esa-sceva/satcom-chunk-collection huggingface dataset:
# High Repetition Rate, TEM\({}_{00}\) Mode, Compact Sub-Nanosecond 532 nm Laser
## 5 Conclusions  
In this study, we demonstrated a compact sub-nanosecond 532 nm laser with an average power of 0.6 \(W\) corresponding to the LD pumped whole power of 10.9 \(W\), a repetition rate of 16 kHz, a pulse duration of 769 ps, and a pulse energy of 37.5 \(\upmu\)J, and the beam was characterized by \(M_{x}^{2}\) = 1.112 and \(M_{y}^{2}\) = 1.164. A MOPA system based on a passively Q-switched monolithic microchip laser was displayed in the experiment. However, the ideal average power of the microchip laser in the experiment was less than 317.8 mW, which was restricted by thermal loading. We proposed the power amplification and beam distortion compensation approaches in our experiments. Lidar with a high average power and corresponding pulse energies of the transmitter gives advantages in the detection distance and detection probability. The high beam quality contributes to increasing the efficiency of the transmitting optical system. Considering its high performance and compact characteristics, the reported sub-nanosecond laser can be widely used.  
Conceptualization, D.M., M.Z. and Z.F.; methodology, D.M.; simulation, T.W. experiment, M.Z. and T.W.; validation, Z.Q. and X.L.; supervision, Z.F.; writing--original draft preparation, D.M.; writing--review and editing, D.M. and X.L. project administration, D.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (2020 YFE0204600) and Key Research and Development Program of Tianjin (19 YFEZCGX00210).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Cavity Length, CL (mm)** & **Reflectivity of Output Coupler, R\%** & **Pulse Width (ns)** \\ \hline  & 35 & 0.909 \\
7 & 40 & 0.851 \\  & 45 & 0.809 \\  & 35 & 1.169 \\
9 & 40 & 1.095 \\  & 45 & 1.041 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Pulse width results for cavity lengths with 45% initial transmittance of Cr\({}^{4+}\): YAG.  
Figure 9: Pulse width change versus initial transmittance and reflectivity at different cavity lengths. (a) CL = 7 mm; (b) CL = 9 mm.
# An Efficient Reliability Method with Multiple Shape Parameters Based on Radial Basis Function
## 2 Radial Basis Function Model and K-Fold Cross-Validation: A Reminder  
### RBF Model  
The RBF model can adopt a linear combination of the radial symmetric function based on the Euclidean distance to estimate the performance function. Assuming the n-dimensional vector \(\mathbf{X}=[\mathbf{x}_{1},\mathbf{x}_{2},\cdots,\mathbf{x}_{n}]\) is the input of the performance function \(g(\mathbf{X})\), the RBF surrogate model can be obtained by \(m\) sample pairs \((\mathbf{X}_{i},g(\mathbf{X}_{i})),i=1,2,\ldots,m\):  
\[\hat{g}(\mathbf{X})=\sum_{i=1}^{m}\omega_{i}\phi(r) \tag{1}\]  
where \(\hat{g}(\mathbf{X})\) is the RBF surrogate model; \(r=\|\mathbf{X}-\mathbf{X}_{i}\|\) is the Euclidean distance between input vector \(\mathbf{X}\) and current sample point \(\mathbf{X}_{i}\); and \(\phi(\ )\) is a kernel function. The frequently used kernel function with shape parameters [29] are shown in Table 1; \(\omega_{i}(i=1,2,\ldots,m)\) is  
Figure 1: Illustration of the distance control.  
