esa-sceva/satcom-chunk-collection huggingface dataset:
# Radio Number for Friendship Communication Networks
## 2 Radio Number of Friendship Graph
** Let \(j\in\{1,2\},\)\(1\leq i\leq k,\)then  
\[\Big{|}L(x_{0})-L\Big{(}x_{jk+i}\Big{)}\big{|}=\left\{ \begin{array}{cc}k+1+3i, &j=1\\ 4k+1+3i,&j=2\end{array} \right.\]  
Since \(d\Big{(}x_{0},x_{jk+i}\Big{)}=1.\) Consequently, \(\big{|}L(x_{0})-L\Big{(}x_{jk+i}\Big{)}\big{|}\geq 5-d\Big{(}x_{0},x_{jk+i} \Big{)}.\)  
**Case 3. ** Let \(j\in\{1,2\},1\leq t\leq k\) and \(1\leq i\leq k\)  
\[d\Big{(}x_{i},\ \ x_{jk+t}\Big{)}=\left\{ \begin{array}{cc}1,&i=t\\ 3&i\
eq t\end{array} \right.\]Consequently,  
\[\left|L(x_{i})-L\Big{(}x_{jk+t}\Big{)}\right|=\left\{ \begin{array}{ll}2i+k-1,&j=1 \text{ {and }}i=t\\ 2i+4k-1,&j=2\text{ {and }}i=t\\ 3i-t+k-1&j=1\text{ {and }}i\
eq t\\ 3i-t+4k-1&j=2\text{ {and }}i\
eq t\end{array} \right.\]  
Therefore, \(\left|L(x_{i})-L\Big{(}x_{jk+t}\Big{)}\right|\geq\left\{ \begin{array}{ll}4,&i=t \\ 2&i\
eq t\end{array} \right.\)  
Hence, \(\left|L(x_{i})-L\Big{(}x_{jk+t}\Big{)}\right|\geq 5-d\Big{(}x_{i},x_{jk+t} \Big{)}.\)  
**Case 4. ** Let \(j,m\) be elements of \(\{1,2\},1\leq t\leq k\) and \(1\leq i\leq k\). Then, \(d\Big{(}x_{jk+i},x_{mk+t}\Big{)}=2\). Moreover,  
\[\left|L\Big{(}x_{jk+i}\Big{)}-L\left(\ x_{mk+t}\right)\right|=\left\{ \begin{array}{ll}3k,&j\
eq m\text{ {and }}i=t\\ 3(i-t),&j=m\text{ {and }}i\
eq t\end{array} \right.\]  
Then, \(\left|L\Big{(}x_{jk+i}\Big{)}-L(x_{mk+t})\right|\geq 5-d\Big{(}x_{jk+i},x_{mk+t} \Big{)}.\)  
**Case 5. ** Let \(j=0,1\leq i<t\leq k\), then \(d\Big{(}x_{jk+i},x_{jk+t}\Big{)}=4\) and  
\[\left|L\Big{(}x_{jk+i}\Big{)}-L\Big{(}x_{jk+t}\Big{)}\right|=\left|3+i-1-(3+t-1 )\right|=t-i\geq 1.\]  
Therefore,  
\[\left|L\Big{(}x_{jk+i}\Big{)}-L\Big{(}x_{jk+t}\Big{)}\right|\geq 5-d\Big{(}x_{ jk+i},x_{jk+t}\Big{)}.\]  
From the above cases, the radio condition holds and \(L\) is a radio labeling of \(F_{4,\ k}\) and  
\[rn(F_{4,\ k})\leq 7k+1.\ \ \Box\]  
The graph \(F_{4,\ k}\) with labeling of vertices is presented in Figure 4.  
