esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Metadata Design
### Metadata Generation Process and Validation
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Measurement Technique** & **Analytical Metadata Elements** & **Utilized Data** & **Utilized Data** \\ \hline XAFS & Coordination Number, Bond Distance, Debye–Waller Factor, R factor & Normalized \(\upmu\)t, \(\chi(k)\), \(\chi(R)\) \\ HAXPES & Binding Energy Peak and Width (FWHM), d-band center & Binding Energy Profile \\ XRD & Crystal Structure Phase, Lattice Constant, Atomic Displacement, Occupancy & Intensity Profile \\ PDF & Rate, Crystalline Diameter, \(R_{\text{NP}}\), \(S\) & \(S(Q)\), \(G(r)\), \(T(r)\), \(g(r)\), RDF(\(r\)) \\ SAXS & Particle Radius, Size Distribution, and Width & Intensity Profile and Particle Size \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Selected analytical metadata and utilized data for key measurement techniques. This table summarizes key analytical metadata elements (e.g., coordination number, binding energy, lattice constant, particle size distribution) and the corresponding utilized data (e.g., normalized \(\upmu\)t, intensity profiles, \(G(r)\)), highlighting the information derived from each measurement technique and its role in data analysis and interpretation. Aligned with open science and FAIR principles, FC-BENTEN's metadata framework continues to evolve, focusing on the precision, adaptability, and seamless integration of new measurement techniques. These advancements contribute to ensuring high-quality and versatile datasets that drive cutting-edge research and foster innovation in diverse scientific fields.
# Potential Possibilities of Using Peat, Humic Substances, and Sulfurous Waters in Cosmetology
## 4 Medicinal Waters
### Biological Activity of Sulphurus Water  
Sulfide waters are used in balneotherapy mainly in the form of baths for diseases of the musculoskeletal system (rheumatic diseases, osteoarthritis, or arteriosclerosis) [99; 112; 113; 114], peripheral nervous system [115; 116], dermatoses [117; 118; 119; 120], circulatory system [121; 122], and respiratory system [123; 124; 125] as well as in many other diseases. The therapeutic effect of sulfurous waters is related to their anti-inflammatory, antioxidant, and antimicrobial activity, among others (Table 5).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Activity** & **Mechanism of Action** & **Ref.** \\ \hline \multirow{8}{*}{Effects on the skeletal system} & -induction of osteogenic differentiation of hMSCs & [112] \\ \cline{2-3}  & -decreases in the activity of alkaline phosphatase & \\  & -increases in the concentration of osteoclastin and & [113] \\  & phosphorus in the serum & \\ \cline{2-3}  & -improvement in quality of life of patients & [114] \\  & -reduction in the consumption of symptomatic drugs & \\ \hline \multirow{4}{*}{Anti-inflammatory effect} & -decreases the expression of IL-1\(\alpha\) & [126] \\  & -downregulation of the expression of TNF\(\alpha\) & \\ \cline{2-3}  & -increases in level of IL-10 & [127] \\ \hline \multirow{8}{*}{Effect on the respiratory system} & -increases in citrulline levels & [123] \\  & -decrease in ornithine levels & \\ \cline{2-3}  & -increases in the impedance curves that correspond to the & \\ \cline{1-1}  & normal ventilation of the tympanic box & [123] \\ \cline{1-1}  & -decreases in pathological impedance curves & \\ \cline{1-1}  & -decreases in IgE concentration & [124] \\ \cline{1-1}  & -increases in IgA concentration & \\ \hline \multirow{8}{*}{Effect on the muscular system} & -improves Visual Analogue Scale (VAS) score, & \\  & -alleviates muscle spasms, & \\ \cline{1-1}  & -alleviates local tenderness, & [114] \\ \cline{1-1}  & -enhances flexion–extension and rotation of the spine & \\ \cline{1-1}  & -improves the Schober’s index & \\ \hline \multirow{8}{*}{Antioxidant activity} & -decreases in values of total oxidative stress (TOS) and & \\ \cline{1-1}  & oxidative stress index (OSI) & \\ \cline{1-1}  & -increases in total antioxidant capacity (TAC) and total nitric & \\ \cline{1-1}  &oxide (NOX) levels & \\ \cline{1-1} \cline{2-3}  & -reduction in peroxide concentrations and SOD activities & [129] \\ \cline{1-1}  & -inhibition of DNA damage & [130] \\ \hline Antimicrobial activity & -reduction in growth of _E. coli_, _E. faecalis_, and _S. aureus_ & [131] \\ \hline \hline \end{tabular}
\end{table}
Table 5: Directions and general mechanisms of biological activity of sulfurous water used in the form of baths or inhalation.
