esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Client Selection in Satellite Federated Learning
## 4 Algorithm
### Client Selection
#### 4.2.4 Optimization Model  
We have proposed the optimization object of CS towards SFL:  
\[\begin{split}\min_{\mathcal{K}}&\left(T_{ggg}+ \frac{1}{K}\sum_{k=0}^{K}\frac{T_{wait}^{k}}{Af_{k}}\right)\\ s.t.&\sum_{k}&\left(T_{wait}^{k}+T_{ k}^{S}\right)\leq T_{m}\\ &\mathcal{K}\subseteq\mathcal{I}\\ & 0\leq K\leq I\end{split} \tag{18}\]  
We cannot exclude the possibility of having no eligible clients at one moment. Therefore, in the constraints, we allow \(K\) equal to 0, which means the server will keep waiting until a suitable client becomes available. In addition, we set a threshold \(T_{m}\) for the total waiting time, which is a hyper-parameter that will be manually adjusted based on the experimental results.  
The above problem can be regarded as a 0-1 knapsack combinatorial optimization problem, where the capacity of the knapsack is \(T_{m}\), the weight of the item \(i\) is denoted by \(W_{i}=T_{i}^{S}+T_{wait}^{i}\), and its value is denoted by \(V_{i}=Af_{k}\). Then, we can solve the optimal CS problem by using Algorithm 3:  
```
1:initialize a 2-dimensional array \(dp_{i,j}\) to storage the max value.
2:for each client \(i=0,1,2\), \(\times s\), \(I\)do
3:for each \(j=0,1,2\), \(\times s\), \(T_{m}\)do
4:if\(i=0\) or \(w=0\)then
5:\(dp_{i,j}=0\)
6:else
7:if\(W_{i-1}\leq j\)then
8:\(dp_{i,j}=\max\Bigl{[}V_{i-1}+dp_{i-1,(j-W_{i-1})},dp_{i-1,j}\Bigr{]}\)
9:else
10:\(dp_{i,j}=dp_{i-1,j}\)
11:endif
12:endif
13:endfor
14:endfor
```  
**Algorithm 3** CS algorithm based on 0-1 knapsack problem
# Buffer with N Policy and Active Management
## 1 Introduction
The central contribution of this study is as follows:  
* A formula for the queue's size distribution (Theorem 1);
* A formula for the energy efficiency, i.e., the long-run time fraction when output radio is off (Theorem 2);
* A formula for the throughput of the mechanism, i.e., the average count of packets transmitted in a time unit (Theorem 3). In addition to that, sample numeric results are shown, illustrating the dependence of the system's performance on the dropping probabilities in the transmission phase, the dropping probabilities in the energy-saving phase, and the critical threshold value. In other examples, solutions to some optimization problems are provided. In these problems, we seek a trade-off between the expected queue size and throughput, expressed by some cost function. The considered model of the system is as general as possible to remain analytically tractable. Specifically, the service/transmission time distribution, which in a real network corresponds to packet sizes, is general in the model of interest. Similarly, the two dropping functions used in the energy-saving and transmission phases are also general and can be chosen arbitrarily. For instance, they can have the form of the shifted Heaviside step function. Assuming this is true, the buffer management policy is, in fact, a passive drop-tail policy. If two such dropping functions are used, then we have the original N policy without active management. Hence, the model studied here encompasses the original model but is much more general than the latter when the dropping functions are not Heaviside steps. The active queue management component of the considered model necessitated the application of a different mathematical approach than in the original model. Namely, in the original policy, when the buffer operates in some phase, say transmission, the stochastic evolution of the system is the same, no matter if the current queue size is 10 or 20 packets. Therefore, the system is vertically homogeneous in each phase. Such homogeneity can be employed to design and solve equations for the characteristics of interest. Whenactive queue management is involved, this homogeneity is disrupted because, in general, the dropping probability is different when the queue is 10, compared to when it is 20. The paper's remaining sections are formatted as follows. In Section 2, the related literature is overviewed. In Section 3, the model of a buffer equipped with N policy and active management is described with the necessary notations. The core contribution is then presented in Section 4. The most demanding derivations, which are divided into three conceptual parts, lead to Theorem 1, with a formula on the queue length distribution. All the remaining derivations are much simpler and based on Theorem 1. They lead to formulae on the energy efficiency and throughput of the mechanism (Theorems 2 and 3). In Section 5, sample results of numerical calculations are displayed and discussed. Finally, some wrapping up comments are collected in Section 6.
