esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Perceived Color
Hot wines had a significantly higher purple hue compared to both Cold/Hot and Hot wines. Cold wines had a significantly lower purple hue compared to the other fermentation temperatures. The same trend was seen in the two-way ANOVA but the interaction between fermentation temperature and cap \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Treatments** & **Saturation** & **Purple** & \
\begin{tabular}{c} **Overall** \\ **Hue** \\ \end{tabular} & **Reduction** & **Rose** &
\begin{tabular}{c} **Red** \\ **Fruit** \\ \end{tabular} & **Tropical** & **White** & **Pepper** & **Mushroom** & **Hot** \\ \hline Cold PD & 3.49 c & 5.02 c & 7.61 ab & 3.53 bc & 5.67 a & 5.57 a & 5.06 ab & 3.23 ab & 2.70 ab & 6.55 a \\ Cold No PD & 2.42 d & 4.36 c & 7.81 ab & 5.10 a & 4.58 b & 5.03 a & 4.42 b & 3.30 ab & 2.99 ab & 6.07 a \\ Cold/Hot PD & 6.29 b & 7.00 b & 7.99 a & 4.20 ab & 4.83 b & 5.41 a & 4.96 ab & 3.49 a & 2.84 ab & 6.94 a \\ Cold/Hot & 6.65 b & 6.84 b & 8.03 a & 4.88 a & 4.95 ab & 5.44 a & 5.26 a & 3.51 a & 3.09 a & 6.10 a \\ Hot PD & 6.53 b & 8.21 a & 7.17 b & 2.77 c & 5.05 ab & 5.71 a & 4.72 ab & 2.80 b & 2.35 b & 6.90 a \\ Hot No PD & 7.98 a & 8.67 a & 7.83 ab & 5.08 a & 4.54 b & 5.43 a & 4.88 ab & 3.22 ab & 3.30 a & 6.25 a \\ \(p\)-value \({}^{2}\) & **-0.0001** & **-0.0001** & 0.291 & **-0.0001** & 0.058 & 0.744 & 0.386 & 0.326 & 0.144 & 0.261 \\ \hline \hline \end{tabular}  
* Different letters within columns indicate a significant difference for [PERSON]’s least significant differences test (\(p\) < 0.05). \({}^{2}\) Significant \(p\)-values are shown in bold fonts.  
\end{table} 
Table 1: One-way analysis of variance (ANOVA) showing mean separation and \(p\)-values of descriptive sensory attributes of Grenache wines assessed by a trained panel (\(t\) = 8). management was statistically significant (\(p\) = \(<\)0.0001) (Supplemental Table S2). Previous research on color as affected by alcoholic fermentation temperature primarily focused on the red hue, likely because the red hue can be directly assessed through the red/green chroma (a*) parameter in CIE Lab analysis. For example, Pinot noir wines were fermented at Cold (10 \({}^{\circ}\)C), Hot (25 \({}^{\circ}\)C), and Variable (10 \({}^{\circ}\)C then 25 \({}^{\circ}\)C) temperature regimes; resulting CIE Lab analysis showed that the Hot wines led to significantly higher a* levels or red hue [27]. The use of red hue has also been seen in sensory tests in the literature. A pair-comparison test on Syrah wines that were fermented at 15 \({}^{\circ}\)C and 30 \({}^{\circ}\)C and received cold soak, extended maceration, or neither, determined that wines with increased fermentation temperature led to more red color [29]. However, it appears that in terms of fermentation temperature purple hue in the present study showed similar behavior to that seen in red hue from past research. Overall, in the present study, a hot fermentation temperature and No PD led to wines with higher color saturation and purple hue.
