esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet of Things (IoT) and the Energy Sector
## 5 Challenges of Applying IoT
### Security Challenge  
The use of IoT and integration of communication technologies in energy systems enhances the threat and cyber-attacks to information of users and the energy systems from production, transmission, and distribution to consumption [142; 143]. These threads define the security challenge in the energy sector [144]. Moreover, IoT-based energy systems are widely deployed in large geographical areas  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Challenge** & **Issue** & **Example Solution** & **Benefit** \\ \hline \multirow{3}{*}{Architecture design} & Providing a reliable & Using heterogeneous & Interconnecting \\  & end-to-end connection & reference architectures & things and people \\ \cline{2-4}  & Diverse technologies & Applying open standard & Scalability \\ \hline \multirow{3}{*}{Integration of IoT with subsystems} & IoT data management & Designing & Real-time data among \\  & & co-simulation models & devices and subsystems \\ \cline{2-4}  & Merging IoT with & Modelling integrated & Reduction in cost \\  & existing systems & energy systems & of maintenance \\ \hline \multirow{3}{*}{Standardization} & Massive deployment & Defining a & Consistency among \\  & of IoT devices & system of systems & various IoT devices \\ \cline{2-4}  & Inconsistency among & Open information models & Covering \\  & IoT devices & and protocols & various technologies \\ \hline \multirow{3}{*}{Energy consumption} & Transmission of & Designing efficient & \\  & high data rate & communication protocols & Saving energy \\ \cline{2-4}  & Efficient energy & distributed & \\ \cline{1-1}  & consumption & computing techniques & Saving energy \\ \hline \multirow{2}{*}{IoT Security} & Threats and & Encryption schemes, & \\  & cyber-attacks & distributed control systems & Improved security \\ \hline \multirow{2}{*}{User privacy} & Maintaining users’ & Asking for & Enables better \\  & personal information & users’ permission & decision-making \\ \hline \hline \end{tabular}
\end{table}
Table 4: Challenges and current solutions of using IoT in the energy sector.  
within the energy sector to offer services. The large deployment of IoT systems puts them in more risk of being under cyberattacks. To overcome the challenge, a study introduces an encryption scheme to secure energy information from the cyberattacks [145]. In addition, distributed control systems which enable control at different IoT system level are suggested to reduce the risk of cyberattack and increasing the security of system [146].
# A Sparse Algorithm for Computing the DFT Using Its Real Eigenvectors
## 1 Introduction
This method can also be extended to other ortho-normal transforms which have real eigenvectors. Our analysis has found that the proposed method requires fewer multiplications than when using a single stage Cooley-Tukey algorithm. In fact, if the proposed method is combined with recursive algorithms like the prime factor FFT, the number of multiplications are comparable to the Cooley-Tukey FFT of size \(2^{k};k\in\mathbb{Z}^{+}\). Moreover, this method is not restricted by the size of the DFT; unlike the Cooley-Tukey FFT which requires the input length \(N\) to be an integer power of 2, or the prime factor FFT which requires \(N\) to be a product of coprime numbers. The methodology is derived in Section 2, which shows the relationship between an input (data) vector, the DFT eigenvectors, and the Fourier transform output. Section 3 deals with the computation of the inverse DFT. The DFT eigenvectors with some favourable properties were derived by [PERSON] [7] and Section 4 provides a brief description of that technique to find the eigenvectors. In Section 5, a 5-point DFT example is shown using our method developed in this work. Accuracy of the proposed method is discussed in Section 6. In Section 7, the number of real-valued operations required for calculating the DFT using our algorithm is derived.
