esa-sceva/satcom-chunk-collection huggingface dataset:
# Designing Pedagogically Effective Haptic Systems for Learning: A Review
## 3 Haptics Applications for Learning
### Additional Applications in Training  
Outside of the traditional learning environment, haptic augmentation can provide substantial benefits in the form of skill acquisition and physical training. A recent study conducted by [PERSON] et al. [58] explored the ability for an electrical muscle stimulation (EMS) haptic device to speed up a user's response. [PERSON] et al. found that the users' reaction time significantly decreased not only while wearing the haptic device which elicited a response, but the faster response time persisted even when the haptic device was removed. This has substantial implications for many fields, such as sports or professional gaming, that require highly precise timing and quick reactions. Haptic intervention has also been found to lead to robust training benefits for sports training via real-time methods of physical feedback, such as posture correction and movement coordination. [PERSON] et al. [59] investigated a haptic system to test a rowing machine's ability to instruct the learning of coordinated movement (e.g., whether during a stroke their back was too early or late in the \"pull phase\"). Three experimental groups were created. Group I received direct real-time positional feedback via vibrators located on the knees and back. These simultaneously vibrated when a certain correct angle was met between the back and knees. Group II received visual positional feedback where circles on a screen corresponded to their posture. Group III received feedback similar to traditional coaching, which was provided once every 25 strokes. Each subject had eight sessions in one day (pre-test, six trainings, and a post-test). While there were not significant differences in the resulting speed or other measures of improvement, the group that had received haptic feedback had an overall lower heart rate during the post-test, which the authors contribute to decreased mental effort. This may indicate that the users who received haptic feedback had a reduced mental workload in line with what we would expect from the off-loading concepts of Embodied Cognition and Cognitive Load Theory.
# Large Field-Size Elliptic Curve Processor for Area-Constrained Applications
## 3 Proposed Crypto Processor Architecture
### Arithmetic Unit
To perform polynomial reduction over \(GF(2^{409})\) and \(GF(2^{571})\), we have implemented NIST-specified algorithms as described in Algorithms 2 and 3, respectively. In our work, these reduction algorithms are implemented like combinational circuit because these require only shift and exclusive (OR) operations. ```
Input: Polynomial, \(D(x)\) with \(2\times m-1\)-bit length Output: Polynomial, \(D(x)\) with \(m\)-bit length \(1\) for\((i\) from 25 down to \(13)\) do \(1.1\)\(T\longleftarrow D[i]\) \(1.2\)\(D[i-13]\longleftarrow D[i-13]\oplus(T\ll 7)\) \(1.3\)\(D[i-12]\longleftarrow D[i-12]\oplus(T\gg 25)\) \(1.4\)\(D[i-11]\longleftarrow D[i-11]\oplus(T\ll 30)\) \(1.5\)\(D[i-10]\longleftarrow D[i-10]\oplus(T\gg 2)\) \(2\)\(T\longleftarrow D[12]\gg 25\) \(3\)\(D[0]\longleftarrow D[0]\oplus T\) \(4\)\(D[2]\longleftarrow D[2]\oplus(T! !23)\) \(5\)\(D[12]\longleftarrow D[12]\) & 0x1 FFFF \(6\) Return \((D[12],\ \ldots,\ D[3],\ D[2],\ D[1],\ D[0])\)
```  
**Algorithm 2**NIST reduction over \(GF(2^{409})\)[14]  
The reconversion in Algorithm 1 requires modular inversion operation. In the existing literature, we have several algorithms to execute the modular inversion over \(GF(2^{409})\) and \(GF(2^{571})\). Our implementation executes the inversion operation using the square version of Itoh-Tsujii algorithm [27]. It requires \(m-1\) squares followed by 10 & 11 multiplications over \(GF(2^{409})\) and \(GF(2^{571})\), where \(m\) is the operand length. From a hardware implementation perspective, there are two possibilities to implement modular inversion: (1) using a separate square and a multiplier block and (2) using existing hardware resources of a modular multiplier. The first method is inconvenient as it utilizes more hardware resources and consumes more power. The second approach is appealing to save the implementation resources and keep low power consumption of the circuit [15; 25; 26]. The instructions in Table 1 contain square, multiplication and addition operations. It is important to note that we have used only a single modular multiplier to perform both square and modular multiplications. The square is computed by providing two similar operands as inputs to the multiplier, as described in [26]. Similarly, the Itoh-Tsujii inversion algorithm is implemented using the existing hardware resources of our proposed modular multiplier, as implemented using hardware resources of different multipliers in [25; 26]. The proposed multiplier architecture is implemented as combinational logic and it requires only one clock cycle (including reduction) to perform one polynomial square and multiplication.
