esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Smart cities represent a complex convergence of urban planning, technology integration, and societal needs. As urban populations continue to grow, cities face unprecedented challenges in managing resources, infrastructure, and services efficiently [1]. The intricate nature of urban environments stems from their multifaceted organisational structures, overlapping jurisdictions, diverse infrastructure systems, and complex operational requirements [2].  
From an organisational perspective, cities comprise numerous departments, agencies, and stakeholders, each with its own objectives and processes. Jurisidictionally, cities often involve multiple levels of government, from local to national, creating a web of regulations and policies. Infrastructure-wise, cities encompass interconnected systems for transportation, energy, water, waste management, and telecommunications, each with its own complexities. Operationally, cities must balance day-to-day functioning with long-term planning and crisis management.  
This inherent complexity necessitates innovative technological solutions to streamline operations, improve decision-making, and enhance the quality of life for citizens. The cloud-edge-IoT (CEI) continuum has emerged as a promising framework to address these challenges. By leveraging distributed computing resources from cloud data centres to edge nodes and Internet of Things (IoT) devices, this continuum enables real-time data processing, adaptive resource allocation, and seamless integration of diverse urban systems. This approach holds the potential to transform urban management, making cities more responsive, efficient, and sustainable in the face of growing demands and limited resources.  
In this paper, we first explore some of the main categories of Smart City applications, highlighting their complex ICT needs. We then introduce the CEI continuum and highlight how this emerging infrastructure can support Smart City applications. To better understand the use of CEI technologies in smart cities, we introduce the COGNIFOG project [3] and then introduce our three main use-case trials as well as a technology demonstrator that has been developed to provide initial performance and functionality feedback to the project. We highlight how this demonstrates the advanced features of the COGNIFOG platform and how these features are able to support Smart City's needs. To validate our approach, we provide some initial performance data from the technology demonstrator and highlight its support for Smart City services and applications. Finally, we finish with observations of the platform, the next steps in its development, and some suggestions for future research.
# A Real Time and Lossless Encoding Scheme for Patch Electrocardiogram Monitors
## 2 Design, Materials, and Methods
### Delta Coding  
Delta coding is applied to reduce the dynamic range of original ECG signals. Subsequent encoded samples are generated from the difference between the current sample and the previous sample of the original ECG signal. An ECG signal contains the shape and size of the P-QRS-T and U waves, and time intervals between various peaks. Each interval contains different frequencies and variations, and the dynamic range of the delta coding volume therefore depends on the input waves. The minimal dynamic range is the P-Q or T-U wave, and the maximal dynamic range is the QRS complex.  
A 30-minute ECG recording of MIT/BIH 100 was taken from the MIT/BIH database, which uses an 11-bit resolution over a 10-mv range, and has a dynamic signal range from 485 (2.3 mv) to 1308 (6.3 mv). In addition to the first recorded value of 995 that has a previous sample of 0, delta coding generates a new dynamic range with a delta volume from \(-217\) (\(-1.05\) mv) to 141 (0.688 mv). Since MIT/BIH has a resolution of 11 bits, the first delta value of the first sampled ECG volume can be calculated using the initial previous value of 1024 (middle value) to reduce the dynamic range of the delta volume. Figure 3 compares the MIT/BIH 100 probability of occurrence of each distinct symbol between the ECG (original) volume and delta volume variance; the distinct delta volume variance is more centralized than the original volume. The dynamic range of the delta volume is limited to between \(-217\) and \(141\), which can be represented by nine bits (in addition to the first record of 995). Figure 3 shows the MIT/BIH 100 ECG volume and delta volume.  
Figure 2: Flow chart.
## 4 Parametric Analysis  
### Height-Thickness Ratio \(\lambda\)  
The \(\lambda\) is the ratio of the height or width (the minimum of the two) to the thickness of the steel plate. The height-thickness ratio reflects the stiffness and strength of infill steel plate. The thickness of 1.5 mm, 2 mm, 2.5 mm, 3.0 mm, 3.5 mm, 4.0 mm, 4.5 mm, 5.0 mm were selected as the basic parameters when concrete grade is C30 and the axial compression ratio is 0.3, and the corresponding height-thickness ratio is respectively 933, 700, 560, 467, 400, 350, 311, and 280. The load-displacement curves of steel plate with the thickness of 1.5 mm\(\sim\)5.0 mm are shown in Figure 16.  
