esa-sceva/satcom-chunk-collection huggingface dataset:
# Fog over Virtualized IoT: New Opportunity for Context-Aware Networked Applications and a Case Study
## 1 Introduction
### The Main Contribution of the Paper  
The technical contribution of this paper focuses on the design of a Fog-based VFC architecture for the energy-efficient joint management of the networking and computing resources under hard constraints to improve the overall tolerated computing-plus-communication latency.  
The main contributions of this paper are as follows:1. To the best of our knowledge, there is no related work that considers FDC across VFC and presents resource allocation and scheduling for the considered real traffic. This is the first work to dynamically schedule incoming traffic into the FNs. Besides, this is the first work to study the impact of a data stream on network resource allocation.
2. A distributed Fog-based VFC architecture is presented that, over moderate-bandwidth, provides high availability operations to mobile users.
3. A case study is defined--\"StreamVehicularFog\" (SVF)--which is an Internet-assisted peer-to-peer (P2P) service architecture, and the stream of data passed/processed over the underutilized networking/computing servers in FDC is evaluated.
4. The simulation results show a significant reduction in the total energy consumption over FDC with various forms of communication links using real datasets.  
The rest of the paper is organized as follows: The background is detailed in Section 2. A description of the FDC architecture is reported in Section 3. A simple case study of the Fog data center data stream management and its correlated problem/solution is reported in Section 4. A performance evaluation of the optimal formulations is reported in Section 5. Finally, Section 6 concludes our work.
# Review on Active and Passive Remote Sensing Techniques for Road Extraction
## 3 Road Extraction Based on Different Data Sources  
High-resolution images, hyperspectral images, SAR images and LiDAR data are primarily used for road extraction. To date, various road extraction methods have been presented in previously reported studies, and the observed differences are due to the use of different data sources. In this section, we summarise the methods, application status and prospects of road extraction based on four data sources.
## 4 Discussion of Challenges and Future Research Directions
Maintaining constant and accurate IDS performance is complicated by factors such as network latency, fluctuations in data quality, and the reliability of data transfer from individual vehicles. To ensure reliability, robust FL algorithms are needed to manage data discrepancies, adapt to changing network conditions, and effectively integrate data from diverse vehicles. Moreover, the timely and accurate deployment of intrusion detection solutions depends on the reliability of model updates and communication protocols. Future research aims to enhance the reliability of IDSs in IoV by refining FL algorithms, improving data preprocessing methods, and optimizing communication protocols. This will ultimately ensure the consistent and reliable operation of FL-enabled IDSs across diverse IoV environments. * Real-time data: In the context of vehicle environments, responding promptly to security threats is crucial for ensuring passenger safety and network security. Swift and effective intrusion detection relies on processing the substantial volume of real-time data provided by vehicles. The primary challenge lies in developing FL algorithms capable of handling this increased data volume efficiently, with a focus on enabling timely anomaly or intrusion identification. Moreover, optimizing communication protocols to efficiently transmit relevant real-time data to central servers for model updates is of paramount importance. Future research in this area is directed towards creating FL models that combine lightweight characteristics with high-performance capabilities. This involves exploring the use of edge computing for local real-time analysis and improving communication protocols to facilitate seamless and swift sharing of real-time data [68]. By effectively addressing this challenge, the utilization of FL-enabled IDSs in IoV can offer immediate responses to security threats, thereby enhancing the overall safety and security of vehicular networks.
## 3 Materials and Methods
### Methods
#### 3.2.3 Retrieval-Augmented Generation  
Retrieval-augmented generation [36; 37] (RAG) leverages the strengths of both retrieval and generation models to enhance the performance of large language models in knowledge-intensive tasks. While traditional generative models can generate fluent text, they often produce misleading or incomplete responses when handling queries requiring specific domain knowledge due to a lack of accurate contextual information. In contrast, RAG incorporates retrieval mechanisms to effectively leverage local knowledge bases during answer generation, thereby producing more accurate and well-supported responses.  
The RAG framework comprises two key components: the retrieval component and the generation component.  
Retrieval Component: Upon receiving a user query, the system employs a predefined retrieval model to search the local knowledge base for the most relevant documents. The retrieved documents are transformed into vector representations using embedding techniques, enabling efficient similarity searches.  