## 5 Conclusions and Future Work  
In this paper, we present an anchor-free localization technique in \(2D\) WSN. Analytical expressions for the average communication range, PLE value, and inter-node distances are derived. The introduction of the CRSSA in this article marks a substantial advancement in wireless sensor networks (WSNs), showcasing a novel anchor-free localization technique that leverages RSS and contextual connectivity. This approach sets a new standard in localization accuracy within WSNs, highlighting the study's innovative edge and technical proficiency. The study's robust theoretical framework and comprehensive simulative evaluations are particularly noteworthy, demonstrating the method's effectiveness and superiority in precision compared to existing localization methods like EAFLA. Additionally, the anchor-free nature of the CRSSA emerges as a pivotal development, offering enhanced flexibility and cost-effectiveness in sensor network deployments, which are crucial in the evolving realm of wireless communications.  
The potential of the CRSSA in the field of WSNs is underscored by its adaptability and economic efficiency. Future research avenues, such as addressing real-time computational efficiency and undertaking experimental validations in diverse real-world settings, will further cement its practicality and application scope. Integrating the CRSSA with emerging technologies and considering dynamic environments where node mobility is a factor will expand its utility and relevance.  
This study, therefore, not only provides a significant contribution to WSN localization techniques but also opens up exciting possibilities for future innovation and practical applications, setting a new benchmark in the field.  
Conceptualization, N.Z. and N.H.; formal analysis, N.Z.; funding acquisition, N.K.; investigation, N.Z.; methodology, N.Z.; software, N.Z.; supervision, N.H. and N.K.; validation, N.H. and N.K.; visualization, N.Z.; writing--original draft, N.Z.; writing--review and editing, N.Z., N.H. and N.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors extend their sincere gratitude to the anonymous reviewers whose invaluable insights and constructive feedback significantly contributed to the enhancement of this manuscript. The authors appreciate the time and commitment invested by the reviewers in the peer review process, which has undeniably enriched the overall quality of this contribution.
## 5 Plasma Precursors
### Hydrogen
#### 5.1.8 Surface Neutralization  
In addition to volume processes like three-body recombination occurring within the plasma itself, significant interactions involve surfaces such as the reactor wall or the lead frame. Surface neutralization, potentially detailed in Equation (20), describes this process occurring on the lead frame surface. This is a highly efficient mechanism initiated when an ion captures an electron from the surface material, often forming a highly excited atom [6; 94]. The work function (\(\Phi\)) of typical materials is between 4 and 6 eV. Crucially, the ion's potential energy (ionization potential, IP, often \(>\)15 eV for common ions) greatly exceeds the work function (IP \(>>\Phi\)). This large potential energy difference ensures that neutralization is highly probable upon contact with the lead frame surface. The combined energy released primarily causes surface heating and may also induce secondary electron emission [27].  
\[\mathrm{e}+\mathrm{A}^{+}+\mathrm{S}\rightarrow\mathrm{A}^{*}+\mathrm{S} \tag{20}\]  
where  
S represents the surface material;  
A\({}^{+}\) is the ion species in the plasma;  
A\({}^{*}\) is an excited molecule or atom.  
In this case, the surface takes up some of the energy from the plasma [95]. When the excited electron goes back to its unexcited state, photons are emitted, with each substance emitting photons at different frequencies, thereby resulting in the different colours of the plasmas [10].  
The rate of reaction for the surface process is regulated by the adsorption and desorption processes. The adsorption reaction, which is exothermic, is presented as Equation (21) [6].  
\[\mathrm{A}+\mathrm{S}\rightarrow\mathrm{A}:\mathrm{S} \tag{21}\]  
The adsorption process can be either due to the Van der Waals forces between molecules and the surface or else due to bonding between the plasma particles and the surface. The latter, known as chemisorption, is more exothermic [6].  
An important parameter in overall surface interactions, especially those involving adsorption and subsequent reactions like recombination, in the surface neutralization discussion is the sticking coefficient (\(\gamma\)), as presented in Table 10. Coefficient \(\gamma\) indicates the probability of ions and atoms recombining when they touch the walls of the reactor.  