# Exploring the Effectiveness of Evaluation Practices for Computer-Generated Nonverbal Behaviour
## 2 Related Work
### Generating and Evaluating Gesture Behaviour  
Numerous studies have focused on generating speech-driven motion for embodied conversational agents. For instance, [PERSON] et al. (2018) leveraged representation learning to map audio to motion, while [PERSON] et al. (2018) used input text with word-level timestamps to generate motion without using speech audio. Subsequently, other researchers have combined both audio and text representations of speech along with speaker identity in their gesture generation models, such as those in ([PERSON] et al., 2019; [PERSON] et al., 2020; [PERSON] et al., 2021). Since the goal of gesture generation for ECAs is to facilitate effective human-agent interaction, some research has furthermore explored generating nonverbal behaviour while considering the interlocutor in the interaction ([PERSON] et al., 2019; [PERSON] et al., 2021). Others have applied more generative approaches to gesture generation, with the aim of learning a probabilistic distribution ([PERSON] et al., 2019). More recently, researchers started picking up diffusion models for gesture synthesis ([PERSON] et al., 2019; [PERSON] et al., 2021; [PERSON] et al., 2021). However, comparing different gesture generation models is challenging, as highlighted by [PERSON] et al. (2018). The GENEA Challenge ([PERSON] et al., 2019; [PERSON] et al., 2021) was set up to address this issue by allowing multiple teams to build models on a shared data set and submit motions from their models to a shared evaluation. A more in-depth review on the field of gesture generation, especially considering deep learning, can be found in ([PERSON] et al., 2020).  
In contrast to the work discussed here, we train an unsupervised probabilistic model on a datas et containing dyadic interactions. These interactions are not cut to only include speech and gesticulation; instead, the interactions contain the full range of nonverbal motion one would expect to see during a one-on-one interaction.
# Multi-Source Image Fusion Based Regional Classification Method for Apple Diseases and Pests
## 3 Results and Discussion
### Evaluation Metrics
#### 3.2.8 Main Contributions of This Work  
To further illustrate the advancements made in this study, we provide a comprehensive comparison between the key contributions of our work and those reported in previous studies. Table 14 summarizes the main differences and advancements achieved in our study.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Evaluation Metrics** & **Aphid** & **Alternaria** & **Mosaic** & **Brown Spot** & **Gray Spot** \\ \hline Accuracy/\% & 94.83 & 97.41 & **99.98** & 93.94 & 96.21 \\ Recall/\% & 73.33 & 59.72 & 60.83 & **91.75** & 76.32 \\ Precision/\% & 91.15 & 93.90 & **94.68** & 87.02 & 92.62 \\ \hline \hline \end{tabular} The bold content is the row optimal value.  
\end{table}
Table 13: Single-class apple disease and pest classification results of AMMFNet under noise interference.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Model** & **Substacc/\%** & **Accuracy/\%** & **Recall/\%** & **Precision/\%** & **F1/\%** \\ \hline Data-level fusion & ResNet18 & **90.02** & **83.60** & **84.51** & **84.90** & **84.71** \\ Feature-level fusion & ResNet18 & 85.64 & 79.06 & 80.08 & 80.34 & 80.21 \\ \hline \hline \end{tabular} The bold content is the column optimal value.  
\end{table}
Table 12: Classification results of the model under different fusion methods.  
This comprehensive comparison highlights the advances and innovations introduced in our research, highlighting the effectiveness of our multi-source image fusion method and the significant improvements made in accuracy.
# Systems Engineering: Availability and Reliability
## 2 Reliability and Risk Assessment
The risk performance analyzed in [3] is essential in relation to the bauxite shipping process, whose associated risk remains to be officially recognized. The output of this paper can support quantitative risk decision-making, as opposed to previously used empirical decision-making, thereby laying a foundation for risk pre-warnings and process safety. Aiming at analyzing and evaluating the inherent risks of the complex system with coupling correlation characteristics objectively, [PERSON] et al. [4] propose a novel risk assessment and analysis method for correlations in the complex system based on multi-dimensional theory. Firstly, the formal description and coupling degree analysis method of the hierarchical structure of complex systems is established. Moreover, considering the three safety risk factors of fault propagation probability, potential severity, and fault propagation time, a multi-dimensional safety risk theory is proposed in order to evaluate the risk of each element within the system affecting the overall system. Furthermore, critical safety elements are identified based on Pareto rules, As Low as Reasonably Practicable and safety risk entropy to support the preventive measures. Finally, an application of an avionics system is provided to demonstrate the effectiveness of the proposed method. Compared with the current methods and technologies, the method proposed [4] mainly reflects the advantages of two aspects. On the one hand, the hierarchical model is modeled in a matrix manner, and the association relationship of each element in the complex system is quickly and accurately analyzed, which reduces the skill requirements of analysts. On the other hand, it provides a feasible and multi-faceted analysis method for the risk assessment of systems in view of fault propagation, which is the core judgment criterion for identifying critical risk factors and is of great significance for ensuring system safety.