the unknown weighting coefficient of \(\phi(r)\), which can be obtained by solving the following equations:  
\[\left\{\begin{array}{lcl}\omega_{1}\phi(\|\mathbf{X}_{1}-\mathbf{X}_{1}\|)+\omega_{2} \phi(\|\mathbf{X}_{1}-\mathbf{X}_{2}\|)+&\ldots&+\omega_{m}\phi(\|\mathbf{X}_{1}-\mathbf{X}_{m} \|)=g(\mathbf{X}_{1})\\ \omega_{1}\phi(\|\mathbf{X}_{2}-\mathbf{X}_{1}\|)+\omega_{2}\phi(\|\mathbf{X}_{2}-\mathbf{X}_{ 2}\|)+&\ldots&+\omega_{m}\phi(\|\mathbf{X}_{2}-\mathbf{X}_{m}\|)=g(\mathbf{X}_{2})\\ &\vdots&\\ \omega_{1}\phi(\|\mathbf{X}_{m}-\mathbf{X}_{1}\|)+\omega_{2}\phi(\|\mathbf{X}_{m}-\mathbf{X}_{ 2}\|)+&\ldots&+\omega_{m}\phi(\|\mathbf{X}_{m}-\mathbf{X}_{m}\|)=g(\mathbf{X}_{m})\end{array}\right. \tag{2}\]  
Denote the vector \([g(\mathbf{X}_{1}),g(\mathbf{X}_{2}),\ldots g(\mathbf{X}_{m})]\) as \(\mathbf{G}_{m}\) and weighting the coefficient vector \([\omega_{1},\omega_{2},\ldots\omega_{m}]\) as \(\mathbf{\Omega}_{m}\). Then, the Equation (2) can be rewritten as:  
\[\mathbf{G}_{m}{}^{T}=\mathbf{\Phi}\mathbf{\Omega}_{m}{}^{T} \tag{3}\]  
where:  
\[\mathbf{\Phi}=\begin{bmatrix}\phi(\|\mathbf{X}_{1}-\mathbf{X}_{1}\|)&\phi(\|\mathbf{X}_{1}-\bm {X}_{2}\|)&\ldots&\phi(\|\mathbf{X}_{1}-\mathbf{X}_{m}\|)\\ \phi(\|\mathbf{X}_{2}-\mathbf{X}_{1}\|)&\phi(\|\mathbf{X}_{2}-\mathbf{X}_{2}\|)&\ldots&\phi(\| \mathbf{X}_{2}-\mathbf{X}_{m}\|)\\ \vdots&\vdots&\ddots&\vdots\\ \phi(\|\mathbf{X}_{m}-\mathbf{X}_{1}\|)&\phi(\|\mathbf{X}_{m}-\mathbf{X}_{2}\|)&\ldots&\phi(\| \mathbf{X}_{m}-\mathbf{X}_{m}\|)\end{bmatrix} \tag{4}\]  
According to the definition of the radial basis function \(\phi(\cdot)\), the inverse matrix \(\mathbf{\Phi}^{-1}\) must exist. Then \(\mathbf{\Omega}_{m}\) can be calculated by:  
\[\mathbf{\Omega}_{m}{}^{T}=\mathbf{\Phi}^{-1}\mathbf{G}_{m}{}^{T} \tag{5}\]  
Finally, substituting Equation (5) into Equation (1), the RBF surrogate model \(\hat{g}(\mathbf{X})\) can be calculated.
## 4 Visual Odometry
### Outlier Rejection  
Because of dynamic factors or repeated textures in the environment, there was a high probability of outlier points, which caused mismatching in the process of feature matching. Although numerous matched points were used in the optimization and a certain number of outliers could be tolerated, when the number of external points was large, the optimization accuracy was significantly reduced or the tracking was even lost. Therefore, it was necessary to remove as many outliers as possible while ensuring calculation efficiency.  
Two efficient mechanisms were used by our algorithm to cull outliers.  
First, after completing the ORB feature point matching, simplified loop chain matching was implemented to detect the quality of the matching. Specifically, the images of the last keyframe and the new keyframe are represented by \(\mathbf{I}_{k-1}^{L}\), \(\mathbf{I}_{k-1}^{R}\), \(\mathbf{I}_{k}^{L}\), and \(\mathbf{I}_{k}^{R}\), for which the superscripts \(L\) and \(R\) represent the left image and right image. For a point \(\mathbf{p}_{i;k-1}^{L}\) in \(\mathbf{I}_{k-1}^{L}\), its corresponding position in \(\mathbf{I}_{k-1}^{R}\) is \(\mathbf{p}_{i;k-1}^{R}\), and their correspondence was obtained by SAD based stereo image matching in the last calculation period. The point \(\mathbf{p}_{i;k-1}^{L}\) corresponds to the matching point \(\mathbf{p}_{i;k}^{L}\) in \(\mathbf{I}_{k}^{L}\) matched by the ORB descriptor; \(\mathbf{p}_{i;k}^{L}\) corresponds to the match point \(\mathbf{p}_{i;k}^{R}\) in \(\mathbf{I}_{k}^{R}\) obtained by SAD based stereo image matching. The calculation of the two parts was performed in the feature matching step mentioned above; therefore, when performing loop chain matching outlier detection, three of four sides of the loop chain were tested, and when four points \(\mathbf{p}_{i;k}^{R}\) were found by SAD, only normalized cross-correlation (NCC) would be used on a patch around \(\mathbf{p}_{i;k-1}^{R}\) and \(\mathbf{p}_{i;k}^{R}\), and features with a low NCC score would be rejected.  
Moreover, in the process of optimization iteration, the outliers were identified and then eliminated according to the reprojection error, as in the process for ORB-SLAM2.
# A Novel Approach for Software Defect prediction Based on the Power Law Function
## 1 Introduction
The power law is a phenomenon that has been found to be effective in characterizing data in many scientific fields such as physics, biology, economics, earth science, and computer science [20]. The power law describes a common behavior in which a few factors play decisive roles in one thing, i.e., a small number of samples holds a large influence. Simultaneously, it is known that in software defects, a small number of instances are responsible for many faults or defects. Software metrics showed fat tails in distributions of software defect data, and the skewness and fat tails of these data were properties of the power law function. Therefore, a correlation between the power law function and software metrics may exist. However, to the best of our knowledge, few people have considered the power law function in predicting software defects. In this study, we propose a novel software defect prediction approach based on power law functions. Section 2 presents fundamental characteristics with respect to the power law function and software defects and the details of the proposed software defect prediction approach. In Section 3, we conduct experimental studies including case studies and comparisons by imposing different datasets and algorithms. Furthermore, we present a complexity analysis in Section 4. Lastly, research conclusions and future work are generalized in Section 5.
# Physicochemical Properties and Sensory Attributes of Yanggaeng Treated with Citrus Peel Powder
###### Abstract  
We aimed to investigate the effect of citrus peel powder (CP) on the physicochemical, antioxidant, and sensory properties of Yanggaeng when treated with various levels (CON (0%), CP2 (2%), CP4 (4%), and CP6 (6%)) of CP. With an increase in CP content, Yanggaeng displayed a significantly elevated free radical scavenging rate, as indicated by increased 2,2-diphenyl-1-picrylhydraz1 (DPPH) and 2,2\({}^{\prime}\)-azino-bis (3-ethylbenzothazoline-6-sulfonic acid) diammonium salt (ABTS) free radical scavenging rates, as well as ferric reducing antioxidant power (FRAP). Furthermore, the addition of CP significantly reduced the pH and increased Brix values compared to the control (CON). CP treatment also exhibited a dose-dependent effect on colorimetric properties, resulting in decreased \(L^{\star}\) and increased \(a^{\star}\) and \(b^{\star}\) values. Moreover, CP addition changed the textural characteristics of Yanggaeng, escalating gumminess, hardness, and chewiness, while reducing adhesiveness. In terms of consumer preferences, Yanggaeng containing 4% CP (CP4) closely resembled CON in terms of attributes such as flavor, taste, sweetness, chewiness, purchase intent, and overall acceptance. However, Yanggaeng containing 2% (CP2) or 6% (CP6) CP led to a decreased overall preference for Yanggaeng. In conclusion, based on our findings, CP4 appears to be the most suitable choice for enhancing both the physicochemical and sensory properties of Yanggaeng. Furthermore, adding CP to Yanggaeng serves as a foundation for novel food production incorporating CP.  
citrus peel; Yanggaeng; physicochemical properties; sensory characteristics +
Footnote †: journal: Applied Sciences
## 4 Conclusions  
1. The core components and working principles of the rigid coaxial twin-rotor helicopter power system were introduced, a torque model of the turboshaft engine, and a rigid rotor aerodynamic model were constructed. A composite helicopter engine/transmission system/rotor model was constructed and coupled with a variable-speed transmission system model.
2. Using the control time points of the upshift and downshift of fuel flow _Fue_, hydraulic pressure _Hyd_, and pitch angle _Pit_ as input variables, 17 upshift/downshift timing strategies were designed using [PERSON]'s center combination sampling method. Furthermore, RSM was used to obtain regression-fitting models between the overshoot values of engine speed, transmission system input torque, rotor speed, transmission system output torque, friction clutch torque, and one-way clutch torque and input variables. The interaction effects between different upshift and downshift time points were analyzed.
3. The upshift optimization models Ha and Hb were designed using nonlinear programming. Compared with model Hb, model Ha has better impact suppression effects in terms of engine speed, input and output torque of the variable speed transmission system, and friction clutch torque. And compared to the baseline upshift strategy, the upshift strategy (\(t_{ha}\) = 15.0 s, \(t_{lb}\) = 13.0 s, \(t_{lc}\) = 10.60 s) optimized by model Ha reduces the engine speed, variable transmission input, output torque, and friction clutch torque overshoot by 5.13%, 8.46%, 10.48%, and 9.25%, respectively.
4. The downshift optimization models La and Lb were designed using nonlinear programming. Compared with the optimized model La, the optimized model Lb is slightly weaker in rotor stability control, but more balanced in engine speed, variable transmission system input and output torque and one-way clutch torque impact suppression. And compared to the baseline downshift strategy, the downshift strategy (\(t_{la}\) = 46.5 s, \(t_{lb}\) = 41.2 s, \(t_{lc}\) = 39.0 s) optimized by model La reduces the overshoot of input and output torque of transmission and one-way clutch torque by 6.51%, 7.61%, and 4.97%, respectively.  
In this paper, the studied power system primarily focuses on the effect of the time discrepancy in controlling the engine and twin rotor on the system's dynamic response. In real-world operational conditions, distinct control methods can be applied to various engines to assess the influence of torque output adjustments between the two engines during the shifting process. Additional research can delve into understanding how flexible blades influence the dynamic properties of gear shifting.  
Writing--original draft, J.W.; Writing--review & editing, H.Y.; Methodology, J.W.; Validation, [PERSON]; Data curation, X.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 52075552.  
Not applicable.  
All data, models, and codes generated or used in the study are included in this article.  
The authors declare no conflict of interest.
## 3 Modal Construction
### Basic Model Construction
\[C_{4}^{a}=P_{o}\cdot max(0,\,E-U_{o})\] (18) \[minC^{o}=C_{1}+C_{2}+C_{3}+C_{4}^{o}\] (19)  
In the above model, the common constraints that need to be satisfied to describe the optimization of multimodal transportation paths are as follows:  
\[x_{ij}^{m}\in\{0,1\},\,\forall i,j\in A,\forall m\in M \tag{20}\]\[min\ C^{t}=C_{1}+C_{2}+C_{3}+C_{4}^{t} \tag{12}\]0 \[min\ C^{t}=C_{1}+C_{2}+C_{3}+C_{4}^{t} \tag{12}\]1  
\[\sum_{i\in A}\sum_{m\in M}x_{ij}^{m}-\sum_{p\in A}\sum_{m\in M}x_{jp}^{m}= \left\{ \begin{array}{ll}-1,\;\forall j\in O\\ 0,\;\forall j\in N\\ 1,\;\forall j\in D\end{array} \right. \tag{23}\]  
\[\sum_{m\in M}x_{ij}^{m}\leq 1,\;\forall i,j\in A \tag{24}\]  
\[\sum_{m\in M}\sum_{k\in M}y_{i}^{mk}\leq 1,\;\forall i,j\in A \tag{25}\]  
\[x_{ij}^{m}.x_{jp}^{k}=y_{j}^{mk},\;\forall i,j,p\in A,\forall m,k\in M\cup m\
eq k \tag{26}\]  
\[x_{ij}^{m}\geq\frac{B}{d_{ij}^{m}},\;\forall i,j\in A,\forall m\in M \tag{27}\]  
\[t_{D}^{arrive}\leq LT \tag{28}\]  
The model formulation adheres to the following constraints: decision variables (20-21) define binary and continuous variable domains to ensure mathematical consistency; path continuity (22) ensures a connected route from origin O to destination D; flow conservation (23) maintains cargo balance at each node with inflows equal to outflows; mode exclusivity (24) constrains adjacent nodes to a single transport mode; single transshipment (25) limits modal shifts per node via cardinality constraints; operational continuity (26) ensures seamless transitions between transportation and transshipment; conditional availability (27) uses a big-M formulation with parameter B (a sufficiently large constant) to enforce infrastructure dependencies; temporal compliance (28) requires arrival times to precede specified delivery windows.
## 3 Results  
### Inhibition of F. oxysporum Growth by Volatile Organic Compounds Produced by Bacillus  
The experiment analysed the effect of volatile compounds produced by bacteria of the genus _Bacillus_ on the growth of the fungus _F. oxysporum_ in a test with two connected plates over a period of 96 h. The interactions between _F. oxysporum_ and bacteria of the genus _Bacillus_ (_B. amyloliquefaciens, B. subtilis and B. thuringiensis_) were analysed.  
The inhibition of the fungus _F. oxysporum_ depended on the bacteria of the genus _Bacillus_ used (Figure 3). The rate of mycelial inhibition of _F. oxysporum_ varied with the duration of the antagonism test (Figure 3). Depending on the bacterial species and the duration of the test, the percentage of inhibition of mycelial growth ranged from 35% to 70% (Figure 3).  
All _Bacillus_ species showed fungistatic properties against _F. oxysporum_ (Figure 3). The highest percentage of inhibition over the entire period of mycelial growth was recorded  
Figure 3: Inhibition of _F. oxysporum_ growth by three species of _Bacillus_ bacteria: blue—_B. amyloliquefaciens, green—_B. subtilis, red—_B. thuringiensis._  
for _B. amyloliquefaciens_, which reduced the growth of _F. oxysporum_ by approximately 70% after 24 h of the test (Figure 3), while the inhibition was 45 % after 96 h of the experiment (Figure 3). The species _B. subtilis_ and _B. thuringiensis_ also showed high antagonistic properties towards _F. oxysporum_, and the calculated percentage of inhibition of the mycelian growth by these _Bacillus_ species after 96 h was 35% and 40%, respectively (Figure 3).  
The bacterium _B. amyloliquefaciens_, which showed the highest ability to inhibit mycelian growth after 96 h of the experiment, was selected for further testing.
# MapReduce Algorithm for Location Recommendation by Using Area Skyline Query
### Area Skyline Query  
Assume that A is a square region in a map. Let \(F=\{F1,F2,...,Fm\}\) be a set of facility types. A traveler wants to find an excellent touristic place in a map where is close to some preferable facilities and is far from some unpreferable facilities. In this scenario, we define _area skyline_ and _area dominance_ as follows:  
**Definition 1**.: _(Area Skyline) We divide a square region \(A\) into \(n\times n\) grids \(G=\{g_{1,1},...,g_{n,n}\}\). A grid \(g_{i}\) is said to be in skyline if there is no other grid \(g_{j}(i\
eq j)\) in a map such that the distance of \(g_{j}\) to the nearest preferable facilities are smaller than that of \(g_{i}\) and the distance to the nearest unpreferable facilities are larger than that of \(g_{i}\)._  
**Definition 2**.: _(Area Dominance) If there exists such grid \(g_{j}\), we say that \(g_{i}\) is dominated by \(g_{j}\) or \(g_{j}\) dominates \(g_{i}\)._  
Figure 3 demonstrates an example of area skyline queries. We compute the area skyline objects by the algorithm proposed in [5] which we call it grid-based area skyline (GASKY). In the map, the area skyline objects are the shaded grids which are not dominated by each other. In other words, the shaded grids are closer to the preferable facilities and are farther from the unpreferable facilities than the unshaded grids. The unshaded grids are the dominated grids which can be eliminated from candidates.  
We use GASKY algorithm as the selection method of areas in a map. However, the time complexity of the GASKY algorithm is worse than those conventional skyline queries. Furthermore, the average processing time increases linearly with the growth of facilities. In this paper, we propose a novel  
Figure 3: An Area Skyline Example.  
algorithm to resolve the poor performance problem of GASKY in the MapReduce framework, which we call \"MRGASKY\".  
The main contributions of this paper are as follows:  
1. We develop MapReduce-based area skyline query computation, which is a distributed algorithm to address the poor performance problem for the grid-based area skyline queries.
2. We propose an efficient algorithm of the MapReduce-based computation.
3. We conduct an extensive performance evaluation, which shows the high efficiency and scalability of our proposed algorithm.  
The remainder of the paper is organized as follows. Section 2 surveys the related works. In Section 3, we formally define the problem and propose our efficient MRGASKY algorithm. Section 4 demonstrate the experimental validation of our proposed algorithm. Finally, Section 5 concludes the paper.
## 4 Sample Corpus Analysis  
We tested SNAP's ability to discover and track semantic concepts in three different corpora of text: a well-known and understood novel that contains the main plot and supporting sub-plots, a committee hearings with only a broadly defined focus, and a news media reports on a wide range of topics for a large geographic region. Each corpus illustrates differently structured text with different semantic concept flows. As each corpus can be analyzed in light of many hypotheses, we will not provide a detailed semantic concept flow analysis of each corpus beyond the annotation of the general observations of semantic flow structures.  
The interactive and semantic concept rich exploration of a text corpus is the main benefit of using the SNAP framework over the existing language processing tools that often (1) implement only one step of SNAP's framework, (2) rely on a manual text coding to identify topics of interest to be explored, (3) lack the interactive visualization layer, and (4) provide limited and pre-defined semantic concept exploration. SNAP allows a user to concurrently explore multiple hypotheses in the text or freely explore the final 3D network of the semantic flows for 'unexpected' synapses. This benefit is best illustrated by exploring the second corpus of a committee hearings (please see Section 4.2 for more details).  
The figures in this section show the semantic concept of an individual document on a x/y plane, while the temporally adjacent documents are stacked on the z-axis. For the visualization clarity the result figures in the top and bottom right panes do not show the inter- nor intra- document edges and only the 3D semantic concept flow. A detail view these edges in a single document is provided in the bottom left pane of the figures.
# Radiometric Identification of Signals by Matched Whitening Transform
## 3 Reversing Phase and Frequency Offsets
### Background  
Phase and frequency offset correction prior to source identification is not always addressed in the radiometric identification literature [17]. The traditional approach to carrier phase recovery is the power law method [53]. Raising the signal to the \(M\)th  
Figure 4: Constellation smearing caused by time-varying phase offset under two SNR values. (**a**) SNR = 20 dB, (**b**) SNR = 10 dB.  
power creates a tone at \(M\) times the offset frequency which can be used to derotate the constellation. However, this method only works for fixed phase offsets. The approach presented here extracts arbitrary phase trajectories by fitting a model to the maximum likelihood estimate of phase points measured over multiple signal segments. The phase trajectory is first estimated from signal segments that are short enough for the phase to be considered stationary; essentially a snapshot of the phase in time. The slope of the line fitted to the phase angles using least squares is proportional to the offset frequency. In addition, the least squares fit method handles nonlinear phase trajectories caused by second order offset frequency effect. This is not possible with he power law method.