Figure 4: \(F_{4,\ k}\) with labeling of vertices. **Theorem 4**. : _Let \(k>2\), \(F_{6,\,k}\) be a friendship graph with blocks each of length 6 and \(\left|V(F_{6,k}\ )\right|=5k+1\) and then \(rn(F_{6,\,k})\leq 17k+1\)._  
Proof. : Define the map \(L\) as follows:  
\[L\Big{(}x_{jk+i}\Big{)}=\left\{ \begin{array}{cc}0,&i=j=0\\ 4+i-1,&j=0,1\leq i\leq k\\ k+6,&j=1,i=1\\ k+10+3(i-2)&j=1,2\leq i\leq k\\ 4k+4+3i&j=2,1\leq i\leq k\\ 7k+7&j=3,i=1\\ 7k+11+5(i-2)&j=3,2\leq i\leq k\\ 12k+5i+1&j=3,2\leq i\leq k\end{array} \right.\]  
Since \(diam(F_{6,\,k})=6\). From the above definition of the labeling function \(L\) and Figure 6, one can prove that the radio condition \(\left|L(x_{u})-L(x_{v})\right|\geq 7-d(x_{u},\ x_{v})\) holds for all \(x_{u},\ x_{v}\in V(F_{6,\,k})\) and \(u\
eq v\). Moreover, \(rn(F_{6,\,k})\leq 17k+1\). Figure 5: \(F_{5,\,k}\) with labeling of vertices. Figure 6: \(F_{6,\,k}\) with labeling of vertices. A friendship \(F_{6,\ k}\) with labeling of its vertices is shown in Figure 6.
## 6 Experimental Evaluation
### Experiment II: Service Allocation
#### 6.2.2 Comparison Algorithms  
We compared the Ubunye classifier with first-fit, round robin, and double price double auction (DPDA) algorithms. The first-fit algorithm is a well-known heuristic model for resource allocation. It aims to allocate services to the minimum number of servers without exceeding their capacity. The algorithm places each service on the first server with sufficient resources. Although simple, it effectively enhances resource utilization and reduces latency. The first-fit algorithm can be optimized for dynamic, multidimensional environments typical in edge computing [35].  
The round robin algorithm is a simple and effective load-balancing technique used in edge computing. It distributes services among edge servers cyclically, ensuring an even load over time, especially when services have similar execution times. This method is beneficial as it does not require detailed server state information. Recent studies [36] have also explored combining the round robin algorithm with AI techniques to improve load distribution and resource utilization in dynamic environments.  
The DPDA algorithm is an advanced technique for resource allocation in edge computing and IoT. Based on double auctions, providers and consumers of resources bid, allowing for an efficient and fair allocation of limited resources. In these auctions, buyers and sellers submit their offers (purchase prices) and bids (sale prices). The goal is to maximize the number of viable transactions while respecting supply and demand constraints. Let \(p_{b}\) be the price submitted by buyers, and \(p_{s}\) is the price submitted by sellers. The auction finds an equilibrium price \(p^{*}\) as follows:  
\[p_{b}\geq p^{*}\geq p_{s}.\]  
A transaction occurs when the price submitted by the buyer is greater than or equal to the equilibrium price, and the price submitted by the seller is less than or equal to the equilibrium price. The algorithm iteratively adjusts the price until the total supply matches the total demand, ensuring an efficient allocation of resources [37].  
DPDA is particularly effective in dynamic and heterogeneous environments, such as edge computing, where resource demand and supply vary rapidly. Ref. [37] demonstratedthat DPDA can significantly improve the resource allocation efficiency in IoT scenarios by reducing latency and increasing resource utilization.
# Quantum Computing Approaches for Mission Covering Optimization
## 1 Introduction
### Quantum Solutions to MCO
#### 1.1.2 Quantum Alternating Operator Ansatz (QAOA)  
Quantum Alternating Operator Ansatz (QAOA) is a method for solving combinatorial optimization problems on NISQ devices [17]. The algorithm is supported by gate-based quantum computers such as the IBM, Rigetti, IonQ or Xanadu machines. In this effort, we focus on implementing the problem on an IBM quantum computer [18]. At the current time, IBM's Qiskit implementation of QAOA uses a Variational Quantum Eigensolver (VQE) to find the expectancy of a parameterized ansatz eigenstate. This quantity is then used to calculate the minimum of the cost function, which is embedded in a cost Hamiltonian \(H_{\mathbb{C}}\). Similar to QA, it is limited to solving binary quadratic problems [19].
