esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Although the potential of lasers for particle acceleration was yet recognized in the early 1960's [1], research over the past 50 years has shown that developing an effective laser acceleration scheme is not straightforward. In the explosion-like interaction of tightly focused ultra-intense laser pulses with matter, electrons are violently expelled out of the focal region and are effectively accelerated only over short distances, typically comparable to the dominant wavelength of the pulse spectrum. Preferably, particles should be kept in phase with the intense laser field over ranges longer than the beam collimation distance. To reach this goal, a myriad of laser-light driven particle acceleration schemes have been proposed. Plasma wakefield acceleration [2,3], the most successful so far, has been proven to be reliable in producing collimated multi-MeV electron beams with per-cent-level energy spread [4, 5, 6, 7]. Energy gains in the multi-GeV range were also reported [8,9]. Laser-wakefield accelerators, combined with a plasma-wakefield afterburner [10], have an exceptional potential for high-energy physics. However, there are also good motivations to develop sub-femtosecond electron sources at nonrelativistic energies for ultrafast electron diffraction [11, 12, 13]. Direct electron acceleration by radially polarized lasers beams (RPLBs) offers an interesting avenue [14, 15, 16, 17]. Independent works suggest that directional and collimated attosecond electron pulses could be produced [18,19]. The experimental realization of this scheme is challenging but promising [20]. The technique could also be used for proton acceleration [21, 22, 23].  
The paper is organized as follows. First, we review the characteristics of RPLBs (Section 2). Second, we present the theory behind electron acceleration in RPLBs (Section 3). Next, we discuss the special case of electron acceleration by tightly focused ultrafast radially polarized laser pulses (RPLPs) by presenting both the theory (Section 4) and experimental results (Section 5). Finally, we conclude by exposing some of the challenges to meet in the future (Section 6).
## 3 Numerical Results and Discussion  
### Numerical Model and Convergence Test  
Hydrodynamic analysis for a hemispherical heaving-buoy-type WEC system was performed based on the aforementioned FR-NWT, a Fortran-based in-house program for hydrodynamic analysis in the frequency domain. The WEC system employs a hemispheric heaving buoy as an actuator. Table 1 lists the conditions for a floating body and wave conditions. Prior to performing the analysis of the WEC system, the shape of the body was fixed, and the unit wave height was used to verify the radiation boundary conditions to be applied to the FR-NWT. In particular, the water depth (\(h\)) was set to 20 m to neglect the seafloor effect in all applied wave frequency ranges. Figure 2 presents the mesh system of 3D-FR-NWT, which has a rectangular shape, to analyze the influence of a flat wall on a floating body motion. A floating body was located in the middle of the free surface. The size of the computational fluid domain was 3/ (wavelengths) 3/. The sensitivity of the number of free-surface nodes was examined by conducting convergence tests on the number of nodes per wavelength by applying from a sparse mesh system to a finer mesh system with the Sommerfeld radiation condition. Figure 3 compares the heave added mass and heave radiation damping coefficient on the number of collocation nodes per wavelength at \(w\) = 3.2 rad/s. The results were compared with a frequency-domain hydrodynamic analysis tool, WAMIT [30]. The results were based on the wave Green function, which satisfied the radiation condition very well. When the number of nodes per wavelength was more than 25, the hydrodynamic coefficient converged, providing the same results as those reported with WAMIT. Twenty-five collocation nodes per free surface were used in the free surface for the remaining numerical simulations.  
Figure 2: Mesh system of 3F-FR-NWT: (**a**) full domain; (**b**) body mesh.
