esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatialization and Analysis of China's GDP Based on NPP/VIIRS Data from 2013 to 2023
## 3 Methodology
### Method for Estimating GDP Using NTL Data  
#### 3.2.1 GDP Modeling Methodology  
The brightness of NTL reflects the strength of economic development in a certain region. Using NTL data, GDP can be spatialized. In recent years, scholars have constructed many GDP spatialization models [29; 30; 31], including linear regression models [32], quadratic regression models [33], neural network models [20], and other models. The regression model is simple to operate and relatively accurate. Therefore, this paper selects five models, including linear regression model, quadratic regression model, exponential model, power function model, and logarithmic model, and sets GDP data as dependent variable Y and NTL index as independent variable X for modeling. At the national scale, the accuracy of different regression models is compared and analyzed from time regression and space regression, and the best fitting model is selected to spatialize China's GDP. In spatial regression analysis, the annual GDP and NTL index data of 31 provincial administrative regions from 2013 to 2023 are used to construct regression models year by year; in temporal regression analysis, the annual GDP and NTL index data of mainland China from 2013 to 2023 are used to construct regression models.
###### Abstract  
To reveal the potential relationship between the bacterial community and quality attributes of vacuum-packaged peeled potatoes, the bacterial community dynamics, visual quality, organic acids, flavor and volatile organic compounds (VOCs) during 12 days of storage under 10 \({}^{\circ}\)C were studied, and a correlation analysis was performed between the bacterial community and VOCs. During the whole storage, the dominant bacteria changed from _Ralstonia_, _Pseudomonas_, _Pantoa_ and _Comomonas_ to _Clostridia_, _Clostridium_, _Lacrimispora_, _Lactococcus_ and _Leuconostoc_. The visual quality and hardness deteriorated significantly on day 12; meanwhile, lactic and acetic acid content accumulated to 0.79 and 4.87 mg/g FW, respectively. Potatoes' flavor deteriorated severely after 8 days, as evidenced by results of an electronic nose (e-nose). A total of 37 VOCs were detected, and the total content showed an increasing trend from 2164.85 to 10658.68 ug/kg during the whole storage. A correlation analysis showed that _Enterobacteriaceae_, _Erwinia_, _Lacrimispora_, _Lacrocccus_, _Serratia_, _Pantoa_, _Clostridium_, _Fluzobacterium_ and _Clostridia_ were positively correlated with the biosynthesis of VOCs. In addition, 10 spoilage markers were screened according to a variable importance in projection (VIP) \(\geq\) 1. Ethanol, which was the most abundant spoilage marker, was significantly related to _Enterobacteriaceae_, _Erwinia_, _Lacrimispora_ and _Lacococcus_. The results of this study have great practical significance for prolonging the shelf life of fresh-cut agricultural produce.  
vacuum packaging; peeled potato; bacterial community; quality; spoilage; correlation analysis +
Footnote †: journal: _foods_
# A Novel Multi-Server Federated Learning Framework in Vehicular Edge Computing
## 6 Experimental Results  
### Simulation Setup  
Various simulations were conducted on a GPU server equipped with an NVIDIA A100-PCIE-40 GB GPU, running Ubuntu 24.04 LTS (GNU/Linux 6.8.0-36-generic x86_64). The environment was configured with CUDA version 12.2 and PyTorch version 2.3.1 [37]. Our FL framework extends the PyTorch FL implementation provided in [38]. The results were averaged over multiple independent trials using a Convolutional Neural Network (CNN) architecture.  
Our experiment employed a CNN due to its superior ability to extract spatial features from image data and its proven effectiveness in image classification tasks. The model consists of two 5 \(\times\) 5 convolutional layers, with the first layer containing 32 channels and the second layer comprising 64 channels, both followed by 2 \(\times\) 2 max pooling layers. Additionally, the network includes a fully connected layer with 512 units and ReLU activation, concluding with a softmax output layer. The entire model contains 1,663,370 parameters.
