esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Communication Model for a Smart Parking System with Multiple Data Consumers
###### Abstract  
A smart parking system (SPS) is an integral part of smart cities where Internet of Things (IoT) technology provides many innovative urban digital solutions. It offers hassle-free parking convenience to the city dwellers, metering facilities, and a revenue source for businesses, and it also protects the environment by cutting down drive-around emissions. The real-time availability information of parking slots and the duration of occupancy are valuable data utilized by multiple sectors such as parking management, charging electric vehicles (EV), car servicing, urban infrastructure planning, traffic regulation, etc. IPv6 wireless mesh networks are a good choice to implement a fail-safe, low-power and Internet protocol (IP)-based secure communication infrastructure for connecting heterogeneous IoT devices. In a smart parking lot, there could be a variety of local IoT devices that consume the occupancy data generated from the parking sensors. For instance, there could be a central parking management system, ticketing booths, display boards showing a count of free slots and color-coded lights indicating visual clues for vacancy. Apart from this, there are remote user applications that access occupancy data from browsers and mobile phones over the Internet. Both the types of data consumers need not collect their inputs from the cloud, as it is beneficial to offer local data within the network. Hence, an SPS with multiple data consumers needs an efficient communication model that provides reliable data transfers among producers and consumers while minimizing the overall energy consumption and data transit time. This paper explores different SPS communication models by varying the number of occupancy data collators, their positions, hybrid power cycles and data aggregation strategies. In addition, it proposes a concise data format for effective data dissemination. Based on the simulation studies, a multi-collator model along with a data superimposition technique is found to be the best for realizing an efficient smart parking system.  
s smart parking system; data consumers; communication model; IPv6 Mesh; RPL; smart city; IoT +
Footnote †: journal: Computer Science and Technology  
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Footnote 1: email: [EMAIL_ADDRESS]
# Welding Image Data Augmentation Method Based on LRGAN Model
## 4 Experimental Results and Discussion
### Comparison of Images Generated by Different Models  
#### 4.2.1 Subjective Evaluation of the Results  
To further prove the effectiveness of the proposed LRGAN model, a comparative analysis was conducted with three adversarial generation models: WGAN, LSGAN, and CycleGAN. Simulations were carried out while ensuring that the model parameters remained essentially the same. The number of iterations was set to 110, and the images generated by each model are shown in Figure 14.  
From Figure 14, it can be seen that from a visual perception perspective, the WGAN model still exhibits a large amount of noise distributed along the welding centerline and its vicinity. The LSGAN model, due to the decision penalty mechanism of the least squares loss function, significantly reduces explicit noise during the training process, but its resolution still needs improvement. The images generated by the CycleGAN model are quite similar to the original welding images, with a significant reduction in blurriness. The images generated by the LRGAN model are clearly superior to the previous three models, more closely resembling the original welding images and showing the best results.
# Distributed Software Build Assurance for Software Supply Chain Integrity
## 4 Our Scheme
### Authority Implementation and Offline vs. Online  
For the authority implementation, we compare our scheme against the centralized server approach. In the centralized server approach, the authority providing the reference inputs for software build assurance is a centralized remote server and requires real-time networking between the server and the user, c.f., remote code attestation discussed in Section 7.  
Our distributed scheme, in contrast, implements the authority using blockchain. We use blockchain to replace the centralized server and to provide the proof reference \(p^{\prime}\). The use of blockchain enables the user to forgo networking beyond its own node for software assurance verification. We describe the blockchain design and application for our assurance scheme to implement the distributed authority in greater detail in Section 4.5.  
We define online vs. offline to better explain the overhead differences. The online operations occur at the time of the software download, while the offline operations can occur before the software download and build. The offline overheads and costs are lower than the online overheads because the offline activity can occur any time before the time of the software download and build. Our work specifically enables the delivery of the authority-reference inputs offline by using blockchain. Blockchain automatically synchronizes and delivers the reference information offline at the time when the software updates are available in the repositories, which is in advance of the online software update to the user.
