esa-sceva/satcom-chunk-collection huggingface dataset:
# Remediation of Metal/Metalloid-Polluted Soils: A Short Review
## 4 Removal Techniques
### Chemical Processes
#### 4.2.5 Electrochemical Remediation  
Electrochemical remediation promotes the migration of heavy metals and metalloids to oppositely charged electrodes under a direct-current electric field. Electrokinetic technology involves the transport of charged chemical species in fluid (electromigration), the motion of a fluid (electro-osmosis), the movement of charged particles (electrophoresis), and the chemical reactions associated with an electric current (electrolysis) [132]. Electrochemical remediation technologies have already been described by several authors, such as [PERSON] and [PERSON] [133]. [PERSON] et al. [134] estimated a model to simulate and describe heavy metal transport in soil under an electrical field.  
Electrodes are generally immersed into wells containing an electrolytic solution and they are inserted into the area to be treated (Figure 4). An electric field gradient is generated so that metal ions migrate towards the oppositely charged electrodes. Then, the contaminants that accumulate at the electrodes are treated and eliminated with various physical-chemical approaches (electrolating, precipitation, pump-and-treat the water near the electrodes, or sorption with ion-exchange resins).  
The electrokinetic method is used since it is efficacious, even in low permeability soils, and does not excessively change soil properties [86]. The combination of extraction with low-molecular-weight organic acids and electrochemical adsorption promotes the decrease of pollutant concentrations in soil [87; 88; 89]. Different acids can be used to extract heavy metals from the soil: sulfuric, ethylene diamine tetraacetic (EDTA), acetic, and citric acid. [PERSON] and [PERSON] [86] demonstrated that the use of citric acid efficiently removes cadmium, cobalt, chromium, copper, lead, and zinc, since it favors the acidification of soil, solubilization of metals, their transportation by electro-osmosis, and their electromigration towards the cathode. The efficiency of citric acid is also relevant in the removal of arsenic [90].  
Soil acidification is not environmentally acceptable, and this must be assessed before the choice of technique that will be adopted is made. A low pH has two severe drawbacks:  
* In these conditions, usually, the metals are in an ionic form; this is to say they can be mobilized from soil.
* The vegetal regeneration of soil could be limited and made more difficult.  
Electrochemical remediation is rather efficient, especially in saturated clay soils, but it is rather complex to carry out.
# Crack Propagation Behavior of Fused Silica during Cyclic Indentation under Incremental Loads
## 4 Conclusions  
Incremental-load cyclic nanoindentation tests were carried out on fused silica, and brittle microfracture phenomena were examined by SEM observation, AFM measurement as well as load-displacement curve analysis. The following conclusions were obtained:  
1. Surface spalling becomes significant as indentation cycle increases when a cube-corner indenter is used, while a Berkovich indenter induces no surface spalling under the present experimental conditions.
2. Four types of characteristic patterns of surface spalling were identified: shallow spalling, deep spalling, internal spalling, and secondary spalling. The latter three kinds of spalling patterns only occur in incremental-load cyclic indentations.
3. The load-displacement curves exhibit unique features such as repetitive elbows, large pop-ins and significant displacement increase at the initial part of loading, which correspond to the incremental propagation of lateral cracks and surface spalling.
4. Multi-cyclic indentation using a cube-corner indenter promotes subsurface crack propagation and surface spalling (material removal) both in depth direction and in lateral direction compared to single-cycle indentation.  
This study reveals the mechanism of microscopic brittle fractures in optical glass induced by cyclic loading/unloading, which is distinctly different from that reported by previous studies in single indentation tests. The findings provide new insights in subsurface damaging and material removal behaviors under cyclic loads, which will help clarify the  
Figure 19: Schematic diagram of cracking/spalling behavior during incremental cyclic indentation using a cube-corner indenter: (**a**) loading and (**b**) unloading in early cycles; (**c**) loading and (**d**) unloading in later cycles; (**e**) loading in final stage.  
mechanism of micro/nanoscale machining processes such as grinding. One of the future directions is to establish the correlations between cyclic indentation and precision grinding, based on which, the optimization of the grinding parameters may be possible to suppress subsurface damage formation and achieve high-quality optical surfaces.  
