dataesr/scientific-paragraphs-categorization huggingface dataset:
As GPR does not penetrate through the clay layer, ERT was able to map the soil strata, identifying anomalies with high organic contents. It was concluded that heaving was caused by high organic content. ASCE  [204]  To develop a rapid testing methodology and testing parameters to assist in detecting and quantifying tented cracks in asphalt pavements by using a multi-sensor non-destructive testing system with a single positioning and navigation system GPR, LiDAR, IRT, video cameras, IRI GSSI 400 MHz ground-coupled antenna The study introduced two parameters; peak height value (PHV) and spacing between two consecutive peaks (SBP) to assess the intensity and frequency of tented cracks; (i) these parameters have a strong relationship to the ride quality indicator IRI; (ii) these parameters can be used to determine the extent and location of critically affected segments of the road; (iii) GPR, LIDAR, video, thermal, the IRI, and pavement inspection data were used to successfully validate the parameters, which were obtained from surface profile measurements; further; (iv) GPR can be used to detect moisture conditions that contributes to the formation of the tented cracks.
ASCE [213] To propose a combined use of GPR, IRT, and TLS techniques to detect road deterioration and its possible root causes GPR, IRT, RGB, TLS Ground-coupled antennas with 500 and 800 MHz central frequencies Joint interpretation of data from different non-destructive techniques can lead to efficient maintenance planning by detecting the internal damages that can affect the internal structure before having any sign of the damage on the surface. (i) GPR is used to identify the defects through the scattering attenuation of the GPR signal, while IRT is used to detect areas with deep cracks or material loss based on their higher surface temperature compared to the surroundings. TLS is used to measure the width and length of any defect detected at a superficial level. (ii) The relation between surface defects and their subsurface root causes was associated by integrating the interpretation of data from the three techniques.
Elsevier [120] GPR scans were effective to a depth of 5 m for identifying near-surface materials, while ERT profiles were beneficial when the bedrock was at depths over 5 m, and they were used for estimating the elevation of the bedrock surface in the study area. GPR data captured using 450 MHz had the best resolution, and it was possible to detect the underground infrastructures in the area. ERT data complemented the interpretation where there were surficial conductive clays, causing GPR signal attenuation. Clay deposits corresponded to areas of low resistivity less than 100 Ω. m while the resistivity of the bedrock has been estimated at values more than 600 Ω. m. Average bedrock depth map for the streets was created based on the interpreted data from GPR and ERT. Elsevier  [216]  To use laser scanning and GPR complemented by finite element method (FEM) calculations to diagnose cracks in bridge approach pavement.
Progress, assessment methodologies, and maintenance techniques are highly dependent on the quality of interpreting the data-driven from field case studies or construc-tion sites. For this reason, the use of new emerging technologies, such as digital twins, is crucial. Furthermore, extracting detailed information and predictive models from GPR combined with other NDTs datasets is highly recommended. Such results could be incorporated with data-driven road-based sensors for real-time monitoring and predictive maintenance. This could deliver significant supporting tools for decision-making owners/operators for road assessment management plans. In the same context, the new trends and current knowledge of using intelligent data analysis such as machine learning and deep learning are employed within the project's lifecycle to automate the process and obtain most of the available data.
It can be used in multiple phases, from operating the data acquisition platforms, processing the NDTs datasets, and employing it in PMS and digital twins. For example, GPR data processing is time-consuming, and the interpretation depends on the experience of the specialist. However, recent research is concerned with applying deep learning techniques to interpret and digitize GPR data and use the existing data to make robust learnable detection models. Similarly, machine learning techniques are applied to other NDTs, such as LiDAR, visual imaging, and IRT, for automating the interpretation and digitization process. In addition, most of the real-world systems that integrate multiple data sources incorporate intelligent data analysis to make sense of the data, as it can detect hidden patterns, build reliable predictive models, and enable real-time monitoring  [228].
Nuclear Magnetic resonance analysis (NMR) 1 H NMR,  13  C NMR, HSQC and COSY spectra were acquired at 277K (4°C) on a Bruker Advance NEO 400 spectrometer equipped with a cryo-probe (multinuclear z-gradient direct cryoprobe-head, 5mm, Bruker), and at 298K (25°C) on a Bruker AVANCE III HD-400 spectrometer. Both work at 400. 3 MHz. Samples were prepared from freeze-dried compounds by their dissolution at 8 mg/mL in 400 µL of D 2 O (Eurisotop). The solvent peak was selected as reference (δ = 4. 69 ppm at 277 K) and the spectra processed on TopSpin software.