## 4 Sampling of Precipitation Conditions for the Constellation Simulation
These simulated data cover a one-day period and RO opportunities with the GPS, GLONASS, Galileo, and BeiDou GNSS telemetries. The right-most gray-shaded boxes in Table 4 correspond to the situation whereby the convective transition characteristics can be separated by at least three tropospheric temperature ranges separated by no more than 1 K (0.5 K in the global tropics). In order to meet the value of N total for this criterion, the four-satellite baseline constellation will have to collect measurements for the largest value of the on-orbit days, which is 14 days. Note that the 14-day value is determined by the Atlantic Ocean basin, which is slightly more demanding in terms of the total required observations compared to the other regions. This procedure was repeated with other variations on the constellation configuration and corresponding values presented in Table 5. For example, a three-satellite constellation would require 28 days to reach the same required total. In general, the required on-orbit time decreases by lowering the constellation inclination angle from 45 degrees to 30 degrees, owing to more on-orbit time concentrated in the desired \(\pm\)20-degree global tropical latitudes. However, the required time also decreases when increasing the constellation inclination angle from 45 degrees to 60 degrees. This results from the similar orbital inclination to the GNSS satellites (e.g., 55 degrees for GPS), resulting in more RO opportunities than the lower inclination orbits. While an additional orbit altitude of 600 km was simulated (Appendix A), the results are not significantly different from the 475-km altitude.
## 6 Conclusions  
This work has demonstrated that the rank-1 polarimetric technique proposed in [31] consistently provides enhanced contrast, making the output images more suitable for the application of any automatic vessel detection method.  
The results show that sea clutter is significantly reduced across all polarization combinations, whether dual- or quad-pol data are employed. Furthermore, it has been shown that the technique delivers improved performance across all L-, C-, and X-bands.  
As future work, we plan to conduct a more detailed analysis focusing on images acquired outside the nominal incidence angles, often referred to as _out-of-performance_ images. These images correspond to scenarios where the SAR system operates outside its optimal design parameters, as with a very shallow incidence angle, resulting in increased backscatter from the sea surface. Such images are particularly important from an operational perspective for ship detection, as customers often need to process the first available image, which may fall into this out-of-performance category. In addition, the behaviour of the polarimetric contrast optimization in the presence of ambiguities also deserves to be studied in the future. Ambiguities are an aliasing effect coming from the sampling of the scene which causes the same target to appear in different image positions. Because backscattered signals coming from the sea surface are much lower than those coming from coastal urban areas and vessels, ambiguities are especially visible in sea areas. Finally, we aim to evaluate the contrast enhancement approach for land targets to further assess its applicability and effectiveness in other scenarios.  
Conceptualization and methodology, A.M.-Q. and J.M.L.-S.; software, A.M.-Q.; validation, A.M.-Q.; formal analysis, A.M.-Q.; investigation, A.M.-Q.; writing--original draft preparation, A.M.-Q.; writing--review and editing, J.M.L.-S.; visualization, A.M.-Q.; supervision, J.M.L.-S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Spanish Ministry of Science and Innovation (State Agency of Research, AEI) and the European Funds for Regional Development under grant number PID2020-117303 GB-C22/AEI/10.13039/501100011033.  
Not applicable.  
The datasets presented in this article are not readily available because they are provided by spatial agencies only for scientific purposes upon request.  
The ALOS-1 image @JAXA/METI ALOS PALSAR L1.1 2008 was downloaded from the Alaska SAR Facility (accessed through ASF DAAC 1 November 2024). RADARSAT-2 Data and Products @ MacDonald, Dettwiler and Associates Ltd. (MDA) (2011)--All Rights Reserved. RADARSAT is an official trademark of the Canadian Space Agency (CSA). The Radarsat-2 image was provided by the MDA and CSA within the framework of project SOAR-EU 6779. All PAZ images were kindly provided by Hisdesat Strategic Services.  
The authors declare no conflict of interest.
## 7 Simulations and Results  
The simulation results and discussions are presented in this section to evaluate the performance of a DSM in the presence of utility, ESS, and PV units. The three different scenarios given in Table 6 have been simulated in the MATLAB version R2016a environment using a seventh generation Intel core m3-7Y30 with a processor @ 1.61 GHz and 8 GB RAM. To tackle with the sub-optimality issue of heuristic techniques, the optimal scheduling problem was solved by running the simulation 50 times for each algorithm. All the results presented in the following subsections are the average cost calculated based on the 50 runs. The results of the proposed HGWGA in all three scenarios were compared with the two different heuristic algorithms (GA and GWO) to validate the effectiveness and better performance of the proposed algorithm. All algorithms used in this paper considered the residential load for the optimal appliance scheduling in the presence of utility, ESS, and PV units. The proposed algorithm was also compared with the unscheduled case where an EMC and SMSU were not installed.
## 2 Materials and Methods
### Ground Data Collection  
Field visits were scheduled during the rainy season at 20-day intervals to the chosen villages according to the satellite passing time and prevailing weather conditions in the district. With the help of the village agriculture extension officers, farmers' fields with different management practices were selected. Moreover, 220 ground truth points were collected during the wet season, which were used to perform classification and validation.  