## 3 Methods  
The proposed on-line monitoring system tracks the physical conditions of the cobot while performing HRC processes. The framework of the online monitoring system is outlined in Figure 2. Basically, the system is composed of several integrated parts: collaborative robot, that communicates with gateway using real time data exchange (RTDE) and MODBUS protocols; data acquisition, which is the gateway to cloud communication; database server, which stores the data necessary for prediction purposes and to feed the on-line monitoring dashboard; data preprocessing which extracts meaningful features from the dataset and transfers them to the ML models; machine learning models, which are exploited to predict the future behaviour of any parts subject to failure; application layer, which is deployed to allow the interactions with human operators under safe conditions. Overall, the monitoring system alerts a human operator whenever a cobot displays improper or erratic behaviour. The operator can access the dashboard remotely. For example, if the temperature of one of the joints is higher than expected, the operator can access the dashboard of the cobot to find out which working situation has led to the anomaly. Moreover, a cobot system can be integrated with additional sensors to detect gas or ambient pollution and then inform the human operator about the hazard. The present paper focuses on the integration of data acquisition and machine learning in a cobot monitoring system; networking communication and the application layer with management indicators have already been discussed in [18; 31; 32].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**HRC Applications** & **Deployed Tasks** & **Important Parameters for HRC** \\ \hline Assembly & HRC assembly in a shared workspace [25] & End-effector force; \\  & Manual guidance collaborative assembly [14] & Payload monitoring; \\  & HRC integrated automotive assembly [15] & Robot Temperature; Joint Speed; \\ Handling & Hazardous material handling [26] & End-effector force; \\  & Aseptic bottling using AR [27] & Joint position and orientation; \\  & Collaborative surface polishing, sanding [28; 29] & Robot Force; Joint Speed; \\  & Collaborative robot injection and moulding [30] & Speed and separation monitoring \\ Welding & Virtual reality HRC welding [21] & Torque/force sensors; Temperatures; \\  & HRC Welding Cell [22] & Position and orientation accuracy; \\  & Spot welding manual guidance using AR [23] & Robot Temperature; Joint Speed; \\ \hline \hline \end{tabular}
\end{table}
Table 1: Human-robot interaction (HRC) applications.
# On-Demand Scheduling of Command and Responses for Low-Power Multihop Wireless Networks +
###### Abstract  
Many IoT applications require a mechanism to disseminate commands and collect responses over a wireless network in order to control and collect data from multiple embedded devices. However, severe collisions may occur if a large number of nodes attempt to respond simultaneously and promptly, not only among the responses, but also with the dissemination of commands. This is because low-power wireless network protocols for dissemination and collection have been designed separately. Tuning the parameters of one side of the protocol has clear trade-off between reliability and latency. To address this challenge, we propose SCoRe, an on-demand scheme for joint scheduling of command and responses on multihop low-power wireless networks to improve both reliability and latency simultaneously at runtime. SCoRe gathers the amount of time required by network nodes for dissemination and collection, and allocates relative timeslots to each node recursively over multihop on-demand when (and only when) disseminating a command. While doing so, information exchange occurs only between local neighbor nodes without a need for global routing table nor time synchronization. We implement SCoRe on a low-power embedded platform, and compare with well-known dissemination and collection schemes through both simulations and testbed experiments on 30 devices. Our evaluation results show that SCoRe can improve both latency and reliability without tuning the parameters for one metric, while the legacy schemes require careful parameter selection to match only one side of SCoRe, never both.  
low-power and lossy network (LLN); multihop wireless network; dissemination and collection; resource allocation +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 5 Conclusions  
According to the hydrogeological and karst hydrogeological conditions, a model coupling karst-fracture media and karst pipes has been developed. A 3D mathematical model of heterogeneous anisotropy was used to describe groundwater flow in the rock mass matrix, and the 1D conduit model was built based on non-Darcy's flow for karst pipes. The permeability of karst pipes was larger along the development direction of pipes. Basically, the permeability coefficients in the other two directions can be neglected. However, there was a certain angle between the coordinate systems and these pipes, Therefore, for 1D pipe flow, its permeability coefficients were a second-order tensor. The initial values for hydraulic conductivities of fracture-k karst media can be obtained by the data of water pressure tests in the boreholes. The relatively accurate values were determined by the inversion method. The calibrated model was used to predict the variation of groundwater flow field and leakage in the dam site area during the reservoir operation. The results show that the coupled model can capture the hydrogeological characteristics of karst and fractured medium and reflect the features of seepage control and drainage measures in the study area. The groundwater level of powerhouse has decreased by about 2-5 m. That of the water conveyance pipeline has risen by 10-20 m and 15-25 m for both banks during the reservoir operation. The leakage of karst pipes for impervious failure was larger than that for normal seepage control. In addition, the leakage of the powerhouse was estimated to be about 1000-3000 m\({}^{3}\)/d.  