# A Middleware-Based Approach for Multi-Scale Mobility Simulation
## 3 The Model
### Composing the Representations of Travelers: The \(\bullet\) Operator  
Consider two simulations \(\mathcal{S}_{1}\) and \(\mathcal{S}_{2}\) with two different representations \(\omega_{1}\) and \(\omega_{2}\). The \(\bullet\) operator transforms the two representations \(\omega_{1}\) and \(\omega_{2}\) to each other. We consider two bi-directional transformations: conversion of agents into flows (and vice versa) and (dis)aggregation of groups of agents.  
To transform agents to flows, the middleware calculates a density and an average speed. We adopt the approach of [19] to do so:  
* For each vehicle, a function of its position \(x\) is defined: if the vehicle is present in \(x\), then the function returns a positive constant, \(0\) otherwise.
* Next, all previously defined functions are summed to create a new function. This new function thus returns a positive constant for each \(x\) position where a vehicle is present, \(0\) if no vehicle is present. This function is called \(D(x)\).
* Finally, if the cells of the model are spaced by \(\Delta x\), we obtain the density \(\rho_{k}\) of traffic on the parts of the arc, necessary for the macroscopic model with the following function: \[\rho_{k}=\frac{1}{\Delta x}\int_{k\Delta x}^{(k+1)\Delta x}D(x)\mathrm{d}x\] (6)  
To transform flows to individual representation, it is necessary to create information for the individual agents. Indeed, a detailed simulation uses individual data that is not present in a simulation representing entities as flows. As a consequence, these data must be generated. To do so, we adopt the method of [19], who propose to create agents at positions determined by probabilities, following a Poisson process.  
On the other side, to transform agents in groups of agents, we adopt the approach of [23], who propose to determine which agents to aggregate by defining distance functions between them and to aggregate the closest ones. However, two agents that are physically close cannot be aggregated if the probability that they will remain close in the future is low. We then use the similarity between itineraries instead of the similarity between positions.  
The transformation of groups to individual agents follows the same procedure as the flows to agents transformation.
# Inter-Annual Variability of Peatland Vegetation Captured Using Phenocam- and UAV Imagery
## 3 Results
### Monitoring Vegetation Phenology
#### 3.2.2 UAV Data  
The spectrally calibrated UAV data, despite their coarser temporal resolution (biweekly to monthly), were able to capture trends in vegetation phenology. Time-series of UAV-derived VIs averaged over the study area were generally consistent with the phenocam data (Figure 4), and a strong correlation was identified between phenocam- and UAV-derived greenness for all calculated VIs (see Figure 5, \(r_{\rm s}\) = 0.77-0.86, \(p\) < 0.01). However, peak greenness values in 2019 and 2021 were notably different. Here the UAV-derived VIs indicated a similar greenness over the two years, whereas phenocam-derived \(G_{cc}\) indicated a greater peak greenness during the 2021 growing season (maximum \(G_{cc}\) = 0.44, Figure 4).  
Figure 5: Correlation between UAV-derived VIs averaged across the scene, and daily phenocam-derived greenness (\(G_{cc}\)) averaged across the ROI. Shown at the top of each of the displayed subplots is the Spearman’s rank correlation coefficient, \(r_{\rm s}\), and its associated statistical significance. \({}^{\star\star}\) indicates \(p\) < 0.01, and n = 26 (i.e., the number of days on which UAV imagery was collected) for all of the eight subplots displayed.