It is found that the trend of the load-displacement curves with different thickness of steel plate shear walls are basically the same by observing the 8 curves. There is a first peak at point A. The curve falls briefly at Point B, then continue to rise to ultimate strength of point C. When thickness of the steel plate is increased by 0.5 mm, the ultimate strength increases by about 200 kN (see Table 3). The ultimate strength corresponding to the displacement increases gradually. However, the range is not very large, which shows that the stiffness and ductility of the entire structure are still dominated by the stiffness  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multicolumn{3}{c}{**Forward Loading**} & \multicolumn{3}{c}{**Reverse Loading**} \\ \cline{2-7}  & \(K_{0}\) (**kN/mm**) & \(P_{\textbf{max}}\) (**kN**) & \(P_{\textbf{u}}\) (**kN**) & \(K_{0}\) (**kN/mm**) & \(P_{\textbf{max}}\) (**kN**) & \(P_{\textbf{u}}\) (**kN**) \\ \hline Experiment & 40.38 & 535.5 & 516.2 & 39.94 & \(-\)506.5 & \(-\)468.0 \\ Finite Element & 42.89 & 502.0 & 502.0 & 43.28 & \(-\)508.4 & \(-\)466.2 \\ Difference (\%) & 6.22 & \(-\)6.67 & \(-\)2.83 & 8.36 & 0.38 & \(-\)0.39 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of experiment and finite element analysis.  
Figure 15: Skeleton Curve.  
The failure model of the steel plate shear wall with PEC columns has a significant \"strip\" feature and buckles along the diagonal line with the maximum stress. The number of stripes and the degree of the deformation are decreased with the increase of the thickness of the plate. With the increase of the thickness of the plate, plate stress develops from the upper right corner to the lower left corner. The plumpness of the Hysteretic curves is increased with the increase of the thickness of the steel plate, as shown in Figure 18. The contour envelope area of the hysteretic loop and the energy dissipation coefficient of 8 specimens are shown in Table 4. As shown in Figure 19, the accumulation of each cycle energy consumption is increased significantly.
# Introduction of a New Index of Field Operations Efficiency
## 2 Materials and Methods  
### Overview  
An arable farming operation typically consists of productive parts (active execution of the main task, e.g., spraying) and non-productive parts (e.g., turnings). In terms of the area to be covered, two parts of the area can be distinguished, the headland area and field body area, where each one of these two areas is covered by a number of paths called either headland passes or field-work tracks, respectively. Additionally, the area covered by the implement traversing a field-work track is denoted a \"row\".  
In order to cover the entire field area, a continuous path that starts at the field gate, traverses headland passes and field-work tracks (the order of these two depends on the operation), and terminates at the field gate again, has to be generated. The method presented here, implements three steps in order to generate such a continuous path, namely:  
1. Generation of the fixed entities. This step includes the generation of the headland passes, and the field-work tracks (presented in Section \"Fixed Entities\").
2. Generation of connectors. Four types of connectors that connects the fixed entities are generated (presented in Section \"Generation of Connectors\").
3. Generation of a continuous path for field coverage. Formulation of the field coverage problem as the problem of traversing an undirected weighted graph (presented in Section \"Continuous Path Generation\"). The input parameters of the planning method include:  
* The coordinates of the edges of the polygon representing the field boundary (B);* The machine's effective operating width (w);
* The driving angle (\(\theta\)), which defines the direction of tracks (in relation to the Universal Transverse Mercator (UTM)-Easting axis);
* The number of headland passes (h).
* The minimum turning radius of the vehicle (r).
* The coordinates of the location of the field gate where vehicles can enter and exit the field (E).
* The fieldwork pattern (F), which determines the traversing order of the track sequence.  
A graphical description of the structure of the tool is presented in Figure 1.  
The presented methodology, in its current form, considers the following assumptions-limitations:  
* Path generation for fields with obstacles is not considered.
* The methodology can only be applied to non-capacitated field operations such as tillage, plough, and so on.
