esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning-Based End-to-End Carrier Signal Detection in Broadband Power Spectrum
## 1 Introduction
One reason is that if a point in one subcarrier array has been categorized as noise, then the subcarrier would be wrongly recognized as two subcarriers. Another reason is that when two or more neighboring subcarriers are very close to each other, the FCN-based method cannot correctly distinguish between the demarcation points, and this would cause severe fault and leak detections. In this paper, to solve the problems above, we propose SCN, an end-to-end CNN model based on deep learning. Contrary to the FCN-based method, which transforms the carrier signal detection problem into a semantic 1D segmentation image problem, we regard each subcarrier in the broadband power spectrum as an independent target object and directly localize its FC position and BW. Therefore, the carrier signal detection problem turns into a 1D image object localization problem. The proposed SCN classifies the broadband power spectrum as inputs and extracts the features of different length scales by the ResNet backbone. Then, the FPN [23] neck fuses the features and outputs the fusion features. Next, the RegNet head regresses the PSD prediction for FC and the corresponding BW prediction. Finally, we can achieve the subcarrier targets by applying NMS. Moreover, we train the SCN on a simulation dataset and evaluate it on an actual satellite broadband power spectrum set. As an improvement of the FCN-based method, the experimental results demonstrate that the proposed method can effectively detect the subcarrier signal in the broadband power spectrum as well as achieve higher and more robust performance than the FCN-based method. The main contributions of this work are summarized as follows:We propose an end-to-end deep CNN-based model for carrier signal detection in the broadband power spectrum, so-called SCN. Without prior knowledge and post-processing, the SCN directly achieves the detection results;
2. We conducted several experiments to demonstrate the superiority of our proposed method compared with other existing methods. Additionally, the model scale and the amount of training simulation samples on the performance of the proposed method are investigated. The remainder of this paper is organized as follows: Section 2 further describes the carrier signal detection problem in the broadband power spectrum. Section 3 introduces the details of the proposed method. Section 4 provides the specific experimentations, including the dataset for the experiments, training details, the evaluation results, and some comparisons with other methods. Finally, Section 5 concludes the paper.
# Simulation of Wind Speeds with Spatio-Temporal Correlation
## 2 Methodology
### Modeling
#### 2.1.1 Data Collection  
A good performance of the model requires well-characterized wind speed data. In addition, it is required that the data collection periods for the evaluation of the wind simultaneity have identical features; this is to say, the starting and finishing times of data collection must coincide for the studied sequences of different locations, and with a minimum period of one year [29]. The previous sentence refers to 10 min mean wind speed values simultaneously measured at such locations.  
The data needed to characterize the wind at a given place can have different origins, such as those obtained during a measurement campaign, those coming from wind farm anemometers or those collected in meteorological stations.  
The fact must be mentioned that wind speed might have been measured at a height that can be different from the one of the wind turbine hubs. For example, in the case of meteorological stations, wind speeds are measured at a few meters above the terrain, i.e., 2, 5 or 10 m. The value of the wind speed at a different height can be calculated with the help of the knowledge of the ground roughness, characterized by the friction factor, and taking the shear effect into account [15].
# Effect of Incorporating Waste Limestone Powder into Solid Waste Cemented Paste Backfill Material
## 4 Discussion  
### Pore Characteristics Analysis  
For NMR results of group A (Figure 4), porosity deteriorated with the admixture of limestone powder, resulting in an increased value (AVG = 27.14%, max = 35.18%). However, optimization of pore distribution increased the proportion of small holes and decreased the proportion of large holes (AVG = 36.26%, max = 46.35%). Binarization parameters, as listed in Table 3, showed an increase in the number of pores (AVG = 6.55%, max= 11.48%) and thereby slightly increased the average pore radius (AVG = 5.41%, max = 7.14%), which was adverse. The analysis of grading parameters, as listed in Table 2, suggested a nonuniform coefficient A1 value of \(Cu\) = 14.56 and a curvature coefficient value of \(Cc\) = 0.72 (strive \(Cu\)\(\geq\) 10 and not too large, 1 \(<Cc\)\(<\) 3), which increased slightly in the presence of limestone powder admixture in which grading was optimized.  
