esa-sceva/satcom-chunk-collection huggingface dataset:
# DNA Barcoding as a Plant Identification Method
## 4 Chloroplast DNA Barcodes
### trnL-trnF (Genic, Intron, and Intergenic Spacer)  
The _trnL-trnF_ intergenic spacer has been proposed as a universal plastid amplicon and has been widely used in plant systematics and phylogeography since the 1990s [35; 47]. This region is located in the large single copy region of the chloroplast genome [19]. Despite its slow rate of molecular evolution, the plastid _trnL_ intron is suggested as a possible marker because of its conserved sites; hence, it could be a useful tool for evolutionary studies at higher taxonomic levels [21; 37]. [PERSON] et al. [51] established primers that work for 19 species tested including algae, bryophytes, pteridophytes, gymnosperms, and angiosperms.
# A Linear Memory CTC-Based Algorithm for Text-to-Voice Alignment of Very Long Audio Recordings
## 4 Experiments and Results
### Performance Benchmark
#### 4.8.2 Comparison with Other Alignment Systems  
The classical datasets used in the MIREX contest ([[https://www.music-ir.org/mir](https://www.music-ir.org/mir)]([https://www.music-ir.org/mir](https://www.music-ir.org/mir)) ex/wiki/2020:Automatic_Lyrics-to-Audio_Alignment_Results (accessed on 31 January 2023)), i.e., [PERSON] and [PERSON]'s datasets, only include up to 20 songs of a few minutes. Although our system was designed to address the very long audio alignment use case, we compared our system with the current state-of-the-art systems for the sake of completeness, as reported in Table 6. For this experiment, our model was trained on the entire DALI dataset, with each epoch processing the whole database. The rest of the above-mentioned training procedure remains identical.  
Comparing first the results of the Jamendo dataset, we find that our system is second in MAAE, with a Q50% value equal to the best SOTA result. For the a compella variant of the [PERSON] dataset, our system achieves results close to the second-best SOTA system. Note that all SOTA systems involve acoustic and language models, while our system relies on a simpler end-to-end architecture.
###### Abstract  
This paper aims to derive an analytical modelling of the downlink exposure in 5G massive Multiple Input Multiple Output (MIMO) antenna networks using stochastic geometry. The Poisson point process (PPP) is assumed for base station (BS) distribution. The power received at the transmitter is modeled as a shot-noise process with a modified power law. The distributions of 5G massive MIMO antenna gain and channel gain were obtained by fitting simulation results from the NYUSIM channel simulator. The fitted distributions, e.g., exponential and gamma distribution for antenna and channel gain respectively, were then implemented into an analytical framework. In this paper, we obtained the closed-form expression of the moment-generating function (MGF) for the total exposure in the network. The framework is then validated by numerical simulations. The sensitivity analysis is carried out to investigate the impact of key parameters, e.g., BS density, path loss exponent, and transmission probability. We then proved and quantified the significant impact the transmission probability on global exposure, which indicates the importance of considering the network usage in 5G exposure estimations.  
Keywords:stochastic geometry; massive MIMO; electromagnetic field exposure; 5G +
Footnote †: journal: Applied Sciences
## 5 Experiments
### Scalability  
Scalability is a crucial aspect of service composition methods, and we verify the generalization ability of our proposed approach by testing it on different experimental scenarios. To achieve this, we refer to [39; 43] and create two types of scenarios. The first scenario involves fixing the number of subtasks to 30, and each subtask contains 300, 400, 500, and 600 candidate cloud services, respectively. The second type fixes the number of candidate services to 500, corresponding to 50, 60, 70, and 80 subtasks. We evaluate the scalability of SkyDRL by observing the execution efficiency of the algorithm for problems of different scales. As demonstrated in Table 3, SkyDRL can always find optimal cloud service compositions, regardless of the scale of the problem. This indicates that the proposed approach has good scalability.  