Generation Component: Once relevant documents are retrieved, the generation component processes them. It typically consists of a large pre-trained language model that integrates the retrieved documents with the user query to generate responses. This mechanism enables the model to generate responses based on the latest and most relevant information from the knowledge base, thereby enhancing accuracy and comprehensiveness.  
In the RAG framework, the generation component does not solely rely on the model's internal knowledge but dynamically integrates information from the local knowledge base to generate responses. This approach significantly reduces the 'hallucination' phenomenon commonly observed in traditional generative models when addressing domain-specific queries, reducing the risk of generating inaccurate or misleading information.  
This study develops a plant protection knowledge question-answering system leveraging RAG technology to enhance answer accuracy and reliability. The system integrates the generative capabilities of a fine-tuned large language model with dynamic retrieval of professional literature, research papers, and technical guidelines in plant protection, ensuring responses that are timely, relevant, and domain-specific.  
The FAISS vector library is employed to convert knowledge base documents into vector representations. When a user submits a query, it is transformed into a vector and matched against the knowledge base using similarity search, enabling rapid retrieval of the most relevant information. The retrieved documents serve as contextual information, which, along with the user query, is fed into the generation module to produce responses that incorporate expert plant protection knowledge and accurately address complex plant pest and disease issues.  
The integration of RAG technology significantly enhances the system's capability to handle multi-domain knowledge and complex agricultural scenarios, delivering more precise and contextually relevant responses to users.
# Performance Optimization of Grounding System for Multi-Voltage Electrical Installation
## 3 Methodology
### Evaluation Criteria  
The performance of the grounding system is evaluated using several parameters that quantify the effectiveness of current dissipation, safety measures, and compliance with regulations.
## 4 Experiments
### Comparison with Baselines  
#### 4.4.1 Evaluation on PHOENIX-2014  
We conduct a comparative analysis of various state-of-the-art models to substantiate the efficacy of our research, as shown in Table 5. We categorize the compared models \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Last Layers**} & \multirow{2}{*}{**Local Size**} & \multicolumn{2}{c}{**Dev**} & \multicolumn{2}{c}{**Test**} \\ \cline{3-6}  & & **del/ins** & **WER** & **del/ins** & **WER** \\ \hline w/o & - & 10.3/3.2 & 28.9 & 10.5/3/4 & 29.3 \\ \hline \multirow{3}{*}{\(1\)D-CNN} & 3 & 11.2/3.9 & 27.2 & 11.8/3.7 & 27.7 \\  & 5 & 7.9/3.8 & 26.1 & 7.8/3.7 & 25.9 \\  & 7 & 7.7/3.7 & 25.6 & 7.9/3.7 & 25.8 \\ \hline \multirow{3}{*}{Local Transformer} & 3 & 8.2/3.1 & 24.7 & 7.6/4.2 & 24.2 \\  & **5** & **7.4/3.3** & **22.4** & **7.3/3.2** & **22.6** \\ \cline{1-1}  & 7 & 8.5/4.7 & 25.0 & 7.7/3.7 & 24.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation of last local layers and local size before output on PHOENIX-2014. The term “w/o” denotes the direct output of results without employing any network. Figure 8: Relationship between inference time and video sequence length during model inference. into two major groups based on their distinct sequence modeling approaches: methods based on Transformer and those employing other network architectures. This is due to the weak performance of the original Transformer on cSLR tasks, which warrants a separate comparison. CMLLR and 1-Mio-Hands primarily utilize traditional HMM models to learn sign language videos. CNN-LSTM-HMM combines CNN-LSTM with HMM to exploit multi-stream data. DNF employs multimodal data comprising RGB and optical flow to enhance the capture of motion features. FCN establishes a fully convolutional network for spatial and temporal modeling. Finally, mLTSF-Net fuses temporal information based on local time similarity, thereby capturing the local consistent semantics of adjacent frames. In comparison to these methods, our proposed approach surpasses them to varying degrees without using additional data, achieving end-to-end training. The VAC model, which integrates VGG and BiLSTM, employs alignment learning to enhance its capabilities, achieving the current state-of-the-art results. Our evaluation metrics on the test set are slightly lower (+0.2 WER). Our approach focuses on the application of Transformer models in continuous sign language recognition (cSLR) tasks, optimizing and improving issues related to single visual features and self-attention mechanisms. The alignment learning and auxiliary learning methods used in VAC aim to strengthen the alignment between visual features and glosses, offering valuable insights for us to learn from and further improve our model's performance.
