esa-sceva/satcom-chunk-collection huggingface dataset:
# Communication Technologies for Smart Grid: A Comprehensive Survey
## Ethernet
### Cellular Communication  
Cellular communication can be used where continuous communication is not required. Advantages with using cellular communication technology is that it is already existing, it has widespread coverage, low cost, and high security. One disadvantage with cellular communication is the fact that the network is shared with many other users, this can in some cases result in network congestion. Universal Mobile Telecommunications System (UMT), Long-Term Evolution (LTE), LTE-Machine Type Communication (LTE-M), and Narrowband IoT (NB-IoT) are technologies used for communication in SGs. The last two were specifically developed for IoT applications. LTE-M and NB-IoT are both low power wide area networks. LTE-M offers higher data rate, but require more bandwidth [75; 76; 77].  
The fifth generation mobile communication network (5G) utilizes wide frequency range including millimeter wave (mm) spectra and operate at higher frequencies than LTE/4G system. Additionally, the bandwidths of 5G are higher than previous generations. The advantages of 5G over earlier generations include higher data rate and low communication latency, improved security and reliability, low power consumption, and ability to connect a higher number of devices. This makes 5G suitable for SG infrastructure [37; 40; 78]. 5G supports ultra-Reliable Low Latency Communication (uRLLC) which is suitable for applications with strict requirements to low latency and high reliability e.g., mission critical applications such as remote control of digital substations [53; 79; 80]. A comprehensive review on the use of 5G for SGs with the future roadmaps and challenges is provided in [81]. The security for SGs in future generation (5G and beyond) mobile networks is discussed in [78].
# Precipitation Estimation Using FY-4B/AGRI Satellite Data Based on Random Forest
###### Abstract
(2018) used an RF to build a retrieval model to estimate precipitation levels based on Himawari-8 satellite data and the GFS numerical forecast product. Their results showed that the RF could describe the precipitation contour well. [PERSON] et al. (2018) used an RF to retrieve precipitation data based on FY-4A/AGRI Levell data. Their results showed the RF had a higher accuracy than that of the AGRI's precipitation product during the day and at night. In general, ML methods have a higher accuracy in estimating precipitation than traditional physical methods. But there are still some problems that we need to solve. There are still numerous errors for heavy rain and torrential rain, and there is no research on precipitation retrieval for the whole region of China based on FY-4B/AGRI satellite data currently. As we all know, ML is a black-box-like algorithm, and it is difficult to determine causal relationships. What can we do to improve ML to make it better? Firstly, we should select more appropriate model feature variables as the input to models. Secondly, an uneven sample distribution will lead an ML model to often overestimate the majority of samples and underestimate the minority of samples, which is more obvious in precipitation samples. Thus, we should select the optimal sample proportion when training the model. And thirdly, because of the complex and diverse terrain of China, it is necessary to evaluate the precipitation retrieval effect of different underlying surfaces. In order to optimize the FY-4B/AGRI precipitation estimation algorithm and improve the accuracy of satellite precipitation retrieval, this paper establishes the FY-4B/AGRI precipitation estimation algorithm based on an RF. The algorithm is based on the analysis of the relationship between FY-4B/AGRI observation data and precipitation, as well as the infrared signal characteristics of precipitation clouds. Thus, more feature variables related to clouds and precipitation are considered in the input of the model. To evaluate the model predictions, the GPM IMERG product is used as a reference and the retrievals are compared with those of the FY-4B/AGRI operational precipitation product to evaluate the retrieval effect of the model. At the same time, the retrieval results are compared and analyzed according to different underlying surfaces.
## 3 Data Filtering for Short-Term Forecasting of Photovoltaic Power Plants Generation
### Empirical Data Filtration  
In determining outliers when analyzing the transparency index dependence on the cloudiness amount, an empirical formula can be used:  
\[k_{T}\leq 0.9-0.5\cdot cc, \tag{1}\]  
Figure 4: Transparency index versus cloudiness for morning/evening altitude angles.  
