esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Primary Stability  
[PERSON] et al. [16] recommended the insertion torque range for miniscrews as 5-10 Ncm in their study. [PERSON] et al. [22] reported that insertion torque values above 15 Ncm would increase miniscrew stability. In this systematic review, MIT measurement was among the criteria included to evaluate primary stability. There are nine articles in which MIT measurement was used [25; 27; 28; 29; 36; 37; 39; 40; 41]. In only three of these articles, MIT values showed significant differences with the control group, and in all three, the MIT values of the control group were higher than the surface-treated group [28; 29; 39]. In the study of [PERSON] et al. [28], MIT values were higher in the control group compared to the SLA  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Author/Year** & **Random Sequence Generation** & **Allocation Concealment** & **Binding of Participants and Personnel** & **Binding of Outcome Assessment** & **Incomplete Outcome Data** & **Selective Reporting** & **Other Sources of Bias** \\ \hline [PERSON] et al./2016 [37] & & & & & & & \\ \hline [PERSON] et al./2015 [38] & & & & & & & \\ \hline [PERSON] et al./2012 [28] & & & & & & & \\ \hline [PERSON] et al./2016 [29] & & & & & & & \\ \hline [PERSON] et al./2009 [39] & & & & & & & \\ \hline [PERSON] et al./2016 [30] & & & & & & & \\ \hline [PERSON] et al./2015 [40] & & & & & & & \\ \hline [PERSON] et al./2021 [41] & & & & & & & \\ \hline [PERSON] et al./2009 [42] & & & & & & & \\ \hline [PERSON] et al./2017 [43] & & & & & & & \\ \hline \end{tabular}
\end{table}
Table 4: _Cont._and SLAO groups, which the authors suggested was because miniscrews with smooth surfaces cause more damage to the bone during insertion, whereas rough surfaces enable a better discharge of blood and bone particles. Only the study by [PERSON] et al. [29] showed mean MIT values of RBM surface miniscrews in the range of 5-10 Ncm.  
Periotest measurements are generally used to evaluate the mobility of teeth and osseointegration of dental implants [52]. The measurement results vary between \(-8\) and \(+50\). The lower the Periotest value the higher the stability, and the higher the Periotest value the lower the stability [53]. This value is also frequently used in vitro and in vivo studies to analyze the primary and secondary stability of miniscrews [25; 40; 54]. In this systematic review, PTV measurement was used in three studies [25; 26; 40]. [PERSON] et al. [26] reported that UV-treated groups showed lower stability than control groups in PTV measurements taken 2 weeks after the placement of miniscrews. Two other studies applied acid etching as a surface modification. There were no differences between the treated groups and the control groups in terms of initial PTV values [25; 40].
# Obstacle Avoidance Path Planning for the Dual-Arm Robot Based on an Improved RRT Algorithm
## 5 Conclusions  
Based on the traditional RRT algorithm, this paper introduces the goal probability bias and A* cost function algorithm. The search time and efficiency of the RRT algorithm is greatly optimized. In addition, the generated paths are also locally optimized. The effectiveness of the GA_RRT algorithm is first verified by the simulation of planar obstacle avoidance path planning. Furthermore, the algorithm is applied to obstacle avoidance path planning simulation of a dual-arm robot. In the process of obstacle avoidance, not only static obstacles are considered, but also the influence of dynamic manipulator obstacles in real time. Finally, the simulation results show that the algorithm can realize the cooperative obstacle avoidance path planning of a dual-arm robot. In terms of search efficiency, it is also superior to the traditional RRT algorithm.  
As a future research task, the experimental verification process will first need to be improved. In addition, the adaptive step size will be further used to improve the search efficiency of the algorithm. The visual method is also considered to detect the dynamic obstacles in the environment, so as to realize the dual-arm path planning in unstructured environments.  
Conceptualization, W.S. and M.T.; methodology, W.S.; software, W.S.; validation, W.S., C.Z. and M.T.; formal analysis, K.W.; investigation, M.T.; resources, W.S.; data curation, W.S.; writing--original draft preparation, W.S. and M.T.; writing--review and editing, K.W. and C.Z.; visualization, W.S.; supervision, K.W.; project administration, C.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by China's manned space program.  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Robotic Arm** & **Path Length** & \
\begin{tabular}{c} **Average Planning** \\ **Time/s** \\ \end{tabular} &
\begin{tabular}{c} **Successful** \\ **Times** \\ \end{tabular} \\ \hline
**Scenario 0** & The main arm & 142.5 & 1.73 & 10 \\  & The slave arm & 195.2 & 2.35 & 9 \\
**Scenario 1** & The main arm & 154.0 & 1.87 & 10 \\  & The slave arm & 234.3 & 2.90 & 10 \\  & The main arm & 153.9 & 1.67 & 10 \\
**Scenario 2** & The slave arm & 163.8 & 2.23 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Simulation results in different obstacle environments.