## Materials and Methods
### SnowSense Testsites in Canada  
#### 2.2.1 The Island of Newfoundland  
The main region of interest for the development of the SnowSense service was the remote location of the island of Newfoundland (97,000 km\({}^{2}\)), which is a province of Newfoundland and Labrador, Canada (Figure 2). The altitudinal gradient of this island ranges between 0 m and 800 m a.s.l. Newfoundland is primarily characterized by having a subarctic and a humid continental climate, with an annual precipitation ranging from 900 mm on the Northern Peninsula to 1700 mm in the southwestern region of the island [23; 24]. This environment, together with a low population, provides the island of Newfoundland ideal conditions for taking advantage of using run-off for hydropower and building up reservoirs. However, the entire region has so far an insufficient instrumentation with only two functioning SWE measurement stations [25].  
As shown in Figure 2, we had access to several run-off gauges to validate our combined and assimilated hydrological service. In this study, we focus on the Humber and the Exploits catchments (Figure 2), which are of of most interest for the operations of the users. As marked in Figure 2, we set-up seven SnowSense in situ stations in Newfoundland, where two are located in the Exploits catchment and two in the Humber catchment. The hydrology of the catchment of the Humber River is influenced by the large lakes Deer Lake, Grand Lake, Sandy Lake and Hindis Lake. The water flow from Hindis to Grand Lake and to Deer Lake is regulated including the Deer Lake Power Plant (130 MW). Water flow from the Upper Humber area to Reidville is more or less natural.  
Figure 2: Overview on locations of SnowSense in situ stations (red triangles) and run-off stations (purple diamonds), within the Humber, Exploits, and Gender catchments in Newfoundland, Canada. Catchments are outlined with a red line. The locations of the two existing snow water equivalent (SWE) stations are indicated as black dots. The background shows the modelled SWE, which was assimilated by in situ stations and Earth Observation (EO) for the 15 March 2018.  
The hydrology of the Exploits River catchment is strongly influenced by the Red Indian Lake (537 km\({}^{2}\)), dividing the catchment into an upper and a lower part. The release of water from the lake at the Millertown Dam is very dynamic (55-700 m\({}^{3}\)/s) and can be assessed from a downstream station.  
In the framework of the demonstration project, we provided information gained from the SnowSense in situ stations and the combined EO and modelling approach as service to two dedicated demo users. Both demo users, namely Nalcor/NL Hydro and the Water Resources Management Division in the Department of Municipal Affairs and Environment (WRMD), are active in hydropower and flood forecast. During the project demonstration phase, they accessed, monitored and investigated the SnowSense products based on their specific requirements, whereof we present a selection of the results in this paper. For the two catchments as test sites, we set up the combined approach of in situ measurements, EO, and modelling.
# Cooperative Localization Using Distance Measurements for Mobile Nodes
## 6 Conclusions  
In this paper, we have presented an efficient cooperative localization algorithm that is based on MDS. The algorithm provides a practical solution for anchorless localization of mobile nodes using noisy measurements. Unlike traditional MDS algorithms, which suffer from an ambiguity problem, the algorithm that is presented here can solve the flip and rotation ambiguities and accurately estimate the positions of nodes in 2D space. The simulation results demonstrate the accuracy of the algorithm, showing that it outperforms alternative methods. At the same time, the proposed algorithm provides greater efficiency than alternative solutions that operate in an iterative manner by providing a closed-form solution. We point out that the main limitations of this algorithm are twofold. Firstly, as mentioned above, this algorithm has been developed in a 2D scenario, which limits its application in more complex situations. Although one may follow a similar procedure to derive the corresponding algorithm for a more general case, i.e., 3D space, this is non-trivial, as the geometry of the network has more degrees of freedom in the 3D space. Secondly, and in a similar vein to other algorithms, the algorithm proposed here requires inertial navigation to provide the displacement of the moving node. Consequently, a deeper analysis of the error that arises from the inertial navigation system should be taken into account in improving this algorithm. These issues will be addressed in future work.  