Size exclusion chromatography The SEC analysis were performed on an Ultimate 3000 device (Thermoscientific®) equipped with triple detection: it includes a diode array detector (DAD), a multi-angles light scattering detector For the ELPs alone, the eluent was a mixture of acetonitrile (ACN, 35%) and aqueous buffer (65%) of acetic acid (0. 3M) and ammonium acetate (0. 2M). The samples were prepared in this buffer at a concentration of 2 mg/mL, with ethylene glycol as flow marker. The separation was done on two TOSOH columns (7. 8 * 300) with respective size exclusion < 10 6 g/mol and < 2 x 10 5 g/mol. Data were processed on Astra software and molar masses were calculated with the dn/dC value of 0. 1391 previously measured for ELPn40.
ACKNOWLEDGMENTS Authors would like to thank  Prof. Bertrand Garbay ,  Ms. Clara Toulouse ,  Ms. Chloé Ilos , and  Ms. Pauline Coutand  for their technical assistance in the recombinant production of the ELPs. The  French Ministry for Higher Education and Research  is gratefully acknowledged for the doctoral grant awarded to ML. China Scholarship Council and Université de Bordeaux  ( UB-CSC 2015 ) are acknowledged for the financial support to YX. This work was also supported by the  French National Research Agency  ( ANR-15-CE07-0002 ). Continuous support from  Univ. Bordeaux ,  CNRS  and  Bordeaux-INP  is greatly acknowledged.
Features of numerical analyses In the analyses, a portion of an underground infrastructure was modeled using the finite element software Comsol Multiphysics c ⃝. The dimensions of the model along the x-, y-, and z-axes were as follows: 8H wall + L tunnel , 8H wall , 5H wall , respectively (Fig. 1 ) where H wall represents the EW height and L tunnel is the tunnel width. The soil and concrete were considered as isotropic and homogeneous porous media. The soil was fully saturated by water. Six adjacent HE pipe loops  28  were modeled as linear entities embedded in the concrete walls. The HE pipes had an outer diameter of 32 mm and an inner diameter of 25 mm. Water was selected as the circulating fluid, with the inflow velocity and temperature specified in Section 2. 4. The pipe layout had a vertical W configuration.
The authors combined this occupancy-grid based discretization with an optimization based path generation to consider the nonholonomic constraints. Finally, they solved the speed planning over the path to solve the dynamic constraints as a convex optimization problem. Gu et al. proposed a sampling-based motion planner fused with a tactical maneuver discovery reasoning in  [16]. Distinct tactical maneuver patterns are extracted from the set of feasible trajectories computed via path generation primitives such as splines (both for path and speed profile). A cost function is then used to choose the final trajectory into the more appropriate tactical pattern set. Risk assessment is an important element in the evaluation of candidate paths using sampling-based approaches. Pierson et al. [90]  applied risk level sets to measure driving congestion, learning the common risk thresholds from the NGSIM and highD driving datasets to classify risk situations into low, medium and high risk.
In recent years, the trend in research has been to 1149 use learning-based methods for decision making and 1150 planning in automated driving, achieving good results 1151 in some specific scenarios. These approaches depend 1152 directly on the training phase and require large train-1153 ing datasets that reflect the environment where the 1154 vehicle will be deployed. In addition, learning-based 1155 approaches have not yet been certified in terms 1156 of verifiability, safety and explainability. Currently, 1157 no deep-learning method applied to decision making and planning has been integrated into production systems.
1148• In recent years, the trend in research has been to 1149 use learning-based methods for decision making and 1150 planning in automated driving, achieving good results 1151 in some specific scenarios. These approaches depend 1152 directly on the training phase and require large train-1153 ing datasets that reflect the environment where the 1154 vehicle will be deployed. In addition, learning-based 1155 approaches have not yet been certified in terms 1156 of verifiability, safety and explainability. Currently, 1157 no deep-learning method applied to decision making and planning has been integrated into production systems.