## 2 Materials and Methods
### Cell Viability  
To determine whether WSM is non-immunogenic, making nacre biocompatible and suitable for using without causing adverse immune reactions, a 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay was conducted to evaluate the impact of WSM on the viability of MC3T3-E1 osteoblast cells. This assay involved seeding approximately 5 \(\times\) 10\({}^{3}\) cells per well in a 96-well microplate. WSM at varying concentrations was introduced, and the cells were incubated for 24, 48, and 72 h so acute and late immune reaction effects could be observed. At each time point, an MTT solution (0.5 mg/mL in PBS) was added to each well, and the cells were further incubated for 4 h to allow the formation of formazan crystals by metabolically active cells. The formazan was dissolved by adding 200 \(\upmu\)L of dimethyl sulfoxide (DMSO). The absorbance was measured at 570 nm using a microplate reader. This method enables the quantitative analysis of cell metabolic activity, providing insights into the cytotoxic or proliferative effects of WSM on osteoblasts. A highabsorbance value at 570 nm indicates higher cell viability, suggesting a non-cytotoxic effect of WSM at the tested concentrations and time points [28].
## 2 Materials and Methods
### Real-Time PCR (RT-qPCR)  
RAW264.7 macrophages were seeded in 6-well flat-bottomed microplates (2.0 \(\times\) 10\({}^{6}\) cells/well). The cells were then treated with SGP (500 or 1000 \(\upmu\)g/mL) inserted into the culture medium for 12 h. After stimulating with SGP for 12 h, total RNA isolated and purified using RNeasy Mini Kit (Qiagen, Valencia, CA, USA) For cDNA synthesis, RevertAid First Strand cDNA synthesis kit (Fermentas, MA, USA) was analyzed following the manufacturer's guidance. RT-qPCR was determined from SYBR Green Master Mix (Applied Biosystems) using the following respective sense and anti-sense primers-IL-6: 5\({}^{\prime}\)-GAGGATACCACTCCCAACAG-3\({}^{\prime}\) and 5\({}^{\prime}\)-AAGTGCCATCATCATCGTTTTCA-3\({}^{\prime}\); TNF-\(\alpha\): 5\({}^{\prime}\)-GCCTCTCTCTCTCTCTCTGTTG-3\({}^{\prime}\) and 5\({}^{\prime}\)-CTGATGAGGGAGGCCATT-3\({}^{\prime}\); GAPDH (used as a housekeeping gene): 5\({}^{\prime}\)-GAGGATACCACTCCCAACG-3\({}^{\prime}\) and 5\({}^{\prime}\)-AAGTGCATTCCTGTTTCA-3\({}^{\prime}\). IL-6 and TNF-\(\alpha\) amplification conditions were evaluated by a Quantstudio 3 real-time PCR system (Applied Biosystems). mRNA levels were calculated from the \(\Delta\Delta\)Ct method [29].
# Age Prediction from Korean Speech Data Using Neural Networks with Diverse Voice Features
## 2 Data and Feature Extraction  
### Data Preparation  
In this study, a conversational speech dataset provided by the AI-Hub ([[https://www.aihub.or.kr](https://www.aihub.or.kr)]([https://www.aihub.or.kr](https://www.aihub.or.kr)), accessed on 1 December 2024) of the National Information Society Agency (NIA) of Korea was used. The dataset comprised recordings from 2547 speakers, both male and female, aged 11-59. Data were collected from various regions of Korea to capture regional accents. The recordings were collected through both online voice chat systems and offline studio environments, following a standardized collection protocol. The audio data were stored in PCM WAV format at a 16 kHz sampling rate. For recordings originally made at 44 kHz, downsampling to 16 kHz was performed to maintain consistency across the dataset. Quality assurance was maintained through a three-stage verification process including worker review, manager inspection, and final validation checks to ensure data integrity.  
To prevent age-biased training, datasets for males and females were constructed by randomly selecting approximately 2200 speech samples for each age. As a result, approximately 11,000 speech samples were obtained for each sex in each 5-year age group (e.g., 20-24, 25-29), forming 10 distinct groups spanning ages 11-59. The data were trimmed to remove silent segments that fell below the typical background noise levels to prevent silence in the audio data from affecting the audio quality and model performance. Sounds under 60 dB--such as whispers or quiet conversations--were considered background noise and excluded to ensure that only meaningful audio was retained in each clip [17].  