# Image Interpretability of nSight-1 Nanosatellite Imagery for Remote Sensing Applications
###### Abstract  
Nanosatellites are increasingly being used in space-related applications to demonstrate and test scientific capability and engineering ingenuity of space-borne instruments and for educational purposes due to their favourable low manufacturing costs, cheaper launch costs, and short development time. The use of CubeSat to demonstrate earth imaging capability has also grown in the last two decades. In 2017, a South African company known as Space Commercial Services launched a low-orbit nanosatellite named nSight-1. The demonstration nanosatellite has three payloads that include a modular designed SCS Gecko imaging payload, PIPEX atmospheric science instrument developed by the University of Dresden and a Radiation mitigation VHDL coding experiment supplied by Nelson Mandela University. The Gecko imager has a swath width of 64 km and captures 30 m spatial resolution images using the red, green, and blue (RGB) spectral bands. The objective of this study was to assess the interpretability of nSight-1 in the spatial dimension using Landsat 8 as a reference and to recommend potential earth observation applications for the mission. A blind image spatial quality evaluator known as Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) was used to compute the image quality for nSight-1 and Landsat 8 imagery in the spatial domain and the National Imagery Interpretability Rating Scale (NIIRS) method to quantify the interpretability of the images. A visual interpretation was used to propose some potential applications for the nSight1 images. The results indicate that Landsat 8 OLI images had significantly higher image quality scores and NIIRS results compared to nSight-1. Landsat 8 has a mean of 19.299 for the image quality score while nSight-1 achieved a mean of 25.873. Landsat 8 had NIIRS mean of 2.345 while nSight-1 had a mean of 1.622. The superior image quality and image interpretability of Landsat could be attributed for the mature optical design on the Landsat 8 satellite that is aimed for operational purposes. Landsat 8 has a GDS of 30-m compared to 32-m on nSight-1. The image degradation resulting from the lossy compression implemented on nSight-1 from 12-bit to 8-bit also has a negative impact on image visual quality and interpretability. Whereas it is evident that Landsat 8 has the better visual quality and NIIRS scores, the results also showed that nSight-1 are still very good if one considers that the categorical ratings consider that images to be of good to excellent quality and a NIIRS mean of 1.6 indicates that the images are interpretable. Our interpretation of the imagery shows that the data has considerable potential for use in geo-visualization and cartographic land use and land cover mapping applications. The image analysis also showed the capability of the nSight-1 sensor to capture features related to structural geology, geomorphology and topography quite prominently.  
nanosatellites; remote sensing; image quality; image interpretability; land use and land cover applications
# Efficient Load Balancing for Blockchain-Based Healthcare System in Smart Cities
## 4 Proposed Scheme
### Nonce Computing Algorithm  
The data blocks received by mining nodes are initially placed in their cache before computing the nonce value. These data blocks may have different priorities depending on the patients' critical vital signs values. Such data blocks are required to be computed on a priority basis. The second algorithm of \(DSPS_{\textit{SHS}}\) is proposed for the mining node to compute the nonce value of a data block according to their data priority. When data arrive at the mining node from the load balancing node, then the mining node needs to compute the data according to their sensitivity level.  
Suppose the load balancer receives five data blocks DB1, DB2, DB3, DB4, and DB5, and waits for their nonce values at 12, 15, 20, 25, and 30 s, respectively. These data blocks have three different priority levels, such as H, M, and L, representing high, medium, and low priority with priority values of 300, 70, and 10, respectively. Their corresponding priority values are calculated according to Table 2.  
A mining node before fetching the data block in computing their nonce value verifies the current value of the data block. Value of each block increments after each interval of time. If there are two different priority blocks available, then their value will be computed with their time of origin. If there are blocks with the same values, then the data block with a higher priority level is selected for nonce computation. If two or more data blocks have the same value with similar priority levels, then that data block is selected, which arrives in the cache of the mining node.  
A complete nonce computing algorithm for the mining node is shown in Algorithm 2. It should be noted that the load balancer node handles \(N\) data blocks at a time and distributes them to the fog mining nodes as per the proposed algorithms.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline _Data Blocks_ & _Priority Levels_ & _Time Since Arrival_ & _Priority Value (\(P_{V}\))_ \\ \hline \(DB1\) & _High_ & _12_ & _3600_ \\ \(DB2\) & _High_ & _15_ & _4500_ \\ \(DB3\) & _Medium_ & _20_ & _1400_ \\ \(DB4\) & _Medium_ & _25_ & _1750_ \\ \(DB5\) & _Low_ & _30_ & _300_ \\ \hline \hline \end{tabular}
\end{table}
Table 2: Priority values of data blocks.