Dielectric function model and fit of experimental reflectance To model the reflectance data, we use an S-matrix code developed in our laboratory  [45]. The reflectance measurements are fitted globally (i. e. all angles, TE and TM polarizations at once) for each sample, using the Python lmfit package  [46]. To model the InGaAs dielectric function, we use the functional of Ref. [37]. The frequencydependent dielectric function ϵ(ω) is described as a sum of oscillators based on Gaussian functions that fulfill causality. ϵ(ω) = ϵ ∞ + N ∑︂ 1 [G rn (ω) + iG in (ω)], (3) where G rn (ω) and G in (ω) are defined as : G rn (ω) = 2A n √ π [︃ D (︃ 2 √ ln 2 ω + ω n σ n )︃ -D (︃ 2 √ ln 2 ω -ω n σ n )︃ ]︃. ( 4 ) G in (ω) = A n exp [︄ -4 ln 2 (︃ ω -ω n σ n )︃ 2 ]︄ -A n exp [︄ -4 ln 2 (︃ ω + ω n σ n )︃ 2 ]︄ , (5) In Eq.
Conclusion In conclusion, we presented an original method to recover the dielectric function of an InGaAs thin film, using a detuned Salisbury screen to enhance the InGaAs layer optical response. We obtained experimental reflectance data that we fitted using a functional form for the dielectric function ensuring causality and thus preventing negative values of its imaginary part. Raman measurement and FTIR global reflectance fits are in good agreement. We provide the complete set of parameters for the calculation of the dielectric function, as well as an estimation of ϵ st. We think this study will be of benefit to the optoelectronics and nanophotonics community.
The process parameters choice such as the laser power, the wire feed rate and the travel speed have an influence on the robot toolpath planning. The toolpath planning includes contour creation, layer height (adaptive or fixed thickness), printing direction, path pattern, and bead geometry settings. Therefore, process planning is important to create a homogeneous and stable printing process. In this work, Grasshopper software is employed to achieve the task. Where, Grasshopper is a visual programming language (VPL) developed in 2007 by McNeel Corporation  [23]. Both, parametric and non-parametric CAD models can be created or imported within Grasshopper software. In this work, both approaches will be performed for toolpath creation. The proposed method enables to produce digitally various shapes and design possibilities parametrically, as shown in the flowchart illustrated in Fig.
3. The parametric design creation is embedded in the bead width and height, layer heights, contour, and toolpath. Then, a robotic control plug-in for Rhino called HAL software  [24]  is used to convert each defined point to robot targets. Finally, a robot programming language is used to include printing process parameters and data related to the robot system to produce the deposition executable by the robotic arm. Figure  3  shows that the proposed parametric 3D printing process begins with first creating a parametric surface. In this step, a cross-section of the CAD model is created. Mathematical formulas of regular shapes (square, rectangle, triangle, circle, parallelogram, etc. ) are used to create the cross-section of the final object. The object dimension and its cross-sectional area parameters are given as predefined inputs, as illustrated in Fig.
4. After the 2D surface is created, patterns are added to it. In most 3D printing processes, patterns such as raster, zigzag, contour, or concentric are commonly used in AM (see, e. g. ,  [25] ). The patterns are created based on the bead geometry dimension, mainly the bead width dimension. The step-by-step process of pattern creation is shown in Fig. 5  and Fig. 6. The next step is to assign points to the generated patterns. These points will later be used by the robot as targets. The points are arranged and connected sequentially to form the toolpath pattern for the robot, as shown in Fig. 7. Noting that, after placing the points on the first layer, the generated toolpath is then copied to the other layers with fixed or variable thicknesses using a Python script. A layer thickness algorithm (see, e.
g. ,  [13] ) is run in the script to create the toolpath patterns for each layer. Hereafter, a layer-to-layer travel strategy is implemented. The travel strategy is created by selecting and connecting the last point of each layer to the first point of the next layer, or vice-versa. Defining a suitable travel strategy is crucial and affects the final object geometry in terms of cooling and the bonding between layers. Lastly, all created points will be converted to robot targets to generate the 3D printing program. HAL framework is used to convert the created points to robot targets. Finally, a digital parametric object, convertible into many robot programs, is now created and ready to be used by the industrial robot to perform the printing process. Remark 1 HAL is a Grasshopper plug-in allowing the translation of parametric designs to industrial robot programs.