## 2 Materials and Methods
### Sensor Data Collection and Pre-Processing
Its spectral capabilities include three VNIR bands at 15 m spatial resolution, six SWIR bands at 30 m spatial resolution, five thermal infrared (TIR) bands at 90 m spatial resolution and a NIR band at 15 m spatial resolution [64]. It has the narrowest swath width of the three virtual constellation sensors at 60 km. We downloaded all available local granules of ASTER covering the entire spatial extent of the study area. Similar to L8 and S2A, the dates where one or more granules are missing result in a sharp edge in the image (Figure 3). The ASTER data were pre-processed using USGS LP-DAAC Science Processor for Missions (S4 PM) [65] processing system that also stores ASTER Level 1, 2, and 3 products. The ASTER Level-1B data were used for NDVI computation. The ASTER Level-1B data are available in the Hierarchical Data Format (HDF) and the bands required to compute NDVI are band 3N (Red) and band 8 (NIR). The R [66] package provided by USGS LP-DAAC was used to convert the HDF data from ASTER Level-1B (in UTM coordinates) to a multi-band geotiff file (UTM zone 16N, WGS 1984) that was then co-registered with the L8 and S2A imagery in ArcGIS. NDVI was computed from the pre-processed image using the bands referenced above and Equation (1). The collective spatial and spectral similarities of L8, S2A, and ASTER enable their synergistic use to map NDVI continuously as a virtual constellation. Since NDVI is calculated using only red and NIR bands, no other bands from the respective satellite sensors were used in this analysis. The spectral characteristics of the L8, S2A, and ASTER standard products are listed in Table 1.  
Figure 3: Issues disrupting long term continuous NDVI coverage. (**A–C**) Sentinel-2A August 2015, overlapping cloudy scenes from the same month merged. (**D**) ASTER May 2018 and (**E**) Landsat 8 February 2018, no adjacent scenes present during those months.
# A 60 GS/s Two-Stage Sampler with a Linearity Calibration Loop for PAM-8 Receivers
## 5 Conclusions  
This paper showed a 60 GS/s 8 \(\times\) 8 TI track and hold in 5 nm FinFET technology with a 6 dB gain at Nyquist, and it proposed a calibration loop applicable to a PAM system, which minimized the distortion of the AFE. The transient co-simulation of the sampler circuit and of the receiver Verilog-A model generated the optimal value of \(v_{corr}\), and the system was able to vary the control voltage to track the temperature change. The histograms at the slicer were evaluated before and after the calibration showing an eye-opening improvement, which resulted in an average reduction in the variances from 34 mV to 27 mV, from 43 mV to 36 mV, and from 27 mV to 19 mV, respectively, for the Typ, SS, and FF corners. Lastly, the obtained \(v_{corr}\) was applied to the buffer during a transient simulation with a 1 GHz, 252 \(mV_{ppd}\) sinusoidal input, showing a THD of the TH circuit higher than 48.5 dB across different PVT conditions.  
Conceptualization, A.D.P., E.M. and N.G.; methodology, A.D.P., E.M., N.G. and C.N.; validation, A.D.P., E.M., N.G. and C.N.; formal analysis, A.D.P., E.M., N.G. and C.N.; investigation, A.D.P., E.M. and N.G.; data curation, A.D.P.; writing--original draft preparation, A.D.P.; writing--review and editing, A.D.P., E.M., N.G., C.N. and L.F.; supervision, C.N. and L.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**System** & **[14]** & **[12]** & **This Work** & **This Work** & **This Work** \\  & **Typ** & & **Typ @ 60 \({}^{\circ}\)C** & **SS @ 125 \({}^{\circ}\)C** & **FF @ \(-\)20 \({}^{\circ}\)C** \\ \hline
**Pre-calibration** & \(\approx\)50 dB (No calibration was implemented; the THD was extrapolated from the plot) & 30.8 dB & 31.6 dB & 36.4 dB & 28.7 dB \\ \hline
**Post-calibration THD** & - & 56.5 dB & 54.0 dB & 55.2 dB & 48.5 dB \\ \hline
**Input Frequency** & 1.01 GHz & 5.5 GHz & 1 GHz & 1 GHz & 1 GHz \\ \hline
**Transistor-level** & **implementation** & Yes & No & Yes & Yes \\ \hline
**Output Dynamic** & **Range** & 500 mV\({}_{ppd}\) & - & 500 mV\({}_{ppd}\) & 500 mV\({}_{ppd}\) & 500 mV\({}_{ppd}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: THD comparison between the literature and this work at three different PVT conditions.
