esa-sceva/satcom-chunk-collection huggingface dataset:
# Time-Dependent Vehicle Routing Problem with Drones Under Vehicle Restricted Zones and No-Fly Zones
## 6 Numerical Experiments  
Experiments are conducted on a computer running Windows 10, with 7.8 GB of RAM and an Intel i7-7700M CPU with a clock speed of 3.60 GHz. The algorithm is implemented in Python 3.12. The solomon R1-type instances and Gehring & Homberger benchmark set ([[https://www.sintef.no/projectweb/top/vrptw](https://www.sintef.no/projectweb/top/vrptw)]([https://www.sintef.no/projectweb/top/vrptw](https://www.sintef.no/projectweb/top/vrptw))) (accessed on 1 March 2024) are adapted to construct the TDVRPD-VRZ-NFZ test cases. The specific steps are as follows: The original customer coordinates are scaled down by a factor of 0.65 to 0.8, and the customer demand is randomly generated within the range of [0.001 t, 0.025 t]. The demand at customer points within the vehicle-restricted zones is set to be less than the maximum drone payload. No-fly zone 1 has a restricted time period in the range [10:00, 12:00], and no-fly zone 2 has a restricted time period in the range [10:00, 14:00]. The locations and sizes of the vehicle-restricted zones and no-fly zones are determined based on the scale of the customer points. The restricted time period for the restricted area is set to [8:30, 9:30], and there is no overlap between the vehicle-restricted zones and no-fly zones. The depot window is set to [8:00, 18:00].  
The system parameters are as follows: \(B^{2}=3\) kWh, \(B^{1}=75\) kWh, \(c_{1}=360\) yuan, \(c_{2}=1\) yuan, \(\varepsilon=0.2\), \(Q^{1}=1\) t, \(Q^{2}=0.005\) t, \(v^{u}=80\) km/h. The algorithm convergence criterion is set such that when the optimal solution remains unchanged for 20 consecutive generations, convergence is determined. The best result and the average result in 10 runs of the algorithm are denoted as best and avg, respectively, with results rounded to one decimal place.
## 2 Proper Orthogonal Decomposition Theory
### Rayleigh Quotient Derivation [35]  
Assuming that \(P_{i}(t)=P_{i}(x_{i},y_{i},t)\) is the fluctuating wind pressure of point \((x_{i},y_{i})\) at time \(t\), and \(\{P(t)\}=\{P_{1}(t),P_{2}(t),P_{3}(t),\ldots,P_{N}(t)\}^{T}\) is the time history of fluctuating wind pressure on the surface of the structure, its maximum projection is:  
\[a_{n}(t)=\{P(t)\}^{T}\{\varphi\}_{n}=\{\varphi\}_{n}^{T}\{P(t)\},\ n=1,2,3, \ldots,N \tag{4}\]  
where \(\{\varphi\}_{n}\) is a set of basis vectors in the orthogonal coordinate system.  
Since the positive and negative of \(P(t)\) is uncertain, the mean square method is used to regularize the above equation:  
\[\overline{a_{n}^{2}(t)}=\frac{\{\varphi\}_{n}^{T}\{P(t)\}\{P(t)\}^{T}\{\varphi \}_{n}}{\{\varphi\}_{n}^{T}\{\varphi\}_{n}}=\frac{\{\varphi\}_{n}^{T}[c]\{ \varphi\}_{n}}{\{\varphi\}_{n}^{T}\{\varphi\}_{n}}=\lambda_{n} \tag{5}\]  
where \([c]\) is the covariance matrix, and \(c_{ij}=\overline{P_{i}(t)P_{j}(t)}\).  
According to the property of Rayleigh quotient, the above Equation (5) obtains the standing value only when \(\{\varphi\}_{n}\) is the eigenvector of \([c]\), and the standing value is equal to the eigenvalue \(\lambda_{n}\):  
\[[c]\{\varphi\}_{n}=\lambda_{n}\{\varphi\}_{n},\ \ n=1,2,3,\ \ldots,N \tag{6}\]  
Assuming that \(\{\varphi\}_{n}\) is orthogonal, the eigenvector and principal coordinate matrix are \([\Phi]=[\{\varphi\}_{1},\{\varphi\}_{2},\ldots,\{\varphi\}_{n}]\) and \(\{a(t)\}=\{a_{1}(t),a_{2}(t),\ldots,a_{n}(t)\}\), respectively, then the projection \(a_{n}(t)\) can be rewritten as:  
\[\{a(t)\}=[\Phi]^{T}\{P(t)\} \tag{7}\]  
Since \([\Phi]\) is an orthogonal matrix, then:  
\[\{P(t)\}=\Big{(}[\Phi]^{T}\Big{)}^{-1}\{a(t)\}=[\Phi]\{a(t)\}=\sum_{n=1}^{N} a_{n}(t)\{\varphi\}_{n} \tag{8}\]  
The above equation is the series expansion of expressing the fluctuating wind pressure as the principal coordinate \(a_{n}(t)\) related only to time and the eigenmode \(\{\varphi\}_{n}\) related only to space, which is an analysis method that separates time and space.
