of L5 NL load. These values give a good approximation of the expected uncertainty from reducing the number of the layer. Thus, the estimated burnup credit value for six-layer model can be higher for L68 load and lower for L5 NL. Overall, the current research focuses on creating a general understanding of the benefit from the BUC method using a best estimate approach. Thus, this level of discrepancy was considered as acceptable.  
Recent research [15; 16] developed and utilised for the burnup credit analysis a detailed 3D model of the BWR GE14 fuel assembly to reflect on various operational difficulties of the BWRs. Although the current research is also focused on developing a realistic BWR FA model, there are substantial differences between the approaches outlined in this investigation and those from studies [15; 16]. For example, the authors of the above study performed depletion calculations using only one coolant density profile. However, the given study used the data obtained from the full core analysis and, thus, considered a more realistic FA behaviour in the reactor core. Another difference is that the BUC method in the research [15; 16] was applied to the fuel discharged at the peak reactivity while, in this study, realistic discharged burnups were considered. Another recent study [14] investigated the effect of the variation of the several operating parameters (compound effect) on the burnup credit using the BWR GE14 fuel assembly design. The study included a wide range of assumptions and simplifications regarding the fuel assembly design and operating parameters, such as lack of part-length rods or gadolinium rods.  
In general, the current modelling approach provides a much more realistic model of the fuel assembly during the operation compared to the studies in Refs. [14; 15; 16] since it uses more realistic axial coolant density distributions and burnup profiles delivered by the thermal-hydraulics module of the coupled core simulation.
# An Efficient System for Automatic Blood Type Determination Based on Image Matching Techniques
## 3 Overview of Image Matching Techniques
### Orb
Consider an image patch around a keypoint \(p\); then, the binary test \(\tau\) is defined in Equation (18):  
\[\tau(p;x,y)= \begin{cases}1:&p(x)<p(y)\\ 0:&p(x)>=p(y)\end{cases}  \tag{18}\]  
\(p(x)\) and \(p(y)\) are the intensity values at positions \(x\) and \(y\). The feature is defined as a vector of \(n\) binary tests:  
\[f(n)=\sum_{1<i<n}2^{i-1}\tau(p;x_{i},y_{i}). \tag{19}\]  
The BRIEF algorithm begins analysis by filtering an image patch around the key point with a Gaussian kernel in order to prevent the descriptor from being sensitive to noise. The sensitivity to noise can be reduced significantly. The filtering step increases the stability and repeatability of the descriptors [36]. Rotation-aware BRIEF (rBRIEF) is a modified version of BRIEF that takes the orientation of the keypoints into account. For any feature set of \(n\) binary tests at location \((x_{i},y_{i})\), we need the \(2*n\) matrix where \(n=256\):  
\[S= \begin{pmatrix}X_{1},.......X_{n}\\ Y_{1},.......Y_{n}\end{pmatrix} . \tag{20}\]  
ORB defines the rotation matrix with orientation \(\theta\) of feature point [25] to construct a steered version \(S_{\theta}\) of \(S\):  
\[S_{\theta}=R_{\theta}S \tag{21}\]  
where  
\[R_{\theta}= \begin{bmatrix}cos\theta&sin\theta\\ -sin\theta&cos\theta\end{bmatrix} . \tag{22}\]  
**Step 6: Keypoints matching using Hamming distance. ** The matching between images is computed using the Hamming distance. The Hamming distance for each keypoint descriptor and the descriptor in the database is computed. The Hamming distance between two descriptors \(S_{1}\),\(S_{2}\) is computed using Equation (23) [37]:  
\[D(S_{1},S_{2})=\sum_{i=0}^{255}x_{i}\oplus y_{i} \tag{23}\]  
The descriptors \(S_{1}\) and \(S_{2}\) are 256 vectors defined by \(S_{1}=x_{0},x_{1}.....x_{255}\) and \(S_{2}=y_{0}\), \(y_{1}.....y_{255}\). The matching is decided based on the minimum Hamming distance. To discard features that do not have any good match to the database, the ratio of distances between the best and second best match must be lower than a threshold. Rejecting all matches where the distance ratio is greater than the threshold should eliminate most of the false matches.