## 3 Estimation of Reinforcement Strain
### Estimation Results
However, at the yield load stage, the average estimated strain of the modular specimen increased rapidly and was estimated to be 14.0% higher than that of the integral specimen. In the ultimate load stage, the strain gap was widened, and the average estimated strain of the modular specimen was 70.9% higher than that of the integral specimen. Although the second-order formula, which was applied for the modular specimen, underestimated the strain, the estimated strain increased rapidly and exceeded that of the integral specimen at the ultimate load stage. Therefore, as the load increases, the stress and strain concentrated at the discontinuity interface of the joint in the modular specimen increase. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multicolumn{3}{c}{**Estimated Strain at Yield Load (\(\chi 10^{-6}\) mm/mm)**} & \multicolumn{3}{c}{**Estimated Strain at Ultimate Load (\(\chi 10^{-6}\) mm/mm)**} \\ \cline{2-7}  & **Integral** (\(\chi_{\text{sp}}\)) & **Modular** (\(\chi_{\text{sp}}\)) & **Ratio** (\(\chi_{\text{sp}}\)) & **Integral** (\(\chi_{\text{sp}}\)) & **Modular** (\(\chi_{\text{sp}}\)) & **Ratio** (\(\chi_{\text{sp}}\)) \\ \hline Average strain estimated using first-order formula & 1600.91 & 2744.55 & 1.714 & 1927.66 & 4214.96 & 2.187 \\ Average strain estimated using second-order formula & 680.98 & 1824.48 & 2.679 & 1007.66 & 3294.85 & 3.270 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the estimated strains for the integral and modular specimens. The strain estimated according to Eurocode2 part.1-1 (Table 8) and the strains measured in the experiments (Table 3) are shown in Figure 8 for each load stage: service, yield, and ultimate load. The measured strains at the same location in the service load stage were very similar (Figure 8a), and the estimated strain of the modular specimen was lower than that of the integral specimen (Figure 8b). At the yield load stage, the measured and estimated strains were very similar. However, when the load increased from the yield load to the ultimate load, the strain of the integral specimen, measured at S2 (1000 mm) and S3 (2000 mm), increased by 770.8%, while the strain of the modular specimen increased by only 37.6% at the same locations. In contrast, regarding the estimated strain, the integral specimen exhibited an increase of 17.8%, and that of the modular specimen was 80.6%. Thus, in the modular specimen, only the steel reinforcement at the discontinuity interface of the joint resisted the load, whereas, in the integral specimen, the steel reinforcement uniformly resisted the load in the central section, where a constant moment occurred.
## 4 Experiments
### Evaluation on VOT2016  
To show the generality of our method, we evaluate the performance of our method on the VOT2016 [10] datasets. VOT2016 includes 60 challenging video sequences, in which background clutters are serious and occlusion is common. The trackers comprehensive performance is evaluated by the expected average overlap (EAO). The EAO combines the raw values of per-frame accuracies and failures in a principled manner and has a clear practical interpretation. We compare our Staple_SCA tracker with five baseline trackers (staple, SRDCF, KCF, SAMF, DSST) on VOT2016. Figure 9 shows the result. Our Staple_SCA tracker with an EAO of 0.298 is superior to other trackers  
Figure 8: Success plots (**a**) and precision plots (**b**) of proposed method against the convention CA method on the 100 video sequences in Visual Tracking Benchmark [8].  
Figure 9: The expected average overlap (EAO) graph with our tracker and five baseline trackers.
## 7 Limitation and Future Work  
In this work, the speed of the vehicle is limited to 60-100 km/h. Therefore, when the node speed becomes higher, the proposed RALAR approach performance might degrade, which requires more  
Figure 8: Average Energy Consumption vs. Time.  
advancement in this method. Moreover, the sensing unit to find the position is based on the GPS, oedometer, and compass; however, the performance proposed RALAR protocol can be further improved by incorporating the inertial measurement unit (IMU) in the sensing system. The performance can also be affected by the noise compass that comes from the influence of the electromagnetic wave. As a future work, the location prediction model can be developed by incorporating a perception-based model such as Simultaneous Localization and Mapping (SLAM). Additionally, the dimensions of the rectangle that were used in the proposed RALAR algorithm can be enhanced to more accurately estimate nodes location. Several other novel solutions can also be explored that were recently proposed, such as an enhanced probabilistic multimeric routing protocol (EProMRP) [47] and Multimedia Multimetric Map-aware Routing Protocol (3 MRP) [48]. This approach not only can improve the end-to-end packet delay and pack loss performance but also can help to identify the current node location while transmitting the data. In addition, several other suitable sets of simulation tools such as OMNeT++ [49], NS [50], Veins [51] and SUMO [52] can be utilized to design a realistic environment. Furthermore, the proposed method can be expanded to the scenario-based mobility model by experimenting with various real-life situations with the mobility patterns of mobile nodes, where it can capture environmental characteristics like the area of operation, obstacles, boundaries, intensity of traffic, type of traffic and density of the network, where a VANET may be deployed [53].  