Conceptualization, K.K. and J.Y.; methodology, K.K.; investigation, K.K.; writing--original draft preparation, K.K.; writing--review and editing, Y.Z. and J.Y.; visualization, K.K., Y.Z. and J.Y.; supervision, J.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
###### Abstract  
The design provisions in current codes for shear resistance of concrete-to-concrete interfaces exhibit significant differences. In this study, the accuracy of design provisions for interface shear resistance was evaluated and compared. From the literature search, a database of 458 push-off test results of interface shear resistance was created to evaluate the shear transfer provisions from the _ACI 318-19, PCI Design Handbook_, _AASHTO LRFD Bridge Design Specifications_, _CSA-S6_, _Eurcode 2_, and _Fib Model Code 2010_. In addition, an equation was derived based on push-off test results collected from the literature to calculate the interface shear resistance for the monolithic uncracked interface. According to many analyses and evaluations of parameters affecting the interface shear resistance, the compressive strength of concrete played an important role, especially for the monolithic uncracked interface. Therefore, the compressive strength of concrete was included in the proposed equation to calculate the interface shear resistance in this study. It is expected that this equation can be applied more accurately than the existing design provisions when high-strength concrete is used. Statistical analyses were carried out for comparison with the existing design provisions to verify the applicability of the proposed equation. The results show that the proposed equation reasonably predicted the interface shear resistance for the monolithic uncracked interface. Appropriate conclusions were also drawn for the design provisions.  
interface shear resistance; different interface conditions; design provisions; monolithic uncracked interface
# Trellis Tone Modulation Multiple-Access for Peer Discovery in D2D Networks
## 3 Trellis Tone Modulation Multiple-Access
### Multiple Access of Trellis Tone Modulation Signals  
We now consider the multiple access scenario where a receiving UE attempts to detect the messages sent from multiple UEs via a DRU. As shown in Figure 4, \(U^{(i)}\) performs the TTM of the discovery message and constructs \(\mathbf{X}^{(i)}\).  
Let the channel gain from \(U^{(i)}\) to the receiver be \(c^{(i)}\). We assume the channel gain is a value considering the channel environments such as path-loss, fading, etc., As described above, the trellis width \(M\) is determined such that the entire DRU passes through a flat fading channel. We also consider the channel is static in time over a single DRU. Of course, if \(N\) is large, the channel can change in a DRU under a time varying channel. However, to ensure the valid operation of the message-passing demodulation, which is introduced in the next section, the quasi-static fading channel is assumed.  
The received signal \(\mathbf{Y}\) is a superposition of transmitted signals weighed by channel constants,  
\[\mathbf{Y}=\sum_{i=1}^{n}c^{(i)}\mathbf{X}^{(i)}+\mathbf{Z} \tag{3}\]  
where \(\mathbf{Z}\in\mathbb{C}^{M\times N}\) is the independent additive complex Gaussian noise with variance \(\sigma^{2}\) for each entry. Figure 4 shows how \(\mathbf{Y}\) is obtained. The detection of the multiple messages from this superposed signal is addressed in the next section.  
The signal-to-noise ratio (SNR) is assumed to be large even if the signal undergoes path loss, since each UE transmits the signal with sufficiently large power through single-tone signal transmission. The network is therefore interference-dominant.
