esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Problem Description
### Proposed Mathematical Model  
It is assumed that WSN(\(S\), AREA, RADIUS, COST) are the WSN we considered, where the location, the levels of radius, and the prices of each radius level are all provided in \(S\), RADIUS, and COST for each sensor, respectively. The proposed budget-limited WSN sensing coverage problem needs to determine the radius level for each sensor to have the maximal effective covered grids of the whole WSN under a limited budget to improve the WSN service quality.  
A mathematical model for the problem is presented below:  
\[\text{Max}\underset{(x,y)}{\cup}\text{ECP}(r(x,y))| \tag{7}\]  
\[\text{s.t.}\sum_{(x,y)}\text{COST}(r(x,y))\ \leq\ \text{COST}_{\text{UB}}. \tag{8}\]  
The objective function in Equation (7) maximizes the number of grids covered by sensors. The only constraint of Equation (8) is the budget-limited total cost of the sensors. Note that, if without Equation (8), each sensor can be set to its maximum radius level, i.e., it is impractical.  
The proposed budget0-limited WSN sensing coverage problem is one of the variants of the knapsack problem. Hence, the proposed problem is also an NP-Hard problem, and it is impossible to be solved in polynomial time [22; 23]. It is always necessary to have an efficient algorithm to solve the important and practical sensor problem. This study thus proposes a new algorithm based on SSO to overcome the NP-Hard obstacles to improve the SSO to enhance the obtained WSN service quality.
## 1 Introduction
Based on the SoundStream approach, the performance of neural codecs has been further enhanced, leading to advancements in the field of audio compression and reconstruction [10; 11]. However, neural codecs have the disadvantage of requiring substantial computational resources because they use the entire acoustic signal as input to artificial neural networks [13; 14]. Consequently, the most lightweight neural codec, LightCodec [14], has recently been proposed. LightCodec is designed to operate in low-complexity environments by dividing the input signal into sub-bands to efficiently extract features [15; 16] and incorporating a Compensation Module (CM) that corrects quantization errors through a shallow network. This approach reduces the high computational complexity of conventional codecs while maintaining performance. Nonetheless, even these neural codecs still demand significant computational power, making them unsuitable for inferior sonobuoy data transmission environments, which are battery-powered with limited power availability. Therefore, there is a need for neural codecs that are specifically tailored to the sonobuoy environment. Therefore, an autoencoder-based signal compression neural network [6] tailored for acoustic signals collected by Directional Frequency Analysis and Recording (DIFAR) sonobuoys has been investigated. Unlike conventional methods that utilize the entire acoustic signal unchanged, this approach is designed to adjust the length of the acoustic signals input into the artificial neural network, enabling data compression with reduced computational requirements. Leveraging the characteristics of autoencoders [17], the encoder mounted on the sonobuoy compresses the signal, and the compressed data is transmitted to the MPA, where a decoder mounted on the MPA reconstructs the acoustic signal. This method addresses the issues of transmission efficiency and security inherent in the conventional FDM signal transmission method used in DIFAR sonobuoys. However, it presents the drawback of degraded reconstruction performance with a relatively large Mean Squared Error (MSE). Accordingly, this study proposes a novel autoencoder-based signal compression model for the DIFAR sonobuoy environment to improve signal reconstruction performance. The contributions of our research are threefold, as outlined below. First, we introduce RVQ [9; 10] and a CM [14], which are representative techniques used in conventional acoustic signal neural network codecs, to develop a neural network tailored for the DIFAR sonobuoy environment. This approach maintains a high compression ratio while improving the accuracy of signal reconstruction. Second, to accommodate the battery-operated sonobuoy environment, we lighten the proposed neural network by reducing the required parameters and computational load, thereby achieving superior signal reconstruction compared to the existing DIFAR sonobuoy-based neural network. Lastly, we validate the effectiveness of the proposed method by establishing an air channel [18] simulation environment that considers the wireless communication between the actual MPA and sonobuoys.