## 2 Main Result
### Robustness Lyapunov Analysis for Error System
#### 2.5.3 Upper Bounding \((P_{x}e_{x},v)\)  
The right-hand side of Equation (31) can be upper bounded by taking the absolute value of terms:  
\[\begin{split}\dot{V}_{eL}+2 aV_{eL}\leq|(P_{x}e_{x},v)|+a| \operatorname{tr}(L_{*}\gamma_{y}^{-1}\Delta L^{\dagger})|.\end{split} \tag{32}\]  
To further bound \(\{|(P_{x}e_{x},v)|,a|\operatorname{tr}(L_{*}\gamma_{y}^{-1}\Delta L^{\dagger})|\}\), recall the Cauchy-Schwarz inequality: \(|(u,v)|\leq(u,u)^{\frac{1}{2}}(v,v)^{\frac{1}{2}}\). Therefore, applying the [PERSON]'s Inequality on \(|(P_{x}e_{x},v)|\):  
\[\begin{split}|(P_{x}e_{x},v)|=|e_{x}^{\dagger}P_{x}v|=|e_{x}^{ \dagger}P_{x}^{\frac{1}{2}}P_{x}^{\frac{1}{2}}v|=|(P_{x}^{\frac{1}{2}}e_{x},P_ {x}^{\frac{1}{2}}v)|&\leq(P_{x}^{\frac{1}{2}}e_{x},P_{x}^{\frac{ 1}{2}}e_{x})^{\frac{1}{2}}(P_{x}^{\frac{1}{2}}v,P_{x}^{\frac{1}{2}}v)^{\frac{1 }{2}}\\ &\leq\underbrace{(P_{x}e_{x},e_{x})^{\frac{1}{2}}}_{=(2V_{eL})^{ \frac{1}{2}}}(P_{x}v,v)^{\frac{1}{2}}\\ &\leq(2V_{eL})^{\frac{1}{2}}(P_{x}v,v)^{\frac{1}{2}}.\end{split} \tag{33}\]  
From Equation (33), the \((P_{x}v,v)^{\frac{1}{2}}\) term can be further bounded by applying Sylvester's Inequality:  
\[\begin{split}\lambda_{\text{min}}(P_{x})^{\frac{1}{2}}\|v\|\leq( P_{x}v,v)^{\frac{1}{2}}\leq\lambda_{\text{max}}(P_{x})^{\frac{1}{2}}\|v\|. \end{split} \tag{34}\]  
Therefore, the upper-bound of \((P_{x}v,v)^{\frac{1}{2}}\) follows:  
\[\begin{split}(P_{x}v,v)^{\frac{1}{2}}\leq\lambda_{\text{max}}(P_ {x})^{\frac{1}{2}}\|v\|.\end{split} \tag{35}\]  
Given that norm of the bounded-stochastic waveform \((\|v\|)\) can vary with time, the upper bound of \(\|v\|\) can be expressed as a _supremum_ (sup) such that  
\[\begin{split} M_{v}>0\
i\sup_{t\geq 0}\|v(t)\|\leq M_{v}.\end{split} \tag{36}\]  
Using the result from Equation (36), the upper-bound of \((P_{x}v,v)^{\frac{1}{2}}\) from Equation (35) can be expressed as follows:  
\[\begin{split}(P_{x}v,v)^{\frac{1}{2}}\leq\lambda_{\text{max}}(P_ {x})^{\frac{1}{2}}M_{v}.\end{split} \tag{37}\]  
Therefore, using the results from Equations (33) and (37), the upper-bound of \(|(P_{x}e_{x},v)|\) can be expressed as follows:  
\[\begin{split}|(P_{x}e_{x},v)|\leq(2V_{eL})^{\frac{1}{2}}(P_{x}v,v )^{\frac{1}{2}}\leq(2V_{eL})^{\frac{1}{2}}\lambda_{\text{max}}(P_{x})^{\frac{1 }{2}}M_{v}.\end{split} \tag{38}\]
# WRTU-16T: Write-Enhanced Low-Power Radiation-Tolerant SRAM for Space Applications
## 2 Proposed WRTU-16T SRAM Architecture
### Write Operation  
During the write state, when transitioning logic from '0' to '1' (Q = S1 = 1, QB = S0 = 0), BL is driven to VDD and BLB is driven to VSS so that P7, P8, N7, and N8 are activated. Q is pulled from 0 to 1 through N8 and QB is pulled from 1 to 0 through N7. Simultaneously, S1 is driven from 0 to 1 through P8 and S0 is driven from 1 to 0 through P7. As a result, the storage and internal nodes are switched in coordination [9].
