esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Final Considerations  
The proposed problem was the optimization of COVID-19 vaccine distribution routes in Central Portugal, minimization time, total cost, and CO\({}_{2}\) emissions, all while considering a capacity constraint. An investigation and study were conducted to examine the existing algorithms that solve this type of problem. It was concluded that the most adequate model to solve the problem at hand would be the generic VRPTW with a capacity constraint.  
A Python 3.10.4 code was developed and run, and the following results were obtained: a total of 11 vehicles; a total cost of EUR 31,949.56; and a total of 998,600.00 g of CO\({}_{2}\) emissions.  
In conclusion, the optimization of vaccine transportation networks can and should be considered. These methods of decision support ensure that the vaccines arrive preserved at the different vaccination centers. All the crucial factors that guarantee the vaccine's viability (such as time) are considered in these methods. In addition, these methods translate into enormous monetary savings, and in most of the clusters, huge savings in greenhouse gas emissions. It is also important to note the main limitation when executing the procedure described in this article: the need to round some numbers and use a mean value due to the inaccessibility of exact data. Specifically, it was difficult to estimate (as seen in Section 2.3) the quantitative needs of each center for the vaccine due to confidentiality. Exact data would produce an even more optimized solution. However, with the values adopted it is possible to see how the optimization of the vaccine transportation networks can be performed.  
In future works, it would be interesting if this decision support method could be adapted and improved. For example, it would be beneficial if each driver was be able to follow real-time information through a mobile application. This application could also control their geolocation, and if there was any traffic impediment, the algorithm could calculate a new optimal route. A real-time calculation and the ability to continuously seek the most optimal route that ensures vaccine viability would save time and guarantee the integrity of the vaccines.  
Conceptualization, E.S., C.O. and J.P.; methodology, E.S., C.O. and J.P.; software, C.O. and J.P.; formal analysis, T.M.L. and P.D.G.; investigation, E.S., C.O. and J.P.; data curation, E.S., C.O. and J.P.; writing--original draft preparation, E.S., C.O. and J.P.; writing--review and editing: T.M.L. and P.D.G.; supervision, T.M.L. and P.D.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors confirm that the data supporting the findings of this study are available within the article.  
This work was supported in part by Fundacao para a Ciencia e Tecnologia (FCT) and C-MAST (Centre for Mechanical and Aerospace Science and Technologies), under project UIDB/00151/2020.  
The authors declare no conflict of interest.
## 3 Diversity and Reliability
### Outage Probability Analysis
#### 3.1.3 Maximum Ratio Transmission  
_MRT is the scheme where the typical_ \(\text{RRH}_{0}\)_, as well as the_ \(k\) _cooperating_ \(\text{RRH}_{k}\)_, are jointly coordinated in transmission to the UE. Under this scheme, we define the SIR from (_1_) as_  
\[\text{SIR}_{\text{MRT}}=\frac{h_{0}d_{0}^{-\alpha}+\sum\limits_{c=1}^{k}h_{c}d ^{-\alpha}}{\sum\limits_{j=k+1}^{N}h_{j}d^{-\alpha}}, \tag{8}\]  
_and we express the outage probability in Theorem_ 3_._  
**Theorem 3**.: _The CDF of the_ SIR _in the case when UE is served through MRT is_  
\[\begin{array}{ll}F_{\text{SIR}_{\text{MRT}}}(\theta)&=\frac{\Gamma(N)}{ \Gamma(N-k)}\theta^{k}\Bigg{(}1+\delta^{\alpha}\theta\Bigg{)}^{-N}\times \Bigg{(}\Big{(}1+\delta^{\alpha}\theta\Big{)}^{N}{}_{2}F_{1}(k,N;1+k;-\theta) \\ &-{}_{2}F_{1}\bigg{(}k,N;1+k;\frac{(-1+\delta^{\alpha})\theta}{1+\delta^{ \alpha}\theta}\bigg{)}\Bigg{)}.\end{array} \tag{9}\]  
_valid for all \(1\leq k\leq N\) and \(\delta<1\)._  
Proof.: Please refer to Appendix C.  