Conceptualization, J.C. and Y.S.; methodology, Y.H.; software, H.S.; validation, J.C., Y.S., H.S. and Y.H.; formal analysis, Y.H.; investigation, J.C. and Y.S.; resources, J.C.; data curation, H.S.; writing--original draft preparation, Y.S.; writing--review and editing, Y.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 41572209.  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Cases**} & \multicolumn{5}{c}{**Karst Pipes**} & \multirow{2}{*}{**Underground Powerhouse**} \\ \cline{2-2} \cline{4-6}  & **1** & **2 and 3** & **4** & **5** & \\ \hline Case 1 & 0.43 & 0.36 & 1.99 & 313.39 & 2272.51 \\ Case 2 & 224.34 & 162.86 & 465.95 & 306.89 & 2307.62 \\ Case 3 & 0.16 & 0.12 & 0.82 & 117.92 & 1205.22 \\ Case 4 & 80.86 & 68.30 & 200.97 & 113.90 & 1232.58 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Prediction of leakage for karst pipes and powerhouse during the operation period of the reservoir (m\({}^{3}\)/d).
## 2 Materials and Methods
### Extraction and Analysis of Volatile Compounds in Oolong Ta  
Volatile profiles were directly collected from each sample with a modified headspace-solid phase microextraction (HS-SPME) method and detected by TRACE-DSQ-II GC-MS (Thermo Fisher, Waltham, MA, USA) as previously reported [17]. Briefly, each sample (2 g) crushed by liquid nitrogen was brewed with 5 mL boiling water in a 20 mL headspace vial, followed by the addition of 20 \(\upmu\)L (4 \(\upmu\)L/100 mL) of ethyl decanoate as an internal standard (IS) and immediate sealing of the vial. After fixing the polydimethylsiloxane/divinylbenzene (PDMS/DVB) fiber (Supelco, Bellefonte, PA, USA) on its upper end, the headspace vial was kept at 60 \({}^{\circ}\)C for 60 min to absorb the volatile gases. The analysis was performed under the following conditions: chromatographic column, DB-5 MS (30 mm \(\times\) 0.25 mm \(\times\) 0.22 \(\upmu\)m); inlet temperature, 230 \({}^{\circ}\)C; carrier gas, high-purity helium (purity \(\geq\) 99.99%); column flow rate, 1.0 mL/min; heating program, an initial temperature of 40 \({}^{\circ}\)C and hold for 2 min, up to 85 \({}^{\circ}\)C at 5 \({}^{\circ}\)C/min and hold for 2 min, up to 110 \({}^{\circ}\)C at 2 \({}^{\circ}\)C/min, up to 130 \({}^{\circ}\)C at 7 \({}^{\circ}\)C/min, up to 230 \({}^{\circ}\)C at 5 \({}^{\circ}\)C/min and hold for 8 min; oven temperature, 40 \({}^{\circ}\)C; injection mode, splitless. Ion source (EI) electron energy, 70 eV; ion source temperature, 230 \({}^{\circ}\)C; mass scanning range, m/z 35-400. Volatiles were characterized based on the mass spectral database and retention index (RI, determined by N-alkanes C3-C25) of the National Institute of Standards and Technology (NIST, Gaithersburg, MD, USA). The relative concentration of the detected volatiles was calculated by Equation (1):  
Relative concentration (\(\upmu\)g/kg) = (Peak area of target/Peak area of IS) \(\times\) 0.1726 \(\upmu\)g/Amount of sample (g) \(\times\) 1000 (1)
## 4 Overview of GNSSs
### BeiDou
#### 4.4.1 BeiDou System Architecture  
In the case of a positioning request, MCS determines the user's location based on the measured signal rotation time and the user's altitude, which is taken from a digital database stored at MCS or provided by the user. The resulting location information is then transmitted back to the user via an outbound signal. For short messages, the message is sent to the addressee by MCS via the outgoing signal. For time requests, the user's exact time adjustment is calculated by MCS and sent to the user via an outbound signal. Based on the time correction, the user adjusts the local clock, synchronizing it with the MCS clock. Requests can be sent, and location information and short messages can be received by user terminals. They can operate in two modes: one mode involves receiving one outgoing signal and transmitting incoming signals via two satellites, while the other mode involves receiving outgoing signals from two satellites when the user is in the common coverage area of two satellites, and transmitting incoming signals via only one satellite. Based on the time difference between the two received outgoing signals and the service re-task transmitted via one satellite, MCS measures the pseudo-distance from the user to the two satellites and calculates the user's position [88; 89; 90].