Fifteen fields were selected in each village with a proper distance from each field to avoid overlapping into a single pixel in the satellite image (Figure 3a). Regular monitoring of the fields was performed. Field IDs were allotted according to the village name for easy identification. Latitude, Longitude, elevation, date of sowing, cultivar details, and quantity of fertilizer applied were some of the major details collected from the farmers. Data collected during the visits are presented in Appendix A. The LAI was estimated using the LP-80 Acicular zeptometer (Figure 3b). In the selected fields, homogenous areas were identified in the crop field where CCEs were conducted on 5 \(\times\) 5 m plots as per the NSSO Guidelines (Figure 3c). After threshing, the crop biomass weight and grain yield were recorded separately.
# Assessing the Capacity and Coverage of Satellite IoT for Developing Countries Using a CubeSat
## 3 Coverage and Capacity Evaluation  
To derive the coverage and capacity of SIoT using the payload design and the GST, Equation (1) is used to estimate the data that can be collected per day by KITSUNE.  
\[Data\ per\ day\ (in\ bytes)=\sum_{i=1}^{n_{GST}}\tau(i)*B*\beta*\dot{\tau}_{i} \tag{1}\]  
Data per day is estimated with the communication time between satellite and GST in minutes in a pass \(\tau(i)\), the number of GSTs visible per day \(n_{GST}\), the number of bytes received per minute \(B\), the ratio of meaningful bytes to total bytes in a packet \(\beta\), the efficiency factor \(\dot{\tau}_{i}\) that incorporates the overall efficiency reduction due to the factors such as performance dependence on temperature and the Doppler effect. These parameters are described in the following subsections that includes overall implementation plan for the mission, the design of the satellite and the GSTs, and the communication protocols between them.
## 1 Introduction
### Software-Related Security Issues in IoT Software
The results showed that while the total number of security errors increases with each version, the error density decreases over time for all examined operating systems, with few exceptions. The most prevalent vulnerabilities in the OSs examined for Cppcheck were CWE-561 (dead code), CWE-398 (7 PK--code quality), and CWE-563 (assignment to variable without use), while CWE-119 (improper restriction of operations within the bounds of a memory buffer), CWE-120 (buffer copy without checking size of input (\"classic buffer overflow\")), and CWE-126 (buffer over-read) for Flawfinder. For RATS, it was CWE-119, CWE-120, and CWE-134 (use of externally-controlled format string). [PERSON] et al. (2019) evaluate the vulnerabilities in IoT software used mainly in smart grid applications through static analysis of the source code. The authors have analyzed open-source software which could be used at any level of the software stack, including operating system level, application level, and library level, in order to obtain a comprehensive understanding of the relevant vulnerability landscape. The assessed software includes jSML, lib60870, libiec61850, JavaSMQ, Pymodbus, Modbus4j, Minnow server, Boa Webserver, thttpd, MicroWebStrv 2, and Busybox. The static application security testing (SAST) is performed by utilizing the SonarCloud ([[https://sonarcloud.io](https://sonarcloud.io)]([https://sonarcloud.io](https://sonarcloud.io)), accessed on 14 May 2023) and Codacy ([[https://app.codacy.com](https://app.codacy.com)]([https://app.codacy.com](https://app.codacy.com)), accessed on 14 May 2023) platforms. The results of the two platforms are manually reviewed to discern between true and false positives. The final results are categorized based on a customized vulnerability categorization scheme which was created by combining the OWASP Top 10 list and the MITRE common weakness enumeration (CWE) scheme. Based on the results received from the static analysis, the custom categorization scheme includes improper certificate validation, buffer overflow, weak cryptography, sensitive data exposure, race condition, and broken access Control. Furthermore, the frequency and the potential impact of the identified vulnerabilities is considered. The article provides a detailed examination of true and false positives which can assist both researchers and practitioners to better focus on the areas requiring review. The vulnerabilities results report 2 for the improper certificate validation category, 6 for buffer overflow, 23 for weak cryptography, 59 for sensitive data exposure, 8 for race condition, and 3 for broken access control. Additionally, the results show numerous false positives reported by the SAST tools utilized.