Only the first three seconds of the trimmed audio data were used for the experiments. Audio samples that were shorter than three seconds after trimming were excluded to ensure that all speech data samples utilized in this study were exactly three seconds long. The speech data preprocessing is illustrated in Figure 1, and the detailed compositions of the datasets used in this study are listed in Table 1.
###### Abstract  
Accurate characterization of surface solar irradiance at fine spatial, temporal, and spectral resolution is central to applications such as solar energy and environmental monitoring. On the one hand, modeling radiative transfer to achieve such accuracy requires detailed characterization of a wide range of factors, including the vertical profiles of gaseous and particulate absorbers and scatterers, wavelength-resolved surface reflectivity, and the three-dimensional morphology of clouds. On the other hand, satellite-based remote sensing products typically provide top-of-the-atmosphere irradiance at coarse spatial resolutions, where individual pixels can span several kilometers, failing to capture fine-scale intra-pixel variability. In this study, we introduce a machine learning framework that integrates large-scale remote sensing satellite data with hyperlocal, second-by-second ground-based measurements from an ensemble of low-cost spectral sensors to estimate the wavelength-resolved surface solar irradiance spectra at the hyperlocal level. The satellite data are obtained from the Harmonized Sentinel-2 MSI (MultiSpectral Instrument), Level-2A Surface Reflectance (SR) product, which offers high-resolution surface reflectance data. By leveraging machine learning, we model the relationship between satellite-derived surface reflectance and ground-based spectral measurements to predict high-resolution, wavelength-resolved irradiance, using target data obtained from an NIST-calibrated reference instrument. By utilizing a low-cost sensor ensemble that is easily deployable at scale, combined with downscaled satellite data, this approach enables accurate modeling of intra-pixel variability in surface-level solar irradiance with high temporal resolution. It also enhances the utility of the Harmonized Sentinel-2 MSI data for operational remote sensing. Our results demonstrate that the model is able to estimate surface solar irradiance with an R\({}^{2}\approx\) 0.99 across all 421 spectral bins from 360 nm to 780 nm at 1 nm resolution, offering strong potential for applications in solar energy forecasting, urban climate research, and environmental monitoring.  
solar irradiance; remote sensing; machine learning [[https://doi.org/10.3390/rs17162753](https://doi.org/10.3390/rs17162753)]([https://doi.org/10.3390/rs17162753](https://doi.org/10.3390/rs17162753))
## 1 Introduction
It was observed that the best-performing model did not exhibit the most ideal residual distribution. Therefore, in this paper, we propose to utilize the spatial probability distribution to enhance the learning on ill-conditioned samples. By incorporating both confidence and spatial objective functions, the model could evaluate the hypotheses from different perspectives, potentially enabling it to learn spatial relationships between points, leading to higher-quality estimations and improved optimization. To this end, we propose Spatial-Aware Net (SA-Net), a dual-branch neural network that consists of a confidence distribution encoder and a spatial distribution encoder. SA-Net not only learns the confidence sampling distribution through the confidence distribution encoder but also introduces the spatial distribution encoder to model spatial correlations between point features. The spatial encoder includes a Cluster-based Graph Mapping block, which allows point matches to share local feature information, while the FPS Aggregate Dispatch Block facilitates information interaction across spatially distant areas. The spatial encoder executes multiple parallel layers to generate a multi-perspective sampling distribution, helping the model  
Figure 3: Spatial distribution analysis of good models and best models. We analyze the minimal sets of good models and the best model by calculating the Euclidean distance between the closest points within each minimal set. It is observed that the minimum Euclidean distance in the best model’s minimal set is significantly greater than that in the good models’ minimal sets. Figure 2: Visualization of ill-conditioned problems. The red line represents outliers, while the green line represents inliers. When outliers are present, the estimated model exhibits large errors. When all points in the minimal set are inliers but are densely distributed, it can lead to model degradation, which we refer to as the ill-conditioned minimum set. On the other hand, when the points in the minimum set are sparsely distributed and all inliers, the estimated model achieves higher precision. avoid suboptimal solutions. Simultaneously, the application of Chamfer Loss constraints addresses the issue of sparsity in the space of samples within minimal sets. In summary, our contributions are as follows:  
1. We reveal the suboptimality in robust estimation models that rely solely on confidence for evaluation. To address this, we propose SA-Net, a dual-branch network that integrates spatial distribution information, mitigating over-reliance on confidence to improve optimization. 2. To improve the model's spatial perception, we introduce the Cluster-based Mapping and FPS Aggregate Dispatch block, which explore feature point relationships from both global and local perspectives. Additionally, we utilize Chamfer Loss to constrain the sampling distribution. 3. Compared with state-of-the-art methods, SA-Net achieves superior results in real-world scenarios on fundamental and essential matrix estimation.