## 2 Modeling of Nuclear and Wind Power  
### UC Modeling of Nuclear Power Generation  
Load following control of a nuclear power plant is obtained by regulating the nuclear reactor power. Generally, through controlling the concentration of the boron solution and the rod displacement height, load following control of the reactor is achieved.  
At present, most nuclear power plants in China are second generation pressurized water reactors (PWRs). They have the capacity to participate in daily load tracking. Considering the restrictions imposed by nuclear power ramp rate constraints and peak-shaving depth constraints, the PWR nuclear power units can participate in daily peak-shaving in the \"12-3-6-3\" power output mode, i.e., they operate at rated power for 12 h, then drop to a light-load power level after 3 h, continue to run at lower power sustaining for 6 h, and then rise to rated power in 3 h before entering the next cycle. The peak-shaving mode of \"12-3-6-3\" is shown in Figure 1.  
In addition, with the development of nuclear power technology, AP1000, EPR and other three generations of nuclear power units show more effective regulation performance, and participate in daily load tracking; peak-shaving operation is also more flexible. They can achieve \"15-1-7-1\" peak-shaving operation mode within 90% of the operating lifetime of the unit, as shown in Figure 1.  
The nuclear power output model with peak-shaving characteristics should be considered in the UC problem. The mathematical model of the output power is expressed as [7]:  
\[P_{N,it} = g_{it}(P_{N,i~{}\text{min}}+\Delta P_{N,i})+h_{it}(P_{N,i~{} \text{min}}+2\Delta P_{N,i})+e_{it}P_{N,i~{}\text{max}}+f_{it}P_{N,i~{}\text{ min}} \tag{1}\]  
where,  
\[e_{it} + f_{it}+g_{it}+h_{it}=1\] \[e_{ik} \geq e_{it}-e_{it-1}~{}~{}~{}k=t,~{}t+1,~{}...,~{}t+T_{i}^{e}-1\] \[f_{ik} \geq f_{it}-f_{it-1}~{}~{}~{}k=t,~{}t+1,~{}...~{},t+T_{i}^{f}-1\] \[h_{it+1} \geq f_{it}+g_{it-1}-1\] \[e_{it+1} \geq h_{it}+g_{it-1}-1\] \[g_{it+1} \geq h_{it}+e_{it-1}-1\] \[f_{it+1} \geq g_{it}+h_{it-1}-1\]  
where, \(e_{it},f_{it},g_{it},h_{it}\) are all \(\{0,1\}\) variables, and denote the operation state index with respect to four nuclear power operating levels: \(T_{i}^{e}\) and \(T_{i}^{f}\) are the minimum running time of the rated power and the  
Figure 1: Two nuclear power generation load following modes.  
low power modes, respectively; \(P_{N,i\ max}\), \(P_{N,i\ min}\) are the maximum and minimum injected power; and \(\Delta P_{N,i}\) represents the power variation of a nuclear generation unit \(i\) within 1 h.  
Under rapid variation of generation, traditional thermal power generation, creep fatigue may come up in the main steam line. Meanwhile, service life of water pump, deaerator, and high pressure heater will be reduced. In general, deep peak-shaving of nuclear power plant is arranged at the 65% of fuel capacity. After, the depth of peak-shaving will be lowered. Under rapid variation of nuclear generation, clad shell may bear limiting stress. Service life of nuclear generation will be reduced.