## 4 Analysis Results
### Structural Model Analysis  
This study conducted a structural model to test the hypothetical relationship of the proposed model using the maximum likelihood method. The model fit index determines whether the sample data conforms to the suggested structural equation model. Table 6 presents several model fits indicators as well as the recommended thresholds. After a Satorra-Bentler scaled Chi-square process, all the model fit indicators have been significantly improved as shown in Table 6.
## 4 Energy and Environmental Evaluation of the System Retrofitting Variants  
### Annual Cooling Energy Production  
Based on information obtained from the chilled water receiver monitoring system at the facility, it was assumed in the annual cooling energy demand analysis that the system's peak demand for cooling power would amount to 78% of the maximum power needs of the installed receivers, i.e., 3635 kW [29]. The entire amount of the chilled water produced was used to maintain the thermal comfort parameters in the production area (air cooling and drying) and in the office and auxiliary area (air cooling).  
The expected annual cooling energy consumption was calculated according to the guidelines on energy certification for water chillers. Assuming that the operation time of the water chillers in the facility covered the period from 15 April to 15 September, i.e., 3600 h/a, and in the remainder of the year, the cooling needs were wholly met by the slow cooling system, the amount of the cooling energy produced in a year would correspond to the values presented in Table 3[29].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Partial Loads** & **Cooling Power Generated** & **Equivalent Operation Time** & **Cooling Energy Production** \\ \hline
**[\%]** & **[kW]** & **[h/a]** & **[MWh/a]** \\ \hline
100 & 3635 & 108 & 392.6 \\
75 & 2725 & 1188 & 3238.8 \\
50 & 1818 & 1476 & 2682.6 \\
25 & 909 & 828 & 752.4 \\ \hline \multicolumn{3}{c}{Total} & 7066.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Seasonal production of cooling energy in a pharmaceutical factory [29].  
Figure 6: Schematic of dual-stage absorption water chiller connections—Variant 2.  
Assuming that the production of cooling energy in the absorption water chillers would account for the core production, and the compression chillers would supply the supplementary and peak production, the annual supply of cooling energy from the absorption chillers would be as follows [29]:  
* Variant 1--1000 kW maximum power, operation for 3600 h/a, 3600.0 MWh/a,
* Variant 2--1650 kW maximum power, operation for 3600 h/a, 4620.0 MWh/a  
The designated volumes of cooling energy production were used to compare the consumption of primary energy and carbon dioxide emissions for the proposed retrofitting variants with reference to the basic variant.
# Edge and Cloud Computing in Smart Cities
## 3 Enabling Technologies
### Artificial Intelligence and Machine Learning  
The integration of AI and ML in edge-cloud computing enhances decision-making, resource management, and predictive analytics. AI-driven techniques help optimize task scheduling, improve computational efficiency, and ensure adaptive service provisioning.  
One of the key applications of AI in edge-cloud computing is intelligent workload distribution. AI models analyze real-time conditions, such as network latency, processing capacity, and energy consumption, to determine whether a task should be executed at the edge or offloaded to the cloud. This dynamic allocation minimizes response times and optimizes resource utilization [87; 88; 89].  
Another important aspect is FL, which allows edge devices to collaboratively train AI models without sharing raw data. Instead of sending complete datasets to a central server, FL enables decentralized model updates, preserving data privacy while improving overall system intelligence. This approach is particularly useful in healthcare, transportation, and other sensitive domains where data confidentiality is a priority [90; 91]. Additionally, RL techniques are used to adapt resource allocation strategies over time. By continuously learning from system performance, RL-based models can dynamically adjust processing power, bandwidth allocation, and task prioritization, ensuring efficient edge-cloud operations [92]. AI and ML significantly enhance the scalability and responsiveness of edge-cloud systems, enabling smarter, more adaptive computing frameworks in smart-city applications.
# Urban Quarry Ground Vibration Forecasting: A Matrix Factorization Approach
## 1 Introduction  
In recent years, blast-induced ground vibrations from quarrying activities and their impact on structures and human beings have attracted significant attention in scientific and industrial communities. Researchers like [PERSON] and [PERSON] have demonstrated through their studies the relationship between blast parameters and resulting ground vibrations [1; 2; 3]. These vibrations can potentially harm structures, especially if they are close to the blasting sites. Other researchers such as [PERSON] et al. and [PERSON] et al. also contributed to this field of study by investigating the simultaneous effects of ground vibration and air blasts [4; 5].  
