esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Persisters of Foodborne Pathogens
### Persister Types and Mechanisms of Formation
#### 2.3.1 Guanosine Pentaphosphate or Tetraphosphate (ppGpp) and Persisters  
ppGpp is an important stress alarmone in bacteria, associated with a stringent response, energy metabolism, virulence, tolerance, and persistence [59]. Under specific stress conditions such as amino acid limitation and antibiotic treatment, ppGpp can be rapidly produced by the RelA/SpoT homologue family and strongly inhibit macromolecular synthesis [60]. Several lines of research provide firm evidence that ppGpp makes a major contribution to persister formation (Figure 3). [PERSON] et al. [61] claimed that stochastic expression of ppGpp is the key cause of type II persister formation in exponentially growing _E. coli_ populations. [PERSON] et al. [62] proposed an ppGpp ribosome dimerization persister (PRDP) model that ppGpp induces _E. coli_ persister formation by directly halting ribosome action through the ribosome modulation factor, a hibernation promoting factor, and a ribosome-associated inhibitor. Furthermore, ppGpp also participates in the toxin-antitoxin (TA) system-induced _E. coli_ persisters. [PERSON] et al. [63] showed that ppGpp accumulation beyond a threshold induced by tRNA limitation results in higher persister levels in an _E. coli_ population. However, there is no clear correlation between ppGpp expression and persistence at a single cell level.  
Figure 2: Different types of persister-cells and bacterial spores. Green images: susceptible bacteria cells. Red images: persisters. Images are not drawn to scale.  
In addition to _E. coli_, ppGpp also participates in persister formation in other species. [PERSON] et al. [55] discovered that the formation of intramacrophage _Salmonella_ persisters is associated with the ppGpp/lon protease-based TA system and is specifically triggered by acid and an energy-limited host cell environment. Recently, [PERSON] et al. [64] proposed that ppGpp-induced GTP depletion is a shared metabolic pathway of both triggered and spontaneous persisters (non-spore form) in a strain of the Gram-positive model species _B. subtilis_. In this process, different pathways of persister formation are activated by various ppGpp synthetases. Clinically, [PERSON] et al. [65] found that _S. aureus_ SCVs isolated from patients who suffer from persistent and recurrent infection have more ppGpp accumulation.
# An Improved Mixed AC/DC Power Flow Algorithm in Hybrid AC/DC Grids with MT-HVDC Systems
## 1 Introduction
### Solving the Power Flow Problem for Hybrid AC/DC Grids with MT-HVDC Systems
#### 1.2.2 The Sequential Method  
The sequential method solves the AC/DC PF equations sequentially, one after another, in each iteration [15, 16]. The main advantage of the sequential method is to make the solution easy to combine the DC PF to the AC PF solution, and it can be implemented easily when the extension of an existing AC PF is needed. In [17], a numerical method based on the Newton-Raphson algorithm to calculate the converter station losses is proposed. A detailed steady-state model of the converter station to solve the AC/DC PF problem sequentially based on the Newton-Raphson technique considering converter station losses and reference power is proposed in [18, 19]. An algorithm with per-unit conversion and changing the bus numbers to simplify solving the PF problem when multiple DC lines and converter station outages occur is developed in [18]. The main drawback of this method is neglecting the AC grid connection in the problem. In [20], a detailed model of the converter station with AC/DC PF equations, including converter station losses, for solving the PF sequentially is proposed. In [21], a method to solve the PF problem through the Gauss-Seidel method is developed.
## 2 Literature Review
### Statistical Techniques for Crash Severity  
#### 2.2.1 Unobserved Heterogeneity in Crash Analysis  
Considering the discrete characteristics of crash severity outcome, several discrete choice models have been introduced. One of the most commonly used analysis techniques is a logit approach. Among various logit approaches, the multinomial logit (MN-logit) model has been widely used due to its simple structure and easy-to-read parameters. For example, the MN-logit model was calibrated to analyze the risk factors of motorcycle crashes and pedestrian-vehicle crashes, respectively [37; 38]. Subsequently, a binary logit model was employed to fit roadside accidents and suggested that rural crashes should be investigated separately [22]. However, the parameters of the MN-logit model are fixed, which cannot identify the unobserved heterogeneity. This regression pattern may not be able to exhibit the real contribution of risk factors on crash severity. Therefore, some advanced modeling techniques, such as random parameters logit (RP-logit) model and random intercept logit (RI-logit) model, have been recommended.  