Conceptualization, W.L., A.K. and B.M.; Data curation, W.L.; Formal analysis, W.L.; Investigation, B.J. and B.M.; Methodology, W.L.; Project administration, A.K.; Software, W.L.; Supervision, A.K.; Validation, W.L., B.J. and B.M.; Visualization, W.L.; Writing-original draft, W.L.; Writing--review and editing, B.J., A.K., X.W. and B.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 5: The Root-Mean-Square Error (RMSE) for the proposed algorithm, the NLS estimator [37], NLSE, and conventional algorithm proposed in [30; 42] for different numbers of nodes and different noise levels.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Experiment and Analysis
### Segment-Level Analysis of the Proposed Model and the Voronoi Model  
The segment-level comparative analysis between the performance of the proposed model and the Voronoi model across the six pipeline segments, as summarized in Table 2, indicates that the proposed model delivers significant improvements over the Voronoi model in segments S1 and S5, with high ILI data complexity, where defect density, distribution, and interaction challenge the feature matching process. For segments S3 and S6 with moderate complexity, the model delivers marginal improvements, while for low-complexity segments (S2 and S4), both models exhibit perfect performance by maintaining a score of 100% through all metrics.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Segment**} & \multirow{2}{*}{**Complexity**} & \multicolumn{3}{c}{**Proposed Model**} & \multicolumn{3}{c}{**Point Matching (Voronoi Model)**} \\ \cline{3-8}  & & **Recall \%** & **Precision \%** & **F1 Score \%** & **Recall \%** & **Precision \%** & **F1 Score \%** \\ \hline S1 & High & 77.0 & 92.2 & 83.9 & 60.7 & 82.2 & 69.8 \\ \hline S2 & Low & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 \\ \hline S3 & Moderate & 95.7 & 100.0 & 97.8 & 91.5 & 95.6 & 93.5 \\ \hline S4 & Low & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 \\ \hline S5 & High & 86.0 & 97.4 & 91.4 & 69.8 & 96.8 & 81.1 \\ \hline S6 & Moderate & 98.1 & 100.0 & 99.0 & 98.1 & 100.0 & 99.0 \\ \hline \multicolumn{2}{c}{**Overall**} & 91.5 & 98.0 & 94.6 & 84.4 & 95.4 & 89.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Segment-level performance comparison between the proposed model and the Voronoi model [7] using \(\varepsilon_{x}=0.220\) m, \(\varepsilon_{y}=0.110\) m, \(\lambda=0.220\) m, \(\delta=0.110\) m, \(\alpha\) (proposed model) = 0.043, \(\alpha\) (Voronoi model) = 0.080, and \(\tau=0.001\)—case study.  
Figure 21: Sensitivity of the proposed model to the merging distance threshold \(\lambda\) using \(\varepsilon_{x}=0.220\) m, \(\varepsilon_{y}=0.110\) m, \(\delta=0.110\) m, \(\alpha=0.043\) m, and \(\tau=0.001\)—case study.
# Optimized Polar Codes as Forward Error Correction Coding for Digital Video Broadcasting Systems
## 3 Design Point Selection Method  
This section discusses a selection method for the optimized SNR design for the AWGN channel. Polar code construction is dependent on the selected value of \(2{x_{0}}^{2}/\sigma^{2}\) as shown before in Equations (3) and (4), which is related to SNR = \({x_{0}}^{2}/2\sigma^{2}\). However, for AWGN channels, the Bhattacharyya \(Z\) parameter can be calculated as follows:  
\[\begin{array}{rl}Z&=\int\limits_{-\infty}^{\infty}\sqrt{W(y|0)W(y|1)}dy\\ &=\int\limits_{-\infty}^{\infty}\sqrt{\frac{1}{\sqrt{2\pi\sigma^{2}}}\;e^{- \frac{(y-x_{0})^{2}}{2\sigma^{2}}\frac{1}{\sqrt{2\pi\sigma^{2}}}\;e^{-\frac{(y +x_{0})^{2}}{2\sigma^{2}}}}}dy\\ &=e^{-\frac{x_{0}^{2}}{2\sigma^{2}}}\end{array} \tag{13}\]  
Figure 3: Adaptive SCL Decoder flowchart.  
