esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of Multimodal Discrete Network Design Problems Based on Super Networks
### The Discrete Network Design Problem  
Generally, the network design problem (NDP) of urban transportation refers to an optimal investment decision problem. For clarity, the decision problem is to determine the expansion of certain links or adding of new links under certain investment constraints and the choices of traffic travelers, which makes the entire transportation network reach certain indicators. Currently, NDP is divided into two forms according to the different types of decision variables: continuous network design problem (CNDP) [9; 10; 11; 12; 13] and discrete network design problem (DNDP) [14; 15; 16; 17; 18; 19; 20]. The first one tends to improve the capacity of existing road sections, while the second one is to add new road sections to the existing network. The multimodal transportation network design problem in this paper is a discrete network design problem (DNDP).  
For DNDP, [PERSON] presented a nonlinear mixed-integer programming model and proposed a branch and bound algorithm to solve the model, which optimized some sections of the urban road network to minimize the overall congestion [14]. [PERSON] et al. improved B & B algorithm based on LeBlanc [15]. [PERSON] et al. transformed the bi-level programming model into a model and proposed a heuristic algorithm based on B & B to solve the model [16]. [PERSON] et al. established a bi-level programming model, and used the support formula method to express the relationship between the expanded links and the added links, and designed an algorithm to solve the bi-level programming model. Considering the optimal number of lanes added for each candidate link in the network [17]. [PERSON] et al. proposed a multi-level planning model with multiple capacity levels and solved the model by the method of system relaxation and user equilibrium reduction [18]. [PERSON] and [PERSON] developed a mixed-integer nonlinear model that decides whether a link is added to the network and developed a solution algorithm of global optimization method with linearization, external approximation, and range reduction technology [19]. [PERSON] et al. studied demand-originated reversible lane design plans for car network and presented a nonlinear bilevel mixed-integer programming model to find the optimal lane combination strategy. In addition, for solving the proposed model, they developed heuristic algorithms to obtain its approximate optimal solution [20].
# Improving the Robustness of Model Compression by On-Manifold Adversarial Training
## 3 Background
### Adversarial Example
#### 3.2.1 Fast Gradient Sign Method  
The fast gradient sign method (FGSM) [10] is one of the early white-box adversarial attack methods designed to quickly find the perturbation direction in a single step so that the loss function of the target classifier increases. When the dimension of input is large, changing each feature of \(x\) by a minimum value \(\epsilon\) yields a perturbation \(\delta,\|\delta\|_{\infty}=\epsilon\). This change can have a significant impact when we calculate the linear product \(w^{T}x\) of \(x\) with the model weight vector \(w\). Taking a first-order approximation of the target classifier's loss function \(\ell(f(x;w),y)\) to the real training example \(x\) with a small perturbation \(\delta\) gives  
\[\ell(f(x+\delta;w),y)\approx\ell(f(x;w),y)+\
abla_{x}\ell(f(x;w),y)^{T}\delta,\]  
and we maximize the right-hand side for \(\delta\) bounded by the \(\ell_{\infty}\) norm ball of radius. This maximization of the right-hand side is exactly the same as follows:  
\[\delta=\epsilon*\text{sign}(\
abla_{x}\ell(f(x;w^{*}),y)),\]  
where \(w^{*}\) is the weight vector of trained target classifier. Finally, we obtain adversarial examples by adding the adversarial perturbation to the input.  
\[x^{\prime}=x+\epsilon*\text{sign}(\
abla_{x}\ell(f(x;w^{*}),y)) \tag{2}\]  
As the FGSM method takes only a single step to find adversarial examples, it is relatively faster than other iterative adversarial attack methods. However, this is not a sophisticated attack method since there is no process of minimizing the perceptual difference between the adversarial example \(x^{\prime}\) and the original input \(x\).
