esa-sceva/satcom-chunk-collection huggingface dataset:
# Framework for Fast Experimental Testing of Autonomous Navigation Algorithms
## 4 Initial Framework Implementation
### Execution Phase
#### 4.2.2 Planning  
At the top level of the motion part of the system, we defined the planning module. At first, we assessed the possibility of implementing the planner available in the navigation stack of ROS. Finally, we discarded this possibility due to the lack of versatility of this system in terms of the localization algorithms that can be used, since the planners of this stack are based solely on grid maps to obtain the trajectories. The interest of our research group is focused on the localization of robots, and we plan to use other ways to represent the environment apart from grid maps, or even transit through unmapped areas (Section 5). For this reason, we decided to develop our own system: a simple and functional planner that is more easily adaptable to any localization algorithm.  
For the developed planning module, we use a topological-metric map that contains all the possible trajectories required by the target application that we want to generate using our framework. This trajectory map is generated in the pre-execution phase as shown in Figure 2. We represent these trajectories by means of a graph in which each node represents an intersection, and each link represents the path between the intersections. Each node contains information of its location in map coordinates, and each link contains equidistant points also expressed in map coordinates. This metric information is obtained in pre-execution by recording the trajectories during manual driving, next sub-sampling them, and finally assigning each point to the node or link to which it belongs. In Figure 4 we can see an example of a graph that expresses in a topological way all the possible trajectories for a certain environment and application. The inputs to this module are the graph described above, the position obtained through AMCL (or an alternative localization module), and the global goal received from the high-level interface. With this data, the module generates as output the combination of nodes and links that describes the shortest path to the global goal within the graph. This path is composed of the points expressed in map coordinates that are sent sequentially to the low-level control as local goals. This planner assures that the robot circulates always through traversable areas.  
Figure 4: Example of a trajectories graph expressed abstractly as a topological map. This map also includes the metric information. Each node represents the location of roads intersections, and each link contains a series of points in 2D Cartesian coordinates that will serve as local goals to the controller. This graph is obtained as shown in Figure 2, and used in execution as shown in Figure 3.
# Study of the Interaction between Regular Waves and a Wind-Fish Combined Structure
## 2 Numerical Simulation
### Free Surface Tracking  
The VOF method has the advantages of small storage, favorable form, and less calculation error, so this paper selects the VOF method as the free surface tracking method. The VOF uses a scalar function between 0 and 1 to simulate the position of a free surface, where 0 indicates that the grid is filled with gas, and 1 means that the grid is filled with liquid. Except for these two aforementioned cases, it is considered that there is an interface between the two fluids in the grid, namely, the free surface. The density and viscosity of the fluid in a single grid are shown in Formula (15),  
\[\begin{array}{l}\rho=\alpha\rho_{w}+(1-  
The horizontal velocity distribution of the numerical wave tank is as follows:  
\[u(x,z,t)=H\omega[\frac{\cosh k(z+h)}{\sinh kh}\cos(kx-\omega t)+\frac{3}{4}Hk \frac{\cosh 2k(z+h)}{\sinh^{4}kh}\cos(2 kx-2\omega t)] \tag{18}\]  
The velocity distribution in the vertical direction, that is, the velocity distribution in the water depth direction is described as:  
\[w(x,z,t)=H\omega[\frac{\sinh k(z+h)}{\sinh kh}\sin(kx-\omega t)+\frac{3}{4}Hk \frac{\sinh 2k(z+h)}{\sinh^{4}kh}\sin(2 kx-2\omega t)] \tag{19}\]  
The wave surface of the wave is expressed as:  
\[\eta=H\Bigg{[}-\frac{Hk}{2\sinh 2 kh}+\cos(kx-\omega t)+\frac{Hk}{4}\frac{ \cosh kh(2\cosh^{2}kh+1)}{\sinh^{3}kh}\cos(2 kx-2\omega t)\Bigg{]} \tag{20}\]  
where \(\omega\) is the frequency of the wave; \(k\) is the number of waves; \(H\) is the wave amplitude; and \(h\) is the water depth of the numerical flume.  