Conceptualization, F.Q. and R.C.M.; Methodology, F.Q. and A.N.J.; Visualization, F.Q.; Writing--original draft preparation, R.C.M. and A.N.J.; Writing--review and editing, F.Q. and R.C.M.; Funding acquisition, R.C.M. All authors have read and agreed to the published version of the manuscript.  
This paper is supported by University Kebangsaan Malaysia (UKM), UKM Grant Code: GGP-2019-023.  
The authors would also like to thank the Editor and the anonymous reviewers for their valuable comments and suggestions.  
The authors declare no conflict of interest.
## 5 Conclusions  
In this paper, we have explored the main technical challenges and potential research directions for RSMA-assisted ISAC systems. We have identified several prime technologies including NTN, RIS, mmWave and THz technologies, and V2X, and their roles in enhancing  
Figure 7: RSMA-assisted ISAC systems with hybrid beamforming.  
the performance and adaptability of RSMA-assisted ISAC systems. With these disruptive technologies, RSMA-assisted ISAC systems are envisioned to achieve ubiquitous global connectivity and precise target detection on the same platform with improved spectral and energy efficiency. We anticipate that this will bring a truly hyper-connected society and advanced systems capable of simultaneously performing communication and radar. While we find these technologies to be quite promising and relevant for RSMA-assisted ISAC networks, in the future, we will explore more technologies, and our perspectives on the significance and effectiveness of various technologies will naturally evolve. An important future direction is to consider AI and machine learning, which enable intelligent resource allocation and self-optimizing network behavior, as well as explore how the potential benefits of prime technologies enable various envisioned use cases and applications, including public safety, industrial automation, autonomous vehicles, smart cities, healthcare, and environmental monitoring, and many more.  
Conceptualization, S.J. and B.L.; methodology, S.J. and N.K.; validation, G.K. and B.L.; investigation, S.J., G.K. and B.L.; resources, S.J. and B.L.; writing--original draft preparation, S.J. and B.L.; writing--review and editing, N.K., G.K. and B.L.; visualization, S.J., N.K. and G.K.; supervision, B.L.; project administration, B.L.; funding acquisition, B.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2024-RS-2023-00259061) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation).  
Not applicable.  
The authors declare no conflicts of interest.
## 5 Sc Range Identification  
Accuracy testing was conducted for the specified dataset in order to identify the _Sc_ range shown in Figure 9, to maximise the number of samples of the cleaned set \(S\), and to maximise the number of samples of the removed set \(R\) in the processed \(D_{PCA-SRP}\).  
Assume that there are two sets of data \(D\): cleaned data \(S\) and \(R\).  
\[D=S+R \tag{5}\]  
\[\%Noise=R/S \tag{6}\]  
\[R=\%Noise\times S \tag{7}\]  
Therefore,  
\[D=S+\%Noise\times S \tag{8}\]  
\[D=S\times(1+\%Noise) \tag{9}\]  
By indexing the dataset \(D\) for identifier, where \(n\) is the number of cleaned data \(S\), we have the following:  
\[D=\begin{cases}S&\text{if }0\leq\text{index}\leq n\\ R&\text{if }n<\text{index}\leq n\times(1+\%Noise)\end{cases} \tag{10}\]  
\(D_{R}\) randomly distributes \(R\) in \(S\) to thoroughly mix the noise in the cleaned data by maintaining its index for identification. Then, \(D_{R}\) is processed in PCA-sample reduction and sorted based on its loading score through the concept of covariance, eigenvalues and eigenvectors, and \(D_{PCA-SRP}\).  
_Sc_ is the selectivity of the data based on the equation in Table 3, \(D_{PCA-SRP}\), where the cut-off is biased to separate the clean-processed data \(D_{PS}\) and to remove processed data \(D_{PR}\). The dataset was transformed from random to sorted PCA-SR-processed data.  