Results The critical value of the Reynolds number, Re c , is computed by solving the system given by the system of algebraic Equations (  23 ) and  (38)  with MATLAB ® 2022a, using the function FSOLVE. Figure  5  shows the variation of Re c with respect to the tilt angle θ for different value of the material parameter λ 1 , when = 0. 01 by considering the regularized Bingham and Casson model. Similar to  [10] , at a given θ, Re c decreases for increasing values of λ 1 and coincides with 5/4 cot θ, when λ 1 = 0, i. e. , B = 0. However, recalling the proportionality relation (  16 ) between λ 1 and τ * 0 , we have that (see Figure  5A ) the yield stress destabilizes the flow when this is modelled using a regularized Bingham flow, while (see Figure  5B ) the regularized Casson flow is more stable than the Newtonian flow (i.
Interleukin-13 receptor alpha 2 The rationale for CAR T targeting of IL13Ra2 has been reviewed elsewhere  (18, 19) , but fundamentally stems from the specific over-expression of this antigen in GBM but not in healthy brain tissue. Preclinical mouse models showed that IL13Ra2 CAR T could cause regression of adult glioma patient-derived xenografts (PDX), including tumours derived from intracranial injection of a stem-cell enriched population  (20). A current Phase I clinical trial (NCT04510051) is investigating the side effects of chemotherapy and cellular immunotherapy for the treatment of IL13R a2-positive recurrent or refractory brain cancer in children. An early study in adult patients with malignant glioma, using donor Tcells modified to express an IL-13 CAR-zetakine/HyTK CAR, administered together with interleukin-2, established the safety of the approach (NCT01082926).
Further, encouraging results were published in an adult patient treated this time with autologous IL13Ra2 CAR T as part of a clinical trial investigating the efficacy of IL13Ra2 CAR T cells, open to patients over the age of 12 with recurrent or refractory glioma (NCT02208362)  (6). These CAR are hinge-optimized, containing the 41BB-costimulatory domain as well as a truncated CD19 domain that permits tracking of the CAR T cells. Enrollment criteria include immunohistochemical evidence of IL13Ra2 protein expression in at least 20% of cells in the sample. In contrast to the systemic delivery of CAR T in Bcell malignancies, the CAR T are delivered directly into the tumour or into the ventricles of the brain. This highlights one of the distinct features of CAR T trials in CNS malignacies discussed later, relating to the potential for the blood brain barrier to limit CAR T access to tumours, and the potential efficacy of direct delivery of CAR T cells.
Currently, there at least two open trials of anti-GD2 CAR T cell therapy for paediatric patients with high grade glioma (HGG) and diffuse midline glioma (DMG), a subtype of DMG located in the brainstem (NCT 04099797 and NCT04196413). A recent breakthrough publication reports results from the first four patients with H3 K27M-DMG receiving a GD2 CAR T cell trial (25). The toxicity, which in some cases was significant, was mainly attributed to inflammatoiry effects and the tumour location. Three out of four patients derived radiographic and clinical benefit, but all patients ultimately succumed to the disease, despite persistance of CAR T cell activity. Interestingly, in preclinical models of anti-GD2 CAR T in neuroblastoma, persistence of neuroblastoma cells with low GD2 expression was observed  (24) , suggesting a possible escape mechanism.
Human epidermal growth factor receptor The Human epidermal growth factor receptor (HER) family is comprised of four transmembrane growth factor receptors; EGFR (HER1), ERBB2 (HER2), ERBB3 (HER3) and ERBB4 (HER4). HER2 is an established immunotherapeutic target in breast and ovarian cancer. In paediatric maligancies, HER2 alterationsprincipally copy number gains and gene over expression -was observed in 6% of all tumours in a paediatric pan-cancer cohort, predominatly in CNS tumours including Diffuse Hemishperic Gliomas (DHG), medulloblastoma, ependymoma, and a subset of neuroblastoma  (14). A phase I trial of HER2 specific CAR T cells in patients with HER2-positive GBM (NCT01109095) concluded that the therapy was safe, although the mean age of this cohort was 49 years, and only 6 paediatric patients were included.