The experimental analysis above provides compelling evidence that SkyDRL is a powerful tool for achieving energy consumption and QoS balance in service composition. Although SkyDRL may not deliver optimal results for optimizing QoS effectiveness since it accounts for energy consumption, its overall performance remains strong. When facing a dynamic environment, SkyDRL showed good adaptability, which proves that SkyDRL can adaptively adjust its learning strategy to find the optimal service composition. Moreover, SkyDRL delivers excellent outcomes for problems of different scales, underscoring its scalability.
## 1 Introduction
To the authors' best knowledge, no comprehensive study on the flexural performance and the post-cracking behavior of FRC with twisted-bundle macro-synthetic fibers is available in the literature. Yet the post-cracking residual strength is the most important property of FRC and, therefore, its flexural performance should be deeply investigated. In order to predict the flexural capacity of FRC, the material behavior and its constitutive law should be examined. The FRC may exhibit strain-hardening or softening behavior. Most of the available literature reports on the strain softening behavior due to the extensive research on SFRC [9; 14; 18; 26]. Nevertheless, while associating the strain-softening behavior with crack opening, the steel FRC and synthetic FRC may not reveal the same response. Since the fibers contribute to the post-cracking stage, fracture mechanics-based constitutive law (stress-crack opening relationship) is usually used rather than continuous mechanics-based model (stress-strain relationship). For simplified stress-crack opening relationship, rigid plastic and linear elastic softening models can be found in available literature [27; 28]. However, the effect of fiber dosage is not considered in these models. This paper presents the findings of experimental investigations conducted on the effect of twisted-bundle macro-synthetic fibers on the compressive strength, tensile strength, flexural strength, and residual flexural performance of high strength self-compacting fiber-reinforced concrete, as well as the fresh properties of the SCC mixtures. Based on the post-cracking behavior observed in the experiments, a simplified stress-crack opening constitutive law is proposed, considering the effect of fiber dosage while determining the section moment capacity.
## 2 Converter Structure and Operation Principle
### Control Strategy  
The overall control block diagram is shown in Figure 2, consisting of four parts: power control, DC/DC converter control, SOC balancing control, and modulation.  
The integration of supercapacitors into each submodule via a bidirectional DC/DC converter enables the MMC-SCES to maintain the characteristic AC and DC port behavior of the conventional MMC. This similarity preserves the effectiveness of the dual-loop power control strategy. The outer active and reactive power loops establish the reference values for the \(d\)-axis and \(q\)-axis currents, while the inner current loop implements decouplingcontrol to generate the modulated voltage. Through \(dq/abc\) coordinate transformation, the system ultimately produces the desired phase-modulated output voltage \(u_{\text{vj}}\).  
The bidirectional DC/DC converters employ a dual closed-loop control strategy to optimize supercapacitor performance and extend operational lifespan. This strategy consists of two main control loops: an outer power loop and an inner current loop. The outer loop maintains the supercapacitor power reference, while the inner loop controls the energy storage current. A feedforward duty cycle is utilized to enhance system tracking performance. The feedforward duty cycle is calculated as the ratio of the supercapacitor rated voltage to the submodule capacitor rated voltage, while the dual-loop control compensates for steady-state errors. Current constraints are implemented to prevent supercapacitor overcharging and over-discharging conditions.  
To minimize hardware complexity, this paper proposes a centralized control strategy where all ESMs per arm share a common energy storage controller. This ensures consistent duty cycles across all ESMs in the arm. Compared to the traditional SOC balancing strategy, the centralized control strategy fundamentally modifies the generation of gate signals for the bidirectional DC/DC converters. This achieves an \(N\) times reduction in controllers, dramatically simplifying the control architecture while maintaining balancing performance.  
The SOC balancing control strategy is introduced in detail in the following section.