This shows that ions hitting the surface are very efficiently converted into neutral hydrogen atoms at the surface, effectively delivering the necessary reactant for the chemical reduction of the copper oxides. A by-product of the reaction is water vapor. If such  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Reaction** & **Sticking Coefficient (\(\gamma\))** \\ \hline \(2\mathrm{H}+\mathrm{wall}\rightarrow\mathrm{H}_{2}\) & 0.03 \\ \hline \(\mathrm{H}^{+}+\mathrm{wall}\rightarrow\mathrm{H}\) & 0.9 \\ \hline \(\mathrm{H}_{3}^{+}+\mathrm{wall}\rightarrow\mathrm{H}_{2}+\mathrm{H}\) & 0.9 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Sticking coefficient expressions [96].  
water condenses on the cold lead frames, it may prove an impediment to further reaction unless removed.
## 2 System and Channel Models
### Angle-of-Arrival Fluctuations  
The hovering of the drone can cause the beam to deviate randomly in orientation due to wind and mechanical jitter and further reduces the energy of the optical beam reaching the receiver plane. Finally, the outage probability increases rapidly when the incident angle of the beam reaches a certain value [23]. The \(\theta_{FoV}\) denotes the threshold of the FoV. Hence, AoA fluctuations can be expressed as [28]  
\[I_{AoA}\simeq\left\{\begin{array}{cc}1,\theta_{a}\leq\theta_{FoV}\\ 0,\theta_{a}>\theta_{FoV}\end{array}\right.\,. \tag{8}\]  
**Theorem 1**.: _The analytical formulae for the PDF and CDF based on [PERSON]'s \(G\) function for the case of drone-assisted FSO communication, considering atmospheric attenuation, atmospheric turbulence, pointing errors, as well as AoA fluctuations, are displayed follows:_  
\[f(\gamma)=\left(1-\exp\left(-\frac{\theta_{FoV}^{2}}{2\sigma_{a}^{2}}\right) \right)\frac{A\xi^{2}C\sigma_{n}^{2}}{2\eta P_{t}\tau}\gamma^{\frac{1}{2}-1} \sum_{i=1}^{b}b_{i}G_{3,5}^{3,2}\left[C\frac{\left(\gamma\sigma_{n}^{2}\right) ^{\frac{1}{2}}}{\eta P_{t}}\right]\begin{array}{c}-1,0,\xi^{2}\\ \xi^{2}-1,a-1,i-1,0,-1\end{array}\right], \tag{9}\]  
\[F(\gamma)=\left(1-\exp\left(-\frac{\theta_{FoV}^{2}}{2\sigma_{a}^{2}}\right) \right)\frac{A\xi^{2}}{2}\sum_{i=1}^{b}b_{i}G_{2,4}^{3,4}\left[C\frac{\left( \gamma\sigma_{n}^{2}\right)^{\frac{1}{2}}}{\eta P_{t}}\right]\begin{array}{c} 1,1+\xi^{2}\\ \xi^{2},a,i,0\end{array}\right]+\exp\left(-\frac{\theta_{FoV}^{2}}{2\sigma_{a} ^{2}}\right). \tag{10}\]  
Proof.: See Appendix A.
# Lavandula Species, Their Bioactive Phytochemicals, and Their Biosynthetic Regulation
## 2 Lavender Genetic Diversity  
_Lavandula_ L., a genus of plants, can be found wild in the Mediterranean region from the North Atlantic to the Middle East. These herbaceous biennial plants thrive in dry, sunny environments and grow in either calcareous or siliceous soils depending on the species. The _Lamiaceae_ family, to which _Lavandula_ L. belongs, is typically defined by the presence of quadrangular stems and opposite, decussate leaves [1; 11; 12].  
The Lavandula section comprises compact shrubs that typically reach a height of 20-60 cm and feature wooden stems. The leaves of these shrubs are elongated, in-line, with opposed arrangement. Foliage shape and vanning may differ between lavender species [1; 13].  
The lavender flowers possess bilabiate form and bee-attracting nectary and are hermaphroditic in nature. Glandular trichomes can be found on the aerial parts of the plant [14; 15], and abundance may depend on the species or the specific plant. Essential oils are contained in this epidermal structure and their form, location, and densities are unique characteristics that distinguish them between the species [14; 16; 17]. The quantity, abundance, and sizes of glandular trichomes in lavender plants have an impact on the essential oil that is extracted from them [18]. Additionally, different glycosides with taxonomic significance are also present in this family [19].  