# Wavelet Packet Method for Locating Critical Slip Surface Using the Strength Reduction Method
## 5 Numerical Examples
### An Idealized Homogeneous Slope  
To verify the proposed method, a homogeneous slope studied in [58] was adopted, which was composed of homogeneous, isotropic soil. The dimensions and the material parameters of the slope are shown in Figure 3. Four discrete models were used to analyze the convergence of the proposed method, as shown in Figure 4, in which the boundary conditions of the computational models were as follows: the left and right boundaries were fixed in the horizontal direction, and the bottom boundary was fixed in the horizontal and vertical direction.  
Figure 3: Dimension of the model in Example 1.  
Figure 2: Flow diagram for the proposed method.  
The EPS contours by the SRM using different models are plotted in Figure 5. The results indicate that the denser the mesh, the smaller the areas of the plastic zone. This is because of the mesh dependence of the FEM, which can be further proved by the distribution of maximum equivalent plastic strain, that is, the calculation model with denser mesh has lower data fluctuation. The traditional SRM can only give the contours of equivalent plastic strain as shown in Figure 5, and the specific position of the CSS cannot be given.  
Taking the slope vertex node as a characteristic point, the reduction factor \(Z_{i}\) versus horizontal displacement \(u_{x}\) curve is drawn as shown in Figure 6. As can be seen from the figure, the horizontal displacement of the slope vertex node has an obvious inflection point.  
Figure 4: Four discrete models for the homogeneous slope. (**a**) Model A (2955 elements); (**b**) Model B (1977 elements); (**c**) Model C (1065 elements); (**d**) Model D (640 elements).  
With the discretization of element length approximate to 0.6 m (Mesh A), 0.75 m (Mesh B), 1 m (Mesh C), and 1.25 m (Mesh D), the proposed procedure was given an FOS of 1.39, 1.40, 1.42, 1.44, respectively. The convergence curves of the numerical solution by the proposed method are plotted in Figure 7. Using an optimization process for the [PERSON] method in STAB, a software developed by [PERSON] [58], the reference solution presented in Figure 7 is 1.39. As shown in Figure 7a, the model's numerical solution gradually approached the reference solution as the number of elements (NoE) increases. In other words, the relative errors (Figure 7b) of FOS by the proposed method gradually decrease as the NoE increases.  
Figure 5: The contours of equivalent plastic strain by the SRM. (**a**) Model A; (**b**) Model B; (**c**) Model C; (**d**) Model D.  
the proposed method can overcome the mesh dependence of FEM, and it is reasonable to take the point with the maximum equivalent plastic strain point as the location of the CSS.
# Using Tablets to Teach Basic Literacy Skills to Kindergarten Students: A Case Study
## 3 Method  
To address the research gaps and issues identified in the preceding section, a research project was designed and implemented, to examine the effectiveness of tablets relative to conventional educational materials. The focus was on basic literacy skills, namely letter name identification and letter sound recognition, as these not only align well to the current curricula for kindergarten students but also are crucial for the development of kindergarten students' literacy skills. A between-subjects research design with two groups was applied; the control group utilized conventional educational materials, while the experimental group engaged with tablets. As it was theorized that sex and prior knowledge play a role in the learning outcomes, it was also decided to take into considerations their effects. Moreover, to ensure the reliability and validity of the data, three sessions per group were conducted. Each session for both the experimental and control groups lasted two instructional hours and was conducted once a week.  
