esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The prevention and eradication of bacteria from tooth cavities still represents one of the major concerns of root canal treatment. Among the materials proposed as intracanal medicament, calcium hydroxide (Ca(OH)2) (CH) has been widely studied for its antimicrobial efficacy, and the benefits of its use are well documented [1, 2, 3, 4]. On the other hand, the dressing material has to be removed entirely before root canal filling [5, 6], since various research studies have shown that CH residues on dentin walls can compromise the canal seal by promoting micro-leakage between the sealer and gutta-perchao, hindering the penetration of the sealer inside dental tubules and interacting with the setting of some sealers [7, 8, 9]. Moreover, different studies have identified an additional issue, which is the difficulty to completely remove CH from the apical third of the canals [7, 10, 11, 12]. Despitethe various protocols and techniques that have been proposed [13; 14; 15; 16], studies have shown that instrumentation and irrigation are unable to clean completely the whole root canal system from CH [17; 18]. The most commonly described root canal cleansing method is the use of a master apical file inserted until the working length (WL) and in conjunction with the abundant irrigation of sodium hypochlorite (NaOCl) and EDTA [19]. However, a recent review reported that the ultrasonic activation of irrigants seems more effective for removing CH than other techniques [9]. [PERSON] and [PERSON] [20] demonstrated that the use of the EndoVac (Kerr Endo, Orange County, CA, USA), an irrigation system using apical negative pressure that sucks the irrigants employing a microcanula placed at the WL, improved CH removal with respect to ultrasonic activation and conventional endodontic needle irrigation. Recently, XP-endo Finisher (FKG Dentaire SA, La Chaux de Fonds, Switzerland) has been developed to improve the efficacy of the final irrigation procedure. XP-endo Finisher exhibits a reduced core size (#25), zero taper, and the peculiar heat-treated NiTi alloy named MaxWire (Martensite-Austenite-electropolis-fileX) [21] that allows the instrument a great flexibility, leading to the removal of debris from hard-to-reach regions while limiting the damage on dentine at the same time. Moreover, the rotational movement can potentially produce agitation of the irrigant solutions, so it was suggested as a final step of disinfection procedures. The first published articles [22; 23; 24] confirmed the XP-endo Finisher's superior effectiveness in cleaning the dentin walls compared to the conventional technique.  
This study was aimed to evaluate quantitatively the residual amount of CH, comparing the effect of four irrigation systems: conventional endodontic needle irrigation, Irrisafe (Satelec Acteon Group, Merignac, France), EndoVac and XP-endo Finisher, by means of scanning electron microscopy equipped with an environmental-dispersive X-ray detector (SEM-EDX). The null hypothesis tested was the absence of a difference in the amount of CH removal between the four different irrigation systems employed in root canal cleaning.
# The DLR FireBIRD Small Satellite Mission: Evaluation of Infrared Data for Wildfire Assessment
## 2 Materials and Methods
### Methodology of Comparison  
The comparison of FireBIRD data to the reference datasets are analyzed regarding the following criteria. The term fire grid cellsubsequently refers to a cell in the hierarchical grid containing at least one active fire location. Accordingly, the term non-fire grid cell refers to a grid cell where this is not the case.  
Figure 2: Results of atmospheric correction and FRP derivation for a TET-1 overpass covering the fire event in California / US, 10 November 2018, at 19:25 UTC. (**a**) shows the atmospherically corrected MWIR band, together with a gray boundary indicating the derived fire pixels. Sub-figure (**b**) shows the same fire pixels with their subsequently derived FRP values.  
* True positives (TP): The ratio between the number of fire grid cells in the FireBIRD data which correspond to the reference data, and the total number of fire grid cells in this reference data.
* False negatives (FN): The ratio between the number of non-fire grid cells in the FireBIRD data which do not correspond to the reference data, and the total number of fire grid cells in this reference data.