Of the 16 cases, eight showed an objective response, including one partial response (tumour regression) and seven patients with stable disease  (26). Whilst the patient numbers were small, the results were encouraging, and the treatment was safe, paving the way for futher trials. Currently, HER2 CAR T cells are being tested in a clinical trial for children with HER2 + brain tumours, over the age of 3 years (NCT02442297). The delivery method, as for the IL13Ra2 studies, was either by direct injection into the tumour resection cavity or by intraventricular delivery. An ongoing trial currently recruiting patients between the ages of 1 and 26 with HER2 + brain tumours, will test HER2 CAR T cell therapy, delivered via an indwelling catheter in the tumour resection cavity or the ventricles (NCT03500991).
In childhood cancers, co-occuring EGFR mutations and inactivating PTEN mutations are rare  (14, 31). There are several potential therapeutic approaches to inhibit EGFR activation, including small molecule inhibitors, monoclonal antibodies and CAR T cells. Both small molecule inhibitors and monoclonal antibodies have not shown significant efficacy  (32, 33). A completed clinical trial in adults (NCT01454596) indicated that in most patients, CAR T could be administered safely (with one treatment-related mortality), but there were no objective responses and no significant improvement in progression free survival. Another Phase I clinical trial (NCT 03638167) using EGFR806-specific CAR T cells in children and young adults with recurrent or refractory EGFR-positive CNS tumours is currently recruiting patients.
However, there is evidence to support the view that combining checkpoint inhibitor drugs with exogenously delivered CAR T may be an effective strategy, although the evidence is primarily derived from adult phase I trials in patients with malignant pleural disease, and not from patients with CNS cancer  (81). A high proportion of T-cells in paediatric CNS tumours exhibit an exhausted phenotype and expression of PD-L1  (60). Observations that concomitant CAR T and anti-PD-1 therapy results in CAR T cell expansion, downregulated CAR T PD-1 expression and tumour regression, particularly in diffuse large B-cell lymphoma (DLBCL)  (82) , further supports the promise of this approach. However, the phase 1b PORTIA study testing tisagenlecleucel, a CD19 directed CAR T cell in combination with pembrolizumab, in patients with refractory or relapsed DLBCL did not report improved efficacy compared to tisagenlecleucel alone, in a small cohort of patients, leading to an early termination of the study (NCT03630159)  (83).
Thermal paths identification The most simple and rough localized time constant equivalent circuit is given in figure  2. Between this ultra simple modelling and a fine modelling, we suggest here a medium complex modelling enough detailed to understand the major thermal aspects. Six main and parallel "thermal ways" are identified (cf. figure  3 ): -Four through the vertical walls (single glazed windows, double glazed window, door and insulated wall) -One through the ceiling, -One through the basement. A typical thermal "equivalent" electrical schematic for one path is given in figure  4. Rint and Rext represent the "in" and "out" thermal resistance of superficial exchange. Each layer is represented by a R, C network. R (°C/W), C (W. s/°C) values are estimated from materials thermal characteristics given in (Louvain catholic university (Belgium) database  [11] ) and from the geometrical characteristics of the each small house parts (surface and thickness).
Result analysis Confirmation of intuitive understanding of the main tendencies in house power consumption and insulation impact can be well demonstrated by SPICE. Simulation time is very short (few seconds) compared to simulation by finite element commercial software such as ANSYS/SILVACO which could take a few hours depending on mesh size and mesh number. The main advantages of our SPICE modelling are given hereafter: -Transposing thermal phenomenon into electronic "world" allows an easy understanding of thermal problems for non thermal specialists, -The "mixed" thermal and electronic simulation for temperature makes the feed back loop design and stability checking easy for electronic specialists. The impact of installed heating power on regulation quality can be precisely visualized, -Displaying node voltages allows a direct access to surface temperature (walls, windows), -Possible use for educational purpose and initiation of electronic students during a standard 4 hours practical lesson.
-Ovejero would like to thank the  European Union  financement through the  European Regional Development Fund (ERDF) , and the  French region of Brittany, Ministry of Higher Education and Research ,  Rennes Métropole  and  Conseil Départemental 35 , through the  CPER  Project  SOPHIE / STIC & Ondes. This work has received a French government support granted to the  Labex CominLabs excellence laboratory  and managed by the  National Research Agency  in the "investing for the future"  program  under reference  ANR-10-LABX-07-01. Digital Object Identifier XXXXXXXXXXXXXXXXXX.