The family _Lamiaceae_ is categorized into seven subfamilies, among which _Nepetoideae_ is a subfamily that comprises lavender species. The genus _Lavandula_ has been meticulously classified by [PERSON] in terms of its taxonomy [1]. The genus _Lavandula_ comprises more than 39 species and 79 intraspecific taxa and hybrids, which can be categorized into three subgenera, namely _Fabricia_, _Sabaudia_, and _Lavandula_, as well as eight sections, including _Dentatae_, _Stoechas_, _Lavandula_, _Pterstoechas_, _Subrudae_, _Cheetostachus_, _Hasikenses_, and _Sabaudia_[20]. The most important cultivated species include fine lavender (_L. _anguifolia_ Mill), lavender aspic (_L. _latifolia_), woolly lavender (_L. _lanata_), and lavandin (_L. \(\times\) _intermediate_, a sterile hybrid of _L. _anguifolia_ and _L. _latifolia_), and all belong to the Lavandula section. In addition, the genus comprises numerous subspecies and several hundreds of varieties [20; 21; 22; 23]. For essential oil production, the most commonly grown genotypes are _L. _anguifolia_ and _L. \(\times\) _intermediate_[24; 25; 26]. Lavender oil (from _L. _anguifolia_) is sold at about 3-5\(\times\) the price of lavandin oil. This is because it is regarded as being of a better standard [27]. The _Lavandula_ genus is characterized by a rich content of phenolic compounds, with the identification of eight anthocyanins and nineteen flavones. Additionally, the content of essential oil of _Lavandula_ species varies greatly, with over 300 terpen, including both mono- and sesquiterpenes, having been accounted for [28].
## 4 Analysis of GPU Parallel Optimization Results  
### Analysis of the Parallel Optimization Efficiency of Different Thread Modes  
Different subject polygons have different numbers of vertices and edges, which results in different total numbers of threads being called when executing parallel algorithms. The total number of threads called by each subject polygon is the same as its number of vertices, and the total number of threads is defined by the number of thread blocks and the number of threads contained in each thread block. Therefore, when calling multithreaded executions of parallel algorithms for each subject polygon, different thread combination modes can be used to test the optimization of the algorithm under different thread modes while ensuring that the total number of threads remains unchanged. For example, when the total number of threads is 128, two thread blocks are used, each of which has 64 threads, and four thread blocks are used, each of which has 32 threads. All 32 threads constitute a warp, which is the basic unit of parallel processing. Threads in a warp execute the same instructions at the same time, so the number of threads in each thread block is best set to a multiple of 32.  
This paper conducts a statistical analysis on the running time of serial and parallel algorithms executed in different thread modes for eight subject polygons with different shape complexities. Six different thread modes are used in the parallel optimization process of each data point, namely 64, 128, 256, 512, 768, and 1024 threads per thread block. The running results are shown in Figure 5.  
Figure 4: GPU parallel optimization of the Greiner–Hormann algorithm.  
According to Figure 5, in a parallel environment, when a single thread block for subject polygons 1, 3, 7, and 8 contains 64 threads, the algorithm running time is the shortest; when a single thread block for subject polygons 2, 4, 5, and 6 contains 128 threads, the algorithm running time is the shortest. Regardless of the amount of data, the parallel algorithm runs fastest when two thread modes are used: a single thread block containing 64 threads and a single thread block containing 128 threads. When the number of threads is greater than 128, the algorithm execution time begins to gradually increase. This is because when there are too many threads, there are resource competition problems. When the number of called threads exceeds a certain limit, the excess threads will enter a waiting state, reducing the overall efficiency of the algorithm. As shown in Figure 5, when the Greiner-Hormann parallel algorithm is executed on data of different volumes, it may not achieve a higher running speed than the serial algorithm. Different data yield different performances; some can lead to speed improvements by several times, and some are several times slower than the serial algorithm.
## 4 Conclusion  
An intrinsic difficulty in Bayesian image restoration resides in determination of a prior law for images. The ME principle solves this problem and enforces the restored image to be positive, so that the spurious negative areas and complementary spurious positive areas are wiped off and the dynamic range of the restored image is substantially enhanced. Image restoration based on image entropy is effective even in the presence of significant noise, missing, or corrupted data. This is due to the appropriate regularization of the inverse problem of image restoration introduced in a coherent way by the ME principle. It satisfies all consistency requirements when combining the prior knowledge and the information contained in experimental data.  