## 3 Preliminaries, Models, and Definitions
### Related Models
#### 3.2.2 Underwater Energy Consumption  
During the node deployment, each node should determine the local information near its position by communicating with its neighbor nodes, and each node should move to the determined position when it knows the position. Therefore, the energy consumption in this study is divided into communication energy consumption and deployment energy consumption.  
(1) Communication Energy Consumption  
The nodes of an underwater sensor network communicate with one another by acoustic signals [28]. Thus, the communication energy consumption of the acoustic medium [29] is adopted in this study and given by  
\[E_{c}(d(s_{se},s_{rec}))=P_{r}\times T_{p}\times A(d(s_{se},s_{rec})) \tag{2}\]  
This expression describes the energy consumption when the data packet is transmitted from the sending node to the receiving node. In Equation (2), \(P_{r}\) is the minimum number of power packets that can be received, and \(T_{p}\) is the data transmission time, whose formulation is as follows:  
\[T_{p}=\frac{L_{b}}{V_{t}} \tag{3}\]  
where \(L_{b}\) is the length of the sent data packet and \(V_{t}\) is the transmission speed of the data packet in water. The energy attenuation during the transmission of the data packet at a given distance is described as follows:  
\[A(d(s_{se},s_{rec}))=d(s_{se},s_{rec})^{\lambda}\cdot\alpha^{d(s_{se}s_{mc})} \tag{4}\]  
where \(\lambda\) is the energy diffusion factor, whose value is generally equal to 1.5, and \(\alpha\) is a parameter determined by the following expression:  
\[\alpha=10^{a(F_{t})/10} \tag{5}\]  
In Equation (5), \(a(F_{r})\) is the absorption coefficient (in dB/m) formulated as follows:\[a(F_{r})=0.11\frac{10^{-3}F_{r}^{2}}{1+F_{r}^{2}}+44\frac{10^{-3}F_{r}^{2}}{4100+F _{r}^{2}}+2.75\times 10^{-7}F_{r}^{2}+3\times 10^{-6} \tag{6}\]  
where \(F_{r}\) is the carrier frequency in kHz.  
(2) Deployment Energy Consumption  
In the process of the node self-deployment for underwater wireless sensor networks, each node moves to the determined position that the node self-deployment algorithm calculates. The energy consumption because of node moving in the process is called the deployment energy consumption. Because the methods of nodes moving are various, it is difficult to express with a specific model. Therefore, in the problem of node deployment, the total distance of all nodes in the deployment process is used to measure the deployment energy consumption of a node deployment algorithm.
# Comparative Study of Energy Efficient Routing Techniques in Wireless Sensor Networks
## 3 The Proposed Taxonomy
### Path Establishment (Route Discovery)
#### 3.5.3 Hybrid  
The main idea of hybrid protocols is to take advantage of proactive and reactive strategies and to exploit their merits in order to incorporate the benefits of proactivity and reactivity. Hybrid protocols are used to speed up delivery and reduce processing overhead by selecting the most efficient routing mechanism to use during packet routing.
## 5 Hierarchical Blockchain Model
### Block Structure and Data Abstraction
#### 5.1.1 Blockchain in Interval Domain  
The interval abstract domain [13] is defined by \(\mathbb{I}=\left\{[l,\ h]\ |\ l\in\mathbb{Z}\cup\{-\infty\},h\in\mathbb{Z}\cup\{+\infty \},l\leq h\right\}\cup\bot\). Given a set of integers \(\mathbb{X}\), this domain approximates \(\mathbb{X}\) by a pair \([l,h]\) where \(l\) and \(h\) represents minimal and maximal element in \(\mathbb{X}\) respectively. For example, the set \(\{2,1,100,4\}\) is approximated by [1, 100].  