## 4 Conclusions  
This study based on the questionnaire responses of residents living in southern Riyadh and air quality monitoring from 2016 to 2020 from a couple of air-monitoring stations close to the area of interest reveals that the adverse effects of poor air quality due to urbanization and industrialization are mostly felt by the residents of Al-Iskan, Ad-Dar Al-Baida, and the Second Industrial City. Respiratory problems and irritation in the eyes were reported by 49% and 50% of the residents, respectively, during the survey. A high concentration of PM10 has been observed in many places in the study area, and it exceeds the permissible limits. Thermal analysis of the area showed the maximum temperature in the Second Industrial City and Al-Misfat, indicating the presence of the thermal island effect in these regions due to industrial activities. The anthropogenic influence on air quality was evident from the air quality data for 2020, which showed a marked decrease in parameters such as NO\({}_{2}\) due to the impacts of lockdown on various activities during the coronavirus pandemic. The land surface temperature image for May 2020 showed a temperature range between 54 \({}^{\circ}\)C and 32.4 \({}^{\circ}\)C, which is less than the temperature recorded during the same period in 2014 and 2017 and clearly shows the positive impact of minimal commercial/industrial activity during the lockdown period in 2020.  
The following are available online at [[https://www.mdpi.com/2076-341](https://www.mdpi.com/2076-341)]([https://www.mdpi.com/2076-341](https://www.mdpi.com/2076-341)) 7/11/5/2107/s1, Table S1: Distribution of questionnaire in the study area; Table S2: Air sampling methods and instruments; Table S3: Relationship between districts and impact of pollution; Table S4: Correlation coefficients at 95% confidence level; Table S5: Eigen values of PC of the data sets; Table S6: Principal Component loadings of the data sets; Table S7: Semivariogram parameters.  
[PERSON] wrote the main manuscript and interpreted the data; [PERSON]-T. prepared questioners and analyzed the statistical data; [PERSON]-E. analyzed the Landsat images; [PERSON] drafted and editing the work; [PERSON]-D. designed maps and prepared Figures 1-3. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Deanship of Scientific Research at King Saud University through research group number RG-1438-023.  
Figure 9: Trend in PM10 and NO\({}_{2}\) during 2016–2020 on the Southern Ring Road and the Al Khaldiyah air station.
# Monitoring of Sugarcane Harvest in Brazil Based on Optical and SAR Data
## 5 Discussion
### Possible Development of the Algorithms
For sugarcane fields in the state of Sao Paulo, a method of object-oriented classification is applied by [PERSON] et al. [50] to discriminate different sugarcane harvest types (burnt, non-burnt, or no harvest) for different soil types using Landsat-TM data. There are the following image characteristics (classifier inputs): Haralick texture features, features of the field shapes, and reflection coefficients in spectral bands. The classification accuracy was estimated in terms of kappa indexes, being between 0.69 up to 0.84. The classification methods, including Support Vector Machine, Naive Bayes, and Artificial Neural Networks were applied by [PERSON] et al. [51] to assess the sugarcane suitability for harvest with the accuracy up to 88%. A combination of the machine learning techniques and methods proposed in the research may improve the overall result and increase the accuracy of detecting the harvested fields. However, that assumption needs additional studies.