## 2 Materials and Methods
### Data Rate Estimation  
For an SAR application, the complete system (UAV with sensors and a communication module) should be portable and not too expensive. Otherwise, it will be unlikely that an average (although trained) rescuer will use it. Requirements for the data rate between the UAV and GS in a mission scenario can be calculated using the following procedure:  
1. Determine the necessary ground sampling distance (GSD) (in cm/pixel);
2. For the chosen camera sensor with \(m\ x\ n\) pixels, calculate the area covered with one orthophoto image and the dimensions in meters (Equation (2)): \[\textit{area width}\ [m]\ =\ \ m\ x\ GSD;\textit{ area height}\ [m]\ =\ \ n\ x\ GSD\] (2) where area width is single image footprint perpendicular to the flight direction and area height is single image footprint in the flight direction.
3. Depending on the UAV camera sensor and applied lenses (field of view (FOV)), calculate the necessary UAV flight altitude: \[H\ =\ \frac{F}{S_{W}}\textit{-area height}\] (3) where \(F\) is the focal length of the camera (in millimeters) and \(S_{W}\) is the camera sensor width (in millimeters).
4. Calculate the time needed between successive images taken by the UAV camera using Equation (4): \[\Delta t\ =\ \frac{s^{\prime}}{v}\ \ ;\ s^{\prime}\ =\ \textit{area height}\ x\ (1-io/100)\] (4) where \(v\) is the UAV velocity in m/s, \(s^{\prime}\) is the distance (in meters) travelled by the UAV between two successive images and _io_ is the image overlap for two successive images (in percent).  
Note that, opposite to common approaches, in Equation (3), area height is used instead of area width. This is done because we rotated images by \(90^{\circ}\), so we have more available image pixels in the flight direction and, therefore, more available time between successive images acquired by the UAV camera. This can be illustrated with the following example.  
Example: For a 20-MP camera, GSD 4 cm/pix and \(io\) = 70%; \(m\) = 3632; \(n\) = 5456; _area height_ = 218.24 m; _area width_ = 145.28 m; \(\Delta t\) = 6.54 s (\(v\) = 10 m/s).  
Since images are compressed in the Joint Photographic Experts Group (JPEG) format, the expected size of a 20-MP image is approximately 8-9 MB. This means that the required data rate per second is between 2.7 and 3 MB/s for a UAV velocity of \(v\) = 20 m/s. If the UAV velocity is 10 m/s, the required data rate per second is between 1.35 and 1.5 MB/s.
## 4 Discussion
### The Potential Applications of Hot Regions Analysis  
The difference in concept, time and space between rocky desertification and rocky desertification research hot regions is of great significance for the comparative analysis of the relationship between real rocky desertification and human society's response to rocky desertification; additionally, it can effectively support the development of rocky desertification planning, governance and research work. In particular, by comparing the spatial distribution differences between regional rocky desertification spatial distribution maps based on satellite remote sensing and ground surveys and rocky desertification research hot regions maps based on web retrieval and text mining, it can be determined which areas have rocky desertification and have received attention from the government, researchers and engineering management, or which areas also have rocky desertification but have not received enough attention from the government, researchers and engineering management. By comparing the spatial disparity and dynamic matching of the above regions, it is possible to examine the prevention and control planning, engineering management, technical research and evaluate the effectiveness of various ecological degradations, including rocky desertification attention from the government and the research community. The role of remnants and shortages and targeted promotion will greatly improve the temporal and spatial pertinence of the rocky desertification control plan, project management and scientific research.  