## 5 Experiment and Results
### Experiments and Discussion
#### 5.4.3 Meter Reading Area Detection Results  
In addition, we compared the performance of the YOLOv8 models for the meter reading area detection dataset. As shown in Table 4, the model introduced in this paper exhibits outstanding performance on the three mentioned datasets. This confirms the model's remarkable learning capability and robustness across various datasets. Moreover, considering the varying levels of task complexity, the pruned model size also adjusts accordingly. In the meter reading area detection task, our model can still rival the YOLOv8 model when the parameters are reduced by 43.93%. Hence, the model can be tailored to different tasks by adjusting suitable parameters while maintaining high accuracy.
## 6 Discussion
### Result of Threshold Calculation
#### 6.2.1 Road Geometry  
As shown in Table 5, the threshold values for the vertical slope, the radius of the curve, and compliance with the minimum radius of the curve design criteria were calculated. In road conditions with a vertical slope \(\geq\) 4%, the number of serious accidents and fatal accidents sharply increased (the number of slight accidents decreased), and the detailed threshold value was 4.3%. On the other hand, when the vertical slope was less than 4% or there was no vertical slope, the probability of a slight accident was higher than that of serious or fatal accidents. When the vertical slope was less than 2%, the probability of a slight accident further increased.  
It can be seen that the ratio of slight accidents was highest on roads with a radius of curve \(\geq\)500 m, and the probability of fatal accidents increased on roads wherein 250 \(\leq\) radius of curve \(<\) 500 m. In particular, because the radius of the curve was applied as a continuous variable, the probability of a fatal accident was very high in the radius range of the radius at 250-370 m. On a road with a radius of curve smaller than 250 m, the probability of a fatal accident was reduced. This is related to the tendency of drivers to reduce the driving speed and focus on safe driving by increasing the level of attention in the case of driving on curved sections with a small radius of the curve.  
It can be seen that the ratio of fatal accidents decreased when there was a non-compliance with the minimum radius of curve design criteria, and the ratio of fatal accidents increased in the case of compliance with the design criteria. Similar to the analysis results that are based on the size of the radius of the curve, it is inferred that the level of driver's attention increased in the curved road section with a curve radius smaller than the minimum design criterion of 280 m, which resulted in safe driving and helped maintain a low driving speed.
# The Influence of Immersive and Collaborative Virtual Environments in Improving Spatial Skills
## 5 Discussion
### Limitations and Future Work  
One limitation of this study is the small sample size. Conducting this study was quite time-intensive, as the students had to spend many hours participating in the modeling activity, which hindered our ability to obtain a larger number of participants. Another limitation is that the students all had an academic background that is already associated with higher performance on spatial assessments. In the future, this application could be tested with students from other academic backgrounds.  
To address the possibility of practice effects, this application could be tested in a context with a control group that either does not participate in any spatial training or who participates in a different type of spatial training activity. Such a study design for a follow-up study could help remove the possibility of improvements in scores being related, in part or in full, to practice effects. Future work could also use a different approach to validate the training, such as by incorporating the pre- and post-spatial testing into the spatial training instrument itself. This may be advantageous, as VR may also be a more accurate medium to use to assess spatial skills in comparison to traditional tests [74].  
It appears that the virtual collaborative environment made the modeling task more engaging for the students than it would have been to perform the same task alone in VR. Future studies could use our tool in both conditions, alone or collaboratively, to assess the specific impact of collaboration on the effectiveness of the tool.  
Regarding the usability of the application, future work could incorporate an embedded tutorial within the application rather than having a live instruction session at the start of the study. This could reduce the amount of work on the instructor's part and facilitate the use of the tool with more groups of students in the future.