## 3 Data and Preprocessing
### Speech Feature Extraction  
To obtain some representative characteristics of speech, it was necessary to implement feature extraction from individual samples and to build a feature matrix. For each intonation of each vowel, 130 features were extracted. This means that 520 features were extracted for all intonations of a particular vowel. For one subject and all corresponding vowel recordings of _/u_/, _/i_/, and _/u_/, the number of features is 1560. The features consist of the following specific types of parameters: energy, low-short time energy ratio, zero crossing rate, Teager-Kaiser energy operator, entropy of energy, Hurst's coefficient, fundamental frequency, mel-cepstral coefficients, formants, jitter, shimmer, spectral centroid, spectral roll-off, spectral flux, spectral flatness, spectral entropy, spectral spread, linear prediction coefficients, harmonics-to-noise ratio, power spectral density, and phonatory frequency range. Some of the features contain multiple subtypes (e.g., shimmer:local, shimmer:apq3, shimmer:apq5, etc.) and some of the features were extracted from smaller time frames of the recording. In this case, several statistical functionals (median, average, minimum, maximum, and standard deviation) were determined.
# Hypergraph Position Attention Convolution Networks for 3D Point Cloud Segmentation
## 3 Method
### Hypergraph Position Attention Convolution Networks
#### 3.3.4 Skip Connection  
In point cloud segmentation, skip connection is a technique that fuses different levels of features to improve the model performance. The process consists of three main steps: interpolation, concatenation and 1 \(\times\) 1 convolution. First, the weight of each node is calculated based on the distance between the known point cloud position information and the target point cloud position information. The magnitude of the weight value depends on the distance. New point cloud features are generated by weighted average interpolation operation. Subsequently, the features are concatenated together to form a richer feature representation. Finally, the 1 \(\times\) 1 convolution operation is applied to reduce the number of channels of the fused features, thus effectively reducing the complexity of the model. The equations for the upsample output features are as follows:  
\[V_{XYZ}=I(V^{l-1}_{XYZ},V^{l}_{XYZ}) \tag{14}\]  
\[V^{l-1}_{upsample}=Conv1D(V^{l}_{out}||V_{XYZ}) \tag{15}\]  
where \(I\) denotes weighted average interpolation operation, and \(V^{l-1}_{XYZ}\) and \(V^{l}_{XYZ}\), respectively, denote the target point cloud position information and the known point cloud position information. \(V_{XYZ}\) denotes the result of the interpolation operation between them. This technique provides an effective means to improve the performance of the model in point cloud segmentation tasks.
# Kinematic Analysis of an Omnidirectional Tracked Vehicle
## 4 Discussion  
The results, summarized in Table 5, indicate that the described kinematic model reproduces the numerically simulated behaviour of omni-track vehicle movement with high accuracy. The largest deviation between the target position (\(-\)173.2, 0) and the obtained final position (\(-\)169.15, 1.83) was 4.45 mm, which corresponds to 2.6% of the assumed distance. This deviation occurred during simulation number 4, for \(\delta_{1}\) = 30\({}^{\circ}\) and \(\delta_{2}\) = 150\({}^{\circ}\), for speeds \(q_{1}\) = 0.1 m/s and \(q_{2}\) = \(-\)0.1 m. In most other cases, the error between the theoretical and simulation-obtained final position did not exceed 1% of the assumed movement distance, typically ranging between 0.4 and 1 mm. The greatest inconsistencies were observed for simulations with rollers oriented at angles \(\delta_{1}\) = 30\({}^{\circ}\) and \(\delta_{2}\) = 150\({}^{\circ}\). This suggest that as the angle of the orientation of the free roll increases (the direction of movement of the roll becomes closer and closer to the straight line perpendicular to the direction of movement of the omni-track), the differences between the simulation result and the equations of kinematics increase from 0.47-0.99% to 0.72-2.57% of the assumed distance. This phenomenon is consistent with expectations. If, in the presented model, the angles \(\delta_{1}\) and \(\delta_{2}\) are 0\({}^{\circ}\) and 180\({}^{\circ}\), respectively, the basic dimensions of the model will change in such a way that the mechanism will assume a peculiar configuration. As a result, the mechanism will no longer be controllable with the proposed actuators. The same effect could be obtained if the values of the angles \(\delta_{1}\) and \(\delta_{2}\) were 90\({}^{\circ}\). Thus, it can be concluded that the closer to the singular configuration, the greater the discrepancy between the kinematic model and the numerical simulation result. This, in turn, would likely translate into similar behaviour of the actual omni-track vehicle. Studies of the effect of mass on the accuracy of trajectory mapping (summarized in Table 6) show that increasing the vehicle mass has a marginal effect. Doubling the vehicle mass, from 60 kg to 120 kg, resulted in a reduction in the final error from 1.02% to 0.98%.