The expected value of \(\text{SIR}_{\text{MRT}}\) is given as  
\[\begin{array}{ll}\mathbb{E}[\text{SIR}_{\text{MRT}}]&=\int_{0}^{\infty}(1-F _{\text{SIR}_{\text{MRT}}}(\theta))d\theta,\\ &\stackrel{{(a)}}{{=}}\int_{0}^{\infty}\Bigg{(}1-\Bigg{(}\frac{ \Gamma(N)}{\Gamma(N-k)}\theta^{k}\Bigg{(}1+\delta^{\alpha}\theta\Bigg{)}^{-N} \times\Bigg{(}\Big{(}1+\delta^{\alpha}\theta\Big{)}^{N}{}_{2}F_{1}(k,N;1+k;- \theta)\\ &-{}_{2}F_{1}\bigg{(}k,N;1+k;\frac{(-1+\delta^{\alpha})\theta}{1+\delta^{ \alpha}\theta}\bigg{)}\Bigg{)}\Bigg{)}\Bigg{)}d\theta,\\ &=\frac{\Gamma(N-k-1)(k+\delta^{-\alpha})}{\Gamma(N-k)}\end{array} \tag{10}\]  
where (_a_) comes after substituting the respective CDF. Using the \(\copyright\)Mathematica integral package to solve the hypergeometric function in (_a_), we have (10). In (10), as the value of \(k\) increases, the numerator increases proportional to the \(k\) and this provides additional diversity gain in MRT with increases in \(\mathbb{E}[\text{SIR}_{\text{MRT}}]\) as compared to TAS and silencing.  
**Remark 1**.: _Notice that by taking \(k=0\) when there is no cooperation, all the considered transmission schemes represent the case of full interference._
# Effect of Laser Parameters on Optical Stealth Transmission System Performance
###### Abstract  
The performance of an optical stealth transmission system based on gain-switched laser depends largely on the laser parameters. Modulation frequency, bias current, and modulation current are considered to study the covertness and bit error rate performance of the optical stealth transmission system. According to optical stealth carrier generation with time spreading and all-optical encoding, the stealth signals are derived. A complementary encoding scheme is adopted in the system simulation. The simulation results show that the temporal and spectral characteristics of the generated stealth signal can be changed by adjusting the bias current, modulation current, and modulation frequency. However, there is a trade-off between bit error rate performance and covertness of the stealth channel. Under the premise of error-free transmission, the bias current and modulation frequency should be reduced and the modulation current should be improved to optimize the covertness of the stealth channel.  
semiconductor lasers; system performance; optical stealth transmission; gain-switching +
Footnote †: journal: journal: Physics Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.  
are studied by system simulations and the effect of laser parameters are evaluated and discussed. Conclusions are presented in the last section.
# Weighted \(z\)-Distance-Based Clustering and Its Application to Time-Series Data
## 2 Self-Constructing Clustering (SCC)  
Suppose \(\mathbf{X}=\{\mathbf{x}_{i}|1\leq i\leq N\}\) is a finite set of \(N\) unlabeled training instances, where \(\mathbf{x}_{i}=[x_{i,1}\ \cdots\ x_{i,n}]^{T}\in\mathbb{R}^{n}\) is the \(i\)th instance. Each instance is a vector with \(n\) features. SCC [20; 55] does clustering in a progressive way. Only one training cycle on the  
* If all the MD's are less than \(\rho^{n}\), where \(\rho\) is a pre-specified similarity threshold in one dimension, i.e., \[\exp\{-Z(i,j)\}<\rho^{n}\] (3) for all existing clusters \(C_{j}\), \(1\leq j\leq J\), a new cluster, \(C_{j+1}\), is created and instance \(i\) is assigned to it by \[\mathbf{c}_{J+1}=\mathbf{x}_{i},\mathbf{\sigma}_{J+1}=\left[\sigma_{0}\ \cdots\ \sigma_{0}\right]^{T},S_{J+1}=1,J\gets J+1\] (4) where \(\sigma_{0}\) is a pre-specified constant.