For NMR results of group B (Figure 5), porosity was optimized in the presence of limestone powder with a slight decrease in value (AVG = 5.87%, max = 7.15%), but pore distribution deteriorated as macroporosity increased (AVG = 43.67 %, Max = 50.50%). Binarization parameters, as shown in Table 3, exhibited pore properties that were optimized by increasing the number of pores (AVG = 20.97%, max = 42.14%), and the average pore radius was decreased. The parameters of grading (Table 2) deteriorated after mixing with the limestone powder, in which \(Cu\) = 40.02 and \(Cc\) = 0.71 for 100% cement group, suggesting a serious loss of intermediate particle size.  
The incorporation of limestone powder had a certain influence on the pore properties of SWCFB A and B, and the optimization effect on B group was apparent. The results of NMR depicted in Figure 5 showed that the porosity of 100% cement B1 was 7.1 times of that of A1. When 15% stone powder was mixed with the B group, the porosity could be reduced to 4.97 times of that of group A. In analyzing the binarization parameters from Table 3, the average pore radius ratio of B and A could be reduced to 1.18-fold from the initial 1.51-fold by incorporating 20% stone powder. In group B, the grading (Table 2) did not require any optimization but deteriorated. Hence, the proportion of particles over 100 \(\upmu\)m in group B were assumed to be over 40% (Figure 1), which was incompatible with the particle size distribution of limestone powder.
## 6 Conclusions  
Combined with the motion characteristics of the hypersonic platform SAR, this paper establishes the imaging geometry model matched with the curved trajectory of this platform, and designs the azimuth multichannel sampling model based on the DPCA technique. When the imaging algorithm is explored to process the SAR echo signal, the high-efficiency spectrum reconstruction method based on the discrete Fourier transform is executed to pre-eliminate the ambiguous spectrum. Then, the uniform azimuth data of the equivalent single channel SAR system is developed. Furthermore, the approximate slant range model is derived to reduce the imaging complexity based on the minimax criterion, and the optimal second-order approximate coefficients of the cosine function are obtained using the two-population coevolutionary algorithm. Simulation results show that the approximate processing of the SAR slant range well satisfies the imaging accuracy in azimuth. On this basis, the ETF imaging algorithm is designed for a hypersonic platform SAR with curved trajectory, and the method of range division is further used to achieve the HRWS imaging. Simulation results of point targets validate the effectiveness of the ETF imaging algorithm.  
This study was supported by the National Natural Science Foundation of China (Nos. 61471019 and 61501011).  
All authors contributed to this work. [PERSON] and [PERSON] proposed the idea and wrote the paper; [PERSON] conceived the spectrum reconstructed algorithm; and [PERSON] revised the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 12: Simulation results for complex scenes using TerraSAR-X real data: (**a**) imaging result processed with the echo data of the first sub-aperture; (**b**) imaging result of the proposed method.
## 4 Conclusions  
The developed TPS/PBAT films containing food preservatives (NaNO\({}_{2}\), C\({}_{6}\)H\({}_{7}\)O\({}_{6}\)Na, Na\({}_{5}\)P\({}_{3}\)O\({}_{10}\), Na\({}_{6}\)(PO\({}_{3}\))\({}_{6}\), and Na\({}_{4}\)P\({}_{2}\)O\({}_{7}\)), produced by blown film extrusion as active packaging, were evaluated for their potential application in food packaging and associated risk assessment. The potential risk posed by substances migrating from the developed TPS/PBAT active packaging was assessed using two analytical techniques: gas chromatography-mass spectrometry (GC-MS) and ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS). These techniques enabled the identification of a broad spectrum of chemical compounds. The analysis identified 1,6-Dioxacyclododecane-7,12-dione, an oligomer from PBAT, as the major migrant. Additionally, various other compounds were detected, including potential additives, like plasticizers or antioxidants. The analysis also revealed the presence of a wide variety of non-intentionally added substances (NIAS), many of which likely resulted from degradation processes during extrusion and film formation. Migration experiments were conducted using three different food simulants to evaluate the potential migration behavior of these compounds. The experiments revealed the formation of new compounds formed through the interaction between food simulants and PBAT monomers or oligomers, particularly in the 95% ethanol simulant. These findings indicate that the active TPS/PBAT biodegradable film containing food preservatives have the potential to impact consumer safety and should be carefully evaluated for use in food contact materials.  