## 2 Materials and Methods
### Characterization of Samples  
Transmission electron microscopy was performed (JEOL-JEM-2100F) in order to establish whether the modification of BaTiO\({}_{3}\) was successful. Morphology of un-sintered and sintered ceramics was investigated via scanning electron microscopy (SEM) (EmCraft cube 2). The composition and homogeneity of the samples was investigated with energy dispersive X-ray analysis (EDX) using Cliff-Lorimer method with all the elements analyzed (Normalized). Distribution of BaTiO\({}_{3}\) size was obtained using Nicomp 380 DLS/ZLS Dynamic Light Scattering and Zeta Potential. Dielectric properties were measured on Microtest LCR Meter 6377@1 KHz and KEITHLEY 2400 SourceMeter. Sample's dimensions were round shaped films with a 3 mm diameter and a thickness of 0.75 mm. All the measurements were performed at room temperature (25 \({}^{\circ}\)C)
## 4 Design and Complexity of Proposed DL Architectures
### Complexity of Proposed DL-Based TAS Schemes  
Table 4 presents the computational complexity analysis of the proposed DL-based systems and traditional EDAS for FGSM. The computational complexity of traditional EDAS is given in [12], while the computational complexity of the proposed DL-based systems is presented in [23]. Both measures are expressed in terms of real valued multiplication. In general, the order of complexity of an RNN or FNN is determined by the number of neurons present in each layer (\(N_{l}\), \(l\in 0\cdots L-1\)) and number of hidden layers (\(L\)); whereas, the order of complexity of a CNN is determined by the number of filters and filter size present (\(FS_{l}=\) (No. of filters \(\times\) filter size) for \(l\)th layer) [23]. As can be seen  
Figure 5: Proposed DL architecture (**a**) FNN (**b**) RNN (**c**) 1D CNN.  
from Table 3, the computational complexity of traditional EDAS grows in proportion to the modulation order and the number of transmit and receive antennas. On the other hand, the complexity of the proposed DL algorithms is unaffected by the different configurations of the system and remains the same throughout. Every random channel matrix sample is processed using the traditional EDAS, and the corresponding antenna indices are saved. During offline training, the features are extracted from the random channel matrix, and the label associated with the antenna index recorded utilizing EDAS is used to train the model. Training requires these computations, and this is a one-time task. Once the model has been trained for the unseen data sample, it will use its prior knowledge to make a prediction about the class label. This simplifies things online and reduces the system's complexity. With an increasing number of these components, traditional EDAS becomes more complex; therefore, DL approaches for TAS are needed to simplify it.
## 2 Data and Methods
### Study Sites  
In this work, we apply and test our methods in French Guiana and in the Gironde area in France. For each site, we select a number of Sentinel-2 images according to cloud coverage and the visibility of wave patterns. A full list of images used and the wave conditions on those dates are provided in Appendix A. Figure 3 shows the measurement area and depth distribution of the surveys used in French Guiana and Gironde.  
To create a supervised dataset for each site, the raw bathymetry survey is coupled with a set of Sentinel-2 images with varying wave conditions. The resulting subtiles and their corresponding depths are grouped into training and validation sets. For the final datasets, we make use of points with depth values ranging between 2 and 40 m only. The distributions of depths used in the training and validation sets are shown in Figure 4.  
By including Sentinel-2 images from different dates in the training and validation sets, we expose the DSPEB models to a wide variety of wave conditions during training, reducing the models' ability to overfit to any specific conditions. The distributions of wave period, wavelength, and the direction of propagation on the dates used for this study are documented in Figure 5.  
Figure 4: Distribution of depths included in the training (**a**), validation (**b**), and test (**c**) sets in French Guiana (blue) and Gironde (red).  
Figure 3: The bathymetry survey obtained from the French Naval Hydrographic and Oceanographic Service (SHOM). (**a**,**b**) show the positioning and depth distribution of survey points included in this study.  
To test our models, we create a test set for each site. In French Guiana, we use the south-eastern section of the raw bathymetry survey as our target area, and we collect Sentinel-2 images from six different dates in 2018 to create the inputs to the models during application. For Gironde, the whole area is reconstructed over four different 2018 dates.