* False positives (FP): The ratio between the number of fire grid cells in the FireBIRD data which do not correspond to the reference data, and the total number of fire grid cells in this reference data. To illustrate that the given number refers to fire grid cells supplementary to the ones in the reference, the value 1.0 is added to represent those reference cells. The resulting ratio is therefore always \(\geq\)1.  
The criterion of true negatives (TN) represents the ratio between the number of non-fire grid cells in the FireBIRD data which correspond to the reference data, and the total number of non-fire grid cells in the reference data. However, since the total number of grid cells not featuring active fires greatly predominates the number of cells which do, this true negative ratio is implicitly very close to 1. This is, however, mostly due to the size of the study region. This measure therefore does not represent a meaningful value for this kind of study. The same applies to the overall accuracy [30]. These measures have therefore been omitted in the statistical analysis.
# Solar Irradiance Forecasting Using Dynamic Ensemble Selection
## 3 Background
### Multilayer Perceptron  
Multilayer perceptron (MLP) is a famous architecture of neural networks. It is a feedforward approach since the information present in the inputs flows in just one direction, from the input layer to the output layer. The MLP is a universal function approximator because it can map any continuous, nonlinear, limited, and differentiable function with a predetermined error threshold.  
The most usual way to train the weights of MLP is the backpropagation algorithm [56], which uses the information of the gradient function to modify the values of the weights in order to reduce the output error. In this work, we address an MLP with three layers (input, hidden, and output layers), and a sigmoid function is employed as an activation function [57].
## 2 Study Area  
The study area is situated in the southern Sichuan Basin, a tectonically complex region at the convergence of the Central Sichuan Uplift, Southern Sichuan Fold Belt, and WesternSichuan Low-Steep Fold Zone (Figure 1) [10]. Paleogeographically, it occupies the Deyang-Anyue Rift Trough, a NW-SE elongated deep-water shelf system subdivided into three subzones: the intra-trough center, trough-margin slope, and extra-trough highland. This structural configuration imposes significant heterogeneity on shale reservoirs, compounded by burial depths ranging from 2800 to 4200 m, which collectively challenge conventional TOC prediction methods in deep-buried Cambrian shales [11,12].  
The Qiongzhusi Formation hosts multiple gas-bearing intervals (Layers 1, 3, 5, and 7), with Layer 5 standing out as the primary target due to its exceptional organic richness [13]. The average TOC content of 2.9% in this layer surpasses the economic threshold for shale gas exploitation, peaking at 3.2% in the intra-trough center. Organic geochemical analyses reveal Type I kerogen dominance and thermal maturity levels (Ro = 2.5-3.0%) firmly within the gas generation window, corroborating high-yield gas flows such as the 1.08 \(\times\) 10\({}^{4}\) m\({}^{3}\)/d recorded at Weiye 1H [14]. Spatially, TOC distribution follows a systematic decline from the intra-trough center (2.0-3.2%) through the trough-margin slope (1.4-2.6%) to the extra-trough highland (0.2-2.0%), a pattern attributed to paleogeomorphic controls on organic matter preservation and sedimentation within the rift trough [15].  
These characteristics underscore the imperative for high-precision TOC prediction models capable of resolving extreme heterogeneity under deep burial conditions. The dataset utilized in this study, comprising 900 validated samples from 12 cored wells predominantly in the intra-trough and trough-margin subzones, ensures comprehensive coverage of TOC variability, thereby providing a rigorous foundation for model development and validation.
# Optimizing Methanol Injection Quantity for Gas Hydrate Inhibition Using Machine Learning Models
## 2 Materials and Methods
### Feature Importance  
A feature importance analysis was conducted to assess the influence of the five input features (flow rate, temperature, pressure, gas specific gravity, and water content) on predicting the minimum methanol quantity required. This analysis elucidated each feature's contribution to the predictive performance of the XGBoost, RF, and DT models, computed via scikit-learn and XGBoost libraries in PyCharm. Feature significance was derived from the models' intrinsic properties, which ranked features based on their role in reducing prediction errors across tree splits [37]. KNN, lacking an inherent importance metric, was excluded from this evaluation.  