Conceptualization, P.W., N.H. and P.V.; methodology, P.W., E.C., C.E. and P.V.; validation, P.W., N.H. and P.V.; formal analysis, P.W., E.C., N.H. and P.V. investigation, P.W., E.C. and P.V.; writing--original draft preparation, P.W., E.C., N.H. and P.V.; writing--review and editing, E.C., C.N., N.H. and P.V.; supervision, N.H., E.C. and P.V.; resources and project administration C.N., E.C. and P.V.; funding acquisition, N.H. All authors have read and agreed to the published version of the manuscript.  
This research project was supported by the National Research Council of Thailand (NRCT), Contract No. N41A640190 (The Royal Golden Jubilee Ph.D. Program) and Kasetsart University Research and Development Institute (KURDI) (KU) 2.68.  
Not applicable.  
Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Assessing Blockchain Consensus and Security Mechanisms against the 51% Attack
## 2 Background
### Blockchain Technology: The Digital Concept
#### 2.1.2 Blockchain Functionality  
Blockchain is a digital technology which stores digital information [5] in blocks. Each block is chained to the blocks adjacent to it, called a blockchain. Any information inside the blocks can never be changed due to the immutable feature. Altering a single piece of information of a block invalidates every block after the one that was altered. The block information is available publicly, and one can easily track almost all key information, such as client records, property contracts, and payment information. Every block comprises a digital signature which correlates with the strings in that particular block. The whole process occurs through a hashing procedure, and a change to a single digit of the block string produces an entirely new digital signature. The blockchain blocks are available for verification by anyone who is part of the network. As a result, any malicious alterations can be immediately detected by the network participants. Regardless of the failure of any part of the network, the blockchain network still keeps running as normal. A block in the bitcoin blockchain typically has a size of 1 MB, and it can contain many transactions as long as it is within the size limit.  
Figure 1 presents a full layout of a bitcoin blockchain block. The block header size is 80 bytes, which includes the version, previous hash, Merkle hash, timestamp, difficulty level, and the nonce. All the information is then hashed to include the inside of the next block header.
# An Open Data Approach for Estimating Vegetation Gross Primary Production at Fine Spatial Resolution
## 2 Materials and Data
### Generating GPP on GEE

Table 2: Data sources for generating GPP on the GEE platform. Next, we optimized the model coefficients for the fine-spatial-resolution revised EC-LUE model. We used the reconstructed fine-resolution NDVI from Section 2.2, 16-day interval climatic data and the FLUXNET sites' 16-day mean GPP from 2000 to 2014 [18] to optimize the model coefficients with the Markov Chain Monte Carlo approach in [PERSON] et al. (2021) [19]. We quantified the GPP data quality by quality tag (NEE_VUT_MEAN_QC) in FLUXNET2015 dataset. When the 16-day mean NEE_VUT_MEAN_QC was less than 0.75, we defined the GPP at this 16-day point as gap-filled GPP; we defined the others as observed GPP. In the step of optimizing the model coefficients, we generated the GPP at 16-day intervals from 95 selected sites, in a similar way to [PERSON] et al. (2020) [3]. Next, in each site, we chose the odd years as the calibration dataset and the remaining half as the validation dataset. We optimized the coefficients with all the data in the calibration dataset at all the sites from nine vegetation types, including deciduous broadleaf forest (DBF), evergreen broadleaf forest (EBF), evergreen needleleaf forest (ENF), mixed forest (MF), grassland (GRA), savanna (SAV), shrubland (SHR), wetland (WET), and C3 cropland (CRO). The optimized coefficients of the fine spatial remote sensing product are shown in Table 3.
