dataesr/scientific-paragraphs-categorization huggingface dataset:
Explaining perception of saltiness and fat The individual studies showed different relationships between lipid composition and salty taste. To better understand the influence of lipid content and fat perception on salty taste, a complementary analysis was carried out on a limited number of projects and samples, for which the intensity of fat perception (fatty descriptor) was available. A subset of 22 samples from three projects (Adi, Boisard, Tarrega projects), in which fatty and springy descriptors were assessed, was extracted. We included in this subset other sensory descriptors (crumbly and sticky) and estimations of these descriptors in the case of the Adi project. Thus, this subset contains a total of six sensory descriptors (2021-01-22_cheese-compo-rheo-senso_Guichard-V3. tab, sheet: dataset_22samples-fatty).
Another important result is that MNFS is a very relevant parameter to predict sensory hardness. In the analyses of the different datasets, the sodium ion content was positively correlated with the Young's modulus and the fracture stress. This supports the knowledge that sodium ions strongly participate in the protein network. In cheese, salt addition improves casein hydration due to a strong binding of sodium ions  (Floury et al. , 2009a,b) , which can influence cheese texture differently according to the type of cheese and its composition in other cations, such as calcium ions. The effect of salt content on cheese texture varies according to the manufacturing process. In rennet-induced lipoprotein gelled matrices, an increase in salt content and pH induced a decrease in hardness and firmness, which was explained by an effect of the substitution of divalent calcium ions by monovalent sodium ions in the casein network  (Lawrence et al.
Acknowledgements This work was supported by the  Qualiment Carnot Institute  from  French National Research Agency  through the  NutriSensAl  project (grant number  16CARN002601 ) and by  French National Research Agency  through the  DataSusFood  project (grant number  ANR-19-DATA-0016 ). We thank  Caroline Peltier  ( ChemoSens platform in CSGA ), for missMDA computing.
[8] , and the APSE MEAM and APSE MED genomes from the H. defensa MEAM and MED strains were aligned using MAFFT  [76]  in Geneious (v. 10; Biomatters  [77] ). Protein similarities were assessed by extracting coding sequences (herein genes) followed by translation and alignment in MAFFT. Genes conserved across all haplotypes were named as designated by van der Wilk et al. [8]  for APSE1 while genes present in some but not all haplotypes were named as designated in subsequent studies  [8, 56]. To assess whether frame shifts observed in p24 from APSE MEAM potentially disables virus production, we used genome sequencing depth as a proxy by accessing the wholegenome shotgun sequence library from the whitefly species in the Bemisia tabaci complex that is the host for H. defensa MEAM (SRR3180082)  [38, 78]  and converting the data from SRA format to fastq using the SRA-Toolkit v.
Candidate ortholog identification We identified candidate orthologs by searching the NCBI non-redundant (nr) database using BLASTP  [80]  for each gene in the APSE3 AS3 prophage genome. Searches were limited to either entries classified as Caudovirales or γ-Proteobacteria. Results were downloaded as tab separated values and filtered to find the best hit for each gene predicted in the APSE genome. Additional searches were conducted to identify candidate homologs of genes absent in APSE3 AS3 genome, but present in other APSE genomes, including cytolethal distending toxin (cdtB) found in APSE8 ZA17 and shiga toxin (stxB) found in APSE1. Additional focused BLASTP and BLASTN searches of NCBI whole genome sequence (wgs) and Ref-Seq genomes databases were used to identify proviral elements that shared multiple homologs with APSE.
Identification of APSE-like elements in bacterial genomes We used a TBLASTX search with APSE3 AS3 genes serving as queries to identify candidate APSE prophage elements in bacterial genomes. Here we searched NCBI RefSeq genomes and wgs databases targeting bacterial genomes classified as Morganelleaceae, Enterobacteriaceae, Pectobacteriaceae, Yersiniaceae, and Erwiniaceae. Once an APSE-like prophage element was discovered, we manually identified the ends of each by comparison with other APSE genomes and then ascertained the extent of shared homologous regions by visualizing alignments using BRIG  [81]. We formally compared each phage-like element to APSE using whole genome TBLASTX (V. 2. 9. 0+) and visualizing the results using EasyFig. py (V. 2. 2. 3)  [82]. We used CD-search to identify toxin-encoding genes in the newly described APSE genome from Arsenophonus species  [83].