Figure 3: The scatter diagram of the PVPP generation forecasted values.  
where \(k_{T}\)--the transparency index, (p.u.); \(cc\)--the cloudiness, (p.u.).  
This expression is determined on the assumptions that come from the experience of PVPP solar forecasting system application at the real power generation facility: under absolutely cloudless weather conditions the transparency index value \(k_{T}\) cannot exceed 0.9 p.u., and under the most cloudy weather the transparency index value \(k_{T}\) cannot exceed 0.5 p.u.  
All observations above the straight line \(k_{T}=0.9-0.5\cdot cc\), are treated as outliers and are considered unreliable. Figure 5 shows the transparency index dependencies on cloudiness, filtered in accordance with the expression (1), for the above-described ranges of the solar altitude angle, characterizing the morning and evening conditions (\(\alpha<15^{\circ}\)).  
From Figure 5 it appears an uncertainty decrease in the cloudiness influence \(cc\) on the transparency index \(k_{T}\) due to the introduced filtering of the observations. The results of PVPP generation forecasting accuracy assessment using empirical filtering are shown in Figure 6.  
The comparison of Figures 3 and 6 allows one to evaluate the efficiency of using simple filtration. The total error value for all the specific periods under consideration using an empirical filter is lower  
Figure 5: Transparency index versus cloudiness for morning/evening altitude angles with empirical filtration.  
Figure 6: The scatter diagram of the PVPP generation forecasted values with empirical filtering.  
than for the calculations without using the filter, which is confirmed by the scatter on the diagrams. The determination coefficient \(R^{2}\) has also increased.  
The advantages of the empirical filtering method are the usability and the fewer required computing resources. Disadvantages of the filtering method are as follows: the difficulty to accurately identify outliers of the transparency index \(k_{T}\), the excessive observations filtering (in addition to outliers, reliable observations can be discarded) and the lack of empirical expressions versatility.
###### Abstract  
The purpose of this study was to investigate the radiation attenuation effect due to reflection and refraction that occurs when radiation passes through the composite material through simulation. The gamma-ray shielding ability of composites with different structures, such as layered composite, fibrous composite, and particle-added composites, was compared. For the layered composites, regardless of the thickness, the gamma-ray attenuation effect due to the structure of the layered composite could not be seen. The simulated attenuation coefficient was found to be different than the theoretical value, which increased with the content of inorganic materials. Assuming that the cesium ions absorbed on the nonwoven fabric are located in the center of the nonwoven fabric and the radioactive material absorbent is used in an appropriate amount, it is expected that more than 50% of gamma rays emitted from radioactive cesium can be shielded. If the nonwoven composites have such a shielding ability, it is expected that after adsorbing 137 Cs, the intensity of gamma rays due to the adsorbed radioactive cesium can be partially attenuated, thereby contributing to radioactive waste treatment.  
attenuation effect; gamma ray; shielding simulation; layered composites; linear attenuation coefficient; nonwoven fabric
## 10 Challenges and Open Research Problems
### Data Availability  
ML algorithms live on data; the more data we feed the algorithms, the smarter they tend to become [336]. To make strategic and intelligent decisions, data are typically required from different sources; these data are integrated and transformed before they are used to make decisions or predictions [337]. This process is the so-called data-driven or evidence-based decision-making [338]. In many cases, large data sets are required both to train and test the ML algorithms as well as to evaluate their efficacy in making predictions. In addition, both labeled and unlabeled data are required to compare the efficacy of different ML algorithms [339], and guide in selecting the best technology tool for a given situation. However, data are not always available or is available in insufficient quantity or quality. Due to the criticality of data to the success of businesses, companies are usually reluctant to share data obtained from UAV deployment trials. There are also cases where it is very difficult to obtain data, especially when a process or technique is still in infancy. Some of the most useful data sets are not publicly available. To make predictions which are generalizable, there needs to be sufficient supply of heterogeneous data repositories that are accessible to many researchers. Failing this, each institution might end up developing individual analytical pipelines that may fail under new circumstances, such as a different operating environment.  