# Recent Advancement of Data-Driven Models in Wireless Sensor Networks: A Survey
## 4 Types of Wireless Sensor Network
### Multimedia WSNs  
Multimedia is a new and emergent type of WSNs. The real-time environmental scalar data, images, video stream, audio data, and tracking movement are only retrieved with multimedia WSNs. Multimedia networks consist of sensor nodes furnished or fixed with microphones, cameras, and other multimedia objects. These types of networks have additional abilities such as the processing time, storage of associates, and fusion of multimedia data from different sources [58]. The aim of the multimedia network is a wide range of possible applications such as military and surveillance [59], smart city surveillance [59], person locator services and environmental monitoring [60], industrial, biomedical sensing, and healthcare [61], object tracking, and traffic avoidance applications [62]. The big challenges in multimedia sensors are source coding, high bandwidth cost, coverage, and energy consumption, QoS application specific, in-network processing, and channel capacity [63]. Two main types of routing protocols are used such as frame sharing (FRASH) and multi-path multi-speed (MMSPEED). One of the event-driven data applications in wireless sensor networks with the most potential is movement tracking. However, existing scalar-based WSNs are incapable of extending various image data, such as object kind and shape. To solve the complexity, a multimedia WSN is used to handle the object's different data. When adopting multimedia, service quality issues always emerge, necessitating the use of network technologies to provide consistent service quality. In the study [62], the authors show that the quality of service (QoS) for multimedia applications varies depending on applications. As a result, delivering QoS while optimizing a network becomes a major issue. Event-driven and query-driven data-driven models are the two most common data models of basic service modes. The service mode based on events only has one sort of service, which is an event-driven service [64]. The majority of event-driven data model applications in multimedia sensor networks are latency and error intolerant. Monitoring the technological ensemble from nodes is essential for an application. Query-driven models have two sorts of services available, including the data query service and stream query service. The majority of services for data inquiry are aimed at the error-prone but query-specific delay-tolerant applications [65; 66].
# Mobile Broadband Performance Evaluation: Analysis of National Reports
## 3 Case Studies
### Connected Nations: UK
* Thus far, the discussion focused on what percentage of the areas where people are present is receiving voice and 4G coverage. Actual physical land coverage is also presented in the report. * There is a 79% from all MNOs and 95% from at least one MNO of voice service in all of the UK's geographic areas. Vodafone and O2 have a 91% geographical coverage, whereas EE and Three are at only 86% for voice service. * As for 4G data service, the is 66% from all MNOs and 91% from at least one operator. EE has the highest 4G geographical presence with 84%, whereas O2 has the lowest with 76%. * _Policies_\(\rightarrow\) The UK government has negotiated with industries to overcome the gap in coverage between rural and urban areas through the \"Shared Rural Network\" (SRN). The proposal allows MNOs to share current and future infrastructures to improve the coverage in rural areas. In addition, an investment to create future-proofed 4G infrastructure and services will be made. From the report, it is possible to observe that all operators had similar and good voice services in indoor premises, and O2 has come out as the winner for 4G data coverage. On the other hand, Vodafone and O2 have a better voice service for outdoor premises whereas the 4G data coverage from all MNOs is the same. Even though all operators have a high coverage for urban areas, the coverage for rural areas is low in almost all cases, which is why the government issued the SRN proposal.