###### Abstract  
As Wireless Sensor Networks (WSNs) grow in popularity, researchers are now focusing more on some challenging issues that significantly degrade overall performance, such as energy hole mitigation, link asymmetry minimization, etc. Link asymmetry is a problem that arises when the coverage distance between two adjacent nodes varies. It creates an obstacle to overcome when designing an efficient Medium Access Control (MAC) protocol for WSNs with low duty-cycling. This phenomenon poses an especially difficult challenge for receiver-initiated asynchronous MAC protocols, which are popular due to their relatively higher energy efficiency. Exploiting the benefits of cooperative communication has emerged as one of the viable solutions to overcome this limitation. Cooperative communication in WSNs has received a lot of attention in recent years. Many researchers have worked to create a MAC layer supporting cooperative communication. However, the association of cooperative communication with an asymmetric link is not studied in the literature. In this research work, COASYM-MAC, a cooperative asynchronous MAC protocol for WSNs, is proposed based on a receiver-initiated MAC protocol that uses the fact that nodes have alternate paths between them to reduce link asymmetry. A key feature of the proposed protocol is that the optimal helper node is selected automatically in case of link asymmetry. Simulations exhibited that COASYM-MAC performs significantly better than a state-of-the-art MAC protocol for WSNs that handles asymmetric links, ASYM-MAC.  
MAC; wireless sensor network; link asymmetry +
Footnote †: journal:  
0000-0001-5888-2220019
## 4 Classification of MAC Protocols Based on Channel
### Cooperative Single-Channel MAC Protocols
#### 4.6.2 Crmac  
A cooperative-based protocol is proposed, where the three types of data transmission modes employed are direct transmission, cooperative relaying, and multi-hop relaying to achieve good throughput. Cooperative communication is performed via optimal relay selection. The RTS/CTS mechanism is used in a direct transmission (DT) mode. In the cooperative relay (CR) mode, all nodes know the sender and receiver SNR information (nodes with higher SNRs are chosen). The sender sends data to the optimal relay and then optimal relay sends it to the destination. If the sender does not receive any ACK after being sent by a short inter-frame space (SIFS) sender, the relay re-transmits the data to the destination. Nodes transmit with an interval of dynamic short-frame space (DIFS). Upon receiving a RACK, a sender knows that the transmission was successful. The mechanism of exchange for the packet is depicted in Figure 13.  
It is basically a relay selection algorithm proposed to select the optimal relay based on the highest SNR among nodes. A performance analysis was performed based on the probability calculation, average transmission time, and Markov-Model used for analysis of the result. The protocol provides enhanced throughput and efficient communication. The demerit of this protocol is the lack of performance analysis on collision avoidance, power consumption, delay factors for real-time applications, and efficient use of channels.
# IDSMatch: A Novel Deployment Method for IDS Chains in SDNs +
## 5 Evaluation
Surprisingly, attack rates have little impact on dropping rates, mainly influenced by switch capacity rather than attack ratios. Increased attack rates lead to higher delays, as switches need to alert the controller before taking action. Consequently, with more attack samples, detection rates, missed detection rates, and dropping rates all rise. In this study, we evaluate our approach using measurements of network delay in the real test bed and the number of hops in the simulation.
# A Bimodal EMG/FMG System Using Machine Learning Techniques for Gesture Recognition Optimization
## 3 Results  
The preliminary results of this study show significant improvement of efficacy on gesture recognition using a bimodal EMG/FMG acquisition system. This is accomplished through a detailed study of the application of different machine learning models.  
Figure 5: Example of FMG raw-signals and EMG raw and pre-processed signals. The green and red vertical lines shown are, respectively, the onset and offset time for each muscle activation.