Open-access data repositories are a key enabler to unlocking insights into addressing the challenges of UAV deployment in cellular networks. With sufficient data shared collaboratively amongst stakeholders in the UAV research and development industry, robust and reproducible ML solutions can be developed for UAV applications in cellular networks. The ability to obtain and reuse data will enable easy collaboration among researchers and the industry, save costs, and minimize the time to market for new products. It is desirable to have an open database for UAV-aided cellular communication, similar to the modified National Institute of Standards and Technology (MNIST) database (_c.f._[340]) widely used in computer vision studies.
## 2 PIESAT-01 Constellation  
The \"Nuwa\" constellation construction plan was proposed by PIESAT Information Technology Co., Ltd. (Beijing, China) comprising the PIESAT-01 constellation (4 high-resolution distributed interferometric X-SAR satellites), the PIESAT-02 constellation (12 SSO orbit X-SAR satellites), the PIESAT-03 constellation (16 LEO orbit C-SAR satellites), and the PIESAT-04 constellation (10 SSO orbit optical satellites).  
On 30 March 2023, the PIESAT-01 constellation was launched from the Taiyuan Satellite Launch Center and successfully entered the designated orbit. This satellite system is the world's first multi-satellite distributed interferometric radar satellite system adopting a \"Cartwheel\" formation. The \"Cartwheel\" formation is a unique satellite constellation configuration where slight differences in the eccentricity of each satellite's orbit ensure that at any given fixed moment, the main satellite remains at the center of the formation with three auxiliary satellites evenly spaced at 120-degree intervals around it. As shown in Figure 1, the PIESAT-01 constellation maintains a coplanar configuration of its four satellites at all times through precise orbit control. The main satellite is in the center of the formation, and the three auxiliary satellites orbit the main satellite in the same elliptical trajectory. The main satellite sends detection signals to the ground, and the PIESAT-01 SAR satellite constellation receives the echo signals synchronously. Four satellites fly in a 528 km sun-synchronous orbit with a recurring cycle of 15 days and an inclination angle of 97.552\({}^{\circ}\).  
The primary mission of the PIESAT-01 constellation is to complete the global land-scale mapping task (between 82.5\({}^{\circ}\) north and south latitude) within two years, and it has high-precision deformation measurement capabilities. Additionally, surface deformation monitoring data with an accuracy of 3 to 5 mm per year can be obtained. It can also offer various types of remote sensing data products, providing high-precision InSAR measurement data and high-quality SAR images for industries such as surveying and mapping, seismic monitoring, land administration, disaster reduction, forestry, transportation, and water resources.  
Figure 1: Schematic diagram of PIESAT-01 constellation distribution.
## 5 Conclusions  
This study used key vegetation parameters (including the FCI, annual average LAI, annual maximum FVC, and annual cumulative GPP) based on quantitative remote sensing products to analyze the status of forest growth, and based on the EQI indicator to evaluate forest health in the Lancang-Mekong region from 2010 to 2020. The findings of this study indicate that, in 2020, Laos outperformed Myanmar, Vietnam, Thailand, and Cambodia in a comprehensive evaluation of the forest ecosystem in the Lancang-Mekong region. From the year 2010 to 2020, Cambodia experienced the highest proportion of decline in all indicators related to the forest ecosystem, followed by Laos and Myanmar, while Vietnam and Thailand demonstrated varying degrees of improvement. Furthermore, it was observed that, as the FCI increased, the absolute values of the change rates of the key vegetation evaluation indicators decreased for all five countries, implying that denser forest coverage tended to result in a more stable forest ecosystem. Apart from the impact of natural activities, the deforestation in Cambodia is mostly caused by urbanization and cultivation. Therefore, measures such as limiting human activities and creating forest nature reserves should be implemented to safeguard the forest's ecological environment in the Lancang-Mekong region.  