# Rock Crack Types Identification by Machine Learning on the Sound Signal+
## 1 Introduction
In recent years, significant progress has been made in sound recognition technology [13]. In particular, the application of the artificial intelligence (AI) method greatly improves speech recognition. Depending on artificially established parameters, such as the short-term energy, short-term zero-crossing rate, and Mel-frequency cepstral coefficient (MFCC), speech recognition with machine learning methods such as support vector machine (SVM) and Gaussian mixed mode (GMM) have already been used. However, these methods are of low robustness. In recent years, the application of deep learning has greatly increased the accuracy and robustness of speech recognition [14; 15; 16; 17]. However, the success of deep learning relies on a large dataset. When only small samples are used in deep learning, overfitting is encountered during the iterative training of the model. In rock mechanics and rock engineering, only a limited number of acoustic signals (sounds or AE signals) can be obtained, which will seriously restrict the application of deep learning in the identification of crack types during rock failure. Figure 1: Different frequencies of acoustic signals and corresponding natural activities (modified according to [PERSON] et al. (2007) [18]). Sound signals are a useful supplement to other monitoring signals (such as acoustic emission and microseismic signals) used to study rock failure. However, the method of distinguishing the rock tensile crack and shear crack based on the sound signals has not appeared yet. Given the current research progress in sound recognition, it is necessary to develop systematic research for sound signal recognition of crack types in rock. In this paper, based on sound spectrogram recognition, a new method for identifying tensile cracks and shear cracks is proposed. The main research steps to construct the identification method are as follows: First, the Brazilian splitting test and direct shear test of granite are carried out to obtain representative spectrograms of tensile cracks and shear cracks. Due to the shortcomings of manually extracting the characteristics of the spectrograms, a pre-trained ResNet-18 is used to automatically extract the abstract characteristics of the spectrograms. Then, based on automatically extracted features, GPC shallow machine learning is selected to automatically recognize tensile and shear crack types with small sample sizes. To verify the accuracy of the proposed method, it is used to recognize the crack development process of a rock specimen under biaxial testing. Finally, this method is applied to the analysis of the tensile and shear crack development processes of rockburst. In the following sections, the basic idea of automatic recognition method-based spectrograms for hard rock crack types is presented; the method implementation, verification, and application are introduced; and, finally, some main conclusions are drawn.
# The Role of Artificial Intelligence in Sports Analytics:
## 4 Discussion
### Study Limitations  
Several limitations should be acknowledged when interpreting the findings of this systematic review and meta-analysis. The included studies exhibited substantial variability in sample sizes, ranging from small cohorts (fewer than 50 participants) to large-scaletracking datasets. Small sample sizes may limit the statistical power and generalizability of AI model performance, particularly in niche sports or elite athlete populations.  
Most of the performance gains reported in this review derive from models tested under controlled, laboratory-style conditions rather than in live competition settings. For example, ref. [33] and [PERSON] et al. [30] evaluated reinforcement learning and IMU-based classifiers in tightly managed trials, and [PERSON] et al. [7] and [PERSON] [15] relied on high-quality video feeds for computer vision assessments. In contrast, only a handful of studies--most notably [PERSON] et al. [18] and [PERSON] et al. [37]--applied AI algorithms directly to match or real-game data, and even these observational analyses reported wider variability and occasional drops in predictive consistency when models trained in one context were applied to another. This uneven distribution of validation settings underscores a critical gap: without systematic \"lab-to-field\" testing and iterative refinement on truly competitive data, it remains unclear how well high accuracies will hold up under the noise, occlusion, and environmental variability of real-world sport.  
Second, differences in data capture methods, such as sensor type (e.g., GPS, IMUs, video tracking) and sampling frequency (ranging from 10 Hz to 100 Hz), introduced heterogeneity in input data quality. Variations in temporal and spatial resolution could influence the effectiveness of AI models but were not consistently reported across studies.  
Third, the methodological diversity across studies--including the choice of AI algorithms (classical ML vs. deep learning) and evaluation protocols (k-fold cross-validation, hold-out test sets, external validation)--complicated direct comparisons and limited the feasibility of fully homogeneous meta-analytic synthesis.  
Fourth, reporting inconsistencies were noted, as some studies lacked detailed descriptions of preprocessing workflows, hyperparameter tuning, model calibration, or data splitting strategies. These omissions affect reproducibility and hinder critical appraisal of methodological rigor.  
Fifth, publication bias may be present. Studies reporting high-performance AI outcomes are more likely to be published, potentially inflating the pooled accuracy estimates despite attempts to mitigate this through sensitivity analyses and subgroup exploration.  
Finally, ethical considerations were insufficiently addressed in many studies. The use of sensitive biometric and performance data without standardized consent frameworks raises concerns about privacy, data ownership, and long-term athlete monitoring, all of which should be integral to future research designs.