# Practical I-Voting on Stellar Blockchain
## 4 Results  
### Scalability  
Scalability is currently one of the most significant problems in blockchain technology. At the time of writing, Bitcoin public network can process 3-7 TPS (transactions per second), Ethereum about 7-15 TPS, Stellar up to 200 TPS. In fact, the limit 200 is denoted in operations per second. In Stellar, there is a distinction between transaction and operation. One transaction can hold up to 100 operations that execute atomically [50]. In most protocols, each node stores all blockchain state and validate all transactions individually. Although this provides high security, the whole blockchain network can not process more transactions than a single node can. If we take presidential elections, where millions of people are casting a vote (sending a transaction), it may quickly turn out that the number is insufficient.  
At the time of writing, the Stellar public network process about 30 operations per second [51], which is about 15% of total capacity. If we assume that the rest 85% is available for our application, then we can state that the Stellar public network is capable of handling about \(\left\lfloor\frac{170}{6}\right\rfloor=28\) votes per second (one vote transaction consists of six operations). If we assume an optimistic scenario where users are voting in uniform distribution during the 12 h. Then Stellar can process \(26*12*3600=1.1\) million total votes. If we compare this number with current attempts highlighted in the introduction section (102k and 82k votes cast), our system is doing very well. We believe that, over time, new scalability solutions will show up. One that is actively developed in the blockchain industry is called Lightning Network (LN).  
Imagine going on to trip with friends, when you buy products that will be used by everyone, or pay in restaurants, you usually do not split the payment across all participants in the time of payment, but rather keep all the receips until the end of the trip and, at the end sum the costs and settle each other via wire transfers. That way, the number of wire transfers is minimalized. The longer the trip, the more payments, the more wire transfers saved.  
The lightning network works similarly. This kind of off-chain peer-to-peer payments can scale almost infinitely, and many modern blockchains can support them. LN also increases anonymity because only the closing ledger settlement is visible on the blockchain, not all the intermediary transactions.  
Stellar plans to introduce a lightning network into its ecosystem [52]; currently, there is one implementation [53], which unfortunately is not ready for production usage.  
Because of the rapid development of lightning network standardization (BOLT) [54], we leave the research of LN in our system for future work.
# A Self-Adaptive Approximated-Gradient-Simulation Method for Black-Box Adversarial Sample Generation
## 2 Related Work
### White-Box Adversarial Attacks  
In a white-box attack, the attacker has full access to the internal structure of a DNN and attacks the DNN with the adversarial samples generated by the DNN gradient. [PERSON] et al. [5] first discovered an intriguing property of DNNs, noting that the input-output mappings are absolutely disconnected so that state-of-the-art DNNs are vulnerable to adversarial examples. It is possible to cause a DNN to misclassify the input images that are added to an imperceptible perturbation. These adversarial examples are explored by optimizing the box-constrained problem of the input images. The box-constrained problem is transferred into an unrestraint problem by the penalty function method, i.e., \(c|r|+loss_{f}(x+r,l)\), \(s.t.\ x+r\in\left[0,1\right]^{m}\). The main task of the attack algorithm is to search the approximate minimum \(r\) by the line search method to cause the DNN to misclassify. The reason for the existence of adversarial samples is the non-linearity and over-fitting of the DNN model. However, it is also possible that linear behavior in higher dimensions can also produce adversarial examples to misclassify the images.  
Therefore, [PERSON] et al. [13] proposed a fast-gradient-sign method (FGSM) attack which generates adversarial examples to misclassify the images by the gradient of the loss function. The gradient is the derivative of the cross-entropy loss function with respect to the input image, which is obtained by the backpropagation of DNN. The input images are perturbed with the direction of the gradient, and only a one-step gradient update algorithm is performed. Therefore, an FGSM attack is very fast at generating adversarial examples. However, it is not efficient that adversarial examples fool the DNN because of the use of one-step modification in isolation. If the step is small, the perturbation preserves the properties of the input images without fooling the DNN; otherwise, the input images are converted to something else. To make the perturbation smaller and more efficient, researchers proposed multiple-iteration optimization to improve the performance of the FGSM, i.e., BIM attack [20] and PGD attack [21]. These iteration-based FGSMs tend to fall into the trap of local optima because they greedily move in the direction of the current gradient. To abscond from local optimal solutions, the momentum method is integrated into the iteration FGSM, i.e., MI-FGSM [22]. The momentum method mainly accumulates the velocity vector to maintain the previous gradient direction. Extensive experiments show that the MI-FGSM is effective in generating adversarial examples.  