## 3 Results and Discussion
### Physical Characterization of Formulations  
The organoleptic properties of formulations were evaluated. Both microemulsions were liquid and formed spontaneously due to the high amount of surfactant. Unloaded microemulsion (Unl-ME) was clear and transparent, while the inclusion of LEE in the ME originated a clear formulation with a yellowish color (Figure 4A). The droplet size and  
Figure 3: SFE extraction kinetics of oil from tomato waste at 500 bar and 60 \({}^{\circ}\)C. S/F: mass ratio of solvent to feed.  
polyldispersity index (PdI) of both microemulsions were also evaluated: Unl-ME presented a droplet size of 5.5 \(\pm\) 0.1 nm and a PdI of 0.15 \(\pm\) 0.01. The incorporation of LEE into the microemulsions enabled the formation of LEE-loaded microemulsions (LEE-ME) with a droplet size and a PdI of 8.4 \(\pm\) 0.4 nm and 0.45 \(\pm\) 0.06, respectively. The presence of droplet size smaller than 100 nm is a described characteristic of microemulsions [28]. The incorporation of LEE did not change the ME droplet distribution. On the other hand, macroemulsions displayed a homogeneous semi-solid aspect. Unloaded macroemulsions (Unl-Macro) were white, while LEE-loaded macroemulsions (LEE-Macro) presented a yellowish color due to the presence of LEE (Figure 4A). The presence of LEE did not change the droplet size distribution and it was observed that 90% of the droplets of both macroemulsions displayed a particle size higher than 1.4 um (d(10), Table S1)). Therefore, these formulations were considered coarse systems, since the internal droplet size is higher than 500 nm [28]. It was also observed that Unl-Macro and LEE-Macro presented shear thinning behavior (Figure 4B), since the viscosity decreased with the increase of the shear rate [29], as well as a similar elastic (G') and the viscous (G\") modulus (Figure 4C). Thus, the presence of LEE did not influence the rheological properties of these macroemulsions.
# Accumulative Roll Bonding--A Review
## 3 Parameters
### Materials Parameters  
Material (i.e., type and extent) is likely the most important parameter in ARB. Sheet materials considered in ARB can be divided into three different categories: (i) similar materials, (ii) dissimilar materials and, (iii) similar/dissimilar materials with reinforcing agent. Based on Google Scholar, Figure 2 represents the number of documents published in the period of 1999-2018 where it is evident that early researches were almost all devoted to the ARB of similar materials. During the interval of2007-2010, a number of researchers used the ARB for bonding similar/dissimilar sheet composites. The trend continued from 2011 to 2014, with a reduction in the bonding of similar materials and an increase in the bonding of dissimilar materials with/without the addition of reinforcements. In recent years, the number of studies devoted to ARB has grown significantly, and despite the increased attention to the bonding of dissimilar materials, attention to the bonding of similar materials has not diminished. Therefore, in the coming years, researches in this field seem to be mostly concentrated on those dissimilar bonding or the reinforced materials.  
To date, \(\sim\)50% of the experiments carried out on the ARB of similar materials have been dedicated to Al alloys; other materials in this category are steel, copper, titanium, magnesium alloys and few researches have been made on miscellaneous metals such as Mo, Zr and Ni alloys. For bonding those reinforced similar materials, the use of Al/Al alloy sheets reaches over 80%. To the best of our knowledge, one of the sheet materials is always Al or its alloys in more than 70% of ARBed dissimilar bonding process.
## 2 Problem Formulation and Modeling
### Grid Model and State Space  
In this paper, towards the aim of discretization, the common airspace is divided into cubic cells called the _grid model_. The reason we use cubic cells rather than other shapes is that it is simpler to locate the position and calculate the distance between UAVs.  
This is not the first use of the grid model in this field. In [30], a trajectory is parametrized by the number of cells that the UAV passes through with entrance and departure time. The size of cells is a parameter. Collision is defined as being when two UAVs lay in the same cell at the same time. [PERSON] et al. improved the collision model, considering the UAVs in the neighboring cells [31]. These models set the size of cubic cells as a large value, which is much greater than the radius of the protected area of the UAVs. However, waypoints of each UAV are recorded roughly in this way. In this paper, we consider a small value as the size of cubic cells, which makes the results more accurate. Then, the waypoints of UAVs can be represented as points in the coordinate.  