## 5 System Development and Design
### The Information Extraction Module  
The information extraction module automatically extracts requirement data based on the principles outlined in Section 4.2, as shown in Figure 8. Users need to input two pieces of data, \"Requirement Text\" and \"Requirement Category\", into the system or upload an Excel file containing these two columns. The system calls the OPENAI GPT-4 API, ex  
Figure 7: Prototype system for requirement representation knowledge graph construction based on prompt engineering.  
tracts structured data based on the prompts, and returns the results. After receiving the structured information from the API, the SRs information extraction module displays the extracted structured data in the text box on the interface and supports downloading it as an Excel file, which serves as the basis for the next module, the construction of the requirement representation knowledge graph. This study introduces a manual correction step in the information extraction process to ensure data consistency. For example, the design constraint requirement in Table 12 involves the \"Integrated Backup Panel (IBP)\", which may be referred to as \"IBP\" in other requirement descriptions. If the same entity is not uniformly processed across different descriptions, it may lead to multiple nodes in the knowledge graph that point to the same concept but have different names, thereby affecting the accuracy of requirement analysis. Therefore, the research team further checked and corrected the results based on GPT-4's structured extraction, ensuring consistency in the entities representing the same concept throughout the knowledge graph construction process and preventing semantic fragmentation. The elements manually corrected in this case are shown in Table 12.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Abbreviation** & **Standardized Term** \\ \hline AFC System & automatic fare collection system \\ FAS & fire alarm system \\ SPD & surge protection device \\ ISCS & integrated supervisory control system \\ ATS & automatic train supervision system \\ IBP & integrated backup panel \\ MTBF & mean time between failure \\ UPS & uninterruptible power supply \\ BAS & building automation system \\ EPS & emergency power supply \\ PLC & programmable logic controller \\ DCS & distributed control system \\ I/O & input/output \\ PIS & passenger information system \\ \hline \hline \end{tabular}
\end{table}
Table 12: Standardized terminology alignment for information extraction.  
Figure 8: Example of the information extraction module.
## 2 Materials and Methods
### Land-Cover Data  
We used the MODIS land-cover product (MCD12C1) to describe land-cover types of the QTP. This annual product describes the geographic distribution of 17 land-cover types defined by the International Geosphere-Biosphere Programme (IGBP) with a spatial resolution of 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) since 2000. To match it with the spatial grid of the ERA5 product presented in Section 2.1, this dataset was resampled by selecting the land-cover types located in the same ERA5 grid cell with the highest appearance frequencies. Given that land cover may change over a long period, the land-cover type with the highest appearance probability during 2000-2021 was assigned for each pixel to minimize the temporal shifts in land-cover types. To this end, appearance probability was estimated using the average of the percentage values affiliated with the MODIS IGBP land-cover product. We further simplified the land-cover classifications (Figure 1), and only three types, i.e., barren or sparsely vegetated areas (BSVs), grasslands, and forests, were used in the following analyses. Other land-cover types, such as croplands and shrublands, whose numbers of available pixels were \(<\) 3% were excluded and were not considered in the following analysis.
## 2 Materials and Methods
### Robotic System  
#### 2.1.1 Hardware Design of the Robotic System  
As shown in Figure 1, the system consists of four main parts: the vision module, the robot body, the upper controller and the lower controller. The vision module is the \"eye\" of the system, which consists of a camera and a holder for capturing images. The robot body consists of a robotic arm and gripper for grasping and placing objects. The upper controller acts as the \"brain\" of the system, receiving data from each component, processing it and making decisions. Its main tasks include target recognition, image localization, and motion planning for the robot. Finally, the lower controller acts as the \"nervous system\" and is directly connected to the robot actuator, sending control signals to drive the robot motion.  
A planar grasping approach was adopted for the experimental manipulation of oyster handling, prioritizing convenience. This method maintains a consistent height of the gripper during each operation, eliminating the need for depth information from the images. As a result, a compact and efficient RGB camera was selected and mounted on a bracket positioned above the conveyor belt (eye-to-hand configuration), with the lens parallel to the belt.  
The robotic system comprises two main parts: the arm and the gripper. The robotic arm (AUBO-i3, AUBO, Haidian, China) weighs 16 kg and has six degrees of freedom, enabling it to reach any position (x, y, z) and orientation (\(\alpha\), \(\beta\), \(\gamma\)) within its working space. The gripper, a key component of the system, is responsible for grasping, transporting, and placing objects. Safe gripping requires securely contacting the object and preventing slipping and damage during handling. Therefore, when robots are used for sorting tasks, the shape, size, and material of the objects must be carefully considered, and the gripper should be chosen accordingly. For this study, a flexible gripper was selected for oyster handling. The gripper, attached to the end of the robotic arm via a connector, features four fingers, providing four points of contact to enhance gripping stability. The gripper operates as a pneumatic device, using a valve to control gas flow in a cylinder, which moves a piston to open and close the gripper. As such, the pneumatic manipulator typically has only two states: fully open or fully closed [23]. Despite this, the manipulator offers a high gripping force and fast response time, making it well-suited for the task [24].