The RP-logit model is a discrete choice model with high flexibility and adaptability that captures the unobserved heterogeneity by allowing parameters to vary across observations. This statistical technique has been known for many years, but it was not fully applicable until the advent of simulation technology. Up to now, it has developed into a satisfactory approach for estimating crash severity [2; 9]. [PERSON] et al. [39] were among the first to employ the RP-logit model in accident analysis and suggested that this approach can be used to verify the distribution of crash severity on roadway segments. Subsequently, the pedestrian injury severity in pedestrian-vehicle crashes was investigated. It was found that the effect of pedestrian age on injury severity was normally distributed across crashes and the probability of fatal injury increased significantly with the increase of pedestrian age [25; 40].  
More specifically, the RI-logit model can be regarded as a special form of the RP-logit model, which addresses the unobserved heterogeneity by allowing model intercept to vary across individual crashes [11]. [PERSON] et al. [41] used the RI-logit model for traffic safety analysis earlier. Based on crash data of I-880N in California, crash risk prediction models were developed for different weather conditions, and the influence of traffic flow variables on crash risk was captured. It was found that model predictive performance can be effectively improved by allowing the model intercept to vary across observations. Additionally, a hierarchical Bayesian approach was proposed to examine the posterior probability of driver injury severity in rural truck-related crashes [42]. The results indicated that capturing random effects plays an important role in predicting injury outcomes.
## 3 Results
### Effects of N addition on C exchange Fluxes, ET, WUE and LUE in a Chronosequence  
In order to provide insights into the influence of fertilization on the C fluxes, ET as well as WUE and LUE during the period of prediction, monthly measured and ANN-modeled values, and their differences are shown for the four post-fertilization years (2007-2010) in Figures 5 and 6. The annual effects of N fertilization during this period are summarized in Tables 2 and 3. Figure 7 compares the annual measured and ANN modeled values of the C fluxes and ET, and shows \(\pm\) 1 standard deviation estimated using the optimized model parameters. Over the four post-fertilization years at DF49, most of the ANN modeled monthly values of GPP were lower than the measured values (Table 1 and Figure 5a), but were higher for \(R\) (Table 1 and Figure 5d) and consequently, modeled monthly values of NEP were lower than the observed values (Table 1 and Figure 5g). At the annual time scale over the four years, N fertilization at DF49 caused a 6% to 11% increase in annual GPP and a 6% to 10% decrease in \(R\), resulting in an 84% to 164% increase in NEP (Table 2). For HDF88, N fertilization caused an 18% to 29% increase in annual GPP, an 11% increase in \(R\) in 2007, and a 6% to 14% decrease during 2008-2010, resulting in a 208% to 321% increase in NEP (Table 2). For HDF00, N fertilization caused a 43% to 70% increase in annual GPP and an 8% to 21% increase in \(R\), resulting in a 27% to 50% increase in NEP (Table 2). Overall, N fertilization had a positive impact on GPP for all three stands with the response being the highest at HDF00, followed by HDF88 (Table 2). No consistent effects on annual ET were evident in the four post-fertilization years in all three stands (Table 3). For annual WUE, N fertilization of DF49 caused an increase of 0.30 to 0.67 g\(\cdot\)C (kg\(\cdot\)water)\({}^{-1}\) during 2007 to 2009, but a decrease of 0.21 g\(\cdot\)C (kg\(\cdot\)water)\({}^{-1}\) in 2010. N fertilization caused increases of 0.59 to 1.05 C (kg\(\cdot\)water)\({}^{-1}\) and 0.76 to 1.75 C (kg\(\cdot\)water)\({}^{-1}\) in annual WUE during 2007 to 2010 at HDF88 and HDF00, respectively (Table 3). For annual LUE, N fertilization had positive impacts for all three stands, especially at HDF00 with the greatest response ranging from 0.0039 to 0.0057 mol\(\cdot\)C\(\cdot\)mol\({}^{-1}\) photons (Table 3).