## 4 Discussion
### Relationship of Fluorescence Emissions to GPP
[27] identified a significant but relatively weak correlation (\(R^{2}\) = 0.25) between airborne-based SIFB and GPP in stressed sugar beet plants and a positive correlation between GPP and SIFB in unstressed plots. We assume that in our study, similar principles applied in the SIFB-GPP relationship in the beech forest, in which the variable related to stress was NDVI and the resulting relationship had a negative slope. SIFB-GPP relationship in the spruce forest was positive. In the evergreen forest, there were no apparent changes in NDVI throughout the season that would have altered the slope of the relationship between fluorescence emission and GPP. It appears that the dependency of the SIFB signal on NDVI kept the scatter of data points from beech trees low. Several recent studies have stated that canopy structure drives diurnal SIF dynamics retrieved from ground-based towers [53; 58]. Measurements confirmed that the detected fluorescence signal is sensitive to plant physiology, related to shading within canopy and dependent also on NDVI (Figures 5-8). We showed daily fluorescence dynamics as viewed from the top of canopy observations with the analysis of data from airborne LiDAR scanner. Canopy shadow fraction explained part of the observed daily variability in fluorescence in addition to changes induced by sunlight. Propagation of \(\alpha_{\mathrm{S}}\) into observed SIF-GPP relationships observed in Figure 11 may be minimal. We expect fluorescence to be more responsive to tree physiology and fluctuations in GPP at a seasonal timeframe.
# A Review on AI for Smart Manufacturing: Deep Learning Challenges and Solutions
## 5 Challenge: DNN Reliability
### Uncertainty Estimation  
For safety-critical applications, it is ideal for deep learning models to output the predictions altogether with the confidence of the model, i.e., uncertainties. Uncertainties can be caused by non-stationary training and testing data, i.e., testing data sampled from different statistical distributions from the training data, such as concept drift, as well as the intrinsic, inevitable randomness of the data. These two situations correspond to epistemic and aleatory uncertainty, respectively [143]. For DNNs, the uncertainty estimation methods fall into three categories: Bayesian neural networks (BNNs), ensembles, and uncertainty prediction. We introduce each of them in the following text.  
In BNNs, the model parameters, i.e., weights and biases, are valued by distributions, e.g., Gaussian, instead of fixed numbers. Multiple inference runs are performed on the same data. In each run, the model parameters are sampled from these distributions. Thevariance of the results after a number of inferences have been run on the same data sample with different parameter samples can tell the uncertainty [143, 144]. [PERSON] et al. apply BNN to machinery for useful life estimation with sensory time series measurements and qualify the corresponding prediction uncertainty [145]. The estimated uncertainty can be utilized to calibrate the prediction and improve accuracy. In ensembles, a series of models are trained on the same data with different initialization. Similarly, the variances of their prediction disagreements indicate the uncertainties [146]. [PERSON] et al. predict the uncertainty-aware remaining life for bearings using ensembles of LSTMs in [147]. After testing on both open and private real-world datasets, they show their method is more superior than BNN methods in both prediction accuracy and uncertainty qualification. Lastly, [PERSON] et al. propose to output the confidence of the predictions directly through training [148]. The models are trained by enforcing the prediction probabilities close to the label distributions (one-hot encoding of the labels).
# Time Domain Spherical Harmonic Processing with Open Spherical Microphones Recording
## 2 Problem Formulation  
We begin this section by reviewing the well-known frequency domain spherical harmonic decomposition method. We then introduce the corresponding time domain formulation, and detail the Fourier Transform relationship between the components in the frequency domain equation and the time domain equivalent. Finally, we show the difficulties in obtaining spherical harmonic coefficients in the time domain.