Conceptualization, J.Z., J.L. and Q.L.; methodology, J.Z.; data processing, J.Z., Y.D. and H.Z.; writing--review and editing, J.Z., L.L. and Y.D. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by National Key Research and Development Program, Grant number 2019 YFE0126700 and 2020 YFE0200700.  
Not applicable.  
Data Availability Statement: Data used in this study are available on request from the first author.  
The authors declare no conflict of interest.
## 3 Case Study--The ANN Models for Load Forecasting
### Experiments and Results
On the other hand, there is always a tradeoff between the computational costs and the level of accuracy in choosing the best model for a specific task. Thus, considering the training time as an indicator of computational costs is useful in such scenarios. As shown in Table 5, the convolutional models are the fastest models, and Transformer is the slowest model. For example, convolutional 2D multivariable is almost 8, 9 times faster than Transformer univariable. Considering the best-performing model, the LSTM multivariable (5989 s and MAE 0.004521), in the whole dataset with convolutional 2D multivariable (1989 s and MAE 0.005036) when sacrificing 0.000515 of accuracy, a 3-fold gain in training time will be achieved. Moreover, it is obvious that as the number of training data increases, the training time rises as well. When the number of training data doubled from 30,000 to 60,000, the training time almost doubled as well. By doubling the number of training data from 60,000 to 120,000, in this case, as the number of available data doubled, the training time will be 1.69 times higher (from 1176.48 to 1988.98 s). Once the ANN models are trained, they can be used for the prediction of any given time in the future. Figures 18 and 19 show the prediction of ANN models for the next 24 days of power consumption and the ANN model prediction with actual data in three different datasets.
# An Efficient Routing Protocol with Overload Control for Group Mobility in Delay-Tolerant Networking
## 3 A Proposed Routing Protocol with Overload Control for Group Mobility
After contact of \(z_{B}\) and \(y_{A}\), \(y_{A}\) receives delivery state and forwarding state information for \(m_{1}(x_{A})\), and delivery state and forwarding state information of \(m_{1}(x_{A})\) are created newly in the overload control table of node \(y_{A}\). In Figure 2(c), \(z_{B}\) is in contact with \(w_{A}\), and \(y_{A}\) is in contact with \(x_{A}\) now. Before contact with \(z_{B}\), \(w_{A}\) has information of successful delivery state to destination \(x_{A}\), as shown in Figure 2(b). Thus, this information is shared with \(z_{B}\), and \(m_{1}(x_{A})\) is removed from the buffer of \(z_{B}\), and information on delivery state and forwarding state are updated in overload control table of \(z_{B}\). Since \(x_{A}\) successfully received \(m_{1}(x_{A})\) before contact with \(y_{A}\) already, \(m_{1}(x_{A})\) is removed from the buffer of \(y_{A}\) after contact with \(x_{A}\). Then, information on delivery state and forwarding state are updated appropriately in the overload control table of \(y_{A}\). \begin{table}
\begin{tabular}{c c c c} \hline
**Message ID** & **Delivery State to a Destination Node** & **Delivery State to a Node in the Same Group as a Destination Node** & **State of Forwarding Permission** \\ \hline \(m_{1}(x_{A})\) & Not delivered & Not delivered & Permission \\  & & & (permitted to forward to any node) Limited permission \\ \(m_{2}(y_{A})\) & Not delivered & Delivered & (permitted to forward to a destination node only) \\  & & & (Not permitted to forward to any node) \\ \hline \end{tabular}
\end{table} 
Table 2: Overload control table of the proposed proactive overload control protocol. Figure 3: Examples of overload control table. (**a**) Overload control table of nodes \(z_{B}\) and \(w_{A}\); (**b**) Overload control table of nodes \(w_{A}\) and \(y_{A}\); (**c**) Overload control table of nodes \(z_{B}\) and \(y_{A}\). Figure 4 shows a flowchart of the proposed algorithm. When node x and node y contact, they update individual and group delivery predictabilities by exchanging summary vectors, which includes information of messages in their buffer and delivery predictability. Then, they update the overload control table. Node x checks if there is any message for forwarding to node y. If there is any, node x checks the overload control table to check if it is permitted to forward. Then, if it is, the message is forwarded based on the forwarding condition, as proposed in Table 1. If node x receives a message from node y, it firstly checks if it is the destination node z of the message. If it is, the corresponding entry in the overload control table is updated. Otherwise, node x checks if it is located within the same group as the destination node z of the message. If it is, the corresponding entry in the overload control table is updated.