# Efficient Low-Complexity Turbo-Hadamard Code in UAV Anti-Jamming Communications
## 1 Introduction
Therefore, it is critical to solve the problems of high decoding complexity and high efficiency losses for the platform of resource-limited UAVs. In this paper, we focus on applying the turbo-Hadamard codes to an FH system for anti-jamming purposes. We employ approximate algorithms to greatly reduce the decoding complexity [22], which results in the effect that the usages of lookup tables (LUTs) are reduced by more than 20% compared to the practical low-rate codes [23; 24]. Considering the fact that the information package is usually short in UAVs [25], we proposed the use of advanced tail-biting turbo-Hadamard codes, which are constructed by concatenating several tail-biting convolutional-Hadamard codes in parallel in order to reduce the communication latency [26]. Simulation results showed that the proposed tail-biting turbo-Hadamard codes performed better than other low-rate codes when faced with various jamming conditions. The contributions of this paper are as follows:  
1. An improved turbo-Hadamard code based on tail-biting is proposed. The proposed codes outperform the conventional terminate-state-zeroturbo-Hadamard codes in terms of the coding gain and anti-jamming performance. Since we introduced no redundant bits, our algorithm has proven to be more efficient than other low-rate codes, especially in the UAV communication scenarios requiring extremely short code lengths. 2. A corresponding decoding scheme is proposed. For easy implementation on the hardware platforms of resource-limited UAVs, we employ addition operations instead of multiplication, division, exponential and logarithm operations. We prove that the proposed algorithm consumes fewer resources than the existing concatenated low-rate codes FPGA platform. 3. The expression of the computational complexity and transmission efficiency are given in closed form. Meanwhile, we analyze the complexity and efficiency of the proposed tail-biting turbo-Hadamard codes in detail. The structure of this paper is organized as follows: Section 2 presents a description of the system model. Section 3 introduces the encoder and decoder of turbo-Hadamard codesbased on tail-biting. Section 4 presents the simulation results and an analysis of tail-biting turbo-Hadamard codes. Section 5 summarizes the contributions of this paper.
# Effect of Space Configurational Attributes on Social Interactions in Urban Parks
## 2 Methodology
### Space Configurational Attributes: Space Syntax Analysis
Therefore, several micro-level design characteristics were also considered in this analysis, including space type, space scale, and lateral visibility [23; 46]. The type and size of the activity space have been widely argued to be influential on social activities in parks [10; 47; 48]. In this study, the type and the scale of the space were measured as a categorical variable (pathways and activity zones) and a continuous variable (length of pathways and area for activity zones), respectively. To include the effect of green elements, vegetation density has been widely used for park environment evaluation [49; 46]. However, as suggested by [PERSON], the perceived openness rather than the physical attributes were more useful in describing how people perceive the environment in parks [50]. Openness was defined as the amount of space perceivable to the viewer [50]. Based on the analysis of the density and type of vegetation in parks, lateral visibility was suggested for the evaluation of space openness [23]. In this study, lateral visibility was measured in three categories according to lateral sightline interruption: (1) open (no interruption on either side of the pathway; interruption along the boundary of the activity zone less than 30%); (2) moderate open (interruption on one side of the pathway; interruption along the boundary of the activity zone between 30% to 70%); and (3) closed (interruption on both sides of the pathway; interruption along the boundary of the activity zone more than 70%).
# Recent Developments in Modulation Spectroscopy for Methane Detection Based on Tunable Diode Laser
## 4 Application of Modulation Spectroscopy for Methane Detection  
Due to the advantage of high spectral resolution, TDLAS has been widely used to measure methane absorptions and concentrations in the applications of molecular spectroscopy [68], natural gas leak detection [69], and trace greenhouse gas monitoring [70] and so on. With TDLAS, the laser frequency is scanned across the absorption line of methane gas, by tuning the output wavelength of the diode laser. Many methane sensor systems are developed utilizing modulation spectroscopy [71; 72] to improve the limits of methane detection. The modulation spectroscopy technique used for TDLAS has been reviewed in detail elsewhere [29; 44; 73; 74], and this section aims to bring relevant methane detection based on modulation spectroscopy up to date with recent developments.
## 3 Data-Driven Robust Scheduling Method  
The proposed data-driven robust scheduling method includes two parts: an uncertain parameter analysis based on kernel density estimation technology and an interval scheduling optimization based on a particle swarm optimization algorithm. First, we estimate the probability density function of uncertain parameters based on the kernel density estimation method, and we adjust the range of the interval number through the confidence level. Second, we design an interval scheduling method that takes the interval number of uncertain parameters as input and optimizes the interval makespan through the PSO algorithm.