This equation demonstrates that polar code construction based on Bhattacharyya parameter depends also on the designed SNR = \(x_{0}{{}^{2}}/2\sigma^{2}\) value, which is related to the mean value for the transmitted signal energy \(x_{o}\), and the noise standard deviation \(\sigma\). The ratio of the standard deviation \(\sigma\) to the mean value \(x_{o}\) defines the coefficient of variation \(C_{v}\), also known as relative standard deviation (RSD). It measures the dispersion in the probability distribution and shows the correlation between the average value and the amount of variation around it. The main idea of polar codes is the creation of synthetic channels, and each synthetic channel becomes either a pure-noise channel or noise-free channel. Our target is to construct good polar codes with optimum channel polarization with respect to any change in RSD at the design point. Thus, the rate of change in the channel reliability measured by Bhattacharyya parameter \(Z\) with respect to RSD should be maximized. To carry out this maximization, we compute Bhattacharyya parameter rate of change as the first derivative of \(Z\) with respect to \(C_{v}\) as follows:  
\[\frac{dZ}{dC_{v}}=\frac{1}{{C_{v}}^{3}}e^{-\frac{1}{2C_{v}^{2}}} \tag{14}\]  
Thus, the second derivative of Bhattacharyya parameter \(Z\) equals to zero as follows:  
\[\frac{d^{2}Z}{{dC_{v}}^{2}}=\frac{-3}{{C_{v}}^{4}}e^{-\frac{1}{2C_{v}^{2}}}+ \frac{1}{{C_{v}}^{6}}e^{-\frac{1}{2C_{v}^{2}}}=0 \tag{15}\]  
Accordingly, the optimum design value of the coefficient of variation equals:  
\[C_{v}=\frac{1}{\sqrt{3}} \tag{16}\]  
This mathematical relation is used to get the optimized SNR design point that is required to construct the optimized polar code according to its code-rate. Moreover, in Figure 4, it is observed that \(Z\) is an increasing function with an inflection point at \(C_{v}=\frac{1}{\sqrt{3}}\) from concave up to concave down which coincides with Equation (16).  
Figure 4: Bhattacharyya parameter optimized design point for AWGN channel.  
To confirm the result of the proposed mathematical relation, a simulation comparison for polar code construction for different coefficient of variation \(C_{v}\) is established in simulation results section.
## 1 Introduction
These values included errors and did not take into account numerical uncertainties in changing circumstances that may take place at the actual sea area in which a number of ships with different maneuverability were dynamically navigated. Accordingly, the T1 FIS did not compute appropriate _CRI_ according to numerical changing of \(D_{CPA}\) and \(T_{CPA}\) in order that navigator could take an action for collision avoidance at optimal distance and time [15; 16; 17; 18; 19]. Therefore, in case the Target-Ship (TS) is approaching to the OS, it would cause that navigator may have difficulty to make an optimal decision for taking an action for collision avoidance based on positioning and timing accuracy. Unlike the type-1 fuzzy logic, the type-2 fuzzy logic [20; 21; 22], which consisted of fuzzy degrees of membership that can take into consideration uncertainties, can help to overcome uncertainties that exist in real changing circumstances. In particular, Fuzzy Inference System based on the Interval Type-2 Fuzzy Logic (IT2 FIS) in mobile navigation requiring positioning and timing accuracy in a dynamic and unknown environment prevented random errors that change unpredictably, and consequently, taking an action for collision avoidance at optimal distance and time showed smooth trajectory, reduction of path deviation, and saving time [23; 24; 25; 26; 27; 28; 29]. At this time, the interval type-2 membership functions are created from deviation of the parameters of the original type-1 membership functions by a percentage of the universe of discourse of the variables that they are associated to. However, these did not take into consideration systematic way of determining uncertainties on membership function. Hence, the application of appropriate uncertainties is needed to develop the IT2 FIS. In this paper, we propose the IT2 FIS in order that the navigator can obtain appropriate _CRI_ in unknown and dynamic circumstances for purpose of efficient taking an action for collision avoidance. This paper has essentially two parts: In the first part, the IT2 FISs are generated by gradually increasing the uncertainty size taking into consideration symmetry over a number of steps. In the second part, from the beginning of the encounter situation to the collision situation, the IT2 FISs are observed and compared how well the appropriate _CRI_ is being computed according to changing \(D_{CPA}\) and \(T_{CPA}\) at optimal positioning and timing. At this time, because the number of collision accidents was very few, near-collision, in which there is the danger of collision between ships approaching each other, but with no collision eventually occurring, is used. The remainder of the paper is organized as follows: Section 2 describes collision avoidance method based on the collision risk. Section 3 presents the IT2 FISs using different uncertainty size. In Section 4, numerical experimental results and discussion are conducted. Finally, a summary of the work and the conclusion are drawn in Section 5.
