dataesr/scientific-paragraphs-categorization huggingface dataset:
Deployment of Cellular Network Testbed Our Github (https://github. com/nischalaryal/cellular-network-testbed-setup (accessed on 21 February 2023)) page provides a clear description for implementing the cellular network testbed. This testbed configuration requires three components: user equipment (UE), radio access network (RAN), and cellular network (CN)  [26]. The primary goal of this configuration is to create an E2E setup, in which the UE equipment communicates with the CN via the RAN. The CN verifies the UE's authenticity, and the UE verifies that it is connected to the correct network. Following successful authentication, the UE can connect to the internet via CN. As shown in Figure  9 , the decentralized application (DApp) part of the system implements a web service connected to BCT nodes.
The web service consists of a front-end written in the Javascript language, connected to the back-end that is deployed using the Java Spring framework (https://spring. io/projects/spring-framework (accessed on 21 February 2023)). The connection to smart contracts and BCT nodes is handled by the web3j library (https:// docs. web3j. io/ (accessed on 21 February 2023)). The smart contracts are deployed in Solidity language and compiled by the solc compiler (https://www. npmjs. com/package/solc (accessed on 21 February 2023)). The code of the deployed DApp is available online, in a GitHub repository (https://github. com/FaribaGhaffari91/AccessControlUsingBCT (accessed on 21 February 2023)).
Once smart contracts are deployed, their code cannot be updated due to their immutability, which ensures their trustworthiness. To overcome this challenge, one solution proposed in  [29]  is to avoid hard-coding the addresses of smart contracts in the operational system of distributed applications. In our proposed method, we provide a solution that enables the update of contracts and replacement of the deployed smart contract with a new one. This can be done by defining SC AB as a reference smart contract to retrieve the current address of other smart contracts in the system instead of using hard-coded smart contract addresses. The admin(s) of the system can update this smart contract as needed. The second concern is related to the flexibility of attributes and policies. Defining the access control policies inside one smart contract (e.
BlendCAC  [35]  is another system that uses smart contracts for storing the access control matrix of the CapBAC model. Each node interacts with the smart contract through the provided contract address and the use of remote procedure calls. Rather than using BCT as a distributed database, the following works exploit this technology as an access validation and enforcement solution. In the field of cellular networks and telecommunications, Ling et al. [41]  proposed B-RAN, an ABAC model implemented as a BCT for access management in Radio Access Networks (RAN). This method implements self-organized access for users and providers, along with enabling mobility management. The user and network provider reach an agreement on cost and digitized spectrum assets, written in a smart contract. After validating the smart contract concerning these parameters, the user can use the resource for a limited time, and the access point will automatically receive payment for the access.
We utilize the Samsung Galaxy S4 smartphone with LTE network capability as our used equipment. A programmable SIM card connects the smartphone to the cellular network. The SIM card stores information such as secret keys, mobile country codes, mobile network codes, etc. This information helps in choosing the right network operator during the authentication process. data regarding PLMN and IP addresses. We utilize the configuration file named enb. band7. tm1. 50PRB. usrpb210. conf to store the PLMN values, IP addresses, and network interfaces of RAN and CN. • Core Network: We utilize OpenAirInterface LTE+ software to implement the core network in the COTS system. The COTS system configuration includes an Intel Xeon W-2102 CPU running at 2. 90 GHz, 16 GB of RAM, and Ubuntu 20. 04 OS. The OAI-based core network utilizes the master branch (https://github.
com/OPENAIRINTERFACE/ openair-epc-fed (accessed on 21 February 2023)) and the MagmaCore uses version 1. 8 branch (https://github. com/magma/magma/tree/v1. 8 (accessed on 21 February 2023)). All the modules of the core network are containerized using either Docker or VirtualBox. The MME functionality of CN initiates a connection with RAN and manages all requests arriving from UE via RAN. The information the user, such as secret keys, is stored in the HSS database and is utilized during the authentication process.