Let \(\mathsf{L}_{c}=\langle\wp(\mathbb{R}),\subseteq,\emptyset,\mathbb{R},\cap,\cup\rangle\) and \(\mathsf{L}_{\mathbb{I}}=\langle\mathbb{I},\subseteq,\bot,[-\infty,+\infty], \cap,\sqcup\rangle\) be the concrete lattice of the powerset of numerical values \(\mathbb{R}\) and the abstract lattice corresponding to the domain of intervals \(\mathbb{I}\) respectively, where  
\[[l_{1},\ h_{1}]\sqsubseteq[l_{2},\ h_{2}]\iff l_{2}\leqslant l_{1 }\wedge h_{2}\geqslant h_{1}\] \[[l_{1},\ h_{1}]\sqcap[l_{2},\ h_{2}]=[max(l_{1}\ l_{2}),\ min(h_{1 }\ h_{2})]\] \[[l_{1},\ h_{1}]\sqcup[l_{2},\ h_{2}]=[min(l_{1},\ l_{2}),\ max(h_{1 }\ h_{2})]\]  
The correspondence between \(\mathsf{L}_{c}\) and \(\mathsf{L}_{\mathbb{I}}\) is defined by Galois connections \(\langle\mathsf{L}_{c},\ \alpha_{\mathbb{I}},\ \gamma_{\mathbb{I}},\mathsf{L}_{ \mathbb{I}}\rangle\), depicted in Figure 7, where \(\forall S\in\wp(\mathbb{Z})\) and \(\forall\overline{\upsilon}\in\overline{\mathbb{I}}\):  
\[\alpha_{\mathbb{I}}(S)=\left\{\begin{array}{ll}\bot&\text{if }S=\emptyset\\ [l,h]&\text{if }round\_down(min(S))=l\ \wedge\ round\_up(max(S))=h\\ [-\infty,h]&\text{if }\
exists min(S)\ \wedge\ round\_up(max(S))=h\\ [l,+\infty]&\text{if }round\_down(min(S))=l\ \wedge\ \
exists max(S)\\ [-\infty,\ +\infty]&\text{if }\
exists min(S)\ \wedge\ \
exists max(S); \end{array}\right.\]  
Figure 6: A Generic Block Structure.  
\[\gamma_{\mathbf{I}}(\overline{v})=\left\{\begin{array}{ll}\emptyset&\text{if } \overline{v}=\bot\\ \{k\in\mathbb{R}\mid l\leq k\leq h\}&\text{if }\overline{v}=[l,\,h]\\ \{k\in\mathbb{R}\mid k\leq h\}&\text{if }\overline{v}=[-\infty,\,h]\\ \{k\in\mathbb{R}\mid l\leq k\}&\text{if }\overline{v}=[l,\,+\infty]\\ \mathbb{R}&\text{if }\overline{v}=[+\infty,\,-\infty].\end{array}\right.\]  
Given a concrete block, its corresponding abstract version by replacing all concrete information with intervals is depicted in Figure 8:
## 4 Simulation Results
### Power Flows
Conversely, in the zonal and nodal pricing schemes, we observed cases wherein the purchase costs increased when the generation and transmission constraints were relaxed, particularly in the KY, JL, and YN zones. For example, when high-cost generators were subject to must-run constraints with minimum output requirements, these generators typically operated at the lower bound of their constraint, precluding them from setting market prices. In this case, lower-cost generators determined the market price. However, upon relaxation of these constraints, previously uncommitted generators with intermediate marginal costs became economically viable for dispatch. This shift in the merit order resulted in these newly committed units setting the market price, potentially leading to higher market prices in the affected zones or nodes, despite the overall reduction in the generation costs. This finding has significant implications for the transition to zonal or nodal pricing, particularly for firms with monopolistic structures in transmission and retail. Although we did not consider the costs of improvements made by a Transco to enhance power system reliability in this single-year analysis, as this would require long-term simulations incorporating network investment characteristics, our results suggest that there may be incentives for a combined Transco-retailer to prioritize addressing the generation and transmission constraints that minimize power purchase costs rather than maximize overall social welfare. This conflict of interest could potentially lead to a Transco intentionally delaying certain transmission investments or biasing the prioritization of projects. Considering these potential conflicts, it may be worth considering having an independent entity assume the role of determining the prioritization and execution of projects related to maintaining power system reliability, such as transmission line expansions, increasing the ESS capacity for grid stability, and reinforcing reactive power compensation equipment. This could help ensure that investment decisions are made with true neutrality, thereby prioritizing overall system efficiency and social welfare. Additionally, implementing functional unbundling of transmission and retail operations or establishing separate accounting systems for each unit could further mitigate conflicts of interest. Periodic audits and transparency measures, including public feedback mechanisms on major transmission investments, could ensure that the Transco's decisions align with both short-term cost efficiency and long-term system reliability. \begin{table}
\begin{tabular}{c
## 5 Implementation
### Phase 3--Selected Methodology Application
#### 5.3.3 Control and Automation  
The improved process was monitored to control the changes over time by maintaining the implemented changes and collecting more data for ongoing monitoring and modifications. New policies and training plans were presented to sustain the changes. Implementation of first-off inspection removal required commitment from the team and  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **With Manual Steps** & **Without Manual Steps** \\ \hline Total cycle time & 314 min & 302.18 min \\ \(\Delta\mathrm{T}\)\% decrease & & 3.76\% & \\ Product output/day & 67 parts & 70 parts \\ \(\Delta\mathrm{P}\)\% increase & & 4.48\% & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Effect of removing first-off inspection.  