## 1 Introduction
Both ICP and NDT are regarded as local scan-matching methods that begin with an initial transformation and then iterate until a local minimum is reached [26]. In addition, there are some feature-based matching methods. The features used mainly include line-plane features [27], viewpoint feature histograms [28], fast point feature histograms [29], 3D neighborhood point feature histograms [30], density characteristics [31], etc. The most representative method is the LOAM [27] scheme, which uses corner and plane point features. Experts and scholars have done a lot of work in scanning matching research [32]. Some work is for specific scenarios [33]. There are also studies on the comparative analysis of different algorithms, including traditional methods [34] and deep learning methods [35]. However, these studies mainly focus on the situation on land and do not explore the application of these algorithms on ASV. Compared with the land field, the situation at sea is more complicated, including wind, waves, currents, sea fog, etc. [36]. In addition, the motion pose of the ship at sea is also more nonlinear. Compared with vehicles constrained by ground and wheels, ASV is completely six-degree-of-freedom motion on the water surface, which poses a challenge to 3D LiDAR scanning matching [37]. In view of the above situation, this paper develops a ship state estimation framework based on a scanning registration algorithm. By comparing and evaluating 10 algorithms, a scanning registration algorithm suitable for marine ASV is selected. In addition, this paper also finds the problems of these algorithms in marine applications, which can provide a reference for the following research. The main contributions of this article are summarized as follows:  
1. This paper discusses ICP and NDT algorithms and their variants. A single-frame point cloud registration scheme based on PCL is built. Considering the accuracy and registration time, the appropriate parameters are selected for the algorithm, and 10 common registration algorithms are tested and analyzed on ASV data. 2. Secondly, a continuous-frame point cloud registration module based on ROS is built. The results are compared with RTK and IMU values, and the performance of the algorithm is analyzed from multiple perspectives. In addition, the problem of large local errors of roll and pitch obtained by the continuous-frame registration module is studied, and the causes of errors are analyzed from many aspects. (3) Finally, the registration algorithm is verified by an experiment with real ASV, and the single-frame and continuous-frame scanning registration schemes suitable for marine environments are obtained.
# Endodontic Management of Endo-Period Lesions
###### Abstract  
An endo-perio lesion is one of the more common tooth-related problems. An association between the presence of apical and marginal periodontitis is known in the literature and has been observed in 5.7% of individuals aged 40-45 years old. The purpose of the present article is to present three case reports describing the successful retreatment of endo-perio lesions. In each of these cases, we used a biologically active bioceramic root canal sealer, GuttaFlow Biosel, which is a bioactive root canal filling material composed of gutta percha, polydimethylsiloxane, platinum catalyzer, zirconium dioxide, and bioglass. All cases were followed up clinically and radiographically for a period of at least 11 months.  
endodontic-periodantal lesions; endodontic retreatment; bioceramic sealer +
Footnote †: journal:However, there remains significant controversy concerning the traditional classification schemes and as to how these conditions should be further subdivided into additional subgroups as the pathology progresses.  
[PERSON] et al. [5] suggested the use of a three-component categorization scheme of endodontic-periodontal lesions, based on the primary etiological factor of the pathology and clinical presentation:  
1. Purely endodontic lesion: when the pulp is necrotic and infected and there is a draining sinus tract coronally through the periodontal ligament into the gingival sulcus.
2. Purely periodontal lesion: when a deep periodontal lesion involves most of the root surface and the dental pulp is vital.
3. Endodontic-periodontal lesion: when the pulp is necrotic and infected and there is a deep periodontal pocket.  
When an endo-perio lesion is diagnosed, both endodontic and periodontal therapy may be required. In this case, the tooth prognosis depends mainly on the success of the endodontic treatment, followed by appropriate supplemental periodontal therapy.  
The aim of this study was to present clinical cases of endo-perio lesions that were successfully treated using a modern endodontic technique.
## 1 Introduction  
### Impact of Climate Change on Biosaline Agriculture and the Role of Halophytes  
Current climate change poses a serious threat to agricultural productivity. A rise in the atmospheric CO\({}_{2}\) level, heat waves, elevated temperature, soil drought, and salinity are all major outcomes of climate change that adversely affect the growth of glycopytes [1, 2]. Halophyte crops are better alternatives for food, fodder, fiber, fuel, essential oil, and medicines, as they have developed special characteristics to cope with environmental extremes. Halophytes possess a greater degree of tolerance than non-halophyte crops. Some parasitic plants develop succulence when growing on halophytic hosts, and an interesting finding is that halophytes growing in their natural habitats do not show signs of oxidative stress [3]. _Halogeton glomerus_ is an annual herbaceous and succulent halophyte belonging to the family Chenopodiaceae. It is considered a potential source for oilseed production and also assists in phytoremediation. Exposure of _H. glomerus_ to long-term salinity and drought stress results in decreased chlorophyll and carotenoid content and inhibition of the photosynthetic rate, transpiration rate, stomatal conductance, water potential, and biomass [4].  