###### Abstract
Therefore, in this study, we developed a fully automated glioma-grading pipeline using post-contrast T1-weighted images using two 3D CNNs--the trained model for tumor region extraction and the original model for grading. The outline of the proposed method is depicted in Figure 1. First, a post-contrast T1-weighted image was fed to a 3D CNN for brain tumor region extraction. The extracted tumor region was then fed to the other 3D CNN for grading and, thus, the glioma grade was obtained. So far, it has been difficult to develop fully automated grading pipeline that extracts and grades the glioma regions from 3D images due to problems such as calculation cost. Whereas the proposed method achieves segmentation and grading with low computational cost by using pre-trained 3D CNNs for the segmentation task, which is computationally expensive to train. Furthermore, although by using only one type of MR image, the proposed method achieves the same or better performance than the method using multi-sequence, which is computationally expensive. Figure 1: Outline of the proposed method. The processing in the method is fully automated, and there is no manual extraction of the tumor region and creation of VOI.
## 3 Results  
### Grain-Size Properties  
Throughout the studied interval of core YDZ-3 (7.7-18.0 m), the mean value of sediment grain size (Mz) is 5.5 \(\pm\) 0.9 \(\Phi\), which indicates a relatively high-dynamic sedimentary processes that changed significantly in the Holocene. The contents of silt (4-63 \(\upmu\)m) and sand (>63 \(\upmu\)m) particles are 5.2 \(\pm\) 2.3% and 8.2 \(\pm\) 5.3%, respectively, while the variance of clay particles (<4 \(\upmu\)m) is relatively large with an average value of 86.4 \(\pm\) 8.5% (Figure 2). To identify the characterized dynamic components, two parameters were selected: M value, the median diameter, indicating the mean hydrodynamic energy; C value, the one percentile of grain size distribution, representing the most hydrodynamic sedimentary component. The C-M diagram can provide valuable insights into sediment transport and hydrodynamic intensity [32; 33]. As shown, these two parameters were coupled (Figure 3b), and this relationship is also evident in marine surface sediments around the present Huanghe River mouth [14], the mud area of the Bohai Sea [34], the Laizhou Bay [35], and the abandoned Huanghe River mouth in the west of the Huanghai Sea [36]. Hence, it is inferred that the most dynamic component in core YDZ-3 was integrated into deltaic and estuary deposition with the mean state of various sedimentary processes.  
For down-core variation, changes in sediment grain-size parameters can be generally grouped into two intervals (Figure 2): small fluctuation prior to \(\sim\)6400 yr BP and greater since then. In specific, prior to \(\sim\)6400 yr BP, all grain-size parameters were characterized by less variation without any distinct changes, and in contrast, since \(\sim\)6400 yr BP, these records varied greater. Eight episodes with a high clay content can be identified, namely 6400-5280 yr BP, 4480-4190 yr BP, 3880-3660 yr BP, 3070-2870 yr BP, 2780-2360 yr BP, 2140-2000 yr BP, 1940-1780 yr BP, and 1700-1650 yr BP. All of the alternations between these sub-stages were not stepwise but abrupt (Figure 2).  
Figure 3: Characteristics of sediment grain size of core YDZ-3 and 35 surface samples. (**a**) Ternary diagrams; (**b**) C–M diagrams; (**c**,**d**) Grain-size distributions, PDF: probability density function, CDF: cumulative distribution function. Comp–1, –2, and –3 are three components by mathematical unmixing in Figure 4.
## 3 Results
### Effect of L. paracasei CFSs on Relative mRNA Expression in 3T3-L1 Adipocytes  
Based on the considerably enhanced glucose consumption, we further evaluated the mRNA expression levels of some critical genes involved in adipogenesis and glucose consumption in adipocytes. The results are presented in Figure 5.  