* Otherwise, instance \(i\) is assigned to the cluster with the largest MD, say cluster \(C_{a}\), i.e., center \(\mathbf{c}_{a}=\left[c_{a,1}\cdots c_{a,n}\right]^{T}\), deviation \(\sigma_{a}=\left[\sigma_{a,1}\cdots\sigma_{a,n}\right]^{T}\), and size \(S_{a}\), are updated by \[\sigma_{a,k}\leftarrow\left\{\frac{\left(S_{a}-1\right)\left(\sigma_{a,k}- \sigma_{0}\right)^{2}+S_{a}c_{a,k}^{2}+x_{ijk}^{2}}{S_{a}}-\frac{S_{a}+1}{S_{a }}\bigg{[}\frac{S_{a}c_{a,k}+x_{ijk}}{S_{a}+1}\bigg{]}^{2}\right\}^{\frac{1}{2 }}+\sigma_{0},\ 1\leq k\leq n,\] \[\mathbf{c}_{a}\leftarrow\frac{S_{a}\mathbf{c}_{a}+\mathbf{x}_{i}}{S_{a}+1},\] (5) \[S_{a}\gets S_{a}+1.\] Note that \(J\) is not changed in the latter case.  
When all the instances have been considered, SCC stops with \(J\) clusters. Algorithm 1 can be summarized below.  
```
for each instance, instance \(i\), \(1\leq i\leq N\)  Compute \(Z(i,j)\), \(1\leq j\leq J\) ; if Equation (3) holds  Create a new cluster according to Equation (4); else  Instance \(i\) is assigned to the cluster with largest MD, according to Equation (5); endif endfor end SCC
```  
**Algorithm 1** SCC  
Note that SCC takes the training set **X** as input and outputs \(J\) clusters \(C_{1},\cdots,C_{J}\). SCC runs fast due to running through the training instances only once. Unlike K-means, the similarity threshold, instead of the number of clusters, is specified in advance by the user.
###### Abstract  
As supply chains become increasingly digitized and decentralized, ensuring security, traceability, and data integrity has emerged as a critical concern. Blockchain technology has shown significant potential to address these challenges by providing immutable records, transparent data flows, and tamper-resistant transaction logs. However, the effective application of blockchain in real-world supply chains requires the careful evaluation of both architectural design and technical limitations, including scalability, interoperability, and privacy. This review systematically examines existing blockchain-based supply chain solutions, classifying them based on their structural models, cryptographic foundations, and storage strategies. Special attention is also given to underexplored humanitarian logistics scenarios. It introduces a three-dimensional evaluation framework to assess security, traceability, and integrity across different architectural approaches. In doing so, it explores key technological enablers, including advanced mechanisms such as zero-knowledge proofs (ZKPs) and cross-chain architectures, to meet evolving privacy and interoperability demands. Furthermore, this study outlines a conceptual cross-chain interaction scenario involving permissioned and permissionless blockchain networks, connected through a bridge mechanism and supported by representative smart contract logic. The model illustrates how decentralized stakeholders can interact securely across heterogeneous blockchain platforms. By integrating quantitative metrics, architectural simulations, and qualitative analyses, this paper contributes to a deeper understanding of blockchain's role in next-generation supply chains, offering guidance for researchers and practitioners aiming to design resilient and trustworthy supply chain management (SCM) systems.  
blockchain; supply chain management; security; traceability; data integrity; smart contracts; cross-chain security; interoperability; transparency; disaster relief +
Footnote †: journal: Applied Sciences
## 1 Introduction
Also scene-based methods, including geostatistical methods, have been used to investigate the effects of time-series smoothing [19]. However, despite the multitude of studies there is a lack of agreement regarding the choice of time-series smoothing methods. To investigate the usefulness of time-series smoothing methods it is appropriate to test the ability of the methods to reconstruct the seasonal variation of the temporal signal. This can best be done by directly relating it to ground measurements. Today, growing networks of ground measured reflectance measurements exist, suitable for process studies and validation of remotely sensed vegetation index data [45; 46]. Furthermore, some indirect data may provide useful time-series records for establishing the ability of different time-series methods to portray the vegetation dynamics. One of these is gross primary productivity (GPP) estimated from carbon flux measurements. This is based on the assumption that GPP is related to the satellite derived NDVI. The relationship between NDVI and fAPAR has been investigated by [PERSON], et al. [47] and [PERSON], et al. [48], and it has been used as major input in models to estimate GPP [2; 3; 49]. This relationship implies that GPP is a useful reference for evaluating the abilities of different methods for smoothing satellite NDVI. Also spatial data may be useful for investigating the different methods. It is well known that topography influences vegetation phenology due to adiabatic temperature difference with height. At high elevation of temperate and boreal regions, slow thermal accumulation leads to delayed canopy development [50; 51] and generally shorter growing seasons than those at low elevation. Based on this concept, a number of studies have used elevation gradients to spatially investigate phenology from satellite data e.g., [15; 52; 53]. Given the difficulty in drawing firm conclusions about different smoothing methods from previous studies, this study aims to address the following questions: (1) is it possible to define one smoothing method that is always best suited, irrespective of situation; (2) if not, what capability do the different methods have for smoothing data in different situations. To achieve this aim, we evaluate the performance of smoothing algorithms for representing seasonal vegetation growth with 8-day MODIS NDVI data by employing a variety of reference data sets. We systematically analyze five different smoothing methods: Savitzky-Golay filtering (SG), locally weighted regression scatterplot smoothing (LO), spline smoothing (SP), least-squares fitting to asymmetric Gaussian functions (AG), and least-squares fitting to double logistic functions (DL), against three different reference data sets: ground measured NDVI from ten locations, ground measured GPP from four sites, and elevation data from an uphill area with a vegetation gradient. Altogether these analyses will support our aim and allow better understanding of how to choose suitable time-series smoothing methods for remote sensing phenology studies.
