esa-sceva/satcom-chunk-collection huggingface dataset:
# Landsat Surface Product Validation Instrumentation:
## 6 Arable Radiometers  
Arable radiometers represent an economical approach for acquiring numerous automated surface reflectance measurements at many different locations. However, while their deployability and automation are highly attractive, their precision and accuracy remain a concern. The characterization of these devices over the past two years at SDSU provides some insights. Figure 20 shows a deployed Arable Mark 2 radiometer on a 10-foot pole and also a plot of calibration results. SDSU initial results indicate that the Arable Mark 2 is potentially capable of an accuracy with respect to ground truth measurements of 0.08 reflectance units if a single, uncalibrated unit is deployed over a growing season. If a minimum of three uncalibrated units are deployed, the accuracy improves through averaging to 0.04 reflectance units. Accuracy potential can be increased further if ground truth measurements are made over the field of view of the instrument at least two times during a growing season. Current analysis indicates that, while the absolute calibration may show an offset to absolute truth, the instruments appear to be stable over time but dependent on solar illumination angles. As such, the SDSU team is developing a methodology to evaluate angle dependency and calibrate the sensors using a series of measurements over the course of a one- to three-day period by taking measurements at various times/solar positions [33]. In this case, a single calibrated unit has been shown to be accurate to 0.005 reflectance units, and three calibrated units are accurate to 0.002 reflectance units. The plot in Figure 20 shows the accuracy for each spectral band based on these four levels of calibration.
## 4 Analysis of Attitude-Prediction Results
### Model Evaluation  
Table 6 shows the error results of the two prediction models (PCA-SVR and SVR). The proposed PCA-SVR model exhibited better prediction accuracy than the SVR model. Its \(R^{2}\) exceeded 90%, its RMSE was below 6.0, and its MAE was below 3.5. In addition, the running time of the PCA-SVR model was 22.8 s, which reduced the unimportant dimensions by analyzing the covariance structure of the original dataset, thereby reducing the model computation cost. Thus, this model can be used to predict the attitude trajectory parameters and guide the operators to optimize parameter settings and obtain a better attitude trajectory of the shield tunneling.
## 5 Conclusions  
This study adopted CFD to investigate the dynamic air exchange between the shaft and the lobby on each floor of a typical 15-story building accompanied by the motion of the elevator car. The transmission of a tracer gas pollutant continuously released in the lobby of the first floor was adopted as an example to illustrate cross-floor pollutant transport. Based on the obtained results, the following conclusions can be drawn:  
(1) Motion of the elevator car created positive pressure in the approaching region of the shaft, which pressed the air from the shaft to the lobbies of the corresponding floors. The elevator car created negative pressure in the leaving region of the shaft, which sucked the air from the lobbies to the shaft. The intermittent air exchange caused airborne pollutant transmission across the floors.  
(2) The airflow rates through the protecting door's clearance opening swung with time due to the fluctuating pressure in the shaft when the elevator car intermittently passed by the clearance opening on each floor. The sudden changes in the airflow rates could be due to the elevator car passing by the protecting door's opening on the concerned floor or the generated water hammer when the elevator car was parked on a specific floor. However, the water hammer effect lasted only for a short while.  
(3) To minimize the transmission of airborne pollutants across floors, the pressures inside the shaft must be better controlled, and the clearance of the elevator's protecting doors could be further minimized.  
Conceptualization, T.Z.; methodology, T.Z. and S.L.; software, G.W.; validation, G.W.; investigation, G.W.; writing--original draft preparation, G.W.; writing--review and editing, T.Z. and S.L.; visualization, G.W.; project administration, T.Z. All authors have read and agreed to the published version of the manuscript.  
This study was partially supported by the National Natural Science Foundation of China (NSFC) through Grant No. 52108084.  
Not applicable.  
Conflicts of Interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Dynamic Modeling and Response Analysis of an Optical Machining Robot with Prismatic Joint Clearance
## 2 Modeling of Spatial Prismatic Joint Clearance
### Contact Mode Description  
The prismatic joint in the UP branched-chain, as shown in Figure 2a, consists of two components: the slide and the guide rail. Let the guide rail be denoted as \(L\) and the slide as \(K\), and define coordinate systems \(O^{l}-e_{1}^{l}e_{2}^{l}e_{3}^{l}\), \(O^{k}-e_{1}^{k}e_{2}^{k}e_{3}^{k}\) at the centers of mass of the guide rail and the slide. When \(L\) is in contact with \(K\), the contact between the two can be  
Figure 1: Hybrid processing robot: (**a**) 3D structure, (**b**) schema.  
determined by the relative position of the surface points. A simplified model of the guide and slider is shown in Figure 2b.  