Genome organization and sites of integration are conserved among APSE haplotypes Sequencing of the MI47, MI12 and 5D strains of H. defensa showed that each contained an APSE in the main host chromosome as a single copy provirus. We then used these APSE genomes together with other sequenced haplotypes  [8, 38, 51, 56, 59]  to compare overall features (Table  1 ). Total genome sizes ranged from 36,522 bp for APSE1 MI47 to 39,884 bp for APSE2 NY26 (Fig. 1 ). Predicted genes further ranged from a low of 41 for APSE3 AS3 to a high of 47 for APSE2 NY26, APSE8 ZA17, and APSE8 5D (Fig. 1 ). BLASTP searches against the NCBI nr database yielded predicted functions for 37 genes while 13 others were classified as unknowns or hypotheticals (Fig. 1 , Table  2 ). Comparing each predicted gene across haplotypes indicated that APSE2 5AT and APSE2 NY26 were nearly identical to one another at the amino acid level (99.
Modules 1, 2 and 4 share features with other phages Given the conservation in gene order and content of modules 1, 2 and 4, we selected APSE3 AS3 as a model haplotype and used BLASTP to ask if any genes shared > 60% identity with predicted products from other fully sequenced viruses. Three genes in module 1, two genes in module 2, and seven genes in module 4 were identified that met this criterion, with each best hit being to another phage assigned to the families Podoviridae or Siphoviridae (Table  2 ). TBLASTX analysis corroborated previously noted similarities in gene order and content between the virion assembly module of APSEs (module 4) and P22-like phages that infect bacteria in the order Enterobacterales  [68]  including Salmonella virus HK620, Shigella flexneri phage Sf6, Morganella phage NV18, and S.
Phylogenetic analyses Since phage elements with similar gene order in APSE modules 1 and 4 were identified in some symbiont and enteric species of Enterobacterales, we generated phylogenies using two module 1 genes, p41 (helicase) and p45 (DNApol), and two module 4 genes, p19 (Portal protein) and p24 (Major capsid protein). These genes were selected to capture phylogenetic signal from each module and represented genes for which we could easily obtain orthologs from other phage and bacterial genomes, hence providing phylogenetic signal. APSE MEAM and APSE MED exhibit structural mutations in p45 that suggest they are pseudogenized  [38]  while BLASTP identified frame shift mutations in p19 from Arsenophonus sp. ex. Aleurodicus floccissimus. Phylogenetic trees further suggested inclusion of p45 and p19 pseudogenes generated phylogenetic error; namely long-branch attraction due to increased rates of nucleotide substitution in pseudogenes.
Availability of data and materials Nucleotide alignments, phylogenetic trees, and prophage assembly from Arsenophonus nasoniae have been deposited in figshare https:// doi. org/ 10. 6084/ m9. figsh are. 14744 397. Genome assemblies of Hamiltonella defensa including proviral elements were obtained from NCBI https:// www. ncbi. nlm. nih. gov/ and include CP017605-CP017610, CP022932-CP022937, CP021663-CP021665, and CP023987-CP023990. Raw sequencing data used in the assemblies of APSE genomes are available on NCBI and include SRR15674828-SRR15674831, SRR15676030, SRR1585572, and SRR15691200.
1) DATA COLLECTION Data collection is the first step in data journey through ITS applications. It provides the capability to gather all basic observable measurements (e. g. , location, speed, neighboring vehicles, road traffic condition, and average travel time) from multiple sources of data (e. g. , road data, vehicle data, driver/passenger/pedestrian data, and traffic flow data) to be exchanged between vehicles and roadside units  [32]. Because of the significant safety implication related to vehicular networks, it is critical to develop reliable collection solutions that take into account VANET characteristics (e. g. , mobility and time sensitivity). In the literature, several contributions propose different architectures and schemes to support efficient data collection. In  [33] ,  Touil et al.