The top view of the encoded airspace is presented in Figure 2, and a 2D example of state space is illustrated. State space describes all the possible future states in the next intervals. When a UAV flies horizontally, the degree it can turn is limited in the fixed interval. In this paper, we set the degree as no more than 45 degrees. Therefore, on the right side of Figure 2, red points are not allowed for UAVs to reach, while only green points are possible waypoints in the next interval for UAVs. Thus, in 2D airspace, one UAV has three choices for the next move, and \(N\) UAVs would have \(3^{N}\) viable states in the next interval, which has formed a state space. In this paper, 3D state space of multiple UAVs is considered, which is much more complicated than the 2D state space.  
Figure 1: A collision case. UAV: unmanned aerial vehicle.
## 3 Research Methodology
### Inclusion and Exclusion Criteria  
We established clear inclusion and exclusion criteria to maintain the rigor and focus of this review. Only original research papers published in English in peer-reviewed journals or conference proceedings were included. To avoid redundancy and potential bias, review papers and meta-analyses were excluded, as they summarize primary research rather than presenting original findings. Similarly, short communications were excluded due to their limited depth and scope. To capture the full spectrum of advancements in AI, IoT, and sensor-based technology, no restrictions were applied regarding publication dates. Each study also had to explicitly focus on the application of AI, IoT, or sensor-based technologies within autism research, forming the basis of our study eligibility criteria.
## 3 Results
### Evaluation of Improved Models
#### 3.2.2 Improvements for Grass-Type Sites  
For the TL-LUE model, the improvement solutions by incorporating SM showed different performances at various sites. For the grass-type, the simulations at most sites showed improvements compared to those by the original model, except for US-Goo (\(\Delta\)R\({}^{2}\) = \(-\)0.01-0, \(\Delta\)RMSE = 0\(\rightarrow\)0.02 gCm\({}^{-2}\) day\({}^{-1}\)). The most significant improvement was observed at US-SRG (\(\Delta\)R\({}^{2}\) = 0.21, \(\Delta\)RMSE = \(-\)0.29 gCm\({}^{-2}\) day\({}^{-1}\)), while at the other sites, the \(\Delta\)R\({}^{2}\) of the improvement ranged from 0.05 to 0.2. A comparison of the different improvement schemes revealed that eight of the nine sites with significant improvements showed that Scheme 1 was the optimal scheme (except for US-AR1, where the optimal scheme was Scheme 2), i.e., it had the highest R\({}^{2}\) and the lowest RMSE.  
For VPM, we conducted similar analysis at fifteen other sites (Figure 9 and Figures S1-S15). The simulations by incorporating SM performed differently at various sites. For the grass-type, 8 of the 10 sites showed an improvement over the original scheme (\(\Delta\)R\({}^{2}\) = 0.02 to 0.32, \(\Delta\)RMSE = \(-\)0.02 to \(-\)0.93 gCm\({}^{-2}\) day\({}^{-1}\)), with the most pronounced improvement at the US-Var site (\(\Delta\)R\({}^{2}\) = 0.32, \(\Delta\)RMSE = 0.93 gCm\({}^{-2}\) day\({}^{-1}\)). Among the eight sites with improvement, seven indicated that Scheme 2 was the best scheme, except for US-AR1 (the most significant improvement was in Scheme 3).  
Figure 8: Performance comparison of GPP estimation with different schemes for incorporating SM information by TL-LUE model for drought years across different sites (The yellow boxes in the figure highlight the minimum R\({}^{2}\) or maximum RMSE at specific sites).  
Figure 9: Performance comparison of GPP estimation with different schemes for incorporating SM information by VPM for drought years across different sites. (The yellow boxes in the figure highlight the minimum R\({}^{2}\) or maximum RMSE at specific site).
# A Physical-Layer Watermarking Scheme Based on 5G NR
## 2 Watermark System Model on 5G NR
### Basic Watermarking Scheme
#### 2.1.2 Watermark Embedding Scheme  
In the uplink of 5G system, the multiple access mode is SC-FDMA. In the protocol, the base sequence of DMRS is modulated by quadrature phase shift keying (QPSK) as follows.  