The following research hypotheses were formulated:  
**H1.**: _Compared to conventional materials and controlling for the effects of participants' sex and prior knowledge, tablets have a significantly greater impact on kindergarten students' learning outcomes, in the area of letter identification/letter sound recognition._  
**H2.**: _Compared to conventional materials and controlling for the effects of participants' sex and prior knowledge, tablets have a significantly greater impact in kindergarten students' retention of knowledge, in the area of letter identification/letter sound recognition._  
**H3a-c.**: _Compared to conventional materials and controlling for the effects of participants' sex, tablets exhibit a more substantial effect on kindergarten students' (a) engagement, (b) enjoyment, and (c) motivation, in the area of letter identification/letter sound recognition._  
**H4a-c.**: _Engagement (a), enjoyment (b), and motivation (c), influence kindergarten students' learning outcomes, in the area of letter identification/letter sound recognition._  
**H5a-c.**: _Engagement (a), enjoyment (b), and motivation (c), influence kindergarten students' retention of knowledge, in the area of letter identification/letter sound recognition._
# Multi-Path Data Distribution Mechanism Based on RPL for Energy Consumption and Time Delay
## 4 Analysis of the Performance
### Analysis of the Network Performance
#### 4.2.3 Analysis of the End-to-End Delay  
Subsequently, we compared the delay between the nodes based on Algorithm 3, Algorithm 4 and the original RPL in Figure 10. According to the results, Algorithm 3 needs the least time because it only focuses the transmission time between the nodes and use the best route to do the timely data forwarding. It also extremely reduces the possibility of the loss of data and the low probability of data retransmission. Although Algorithm 4 is not as effective as Algorithm 3, its delay is still smaller than the original RPL. It firstly considers the energy balance and ensures the network lifetime for as long as possible. On the basis of this, it considers the cache utilization. However, the original RPL always carries out packet forwarding by choosing the preferred parent node. The choice of the parent node is determined by the objective function, which is determined by certain metrics. As long as the topology does not change, the preferred parent node will not change. There is no guarantee of the end-to-end delay.  
From the figure we can see that there is irregular fluctuation of delay under different network sizes. It responds to the position of the nodes.
# Recent Developments in Modulation Spectroscopy for Methane Detection Based on Tunable Diode Laser
## 4 Application of Modulation Spectroscopy for Methane Detection
### Two-Tone Frequency Modulation Spectroscopy Applied for Methane Detection
#### 4.3.2 Methane Detection Systems Based on TTFMS  
In 1982, the TTFM technique was first proposed by [PERSON] et al. [118]. Then modulation frequencies increased from the kHz range to the tens of GHz range due to using a CW dye laser source and an electro-optic modulator [119]. In the same year, the frequency of hundreds of MHz was obtained by using a lead-salt diode laser [120]. [PERSON] et al. [121] employed a DFB diode laser to develop a TTFMS spectrometer for monitoring methane. A balanced homodyne detection technique was adopted so that the spectrometer had high sensitivity. The modulation frequency and the rf frequency were 2 GHz and 5 MHz, respectively. The sensitivity of the spectrometer was up to \(7(2)\times 10^{-8}\) at a 1-Hz bandwidth. Recently, the TTFMS technique has been used in conjunction with a DFG radiation source. [PERSON] et al. [122] developed a portable DFG spectrometer, as shown in Figure 5. Compared with direct absorption, SNR has been enhanced by a factor of 100 by using TTFMS. [PERSON] et al. [65] described another portable spectrometer based on QCLs and TTFMS for monitoring CH\({}_{4}\). The wavelength tunability vs. temperature was 2 GHz/K for QCL emitting at 7.3 \(\upmu\)m. The output SNR was enhanced about six times than that of direct absorption.
# A Polarity Capturing Sphere for Word to Vector Representation
## 1 Introduction
The answer is yes. It is logical that unrelated words, e.g., \"train\" and \"star\", would have their vectors far away from each other, because of our principle that vector similarities or closeness should reflect relatedness. This is consistent with the theory that at a high dimension, the dot product of randomly occurring unit vectors on the sphere is close to zero [16]. The algorithm has a number of beneficial features:  
* The proposed algorithm is a very simple relaxation algorithm, and it converges very well and fast. It is simpler than the typically used word to vector design methods, such as neural networks or deep networks. * We believe that embedding the vectors into a sphere provides a natural representation, unlike distributional models for learning word vector representations and other co-occurrence-based models. Some of these models often fail to distinguish between synonyms and antonyms,  
Figure 1: Semi-supervised illustration. since antonymous words occur in similar context the same way synonymous words do. Other models fail to distinguish between antonymous word pairs and unrelated word pairs. * The learned word vectors can always be continuously augmented. The database can flexibly be extended to include more words by simply feeding the algorithm with more word pairs with the desired target similarity scores. The algorithm guarantees that these new vectors will be placed correctly with regard to the existing vectors. * Composite words can also be handled by our algorithm. This is because any word pair with the desired similarity score can be fed into the algorithm. For example, \"old-fashioned\" is an expression that combines two words: \"old\" and \"fashion\"; this combined expression can be given to our algorithm as a synonym to a word that conveys its meaning: e.g., \"outdated\". * Generally it is a supervised procedure. The designer typically gives synonyms/antonyms from thesauri or other sources. However, it could also be designed in a semi-supervised setting. This means that in addition to the user-defined similarity scores between the collected words, there could be many more unlabeled pairs that have to be learned from the text pieces available (for example, through co-occurrence arguments). The labeled pairs will provide some anchor around which the unlabeled pairs will organize, and are therefore essential for guiding the correct vector placements. In other words, new relations between word pairs will be inferred from existing ones.