Due to the presence of prismatic joint gaps, the guideway and the slider are subject to different contact patterns, each corresponding to a different attitude. By calculating the positions and velocities of points \(P_{1}\)-\(P_{8}\), the contact state between the track and the slider can be obtained.  
\(\xi^{l}\) and \(\xi^{k}\) are the position vectors of the center of mass of \(L\) and \(K\) in \(O\)-\(i_{1}i_{2}i_{3}\), respectively. Let the vectors between the two center of mass coordinate systems be \(\overrightarrow{O^{l}O^{k}}=\mathbf{q}\), \(\overrightarrow{O^{l}P_{1}}=\mathbf{p}_{1}\), and \(\overrightarrow{O^{k}P_{1}}=\mathbf{P}_{1}\), and the position vectors of point \(P_{1}\) in \(O^{l}-e_{1}^{l}e_{2}^{l}e_{3}^{l}\) and \(O^{k}-e_{1}^{k}e_{2}^{k}e_{3}^{k}\) coordinate systems are \(\mathbf{r}_{1}^{l}\) and \(\mathbf{R}_{1}^{l}\) respectively. \(\mathbf{T}_{l}\) and \(\mathbf{T}_{k}\) are the coordinate transformation matrices of \(L\) and \(K\) relative to the coordinate system \(O\)-\(i_{1}i_{2}i_{3}\). The following, Equation (1), can be derived from the closed-loop vector relationship:  
\[\left\{\begin{array}{l}\mathbf{R}_{1}=-\mathbf{q}+\mathbf{p}_{1}=\mathbf{\xi}^{l}-\mathbf{\xi}^{ k}+\mathbf{T}_{l}\mathbf{r}_{1}^{l}\\ \mathbf{R}_{1}^{k}=\mathbf{A}_{K}^{T}\mathbf{p}_{1}=\mathbf{A}_{K}^{T}(\mathbf{\xi}^{l}-\mathbf{\xi}^{ k}+\mathbf{T}_{l}\mathbf{r}_{1}^{l})\end{array}\right. \tag{1}\]  
When \(\mathbf{R}_{1}^{k}\cdot\mathbf{e}_{1}^{k}>a^{\prime}/2\), point \(P_{1}\) contacts the upper surface, and when \(\mathbf{R}_{1}^{k}\cdot\mathbf{e}_{2}^{k}>b^{\prime}/2\), point \(P_{1}\) contacts the side surface. The relative position vector of \(P_{1}\) is \(\hat{\mathbf{\xi}}=\hat{\mathbf{R}}_{1}^{k}\). The normal contact force between two objects in contact acts in a direction is \(\mathbf{e}_{1}^{k}\) or \(\mathbf{e}_{2}^{k}\). Similarly, the contact mode of points \(P_{1}\)-\(P_{8}\) can be obtained.  
Table 1 provides a summary of the identified contact modes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Order** & **Diagram** & **Description** & **Order** & **Diagram** & **Description** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Contact mode of the prismatic joint.  
Figure 2: Prismatic joint in UP branch chain: (**a**) structure, (**b**) simplified model.
## 2 Laser-Generated Ultrasound
### Design and Structures
#### 2.2.3 Array Transmitters  
LGUS is a promising approach to realize high-frequency ultrasound arrays for ultrasound beam steering and high-resolution ultrasound imaging [73; 74; 75; 27]. An array element is defined by the size and location of a focused laser beam. The tiny focused spot size of the laser beam facilitates a wide-angle radiation pattern of generated ultrasound. Furthermore, a tiny array element allowing high spatial sampling is imperative for a high-frequency phased array. Several array transmitters are studied. (i) A 75-MHz, 2D phased array is demonstrated based on LGUS transmitters by mechanically scanning the focused laser beam [73]. (ii) An LGUS array transmitter using AuNPs with different diameters for different absorption wavelengths is proposed [74]. Photoacoustic excitation of an array element can be controlled by selecting different excitation wavelengths. By varying the phase delay for each laser wavelength, the ultrasound beam steering can be achieved, which eliminates the requirement for a mechanical scan. (iii) An LGUS array transmitter is created by holographic projection using a microelectromechanical systems spatial light modulator [75]. Ultrasound focusing with a focal size of 1.6 mm is achieved by projecting ring-shaped laser intensity. Focal length can be varied by changing the diameter of the ring-shaped laser excitation. Without the need of the mechanical scan, fast axial scanning microscopy is feasible.