# Effect of Aggregate Crystalline Surface Anisotropy on
###### Abstract  
To investigate the influence of aggregate crystalline surface anisotropy on the interfacial effects and understand the bonding mechanisms, molecular dynamics simulations were employed to analyze the spatial distribution, diffusion, and adhesion properties of asphalt on typical acidic (\(\alpha\)-quartz, SiO\({}_{2}\)) and weakly alkaline (calcite, CaCO\({}_{3}\)) aggregates. The results indicated that different types and crystalline surfaces of aggregates did not alter the distribution patterns of the asphalt components on their surfaces. However, the magnitude of the radial distribution function (RDF) varied with different crystalline surfaces, and a higher RDF value was correlated with better adhesion performance. Different diffusion behaviors were exhibited by asphalt molecules on different crystalline surfaces: slower diffusion was correlated with stronger adhesion and faster diffusion with weaker adhesion. The adhesion performance was significantly affected by the anisotropy of the aggregates. In the asphalt-SiO\({}_{2}\) system, the van der Waals energy and surface atomic density were the major influencing factors, whereas, in the asphalt-CaCO\({}_{3}\) system, the electrostatic energy was significantly influenced by ionic bonding. Overall, alkaline aggregates showed greater adhesion performance with asphalt than acidic aggregates.  
molecular dynamics; asphalt; aggregate; aggregate crystalline surface; anisotropy; interface
## 3 Tactical Calculation Methodology and Situational Awareness Evaluation Algorithms
The orientation measurements are stored in a configurable 20-element buffer using a weighting strategy to determine correction for the new value inserted into the buffer. Figure 3: Here, mCOP AR view with unit-detailed labels (1: unit name, 2: unit APP6C code, 3: latitude of given unit, 4: distance to given unit, 5: unit potential from heterogeneous perspective, 6: unit’s tasks, 7: threat level value, 8: icon representing threat level by color, 9: estimated battle outcome and duration, 10: ETA (estimated time of unit arrival) or time to encounter, 11: longitude of given unit, and 12: bearing of given unit). Tactical orientation (which is aimed at identifying and calculating the locations of any blue or red forces and their spatial relation to the mCOP user) is crucial for effective mission planning and execution and therefore for the application of threat-level evaluation algorithms [5]. The first method used for that purpose is a threat-level evaluation [18], which is based on a model of comparison of two homogeneous forces, which requires the selection of factors that can be measured and assessed for their impact and association with the locally performed combat. Therefore, with this in mind and considering the functionality of mCOP, for this mathematical operation, there is a unit's aggregated combat potential. This is useful because it gives a sense of requirements for the unit's size to achieve the desired effectiveness in a specified military action. The definition of combat potential can be found in Reference [4]. Linear interpolation in conjunction with the tables from Reference [19] is a necessary means to acquire the values that can be quickly and easily interpreted by the soldier on the battlefield. The function of the threat level based on the unit's combat potential is defined in Reference [4]. One of the limitations of this method is that it can be used only as a reference. To improve calculation reliability, mCOP provides an additional method for combat situation evaluation: an attrition model based on Lanchester-type models of warfare [10; 14; 20]. Figure 4: Tactical view of surrounding battlespace objects in tactical augmented reality view (TARV). Handheld devices utilize built-in inertial and geolocation sensors to locate and orientate the operator within the battlespace. Surrounding battlespace objects are projected based on an object’s azimuth and calculated distance. Object annotations data can contain recon data and supplemented potential information. The unit recognition and threat evaluation model provides configurable parameters, thus providing complex analytic capabilities. It delivers information about estimated combat times and some predictions about who may win the fight. This heterogeneous model better reflects the actual battlefield as opposed to the first method, which is homogeneous. It comprises five categories of a weapon's
## 3 Results
### Analysis of SCO Properties  
The lipids extracted in the miniPilot reactor were analyzed with regard to the quality parameters of iodine value, peroxide value, and acid value. The iodine value, which gives information on the saturation of the lipids, was determined to be 48.0 \(\pm\) 1.2 [38]; the peroxide value is used to monitor lipid oxidation and was 7.28 \(\pm\) 0.07 [39]; and the acid value, which gives information on the amount of free fatty acids in the lipids, was 4.27 \(\pm\) 0.43 [40]. The extracted lipids showed a fatty acid distribution of 49.8% saturated fatty acids (SFA), 42.5% monounsaturated fatty acids (MUFA), and 8.2% polyunsaturated fatty acids (PUFA).