To our surprise, the seemingly similar effects in glucose uptake provoked by the studied _L. paracasei_ CFSs were grounded by divergent metabolic pathway modulations. The data revealed upregulation in PI3K and Slc1a4 gene expression in P4 compared to MRS_C (_p_ < 0.05), suggesting an enhanced insulin-dependent influx of glucose in these cells. In contrast, the mRNA gene expressions of IRS-1, PI3K, and Slc1a4 in C8 and C15 were significantly downregulated compared to MRS_C (_p_ < 0.01). However, we found a notable increase in Slc1a1 gene expression in M2.1, C8, and C15 compared to MRS_C (_p_ < 0.01), which indicates intensification of basal (insulin-independent) glucose uptake in these groups. Gps2--a newly identified crucial factor of adipocyte biology, involved in the modulation of PPARy expression and the insulin signaling pathway--was downregulated in C8 (_p_ < 0.01) and C15 (_p_ < 0.05), compared to the control group. The PPARy mRNA gene expression in these groups was also suppressed (_p_ < 0.01), but these changes were not accompanied by variations in intracellular lipid deposition after 48 h of treatment.  
Figure 5: Relative mRNA expression of markers involved in adipogenesis and glucose metabolism in fully differentiated 3T3-L1 adipocytes, treated with 10% cell-free supernatants from _Lactiaseibacillus paracasei_ strains. (**a**) Peroxisome proliferator-activated receptor gamma (PPARy); (**b**) G-protein pathway suppressor 2 (Gps2); (**c**) insulin receptor substrate 1 (IRS-1); (**d**) phosphoinositide 3-kinase (PI3K); (**e**) solute carrier family 2, member 1 (Slc2a1); (**f**) solute carrier family 2, member 4 (Slc2a4). Abbreviations: MRS_C—induced control, treated with MRS; M2.1, C8, C15, P4—experimental groups, induced and treated with supernatants of the respective strains for 48 h. Data (mean \(\pm\) SEM) were relatively estimated by applying the modified 2\({}^{\rightarrow}\Delta\Delta\Gamma\) approach with GeoMean value of the reference genes (ribosomal protein, large, P0, and beta-actin) and are expressed relatively to MRS_C. The asterisk indicates the significant differences between MRS_C and all other groups, as *—_p_ < 0.05; **—_p_ < 0.01.
## 4 Calculation Results and Discussion
### Validity Analysis Considering the Spatial Distribution of Precipitation  
To verify the effectiveness of considering the spatial distribution of precipitation, the forecasting results of the proposed CM-MDLN model before and after considering the spatial distribution of precipitation are compared. We divide the data into two categories. The first category considers the spatial distribution of precipitation, that is, the grid precipitation grid data generated by combining satellite remote sensing data in Section 3.2. The second category does not consider the spatial distribution of precipitation; that is, only the precipitation data observed by the original meteorological station is used. In this subsection, we take the data from June to August 2018 as an example for verification. This period belongs to the rainy season in HC and GL, and the spatial distribution of precipitation is highly variable. Therefore, this period, which is obviously representative, is selected as an example to verify the impact of the spatial distribution of precipitation on the forecast of power generation.  
Figure 7 shows the comparison of the average daily precipitation in HC city from June to August and the forecasting results of daily power generation before and after considering the spatial distribution of precipitation in the model. Table 2 shows the statistical results of the evaluation metrics of the forecasting results in HC city in the two cases. Parts of the peak and valley values in the curves of the forecasting results are enlarged and displayed in Figures 8 and 9, respectively. Combining the table of statistical evaluation metrics and the magnified view of local peak and valley values, it can be seen that the fitting effect of the prediction results considering the spatial distribution of precipitation (green line, accuracy of 94.52%) is better than that of the prediction results without considering the spatial distribution of precipitation (purple line, accuracy of 88.72%) in both the trend of the curve and the peak and valley values.  
Figure 7: Comparison of forecasting results before and after considering the spatial distribution of precipitation in HC city. The \(x\)-axis represents the time in days, the primary \(y\)-axis represents the power generation, and the secondary \(y\)-axis represents precipitation.