## 1 Introduction
One of these rare cases is the airline industry, where several attempts were made by various industry players to develop such a label but no industry-wide standards currently exist [17]. Another example is the ongoing research project _Labeling road surfaces in the Netherlands_, which aims at labeling road surfaces according to four indicators: skid resistance, noise reduction, rolling resistance, and lifespan [18]. Indeed, in the last few years, public administrations are willing to reward those road works whose quality is beyond the thresholds required by the specifications, and recognize these improvements in the construction practices and/or used materials in the technical evaluation of the tendering process. One example of this willingness is the promotion of public-private partnerships (PPP) for roadways based on performance-based indicators (PI) linked to bonuses and penalties [19]. The Spanish administration launched many PPP contracts based on availability payment depending on the fulfillment of a set of performance-based indicators. In this sense, a labeling system would smooth the introduction of award criteria into the road construction contracting process and would facilitate the assessment of the level of compliance. For the abovementioned reasons, the aim of this research was to establish a multi-label scheme for wearing courses according to three categories: mechanical performance, functional performance, and environmental performance. In particular, this study focused on establishing a guideline to create labels for thin wearing courses because thin overlays are a preservation technique that is gaining popularity given the growing concern of IMs on safety and ride comfort. Each category is influenced by different properties, but a unique global label is obtained for each category. The performance of each category is rated in terms of a set of performance classes from A to D on the label. The ultimate goal of this study was to propose a guideline to create a flexible tool that allows evaluating any wearing course based on its mechanical, functional, and environmental performance, comparing attributes among different mixtures to select the optimum solution for each specific situation. The purpose was to develop a tool that facilitates communication between clients and contractors and stimulates better road surface practices. To make the proposed methodology more tangible, the establishment of the labels is particularized to the Spanish case.
# FollowMe: One Social Importance-Based Collaborative Scheme in MONs
## 1 Introduction  
Mobile opportunistic networks (MONs) utilize node mobility to form communication opportunities to overcome challenging scenarios. This approach naturally makes it possible to achieve the goals of ubiquitous interconnection and a thorough perception for the Internet of Things, and can be applied to many fields such as device-to-device communication, data offloading, and intelligent transportation [1].  
The collaboration behavior of nodes plays an important role in MONs because nodes need to store/forward packets from/to others. However, considering the limited storage space and the consumption of battery power, nodes show a certain degree of selfishness in MONs. They tend to maintain their own interests sometimes and refuse to serve others. Therefore, it is highly important to encourage additional nodes to participate in the data forwarding process.  
Many incentive models have been proposed to improve the cooperative behavior of nodes, such as the reputation system [2], the virtual money mechanism [3], and the tit-for-tat method [4]. All of these models are based on a passive scheme, that is, nodes have to forward/receive packets as much as possible to enable them to gain a higher reputation, or earn more money, or send a larger number of packets. In fact, the nodes in MONs show strong self-similarities as they are carried by people. Based on the social learning and psychological theories, they always imitate others' behavior, especially those of high social importance. Recent studies verified the existence of imitating behavior in ethics [5], consumption [6] and cooperation [7]. Taking this into account, in this paper, we propose FollowMe, an evolutionary game-based solution to motivate nodes participating in MONs. FollowMe introducesthe sociality of nodes into the signal game model, in which the signal indicates the social importance of nodes and will be sent to the other nodes in each round. A node designated as a social node in an MON sends the signal reference \(m_{1}\) with high social importance; otherwise, it sends the signal \(m_{2}\) with low social importance. Our main contributions are summarized as follows:  
* We establish the signaling game model between nodes by introducing the social importance, which is deemed to be a signal.
* We calculate the income of a node based on the prisoner's dilemma model and combine the income with the forwarding probability of nodes, rather than merely considering the cumulative incomes for all rounds.
* Compared with the random update strategy, FollowMe achieves more effective network performance in terms of routing.  
The remainder of this paper is organized as follows. Section 2 reviews related work. In Section 3, we introduce the preliminaries. Section 4 describes the FollowMe solution. In Section 5, we apply FollowMe to the classical opportunity routing algorithm PRoPHET and SimBet. Section 6 presents the experimental results and analysis. Section 7 concludes the paper.