## 1 Introduction
Altogether, the synergy between IoT, DTs, and VR tools amplifies the impact on urban planning, governance, and sustainability [17]. While each of these technologies (IoT, DTs, and VR) individually contributes to the smart city ecosystem, their true potential is achieved through seamless integration, i.e., interoperability [18]. The use of a common data model for IoT devices enables consistent interpretation and utilization of data across various platforms, applications, and smart city services. Current efforts among standardization bodies and industry associations are focused on the importance of metadata to build comprehensive data models that enable interoperability [19]. There seems to be an alignment between many of them, creating an ecosystem definition where oneM2M, FIWARE, ETSI ISG CIM, and OMA LwM2M are co-existing and playing a clear complementary role [20]. There are a few research works focused on erasing urban services silos. Most of them aim at the use of semantic interoperability in different application fields such as energy [21], agriculture [22], or building management [23]. Other approaches concentrate on the integration of BIM and IoT, both for building [24], public facilities [25], and cities [26]. Finally, there are initiatives that work above the IoT platform level, focused on data integration [27], analytics [28], and applications [29]. However, to the best of our knowledge, there is not a previous work that proposes the combined use of IoT, DTs, and VR technologies towards breaking down the barriers between different urban service domains to enable integrated and collaborative approaches to city management. The objective of this paper is to present the design principles and practical implementation of a DT concept for management of cross-domain smart city services. The DT solution, which has been developed in the framework of the IoTMADLab initiative, consists of a VRtool that creates a virtual replica of the real environment and the main functional features of the IoT devices. This virtual environment offers intuitive interaction to perform typical monitoring tasks through Head Mounted Displays (HMDs) and its controllers. The rest of the paper is organized as follows: Section 2 introduces the IoTMADLab initiative, explains the methodology followed to develop the work, and provides some insights about the proposed IoT network architecture. Section 3 describes the main components of the DT platform, at both physical and virtual level, the technologies used for its deployment and the interconnections between the different elements. Section 4 presents the services currently implemented for both monitoring and controlling urban assets. Finally, Section 5 summarizes the main achievements and benefits of the proposed work while Section 6 discusses the future research lines to expand the DT capabilities.
## 6 Results
Should these results be cross-examined with the transmission powers, it is clear that players increase their transmission powers to accomplish higher utilities (e.g., player 2), while in other cases, the players select a smaller transmission power level since it is not in their incentive to increase it because it will cause more interference (e.g., player 5). In Figure 5, the reader can observe the evolution of the utilities of each player with SIC and non-SIC. In Table 2, the reader can see the final values of the utilities of each player. Players achieve higher SINR values overall with SIC, as interference is mitigated more effectively through the successive decoding process. Player 2 has the highest SINR of 0.4556, likely benefiting from favorable channel conditions or a lower interference environment. SINR values with non-Sic are uniformly lower than in the SIC scenario due to the cumulative impact of interference from all players. The highest SINR in this case is 0.2832 for Player 5, reflecting the inability of players to decode and cancel out other signals effectively. Overall, the SIC mechanism demonstrates its advantage by enabling better SINR performance for all players, as shown by consistently higher values in the SIC column compared to No SIC. This improvement emphasizes the effectiveness of SIC in enhancing signal quality in multi-user environments. Figure 3: Transmission power evolution. Also, a Deep Reinforcement Learning (DRL) algorithm has been implemented to show the differences with the proposed method. The potential function over the iterations can be seen in Figure 6. Here, the observation is that the potential function is not that smooth compared to the game theoretic algorithm even though it may seem that it surpasses it. Moreover, a general comment is that the method takes too long to produce results. Moreover, another plot using the DRL, Figure 7, illustrates the utility of each player. It is evident that two of the players accomplish quite high utility while the remaining three accomplish very small utility. This may be a result of the learning process of the game theoretic algorithm. Figure 4: Final utilities of players with respect to SINR. Figure 5: Utility based on SINR over iterations.