Bayesian ME approach is a statistical method which directly operates in the spatial domain, thus eliminating the inherent errors coming out from numerical Fourier direct and inverse transforms and from the truncation of signals in the transform domains. Theoretically, no artifacts should be introduced by processing, since the entropy maximization produces the most unbiased and featureless solution that is consistent with available data and complies with the errors in measurements and modeling.  
Figure 9: Close-up of malignant abnormalities in mammograms: Case mdb245, MIAS - microcalcification cluster, fatty tissue (left), case #9, UNC Radiology - breast carcinoma (middle), Salus Hospital - stellate patterns (right). Digitized X-ray images by optical scanning (top) and digitally restored images (bottom).  
In recent years, imaging equipment and techniques have significantly been improved that entailed the acceptance of mammography if performed with excellent positioning, firm compression of the breast, and dedicated processing facilities. The challenge today is to disclose early stages of breast cancer in the 35 to 50 age groups. Manufacturers exhibit an increasingly interest in diagnosing cancer in dense breast, which has substantial amounts of fibroglanduar tissue. Pattern recognition and classification methods will further be developed for computer assisted diagnosis ending up with expert systems that may perform all tasks required by digital biomedical data analysis and interpretation.
## 5 Experimental Results and Discussion
### Comparison between Single-Frequency Hammering and Variable-Frequency Hammering  
In this study, the vector data of the single-frequency hammer MFCCs and variable-frequency hammer MFCCs obtained from two and four categories were imported into the SVM model, which used linear, quadratic, and Gaussian kernel functions and a one-to-one multi-class classification strategy. For accuracy testing, the six-fold cross-validation method was used to divide the dataset into six parts, five of which were used for training and one for testing, and the mean of the results of the six times was used as an estimate of the accuracy of the algorithm.
# Improved Duplication-Transfer-Loss Reconciliation with
## 2 Definitions and Preliminaries
### Transfer-Loss Events  
As shown in Figure 1, a \(\mathbb{TL}\) event is equivalent to a standard transfer event with the exception that the donor lineage thereafter loses all copies derived from the gene that was transferred. \(\mathbb{TL}\) events were first defined by [PERSON] et al. and implemented in their DTL reconciliation framework and software Mowgli [3]. The software ecceTERA [16] later inherited the same reconciliation framework. No other parsimony-based DTL reconciliation framework can currently handle \(\mathbb{TL}\) events. Note that \(\mathbb{TL}\) events do not represent a distinct biological event type; instead, they are simply standard transfer events followed later by one or more loss events that result in the donor lineage losing all copies derived from the gene that was transferred. Thus, the only reason to explicitly define \(\mathbb{TL}\) events and distinguish them from standard transfer events is that most DTL reconciliation models and algorithms are not able to correctly handle evolutionary scenarios involving \(\mathbb{TL}\) events. This difficulty in handling \(\mathbb{TL}\) events stems from the fact that \(\mathbb{TL}\) events are _unary_; unlike standard transfer events, they do not result in a bifurcation in the gene tree topology. Thus, there does not exist any node in the input gene tree that can represent a \(\mathbb{TL}\) event. However, the DTL reconciliation model can be easily adapted to allow for \(\mathbb{TL}\) events by explicitly augmenting edges in the input gene tree with additional nodes, referred to as _hidden_ nodes. Each such hidden node simply subdivides the edge that they are augmented to, resulting in a non-binary node with a single child. This property allows hidden nodes to represent possible \(\mathbb{TL}\) events, as the unary property is kept intact. More formally:  
**Definition 3** (Augmented gene tree and hidden nodes).: _Given a gene tree \(G\), an augmented gene tree \(G^{\prime}\) is defined to be the tree obtained from \(G\) by (i) selecting a subset of edges \(A\subseteq E(G)\), and (ii) subdividing each edge in \(A\) by a hidden node such that each edge \((g,g^{\prime})\in A\) is replaced by the two edges \((g,h)\) and \((h,g^{\prime})\), where \(h\) is a new hidden node._  
Figure 2 shows an example of an augmented gene tree. The hidden nodes in an augmented gene tree can be used to represent \(\mathbb{TL}\) events. Thus, under the DTLx reconciliation framework, the reconciled gene tree is in fact a reconciled _augmented_ gene tree, where each hidden node shown on that augmented gene tree represents a \(\mathbb{TL}\) event.