###### Abstract
As structured by architecture frameworks such as the Department of Defense Architecture Framework (DoDAF), the architecture design determines the system selection, connection pattern, and operational process, among others. When it comes to the operational phase, reconfiguration refers to re-sequencing operational flow, re-connecting physical systems, re-allocating systems to activities, etc., in the case of external and internal changes. Although reconfiguration has been proposed as an important principle for designing resilience [3], the definition, enablers, and implementation strategies of reconfiguration in SoSs, especially combat SoSs, remain vague. Plus, the SoS architecture selection methods do not usually account for the operational-time reconfiguration options, while the reconfiguration optimization methods for combat SoSs often make an architecture hypothesis in advance. There is a lack of an integrated method that selects architecture alternatives and reconfiguration options together and suggests a proper architecture that can facilitate optimal reconfiguration strategies for better resilience. Therefore, this paper contributes to the SoS and defense domains by proposing a method that can select architecture alternatives and reconfiguration options in an integrated manner towards enhanced SoS resilience. This method defines the reconfiguration at the architecture level and identifies the enablers for SoS architecture reconfiguration. Meanwhile, the method develops an integrated formulation for the initial architecture selection and reconfiguration process with an SoS effectiveness evaluator using a functional dependency network, and employs an intelligent optimization algorithm to solve the problem. The goal of the method is to support SoS decision-makers in designing resilient SoS architecture at the design phase by exploring the possible outcomes of reconfiguration. This paper consists of five sections in total. Section 2 reviews the existing literature associated with SoS resilience and reconfiguration strategies. Section 3 describes the proposed quantitative reconfiguration framework. This section begins with the problem description regarding SoS architecture configuration, reconfiguration enabler identification, effectiveness evaluator, and the reconfiguration process. This section further develops the mathematical formulation and corresponding solution algorithm based on the problem description. The next section applies the proposed method to a naval-integrated air and missile defense SoS problem and demonstrates the effectiveness of the method. The final section concludes the paper and indicates future research.
## 4 Results and Analysis
### Effect of Network Parameters on Welfare of P2P Platform
#### 4.5.3 Sensitivity towards Charge Rate Coefficient  
Figure 17 shows the effect of the charge rate coefficient \(\gamma\) on the welfare of the prosumers for Interval I. The same trend can be observed as with the loss coefficient, where the prosumers' welfare decreased to a negative value for the ZI strategy. This is because the network utilization fee is directly proportional to the charge rate coefficient \(\gamma\). Increasing \(\gamma\) to a high value results in the prosumers paying more to the market operator as compared to what they will earn from the market for P2P transactions. This results in a negative revenue for the prosumers participating in the P2P market at high values of \(\gamma\). For the GT approach, the prosumers increase the value of \(\alpha_{j}\) to decrease the amount of traded energy with the customers. Note that for high values of the charge rate coefficient, the welfare also remains negative for the GT approach. This is because at such values of \(\gamma_{j}\), the prosumers will have to pay more to the market operator when compared to the revenue they will earn from the P2P market for any amount of energy traded. However, the GT approach manages to limit the welfare of the prosumers to a very small negative value, which is more competitive compared to the ZI results. Because the suggested P2P platform depends on the day-ahead market and intermittent nature of the PV resources, the performance of the presented trading strategies can be improved by introducing the intraday market to model the uncertainty of the PV source. However, this aspect of the suggested P2P platform has been left for future research [35].  
Figure 16: Prosumers’ welfare for different values of loss coefficient. (**a**) ZI strategy. (**b**) GT approach.