# Two-Stage Metaheuristic Algorithms for Order-Batching and Routing Problems
## 4 Proposed Algorithm
### SA Algorithm
Step 6: In the current picking order, randomly choose the swap or insertion procedure to generate neighboring solutions for the picking order \(P_{neigh}\). Step 7: Calculate the picking distance of each batch according to \(P_{neigh}\), and sum the distances of all batches to obtain the total distance of the neighboring picking order \(D_{neigh}\)Step 8: Calculate the objective value deviation \(\Delta D=D_{neigh}-D_{curr}\). Step 9: Determine whether \(\Delta D<0\). If yes, proceed to Step 10; otherwise, proceed to Step 13. Step 10: Substitute \(P_{curr}\) with \(P_{neigh}\), and then, proceed to Step 11. Step 11: When the solutions are substituted, reset the picking chain length as \(L=L+1\) and proceed to Step 12. Step 12: If \(D_{neigh}\geq D_{best}\), then proceed to Step 15; otherwise, substitute \(P_{best}\) with \(P_{curr}\). Step 13: According to the uniform (0,1), generate a random probability value \(U\) and proceed to Step 14. Step 14: Determine whether \(U<\exp\left(\frac{-\Delta D}{KT}\right)\). If yes, return to Step 10; otherwise, proceed to Step 15. Step 15: Determine whether \(L\) is \(L_{max}\). If yes, then perform Step 16; otherwise, return to Step 6. Step 16: Execute the annealing procedure. Set the picking temperature as \(T=T/(1+\beta\times T)\) and the picking chain length as \(L=0\); subsequently, proceed to Step 17. Step 17: Determine whether the picking temperature \(T<T_{b}\). If yes, then export \(D_{neigh}\) and proceed to Step 18; otherwise, return to Step 7. Step 18: Calculate the objective value deviation \(\Delta D=D_{neigh}-D_{curr}\). Step 19: Determine whether \(\Delta D<0\). If yes, proceed to Step 22; otherwise, proceed to Step 20. Step 20: According to the uniform (0, 1), generate a random probability value \(U\). Step 21: Determine whether \(U<\exp\left(\frac{-\Delta D}{KT}\right)\). If yes, then proceed to Step 22; otherwise, return to Step 5. Step 22: Substitute \(B_{curr}\) with \(B_{neigh}\), and then, proceed to Step 23. Step 23: When the solutions are substituted, reset the batching chain length as \(LL=LL+1\) and proceed to Step 24. Step 24: If \(D_{neigh}>D_{best}\), then substitute \(B_{best}\) with \(B_{neigh}\) and proceed to Step 25. Otherwise, no substitution procedure is required; proceed to Step 25. Step 25: Determine whether the batching chain length \(LL>L_{max}\). If yes, perform Step 26; otherwise, return to Step 6. Step 26: Perform the annealing procedure. Set the batching temperature as \(TT=TT/(1+\beta\times TT)\) and the batching chain length as \(LL=0\) and proceed to Step 27. Step 27: Determine whether the batching terminal temperature \(TT\) is lower than \(T_{b}\) or the computation time is met. If yes, end the procedure; otherwise, return to Step 5.