# A Novel Relay Selection Scheme Based on Q-Learning in Multi-Hop Wireless Networks
## 5 Conclusions  
In wireless networks, multi-hop communication system becomes an important issue. In this situation, it is important to select an appropriate relay for signal transmission in multi-hop environment. If the proper relay is not selected, it may cause performance degradation of the device node and additional damage such as an error propagation, latency and waste of resources. So, the proposed scheme can achieve the same performance with the conventional BHM scheme and satisfy the target BER while reducing the number of hops. In the proposed scheme, the optimal relay is selected which maximizes the reward based on SNR by using Q-learning algorithm in the multi-hop system. This paper proposes a method to define the received SNR at each node as a reward. The simulation results show that the proposed scheme has same BER performance and throughput but fewer relays than the conventional BHM scheme when the target BER is satisfied. This reduces the latency and the waste of resources. Therefore, the performance of the proposed Q-learning based multi-hop transmission in wireless networks is enhanced.  
Figure 11: The number of steps in proposed scheme according to the learning episode.  
Figure 10: The number of average selected hops.  
**Author Contributions:** [PERSON] proposed an algorithm for relay selection in multi-hop wireless communication networks; [PERSON], [PERSON] and [PERSON] supplemented an algorithm for higher error performance. They tried to minimize the loss of error performance; [PERSON] gave feedbacks about an modified algorithm and all simulation results. Furthermore, [PERSON] provided the experimental materials for better computational simulations and revised critical errors of the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2019-2018-0-01423) supervised by the IITP(Institute for Information & communications Technology Promotion) and supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(2020R1A6A1A03038540).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Lossless and High-Throughput Congestion Control in Satellite-Based Cloud Platforms +
## 3 Integrated System and Algorithm Design  
In this section, we provide a detailed description of the system and algorithm design of CCLF. We first outline our design goals and then elaborate on the procedures for congestion control and Data scheduling. In this paper, we use \"Interest\" to represent Interest packets and \"Data\" to denote Data packets in NDN.
# Vehicle Acceleration Prediction Based on Machine Learning Models and Driving Behavior Analysis
## 2 Methodology
### Hmm  
The HMM includes two stochastic processes: the first one is the basic stochastic process that depicts the interconversion between states, i.e., a Markov chain; the second one is the stochastic process that depicts the correspondence between states and observations [13]. That is, the observer cannot observe the original state through the observations but can only use the stochastic process to determine the existence and characteristics of the model state.  
In Figure 2, where \(x_{i}\) is the hidden state, \(y_{i}\) is the observation, \(a_{ij}\) is the transition probability of the hidden state, and \(b_{ij}\) is the output probability from the hidden state to the observed state. A complete definition of HMM is as follows:  
Figure 1: Flowchart of the proposed vehicle acceleration prediction method.  
\(Q\) and \(V\) denote the set of all possible states, and the set of all possible observations, respectively.  
\[Q=\{q_{1},q_{2},\cdots,q_{N}\},\;V=\{v_{1},v_{2},\cdots,v_{M}\} \tag{1}\]  
where \(N\) is the number of possible states and \(M\) is the number of possible observation sequences.  
\(I\) is the state sequence of length \(T\), and \(O\) is the observation sequence corresponding to \(I\):  
\[I=\{i_{1},i_{2},\cdots,i_{T}\},\;O=\{o_{1},o_{2},\cdots,o_{T}\} \tag{2}\]  
\(A\) is the state-transferring probability matrix:  
\[A=\left[a_{ij}\right]_{N\times N} \tag{3}\]  
where \(a_{ij}=P\big{(}i_{i+1}=q_{j}\big{|}i_{t}=q_{i}\big{)},i=1,2,\cdots,N;j=1,2,\cdots,\)and \(N\) is the probability of moving from moment \(t\) in state \(q_{i}\) to moment \(t+1\) in state \(q_{j}\).  
\(B\) is the observation probability matrix:  
\[B=\left[b_{j}(k)\right]_{N\times M} \tag{4}\]  
where \(b_{j}(k)=P(o_{t}=v_{k}|i_{t}=q_{i}),k=1,2,\cdots,M;j=1,2,\cdots,\) and \(N\) is the probability of obtain ing the observation \(v_{j}\) under the condition of being in state \(q_{j}\) at moment \(t\).  
\(\pi\) is the initial state probability vector:  
\[\pi=(\pi_{i}) \tag{5}\]  
where \(\pi_{i}=P(i_{1}=q_{i}),i=1,2,\cdots,\)and \(N\) is the probability of being in state \(q_{i}\) at moment \(t\).  
The HMM is determined by the initial state probability vector \(\pi\), the state transferring probability matrix \(A\), and the observation probability matrix \(B\). \(\pi\) and \(A\) determine the state sequences, and \(B\) determines the observation sequences. Thus, the HMM can be simplified as \(\lambda=(A,B,\pi)\). In addition, as can be seen from the definition, the HMM makes two basic assumptions (i.e., the homogeneous Markov hypothesis and the observation independence assumption) [14].