1) INTRA-VEHICLE DOMAIN The number of electrical components and embedded devices in modern vehicles is continually increasing. A multitude of interconnected embedded computer systems, called Electronic Control Units (ECUs), have been widely used in vehicles forming a distributed network to control a broad range of automobile functions  [42]  including powertrain and in-vehicle infotainment. In general, ECUs can communicate with each other over many in-vehicle bus communication networks  [42] -  [44] : Controller Area Network (CAN), Local Interconnect Network (LIN), FlexRay, Media Oriented System Transport (MOST), and Ethernet (see Table  2 )). The use of each one of them depends on the criticality, cost, bandwidth, and timing requirements of the desired functions. CAN and FlexRay are mainly designed to provide a low-cost and fast data transmission; this makes them more appropriate for critical applications, such as powertrain and safety control.
It is specified as CWE-120 under the CWE dictionary of weakness types. Typically, it occurs when a program overruns the buffer's boundary and overwrites adjacent memory locations while writing data into a memory buffer. By the exploiting this vulnerability, it becomes easy to inject malicious code into a program to gain illegitimate access to the targeted system. In 2017, a stack-based buffer overflow issue was discovered by Shkatov et al. [84]  in several vehicles, including BMW and INFINITI. These vehicles had telematics control modules (TCUs) which are built by Continental AG; they contain the S-Gold 2 (PMB 8876). The exploitation of this vulnerability allows the attacker to disable the infotainment system and affect the functional features of the vehicle. There are advanced types of injection vulnerabilities.
3) ATTACKS ON CONFIDENTIALITY Attacks targeting confidentiality aim to disclose network sensitive's information to an unauthorized party. Due to the message broadcast characteristic in VANET, data exchanges could be easily compromised. Passive attacks (AKA Eavesdropping, sniffing, or snooping attacks) do not disrupt the operation of the network; they passively target weakened and unsecured connections to cause privacy leakage. The basic idea is to maliciously intercept relevant traffic in order to gain access to sensitive information such as credentials, personal location, or node configuration. Based on the extracted information, the attacker may continuously capture and analyze broadcast messages to track nodes based on their physical positions  [122] -  [124]. The impact of passive attacks could be very high since it could be used as a preliminary stage to perform more sophisticated and destructive attacks (e.
Timing attacks can be performed by an attacker who intentionally adds some extra timeslots to forwarded messages aiming to impact information freshness  [127]. Another study conducted by Arsalan and Rehman  [128]  discusses thoroughly timing attacks in VANET. They propose a scheme, called Timing Attack Prevention (TAP), to detect and mitigate this type of attacks. The proposed scheme eliminates the risks of delayed and duplicated emergency packets by controlling the broadcasted messages. This is done through the use of Software Defined Network (SDN) controllers and Named Data Networking (NDN) communication paradigm in VANET.
Islam et al. [134]  report that the use of either elliptic curve or bilinear-pairing causes a heavy computational burden making them infeasible for VANET. Thus, to overcome this issue, they introduce a password-based conditional privacy preserving authentication and group-key generation (PW-CPPA-GKA) scheme for VANET. The usage of this scheme allows vehicles to join or leave a regional group of nodes and also facilitates password updates. In terms of communication overhead and latency, PW-CPPA-GKA outperforms other existing schemes  [148] -  [150]. However, the authors  [134]  did not simulate the proposed scheme in realistic scenarios (e. g. , urban or highway scenarios) that consider traffic density, speed of moving vehicles, or some other metrics. Huang et al. [14]  investigate the possibility of exploiting the potential of the 5G technology in supporting higher data rates with larger numbers of connected devices to overcome the issues of public-key cryptography.
a: SUPERVISED ML-BASED IDS Signature-based IDS consists of matching an observed activity with a predefined set of rules (signatures) and patterns characterizing a well-known threat. With the use of this detection strategy, the system can accurately identify known attacks without exhausting the computational resources of the network. Supervised ML based schemes represent good candidates to outperform signature-based IDS algorithms since both rely on classification and knowledge databases  [176]. Hence, making predicting outcomes for unforeseen data becomes effective and more accurate. In the literature, there are numerous contributions (e. g. ,  [12] ,  [169] -  [172] ) that have investigated the use of supervised ML algorithms along with signature-based IDS to examine their applicability in enhancing security in VANET.