# A Survey on Deep Learning in 3D CAD Reconstruction
###### Abstract
The extrusion modeling operation is a common parametric modeling method that constructs three-dimensional geometry by extending a two-dimensional cross-section along a given direction. Compared with traditional manual tool operation methods, the emergence of these studies will not only reduce the dependence on professional knowledge for CAD 3D reconstruction, but also greatly speed up the reconstruction process. Figure 1: Real-world applications of CAD-related technologies: (**a**) automotive manufacturing; (**b**) education; (**c**) aerospace engineering; (**d**) the medical field; (**e**) robotics; (**f**) architectural design. The reconstruction of 3D CAD has always been a challenge in the field of deep learning. In the past two decades, with the continuous development of technology, the realization of 3D CAD reconstruction in this field has become increasingly feasible [29]. The research community has been committed to reconstructing 3D CAD models from point clouds, sketches, and other forms, and there have also been related studies on the generation of 2D CAD sketches [30; 31; 32]. In recent years, CAD 3D reconstruction has made remarkable progress, especially with the introduction of deep learning, which has led to more intelligent and automated reconstruction processes. Deep learning not only overcomes the limitations of traditional methods but also provides the technical foundations for large-scale industrial applications in the future. The aim of this survey is to comprehensively summarize the relevant work on deep learning in the field of 3D CAD reconstruction. The main contributions of this paper are summarized as follows:  
* We briefly review the recent progress in deep learning-based 3D CAD reconstruction. In addition, recent research methods and research trends over the past years are also given. * We summarize existing reconstruction approaches based on different input modalities, including point clouds, sketches, and other forms. We also summarize CAD sketch design generation based on deep learning. * We introduce commonly used CAD data representation formats in deep learning frameworks, which helps improve our understanding of model structure and design consistency. * We summarize the commonly used public CAD datasets. * We discuss the main challenges, existing limitations, and potential directions for future research. This paper is organized as follows: In Section 2, we present deep learning-based methods for reconstructing 3D CAD models from point cloud data. Section 3 introduces the reconstruction of 3D CAD models from sketches. Section 4 introduces the reconstruction of 3D CAD models from other forms. Section 5 discusses deep learning methods for CAD sketch generation, as shown in Figure 3. In Section 6, CAD data representation formats are introduced and explained. Section 7 reviews the commonly used CAD datasets. Section 8 introduces some evaluation metrics. Section 9 concludes the entire paper and looks forward to future research directions. Figure 2: This is the annual citation data of CAD papers.
# COMA: Propagating Complementary Multi-Level Aggregation Network for Polyp Segmentation
## 5 Conclusions  
This study has focused on the propagation of complementary multi-level aggregation to overcome multi-level distribution discrepancy. The proposed method, COMMA, leverages refined low- and high-level representations in a multi-decoder structure to discriminate polyps in ambiguous contexts. The decoder, which comprises a complementary masking module (CMM) and boundary propagation module (BPM), refines the complementary features by masking the low-level representation through the high-level representation, and propagates the complementary information to the next decoder. We also introduce a hybrid loss function combining weighted BCE, weighted IoU, and L1 distance loss to address class imbalance and noisy label problems. To verify the effectiveness of the proposed approach, we evaluated COMMA compared to existing segmentation methods on five benchmark datasets. COMMA achieved state-of-the-art performance, as well as significant generalization performance. In future work, we intend to study complementary multi-level aggregation to achieve greater memory efficiency and improve segmentation performance.  
Conceptualization, W.S.; methodology, W.S.; data curation, W.S.; formal analysis, W.S.; writing--original draft, W.S.; visualization, W.S.; methodology, M.S.L.; writing--original draft & editing, M.S.L.; data curation, M.S.L. and S.W.H.; formal analysis, M.S.L.; supervision, S.W.H. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Brain Korea 21 FOUR. This research was also supported by National Research Foundation of Korea (NRF-2019R1F1A1060250).  
Not applicable.  
The authors declare no potential conflict of interest.