esa-sceva/satcom-chunk-collection huggingface dataset:
# On Maximizing the Probability of Achieving Deadlines in Communication Networks
###### Abstract  
We consider the problem of meeting deadline constraints in wireless communication networks. Fulfilling deadlines depends heavily on the routing algorithm used. We study this dependence generically for a broad class of routing algorithms. For analyzing the impact of routing decisions on deadline fulfillment, we adopt a stochastic model from operations research to capture the source-to-destination delay distribution and the corresponding probability of successfully delivering data before a given deadline. Based on this model, we propose a decentralized algorithm that operates locally at each node and exchanges information solely with direct neighbors in order to determine the probabilities of achieving deadlines. A modified version of the algorithm also improves routing tables iteratively to progressively increase the deadline achievement probabilities. This modified algorithm is shown to deliver routing tables that maximize the deadline achievement probabilities for all nodes in a given network. We tested the approach by simulation and compared it with routing strategies based on established metrics, specifically the average delay, minimum hop count, and expected transmission count. Our evaluations encompass different channel quality and small-scale fading conditions, as well as various traffic load scenarios. Notably, our solution consistently outperforms the other approaches in all tested scenarios.  
ad hoc networks; deadline achievement probability; delay-aware routing; real-time systems; time-critical communications +
Footnote †: journal: Sensor and Actuator Networks
# Using Optimization Techniques in Grammatical Evolution
## 2 The Proposed Method
### The Neural Network Construction Algorithm  
The main steps of the algorithm used to construct artificial neural networks with Grammatical Evolution are listed below:  
1. **Initialization** step: 1. **Set**\(N_{g}\) as the maximum number of generations allowed. 2. **Set**\(N_{c}\) as the number of chromosomes. 3. **Set**\(p_{s}\) as the selection rate and \(p_{m}\) as the mutation rate. 4. **Define** as \(L_{i}\) the number of generations that should elapse before applying the local optimization technique. 5. **Define** as \(L_{c}\) the number of chromosomes involved in the local search procedure. 6. **Initialize** the chromosomes. Each chromosome is considered as a series of randomly initialized integers. 7. **Set** iter=0.  
Figure 2: Flowchart of the used Simulated Annealing variant.  
2. **Genetic** step: 1. **For**\(i=1,\ldots,N_{g}\)**do** 1. **Create** for every chromosome a neural network \(C_{i}\) using the Grammatical Evolution procedure of Section 2.1 and the associated grammar given in Figure 1. 2. **Calculate** the fitness \(f_{i}\) on the train set of the objective problem as: \[f_{i}=\sum_{j=1}^{M}\bigl{(}C_{i}\left(\overrightarrow{x}_{j}\right)-t_{j} \bigr{)}^{2}\] (4) where the set \(\left(\overrightarrow{x}_{j}^{\prime},t_{j}\right)\), \(j=1,...M\) is train dataset, with \(t_{i}\) being the actual output for the point \(\overrightarrow{x}_{i}^{\prime}\). 3. **Perform** the selection procedure. Initially, the chromosomes are sorted according to their fitness values. The best \((1-p_{s})\times N_{c}\) chromosomes are transferred to the next generation. The remaining chromosomes will be replaced by offspring created during the crossover procedure. 4. **Perform** the crossover procedure. The crossover procedure produces \(p_{s}\times N_{c}\) offspring. For every pair of produced offspring \(\hat{z}\) and \(\widehat{w}\), there are two offspring \((z,w)\). The selection is performed using tournament selection. The new offspring are produced using the one-point crossover procedure. An example of the one-point crossover procedure is shown in Figure 3. 5. **Perform** the mutation procedure. For each element of every chromosome, a random number \(r\in[0,1]\) is drawn. The corresponding element is altered if \(r\leq p_{m}\). 6. **EndFor**
3. **Local Search step:** 1. **If**\(\text{iters}\mod\ L_{i}=0\), **Then** 1. **Create** a set of randomly \(L_{\text{C}}\) chosen chromosomes from the genetic population. Denote this set as \(L_{S}\). 2. **For** every \(X_{k}\)**in**\(L_{S}\) apply the modified Simulated Annealing algorithm given in Algorithm 1: \(X_{k}=\text{sim}(X_{k})\) 4. **set** iter = iter + 1. **If**\(\text{iter}>N_{g}\)**goto** Evaluation step else **goto** Genetic step. 5. **Evaluation** step: 1. **Obtain the chromosome with the lowest fitness value and create the associated neural network \(C^{*}\). 2. **Evaluate the neural network \(C^{*}\) in the test set of the underlying dataset and report the results.**  
Figure 3: An example of the one-point crossover method, used in the Grammatical Evolution procedure.