The prediction and control of ground vibrations induced by blasting have been extensively researched. [PERSON] and [PERSON] provided insights into the damage to surface structures due to these vibrations [6]. Artificial Neural Network approaches, as proposed by [PERSON] and [PERSON], have been employed for the prediction of these vibrations [1; 3]. Thismethod of prediction was further expanded by other researchers such as [PERSON] et al., [PERSON] et al., and [PERSON] et al. [7; 8; 9].  
Apart from neural network methods, multivariate analysis, as applied by [PERSON], has also been employed for the prediction of blast-induced ground vibrations [10]. Moreover, Australian and British standards provide guidelines related to the use of explosives and evaluation of human exposure to vibrations, respectively [11; 12]. The studies by authors like [PERSON] et al., [PERSON] et al., and [PERSON] offer in-depth knowledge on blasting vibrations and their effects on structures [13; 14; 15].  
Historical perspectives on the topic have been provided by [PERSON] and [PERSON] and [PERSON] and [PERSON] [16; 17]. In addition, some research works like those by [PERSON] and [PERSON] have proposed other models to predict vibrations [18; 19]. With the rise in the relevance of technology, web-based visualization systems for predicting ground vibrations, as suggested by [PERSON] et al., are gaining traction [20].  
Recommendation systems using techniques like matrix factorization have also found application in this domain [21; 22; 23]. The critical essence is to find a balance between efficient blasting and minimizing the adverse impacts on the surroundings.
# Entity-Centric Fully Connected GCN for Relation Classification
## 3 Experiment
### Performance Comparison  
The performances of the proposed model and baselines on TACRED dataset are illustrated in Table 2. We employ four types of models as the baselines: (1) Logistic regression (LR) based model is a traditional method for relation classification, which utilizes the dependency information and the lexical information of sequences to form sentence-level features. (2) The convolutional neural network based model proposed by [PERSON] et al. [10] is employed to deal with the relation classification task, which develop a feature extractor to automatically capture the semantic features of sentences for relation classification. (3) The long short term memory (LSTM) based methods, including PA-LSTM [31] model, tree-LSTM model [33] and SDP-LSTM model [14], are introduced as the baselines. The PA-LSTM model employs a position-aware attention mechanism and a LSTM encoder to extract the sentence features.  
The SDP-LSTM model focuses on the shortest dependency path between an entity pair, in which the key semantic information is contained. The tree-LSTM model encodes the whole tree structure to obtain the semantic information of sentences. (4) The graph convolutional network based models C-GCN proposed by [PERSON] et al. [29] and AGGCN proposed by [PERSON] et al. [34] are also introduced as a baseline in our experiment.  
From Table 2, we can observe that the GCN based models achieve better performances that other baselines and our propose method outperforms all the baselines. Our model achieves significant improvement in terms of F1 with at least 0.7. The CNN based model has the best precision score 75.6 and the lowest recall score with only 47.5. Compared  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Para** & **Description** & **SemEval** & **TACRED** \\ \hline \(d_{w}\) & _Word embedding_ & 300 & 300 \\ \(d_{p}\) & _Position embedding_ & 30 & 30 \\ \(\gamma\) & _Initial learning rate_ & 1.0 & 0.5 \\ \(\zeta\) & _decay rate_ & 0.9 & 0.95 \\ \(\zeta\) & _Initial dropout rate_ & 0.5 & 0.5 \\ \(h_{l}\) & _Bi-LSTM hidden size_ & 100 & 100 \\ \(h_{f}\) & _FCGCN hidden size_ & 200 & 200 \\ \(d_{l}\) & _FCGCN layers_ & 2 & 2 \\ \(h\) & _Attention heads_ & 3 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hyper-parameters used in the experiments.  
to AGGCN and C-GCN, the proposed FCGCN achieves better performance. We believe the reason behind it is that the global entity nodes can effectively accumulate semantic information from the whole dependency tree and form more informative semantic features. The experimental results prove the effectiveness of the proposed FCGCN model.  
In order to evaluate the universality of the proposed proposed model, we also conducted experiments on SemEval 2010 Task 8. We mainly carried out experiments on some dependency models, as shown in Table 3. The proposed model can still get the F1 score 86.0 in SemEval 2010 Task 8, and and outperforms the compared baselines.