com/OPENAIRINTERFACE/ openair-epc-fed (accessed on 21 February 2023)) and the MagmaCore uses version 1. 8 branch (https://github. com/magma/magma/tree/v1. 8 (accessed on 21 February 2023)). All the modules of the core network are containerized using either Docker or VirtualBox. The MME functionality of CN initiates a connection with RAN and manages all requests arriving from UE via RAN. The information the user, such as secret keys, is stored in the HSS database and is utilized during the authentication process. We utilize a SIM card reader and pySim (https://github. com/osmocom/pysim (accessed on 21 February 2023)) software to program the SIM card and store the necessary information. • Radio Access Network: We employ OpenAirInterface5g (https://gitlab. eurecom. fr/ oai/openairinterface5g (accessed on 21 February 2023)) software to implement a softwerized RAN in COTS system.
Similar modeling approaches building on analytical formulations for ground temperature and active layer thickness have also been applied at regional scales (e. g. GIPL1 in Alaska,  Sazonova and Romanovsky, 2003). Being an equilibrium model, CryoGrid 1 is generally not well suited for climate change simulations, missing transient processes. For instance, changes of the model forcing impact ground temperatures without time delay in the this equilibrium model, when in reality subsurface processes like ground ice melt delay the response of ground temperature. Therefore, the transient model CryoGrid 2 is employed for mapping climate change impacts, using similar spatially distributed forcing data sets as CryoGrid 1  (Czekirda et al. , 2019; Westermann et al. , 2013 Westermann et al. , , 2017)).
Efficient Global Optimization (EGO)  [25]  is one of the most powerful existing approaches for global optimization using response surfaces. It is particularly suitable for the optimization of black-box problemsi. e. , problems with no explicit formulation of the objective function to be optimized -where only a limited budget of function evaluations is available. This is the case for the optimization of parameters in a numerical model, where each objective function evaluation is computationally expensive since it is read from the output of a finite element numerical simulation. This paper focuses on an optimization study performed on flax/epoxy composite laminates impacted at three energies -5 J, 10 J, and 15 J -using the commercial software LS-OPT  [26]. In particular, the main objective of the optimization strategy is the numerical characterization of the material in order to achieve a uniform parameter configuration, regardless of the energy level tested.
To characterize the material, we first perform a series of experimental tests, including tensile tests, four-point bending tests, and LVI tests. Second, we design a finite element impact model and simulate it using explicit software LS-DYNA  [27] , which provides different damage mechanic material models for composites. In this study, we use a MAT54/55 card  [28] , described in Section 4. Then, we perform a sensitivity analysis to select the parameters with the greatest influence on the output response. Finally, we use EGO to find the parameter configuration that best reproduces the experimental load-displacement curves for the different impact energies. Here we consider a multi-target approach aimed at obtaining satisfactory results for multiple energy levels, which we believe is a good practice to achieve a good level of generalization in material characterization.
Dynamic time warping Dynamic Time Warping (DTW) is one of the methods available in LS-OPT to calculate the distance between two curves. Only recently (from LS_OPT version 6), DTW has been introduced in parameter identification problems as a powerful method for matching simulated and experimental plots. It considers both the shape and the discrepancy between two curves in the abscissa direction, unlike the mean squared error (MSE) which only considers the pointwise difference in the ordinates. The distance between two datasets is computed through a warping path, which represents the minimum cumulative distance required to traverse all points in the curves. If the curves have not the same length, as in this case, the option Truncate computed using target must be selected  [26] , leading to the Partial Dynamic Time Warping (DTW-p) method.
Acknowledgments The authors would like to thank  HP Composites SpA  for the realization and provision of the laminate materials. The authors also thank  Prof. Alessandro Scattina  for the assistance in the experimental tests carried out with professional equipment in the laboratories of  Politecnico di Torino. Elena Raponi  acknowledges funding by the  PRIME programme  of the  German Academic Exchange Service (DAAD)  with funds from the  German Federal Ministry of Education and Research (BMBF).