(2) Wave elimination  
The Waves2 Foam solver uses a relaxation zone for wave attenuation, and defines the relaxation function in the solver, as shown in Formula (21).  
\[\begin{array}{l}\chi_{R}(\xi)=1-\frac{\exp(\xi^{3.5})-1}{\exp(1)-1}\\ \xi\in[0,1]\end{array} \tag{21}\]  
This function makes the values of the starting point of the relaxation zone 1, the ending point 0, and the middle of the relaxation zone between 0 and 1. In the relaxation zone, the velocity of fluid and the volume fraction of the wave surface are constantly updated in time steps. See Formula (22) for the specific expression,  
\[\psi=\chi_{R}\psi_{computed}+(1-\chi_{R})\psi_{target} \tag{22}\]  
where \(\psi\) is the volume fraction or velocity of the fluid; \(\psi_{computed}\) is the updated volume fraction or velocity calculated in real time in the relaxation zone; and \(\psi_{target}\) is the theoretical value corresponding to the entrance, with the exit value of 0. Figure 2 is the schematic diagram of fluid volume fraction. The value of the volume fraction is mainly based on the relative position from the static water surface, which is 0 above the static water surface and 1 below it.  
Figure 2: Schematic diagram of fluid volume fraction.
## 3 Methodology
### Diving into Pairing Approach
#### 3.2.3 Memory Considerations  
In contrast to earlier methods like witness-based approaches [50; 51], where each electronic component needed extra buffer storage as a witness, our new approach follows a different direction. In those previous methods, the confidential data from the source component had to be stored in buffer memory to verify its authenticity, leading to higher memory usage. Unlike previous approaches that burdened ECUs with storing confidential data for verification, our solution adopts a different strategy. Rather than relying on ECUs to store such data, our method utilizes a centralized module. This module utilizes machine learning techniques to authenticate event data collected from paired ECUs. Centralizing the verification process eliminates the necessity for ECUs to retain extra buffer storage for the private information of their counterparts. By removing the need for ECUs to store sensitive data, our approach notably reduces memory usage. Furthermore, this decrease in memory load does not compromise the ability to effectively detect security breaches. Our approach successfully detects security breaches without adding any additional strain on the memory or network resources of the ECUs.
# Design of Safety Evaluation and Risk Traceability System for Agricultural Product Quality
## 2 Design of Agricultural Product Quality and Safety Evaluation System
### Design of Evaluation System  
By inviting experts from agricultural product industry research institutes, relevant universities, and industry associations to evaluate the rationality of the selection of indicators, the importance of each indicator was compared according to the 1-9 scale method, and the relative importance of each indicator was assigned. The final weight of each indicator was determined by weighted addition and equalization according to the assignment of experts. When constructing the evaluation index system, we followed the principle of completeness as much as possible. The scoring methods and results of secondary indicators in the specific agricultural product quality and safety risk evaluation model are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Primary Indicators** & **Secondary Indicators** & **Unit of Measurement** \\ \hline Available content of heavy metals in soil (_a_) & Total content of heavy metals in soil (_a_) & mg/kg \\ heavy metals in soil & Soil pH (_b_) & / \\ (_Acs_) & Organic matter (_c_) & g/kg \\  & Redox potential (_d_) & mV \\ \hline Absorption coefficient of heavy metals in & Crop varieties (_e_) & / \\ crops & Nutritional status (_f_) & / \\ (_Ac_) & Cultivation measures (_g_) & / \\ \hline Heavy metal content & Heavy metal content in pesticides (_h_) & mg/kg \\ from other sources & Heavy metal content in fertilizers (_i_) & mg/kg \\ (_Os_) & Heavy metal content in the atmosphere (_j_) & mg/kg \\  & Heavy metal content in irrigation water (_k_) & mg/kg \\ \hline \hline \end{tabular}
\end{table}
Table 1: Risk assessment indicators for agricultural product quality and safety.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Risk Level Evaluation Index** & **Scoring Criteria** & **Score (Point)** \\ \hline Total content of heavy metals & Exceeding the standard & 1 \\ in soil (_a_) & Not exceeding the standard & 0 \\ \hline  & pH value \textless{} 4.5, Extremely acid & 6 \\  & 4.5 \(\leq\) pH value \textless{} 5.5, Strong acid & 5 \\  & 5.5 \(\leq\) pH value \textless{} 6.5, acid & 4 \\ Risk level of soil pH & 6.5 \(\leq\) pH value \textless{} 7.5, neutral & 3 \\ (_b_) & 7.5 \(\leq\) pH value \textless{} 8.5, alkalinity & 2 \\  & 8.5 \(\leq\) pH value \textless{} 9.5, Strong alkalinity & 1 \\  & 9.5 \(\leq\) pH value, Extreme alkalinity & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Scoring of agricultural product quality and safety risk assessment.