## 2 TH-2 Spaceborne GNSS Data Assessment
### Quality of GNSS Observations  
#### 2.3.1 Carrier-to-Noise Ratio  
The signal strength can be measured by the carrier-to-noise ratio (C/N\({}_{0}\)), which shows the ratio of the carrier signal power to the noise power in a 1 Hz bandwidth [25]. Figure 5 indicates that the average C/N\({}_{0}\) values of all signals increase as the elevation angle increases. For the GPS, the C/N\({}_{0}\) values of the L1 frequency are larger than those of the L2 at all elevations, which is attributed to the differences in their tracking method. For the BDS2, the B3 frequency has about 2-4 dB\(\cdot\)Hz larger C/N\({}_{0}\) values than the B1. In addition, it can be noted that the C/N\({}_{0}\) values for the MEOs are slightly larger than for the GEOs and IGSOs, which may be due to the higher free-space loss of the GEOs and IGSOs than for the MEOs.  
Figure 4: Statistics of the average number of available GPS and BDS2 satellites per epoch for TH-2A and TH-2B.  
Figure 3: Average number of GPS (**left panel**) and BDS2 (**right panel**) satellites observed along TH-2A orbits from DOY 305 to 310 in 2019.
## 2 Problem Definition
### Problem Modeling
#### 2.2.1 User Requests  
Satellite imaging missions commence with user requests, and in the scheduling of these missions, the requirements of the users are the most critical considerations. Therefore, the foremost factor to prioritize in mission scheduling is the parameters related to the targets requested for imaging by the users. In this study, \"significance \((s_{i})\)\" is a measure that reflects the hierarchy of importance of the targets desired for imaging from the user's standpoint, while \"urgency \((u_{i})\)\" indicates the time sensitivity concerning the user's need for images of the target. To enhance our mission scheduling approach, we adopt the Eisenhower matrix [42] as a guiding framework in Figure 3. This matrix, dividing tasks based on their significance and urgency, creates a comprehensive nine-cell grid, each cell representing a combination of low, medium, and high levels of these two dimensions, with low assigned a value of 0.3, medium a value of 0.6, and high a value of 0.9. The profit \((p_{i})\) derived from imaging a target is then determined by combining the target's significance and urgency with the weighting factors \((\alpha,\beta)\). Specifically, the profit for a target with the highest significance and urgency is calculated as \(\alpha\times 0.9+\beta\times 0.9\), reflecting the structured approach to prioritize satellite imaging tasks. This enables a nuanced prioritization of imaging tasks, facilitating a strategic allocation of satellite imaging resources to address the most pressing and significant targets first. This strategic application of the matrix is expressed in the following Equation (1):  
\[p_{i}=\alpha s_{i}+\beta u_{i} \tag{1}\]  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Variable** & **Definition** \\ \hline \(i\) & Index number of target candidates, \(i\in\{1,...,|I|\}\) \\ \(j\) & Index number of satellites, \(j\in\{1,...,|J|\}\) \\ \(k\) & Index number of visible time windows, \(k\in\left\{1,...,\left|V_{ij}\right|\right\}\) \\ \(l\) & Index number of time intervals, \(l\in\left\{1,...,\left|T_{j}\right|\right\}\) \\ \(I\) & Set of target candidates \\ \(J\) & Set of satellites \\ \(V_{ij}\) & Set of visible time windows of target \(i\) by satellite \(j\) \\ \(T_{j}\) & Set of time intervals by satellite \(j\) \\ \(v_{ijk}\) & \(k\)th visible time window of target \(i\) by satellite \(j\) \\ \(t_{jl}\) & \(l\)th time interval by satellite \(j\) \\ \(x_{ijk}\) & Decision variable of target observation in \(v_{ijk}\) \\ \(t_{jl}^{*}\) & Start time of \(t_{jl}\) \\ \(t_{jl}^{c}\) & End time of \(t_{jl}\) \\ \(t_{jl}^{*}\) & Start time of observation in \(v_{ijk}\) \\ \(t_{jl}^{*}\) & End time of observation in \(v_{ijk}\) \\ \(t_{ijk}^{*}\) & Observation time duration in \(v_{ijk}\) \\ \(t_{j}^{*}\) & Gap time of satellite \(j\) \\ \(d_{j}\) & Duty time per pass of satellite \(j\) \\ \(p_{i}\) & Profit obtained when observing target \(i\) \\ \(s_{i}\) & Significance measure of target \(i\) \\ \(u_{i}\) & Urgency measure of target \(i\) \\ \(\alpha,\beta\) & Weighting factor \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notation.