In the above attack methods, the adversarial examples are obtained by solving non-linear combinatorial optimization problems.
## 2 Multiphysics-Coupled Analysis-Based Induction Hardening Process
### Finite Element Analysis (FEA)  
The model utilized in this study simplifies the actual geometry of the CTBA and the induction heating coil setup. While real induction heaters consist of multiple components in addition to the coil and the CTBA, their effects on the simulation are considered negligible and were therefore excluded from the analysis model. Although the actual CTBA exceeds 1200 mm in length, simulating the entire geometry would significantly increase computational time and hinder convergence. Consequently, a half-model was used, focusing on the interaction between the CTBA and the coil. The center of the CTBA, which is fixed during real-world heating, was represented using a fixed boundary, as shown in Figure 4. The finite element model applied in this study used 1 mm sized eight-node solid hexahedral elements with constant stress. To capture the skin effect, the mesh was constructed with three layers in the thickness direction, and hexahedral elements were used to improve convergence. The skin effect, characterized by induced currents concentrating near the conductor's surface, was modeled to ensure that the simulated temperature distribution closely matches real-world phenomena [16; 17].  
The induction hardening process was divided into two phases: heating and quenching. In the heating phase, involving an electromagnetic-thermal coupled analysis, a current of 1500 A at 58 kHz was applied to both ends of the coil. eCoil surroundings and the CTBA was included in the model, but convection effects were excluded as their impact was expected to be negligible due to the short heating duration. In the quenching phase, involving a thermal-structural coupled analysis, the initial temperature and stress values obtained from the heating phase were used. Cooling was modeled by applying an external surface temperature of 25 \({}^{\circ}\)C to the CTBA submerged in water.  
Material properties for the simulation were sourced from the software's database. Boron steel was used to model the CTBA, while copper was used for the coil. The material properties are detailed in Table 1[18]. Electrical, thermal, and structural properties were incorporated into the simulation, with electrical conductivity specified as temperature-dependent, as illustrated in Figure 5. The electrical conductivity decreases with increasing temperature due to the heightened vibration of metal atoms, which disrupts the flow of free electrons by increasing the frequency of collisions.  
Figure 4: FEA modeling and meshing.
## 2 Literature Review
By drawing parallels with LS-MIMO's challenges, our study highlights the importance of real-time scheduling strategies for efficient vehicular communication. [PERSON] et al. [7] quantitatively examined the challenges of radio resource allocation for sidelink communication in Cellular V2X, using collective perception (CP) as a use case. The study introduced data traffic models representing different message periodicities and determinism levels in real-world vehicular networks. Additionally, the paper derived analytical expressions to calculate radio resource utilization and collision probability, comparing unmanaged mode (Sensing-Based Semi-Persistent Scheduling (SB-SPS), Mode 2 in 5G-NR V2X) with managed mode (CG Type II, Mode 1 in 5G-NR V2X). The findings emphasize the impact of CP services on enhancing environmental awareness in C-V2X networks. However, while the study provides valuable insights into resource allocation mechanisms, it does not focus on the probability of packet collisions in time-domain-based resource selection strategies. [PERSON] et al. [23] analyzed the impact of different contention-based resource group (CBG) configurations on collision probability, message success rate and waiting time in 5G NR grant-based structures. The study leveraged Monte Carlo simulations to assess the trade-off between collision probability and resource utilization efficiency, demonstrating that multiple CBGs can improve performance when the number of UEs ranges between 10 and 50. However, it also highlighted that in certain scenarios, additional complexity in handling multiple CBGs may not always yield performance benefits. This study provides valuable insights into contention-based resource allocation, which complements our work by further emphasizing the importance of efficient dynamic scheduling in reducing collisions in 5G NR sidelink communications. In contrast, our study extends these findings by introducing a dynamic time-domain resource allocation strategy that optimizes resource selection based on real-time changes in UE behavior. This approach enhances collision avoidance and improves overall network efficiency in rapidly evolving vehicular environments.