# Winter Wheat Mapping in Shandong Province of China with Multi-Temporal Sentinel-2 Images
## 2 Materials and Methods  
### Study Area  
Shandong Province is located in the eastern part of the North China Plain (Figure 1), in the lower reaches of the Yellow River, and is also an eastern coastal province bordering the Bohai and Yellow Seas [27; 28]. The land area of the province is 155,800 square kilometers, accounting for about 1.6% of the total land area of the country [29; 30]. The topography of Shandong is characterized by a high elevation in the center, hills and plains interspersed in the east, and plains dominating in the west, with the plains of Shandong Province accounting for 65.56% of the entire land area [31; 32]. Shandong belongs to the warm temperate monsoon climate type, there is rain and heat at the same time, the precipitation is sufficient and concentrated, light resources are abundant, and the heat conditions of the province can meet the demand of the crop biannually [33]. With suitable climatic and favorable topographical conditions, Shandong is one of the major grain crops producing areas in China. Winter wheat is one of the major grain crops in the province [25]. In 2021, the sown area of winter wheat reached 23,567 thousand hectares, accounting for 16.95% of the national sown area of winter wheat, and it is the main planting area of winter wheat after Henan [26]. The growing period of winter wheat in Shandong is about 220 to 270 days, with seeding about between September and October, and stops growing in December of the same year to enter a dormant period [34]. From February to March of the following year, as temperatures rise, wheat re-enters the green growth period. It enters the jointing stage in early April, the heading stage in April and May, and the final maturity and harvest in June [35].
## 3 Results  
The contents of styrene monomer in the investigated PS sample materials (pellets, extruded sheets, and thermoformed pots) are summarized in Table 1.  
The migration rates of styrene into food simulants and into yoghurt under different time/temperature conditions from the extruded sheets (PS-S-11, PS-S-22, PS-S-32) and the thermoformed pots (PS-P-11, PS-P-22, PS-P-32) are depicted in Figure 1.  
The relevant exemplary chromatograms are available in the Supplementary Materials for the detection of styrene with: (a) GC-FID (in the acetone extracts of the PS samples), (b) HS GC-FID (in 20% and 50% food simulants), and (c) P&T-GC-MS (in yoghurt).  
All migration experiments in food simulants and in yoghurt were carried out as independent triplicates. The migration from extruded sheets and thermoformed pots was determined in the yoghurt after storage at 8 \({}^{\circ}\)C for 10, 20, 30, 40, and 50 days. The mean values and standard deviations of all the measurements are reported in Table 2. Migration values are given in \(\upmu\)g/kg (food), which correspond to \(\upmu\)g/6 dm\({}^{2}\) according to the EU cube model (where 1 kg of food is in contact with 6 dm\({}^{2}\) of packaging), and which were used for the migration contact experiments in this study.  
In Table 3, the relative migration of styrene is given, expressed as a percentage; it was calculated by dividing the mass of styrene found in the food (simulant) by the initial mass determined in the sample material.  
Figure 1: Specific migration of styrene into food simulants from the extruded sheets (solid bars) and the thermoformed pots (dashed bars).
# A Graph-Based Approach for Modelling Quantum Circuits
## 6 Evaluation of the Proposal
### Ease of Model Manipulation  
We will focus on the discussion of modifiability on those models that are manipulated by the user. We will keep the discussion simple and abstract, and we will not compare the strategies according to the availability of supporting graphical or textual model editors available for each one, or the difficulty of developing such tools. If we only consider the number of modelling elements that make up a model, and with which the user has to interact (create, modify, move or delete), according to the statistics and equations described in Section 5.3, the \"slice\" strategy is the best, as it requires the least number of additional modelling elements, and then the \"mixed\" ones followed by the other two strategies.  