The importance scores were visualized using bar plots generated for XGBoost, RF, and DT to highlight the relative impact of each feature on methanol predictions. This approach provided valuable insights into the dataset's dynamics, enabling engineers to prioritize critical operational parameters to optimize methanol dosing in gas pipelines. The analysis leveraged the models' ability to identify the key drivers of hydrate inhibition, enhancing the practical applicability of the predictive framework.
# A Magnetic-Balanced Inductive Link for the Simultaneous Uplink Data and Power Telemetry
## 8 Conclusions  
This paper proposes a new magnetically-balanced inductive link for the simultaneous power and uplink data transmissions under ultra-weak coupling. An extra external coil is specially added for the uplink receiving. The power carrier interference is minimized to approach zero by balanced canceling of the magnetic field of the power carrier. We optimize the coil parameters to achieve higher SIR based on the modeling of the mutual inductance. We also optimize the circuit parameter to minimize the impact of the uplink data transmission on the PTE. We use our intraocular sensor device as an application example to present the modeling and the optimization procedure in detail. The prototype achieves as high as a 65.72-dB improvement of the SIR compared with the conventional two-coil inductive link. Benefiting from this substantial improvement of SIR, the inductive link achieves a 1 \(\times\) 10\({}^{-7}\) BER uplink data transmission even though the coupling coefficient is as low as 0.005, and at the same time, the uplink coexists well with the power link with only \(\sim\)5% PTE loss and \(\sim\)5% voltage ripple on the implant load. This magnetically-balanced inductive link could be useful for small-sized biomedical sensor devices, which require transmitting data and power simultaneously under ultra-weak coupling. This work is supported by NSFC (National Natural Science Foundation of China) under the National Major Projects for Instrument Development of China 31427801. The authors would like to thank the cooperative hospital (Peking University Third Hospital) for providing medical requirements on the implantable sensor system for glaucoma treatment. Thanks also go to Beijing open lab of Keysight Technologies, for the support of test instruments.
# Design of a Cargo-Carrying Analysis System for Mountain Orchard Transporters Based on RGB-D Data
## 3 Model Construction and Volume Measurement Strategy and Deployment
### Volume Measurement Strategy  
To obtain the load volume for citrus transportation, we used detection results, depth maps, and the spatial point height measurement model to determine the number of layers where a fruit basket was located. We converted the 2D coordinate information into an ROI for height analysis. The depth values were converted into the height values in the ROI by fusing the depth map with the height measurement model to obtain the number of layers of the carried cargo. A flowchart is shown in Figure 8.  
Figure 6: Model pruning principle.  
Figure 7: The workflow of model pruning.  
According to the transport, the load reached the rated carrying capacity when the transporter carried two layers of \"fullbasket\", so we considered the cases of the transporter carrying one or two layers of fruit baskets. Because we used standardized baskets (length \(\times\) width \(\times\) height = 490 \(\times\) 350 \(\times\) 260 mm) for transportation, when a basket was located on the first or second layer, the heights of the spatial points in the ROI were more or less than 260 mm. Considering the measurement error and the analysis of the experiment, we set 300 mm as the threshold to determine whether a fruit basket was on the first or second layer.  