As shown in Table 1, the Guizhou Plateau, which is a typical rocky desertification area, has been fully studied by researchers. However, in the Qionglai Mountain--Liangshan Area of Sichuan, the Wushan-Shennongjia Area of Hubei and the Hengduan Mountain Area of western Yunnan, the sensitivity to rocky desertification is high or very high; however, the amount of research conducted here is very low. Therefore, more researchers can choose these regions as a new study area to detect occurrence regularity and the evolutionary process of rocky desertification.
## 3 Results and Discussion
### Skid Resistance and the Polishing Effect  
A British Pendulum Skid Resistance Tester was used to obtain the BPNs of the asphalt samples. Table 8 shows the results of the Skid Resistance test. It has been established from the results that the asphalt modified with GNPs has a higher skid resistance in comparison to non-modified asphalt. The reason behind this is the nano-texture provided by the GNPs.  
The reduction in the skid resistance due to the polishing of the asphalt surface (caused by the continuous movement of the wheel) is also lesser for GNP-doped asphalt. In the case of the non-modified asphalt, the reduction in the skid resistance was around 47%. On the other hand, the reduction in the skid resistance in the case of 2% and 4% GNP-modified asphalt was 27% and 28%, respectively. This is due to an increase in the stiffness of the GNP-modified binder which improves the structural performance of highways [18]. An improved structural performance ensures the intactness of the macrotexture, resulting in a high skid resistance [10].  
Typically, BPN values are obtained in the field after the pavement has been subjected to traffic but, in this case, they were obtained in a laboratory to compare the skid resistance of GNP-doped asphalt with that of conventional asphalt. It is pertinent to mention here that the focus of this investigation was  
Figure 16: A piece of aggregate coated with Graphene Nano-Platelet (GNP)-modified binder.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**British Pendulum Number (BPN) Before Wheel Tracking Test**} \\ \hline \multirow{2}{*}{Number of Samples} & \multirow{2}{*}{Non-Modified Asphal} & 2\% Graphene & 4\% Graphene \\  & & Nano-Platelet & Nano-Platelet \\  & & (GNP)-Modified Asphalt & (GNP)-Modified Asphalt \\ \hline
1 & 45 & 60 & 61 \\
2 & 46 & 59 & 60 \\
3 & 44 & 60 & 60 \\ \hline \multicolumn{4}{c}{**British Pendulum Number (BPN) After Wheel Tracking Test**} \\ \hline
1 & 25 & 45 & 44 \\
2 & 23 & 44 & 43 \\
3 & 24 & 42 & 44 \\ \hline \multicolumn{4}{c}{**Percentage Reduction in Skid Resistance**} \\ \hline  & 47\% & 27\% & 28\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: The British Pendulum Numbers (BPNs).  
to determine the effect of the surface texture/roughness provided by the GNPs on the skid resistance of the asphalt.
# Enhancing Street-Crossing Safety for Visually Impaired Pedestrians with Haptic and Visual Feedback
## 2 Related Works
### Visual Feedback for Low-Vision and Blind Users  
In the field of visual feedback for assisting visually impaired individuals, researchers primarily focus on providing informational support for those with low vision. These systems leverage the residual visual abilities of low-vision individuals and have achieved significant results in various application scenarios, such as obstacle recognition through visual enhancement technologies [28; 69; 70; 71; 72], path navigation guidance [73], gesture interaction [74], and stair-climbing assistance [75]. To enhance the usability of these systems, researchers have developed a range of visual optimization methods [76], such as image magnification, contrast adjustment, and edge enhancement, which are presented to users via head-mounted display devices or other visual interfaces.