consent from quality engineers. Poka-yoke was used to reduce the number of defects from machine set-up by providing operators with updated training that outlines all the new critical tools and related quality issues [89]. A checklist was prepared to be used by the operator before proceeding to machine a batch in the CNC to ensure that the tools are in the right condition. The steps carried out were documented as a standard. It was essential to store quality records and manage document control using a secured line and allowing for future access. Data storage requirements needed assessment. Process validation steps provided input into determining which process parameters need to be monitored and/or controlled and these factors be used for Continued Process Verification. Risk management included designing change controls to account for any changes and to assess the impact of the change.
## 3 Results and Discussions
### Dynamic Light Scattering (DLS) Analysis  
The particle size distribution of Au-20 and Au-PVP-20 colloids was analyzed by Dynamic Light Scattering Techniques using a standard He-Ne laser (632.8 nm) as a light source. BI-200 SM, Brookhaven Instrument Corp., was used for this purpose. The Particle Size Distribution (PSD) profiles of Au-20 and Au-PVP-20 are shown in Figure 6. In the case of Au-20 (Figure 6a), there were two size distributions. The first distribution lay in the range from 8 nm to 27 nm, with a peak at 19 nm, while a majority of the nanostructured particles lay in the second distribution, which ranged from 74 nm to 288 nm, with a peak at 121 nm. In Au-PVP-20 (Figure 6b), again, there were two size distributions. The first distribution lay in the range from 15 nm to 19 nm, with a peak at 19 nm, while a majority of the nanostructured particles lay in the second distribution, which ranged from 98 nm to 297 nm, with a peak at 153 nm.  
With PVP capping, the hydrodynamic size distribution was 15 nm to 297 nm. Conversely, without PVP capping, the hydrodynamic size distribution was 8 nm to 288 nm. These size distributions lie within the range of the size distribution determined by [PERSON] and co-workers for Au-PVP nanocrystals, which range from 100-300 nm [33; 34].  
It is to be noted that in DLS measurements, the size distribution of the AuNPs in composites is not very different from fructose-stabilized AuNPs. This justifies the co-stabilizing character of PVP in controlling the size distribution.
## 4 Conclusions  
Power consumption forecasting plays a vital role in describing future demands on power management to help plan sustainable policies. The main issues in hourly power consumption forecasting are the dynamic and intricate patterns. In addition, some burstiness and noise arises in the traffic. The decomposition operation can enhance the understanding of the time series relationship. The hybrid model of RobustSTL decomposition and the TCN model as the means of forecasting indicates excellent results compared to counterpart models. The proposed model obtains optimal accuracy with the lowest value of MAPE, MAE, and RMSE in hourly power consumption forecasting which outperforms the other models. The proposed model also achieves the best values in precision, recall, and F1-score metrics. Moreover, the proposed model can learn the noise and burstiness of the time series dataset to understand the data pattern.  
Not applicable.  
The data presented in this study are openly available in Kaggle ([[https://www.kaggle.com/datasets/hguletein/hourly-power-consumption-of-turkey-20162020](https://www.kaggle.com/datasets/hguletein/hourly-power-consumption-of-turkey-20162020)]([https://www.kaggle.com/datasets/hguletein/hourly-power-consumption-of-turkey-20162020](https://www.kaggle.com/datasets/hguletein/hourly-power-consumption-of-turkey-20162020)), accessed on 1 November 2021).  
The authors declare no conflict of interest.