Transformation:  
\[D\to D_{R}\to D_{PCA-SRP} \tag{11}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**PCA–SRP and ANN Parameters** & **Values** \\ \hline Most significant & \(>\)_Sc_ \(\times(PCA_{SRP}(M)_{highest})\) \\ sample size & \\ Learning rate & 0.1\% \\ Epochs & 100 \\ Number of ANN & 2 hidden layers (32 and \\ neurons & 16, respectively) \\ Activation functions & ReLU in hidden layers \\ used & SoftMax in final layer \\ \hline \hline \end{tabular}
\end{table}
Table 3: PCA–SRP and ANN parameters.  
Figure 9: Detailed sensitivity vs. specificity testing procedure.  
Thus,  
\[D=S\cup R \tag{12}\]  
\[D_{PCA-SRP}=S_{PCA-SRP}\cup R_{PCA-SRP} \tag{13}\]  
By definition, \(S_{accuracy}\) (true positive rate or sensitivity) is the number of correct samples in a clean-processed \(S\) in \(D_{PS}\) with a relative to number of \(n\).  
\[S_{accuracy}=S_{D_{PS}}/n \tag{14}\]  
However, \(R_{accuracy}\) (True Negative Rate or Specificity) is the number of correct samples of \(R\) in \(D_{PR}\).  
\[R_{accuracy}=R_{D_{PR}}/(n\times\%Noise) \tag{15}\]  
Therefore, the most efficient \(Sc\) is where \(S_{accuracy}\) and \(R_{accuracy}\) meet.
## 3 Results
### Analysis of Drainage Coefficient  
According to the results of theoretical runoff, the drainage coefficients calculated by the average draining method and the empirical formula are shown in Tables 3 and 4. Considering the current actual farmland drainage demand, the table only lists the drainage coefficient results for the 3-, 5-, and 10-year return periods.  
As shown in Table 3, \(Q_{A}\) was greater than \(Q_{E}\) when the return period was greater than 3-year, and less than \(Q_{E}\) when the return period was 3-year under 1-day rainfall duration. Table 4 shows that \(Q_{E}\) was always greater than \(Q_{A}\) for any return period under 3-day rainfall duration. The longer the drainage duration, the greater the regulating effect of farmland on runoff. The average draining formula could effectively reflect the regulating influence of the drainage duration on runoff. Thus, the \(Q_{A}\) was small when discharging 3-day rainfall in 4 days.  
It can be seen from Tables 3 and 4 that \(Q_{A}\) and \(Q_{E}\) both decreased with the decrease in the rainfall return period under the same rainfall duration in the same year. \(Q_{E}\) increased with the increase in rainfall duration under the same rainfall return period in the same year. The effect of rainfall duration on \(Q_{A}\) had no obvious regularity. For 1-day rainfall duration, the drainage coefficient increased with time, and its annual change rate decreased with the decrease in the return period. The annual change trend of \(Q_{A}\) was greater than that of \(Q_{E}\) when the return period was greater than 3-year. The drainage coefficient decreased with time for the 3-day rainfall duration. According to the absolute value of the drainage coefficient change rate under 3-day rainfall duration, the change trend of the annual drainage coefficient increased with the decrease in the return period, and that of 3-day rainfall duration was less than that of 1-day rainfall duration. The change trend of the annual drainage coefficient was the same as that of theoretical runoff.  
As shown in Table 1, the observed runoff of the Wanyan River pumping station in 2018 was 8.17 mm under the 3-day rainfall duration with 3-year return period. Therefore, the observed Q\({}_{E}\) was 2.21 mm/d, and Q\({}_{A}\) was 0.98 mm/d when the drainage duration was 4 days, so the observed drainage flow of the Wanyan River pumping station was 13.97-31.49 m\({}^{3}\)/s. As can be seen from Table 4, under 3-day rainfall duration with 3-year return period in 2018, the theoretical Q\({}_{E}\) was 4.83 mm/d, Q\({}_{A}\) was 2.47 mm/d, and the corresponding drainage flow of the pumping station was 35.11-68.73 m\({}^{3}\)/s. The theoretical flow was greater than the actual flow. In the same way, under 5-year return period in 2018, the theoretical drainage flow was 79.74-87.32 m\({}^{3}\)/s for 1-day rainfall duration and 67.97-105.6 m\({}^{3}\)/s for 3-day rainfall duration. The design flow of the Wanyan River pumping station is 57.97 m\({}^{3}\)/s, which cannot meet the drainage demand of the 5-year return period and is also an important reason for the local waterlogging disaster.