# Simulation Tool for Winter Navigation Decision Support in the Baltic Sea
## 6 Implementation
In the current version, the service times of vessels have been estimated based on distributions obtained from historical data analysis. The service time (gross berthing time) is not relevant to decisions regarding icebreaker assistance as described in this article. The simulation framework here briefly shows the capability of also incorporating berthing operations-related parameters, which is a possible extension of this decision-making tool. The decisions addressed by those processes would be different from the ones mentioned in this article. However, having both these systems in one simulation model enables the user to observe  
Figure 4: Statechart for icebreaker agents. interactions (if any) and capture feedback information between one process and another. Port capacities have been estimated by the practitioners and traffic controllers. In the second process flow (B), sequence of action for icebreakers at ports is modelled. Icebreakers need to regularly visit ports for crew change, provisions, and refueling. They can choose to visit any port from a list of acceptable ports. For routine visits, their downtimes are estimated from previous data, varying from 6 to 12 h. The icebreakers need to visit a port once every 10 days during the winter months. Not all icebreakers can visit a port at the same time so as to ensure that there are enough icebreakers available to support the traffic.
# Addressing the Potential of L5/E5a Signals for Road ITS Applications in GNSS-Harsh Environments +
## 1 Introduction  
With the release of Android Nougat in August 2016, the raw GNSS measurements on L1 (1575.42 MHz) became available to users on an increasing number of low-cost devices. Since then, research from various centers worldwide have tested and analyzed the performance capability of their properties including the satellite elevation angle, the received C/N0 (Carrier-to-Noise density ratio), the measurement accuracy with respect to high grade receivers, the sensitivity to cycle slips and multipath [1]. Published results [1] have pointed out that GNSS measurements on the L1 frequency are C/N0 dependent, featuring values approx. 10 dBHz less than those from high-grade receivers, suggesting that the adoption of C/N0 weighting schemes being a more appropriate approach. Concerning the satellite signal multipath, it was demonstrated that GNSS measurements on L1 are affected more severely than those in high grade receivers, especially in GNSS-harsh environments [1]. However, the main challenge for the quality of the GNSS measurement is that the embedded GNSS antenna in the smartphones lead to L1 pseudorange accuracies of the order of 4-5 m [2].  
Following these developments, an increasing number of smartphones featuring dual-frequency capabilities was developed. Concerning L5, E5a and B2a, all three signals can be tracked from the GPS, Galileo and BeiDou satellites, respectively. L5/E5a/B2a signals are superior to L1/E1/B1 ones as a result of their design specifications [1,3]. For example, the chipping rate of L5/E5a/B2a's is higher than that of the L1/E1/B1 leading to more precise measurements. The lower level of observation noise in the code observables and their resilience against the multipath for GPS/Galileo/BeiDou L5/E5a/B2a signals has been validated through field surveys using various smartphones [1]. Contrarily, fever satellites arebeing tracked on L5/E5a/B2a than on L1/E1/B1, whilst featuring lower C/N0 values. However, the rapid expansion of the European Galileo and BeiDou systems and the modernization of GPS raise the number of satellites (>60) transmitting on L5/E5a/B2a signals.  
In this evolving landscape, this study aims to address the capabilities of L5/E5a/B2a signals in GNSS-harsh environments with a focus on land vehicle ITS applications requiring meter-level accuracy [4; 5; 6]. The structure of the paper is organized as follows. Firstly, the background and state of the art on L5/E5a/B2a signals is presented. Section 2 presents the methodology and fieldwork undertaken. The analysis of the raw GNSS measurements at the pre-processing and post-processing stage is presented in Section 3. Finally, Section 4 presents the key conclusions and discussion for future work.