Song et al. [12]  study the feasibility of supervised ML in designing an IDS to protect the in-vehicle network (i. e. , CAN bus). The proposed model uses a Deep Convolutional Neural Network (DCNN) architecture, called Inception-ResNet  [177] , due to its superior performance in natural image classification tasks. The authors build a new DCNN model optimized for data traffic in CAN bus that provides better detection and latency performance. Simulation results show that the proposed model outperforms existing machine learning models, such as Support Vector Machine (SVM), K-Nearest Neighbors, and Decision Trees in terms of detection accuracy, training cost, and latency. However, the model  [12]  is unable to detect unlearned types of attacks. Unlike the previous work  [12]  that address the security of in-vehicle network, Eziama et al.
Simulation results, based on the NSL-KDD dataset, show that the proposed system outperforms existing schemes in terms of the convergence of collaborative Learning, the minimum training data size, and the security-privacy tradeoff. However, it cannot precisely identify the type of attacks. Furthermore, the authors in  [170]  propose a new model that uses plausibility checks and ML to detect and mitigate the risks of location spoofing attacks in VANET. In this model, a combination of K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM) has been adopted to classify misbehaviors for further mitigation plans. Moreover, the authors have introduced a friendly version of the VeReMi dataset  [178] , which is created specifically to train the ML-based models with a wide range of misbehaving traffic scenarios for testing V2X security.
b: UNSUPERVISED AND REINFORCEMENT-ML BASED IDS Because datasets cannot be exhaustive, it is extremely difficult to catch unknown threats for which no characterizing patterns are available. Thereby, considerable attention has been paid to anomaly-based IDS approaches. It provides the capability to overcome the limitations of signature-based IDS in ensuring an effective detection of abnormal behaviors by continuously checking network traffic for any deviation from legitimate network profiles  [179]. Recently, anomaly-based detection strategies can benefit from advances in the field of machine/deep learning, particularly unsupervised and reinforcement learning. The operational logic of unsupervised learning helps models crafting representative features of legitimate profiles and also generating analytic insights from patterns and structures in unlabeled data  [180].
In this regard, various anomaly-based approaches have been proposed. Furthermore, the constructed knowledge can be labeled with signatures to enrich datasets for hybrid detection strategies  [165]. The authors in  [13]  introduce a new mechanism based on unsupervised ML to detect a specific type of DDoS attacks, namely RF jamming attacks. Through clustering using the K-means algorithm, the authors have evaluated the capability of a new metric, called Relative Speed Variation (RSV), in distinguishing intentional from unintentional jamming and identifying the unique characteristics of each jamming attack. The authors do not rely on the specific characteristics of k-means algorithm  [13]. This opens up the door for further studies using different clustering algorithms (e. g. ,  [179] ,  [181] ) especially, with the potential demonstrated by RSV.
For in-vehicle security design, Hanselmann et al. [173]  propose a scheme to secure CAN buses. They propose CANet as a new deep learning-based IDS to process signals to catch unknown attacks and to detect earlier technical failures. They implemented CANet using a new neural network architecture, called Long Short-Term Memory (LSTM), to handle the challenging structure of CAN data and calculate anomaly scores. One of the strongest points of CANet, compared to existing techniques (e. g. ,  [182] ,  [183] ) is (a) its capability to work on signals of multiple CAN IDs simultaneously; (b) its high true negative rate, which is necessary for real-world applications; and (c) its reliability in detecting unknown attacks. Furthermore, Xing et al. [174]  introduce a novel intrusion detection strategy for Autonomous Vehicle Networks (AVN) based on an assessment of Autonomous Driving Vehicles (ADVs) and a reinforcement Q-learning method.
2) CHALLENGES IN ML-BASED SECURITY MECHANISMS Machine learning, deep learning, and reinforcement learning (RL) are one of the most rapidly growing fields to realize next-generation ITS. However, to achieve the full potential of ML/DL, many challenges and open issues still need further investigation. Successful ML applications require a sufficient amount of representative datasets that can be used to train models. The generation of such datasets is particularly challenging in high scale and heterogeneous systems like VANET  [184]. Even with the richness of data, it is yet challenging to develop a suitable model that processes data collected from various sources (e. g. , vehicular sensors, wireless technology, and network traffic). Complex and time-consuming steps in preprocessing and cleaning of datasets are required in order to accurately reflect the actual environment and avoid data anomalies and misinterpretation.