The steps for the height analysis of the fruit baskets were as follows:  
1. The improved YOLOv7-tiny model was used to obtain the 2D coordinate information \(\left(\left(x_{1},y_{1}\right)_{RGB},\ (x_{2},y_{2})_{RGB}\right)\) in the RGB image;
2. The ROI of the height measurement in the depth map was obtained according to the 2D coordinate information;
3. The depth value \(\left(Depth(x,y)_{x\in[x_{1},x_{2}],y\in[y_{1},y_{2}]}\right)\) and the spatial point height measurement model were combined to obtain the height value \(\left(H(x,y)_{x\in[x_{1},x_{2}],y\in[y_{1},y_{2}]}\right)\) of each point in the ROI;
4. The height values of each spatial point in the ROI were summed and averaged to obtain the approximate height value (\(Hc\)) of the basket;
5. The approximate height of the basket was compared to the height threshold to determine the number of layers where the basket was located;
6. Combined with the classification and the number of layers, the load volume measurement was realized.
# Strategies for Enhancing Extracellular Electron Transfer in Environmental Biotechnology: A Review
## 3 Strategies for Enhancing EET in Environmental Biotechnology
### External Electricity or Electric Field  
EET can be enhanced through the supply of external resistance. The rate of transfer of electrons amongst microorganisms and the electrode or electron acceptor can be regulated by adding external resistance to the circuit of a bioelectrochemical system. The resistance value affects the kinetics of EET by controlling the flow of electrons through the circuit.  
Figure 2: The structure of MFC carbon electrodes: (**a**) ordinary electrodes made from carbon with identical geometric macro size, and (**b**) a comparison between carbon veil and carbon felt (or mat) electrodes. V denotes the ratio of surface area to volume [71].  
While reduced resistance enables faster transfer rates, higher resistance delays the transfer of electrons [73]. In bioelectrochemical systems, external resistance enables the optimization of energy harvesting. It is possible to balance the electron transfer rate with the system's aims for power output or energy recovery by altering the resistance. This optimization guarantees the effective use of available electron donors, maximizing the capacity for energy production or storage [74].  
Several studies have been conducted to examine the effect of external resistance on EET. One study employed a highly varied mixed culture MFC [75] to investigate the effects of different resistance loads on biofilm formation and power output. The researchers noticed that a low resistance load resulted in the formation of thinner biofilms, accompanied by increased current production. Conversely, electrodes subjected to high external resistance loads exhibited the formation of thicker biofilms, but with lower power output. The influence of biofilm permeability and external resistance on MFC current production, microorganism growth, and the transfer of electrons from the redox mediator to the anode was investigated by [PERSON] et al. [76]. Their findings show that lower external resistances resulted in improved MFC performance. MFCs with lower external resistance produced more extracellular mediators in addition to building biofilm with more exoelectrogens. Similarly, it was discovered that a larger external electrical load may severely restrict the flow of electrons between the electrodes [73] and is thus generally unfavorable for MFC power generation. The influence of external resistance on cellular performance was also investigated by [PERSON] and [PERSON] [77], and it was discovered that modifying the external resistance during the acclimation of MFCs can significantly affect the performance of the anode, and this impact is observed in terms of charge transfer, diffusion, and the overall resistance of the anode. In the majority of cases, a load below 1 k\(\Omega\) was identified as the most appropriate choice of external resistance [78]. Therefore it was suggested that future investigations aiming to optimize system performance in terms of power production should consider experimenting within this range of load values [78].