## 2 Materials and Methods
### Methodology
#### 2.3.2. Delineation of Built-Up and Non-Built-Up Areas  
In the investigation of urban development and the impact of human activities, the precise demarcation of urbanized and non-urbanized areas is of paramount importance. Night-time light data, as a crucial indicator of the intensity of human activities in cities, have been extensively applied in recent years, particularly in delineating the boundaries of urbanized areas. However, it is essential to recognize that the accuracy of such demarcation based solely on night-time light data is not satisfactory due to the low resolution of the data and the interference of light spill-over effects [30]. To address this challenge, we introduce cleaned point-of-interest (POI) data, which include various facilities closely related to daily human life, such as restaurants, supermarkets, banks, and hospitals. Previous studies have confirmed that the integration of night-time light data with POI data can effectively eliminate noise in the night-time light data and significantly mitigate the impact of light spill-over effects [31; 32]. Therefore, we constructed the NP index by combining POI data with night-time light data and set appropriate thresholds for the classification of urbanized and non-urbanized areas.  
The cleaned POI data were analyzed using kernel density estimation. After iterative experimentation, the optimal parameters were determined as an output resolution of 10 m  
Figure 2: Workflow diagram of the estimation of the HVR.  
and a search radius of 650 m. On this basis, we integrated night-time light data using the following formula:  
\[\text{NP}_{i}=\sqrt{P_{i}\times\text{NTL}_{i}} \tag{1}\]  
where NP\({}_{i}\) is the NTL and POI index, \(P_{i}\) is the POI kernel density value at point \(i\), and NTL\({}_{i}\) is the night-time light luminance value at point \(i\). At the same time, in order to create a better delineation effect, we resampled the night-time light data to a resolution of 10 m of the POI kernel density data.
## 2 Experiments  
For the synthesis of pure SnSe ingots, a stoichiometric amount of the Sn (99.5%, Alfa Aesar, Haverhill, MA, USA) and Se (99.5%, Alfa Aesar) mixture powder was loaded into a fused silica tube and then it was heated up to 1220 K and held at the same temperature for 24 h under a vacuum condition. The pure SnSe ingots were pulverized manually with a mortar and pestle and then the pulverized powder was passed through a sieve to obtain a mixture powder of less than 45 um. For the single-layer metallization on SnSe, a set of Ag powder (325 mesh, 99.99%, Sigma-Aldrich, St. Louis, MO, USA), Ni powder (325 mesh, 99.8%, Alfa Aesar), Ti powder (325 mesh, 99.99%, Alfa Aesar) was prepared. The spark plasma sintering (SPS, Fuji Electronic Industrial Co., Ltd., Kyoto, Japan) system was used to perform the SnSe sintering and metallization at the same time as shown in Figure 1.  
A previous study [5] reported that both the metal powder and foil metallization methods showed no big differences on their interface bonding and electrical properties. Thus, for the multi-layer  
Figure 1: Schematic of co-sintering process of thermoelectric material and metal layer using SPS (Spark Plasma Sintering).  
metallization, we prepared different metal foils such as Ag foil (25 \(\upmu\)m, 99.99%, Alfa Aesar), Co foil (50 \(\upmu\)m, 99.99%, Alfa Aesar), and Ti foil (50 \(\upmu\)m, 99.99%, Alfa Aesar) with the intention of making regular interfaces over the entire region which are easily distinguishable from one another. The SnSe powder was sintered together with metallic layers at 550 \({}^{\circ}\)C for 10 min with 40 MPa pressure. This sintering step completed the formation of metallized SnSe ingots. In order to investigate their interfaces, SnSe ingots were cut into several small thermoelectric legs using a diamond wire saw. The microstructures of the interfaces were analyzed using a scanning electron microscope (SEM, Hitachi, Tokyo, Japan). The composition distributions of SnSe and metal elements were investigated using an energy dispersive spectroscopy (EDS) line-scan (Hitachi, Tokyo, Japan). The EDS line scan was conducted several times to verify that interface characteristics are almost the same over the entire region. Heat treatments were conducted at 723 K in an Ar atmosphere to verify the thermal stability of metallization interfaces [6; 7]. To understand the effects of heat treatments, the electrical contact resistances were measured before and after heat treatments by home-made resistance scanning measurement equipment [8].