## 3 Results and Discussion  
### Physico-Chemical Indications and Phytochemical Proprieties of PPMS  
The physico-chemical indicators and phytochemical properties of PPMS are presented in Table 3.  
In the result of the analysis of the physico-chemical properties and phytochemical chemical proprieties of the raw material (Table 3), it was found that PPMS has a moisture content of 6.21% and is a rich source of ash (6.27%). The approximate PPMS compositions were more or less similar to the results of [PERSON] et al., who observed lower AC (5.37%) but slightly higher MC (10.96%) in pumpkin powder [9]. On the other hand, an AC of 3.8% in PPMS was reported by [PERSON] et al. [38]. The same MC was within the safe limit, as [PERSON] et al. [39] noted that pumpkin powder with an MC greater than 14% was susceptible to fungal and mold growth. In PPMS, the TA was 1.16%, expressed as malic acid, close to the values recorded by [PERSON] et al. of 1.03% [40]. In the research of this study, high TCC (27.76 mg/100 g DW) and TPC (237.15 mg/100 g DW) were found in PPMS, providing high AA. These values correlate with data from [PERSON] et al. [41]. [PERSON] et al. [42] recorded lower values for TPC (134.59 mg GAE/100 g DW). [PERSON] et al. [43] noted that the TPC determined in hot-air-dried samples was 67.6 mg/100 g DW, and that in freeze-dried samples was 63.7 mg/100 g DW.  
Our study has shown excellent antioxidant potential (mg TE/100 g DW) in both assays of 681.01 (ABTS) and 300.03 (DPPH), respectively, for pumpkin powder used in foamy confectionery products (Table 3). Other studies have shown the AA of the powder pumpkin  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Indicators** & **The Value of the Indicator** \\ \hline MC, g/100 g & 6.21 \(\pm\) 0.05 \\ AC, g/100 g DW & 6.27 \(\pm\) 0.06 \\ pH & 7.45 \(\pm\) 0.05 \\ TA, \% expressed in malic acid & 1.16 \(\pm\) 0.07 \\ TCC, mg/100 g DW & 27.76 \(\pm\) 5.01 \\ TPC, mg GAE/100 g DW & 237.15 \(\pm\) 16.8 \\ AA (ABTS), mg TE/100 g DW & 681.01 \(\pm\) 21.6 \\ AA (DPPH), mg TE/100 g DW & 300.03 \(\pm\) 14.4 \\ \hline \hline \end{tabular}  
* Antioxidant activity of ABTS and DPPH was determined for hydroethanolic extracts of pomace powder of musky squans (PPMS). MC—moisture content; AC—ash content TA—titralte acidity; TCC—total carotenoid content; TPC—total polyphenol content; AA—anticohat activity. ABTS—2,2-azinobis-(5-ethylbenzothazoline-6-sulfonates); DPPH—2,2-diphenyl-1-picrylhydraz; EAE—galic acid equivalent; TE—trolox equivalent; DW—dry weight. Results are presented as mean \(\pm\) standard deviation.  
\end{table}
Table 3: Physico-chemical indicators and phytochemical properties of PPMS.  
pulp to be 0.53 mmol of AAE (ascorbic acid equivalent)/100 g, as well as a TPC of 192 mg GAE/100 g and a \(\beta\)-carotene content of 32.87 mg/100 g DW [44].