## 1 Introduction
Thus, investigating the accuracy of 3 DP surgical guides is essential given their increased use as a part of the digital workflow in implantology. Post-processing 3 DP implant surgical guides is essential in ensuring the accuracy of the implant surgical guides fabricated, affecting the accuracy in terms of positive, negative, intaglio, and cameo surface discrepancies. One of the primary sources of error identified is excessive alcohol washing and insufficient removal of uncured resin [17]. Literature looking at single-implant tooth surgical guides comparing the automated method (solely ultrasonic washing) to handwashing along with ultrasonics found that the latter resulted in higher accuracy, with handwashing demonstrating discrepancy values that were consistently higher than the automated group. This trend was seen for all surface accuracy measurements in a similar study comparing post-processing methods [18]. [PERSON] et al. [18] reported that handwashing and ultrasonic cleaning had a positive discrepancy, whereas the automated group had a greater negative error. The errors for both, however, were <0.18 mm, and hence deemed clinically acceptable [18]. Nonetheless, previous studies investigated single-implant guides with little evidence supporting specific post-processing recommendations for full-arch surgical guides. Post-curing in a nitrogen atmosphere has also been reported to affect the properties of 3D printing. Two studies, [PERSON] et al. [19] and [PERSON] et al. [20], identified that curing in a nitrogen-rich atmosphere prevents the formation of an oxygen-inhibited layer, ensures complete surface polymerisation, and improves monomers' conversion. Similarly, [PERSON] et al. [21] looked at 3D-printed occlusal splints with different printers and post-curing techniques, and reported that stroboscopic post-curing with nitrogen improved the mechanical properties. When using Asiga printers, the double bond conversion of monomers (%) was 75.4 \(\pm\) 4.5 with nitrogen compared to 56.7 \(\pm\) 6.2 without (\(p\) < 0.001), and in terms of the Vickers hardness number (VHN), post-curing with nitrogen was 13.3 \(\pm\) 0.5 compared to 11.8 \(\pm\) 0.6 without (\(p\) < 0.001). Thus, curing in the presence of nitrogen is proven to have better mechanical properties. Nonetheless, and as previously identified with washing techniques, the effect of nitrogen for post-curing full-arch surgical guides remains to be explored. This study aims to address the gap in the existing literature on the accuracy of full-arch surgical implant guides, as previous studies focused on single-implant surgical guides. The objective of this study is to assess the impact of photopolymer resin type, location of the arch, and post-processing techniques on the trueness and precision of full-arch 3 DP surgical implant guides prior to implant placement.
## 1 Introduction
Ambient Detection addresses boundary ambiguity inherent in single-object detection methods through the examination of distance, spatial, and attribute relationships among objects. Specifically, this study aims to accurately monitor compliance with concrete pouring height regulations on construction sites. Pouring height is a critical standard intended to prevent concrete quality deterioration, yet it remains challenging to measure precisely using current visual inspection practices. The proposed Ambient Detection-based approach explicitly considers the relationship between the pouring boom head and the concrete object during pouring operations, significantly improving detection accuracy. During pouring, the pouring boom head and discharged concrete maintain a continuous physical relationship. Therefore, our approach simultaneously analyzes the relative spatial positioning and visual attributes of the pouring boom head and the concrete to enhance object detection performance. Such relational analysis is particularly effective in resolving boundary ambiguity, a frequent issue on construction sites. Consequently, this research overcomes the shortcomings of existing YOLO-based object recognition algorithms, enabling objective and precise assessments of compliance with pouring height regulations. Furthermore, the proposed approach aims to establish a technological foundation capable of expanding beyond concrete pouring tasks. By leveraging object relationships, the developed detection methodology can be applied more broadly across various construction monitoring scenarios. Guided by these observations, we hypothesize that relationship-based ambient detection can markedly outperform single-object YOLOv11 detection in concrete-pouring scenes plagued by boundary ambiguity and visual similarity. This approach explicitly embeds distance, attribute, and spatial relationships into the detection pipeline. To verify this hypothesis we set five research tasks: (T1) design a framework that fuses the three contextual cues without adding an extra trainable network; (T2) implement a rigorous region-of-interest (ROI) definition and closed-form formulas for all three relationship scores--distance \(D_{R}\), attribute similarity \(A_{R}\), and the binary spatial constraint \(S_{R}\); (T3) compare accuracy, misdetection, and complete-failure rates of YOLOv11 and the proposed Ambient Detection on a hold-out test set of 232 unseen field images; (T4) analyse the computational overhead on an RTX 3090 GPU, noting that additional calculations inevitably slow Ambient Detection, yet may be justified by the accuracy gain; and (T5) identify current limitations, such as the fixed 1.5 m neck-height assumption, and outline future work for lightweight optimization and cross-trade generalization.