## 6 Conclusions  
This paper has presented a fully integrated linear 2.4-GHz CMOS power amplifier with two operating modes for WLAN applications. The mode switching is achieved by statically or dynamically controlling the number of power cells. The PA achieves an output P1 dB of 27.6 dBm and 17.7 dBm with a power-added efficiency (PAE) of 32.7% and 17.7% in the high-power mode (HPM) and low-power mode (LPM), respectively. Additionally, the efficiency of the low-power mode is 1.5 times higher than that of the high-power mode within the output power range of 10-16 dBm. In the high-power mode, the use of gm linearization and PMOS compensation result in an output 1 dB compression point of 27.6 dBm and a saturated output power of 29.7 dBm. Furthermore, under a WLAN 802.11n 64 QAM modulation signal, the power amplifier achieves \(-\)27 dB EVM at 19 dBm output power, demonstrating its ability to meet the linearity requirements of WLAN standards.  
The proposed PA could be used in various termini, including routers and tablets. The future direction is to integrate this PA into an entire transceiver to realize a fully integrated WLAN SOC chip. Moreover, the proposed PA has only two modes to switch, and more modes can be achieved in the future to further improve efficiency.  
Conceptualization, H.S., T.M. and B.W.; Methodology, H.S., T.M. and B.W.; Software, H.S., T.M. and B.W.; Validation, H.S. and T.M.; Formal analysis, H.S. and B.W.; Investigation, H.S. and T.M.; Resources, H.S., T.M. and B.W.; Data curation, H.S.; Writing - original draft, H.S.; Writing - review & editing, H.S., T.M. and B.W.; Visualization, H.S.; Supervision, H.S. and T.M.; Project administration, H.S., T.M. and B.W.; Funding acquisition, T.M. and B.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data in this paper are all from simulation and test results of real circuits, and there is no data plagiarism or falsification. Due to project confidentiality requirements, we regret that we cannot release more details of the data at this time.  
The authors declare no conflict of interest.
# Hybrid Renewable Energy Systems--A Review of Optimization Approaches and Future Challenges
## 4 HRES Optimization
### Computation Efficiency
#### 4.4.3 Hardware Architecture  
Here assessed the dependence of methods on computing resources. MILP and MATLAB 9.1-9.13 are both assigned to \"High\", reflecting their reliance on high memory capacity for large-scale computations, leverage advanced hardware capabilities such as parallel processing but are not inherently optimized for graphics processing units or distributed computing environments [69; 70]. NSGA-II, MOPSO and LF-SSA, GWO, CSA, PSO are in group \"Medium\", as they require robust hardware for managing multi-objective optimization problems but can perform adequately on less advanced systems with minor adjustments [72; 76], heuristic-based methods, as they operate efficiently on standard hardware, making them more accessible [71; 74], though they lack specialized hardware designs [20; 74; 77]. HOMER Pro 3.12-3.16 and TOPSIS are a part of \"Low\", as they require minimal hardware resources, reflecting their lightweight computational nature [9; 62].  
The advent of new quantum artificial intelligence (AI) processors has the potential to revolutionize the scientific world, particularly in the domain of HRES optimization. Quantum computing's unparalleled ability to perform parallel computations and solve complex optimization problems exponentially faster than classical processors could drastically reduce computation time for resource-intensive methods like PSO, Genetic Algorithms (GA), and machine-learning-based predictive models. On conventional processors, calculations that traditionally require hours or days could be completed in seconds or minutes on a quantum chip, enabling real-time simulations and optimizations for HRES design and operation. This speed-up would allow scientists to explore larger solution spaces, refine multi-objective optimization problems more effectively, and model more complex systems without the bottleneck of computational delays [78, 79, 80].
# Design of an Isolated Bidirectional Symmetric Resonant Converter
## 4 The Design Procedure of Circuit Component Parameters
### Step 11: Determining the Duration of Dead Time (\(t_{dead}\))  
The duration of dead time plays the key role of success or failure of ZVS of power switches. Too short of a dead time will lead to the unsuccessful removal of the charge on the parasitic capacitors of the power switches and yield ZVS failure while too long of a dead time would sacrifice the energy propagation time and thus increase the current stress on the primary side [21]. Therefore, the silicon carbide (SiC) NMOS power switches C2M0160120D manufactured by the CREE Inc. with very small parasitic capacitance \(C_{OSS}\) being equal to 55 pF are used, and thus the ZVS is easily accomplished. By using (22), the required dead time can be attained.  
\[t_{dead}\geq 8\text{-}C_{OSS}\text{-}f_{s\_max}\text{-}L_{m}=8\text{-}55p \text{-}145k\text{-}209.65u\cong 13.38n\text{s} \tag{22}\]  
Because the stray capacitance and parasitic inductance are not taken into account, the dead-time interval was kept up to 200 ns for more tolerance.