We now extend this reflection to consider the ease of adding and deleting elements according to the quantum circuit, rather than these operations themselves (which are easily performed in any editor). That is, from the point of view of how easy it is to insert a new QuantumGate or Qubit in its correct place and how easy it is to restore the model to a valid and correct state after deleting such an element.  
The main criterion for ranking the strategies by level of preference is the difficulty of identifying the point in the graph where a quantum gate needs to be inserted or removed. In addition, the edges connections have to be redone to bring the graph back into a coherent state.  
The strategies that use composite Nodes are the preferred ones because in them it is easy to establish a correspondence between the quantum circuit and the elements of the model, and therefore it is easy to identify where to add the new element or how to restore the model to a valid state. Among them, \"mixed swim-lane\" is preferred over the other two because the circuit structure is better represented than in the \"mixed linear\" strategy and because changes only require updating Edges, whereas in the \"slice\" strategy the user may have to move gates to different Nodes to rearrange the model to represent the quantum circuit. Then come the \"swim-lane\" and \"linear\" strategies, because there is no horizontal structure in \"linear\" that can help the user to perform the operations.  
In summary, from best to worst, the preferred strategies in terms of model modifiability are: \"mixed swim-lane\", \"mixed linear\", \"slice\", \"swim-lane\", \"linear\".  
This order is not affected by the transformation that reduces the number of modelling elements described in Section 5.3, but from our point of view it hinders this task because Edges give the user freedom to place Nodes in any order. Nevertheless, with the appropriate support from editors, it can be useful and even easier for the user to manipulate models in this reduced form.
## 4 Simulation Experiment and Results Analysis
### Experimental Analysis and Summary
#### 4.3.5 Comparison Experiment of Applicability of HSP-QGA in Heterogeneous and Homogeneous Systems  
The above experiments have verified the advantages of HSP-QGA in task scheduling. To verify the applicability of HSP-QGA to heterogeneous platforms, this experiment is carried out in heterogeneous and homogeneous systems. Set three data groups with 10, 20, and 30 tasks to test the HSP-QGA algorithm and compare their efficiency. The other relevant parameters except for the number of tasks in the two systems are set as follows: the number of processors is 4, CCR is 0.3, and the number of DAGs is 2.  
The experimental results show scheduling efficiency in 10, 20, and 30 tasks in heterogeneous and homogeneous systems, as shown in Figure 13. When the number of tasks is 10, the scheduling efficiency of the heterogeneous system is 41.7% higher than that of the homogeneous system; when the number of tasks is 20, the scheduling efficiency of the heterogeneous system is 24.8% higher than the homogeneous system; when the number of tasks is 30, the scheduling efficiency of the heterogeneous system is 43.3% higher than that of the homogeneous system. Considering the negligible error caused by the difference in processor release performance and the randomness of generated DAG, the results show that HSP-QGA is more appropriate for heterogeneous scheduling platforms.
## 3 Results
### Geographical Detection of the Spatial Differentiation of CI in the Sanjiang Plain
#### 3.4.4 Detection of the Factor Ranges/Types Suitable for Clumping Effects of Foliage  
The risk detector was applied to determine the optimal range/type of the driving factors for foliage clumping effects, with a confidence of 95%. The factor ranges/types with the smallest mean values of \(\text{CI}_{\text{GS}}\) were most suitable for the clumping effects of foliage. The relationships between the spatial differentiation of CI and different driving forces differed (Table 6). Only relationships between \(\text{CI}_{\text{GS}}\) and elevation, slope, and annual precipitation were monotonic; thus, the mean value of \(\text{CI}_{\text{GS}}\) increased with them. The most optimal ranges of elevation, slope, and annual precipitation for the clumping effects of foliage were 589-1035 m, 5.8-13\({}^{\circ}\), and 638.7-725.7 mm, respectively. Therefore, areas with high elevation, steep terrain, and abundant precipitation were more likely to be conducive to the clumping effects of foliage.  