# A Comprehensive Review and Assessment of Cybersecurity Vulnerability Detection Methodologies
## 6 Discussion and Synthesis
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Domain** & **Features** & **Connected** \\ \hline \multirow{8}{*}{IA-based approach} & \multirow{3}{*}{\begin{tabular}{c} Vulnerability detection \\ based on Deep \\ and Machine Learning. \\ \end{tabular} } & Deep and Machine Learning (CNN, DNN, RNN, \\  & LSTM, BLSTM, FNN, VAE, GNN, AEs, GANs, DRL, \\  & & RF, LR, DT, ETC, VC, BC, AC, GB, XB, GRU, DBN, \\  & & MLP, K-fold Stacking Model (RF, GNB, KNN, SVM, \\  & & GB, AdaLR, ADA, SVC, RFC, XAI)). \\ \cline{2-3}  & & Large Language Model (LLM, GPT-2, GPT-3, GPT-3.5, \\  & & GPT-4, Llama, PaLM2)-Metaheuristic algorithms \\  & & (Genetic Algorithm (GA), Genetic Programming (GP), \\  & & Particle Swarm Optimization (PSO), \\  & & Teaching–Learning-Based Optimization \\  & & (TLBO), among others). \\ \hline \multirow{4}{*}{\begin{tabular}{c} Feature \\ model-based \\ approach \\ \end{tabular} } & \begin{tabular}{c} Vulnerability feature \\ model-mapping, \\ dependencies, and \\ correlations of \\ system components. \\ \end{tabular} & Cybersecurity knowledge base, reverse engineering, \\  & & metamodel, Algorithms FM (SubFM/Vendor, \\  & & SubFM/RC and SubFM/Tree), FaMaPy. \\ \hline \multirow{4}{*}{\begin{tabular}{c} Graph-based \\ approach \\ \end{tabular} } & \begin{tabular}{c} Vulnerability detection \\ based on graph structure \\ information related to target \\ input and strengthened by \\ certain AI techniques. \\ \end{tabular} & AST-PDG-CPG-Gremlin graph-EDG- \\  & & Graph-based analytic-Graph traversal- \\  & & Threat knowledge graph -GNN-SPG- \\  & & LLM and AI model. \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Matching- \\ based \\ approach \\ \end{tabular} } &
\begin{tabular}{c} Vulnerability detection \\ based on string-matching \\ algorithm and AI models. \\ \end{tabular} & RE–Levenshtein edit distance–TF-IDF- \\  & & Ratcliff/Obershelp–fuzzy matching; \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Connected papers, from 2016 to 2024, related to vulnerability detection per approach. In addition, the graph-based approach describes target components, their relationships, and potential dependencies (asset inventory, snippet code, software system, CVE, CPE, etc.). Other AI models can thoroughly analyze every system component for vulnerabilities. The training phase of models can use data from the graph representation (data, flow, and control). To prevent a high rate of false positives and negatives, it is helpful to build an accurate representation and reflect an exact dependence. Furthermore, accurate and thorough representation helps pretrain the model, improving performance with LLM or other AI models.
## 1 Introduction
In Section 2, the tracking algorithm based on modified aerodynamic model (MAM) is proposed, including the HGV aerodynamic model, radar measurement model and the MAM tracking algorithm. In Section 3, the multiple model tracking algorithms are proposed, including IMM tracking algorithm, AGIMM tracking algorithm and HGMM tracking algorithm. In Section 4, two typical HGV trajectories are generated and tracked using different algorithms by simulation, and the tracking performances are compared and discussed systematically. Finally, some valuable conclusions are drawn in Section 5.
# A Novel Effective Multipath Routing Technique Providing High Availability in Wireless Networks
## 5 Simulations and Discussion  
### Simulations  
To validate the analytical results derived in Section 4, several simulations have been conducted using the network simulation tool OMNeT++ v4.6 [20].