## 3 Results
### Performance of AI Models  
The most commonly used metric is the Dice index, which is used by 25 articles. The Dice index assesses the similarity between segmentation results and ground truth by calculating pixels in the intersection and comparing this to the average size of two areas, visually evaluating the accuracy of AI segmentation models [33]. Similar metrics, including IoU and Jaccard coefficient, are calculated by dividing the area of intersection by the area of the union [61]. A total of 11 and 6 articles, respectively, used them to measure the accuracy and similarity. Compared to IoU, the Jaccard coefficient is not only used for image segmentation but can also be applied to text. In summary, the existing AI tooth segmentation models can achieve a Dice index of 0.935 \(\pm\) 0.035 (Mean \(\pm\) SD) and an IoU or Jaccard coefficient of 0.877 \(\pm\) 0.075. The CNN model developed by [PERSON] et al. showed the highest DSC of 0.99, as well as the highest IoU, precision, and recall score of 0.99, and the lowest 95% HD of 0.12 mm, demonstrating near-perfect segmentation results [31]. In contrast, CAT-UNet exhibited the lowest segmentation accuracy, with a DSC of 0.865 and an IoU of 78.12 [32]. The method of Point-based Detection and Gaussian Disentanglement achieved only a minimum average IOU of 0.704 in tooth detection [61]. Moreover, there are four articles used the Boundary F1 Score to assess the identification and segmentation of boundary regions, achieving an accuracy of 0.97 \(\pm\) 0.02.  
In addition, accuracy, precision, and recall are widely used metrics for evaluating the model performance. Accuracy quantifies the proportion of pixels correctly classified by the model to the total number of pixels. Precision measures the correctness of the segmented area, while recall evaluates the success of the model in segmenting individual teeth [39]. Current AI models can achieve an accuracy of 98.00% \(\pm\) 1.55%, a precision of 94.52% \(\pm\) 4.53%, and a recall of 94.17% \(\pm\) 4.08% in the tooth segmentation in CBCT images. [PERSON] et al. and [PERSON] et al. applied the same segmentation model based on the U-Net to the images [39; 42]. [PERSON] et al. focused on images with small dentulous areas, achieving 100% accuracy and 100% precision. [PERSON] et al., on the other hand, focused on images containing dental fillings and achieved the highest recall of 99.7%.  
Furthermore, 15 papers used ASSD and 11 papers used HD to evaluate the image segmentation performance, respectively. ASSD considers the distance between each segmented area and the ground truth, while HD only considers the maximum distance, typically using the 95% Hausdorff Distance for evaluation. The results indicate that AI segmentation methods achieved an ASSD of 0.22 \(\pm\) 0.13 mm and a 95% HD of 0.94 \(\pm\) 0.59 mm. [PERSON] et al. improved the U-Net-based segmentation model proposed by [PERSON] et al. through integrating high precision pulp cavity images from micro-CT with the tooth images from CBCT, achieving the lowest ASSD of 0.09 mm [29; 52]. Other evaluation metrics, utilized in only one article, are not described separately in this section.
# Advancing Smart Zero-Carbon Cities: High-Resolution Wind Energy Forecasting to 36 Hours Ahead
### Wind Energy Forecasting  
Wind energy shares a key role in preventing global climate change and supporting the United Nations' Sustainable Development Goals (SDGs). In 2024, global wind power capacity increased by 59.70 GW, bringing the total capacity to 1017.20 GW [1; 2]. The inherent variability of wind makes it an unpredictable energy source [30]. As a result, wind energy has not reached its full potential and cannot consistently deliver power at specific times. Wind power stations, however, can collect Big Data on various meteorological factors such as temperature, humidity, air pressure, wind direction, wind speed, and turbine performance [31]. Numerous devices, sensors, drones, space satellites, and anemometers continuously measure wind and meteorological data. These data can be transformed into valuable insights to enhance wind station management [32; 33].  
The wind power potential (W/m\({}^{2}\)) atlas displayed in Figure 1 provides a visual representation of the wind energy potential with an infrastructure of electricity from 66 kilovolts (kV) to more than 500 kV grids across the globe [34]. The color scale indicates the wind speed and power density at different locations, with darker shades of red representing areas with higher wind speeds and potential for significant wind power generation. The map highlights several regions with favorable wind conditions, which are ideal for installing wind turbines. On the other hand, this map highlights the importance and promising potential of wind energy globally.  
As shown in Figure 2, wind speed and wind power predictions are further grouped according to their time horizons [35; 36]. Weekly forecasts assist in planning maintenance operations at wind farms. Daily forecasts benefit electricity market participants, and intra-day predictions are crucial for short-term markets and maintaining a real-time balance between supply and demand [37; 38].  
Figure 1: Wind power potential atlas in the world map at 100 m height [34].