Halophyte plants survive in soil where salinity is around 200 mM NaCl [5]. Plant ecologists classify halophytes into three main groups. The first is euhalophytes that dilute salt within their stems or leaves and have a strong capability to endure salt stress. Secretohalophytes secrete salt from their leaves and grow widely around the world, inhabiting inland saline lands and seawater. Pseudo-halophytes can not only hold up ions in roots but also minimize their transport to the shoot parts. On the basis of morphology, they are classified into two groups: excretive and succulent. Based on salt demand and tolerance to NaCl, they can be classified as obligate halophytes, facultative halophytes, and habitat-indifferent halophytes. Obligate halophytes are true halophytes, as they need salt for their growth. Members of the family Chenopodiacea belong to this category. Faculative halophytes generally grow in soil where salts are in quite low concentrations, but they can also grow under saline conditions. Monocots belonging to families Poaceae, Cypraceae, and Juncaceae and a large number of dicotyledons belong to this group. Habitat-indifferent halophytes prefer to live in saline-free soil but can also thrive in saline soil. Examples are _Festuca rubra, Agrostis stolonifera_, and _Juncus bufonius_. On the basis of habitat, halophytes are classified into hydrophalophytes (grow in saline water or salt marshes) and xerohalophytes (grow in desert). Salinity induces the release of oleanolic acid in the root exudates of _Salicornia_, which act as chemoattract for colonizing halophilic siderophores producing _Halomonas anticariensis_ FP35\({}^{\text{T}}\)[6]. In return, colonization of this bacterium enhances the positive effects on the root length, shoot length, germination, and vigor index of _S. hispanica_[6].
## 2 Data Collection from the Target Area  
### Area Data of Energy Self-Sufficient Households  
The target town, which was designed to secure 90% energy self-sufficiency by minimizing power usage and the number of sunless days, consisted of approximately 70% farming and 30% fishing households. Out of a total of 905 households, 207 were fishing, 346 were farming, and 352 were sustained by both farming and fishing. Figure 1 shows detailed information about the 905 target households, such as the method of classifying them into five villages. \"Building\" indicates the number of households in the villages that installed PVs on the rooftop or at the side of the residential building. \"Land\" indicates the number of households in the villages that installed PVs in their yard or at other sites near their house because the residential building was too fragile to install PVs on it. \"Forest/Farmland\" indicates the number of PVs installed in households that live off farming, for example, PVs installed in barns. \"Mobile\" indicates the number of PVs installed in a mobile rack or frame so that they could be moved to places that consume power occasionally, such as a greenhouse.
# Fatigue Life Analysis of Cylindrical Roller Bearings Considering Elastohydrodynamic Lubrications
## 5 Bearing Fatigue Life Test  
This study focuses on the SKF N324 supporting bearing located on the main shaft of the SFW250-8/850 horizontal hydro-generator, manufactured by Harbin Electric Machinery Co., Ltd. in Harbin, China. The fatigue life tests conducted on the medium-sized 2-CGSM-220 conventional life testing machine produced by Luoyang Bearing Research Institute Co., Ltd. in Luoyang, China validated the correctness of the theoretical model established in this study.  
Bearing test operating conditions: Fatigue life tests were performed on the bearing under the light-load conditions specified in Table 3 and the heavy-load conditions provided in Table 4.  
Monitoring and failure criteria for bearing fatigue tests: An increase in bearing vibration amplitude accompanied by regular spike waves--fatigue spalling, indentations, or localized damage on the bearing contact surfaces during testing will significantly amplify the vibration amplitude--and a vibration amplitude within 60 \(\upmu\)m-100 \(\upmu\)m are identified as bearing failures, requiring shutdown for inspection. Fatigue failure is confirmed when flaking of the base metal with an area not less than 1 mm\({}^{2}\) and a depth not less than 0.05 mm is observed on the working surfaces of the rolling elements or rings of the tested bearing.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Viscosity/(Pa\(\cdot\)s)** & **De Mul Model** & **Our Model** & **ISO/TS 16281:2008** \\ \hline
0.013 & 172 & 169.7 & 162.9 \\
0.043 & 172 & 166.6 & 159.9 \\
0.073 & 172 & 163.1 & 156.5 \\
0.103 & 172 & 159.6 & 153.2 \\
0.133 & 172 & 155.5 & 149.3 \\
0.163 & 172 & 151.7 & 145.6 \\
0.193 & 172 & 147.8 & 141.8 \\
0.223 & 172 & 143.2 & 137.5 \\ \hline \end{tabular}
\end{table}
Table 7: Fatigue life of bearings at different viscosities (\(\times\)10\({}^{6}\) r).  
Figure 13: Results and relative deviation of three models with different initial viscosities.  
Fatigue test protocol: The test is terminated upon fatigue failure, with the recorded fatigue life serving as the experimental result.