# Support Vector Machine-Based Transmit Antenna Allocation for Multiuser Communication Systems
## 2 System Model and Conventional Optimization Approach  
### System Model  
As illustrated in Figure 1, we consider a multiuser communication network consisting of one multi-antenna transmitter (Tx) and \(U\) selected/scheduled single-antenna receivers (Rxs or users). Let \(N_{t}\geq U\) denote the number of antennas at the Tx. We use \(\mathbf{h}_{i}\in\mathbb{C}^{N_{t}\times 1}\) to denote the channel coefficient vector from the transmitter to receiver \(i\), where \(i\in\{1,2,\cdots,U\}\). Let \(\mathbf{H}\in\mathbb{C}^{U\times N_{t}}\) be an overall channel coefficient matrix from the transmitter to all the receivers, where the \(i\)th row of \(\mathbf{H}\) is given by \(\mathbf{h}_{i}^{T}\) and the \((i,j)\)th entry of \(\mathbf{H}\) is expressed as \(h_{i,j}\), that is, the channel from Tx antenna \(j\) to user \(i\). In this study, we consider a low-cost Tx that has a limited number of radio frequency (RF) chains and computational capability. Thus, an antenna selection scheme using a part of transmit antennas is relevant, rather than the highly complex optimal multiuser pre-processing or beamforming schemes. Accordingly, channel magnitude information, i.e., partial CSI, is sufficient for simple antenna selection at the Tx (refer to Section 3 for the overall procedure). Note that the main motivation of this study is to effectively reduce the computational complexity at the Tx by using a machine learning-based method. The partial CSIT is available through channel gain feedback from Rxs to Tx. Specifically, each Rx \(i\) estimates its own channel gains, i.e., \(h_{i,j}\) for all \(j\), obtains their magnitude information \(g_{i,j}=|h_{i,j}|^{2}\), and feeds them back to the Tx. The Tx accumulates the feedback information from all Rxs and constructs a channel gain matrix \(\mathbf{G}\in\mathbb{R}^{U\times N_{t}}\) whose real-value entries are given by \(g_{i,j}=|h_{i,j}|^{2}\).
## 1 Introduction  
In the past 10 years, cryptography has made great progress in expanding the adversary model to cover side-channel attacks [1, 2, 3, 4], and researchers have built some provably secure cryptographic schemes that can resist some side-channel attacks. In most theoretical work, it is assumed that the participants have complete confidentiality to their local computation. The attacker may only obtain the signature of the selected plaintext or the decryption of the selected ciphertext, but it is usually assumed that the signature or encryption process itself is completely secret to the adversary. In particular, theoretically, the related information of the private key that an adversary can obtain is only contained in a clear boundary, such as signature or decryption. Such adversaries are sometimes called \"black box\" attackers. [PERSON] and [PERSON] pioneered work for modern cryptography. Based on some computational complexity assumptions, they proved the security of many cryptographic schemes under the black box model, such as encryption [5], signature [6] and the zero-knowledge proof [7].  
However, real attackers do not always follow such clear boundaries. Various successful side-channel attacks have proven that the key information and internal state information related to the specific calculation may be leaked to a certain adversary. Since each cryptographic algorithm is ultimately implemented on the physical platform, it will inevitably affect the surrounding environment in a measurable way. The side-channel attack obtains secret information about the cryptographic system by measuring the surrounding environment of the machine that is executing the related algorithms. For example, an attacker obtains the relevant confidential information of the cryptographic system by measuring and analyzing the time [4] or the electromagnetic radiation [8] of the specific algorithm. Through the \"cold start\" attack [9], if an adversary can access the corresponding physical device, it can recover part of the key of the cryptographic system even when the power has just been cut off. Side-channel attacks [10, 11] allow processes to violate isolation boundaries and read information from other processes on the same machine. In other words, the real attacker may not be the black box.  
The emergence of side-channel attacks leads cryptographers to reevaluate the black box model and create new adversary models and provable security schemes. This work is called leakage-resilient cryptography.  
As leakage-resilient cryptography is a relatively young research direction of cryptography, the theory and practice of leakage-resilient cryptography have made remarkable achievements in the past decade.
## 3 Methodology
### Our Approach to Evaluating Uncertainty in QoE due to Statistical Errors in QoS Measurements  
We adapted the relationship reported in [8] to evaluate the uncertainty in QoE due to sampling error in QoS metrics is given here as Equation (5).  
\[\sigma_{\text{QoE}}=\sqrt{(\frac{\partial QoE}{\partial PLR})^{2}*\sigma_{PLR }^{2}+(\frac{\partial QoE}{\partial jitter})^{2}*\sigma_{jitter}^{2}+2*PCC(PLR, jitter)*\frac{\partial QoE}{\partial PLR}*\sigma_{PLR}*\frac{\partial QoE}{ \partial jitter}*\sigma_{jitter}} \tag{5}\]  
In Equation (5), PCC (PLR,jitter) is the Pearson Correlation coefficient between PLR and jitter calculated from the data set. It can be seen from this equation that the partial differential of QoE with respect to PLR and jitter is a key factor in evaluating the uncertainty in QoE, here evaluated as asymptotic standard error of QoE.  