## 5 Methodology for Reliability and Availability Evaluation
### Topology  
The first step of the proposed methodology is to define a structure through which the network can be modeled. In the proposed approach, the network is organized as a graph \(G(V,E)\) with \(n\) vertices (\(V\)) and \(k\) edges (\(E\)). The vertices represent field devices whereas the edges represent the wireless links between devices. The network topology can be stored in the adjacency matrix (\(A_{n\times n}\)) of graph \(G\). If a device \(N_{i}\) has a neighbor \(N_{j}\), then the entries \(a_{ij}\) and \(a_{ji}\) of \(A\) will receive the value \(1\), otherwise they will receive the value \(0\). Thus, by using this structure we can represent any WSN topology. Figure 5 shows an example of a WSN represented using the aforementioned structure. In this example, the network is composed by 1 gateway (\(Gtw_{0}\)), 1 access point (\(Ap_{0}\)), 2 routers (\(R_{0}\) and \(R_{1}\)) and 3 field devices (\(Fd_{0}\), \(Fd_{1}\) and \(Fd_{2}\)). The indexes located more close to the vertices are used to identify the devices in the adjacency matrix.  
Figure 5: Example of a Wireless Sensor Network represented through a graph and its respective adjacency matrix.
# Enhancing Sustainable IoT Systems Through a Goal-Oriented Requirements Analysis Framework
## 4 Goal-Oriented Requirements Analysis Approach for IoT Software Systems
### Goal-Oriented Requirements Analysis Model in a Nutshell
#### 4.1.1 Adaptation Explanation  
Technically, considering the adaptation of the i* language for the IoT requirements analysis, the representation of each modeling element in the i* 2.0 language for use in the requirements analysis for IoT systems is as follows:  
* Actor: In numerous goal-oriented methodologies, actors play a crucial role by having intentions and goals. This introduces a seamless and powerful approach for scrutinizing, formulating, and executing various software solutions. Actors possess characteristics such as reactivity, proactivity, and social ability, making them practical for representing, analyzing, and designing software solutions and various complex systems that involve cooperation and co-creation of value. In this concept, actors can be employed to symbolize the end user, a software system (whether web-based, mobile application, or desktop), and the IoT system. They are labeled as \"IoT\" for the IoT system, \"U\" for the end user, and \"S\" for the web-based, mobile, or desktop software.
* Goal: These are the objectives that actors aim to achieve. The symbol is the same as the i* 2.0 language.
* Quality: Represents quality attributes. The symbol is the same as the core i* language. It can be used for quality issues in IoT system (\"IoT\") and user (\"U\") actors, as well as software attributes for the actor \"S\".
* Task: The actors perform actions to achieve their goals, acting as mechanisms to achieve the goals. In this proposal, tasks are essentially identified as the services provided or consumed by \"things\". They are usually associated with IoT system functionalities or software functions.
* Resource: This is an entity that is essential for an actor to perform a task. In the original i* language, the symbol was contemplated by simply adding a label with \"T\" since it represents a \"thing\". The \"thing\" is the essential element of the IoT system. This can be integrated with other \"things\" to construct an IoT system.  
The adaptation previously introduced was implemented using the Eclipse Modeling Framework (EMF) through the use of a metamodel. EMF is a framework designed for modeling and code generation, facilitating the development of tools and applications based on a structured data model. Within this framework, a metamodel defines the structure and constraints of other models, serving as a formal specification that describes the permissible elements, their attributes, relationships, and constraints. In EMF, metamodels are typically defined using the Ecore metamodel, a subset of UML specifically designed to describe the structure of models within the EMF environment.