## 2 RSR Measuring Technique
### Monitoring Sensor Design  
Based on the RSR method, a monitoring transducer was designed. As shown in Figure 1, the external structure of the transducer is approximate cube with a side length of 80 mm, and it contains three orthogonal sensor faces. The transducer uses a vibrating wire as the sensor element, which has a measuring range from 0 to 35 MPa. Since only three normal stress components can be obtained for each transducer, two transducers should be buried together as a group for each measuring point during the monitoring process. As shown in Figure 2, the two transducers are placed in advance into the grouting materials to form a sensing column for convenience. According to the research of [PERSON] et al. [16] the directions of the six sensor faces should be recorded and the space of the two transducers should be more than 1.2 times the side length in order to avoid interactions during grouting.  
Further, the measuring instruments for the 3D compressive stress sensors, including installing equipment and data acquisition instrumentation, were also developed. The installing equipment is used to push the sensing column and record the burying depth and rotation angle during the installation procedure. As shown in Figure 3, the equipment consists a pushing rod, a three-dimensional roller and a goniometer. The pushing rod includes two alternative lengths (1 m and 1.5 m) of hollow bar made of stainless steel or aluminum alloy. The pushing rod also can be used as the grouting pipe when necessary. The three-dimensional roller is 130 mm in diameter and it is installed in front and between the pushing rods. The goniometer is connected to the transducer column via the front-end rabbet and the instrument displays the angle with the vertical direction during pushing of the sensing column. The function of the data acquisition instrument is to display and store digital signals that are converted from the analog transducer data. Note that electronic productions are not safe in some underground engineering because the existence of gas, especially in a deep coal mine. Therefore, an explosion-proof data acquisition instrument was developed to satisfy the application requirements for using in coal mines (as shown in Figure 4). The explosion-proof data acquisition instrument has a Chinese National explosion-proof certificate and can output many kinds of digital signal, such as temperature, voltage, current, resistance, bridge, and strain, etc.  
Figure 1: External view of 3D comprehensive stress transducer ((**a**). sensor faces; (**b**). sealing washer; (**c**). supporting body; (**d**). cable).  
Figure 2: Schematic diagram of the grouted sensing column showing the placing of the two transducers.
## 2 Methodology
### Data Collection and Preprocessing for Labeling  
#### 2.1.1 Data Collection  
As mentioned above, this research used both online sentiment data and the CNN-LSTM method to improve the performance of predictions of the car sales movement direction of Taiwan's top six leading imported car brands. As shown in Figure 2, for each of Taiwan's six car brands, namely, BMW, Lexus, Mazda, Benz, Toyota, and VW, the historical car sales data and online sentiment data were collected mainly from 2014 to 2019.  
The historical car sales data were collected from the Ministry of Transportation and Communications, R.O.C. (MOTC) [29]. The MOTC website is a platform for commonly used transportation statistics and is operated by Taiwan's government. Since the MOTC website provides new car registration data on a monthly basis, as shown in Table 1, the new car registration data from 2014 to 2019 were retrieved as the historical monthly sales volumes for six car brands. In addition, the new car registration data of January 2020 were also collected for the continuing labeling work.  
The online sentiment data were collected from the OpView Insight: Social Media Monitoring Tool (OpView) [31]. OpView is the largest social media monitoring service platform in Taiwan. It collects eWOM and news every day from five online media sources in Taiwan [32], including more than 6100 discussion forums (e.g., the Mobile01 and the Dcard), more than 36,000 social media (e.g., Facebook and Instagram), more than 400 Q&A websites (e.g., Yahoo! Answers), more than 1800 blogs, and more than 3600 news websites (e.g., EToday and Line Today) [31]. The collected daily eWOM and news are then analyzed as three types of sentiments, i.e., positive, negative, and neutral, for various products and brands. For the study, three types of daily online sentiment volumes, positive (P), negative (N), and total (T), from 2014 to 2019 for six car brands were collected. Table 2 shows the collected daily online sentiment volumes for BMW.  
Figure 2: Summary of the predicted dataset.