## 2 Materials and Methods
### Microcapsule Characterization
#### 2.3.2 Encapsulation Efficiency (EE) Determination  
EE was determined as described in [18]. Before each experiment, the weight of the container without the sample needs to be constant, and the specific method was determined as follows: The glass beaker was dried at 80 \({}^{\circ}\)C in the blast drying oven (LICHEN, 101-00 BS, Shanghai, China) for 1 h and then transferred to the glass desiccator (KESD, Shenzhen, China) until room temperature, and the weight was A\({}_{0}\). Petroleum ether (5 mL) was shaken vigorously at room temperature for 5 min with FS, FG, SS, and SG (0.5 g). The solvent was recovered by filtration and the residue was washed with petroleum ether (5 mL). The surface oil was calculated according to the mass difference of the pre-weight glass beaker (A\({}_{2}\)).  
The extractor was treated the same way as the glass beaker and weighed as A\({}_{1}\): by precisely weighing FS, FG, SS, and SG (0.5 g), and extracted with petroleum ether at 80\({}^{\circ}\)C in a Soxhlet apparatus (Hanon, SOX406, Shanghai, China) for 4 h. After extraction, the solvent was evaporated completely to constant weight. The total oil was calculated according to the mass difference of the pre-weight extractor (A3) [18]:  
\[\text{EE (\%)}=1-(\text{Surface oil}/\text{Total oil})\times 100\% \tag{1}\]  
\[\text{Surface oil}=\text{A}_{2}-\text{A}_{0}\text{, Total oil}=\text{A}_{3}-\text{A}_{1}\]
# Improving IoT Data Security and Integrity Using Lightweight Blockchain Dynamic Table
## 5 Proposed Solution
### Key Exchange Mechanism  
One of the core cryptographic building blocks, key exchange (KE), allows users to establish secure communication by exchanging symmetric keys. Therefore, to establish data encryption and blockchain generation, an AES key has to be generated and exchanged between the server and IoT node [47].  
In the proposed solution, the data have been encrypted with four key parts to maximize the security and efficiency as mentioned before. This is required to exchange those keys between the server and IoT node in a secure mechanism.  
First, both the server and IoT node generate new ECC public and private keys. Then, the server generates the four keys of AES with 64 bits. The IoT nodes send the ECC public key to the server so it can use it to encrypt the four AES keys and then re-send it to the IoT nodes. The data encryption using the ECC algorithm is heavier than data decryption, as it requires more mathematical operations; therefore, such a heavy technique will be performed on the server side to ensure that it does not affect the efficiency of the IoT node. In addition, for the best performance, the TinyECC algorithm was used [48].  
Figure 5 summarizes the key exchange mechanism in the proposed solution using the TinyECC algorithm.  
Figure 5: Key exchange mechanism.
## 5 Hardware System Design  
FPGA's inherently parallel architecture enables it to be used as a hardware accelerator to improve the performance from execution time perspective. Thus the objective is to exploit the available parallelization in the algorithm with respect to the hardware. It is achieved either by executing tasks concurrently with the help of multiple processing units or by pipelining the computational tasks. The former results in excessive use of available resources. Thus can only be used with a limited number of tasks and iterations. Pipelining increases the throughput of the given design. In ideal parallel program structure, pipelining initiation interval (_ii_) is 1, which means next input can be applied to the pipeline after one clock cycle. This way the pipeline stages are always busy and maximum possible throughput is achieved. Bigger values of _ii_ correspond to the lower gain from the pipelining, for instance \(ii=n\) implies that pipeline stage stalls after each operation for (\(n-1\)) clock cycle. There are various performance bottlenecks which restrict the associated gain from the parallelization.  
Sequential dependencies within the algorithm substantially limit the performance of the hardware. Therefore, it is essential in designing process to define all the sequential dependencies between internal sub-modules of the algorithm. Figure 8 illustrates the UML composite internal structure diagram that includes dependencies between different components of the algorithm. Each component (sub-modules) of the algorithm requires an interface with the external storage that retains the input and output data related to the algorithm. Similarly, these components also require an interface with internal storage to save their intermediate results. Depth-evaluation depends on _best-match computation_ that can only start processing as soon as the pre-processing and non-parametric transform come to an end. For this reason, all three components cannot start executing independently. However, intra-component tasks can be executed in parallel, such as defining valid pixels and defining lens type are independent of each other and hence can run concurrently. The next sections explain how the algorithm is adapted and optimized with respect to the performance.  
Figure 7: Two parallel micro-images depicting the epipolar geometry of the stereo pair.