## 4 Applications of Microextraction Techniques to the Determination of Organic Micropolultants
### Pharmaceuticals
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Compounds** & **Matrix** & \begin{tabular}{c} **Extraction** \\ **Technique** \\ \end{tabular} & **Optimal Times** & **Handing** & \begin{tabular}{c} **Recovery** \\ **Accuracy (\%)** \\ \end{tabular} & **LoD** & **Ref.** \\ \hline Organic tin compounds (trimethyltin chloride, tripropyltin hydroxide, tripropyltin carboxylate, triphenyltin hydroxide, tributyltin chloride) & Freshwater and seawater & SPME & \begin{tabular}{c} Extraction: 45 min \\ Desorption: 5 min \\ \end{tabular} & Easy to use & 71–104 & 6–185 [59] \\ Benzylic and aliphatic quaternary ammonium compounds & Tap water and surface water & SPME & \begin{tabular}{c} Extraction: 45 min \\ Desorption: 15 min \\ \end{tabular} & 96 well system & 97–143 & 10–500 [60] \\ Polar pesticides (diruron, floumeuron, linuron, monuron, neburon, siduro, hurnt, carbaryl, chlorograph, \\ methoicath, promecarb, propomarch, propomarch) & Tap water, surface water and well water. & IT-SPME & 15 draw/eject cycle & Lower handling & 77–104 & 10–1200 [61] \\ \hline Multiresidues (atrazine, chlorforwinphos, chlororypphos, di(2-ethyl)phthalate, dinuro, isoproturon, simazine, tetrbutylnzine, trifuran) & Wastewater, superficial and coastal water & IT-SPME & 18 min & Lower handling & 8–166 & 25–2500 [62] \\ \hline Pestictics (alcharo, borpefezin, chlororpypphos, chlororri, renathion, heeythizasz, isoproturon, malathion, telolodes methyl, prochlora, imazalli, abmancetin, diazntez, amazine, simazine) & Surface water & SBSE & \begin{tabular}{c} Extraction: 60 min \\ Desorption: 30 min \\ \end{tabular} & Practical & 3-62 & 10–1000 [63] \\ \hline Antimicrobial compounds (triclesan, tricolcartan) & River water and wastewater & SBSE & \begin{tabular}{c} Extraction: 180 min \\ Desorption: 15 min \\ \end{tabular} & Practical & 25–89 & 2.5–10 [64] \\ \hline Pestictics (carbofuran, clamazone, tebouconazole) & Tap water and surface & DLIME- & Extraction: seconds & Fast. Ease of operation & 62.7-120 & 20 [65] \\ \hline Triclosan and 2,4-dichlorophenol & Tap water and surface & DLIME- & Extraction: 1 min & \begin{tabular}{c} Easy \\ extraction-solidification \\ \end{tabular} & 83–119 & 2–20 [66] \\ \hline  & Wastewater, river water & & & & & \\ Trazine herbicides (cyanazine, simazine, atrazine) & underground water and & \
\begin{tabular}{c} Extraction: 30 min \\ Unrifugation: 15 min \\ \end{tabular} & Simple & 85.1–100 & 50–60 [67] \\ \hline Triclosan and tricolcurban & Wastewater and water & & & & & \\ water & IL-DLPME &
\begin{tabular}{c} Extraction: short time \\ Centrifugation: 10 min \\ \end{tabular} & Simple & 70.0–103.5 & 40-580 [68] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Microextraction techniques to determine pesticides in environmental water samples by liquid chromatography-tandem mass spectrometry.