## 1 Introduction
The key novel contribution of this paper is related to the architectural principles for the autonomic M2M service networks relying on the horizontal approach, created key enablers called as autonomic M2M manager, M2M service capabilities, M2M messaging system, M2M gateways towards energy constrained M2M asset devices and creation of trust to enable end-to-end security for M2M applications and their experimental based evaluation with three different cases dealing with smart metering, car sharing and electric bike systems. The same type of challenges have also been focused e.g., within FI-Ware [51] Public Private Partnership (PPP) project, which has created an IoT platform, architecture and a set of generic elements related to cloud hosting, data/context management, IoT services enablement, application/services ecosystem and delivery framework, security and Interface to Networks and Devices (I2 ND). However, it is seen that the approaches are different in the sense that we rely more on open standards and have an open multiple stakeholder system as the goal, and FI-Ware is more relying on open application programming interface (API) based implementations of specific industrial companies. There are also other projects which are/have been working in the area such as e.g., Hydra [52], Runes [53], IoT-A [54], iCore [55] and Sofia [56]. Each of these projects has specific contributions to be added, however, they have still not solved the described grand challenges related to the technological complexity and vertical M2M silos. The rest of this article is organized as follows. The architectural principles are defined in Section 2. The key building blocks are described in Section 3. Experimental evaluations are discussed in Section 4. Finally, conclusions are provided in Section 5.
# Entropy Weighted TOPSIS Based Cluster Head Selection in Wireless Sensor Networks under Uncertainty
## 3 Some Assertions and Symbols  
We have considered a WSN here under the following assertions:  
* Nodes are distributed at random places inside a square area;
* The base station is positioned outside the square's bounds, enabling communication with nodes inclined to multi-path attenuation. Multi-path attenuation does not influence communication between nodes;
* The nodes are cohesive because they share the same capabilities and initial battery energy while performing different tasks depending on the time of day;
* Communication between any node, the BS, or any other node is possible.
* The nodes are immobile;
* Every node senses its environment and emits a signal of the same length;
* Numerous aspects of sensor nodes, including the primary energy of nodes, the distance between sensor nodes and receiving stations, the size of information packets, and estimates of voltage and transmission power, among others, have imprecise values due to erratic/dangerous natural conditions.  
Table 1 provides a list of symbols used in the paper.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Description** \\ \hline \(db\) & Distance to the base station \\ \(db_{0}\) & Fixed measuring distance to the base station \\ \(ds\) & Distance from the sink \\ \(dn_{c}\) & A node’s distance from each node in a cluster or its number of neighbors \\ \((X,Y)\) & Position of CHs in a WSN \\ \((W_{x},W_{y})\) & Position of nodes in a WSN \\ \(l_{energy}\) & Initial energy \\ \(EL_{e}\) & Electronics energy \\ \(ET_{e}\) & The energy used for data transmission \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of symbols.