###### Abstract
To address the issues discussed above, a novel blind adaptive DIW scheme is proposed for secured watermark transmission with higher imperceptibility, robustness, and an embedding rate with an optimum computational cost. The structure of the paper is as follows: Section 2 is devoted to reviewing the State-of-The-ART (SoTA) in the field. Next, a detailed description of the proposed scheme is introduced in Section 3. Section 4 provides a broad discussion on the results reported in the experimental section. Finally, some of those more relevant results and future investigations are accordingly summarized in Section 5.
# A Review of Acoustic Emission Source Localization Techniques in Different Dimensions
## 3 Sound Source Localization Methods for Two-Dimensional (2D) Structures
### Modal Acoustic Emission (MAE)
[50] developed a new AE source localization method for large slabs that utilized a combination of time-of-flight and modal source localization algorithms to evaluate the location of sound sources.
# Analysis of Blasting Efficiency Using Crack-Inducing Holes and Pre-Splitting Method in Blasting
###### Abstract  
In an era of increased need for underground tunnel excavation to address growing urban population and traffic concerns, complaints resulting from blasting vibrations and the frequent execution of uneconomically inefficient blasting operations due to excessive overbreak have become more prevalent. Therefore, it is necessary to develop blasting methods that can reduce blasting vibrations and minimize overbreak. Various patterns of crack induction holes were placed between the presplitting holes to facilitate the formation of controlled pre-cracks to address the limitations of the presplitting blasting method in this study. The author conducted full-scale experimental blasting at a railway tunnel site and analyzed the blasting effects of the crack induction hole method and pre-splitting technique. As a result of the field test, the pre-formed cracks effectively attenuated vibrations generated in the cut blasting area, reducing blasting-induced vibrations by from 9.3% to 33.5%. Additionally, the amount of overbreak was decreased by from 17.9% to 20.2%. Therefore, the use of crack induction holes and pre-splitting blasting methods in underground tunnel blasting is expected to reduce overbreak, thereby lowering reinforcement costs and minimizing vibrations, preventing damage to adjacent structures. This is expected to enable economically and safely executed tunnel blasting operations both directly and indirectly.  
crack inducing hole; pre-splitting; combined blasting; vibration reduction; overbreak minimization 2020 IEEEexample:BSTcontrol  
31 January 2025  
Revised: 19 February 2025  
Accepted: 21 February 2025  
Published: 27 February 2025  
**Citation:** [PERSON]; [PERSON] Analysis of Blasting Efficiency Using Crack-Inducing Holes and Pre-Splitting Method in Blasting. _Appl. Sci._**2025**, _15_, 2599. [[https://doi.org/10.3390/app1505259](https://doi.org/10.3390/app1505259)]([https://doi.org/10.3390/app1505259](https://doi.org/10.3390/app1505259))  
**Copyright:** 6 2025 by the authors.  
License MDPI, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license  
[[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).
# Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review
## 4 Technical Discussion
### 4.1. Note Specifications and Applications
#### 4.1.8 Programming of Sensor Nodes  
In this paper, it is noted that most of the sensor nodes are programmed using the standard languages (e.g., C/C++). The majority of the commercial motes utilize a generic, open-source programming environment. The software is developed using Arduino IDE as the base code environment and compiler. One of the key features to consider when designing sensor nodes is the operating system (OS) to utilize in the sensor modes. The OS significantly influences the stability, capability and security of the device. It should be easily upgradable without making several changes to the hardware. The open source TinyOS and Contiki are the most commonly utilized OSs. The commercially available Waspmote Plug & Sense! can be programmed using Libelium's integrated development environment (IDE) that can be downloaded and installed on the Windows, macOS and Linux operating systems.