# Service Interface Translation. An Interoperability Approach
## 3 Service Interface Translation
### Methodology  
This work is framed into experimental computer science and engineering (ECSE) research, which can be broadly defined as \"the building of, or the experimentation with or on, nontrivial hardware or software systems\" [26]. Based on a top-down strategy, the research process has been divided into sub-problems, which have been individually characterized and addressed to achieve an overall solution. The sub-problems addressed in this paper are (1) identification of requirements; (2) payload translation design; (3) definition and analysis of the consumer interface generation system and the interface instance; (4) problem definition and generalization of the runtime deployment.
# Smart Grid Protection, Automation and Control: Challenges and Opportunities
## 3 Evolution of Substation Architectures and Digital Modernizations for Smart Substations
### Virtualization of Protection, Automation and Control (VPAC)
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Consideration Area** & **Description** & **Additional Details** \\ \hline \multirow{4}{*}{Compliance} & Ensure compliance with laws, & \\  & regulations, and security standards; & Compliance with standards like IEC 62443 and NERC CIP \\  & obtain necessary certifications & ensures cybersecurity and operational consistency across all \\  & and audits. & substation systems. \\ \hline \multirow{4}{*}{Availability and Reliability} & Design with high robustness, & \\  & including redundancy at all levels & \\  & to ensure continuous operation. & \\ \hline \multirow{4}{*}{Real-Time Performance} & Challenge: Historically, & \\  & virtualization technologies have & Handling real-time operations is critical in VPAC applications, \\  & struggled to deliver the real-time & which require immediate responses. Current improvements in \\  & performance required for & hardware and software are addressing these limitations. \\  & critical applications. & New hardware/software solutions address historical \\  & Current Shift: New solutions are & performance issues. Immediate responses are critical for \\  & emerging that promise improved & handling SMV and GOOSE traffic. \\  & real-time performance. & \\ \hline \multirow{4}{*}{Software Development Lifecycle (SDLC)} & Follow SDLC best practices, & \\  & excluding unnecessary services and & Integrating security into every stage of the software \\  & components to minimize the & development lifecycle helps reduce vulnerabilities and ensure \\  & attack surface and & performance optimization. \\  & performance issues. & \\ \hline \multirow{4}{*}{Integrated Security} & Implement strong encryption, & \\  & authentication protocols, and & \\  & protection against internal and & \\  & external threats. & \\ \hline \multirow{4}{*}{Interoperability} & Adhere to open standards and & VPAC systems should work seamlessly across different \\  & communication protocols to interact & manufacturers, especially under IEC 61850-9-2 LE for \\  & with diverse devices and systems. & SV communication. \\ \hline \multirow{4}{*}{System Monitoring and Auditability} & Implement continuous monitoring & \\  & and auditing to detect and respond & Monitoring is critical for maintaining stability, especially in \\ \cline{1-1}  & to anomalies, ensuring & real-time VPAC systems. Implement logs for real-time event \\ \cline{1-1}  & system integrity. & detection and response. \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Key technical requirements for VPAC systems.
## 4 Experimental Results
### Throughput Analysis  
Figure 4 illustrates the throughput achieved by the selected LWSCs while encrypting the video feed stream. Sosemanuk has the highest throughput, followed by Salsa and Grain-128a. In contrast, Grain-v1, Mickey-v1, and Trivium have the lowest throughputs, with their performance improving in the specified order.
# Orthogonal Subblock Division Multiple Access for OFDM-IM-Based Multi-User VLC Systems
## 4 Conclusions  
In this paper, a novel OSDMA scheme is proposed and investigated for OFDM-IM-based MU-VLC systems. Moreover, DFT spreading and interleaved subcarrier multiplexing are further introduced to address both the low-pass frequency response and the LED nonlinearity issues of practical MU-VLC systems. It has been verified through numerical simulations and hardware experiments that both OFDM-SM-IM and OFDM-DM-IM can  
Figure 10: The received constellation diagrams for: **(a)** OFDM-SM-IM, 16 QAM, 50 cm, without interleaving, **(b)** DFT-S-OFDM-SM-IM, 16 QAM, 50 cm, without interleaving, **(c)** OFDM-SM-IM, 16 QAM, 50 cm, with interleaving, **(d)** DFT-S-OFDM-SM-IM, 16 QAM, 50 cm, with interleaving, **(e)** OFDM-DM-IM, circular (7,1)QAM, 60 cm, without interleaving, **(f)** DFT-S-OFDM-DM-IM, circular (7,1)QAM, 60 cm, without interleaving, **(g)** OFDM-DM-IM, circular (7,1)QAM, 60 cm, with interleaving, and **(h)** DFT-S-OFDM-DM-IM, circular (7,1)QAM, 60 cm, with interleaving.  