Acknowledgements. The authors acknowledge the support of the  Observatoire de Paris , the  CNRS/INSU , and the  ANR (french 'Agence Nationale de la Recherche')  via the program  NT09-635931  ' Study and Prototyping of a Super Station for LOFAR in Nançay '. Support to nenuFAR from  Region Ile-de-France program DIM-ACAV. Support to  NenuFAR from Region Centre Val de Loire via the Initiatives Académiques de l'Univ. Orléans-Tours. The authors thank  Frederic Boone  for his useful inputs.
In complement to the CML baseline method, an energy analysis was conducted following standards for considering primary and secondary energy (IFIAS, 1978) using the Cumulative Energy Demand (v1. 09) impact assessment method, also known as CED  (Frischknecht et al. , 2007). The model itself was initially developed in SimaPro (v9) but was transferred over to Microsoft Excel 2016. The impact factors of the production system were sourced from one of three databases: Ecoinvent (v3. 3), USLCI 2015 and ELCD (2016). The names of all processes selected from these databases are provided in table B of the "Textual supplementary. docx".
2. 1 and 2. 2. 2. In addition to these 4 reactors, three smaller bench-scale reactors or mini-reactors (MR1-3) mimicking the design of the LEB are also assessed, but varying in terms of the light distribution configurations (described in Section 2. 2. 2). To enable a thorough comparison of all reactor technologies and subsystems, and as shown in Fig. 2 , the cultivation system components are separated into seven sub-systems (based on the provision of water, air, nutrients, light, agitation, physical reactor infrastructure and maintenance), harvesting systems are split into three sub-subsystems (harvest transfer, electrocoagulation and centrifugation) and the downstream processing into biostimulant was split into two sub-systems (cellular disruption and enzymatic hydrolysis). For each sub-system, average operational data was acquired from the pilot facilities in Almeria, Spain, and is based on measurements of average energy and material consumption over time (under usual local conditions).
85 kW of LED strips. Finally, MR3 consists of an arrangement of 19 internal light tubes (Ø 40 mm) also made of glass, lit internally by 2. 28 kW of LED strips. The tubular external structures in MR1-3 are not liquid filled. These three miniaturised versions of the LEB serve to explore effects of internal light distribution on yields, but also on the scaling potential of the LEB system. The biomass yields for the novel reactors (LEB, and MR1-3) are based on data from a combination of preliminary batch and continuous culture trials between 2019 and 2021, whereas data averages for conventional reactors (ORP, TLC, and VST) are collected from commercial pilot units operating in 2019 (see digital supplementary material. xlsx for datasets, inputs, and emissions).
Sensitivity analysis To manage uncertainties and likely variations of operational input data of the LCA model, a sensitivity analysis was performed. Key impact parameters considered to be subject to a high degree of uncertainty or that were identified as responsible for impact hotspots were selected for further analysis. First, the average biomass concentration at harvest measured during the testing was varied by ±25 % to illustrate the effect of varying this particularly sensitive parameter across a wide yield range. The second key parameter affecting all reactors was the type of electricity mix selected and its associated impact factors, which in the base case of operation, is a Spanish electricity mix with a climate impact of 0. 13 kg CO 2 equivalents per MJ of consumed electricity. To illustrate how the selection of different typical energy sources would affect the performance of the reactor, the climate impact of each MJ consumed was varied from 0.
e. per kg biomass (DM). The supplementary material also contains a. csv file of this LCA model, as developed using Microsoft Excel. Therein is contained all other information necessary to trace and identify specific processes (and database origins) for each item in the inventory. Nutrient and carbon inputs to the LEB under modelled urban-industrial symbiosis conditions, as described in Section 2. 2. 5, are considered as waste streams from wastewater treatment and wasteincinerator plants. These are therefore modelled as avoided emissions to the environment and are credited with impact mitigation effects in terms of eutrophication and climate impacts, respectively. Similarly to the comparative LCA of reactor technologies conducted by Pérez-López et al. (  2017 ), all other environmental burdens were allocated to the biomass yielded by each reactor.