## 2 Measurement Principles and Device  
### Theoretical Formulas for Emissivity Measurement  
According to the relevant literature [24; 25], the derivation of the firing rate formula is generally based on [PERSON]'s law of thermal radiation. This means that for an opaque object, its emissivity is equal to its absorption, which is equal to 1 minus the reflectance. There is no issue for objects with a uniformly diffuse surface in all directions. However, for the normal emissivity, when the spatial reflect characteristics of the surface of the object's surface are complex, it cannot be guaranteed that the normal emissivity equals the normal absorptivity, thus posing a certain risk. To avoid this situation, we derived an emissivity measurement formula, specially to avoid using [PERSON]'s law of thermal radiation, and provided the emissivity formula for materials under vacuum low-temperature conditions as below.  
For any object, its observed radiance is the following:  
\[L=\varepsilon L_{T}+\rho L_{E} \tag{1}\]  
where \(\varepsilon\) and \(\rho\) --the emissivity and reflectance of the object, respectively; \(L_{T}\) --the radiance of an ideal blackbody at temperature \(T\); \(L_{E}\) --the radiance of the environment.  
Assuming the sample temperature is \(T_{1}\), and the blackbody temperature for comparison is \(T_{2}\), then the radiance of the sample is as follows:  
\[L_{1}=\varepsilon_{r}L_{T_{1}}+\rho_{r}L_{E} \tag{2}\]  
and the radiance of the blackbody is as follows:  
\[L_{2}=\varepsilon_{b}L_{T_{2}}+\rho_{b}L_{E} \tag{3}\]  
Assuming the total responsivity of the detection system is k, the signal detected from the sample is \(S_{1}\), and the signal detected from the blackbody is \(S_{2}\), where subscript r represents the sample, and subscript b represents the blackbody.  
Then, the sample signal --through calculation, it can be found that even at \(-20\)\({}^{\circ}\)C, the radiance of the ideal blackbody can be at least 3 orders of magnitude higher than that of the liquid nitrogen blackbody at the wavelength range from 4 um to 16 um which is the concern in this facility--is the following:  
\[S_{1}=k(\varepsilon_{r}L_{T_{1}}+\rho_{r}L_{E})\approx k\varepsilon_{r}L_{T_{ 1}} \tag{4}\]  
The blackbody signal is the following:  
\[S_{2}=k(\varepsilon_{b}L_{T_{2}}+\rho_{b}L_{E})\approx k\varepsilon_{b}L_{T_{ 2}} \tag{5}\]  
Dividing the sample signal by the blackbody signal:  
\[\frac{S_{1}}{S_{2}}=\frac{k\varepsilon_{r}L_{T_{1}}}{k\varepsilon_{b}L_{T_{ 2}}}=\frac{\varepsilon_{r}L_{T_{1}}}{\varepsilon_{b}L_{T_{2}}} \tag{6}\]  
The resulting emissivity is as follows:  
\[\varepsilon_{r}=\frac{S_{1}}{S_{2}}.\frac{L_{T_{2}}}{L_{T_{1}}}\varepsilon_{b} \tag{7}\]  
If the sample temperature \(T_{1}\) is equal to the blackbody temperature \(T_{2}\), then the emissivity is the following:  
\[\varepsilon_{r}=\frac{S_{1}}{S_{2}}.\varepsilon_{b} \tag{8}\]  
In Equation (8), there are only three components; this facilitates the calculation of emissivity from the measured signals and the assessment of the uncertainty in emissivity.
## 5 Structural Displacement Extraction Validation
### Ground Truth Measurements and Comparison Plan  
#### 5.1.1 Processing Steps for Ground Truth Measurements  
For each reference target, ground truth measurements are roof level displacements, determined by double integrating the accelerations recorded by an accelerometer installed close to the target. The processing steps for ground truth measurements include the following [28]:  
* **Baseline correction**: The raw acceleration time series is baseline corrected using the mean value of the pre-event data (the first 100 data points for each test).
* **Zero-padding**: The baseline corrected acceleration data is tapered for the first and last second, then the data is zero-padded for 20 s at both the beginning and the end of the time series.
* **Filtering**: A fourth-order bandpass Butterworth filter with cutoff frequencies of 0.1 and 50 Hz is applied to remove measurement noise above and below those limits.
* **Integration**: The fourth-order Runge-Kutta method is applied to integrate the filtered acceleration to obtain the velocity.  