# Hyperspectral Super-Resolution Technique Using Histogram Matching and Endmember Optimization
## 4 Experiment
### Proposed Method Evaluation
Comparing Figure 6a,b with the per-pixel error image in Figure 4, we can observe that the error rate for the overall image has been significantly reduced. In particular, the phenomenon where two images are not merged into one image by the pixel mismatch in Figure 4a is significantly reduced in Figure 6a,b. Also, this phenomenon is hardly observed in the reconstructed RGB images of Figure 6c,d, which confirms that removing the high-resolution step in [PERSON] et al. (2018) gives a better performance in pixel-mismatch environments, by relaxing the spatial constraint on two images with different FOVs. Also, we showed that the color distribution difference that can be caused by removing the high-resolution step in [PERSON] et al. (2018) can be reduced by using histogram equalization. However, further research might be needed to study how to update the endmembers, even in an environment of pixel mismatch, using additional optimization techniques. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method of Field of View Mismatch**} & \multicolumn{4}{c}{**[PERSON] et al. (2018)**} & \multicolumn{4}{c}{**Proposed Method**} \\ \cline{2-10}  & \multicolumn{3}{c}{**RMSE**} & \multicolumn{3}{c}{**SAM**} & \multicolumn{3}{c}{**RMSE**} & \multicolumn{3}{c}{**SAM**} \\ \cline{3-10}  & **Average** & **Median** & **Average** & **Median** & **Average** & **Median** & **Average** & **Median** \\ \hline
**Without Transformation** & & 2.68 & 2.11 & 5.58 & 5.47 & 2.68 & 2.57 & 6.69 & 5.32 \\
**Shear Transformation Using** & \(C_{x}=0.1\) & 9.79 & 7.70 & 7.16 & 7.35 & 3.64 & 2.96 & 6.73 & 6.03 \\
**Affine Matrix** & \(C_{x}=0.2\) & 11.76 & 9.33 & 7.88 & 8.00 & 4.4 & 3.56 & 6.9 & 6.26 \\
**C\({}_{x}=0.3\)** & 12.89 & 10.62 & 8.37 & 8.68 & 5.19 & 4.30 & 7.2 & 6.18 \\
**P\({}_{n}=20\)** & 9.12 & 6.93 & 6.71 & 6.83 & 3.56 & 2.87 & 6.65 & 5.65 \\
**Translation by Cutting off Upper** & \(P_{n}=40\) & 10.95 & 8.17 & 7.31 & 7.40 & 4.25 & 3.27 & 6.86 & 5.80 \\
**and Left Sides** & \(P_{n}=60\) & 12.02 & 9.27 & 7.80 & 8.03 & 4.93 & 3.68 & 7.08 & 5.98 \\
**P\({}_{n}=80\)** & 12.81 & 9.99 & 8.24 & 8.52 & 5.60 & 4.13 & 7.24 & 6.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results for the Harvard database.
# Leveraging Retrieval-Augmented Generation for Automated Smart Home Orchestration
## 4 Test Scenarios and Validation Results
### Model Validation and Future Direction for Failure Management  
Because the orchestrator generates executable code that directly interacts with smart home devices, ensuring that this code is safe, correct, and functional is essential. While we do not yet employ formal verification techniques or automated test suites, the current system incorporates several manual validation mechanisms integrated into the deployment and monitoring process. The generated Java bundles are organized into the following categories:  
* Compiled and deployed directly into the Apache Felix runtime, which serves both as the development and execution environment.
* Manually verified using logs and OSGi SCR tools, such as _scr:list_ and _scr:info_, to ensure successful bundle activation and service binding.
* Checked by developers for correctness, including structural validity of the Java class, proper annotations, and matching component XML descriptors.
* Monitored at runtime, where any failed activations or unsatisfied references are detected through Felix logs, and the corresponding orchestration process is halted. This setup enables effective testing in small-scale experiments; it is not isolated from the core system and does not constitute a true sandbox. As a result, validation and failure management rely on manual oversight and cannot guarantee safety in larger, unattended deployments. We acknowledge that this is a limitation of the current system and that scaling the orchestrator will require the introduction of more automated and formal validation strategies. In future iterations, we plan to:
* Introduce a dedicated sandbox environment, such as a separate Apache Felix instance, to safely test and validate generated bundles before integration.
* Integrate static analysis tools to detect code structure issues, annotation usage errors, and unresolved references before compilation.
* Explore automated testing pipelines, where test cases are generated and matched against expected orchestration outcomes.
* Investigate the use of formal verification techniques, including model checking and symbolic execution, to validate behavioural properties such as state reachability, sequencing correctness, and safety constraints.
Although these directions are not yet implemented, they are recognised as essential for advancing the system toward reliable, fully autonomous orchestration. Our current work provides a foundation for these capabilities by emphasising structured generation, logging, and controlled error handling. Future development will focus on formalising this process and enabling safe scalability through automation and validation frameworks.