Acknowledgements Authors would like to acknowledge guidance provided by  Professor Elzbieta Plaza  regarding nutrient speciation in wastewater and applications thereof,  Researcher Rajib Sinha  for his guidance in applying material flow analysis modelling methods in Life Cycle Assessment, Associate  Professor Miguel Mendonca Reis Brandao  for his guidance on carbon credit methodologies, and  Professor Maria Malmström  for her guidance with regards to application and integration of environmental modelling techniques.
1% protein tertiary structure similarities (median values, native Spike). Machaon's meta-analysis revealed Spike's relationships with biological processes such as ubiquitination and angiogenesis and highlighted different patterns in virus attachment among the studied variants. Available at: https://github. com/anastasiadoulab/machaon. structure-based approaches, such as the Dali server 10 and RCSB PDB 11 search, emphasize on high rate of structure similarity with fixed thresholds. Performance-wise, most standardized metrics for structural comparison 12 such as the Template Modelling Score (TM-Score)  13  require prior superposition of whole structures, which is a computationally intensive task. It is becoming apparent from the above that there is an unmet need for structural comparison. Towards that direction, newer attempts appear such as the recent structural search module of RCSB PDB, BioZernike 14 that relies on electron density.
The method assigns each candidate protein to a cluster by its metric vector and chooses a cluster by considering a mixed ranking of the metrics. Machaon builds a profile of each finalist protein with metadata from established third-party services including UniProt  25  , RefSeq  26  , QuickGO 27 (Supplementary Fig. 1 ) and performs filtering by the coding gene. The method extends this profile by computing established metrics for tertiary and chemical structure similarity and by performing sequence alignments between the transcripts and protein secondary structures of each identified protein and the reference molecule. Finally, Machaon provides indications on the reference protein's functions based on the accumulated information of the selected proteins, pinpointing potentially related areas in the reference protein 2D structure.
Validation of method's accuracy on public datasets To validate the method, we appraised the accuracy and limitations of Machaon on tasks of whole structure comparisons on different datasets and cases (Table  1 ). The datasets are retrieved from public repositories to avoid biased results. Machaon compares two proteins in an alignment-free manner and detects not only proteins with a common domain but also ones with structural similarity resulting from smaller/dispersed common fragments, covering cases where a protein does not have large consecutive similar chunks with a target protein set. Therefore, it cannot be directly compared with methods focusing on structure alignment or being evaluated on restricted domain searches. The interpretation of our tests emphasizes on the structural similarity of the identified proteins which is an effective validation strategy since there is not a universal ground truth available  17.
We measure 3D similarity with TM-Score  28  , an established metric which is independent of protein length, and 2D sequence identity from 2D fold sequence alignments (see Methods: 'Enrichment and assessment of the selected candidate entries'). The results are pseudo-labeled according to the different criterion of each task for the purpose of validation. Firstly, we investigated Machaon's detection scope on identifying proteins of the same CATH  29  family utilizing BioZernike's validation set. This dataset is a non-redundant set of 2685 structures belonging to 151 CATH families. We employed Machaon for a random protein per family in the set (Fig. 2a ) for families with more than one occurrence. We labeled the protein in the results that belong to the CATH family of the reference protein as true positives.
The method found such proteins with high recall, specificity and low precision, f1-scores close to zero since many more structurally relevant proteins were included in the final results (Table  1 ). The true and false positives had high and medium-to-low TM-Scores respectively and this conveys that Machaon operates outside the CATH system (Fig. 2b ). A subset of proteins with different superfamilies/families in the final cluster exhibited higher structural similarity than some proteins of the target CATH hierarchy, which is in accordance with previously reported observations  21. These identified proteins would have been overlooked if our method had a search criterion restricted to the CATH classification. The second task involved the dataset of SHREC 2018 challenge  30  (2267 structures deriving from the conformational space of 107 proteins) and a series of comparisons between every protein represented more than once and the rest of the proteins in the set (Fig.