The baseline correction, filtering, and integration steps outlined above are repeated to obtain the calculated velocity and then to obtain the displacement time series. The final displacement result is obtained by baseline correcting, filtering, and truncating (removing the zero-padded portions) of the time series.
### Literature Review
[34] proposes a method for detecting inter-turn short circuits in DFIG generators using external leakage flux sensors and combined VMD-HHT analysis. Multiple load scenarios and fault levels were simulated, achieving efficient decomposition of non-stationary signals and identifying characteristic spectral patterns of faults. The detection rate exceeded 96%, even in signals with low signal-to-noise ratios. The study also evaluated different sensor locations, thus optimizing the system sensitivity. Experimental validation confirmed the applicability of the approach in real power systems.
## 6 Performance Evaluation
### Metrics  
* Request Round Trip Time (RRTT): The time required to send a path request packet and receive the path reply.
* Computation Cost (CC): The number of operations the server performs to determine the optimal path for a given RReq and maintain the road reservations.
* Trip Time (TT): The time required by a vehicle to reach its destination after receiving the RRep packet from the server.
* Traffic Messages (TM): The number of exchanged messages to compute and maintain the optimal path. In this context, only the traffic related messages are counted, such as route request messages, server election messages, route reply messages and route update messages.
## 4 Optimization Problem Modeling
### System Cost Model  
The system cost function, denoted by \(U\), takes into account both the task queue length and energy usage and is represented as follows:  
\[U=\sum_{t=1}^{T}U_{t}=\sum_{t=1}^{T}\mu_{1}E_{t}+\mu_{2}Q_{t} \tag{11}\]  
Here, \(U_{t}\) indicates the cost at each time \(t\). The energy consumed, \(E_{t}=E_{t}^{c}+E_{t}^{o}\), includes computation and data transmission at time \(t\), while \(Q_{t}=\sum_{m=1}^{M}Q_{m,t}^{o}+\sum_{k=1}^{K}Q_{k,t}\) reflects the system's queue length at the same time. The parameters \(\mu_{1}\) and \(\mu_{2}\) prioritize energy and queue length, respectively.
# Manipulating Stress Responses during Spaceflight Training with Virtual Stressors
## 4 Materials and Methods
### Data Analysis  
Data analysis was performed using SPSS software (Version 28.0; IBM Corp.). Distributions were tested for normality using skewness and kurtosis divided by the standard error and concluded to be normal if less than 1.96 [61]. Each dependent variable was visually inspected for errors (e.g., signal artifacts, miscalibration, electrode disconnect) and participant trials were omitted if they displayed erratic patterns. Alterations were made to discontinue some questionnaires during the experiment, resulting in analysis on only a subset of the sample. Repeated measure analysis of variance (RM-ANOVA) was used to calculate the fixed effect of stressor level. Physiological data were standardized against the first 90-s of the low stressor trial to emulate change from baseline, to which the 90-s were then omitted from the analysis. The first 90-s was also omitted from the medium and high stressor trials to account for the physiological transition in response to a stressor. To account for correlation of physiological data, autoregressive (AR1) models were used for the covariance matrix with a participant random effect. AR1 model fit was assessed with Akaike's Information Criteria (AIC). Significant differences were located using pairwise comparisons, and acceptance level was adjusted to control for type I errors (Bonferroni adjustment). Results were considered significant for \(p\leq 0.05\) and marginally significant for \(p<0.10\)[62]. The effect sizes, given in partial eta squared for the models, were transformed, and reported as [PERSON]'s \(d\) effect size under the conservative assumption the stress level means are separated with maximum variability [63]. [PERSON]'s \(d\) was used for assessing effect size, where \(0.2<l\,d\,|<0.5\) is considered small effect size, medium effect size when \(0.5<l\,d\,|<0.8\), and large effect size for \(l\,d\,|>0.8\)[63].  
The three-factor SSSQ scale scores for pre- and post-trial were calculated for each participant. The factor scores from both pre- and post-trial are standardized against normative means and standard deviation values from a large sample of British participants [64] and standardized using methods in [55]. Change scores were calculated for each factor using the z-score Formula (1) which has been used in previous studies [55]. The z-score then represents the change between pre- and post-trial in units of the deviation from the population mean.  
\[\text{z}=(\text{standardized post-score}-\text{standardized pre-score}). \tag{1}\]