###### Abstract  
In this research, a new electro-optical-thermal modeling is proposed and built by simulation program with integrated circuit emphasis (SPICE). In particular, it is constructed for use in the non-dispersive infrared (NDIR) sensing technique of gas concentration. This model, based on the theory of circuitry and the Beer-Lambert law, includes various equivalent elements for the optics, sensor, and circuits. To build and investigate the validity of the proposed model, an NDIR for measurement of CO\({}_{2}\) is built with the hybrid combination of a thermopile sensor with a specific wavelength filter, an infrared micro electro mechanical systems (MEMS) heater, an optical tube, amplification circuits with a chopper amplifier, advanced RISC machine (ARM)-based micro processing unit and discrete electronic devices. The thermal properties of the light source with periodic modulation have been studied from the output signal of a thermopile within the limit of modulation frequency. Based on the thorough measurements of output signals and transient responses, the thermal and optical parameters of the sensor and optical components for this model are extracted. The comparison of the simulation and experimental data of the NDIR measurement for different CO\({}_{2}\) concentrations shows a great agreement with a maximum error of 0.27% at 3500 ppm. This approach allows for the development of a high-level sensor and circuit integrated simulation based on the most fundamental principles and multiple variables.  
electro-optical-thermal; NDIR; CO\({}_{2}\); thermopile
## 3 Cognitive Mobile BTS
### Downlink Channel Control  
Although the SSM provides information about downlink channel usage, this information is incomplete. MSs may experience hidden node phenomena, just like any wireless system. This situation is represented in Figure 6. The external BTS interferes with MS1, but the SSM is unaware of this (note that the cognitive BTS selects channels statistically, rather than following a plan as in conventional networks, meaning that hidden BTSs are possible). Besides, even if the external BTS was not hidden to the cognitive BTS, the received power levels could differ drastically from those received by the MSs, and these effects would prevent good perception of downlink channel status as sensed by the MSs. The solution thus is to use information obtained by the MSs themselves.  
Figure 5: Decision Module.  
Figure 6: Hidden node phenomenon.  
According to the GSM standard [32], MSs with ongoing communications (in Radio Resource connected mode) send Measurement Report messages regularly to the network. These messages contain measurement results about reception characteristics from the current cell and from neighbour cells. The BCCH Allocation list, which is the reference for the measurements, is compiled from information received on the BCCH in System Information 2 and on the SACCH in System Information 5 messages. If neighboring cell information is not available for the serving cell, the mobile station indicates this in a Measurement Report message, which is sent on the link layer Slow Access Control Channel (SACCH). These messages report to the network measurement results about the dedicated channel and the neighbour cells [33].  
Channel reselection takes place when the received signal quality level (for any ongoing communication) drops below a minimum RX-Qual (RX-Qual indicates the corresponding Bit Error Rate (%) for the measured channel) threshold (we selected the minimum 9 dB \(C/I\) ratio for GSM communications [34]) or when the periodic monitoring message is not received (note that SACCH messages are always transmitted in confirmed mode).  
In order to select a new channel, and since the information provided by the SSM may be insufficient as discussed above, the channel measurements from the MSs are also taken into account. In this case, the parameter considered from the Measurement Reports is the RX-Level (the spectral power detected in the channel) of neighbouring cells, since we want to avoid non-GSM radio sources as well. The cognitive BTS selects candidate cells by considering the four channels where the SSM senses the lowest power. Then, the estimation is completed by jointly averaging the power levels received by the MSs and the perceived power level reported by the SSM. Finally, the cell with minimum detected power is selected for the cell reselection. The rationale behind considering SSM perceiver power level as well is that new MSs might become active, and since the number of MSs with ongoing communications may be low, the SSM represents an average view of the channel. Figure 6 shows this: MS2 may become active and its channel perception would be very different from that of MS1.
## 6 Conclusions and Future Directions  
### Main Conclusions  
Main Research Objectives: (1) Develop a systematic trajectory data processing framework ensuring consistent data quality; (2) create an innovative trajectory point-labeling algorithm for accurate road network mapping; (3) establish multi-source data fusion methods to improve parameter estimation reliability; (4) build automated and reusable processing tools for practical engineering applications; (5) validate methodology effectiveness using large-scale real-world trajectory data. This research addresses challenges faced by traditional traffic parameter acquisition methods, including poor data quality and acquisition difficulties, by proposing a comprehensive method for traffic parameter extraction and analysis based on vehicle trajectory data. Through theoretical innovation and engineering practice, the following main achievements and conclusions were obtained: