esa-sceva/satcom-chunk-collection huggingface dataset:
# A Low Overhead Mapping Scheme for Exploration and Representation in the Unknown Area  
[PERSON]  
1 Department of Computer Engineering, Konkuk University, Seoul 27478, Korea 1, [PERSON]  
2 Department of Multimedia Engineering, Gangneung-Wonju National University, Gangwon-do 25457, Korea 2, [PERSON]  
3 Department of Software, Korea National University of Transportation, Chungcheongbuk-do 27469, Korea 3, [PERSON]  
4 Department of Computer Information, Yeungnam University College, Daegu 42415, Korea 4, [PERSON]  
5 Department of Computer Software Engineering, Changshin University, Gyeongsangnam-do 51352, Korea 5  
[PERSON]  
1 Department of Computer Engineering, Konkuk University, Seoul 27478, Korea 1,* 1,  
as an array. This data consists of all cells from \(x_{0}\), \(y_{0}\) to \(x_{map\ of\ width}\), \(y_{map\ of\ height}\). This format means that the map should remain a rectangular structure, regardless of the shape of the map. For example, to represent a completed map of a triangle, the grid map creates a rectangular map that matches the array structure containing all the vertices of the triangle. Here, the area of the array created for only the representation, except for the triangle map, is not the information of a map, although it must always be loaded when SLAM uses the map. These problems cause SLAM to do more calculations.  
A topological mapping method has been proposed to overcome problems of the grid map, as shown in Figure 1c. It is a way to create a graph based on landmarks such as walls, corners, and obstacles [12; 13]. This method represents an area as a node. The node is the area where the mobile robot can move. If an area and the other area are connected, these two areas are connected using the edge. The edge is the path that can move in these two areas. Therefore, this map representation method efficiently uses memory to use the map through nodes and edges [14]. The topological map efficiently reduces memory. However, there is a problem that the detailed movement of the mobile robot is difficult because the information on the map is insufficient.  
Therefore, a hybrid mapping scheme has been proposed, which has both grid map information and topological map efficiency. The approximate cell decomposition algorithm uses a recursive method to continue subdividing the cells and connects divided cells through the edge as a topological map [15]. Additionally, a path planning method using approximate cell decomposition has been proposed [16]. However, there is a limit to maps that must be known in advance to divide the cells by a recursive process in this method. The SLAM without the information of the area in advance cannot use this method.  
In this paper, a new mapping scheme is proposed for reducing overheads in the SLAM, which the authors called the Rmap. It uses rectangles instead of cells of the grid map to enable efficient exploration while performing the SLAM on unknown areas.  
The rest of this paper is organized as follows. Section 2 introduces the Rmap scheme in detail. Section 3 shows the performance evaluation results of the Rmap. Finally, conclusions are discussed in Section 4.
## 3 VRIS Image Steganography Model
### Hybrid Loss Function
#### 3.4.4 A Variant of the Binary Cross-Entropy Loss  
The binary cross-entropy loss function updates the parameters of the model through optimization algorithms such as gradient descent, making the model's predictions closer to the true labels. By minimizing the binary cross-entropy loss function, we can train more accurate and efficient image steganography models. The basic form of the binary cross-entropy loss function can be expressed as follows:  
\[L(y,\hat{y})=-ylog(\hat{y})-(1-y)\log(1-\hat{y}) \tag{7}\]  
where \(y\) represents the true label of the sample, and \(\hat{y}\) represents the predicted probability of the discriminator on the sample.  
The discriminator in GAN is essentially a binary classifier that can distinguish whether the input sample is a real sample or a generated sample. Therefore, during training for image steganography tasks, we use a variant of the binary cross-entropy loss function that is often used to measure the accuracy of the model in predicting hidden information.  
The loss function of the discriminator is divided into two parts: the first part represents the loss of the discriminator's prediction success for the true samples, and the second part is the loss of the discriminator's prediction success for the generated samples, where in general we would like \(D(x)\) to be close to 1, and \(D(G(z))\) close to 0. The loss function of the discriminator is as follows:  
\[L_{D}=-E_{x\sim P_{data}}[\log D(x)]-E_{z\sim P_{z}}[\log(1-D(G(z)))] \tag{8}\]  
where \(D\) represents the discriminator, G is the generator, \(P_{\text{data}}\) is the real data distribution, and \(P_{z}\) represents the noise distribution.  
The generator goal is to trick the discriminator into producing false predictions, and in general, the generator wants to maximize the discriminator's prediction probability for the generated samples. Therefore, in the actual training, we will maximize \(E_{z\sim P_{z(z)}}[\log(D(G(z)))]\). The generator's loss function is shown below:  
\[L_{G}=-E_{z\sim P_{z(z)}}[\log(D(G(z)))] \tag{9}\]
# Aroma Characteristics of Lavender Extract and Essential Oil from _Lavandula angustifolia_ Mill.
## 2 Results and Discussion
### Principal Component Analysis  
To further distinguish the differences between lavender essential oil and lavender extracts, principal component analysis (PCA) was conducted on the common volatiles, which were the same volatiles identified in LEO and LE-EF. According to the PCA analysis in Figure 3A, the accumulated contribution rate of the first two principal components (PC 1, 77.5%; PC 2, 20.7%) was 98.2%, which appeared to represent the sufficient information of samples, and the two samples were well-separated. The LE-EF exhibited high scores on negative PC 1, where the loadings of characteristic volatile compounds including 1-octen-3-yl acetate, _trans_-\(\alpha\)-bergamotene, lavandulyl acetate, carvone, coumarin, linalool oxide I, linalool oxide II, cryptone and linalyl acetate were high, among which linalyl acetate was the most characteristic aroma compound in lavender extracts, and most of them imparted a floral note. The LEO showed high scores in positive PC 1, which contained high loadings of compounds, including lavandulol, camphol, (_E_)-\(\beta\)-farnesene, \(\alpha\)-santalene, caryophyllene oxide, terpinen-4-ol, 1,8-cineole, limonene--the characteristic aroma compounds with camphor-like, spicy or herbal odors in lavender essential oils (Figure 3B).
###### Abstract
* A lightweight roaming authentication protocol for SGIN: The roaming authentication protocol provides a strategy for roaming between different operator networks for satellite-connected UE in SGINs, which needs to pre-negotiate with the corresponding core network after the last authentication is completed. The UE only needs to complete mutual authentication with the satellite when roaming, thus reducing the propagation delay. The remainder of this article is organized as follows: We review the related work in Section 2. Then, we introduce the prior knowledge involved in the protocol and SGIN system model in Section 3. The details of our scheme are presented in Section 4. The security of the proposed scheme is proven in Section 5. We compare the performance of related schemes in Section 6. Finally, we summarize the article in Section 7.
## 3 Results
### Subjective Survey Results  
In the subjective survey of symptoms and performance under each lighting condition, most subjects believed that they had performed better (43.4%, 23 responses) and  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(p\) **Values** & **C0 (2, \(-\)2)** & **C0 (2, 0)** & **C0 (2, 2)** & **C0 (3, \(-\)1)** & **C0 (4, 0)** & **C0 (6, 0)** & **C0 (6, 2)** \\ \hline Basal–White & \(p=0.631\) & \(p<0.001\)* & \(p=0.267\) & \(p=0.224\) & \(p=0.079\) & \(p=0.280\) & \(p=0.009\)* \\ \hline Basal–Red & \(p=0.417\) & \(p=0.042\)* & \(p=0.267\) & \(p=0.302\) & \(p=0.023\)* & \(p=0.015\)* & \(p=0.961\) \\ \hline Basal–Blue & \(p=0.660\) & \(p=0.006\)* & \(p=0.396\) & \(p=0.037\)* & \(p=0.050\)* & \(p=0.003\)* & \(p=0.954\) \\ \hline White–Red & \(p=0.183\) & \(p=0.002\)* & \(p=0.827\) & \(p=0.439\) & \(p=0.849\) & \(p=0.584\) & \(p=0.084\) \\ \hline White–Blue & \(p=0.836\) & \(p=0.008\)* & \(p=0.721\) & \(p=0.392\) & \(p=0.471\) & \(p=0.045\)* & \(p=0.051\) \\ \hline Blue–Red & \(p=0.151\) & \(p=0.653\) & \(p=0.702\) & \(p=0.033\)* & \(p=0.841\) & \(p=0.421\) & \(p=0.809\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical significance (\(p\) value) for order 2 of LOA (A) and for the coefficients C0 (3, \(-\)1), C0 (4.0), C0 (6.0), and C0 (6.2) belonging to HOA orders (B). An asterisk (*) indicate statistical significance (\(p\leq 0.50\)).  
Figure 8: Comparison between the basal measurement and the three types of lighting in terms of M, J0, and J45. Each \(p\) value is represented on the bars, where * and bold letters indicate statistically significant differences (\(p\leq 0.05\)).  
experienced a greater sense of visual well-being (41.5%, 22 responses) overall during tests conducted under white lighting conditions, followed by blue lighting, where performance (34%, 18 responses) and visual well-being (35.8%, 19 responses) were also perceived positively. Consequently, the subjects perceived themselves to have performed worse (22.6%, 12 responses) and experienced a lower sense of visual well-being (22.6%, 12 responses) under red lighting.
## 3 Results
### A Bowed Oscillator  
In addition to arranging simple harmonic oscillators in delay-free feedback networks, we may use ODEs and RK4 as a means of introducing physically motivated nonlinearities. As an example, consider a harmonic oscillator driven by friction--such as a point on a bowed violin string.  
The physics of bow-string interaction is well understood [27; 28; 29]. The effect of the bow on the point of bowing is usually modeled through a nonlinear function which is coupled to the point of bowing (here given as a single oscillator). The motion of this oscillator is  
\[\ddot{u}=-\omega^{2}u-F_{b}\Phi(v-v_{b}) \tag{23}\]  
In this model \(u\) is displacement of the oscillator, \(f=\omega/2\pi\) is the fundamental frequency, \(F_{b}\) is the force of the bow divided by mass (which is a quantity we neglect to include in our model in favor of using \(\omega=\sqrt{k/m}\)), \(v\) is the oscillator's velocity, \(v_{b}\) is the velocity of the bow, and \(\Phi\) is a nonlinear friction model such as:  
\[\Phi(v_{rel})=\sqrt{2a}v_{rel}e^{-2a\
u_{rel}^{2}+1/2} \tag{24}\]  
where \(a\) is a coefficient of friction and \(v_{rel}=v-v_{b}\).  
As is the case with the second order spring-mass system given in Equation (11), we can re-write Equation (23) as a pair of ODEs:  
\[\begin{split}\dot{y}=-\omega^{2}x-F_{b}\Phi(y-v_{b})\\ \dot{x}=y\end{split} \tag{25}\]  
and solve with RK4.  
A slightly different form of this model is discussed in Chapter 3 of [21]. There, the linear part of Equation (23) is solved with Backward Euler, and the nonlinearity \(\Phi\) is solved with Newton-Raphson. This requires, of course, that \(\Phi\) be differentiable and that the derivative be determined prior to computation. Furthermore, Newton-Raphson is an iterative solver so it may not be appropriate for real-time implementation due to convergence issues. These two implementations are compared in Figure 7. The code used to generate the Newton-Raphson version of the system was taken directly from Appendix A in [21].  
Figure 7: The bowed oscillator model given here (solved with RK4) and that given in [21]. In both implementations, oscillator frequency \(f=200\), \(F_{b}=500\), \(v_{b}=.2\) and \(a=100\). **(a)** The system given in Equation (25) solved using RK4; **(b)** Using Newton-Raphson and Backward Euler to solve Equation (23).  
Since RK4 puts us in a position to solve the system without having to differentiate \(\Phi\), we are free to experiment with friction models that are discontinuous. For example, we may study the discontinuous friction model given by  
\[\Phi(v_{rel})=\text{sign}(v_{rel})e^{-a|v_{rel}|} \tag{26}\]  
A comparison of the bowed oscillator using this friction models and the one given in Equation (24) are compared for various values of \(a\) in Figure 8.
# An Improved Back-Projection Algorithm for GNSS-R BSAK Imaging Based on CPU and GPU Platform
## 6 Conclusions  
With the improvement of GPU computing power, the BPA has become a feasible imaging method for processing GNSS-R BSAR data. This paper proposes an improved BPA for processing GNSS-R BSAR data using a heterogeneous architecture platform of CPU and GPU. The improved BPA can accurately compensate for the nonlinear motion error of the satellite and improve the synchronization performance of the direct signal and echo signal. The improved BPA on the CPU and GPU platform greatly reduces the total running time and can meet the needs of many offline GNSS-R BSAR imaging applications. As shown by the simulation and the experimental results, the algorithm performs well in imaging quality and efficiency. Using GPS L5 signal for a long-time synthetic aperture, the range resolution of the generated image is about 19 m, and the azimuth resolution is about submeter level. Images of this quality can meet the needs of many applications; an important application of GNSS-R BSAR imaging in one-station fixed mode is deformation monitoring. The next step is to apply this algorithm to the research of deformation monitoring, which will greatly reduce time consumption and improve emergency response time.  
Conceptualization, D.Y. and F.W.; methodology, S.W.; software, S.W.; validation, S.W., F.W.; formal analysis, S.W.; investigation, S.W., G.G. and Z.X.; resources, S.W.; data curation, S.W.; writing--original draft preparation, S.W.; writing--review and editing, S.W. and F.W.; visualization, S.W., G.G. and Z.X.; supervision, D.Y.; project administration, D.Y.; funding acquisition, D.Y and F.W. 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 (NNSFC) under grant no. 41774028, China Postdoctoral Innovation Talent Support Program (BX20200039), Beihang University Beldou technology achievement transformation and industrialization fund support (BARI2003).  
Not applicable.  
The processed data cannot be shared at this time because they are also part of an ongoing research project.  
The authors acknowledge no more support.  
The authors declare no conflict of interest.
# Ramjet Nozzle Analysis for Transport Aircraft Configuration for Sustained Hypersonic Flight
## 4 CFD Analysis of Ramjet Nozzles
### CFD Setup
#### 4.1.6 Parametric Study  
The parametric study of the nozzle was comprised of the geometric parameters to be optimised for maximum thrust. This CFD study encompassed all the elements mentioned in the previous sections and performed the tests in an iterative manner on combinations of different geometrical variables to ensure maximisation of the performance criteria discussed previously. The method also provided insight on the comparison between the three geometries and the geometrical aspects that render one superior to another.  
The conducted simulations took into account the following assumptions for compressible flow in a ramjet nozzle [18]:  
* For simplicity, the exhaust gas was assumed to be an ideal gas with specific heat ratio of \(\gamma=1.4\) for simplicity.
* The gas flow was isentropic, adiabatic, and frictionless.
* The gas flow was constant (i.e., steady).
* Since the exhaust accelerates exponentially, the gas flow behaviour was compressible.
* The gas flowed in a straight line along the nozzle axis of symmetry.  
The details of the geometric parameters tested in the study for all three nozzles are provided below in Tables 3-5, along with the exact geometry of each tested nozzle type illustrated in Figures 12-14. The range of values selected for the bell nozzles in particular crossed the regime from TIC to TOC to investigate the effects of shifting the internal shock and enlarging the kernel zone when moving from TIC to TOC. In addition, the nozzle pressure ratio was another output parameter that resulted from the specific geometric constraints used, and this is illustrated in the figures associated with the corresponding nozzle types in the sections that follow.  
Figure 12: Conical nozzle geometry.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Geometrical Parameters** & **Parameter ID** & **Value Range (m)** \\ \hline Nozzle Horizontal Length & H1 & 3, 3.55, 3.8 \\ Nozzle Exit Radius & V1 & 1.65, 1.8, 2 \\ Total Combinations & & 3 \(\times\) 3 = 9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Bell nozzle dimensional range.  
Figure 14: Dual nozzle geometry.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Geometrical Parameters** & **Parameter ID** & **Value Range (m)** \\ \hline Nozzle Horizontal Length & H1 & 3, 3.55, 3.8 \\ Nozzle Exit Radius & V1 & 1.65, 1.8, 2 \\ Nozzle Contour Radius & R1 & 6, 8, 10 \\ Total Combinations & & 3 \(\times\) 3 \(\times\) 3 = 27 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Dual nozzle dimensional range.  
Figure 13: Bell nozzle geometry.
## 3 Problem Description
### Batch-Processing Time  
Our approach to the scheduling problem seeks to sensitize the batch-processing time considering a decrease in the flow over time due to clogging. Each production line comprises two stages: filtering and bottling. The nominal flow for both stages is determined by SKU-specific parameters. In the case of filtration, the admissible flow rate is determined by the filtering area available, calculated for the number of filters that a housing can contain and the pore diameter of the filter itself, which is specified in accordance with the filtering requirements of the batch. For the bottling stage, bottle size is the parameter that conditions the capacity of the line; furthermore, it is assumed that the flow will remain constant for this stage. With regard to the aggregated flow, the minimum of both is considered.  
In the literature, authors have considered a processing-time calculation based on the nominal flow of the line, thereby overestimating the actual production capacity.  
In order to model the reduction in flow rate resulting from filter clogging, a Darcy estimation is employed. This is a validated approach for describing the hydraulic performance of a fluid in a porous medium [43].  
In principle, there are two possible scenarios: (1) The admitted flow rate through the filtration line (i.e., the Darcy flow rate, \(Q_{Darcy}\)) is greater than the flow rate through the bottling line (i.e., the operational flow rate, \(Q_{op}\)). In this case, the latter will be considered the initial flow rate. (2) Conversely, if the operational flow rate is greater than the Darcy flow rate, the initial flow considered is that from the filtration stage. In both scenarios, the objective is to model the decline of the flow rate, represented by the function \(Q(t)\), to the minimum acceptable threshold, \(Q_{min}\), as a fraction of the initial condition. At this point, the filters must be cleaned or replaced to restore efficient operation within tolerable margins. The reduction in flow rate is attributed to the accumulation of solids within the filters, which decreases the available filter area, \(A(t)\), resulting in an exponentially distributed decline. The rate of filter clogging depends on the physicochemical characteristics of the wine batch being processed.  
The variables and the modeling procedure will be defined in the following section, according to the cases described.
# A Systematic Review and Future Development of Automotive Classis Control Technology
## 5 Automotive Suspension by Wire System
### Key Technologies Regarding Fault Tolerance  
When the wire-controlled suspension system malfunctions, the parameters of Kp, Ki, and Kd set in the early stage of PID suspension are fixed values and cannot quickly adapt to changes. Therefore, a fault-tolerant mechanism is needed to perform gain and compensation processing on the control signal.  
Passive fault-tolerant control mainly designs fixed and invariant fault-tolerant control laws for pre-known fault types, so that the system is not sensitive to preset faults (such as sensor failure) [82]. This method does not require pre-diagnosis of faults or real-time adjustment of control laws, and the implementation time and cost are relatively low.  
The theoretical basis of active fault-tolerant systems is FDD (Fault Self-Test Feedback System). When a system malfunctions, the diagnostic unit detects and diagnoses it, and controls the feedback/feedforward controller to obtain appropriate fault-tolerant control laws, ensuring the stability of the closed-loop system. In the implementation process of active fault-tolerant control system, the system model is first established, and based on this, a system fault model is established: An FDD system is designed based on system characteristics, and then a fault-tolerant control law is designed based on compensation rules.  
In addition to conventional chassis support and shock absorption, wire-controlled suspension has been extended to the driver's cabin and seat of commercial vehicles due to its good load adaptability and low natural frequency, greatly improving long-distance driving comfort and fatigue resistance. The wire-controlled suspension can meet the requirements of driving smoothness and vehicle handling under different working conditions. However, due to its complex structure, it has a certain negative impact on the failure rate, safety risks, and energy consumption of the entire vehicle.
# A Hybrid Text Generation-Based Query Expansion Method for Open-Domain Question Answering
## 3 Experimental Results and Analysis
### Fusion Strategies for Retrieval Results  
In this section, we explore how to fuse the three types of retrieval results better to achieve improved retrieval accuracy. We have experimented with four fusion methods: \(ctx(AS)\), \(ctx(AT)\), \(ctx(ST)\) and \(ctx(AST)\) (for example, \(ctx(AS)\) refers to the combination of results retrieved using \(ctx(A)\) and \(ctx(S)\) as query expansion terms). According to Table 4, the overall performance is better when fusing the results of all three types, with \(ctx(AS)\) being the second best. This shows that the retrieval results obtained using \(ctx(A)\), \(ctx(S)\), and \(ctx(T)\) as query expansion terms are complementary.  
Furthermore, this paper employs a uniform fusion approach, in which an equal number of candidate passages are extracted from each retrieval result to form a new candidate passage set. For instance, in the \(ctx(AS)\) method, for each query, we extract the top 500 candidate passages from the retrieval results corresponding to the query with \(ctx(A)\), and the top 500 candidate passages from the retrieval results corresponding to the query with \(ctx(S)\). These two sets are then intermixed to form the final retrieval results. This fusion approach is relatively simple, but other fusion methods, such as rank reciprocal, can also be considered.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Methods** & & **NQ** & **Trivia** \\ \hline
**Extractive** & Hard EM [17] & 28.1 & 50.9 \\  & Path Retriever [18] & 32.6 & - \\  & ORQA [19] & 33.3 & 45.0 \\  & Graph Retriever [20] & 34.5 & 56.0 \\  & REALM [21] & 40.4 & - \\  & DPR [11] & 41.5 & 57.9 \\ \hline
2-4 **Generative** & GPT3 [22] & 29.9 & - \\  & T5 [3] & 36.6 & 60.5 \\  & SpanSeqGen [23] & 42.2 & - \\  & RAG [4] & 44.5 & 56.1 \\  & FD [6] & **51.4** & **67.6** \\ \hline
2-4 **Sparse** & BM25 (E,ours) & 27.2 & 35.4 \\  & GAR (E) & 30.4 & 44.4 \\  & HTGQE (E) & 35.0 & 50.7 \\  & BM25 (G,ours) & 28.8 & 47.1 \\  & GAR (G) & 24.0 & 39.7 \\  & HTGQE (G) & **38.3** & **56.8** \\ \hline \hline \end{tabular}
\end{table}
Table 3: OpenQA system performance comparison between HTGQE and other methods.