## 6 Edge Computing
### MEC for Smart Cities
Local data analysis: MEC provides real-time and critical data analysis of the massive amounts of collected data from different smart city applications. Various data analysis schemes can be deployed locally in a smart way. When edge computing undertakes local data analysis, it minimizes the delay in forwarding data to the remote cloud and waiting for the response.
### Literature Review  
Existing research has conducted evacuation experiments in real scenarios using hypothetical strategic level small group data [9], or computer evacuation simulations in hypothetical scenarios [10; 11; 12; 13; 14; 15; 16; 17; 18; 19] (Table 1). However, in both experiments and simulations, the scenario settings were typically simplistic. Researchers made assumptions about the number of small group members and their proportion to the total number of occupants, the number and size of small groups, the location of small group members, and the distance between members (Figure 2). The building spaces in experiments and simulations predominantly featured spatial functions such as rooms [6; 7; 9; 10; 12; 13; 14; 15; 17; 19] and pedestrian zones [11; 16; 18; 20; 21]. The spatial layouts mainly consisted of rectangular shapes [6; 7; 9; 10; 11; 17; 20; 22] and T-shaped configurations [18]. The outcomes derived from experiments and simulations in hypothetical data and simple scenarios were different. One group of researchers believed that small group evacuation behaviors reduced the overall evacuation efficiency [6; 7; 10; 12; 15; 16; 19; 20; 21], another group of researchers believed that they could increase the evacuation efficiency [14; 17; 22], and yet another group of researchers believed that the positive or negative effect of small groups on overall evacuation depended on the competition mechanism within the small groups [9] as well as the density of occupants [18].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Strategic Level** & **Scenario Setting** & **Simulation Results** & **Attitude** & **Ref.** \\ \hline \multirow{6}{*}{\
\begin{tabular}{c} Evacuation \\ experiments in \\ hypothetical \\ scenarios \\ \end{tabular} } & Rectangular room &
\begin{tabular}{c} Slov down initiate movement \\ \end{tabular} & Negative & [7] \\ \cline{2-5}  & & & Decrease evacuation time & Positive & [22] \\ \cline{2-5}  & & & Increase evacuation time & Positive & [23] \\ \cline{2-5}  & & Rectangular pedestrian zone & Increase the average & Negative & [20] \\ \cline{2-5}  & & & & \\ \cline{2-5}  
This paper described the methods of the study, followed by an introduction to small group data. The effect of small groups on evacuation was then analyzed through simulation. Comparison between the present study and existing studies was carried out, and directions for further research were explored. Finally, the key findings and implications of the study are summarized.
# MPEG and DA-AD Resilient DCT-Based Video Watermarking Using Adaptive Frame Selection
## 4 Experimental Results
### Experimental Setup
#### 4.1.1 Benchmark Data Preparation  
For our evaluation set, we used five benchmark video clips, i.e., ITE/ARIB Hi-Vision Test Sequence numbers 2, 8, 20, 23, and 46 [29]. First, we converted raw YPbPr data into an RGB uncompressed video using the following equation:  
\[\begin{array}{rcl}R&=&Y+1.576P_{r}\\ G&=&Y-(0.159P_{b}+0.334P_{r})/0.701\\ B&=&Y+1.826P_{b}.\end{array} \tag{5}\]  
The original 600 video frames of each clip had a duration of 20 s. The display resolution of the original frames is 1920 \(\times\) 1035. We cropped the last three rows because the MPEG-4 part 10 codec (H.264) in MATLAB automatically resizes the video resolution to 1920 \(\times\) 1032. To obtain a 15-s video, we removed the first 150 frames. Finally, we obtained uncompressed AVI videos with a display resolution of 1920 \(\times\) 1032 at 30 frames per second with a 15-s length. The total size of each video is 2.49 GB (1, 430,826 kbps).