To cope with the challenges of the availability of datasets, the authors in  [178]  introduce the Vehicular Reference Misbehavior (VeReMi) as a first public extensible dataset specifically designed to train ML-models for the evaluation of misbehavior detection mechanisms for VANET. Security applications are not static in nature; this means ML/DL models must continuously monitor activities and analyze behaviors looking for deviations. Therefore, whenever there is an adjustment in the state of the network, ML/DL models need to be retrained according to the freshly acquired data; this leads to another challenge, namely the cost of training ML/DL models. For real-time VANET applications, it is difficult to frequently retrain ML/DL models since the process is expensive in terms processing and storage overhead.
g. , deep packet inspection (DPI), Firewalls, IPS, and IDS) into virtual security appliances (e. g. , vDPI, vFirewalls, vIDS, and vIPS). This certainly has the potential to achieve a higher level of agility and enables optimal orchestration of resource allocation  [193]. SDN and NFV paradigms are complementing each other, and both are essential parts of the 5G network. They have the potential to boost the development and deployment of secure network applications due to the capability of enabling unlimited creativity of network functionalities. Various protocols have been proposed to extend network security. In the context of security, Floodlight is a Java-based open-source SDN controller that supports virtual switches. This makes it easier to develop and test modules in a flexible and extendable way to react to changes in network configuration  [196].
Fuzzy Logic Methods FL techniques are widely used for energy management in standalone or grid-connected hybrid renewable energy systems. FL can effectively manage the multi-control functions. Several studies have been found in the literature discussing FL-based EMSs. The design and implementation of an EMS based on fuzzy control for a DC microgrid system was presented in Reference  [72]. In this work, the modeling, analysis, and control of the DERs and ES devices were completed in a MATLAB/Simulink environment. The FL controller managed the battery SOC to ensure a longer battery life and to offer better performance for the entire system at low cost. Different membership functions with variables were defined for both inputs and outputs of the system. The simulation result validated the accuracy of the proposed system with a fuzzy controller that can maintain the battery SOC at a certain level, irrespective of the amount of power generated from the microgrid system.
Existing Software Tools and Sardware Components for Microgrid EMS This section introduces both software tools and hardware components used for microgrid applications. In the literature, many open-source software tools have been used for the optimization of hybrid renewable energy systems. The most used software tool in size optimization for PV-WT HES is Hybrid Optimization Model for Electric Renewables (HOMER)  [85]. Another software, named Improved Hybrid Optimization by Genetic Algorithm (i-HOGA), has been used in several studies for sizing optimization for standalone PV-WT HES  [86]. TRNSYS and graphical user interface (GUI) are graphically based software tools and are extremely flexible  [87]. Several hardware components, such as Arduino, Raspberry Pi (RPi), and Color Control GX (CCGX), and SCADA, are explored in  [39, 88].
Software Tools Primarily, researchers throughout the world have used HOMER to optimize their suggested hybrid systems. HOMER is a computer software that can evaluate various design options for both standalone and grid-connected energy systems. Three main tasks that HOMER can perform are simulation, optimization, and sensitivity analysis  [89]. Bhakta et al. [90]  utilized HOMER for sizing, optimizing, and performing the economic analysis of a PV/wind hybrid system with a battery bank for microgrid residential application in Northeast India. In Reference  [91] , Das et al. carried out a feasibility study to find the optimal size combination of hybrid energy system for the household user in Kuakata, Bangladesh. Giannoulis and Haralambopoulos  [92] , Al-Karaghouli and Kazmerski  [93] , Bekele and Palm  [94] , Ma et al.
[95] , Adaramola et al. [96] , Olatomiwa et al. [97] , Nandi and Ghosh  [98] , Sen and Bhattacharya  [99] , and Lau et al. [100] , used HOMER to optimize their proposed standalone hybrid systems. Different locations throughout the world (Nigeria, Bangladesh, Ethiopia, Iraq, Hong Kong, Ghana, India, Malaysia, and Greece) are covered. Each survey suggested a particular combination of components which is different from others in terms of performance, according to a certain location. i-HOGA is a hybrid energy system optimization software developed by the University of Zaragoza  [101]. It utilizes GA for the size optimization of single-or multi-objective functions. i-HOGA performs optimal control strategies with less computational time compared to the use of GA alone. In Reference  [102] , Fadaeenejad et al.