It can be seen from Figure 1 that, if the shape of the functional relationship between QoE and QoS is known, then for any small change in QoS, the resultant change in QoE can be determined. To find the propagated uncertainty we calculate partial derivative of QoE w.r.t QoS parameters and the asymptotic standard errors of PLR and jitter.  
The approach to calculate the propagated uncertainty also depends on whether the dependent variables are completely independent of each other or have some correlation between them. If the measured values of PLR and jitter are considered to be independent of each other then the model proposed in Equation (5) can be simplified.  
Since, we have already established that PLR and jitter has considerable (20-30%) correlation between them, we evaluate the asymptotic standard errors of QoE using Equation (5).
## 5 Experiment and Results
### Evaluation Measures  
This section presents the evaluation measures applied to the experiments, which include the accuracy and F-measure.  
The accuracy measure is calculated by finding the number of correct predictions for all the classes and dividing it by the number of instances [59; 60], as observed from the following equation:  
\[Accuracy=\frac{TP_{positive}+TP_{negative}+TP_{neutral}}{\#instances} \tag{14}\]  
where \(TP_{positive}\), \(TP_{negative}\), and \(TP_{neutral}\) represent the number of instances that are predicted correctly as positive sentiment, negative sentiment, or neutral sentiment, respectively. _instances_ is the number of instances in a dataset.  
On the other hand, the _F-measure_ is the harmonic mean of precision and recall [61], which is calculated by the following equation:  
\[F\text{-}measure=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{15}\]  
where the _Precision_ is the percentage of correct predictions for a class relative to all the predictions of the same class [61], and the _Recall_ is the percentage of correct predictions for a class relative to all instances that actually belong to the class [62].
## 3 Novel Taxonomy for SSNs
### Low Level Virtualization
#### 3.1.2 LLV Based on Virtual Machine Solutions
* **Darjeeling**[47]: is the first open source Virtual Machine that is based on java for resource constrained devices (2-10 KB of RAM), It is considered for SSNs, because it is a completely thread based programming model that can be ported in three different platforms with three different operating systems. Darjeeling provides a group of features, like light-weight threads, dynamic memory management, and exception handling, while targeting to optimize utilization of the constrained resource. It uses an offline tool, called infuser, to convert the java bytecode into custom bytecode and links between them through loadable modules, called infusions. Thus, by repeating that procedure, multiple applications can run concurrently. The performance evaluation of Darjeeling was run using three platforms with different microcontrollers (ATmega128, MSP430), operating systems (TinyOS, Contiki, LiteOS [38]), and radios (CC1000, CC2420, nRF905). This study proved that Darjeeling is slower than native C by 50%, while the static linking was reduced by 55-83%, and the memory efficiency designed for a 16-bit architecture stack space was reduced by 27% for a real-world environmental monitoring application.
## 5 Numerical Results
### Analyzing the Effects of Cybersecurity and Cyberattack Costs on \(E_{6}\)
#### 5.2.1 The Impact of Cybersecurity Costs  
We first focus on the cybersecurity costs (\(C_{12}\) and \(C_{20}\)) and numerically evaluate their impacts by changing one of them. Recall that \(C_{12}<0.2\) is required by (15) for making \(E_{6}\) ESS. Similarly, \(C_{20}<0.3\) is required by (16). Figure 5a presents the evolution of population fractions over \(C_{12}\). We can see that the system converges to \(E_{6}=(1,1,0,0)\) and \(E_{8}=(0,1,1,0)\) when \(C_{12}<0.2\) and \(C_{12}>0.38\), respectively. On the other hand, the population fractions \(x\) and \(z_{1}\) fluctuate when \(0.2<C_{12}<0.38\). Figure 5b presents the evolution of population fractions over \(C_{20}\). The system converges to \(E_{6}=(1,1,0,0)\) and \(E_{9}=(1,0,0,1)\) when \(C_{20}<0.3\) and \(C_{20}>0.86\), respectively. On the other hand, the population fractions \(y\) and \(z_{2}\) fluctuate when \(0.3<C_{20}<0.86\). The evaluation of cybersecurity cost parameters shows that \(C_{12}<0.2\) and \(C_{20}<0.3\) satisfy the ESS conditions for \(E_{6}\).