# Experimental Evaluation of Unicast and Multicast CoAP Group Communication
###### Abstract  
The Internet of Things (IoT) is expanding rapidly to new domains in which embedded devices play a key role and gradually outnumber traditionally-connected devices. These devices are often constrained in their resources and are thus unable to run standard Internet protocols. The Constrained Application Protocol (CoAP) is a new alternative standard protocol that implements the same principals as the Hypertext Transfer Protocol (HTTP), but is tailored towards constrained devices. In many IoT application domains, devices need to be addressed in groups in addition to being addressable individually. Two main approaches are currently being proposed in the IoT community for CoAP-based group communication. The main difference between the two approaches lies in the underlying communication type: multicast versus unicast. In this article, we experimentally evaluate those two approaches using two wireless sensor testbeds and under different test conditions. We highlight the pros and cons of each of them and propose combining these approaches in a hybrid solution to better suit certain use case requirements. Additionally, we provide a solution for multicast-based group membership management using CoAP.  
Keywords:wireless sensor networks; Internet of Things; CoAP; sensors; group communication; multicast; entities +
Footnote †: journal: Sensors
## 5 Distributed Machine Learning Algorithms
### Collaborative Learning  
While DL refers to a general approach where multiple agents works in parallel sharing, broadly speaking, in the ML effort, whether it is for training or testing, collaborative learning can be considered as a specific type of decentralized learning where multiple agents collaborate to implement an ML algorithm. This is a very efficient way to implement a DML algorithm in an IoV scenario since V2X connection can be used. Let us focus indeed on a scenario where multiple vehicles travel around; thanks to proper V2V connections, they can exchange ML data, allowing a tighter interconnection among vehicles.  
[PERSON] et al. (2019) survey on the importance of collaborative learning techniques in IoV scenario exploiting 5G communication systems. In particular, they focus on the importance of this approach when dealing with autonomous driving use cases. Five main questions are solved in the paper regarding the implementation of an autonomous driving system, that are: (1) How can autonomous vehicles (AVs) effectively use wireless communications for transmitting data on the road? (2) How can AVs manage the shared data? (3) How can shared data be used for improving AVs environment perception? (4) How can shared data be used to drive more safely and efficiently? (5) How can shared data privacy be protected and cyberattacks prevented? The authors have shown how vehicles can collaborate for improving the machine learning process aiming at better complying with environmental perception and data fusion. When information is exchanged through different vehicles, the environment is better understood and AV can move around relying with a higher safety and collision avoidance behavior as well as employing collaborative traffic analysis.  
[PERSON] et al. (2019) introduced, for the first time, the possibility of collaboration among different sources for better estimating the best route to be taken by vehicles in an urban scenario. In particular, through the presence of different learning automata, the information coming from different sensors is collaboratively used to find the best route. A similar scenario is also considered by [PERSON] et al. (2019), where the best route selection problem is solved. Here, instead, the use of an edge computing layer is considered. Through a strict collaboration between vehicles, edge nodes, and cloud computing nodes, a collaborative learning solution is proposed.
# Management Platforms and Protocols for Internet of Things: A Survey
## 7 IoT Device Management Protocols
### COntrained Networks and Devices MANagement (COMAN)
#### 7.1.3. OMA-LwM2M--Open Mobile Alliance for Lightweight M2M  
The OMA LWM2M enables M2M Device Management, acting as an OMA-DM successor using the same protocol, and provides a compact and secure communication for this management [79]. It provides a sub-layer to allow management of LWM2M Server/Client, using a CoAP client-server architecture over UDP as a transport layer, as shown in Figure 18.  
The M2M Service Provider, Network Service Provider, or Application Service Provider can be hosted by the LWM2M Server that provides a private or public data center [80]. The LWM2M Client is integrated into a software or device [81]. The LWM2M communication model [82] uses the CoAP methods (GET, PUT, POST, and DELETE) with bindings over UDP transport layer.  
_Pros: Communication model and secure. Cons: No supports the heterogeneity networks._  
Use Case  
Nowadays, there are several solutions (CoAP, OMA-LwM2M, or OMA-DM) with COMAN requirements, e.g., energy states, logging, system authentication, peripheral management, and access controls to the system [66]. Sprint is globally one of the best examples of a mobile Operator that has made FOTA part of its services strategy.  
It is fully committed to providing FOTA updates according to the OMA-DM standards [83].