We extended our evaluation to a third task involving a non-redundant dataset PDB70 (~15211 structures) that represents the classified proteins in SCOPe 2. 07 from a snapshot of the entire PDB (2018) and was targeted in the SCOP140 benchmark  31. The provided dataset consisted merely of ATOM records, so we kept just the first conformation in each file for compatibility (1867 PDB files were edited). We also excluded d2zjq51/q105A domain that contained only the coordinates of alpha-carbons (no angle information available), updating the ground truth table. Fmax scores were computed with the benchmark's publicly available code for the untruncated final cluster per query domain, evaluating the ranking of the proteins in the results against the target SCOP classification (Table  1 ). The low scores conveyed that Machaon does not conform to SCOP classification but identifies structurally similar proteins beyond SCOP folding classes (Fig.
Identifying structurally similar proteins to SARS-CoV-2 Spike To further validate the method, we applied Machaon on a large number of structural comparisons between Spike protein monomers of SARS-CoV-2 variants (native  32  , Delta  33  and Omicron  34  ) as references and a dataset of ~12500 PDB files containing ~40000 structures (median resolution: 2. 64 Å) belonging to proteomes of viruses and other organisms (Fig. 3 ). We compared the proteins considering their whole available structure but also in a more localized scope such as domains or parts resembling to the binding sites of the reference molecule. Then, we investigated properties of the Spike protein and its domains based on the emerging information by the aforementioned comparisons. As expected, whole structural comparisons for each of the three Spike variants (native, Delta, Omicron) revealed several viral proteins in the top similarity results belonging to the family of Coronaviridae.
On a global scope, ACE2 PDB structure has 15. 68% 3D similarity and 15. 8% 2D sequence identity with the reference Spike protein PDB of native strain, meaning that they share common short structural areas that could include functional structural motifs. Additionally, the transcripts of ACE2 and Spike protein exhibit remarkable similarity (38. 79% 5'-UTR, 37. 72% CDS and 17. 59% 3'-UTR sequence identities) and that prompts to a potential correlation between Spike and ACE2 gene expressions.
These files contain data on the intersection of the residue positions present in the three original PDB files and therefore each one covers the same residue positions. Meta-analysis of the results concerning the virus attachment process, convey an altered binding pattern between the three proteins that could relate to the different infection rates (Fig. 7d-f ). Additionally, this difference can be attributed to structural differences between the proteins as we observed by comparing their secondary structures (Supplementary Fig. 9 ). We also performed meta-analysis on the results of the NTD, CTD and RBD domains (native Spike) and we came across links with host biological processes: angiogenesis  48  and CTD, blood coagulation  49  and RBD, heart-related  50  processes and NTD (Fig. 8 ). Similar secondary structural elements were identified with short protein domains in the dataset that could potentially pose as structural equivalents to short linear motifs (SLiMs)  51.
Evaluating the core metrics of Machaon We tested how different setups would alter the previously presented results on Spike protein, showcasing the robustness of the metrics. Firstly, we computed the metrics on different versions of SARS-CoV-2 Spike protein: alternative resolutions, conformations, lengths and variations (516 PDB chains). The measurements suggested that most of these versions would have been identified if they were present as unknown proteins in the whole structure comparisons results for Spike. The median values of the metric vector [B-phipsi, W-rdist, T-alpha] in the final cluster were [0. 0180, 0. 9410, 0. 0378] and for the Spike proteins set were [0. 0025, 0. 7334, 0. 0666] (Supplementary Data 4). We also performed an alternative search session with Machaon having a preprocessed version of Spike's protein PDB structure (see Methods: 'Prediction of Spike protein's binding sites') as a reference.