# Design of a Device Based on an LED Matrix for Water Sterilization
## 2 Materials and Methods
### Effect of LED Exposure on Microorganism Viability  
To compare the antibacterial capacity of the different LED devices tested, _E. coli_ was used as a control. Cultures of _E. coli_ in exponential phase were washed 10 times by centrifugation with deionized distilled water and diluted in 10 mL of water to reach 5 \(\times\) 10\({}^{3}\) colony-forming units (CFU) per milliliter. At this point, the cells were exposed to the LEDs for different periods of time, under both static and stirring conditions. Dilutions were then plated on solid BHI medium plates, and after overnight incubation at 37 \({}^{\circ}\)C, the colonies obtained were quantified. After selecting LED 2 as the best option to build a disinfection prototype, its effect on different species of bacteria and yeasts was analyzedfollowing a similar protocol. The effect on yeasts was carried out similarly, but using Sabouraud medium instead of BHI.  
To obtain _C. perfringens_ endospores, cultures were washed by 10 centrifugations with distilled water, and finally resuspended in distilled water. Under these stringent conditions, vegetative cells disappeared from the culture and were replaced by endospores. Viability tests were carried out by culturing dilutions of endospores on McConkey agar in a manner analogous to that used for vegetative cells.
# Chemo-Mechanical Model for the Expansion of Concrete due to Alkali Silica Reaction
## 1 Introduction
Some aggregates may also release significant amounts of alkalis, such as volcanic glass, unwashed marine sand, alterated feldspars, micas, clay minerals and zeolites, and soda-time glasses [25; 26; 27]. This could explain why many concrete structures are damaged by ASR, despite having been built with low-alkali cements [28; 29]. It was reported that the ASR expansion in 14 days in 80 \({}^{\circ}\)C increased from 0.83% to 1.04% due to the alkali released from aggregates with a cement containing only 1.0% Na\({}_{2}\)O\({}_{\text{e}}\). The increase in 360 days in 20 \({}^{\circ}\)C was about 35.7% (from 0.95% to 1.29%) [30]. Such an effect of alkali released from aggregates on ASR was not considered in the above mentioned models either. As the reaction goes on, the calcium in the pore solution penetrates into the gel layer producing more C-S-H gel resulting from the recycling of alkalis in the basic gel by calcium, which is called alkali-recycling by calcium [1]. This also play a role of alkali source. The formed C-S-H gel would play a barrier shells surround the particle. Microstructure models are developed for the microstructure evolution and influence on the creep behavior of cement paste [31; 32]. The purpose of this paper is to develop a model to predict the expansion caused by ASR. The formation of the basic gel and its swelling are considered separately in this model. The formation of the basic ASR gel is driven by alkali charge-balanced by hydroxide ions. The swelling of the gel is driven by water imbibition. This two steps reaction sequence is more in line with the reaction sequence for the formation of ASR products. The alkali recycling effect by calcium is included in the model because it plays an important role in the long term reaction. Both external alkalis (deicing salt, etc.) and internal alkalis (cement and aggregates, etc.) are considered for the mass balance of alkali. The expansibility of the gel is modeled as a function of sodium to calcium ratio in the pore solution. Swelling of the gel is calculated based on the water imbibition process according to the expansibility. The pressure due to swelling is coupled with a damage model to calculate the expansion and damage caused by the alkali-silica reaction.
# Feedback Approach for the Relay Channel with Noisy Feedback and Its Security Analysis
## 4 An SK-Type Scheme for the MISO Fading Relay Channel with Noisy Feedback and Its Security Analysis  
In this section, an SK-type scheme for the MISO fading relay channel with noisy feedback is proposed. In this extension scheme, to deal with complex signals in the fading channel, we introduce the two-dimensional complex plane mapping method. Furthermore, a pre-coding strategy is used to transform the feedforward MISO channel into a complex-valued SISO channel, while a beamforming strategy is used to transform the feedback SIMO channel into a complex-valued SISO channel. Consequently, by following the scheme for the AWGN RC with AWGN feedback, we obtain the scheme for the MISO FRC with noisy feedback. The main result and security analysis of this extension scheme are given below.