Moreover, the relationships between other driving factors and \(\text{CI}_{\text{GS}}\) were non-monotonic. The mean values of \(\text{CI}_{\text{GS}}\) decreased first and then increased with increasing average annual temperature, and the minimum mean value of \(\text{CI}_{\text{GS}}\) appeared when the average annual temperature was 0.9-2.1 \({}^{\circ}\)C. In terms of the aspect, the mean value of \(\text{CI}_{\text{GS}}\) reached 0.852 in the north. The geomorphic type with the smallest mean value of \(\text{CI}_{\text{GS}}\) (0.648) was the moderate relief mountain; among all vegetation types, the mean value of \(\text{CI}_{\text{GS}}\) of the coniferous and broad-leaved mixed forest was the smallest. For land use type and soil type, the minimum mean values of \(\text{CI}_{\text{GS}}\) were found for forestland and leached soil, reaching 0.746 and 0.808, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Factor** & **Optimal Range/Type** & **Mean Value of \(\text{CI}_{\text{GS}}\)** \\ \hline Elev (m) & 589–1035 & 0.643 \\ Geomt & Moderate relief mountain & 0.648 \\ Slop (\({}^{\circ}\)) & 5.8–13 & 0.670 \\ Veget & Coniferous and broad-leaved mixed forest & 0.682 \\ Landt & Foresland & 0.746 \\ Soilt & Leached soil & 0.808 \\ Temp (\({}^{\circ}\)C) & 0.9–2.1 & 0.818 \\ Prec (mm) & 638.7–725.7 & 0.822 \\ Aspe (\({}^{\circ}\)) & 0–22.5, 337.5–360 & 0.852 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The optimal range/type for clumping effects of foliage.  
Figure 12: Interactions between driving factors.
## 2 Predictive Model Based on ESN
### Network Architecture
#### 2.2.1 Network Learning of an ESN  
In the training process of an ESN, the weight, \(\mathbf{W}^{in}\) and \(\mathbf{W}\) are randomly generated through a uniform distribution and remain unchanged after initialization. The weight matrix \(\mathbf{W}^{out}\) is obtained by offline learning. Since the output of an ESN model is linear and forward, Equation (4) can be further written as  
\[\mathbf{Y}=\mathbf{W}^{out}\mathbf{X}, \tag{5}\]  
where \(\mathbf{Y}\in\Re^{N_{y}\times T}\), \(\mathbf{X}=[1;u(n);x(n)]\in\Re^{(1+N_{x}+N_{u})\times T}\). The optimal \(\mathbf{W}^{out}\) can be obtained by solving a linear equation system  
\[\mathbf{Y}^{target}=\mathbf{W}^{out}\mathbf{X}, \tag{6}\]  
Notice that \(T>>1+N_{x}+N_{u}\), Equation (6) is overdetermined. In order to improve the stability and generalization ability of the network, the ESN model proposes two methods to solve the problem of parameter learning. A general method is to add white noise \(\
u(n)\) during training, namely  
\[\widetilde{x}(n)=\tanh\Bigl{(}\mathbf{W}^{in}[1:u(n)]+\mathbf{W}x(n-1)+\mathbf{W}^{fb}y(n -1)+\
u(n)\Bigr{)}. \tag{7}\]  
The added noise can propagate forward through \(\mathbf{W}\), which plays the role of modeling the noise signal in the reservoir. As a result, the output can learn some recovery information from the interference signal so as to increase the stability of the model.  
As an alternative, ridge regression can be used to replace linear regression, that is, a regularization term can be used to increase the stability of the model. Hence, the computational result is  
\[\mathbf{W}^{out}=\mathbf{Y}^{target}\mathbf{X}^{T}(\mathbf{X}\mathbf{X}^{T}+\beta\mathbf{I})^{-1}, \tag{8}\]  
where \(\beta\) is the penalty parameter of the regularization term.  
In a word, the training and prediction of an ESN model can be summarized in the following Algorithm 1.  
```
1. Randomly generate the input connection matrix \(\mathbf{W}^{in}\) and the reservoir connection matrix \(\mathbf{W}\) and make sure that the following conditions and satisfied, i.e., \(\mathbf{W}\) is sparse, and its spectral radius \(\rho(\mathbf{W})<1\).
2. Use the input sample \(u\) to drive the network operation and store the corresponding reservoir activation state \(x(n)\).
3. Based on the calculated reservoir activation state, obtain the output connection matrix \(\mathbf{W}^{out}\) by minimizing the MSE between \(\mathbf{Y}\) and \(\mathbf{Y}^{target}\).
4. Given a new input \(u(n)\), use the trained network, obtain the predicted output \(y(n)\) by forward propagation.
```  
**Algorithm 1** The Main Steps for the Training and Prediction of an ESN Model.