\[x(n)=e^{j\varphi(n)\pi/4} \tag{1}\]  
where \(n\) is the number of the subcarrier, \(\varphi(n)\) determines the phase of the \(n\)th subcarrier, and its values are \(\pm 1\) and \(\pm 3\). \(x(n)\) is the base sequence of DMRS, which is one of the 30 groups specified in the protocol. When embedding the watermark, the QPSK signal can be rotated clockwise or counterclockwise according to the watermark bit--that is, the watermark signal is embedded into the phase of DMRS:  
\[y(n)=x(n)e^{j\alpha n(n)}=e^{j(\varphi(n)\pi/4+\alpha w(n))} \tag{2}\]  
where \(e^{j\alpha n(n)}\) is the embedded watermark signal, \(a\) is the embedded strength, \(w(n)\) is the watermark bit embedded in the nth subcarrier, and its value is \(\pm 1\). \(y(n)\) is the base sequence of DMRS after embedding the watermark. The constellations are shown in Figure 3.  
In Figure 3, blue circles indicate the symbols of DMRS specified by the protocol, which are the standard QPSK symbols. Green circles indicate the symbols after embedding the bit 0 of the watermark, and rotating them clockwise by a radian. Red circles indicate the symbols after embedding bit 1 of the watermark, and rotating them counterclockwise by a radian. The watermark bit is embedded through different rotation directions.
## 4 Conclusions  
The article proposes a method that enables an assessment of any given navigation positioning system in terms of its compliance (or non-compliance) with the minimum accuracy requirements specified for hydrographic surveys. The unique feature of the model is the possibility of carrying out an assessment based on actual measurements and analyses of a positioning system's operating and failure times. In the example calculations, it was assumed that the distributions of operating and failure times are exponential (in accordance with Equations (13)-(18)). However, where sufficient series of measurements of the analyzed system are available, it is possible to determine through measurements the actual statistical distribution parameters and their distribution functions while enabling the application of Equation (11), which describes the system's positioning process, in statistical terms, in more detail. The presented model was applied to three different selected GNSS solutions, i.e.,  
Figure 7: The availability of the DGPS and EGNOS systems, as well as multi-GNSS receivers, for the IHO special order (**a**) and for the order 1a/1b (**b**).  
DGPS and EGNOS systems, as well as multi-GNSS receivers. Based on analyses using very extensive and real measurement data, it has been shown that the proposed model can be successfully used in practice.  
It should be noted that the proposed method is not limited only to autonomous and augmentation GNSS systems. It can be successfully applied to other positioning systems that require the indication of the range of their use in various navigation types [44; 45].  
The proposed method can also be applied outside of hydrography. With minor modifications, it can be successfully applied to all positioning applications listed in Figure 1.  
This research has been financed from the science budget for 2016-2020, as a research project within the _\"Diancentowy Grant\"_ Program No. DI2015 008545.  
The author declares no conflict of interest.
## 1 Introduction  
In recent years, science and technology have revolutionized our way of life, improving well-being and comfort for all mankind. The discovery of new materials with unique features at macro- and nano-scales has played a significant part in this advancement. The possibility of producing materials able to perform different functions and of responding to external stimuli will undoubtedly be an extremely important research area for the foreseeable future.  
There is increasingly intensive research for energy storage technologies development due to the enhanced energy needs of the contemporary societies. Global energy consumption, together with greenhouse gasses and CO\({}_{2}\) emissions, is increasing at a very fast pace due to rapid global economic growth, global population growth, and the human dependence on energy-consuming appliances. This increment in global energy consumption has specific environmental implications that pose serious challenges to the environment and human health. Moreover, the enhanced global energy consumption results in the reduction in the availability of traditional energy resources, such as coal, oil, and natural gas. Therefore, there is an urgent need for new systems development based on the conversion and storage of sustainable and clean energy.  
An effective way for the energy systems efficiency increment is the use of latent heat thermal energy storage because of its high energy density in comparison to sensible heat storage systems. Phase change materials (PCMs) are one of the key components for the development of advanced sustainable solutions in renewable energy and engineering systems. PCMs enable either the storage or release of large amounts of energy, while their temperature is slightly changed or kept constant. PCMs have the ability to accumulate and store lots of energy. The activation of this high storage potential of PCMs is accomplished when their phase is changed.  
In order to update the field of renewable energy and engineering systems with the use of PCMs, a Special Issue entitled \"Phase Change Materials: Design and Applications\" has been introduced. This editorial manuscript gathers and reviews the collection of ten contributions (nine articles and one review), with authors from Europe, Asia, and America accepted for publication in the aforementioned Special Issue of _Applied Sciences_.