Figure 9: Average BER vs. distance for **(a)** OFDM-SM-IM and DFT-S-OFDM-SM-IM without and with interleaving and **(b)** OFDM-DM-IM and DFT-S-OFDM-DM-IM without and with interleaving (w/o: without, w /: with, interl.: interleaving).  
achieve substantial performance improvements when DFT spreading and interleaving are performed. More specifically, simulation results show that more than 3-dB SNR gains can be obtained when DFT spreading and interleaving are performed for both OFDM-SM-IM and OFDM-DM-IM over a low-pass VLC channel, while experimental results demonstrate that substantial distance extensions of 40.2% and 19.1% can be achieved by DFT-S-OFDM-DM-IM with interleaving compared with OFDM-DM-IM with interleaving and DFT-S-OFDM-DM-IM without interleaving, respectively. Therefore, OSDMA with DFT spreading and interleaving can be a promising candidate for practical high-speed MU-VLC systems.  
Formal analysis, W.W., M.L., X.D. and C.C.; funding acquisition, C.C.; investigation, Y.N. and J.C.; methodology, J.C.; project administration, C.C.; resources, X.D.; software, J.C.; supervision, M.L. and C.C.; validation, Y.N.; writing - original draft, Y.N.; writing - review & editing, W.W., X.D. and C.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (61901065 and 62001174), the Natural Science Foundation of Chongqing (cstc2021 jcyj-msxmX0480), and the Fundamental Research Funds for the Central Universities (2021 CDJQY-013).  
Not applicable.  
The authors would like to thank the anonymous reviewers for their valuable comments and suggestions.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Characterisation
#### 3.2.3 Dls  
Particle hydrodynamic size, polydispersity, and surface charge were measured before and after loading MTX for UiO-66 and UiO-66-NH\({}_{2}\) in water (Table 2). Synthesis of UiO-66 was performed by an ageing method to obtain nano-sized particles of 156 \(\pm\) 0.4 nm and a polydispersity index (PdI) of 0.279 for better drug delivery to tumour cells [45]. After MTX loading, the nanoparticles showed an increase in the size of UiO-66-MTX (211 \(\pm\) 8 nm) and a PdI of 0.42. UiO-66-NH\({}_{2}\) exhibited a hydrodynamic size of 170 \(\pm\) 2.3 nm [46] and a PdI of 0.212 before MTX loading and a size of 487 \(\pm\) 3 nm and a PdI of 0.179 after loading of MTX (Figure S7). The size of UiO-66-NH\({}_{2}\)-MTX was bigger than UiO-66-MTX, and this may be due to the blockage of pores by binding to an -NH\({}_{2}\) group [56].  
Figure 4: PXRD patterns for (**A**) calculated UiO-66 and pristine UiO-66, UiO-66-MTX, and MTX and (**B**) calculated UiO-66-NH\({}_{2}\) and pristine UiO-66-NH\({}_{2}\), UiO-66-NH\({}_{2}\)-MTX, and MTX are shown.  
The surface charge of UiO-66 decreased from 26.7 \(\pm\) 2.23 mV to 19.8 \(\pm\) 0.94 mV after loading with MTX, indicating possible internalisation of the drug. MTX contains negatively charged functional groups, which can interact with the positively charged sites on the surface of UiO-66, leading to a decrease in the overall positive charge of the MOFs. This reduction in surface charge might imply that MTX is adsorbed or bound to the MOFs, and the negative charges of MTX contribute to the altered electrostatic characteristics of UiO-66.  
The zeta potential of UiO-66-NH\({}_{2}\) decreased from 30.8 \(\pm\) 0.81 mV to 14.1 \(\pm\) 0.66 mV after MTX loading, and the particle size of UiO-66-NH\({}_{2}\)-MTX was larger compared to UiO-66-NH\({}_{2}\). According to [PERSON] et al. (2019), UiO-66 and UiO-66-NH\({}_{2}\) carry a positive charge in aqueous solutions with a pH below 8.3, resulting from the formation of Zr-OH\({}^{2+}\) by Zr-OH at the Zr6 node. Furthermore, at pH levels below 5, UiO-66-NH\({}_{2}\) can undergo protonation at the amino group. These varying surface charges, influenced by factors such as H-bond ability to donate or accept, amino group, Zr cluster, and host-guest chemistry, are utilised to effectively load and deliver MTX ([PERSON] et al., 2019). Water stability of UiO-66 and UiO-66-NH\({}_{2}\) at different pH values may be due to different pKa values generated by oxo-hydroxy groups of the Zr cluster ([PERSON] et al., 2019).