esa-sceva/satcom-chunk-collection huggingface dataset:
# Dual-Indicator Micro-Electro-Mechanical System Monitoring Method for Rock Instability Early Warning
## 3 Experimental Validation and Performance Evaluation
### Experimental Setup and Modeling
#### 3.1.3 Dynamic Simulation of Collapse Mechanisms  
The collapse process of unstable rock masses was simulated through the artificially induced cutting of the dominant structural plane. As defined in Equation (9), the relative bonding length \(\Delta L\) serves as a quantitative metric for structural plane degradation. Reductions in this length correlate directly with heightened deterioration severity and diminished rock mass stability.  
\[\Delta L=1-\frac{L}{L_{0}} \tag{9}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Number** & **Frequency Range/kHz** & **Range** & **Resolution** & **Size/mm** \\ \hline INV9832-50 & 1–10 & 50 g & 0.01 mg & 19 \(\times\) 19 \\ RSV-150 & 0–25 & 1 m/s & 0.5 \(\upmu\)m/s & 402 \(\times\) 165 \(\times\) 145 mm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main performance parameters of the device.  
Figure 6: Data acquisition sensing equipment. (**a**) MEMS accelerometer. (**b**) Signal collector. (**c**) LDV equipment.  
where \(L\) is the cumulative length of artificially cut structural plane; and \(L_{0}\) is the height of unstable rock mass, taking 10 cm.  
The collapse simulation protocol involved progressive 0.5 cm depth cuts to structural planes until failure initiation. Table 3 quantifies the cutting length-to-relative bonding length correlation across 14 distinct working conditions for unstable rock masses.
## 3 Results and Discussion
### Establishment and Validation Calibration Model
#### 3.3.2 Validation of Calibration Models  
The KPLS calibration models of crude protein content, oil content, erucic acid content, and glucosinolate content were built in Section 3.3.1. Then, these calibration models were used to estimate the crude protein content, oil content, erucic acid content, and glucosinolate content at each NIR hyperspectral image. A total of 30 independent samples were used to  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**Preprocessing**} & \multirow{2}{*}{**KPLS Factors**} & \multicolumn{2}{c}{**Training Set**} & \multicolumn{2}{c}{**Cross-Validation**} & \multicolumn{2}{c}{**Test Set**} \\  & & & **R\({}^{2}\)c** & **RMSEC** & **R\({}^{2}\)cv** & **RMSECV** & **R\({}^{2}\)p** & **RMSEP** \\ \hline \multirow{4}{*}{Crude protein} & MSC + 1 st Der & 6 & 0.9692 & 0.4500 & 0.9548 & 0.5522 & 0.9461 & 0.5514 \\  & MSC & 7 & 0.9632 & 0.4975 & 0.9481 & 0.5908 & & \\  & SNV & 8 & 0.9687 & 0.4559 & 0.9504 & 0.5736 & & \\  & SNV + 1 st Der & 6 & 0.9468 & 0.5957 & 0.9219 & 0.7369 & & \\ \hline \multirow{4}{*}{Oil content} & MSC + 1 st Der & 6 & 0.9653 & 0.5399 & 0.9528 & 0.6285 & 0.9503 & 0.5680 \\  & MSC & 6 & 0.9511 & 0.6185 & 0.9423 & 0.6812 & & \\  & SNV & 6 & 0.9474 & 0.6397 & 0.9393 & 0.7105 & & \\ \hline \multirow{4}{*}{Erucic acid} & MSC + 1 st Der & 9 & 0.9774 & 2.5018 & 0.9603 & 3.2581 & 0.9572 & 2.8113 \\  & SNV + 1 st Der & 7 & 0.9610 & 3.3473 & 0.9423 & 4.1374 & & \\  & OSC + 1 st Der & 6 & 0.9362 & 4.2488 & 0.9134 & 5.0265 & & \\ \hline \multirow{4}{*}{Glucosinolate} & MSC + 1 st Der & 8 & 0.9451 & 9.5087 & 0.9182 & 11.7602 & 0.9335 & 10.3209 \\  & MSC + 2 nd Der & 7 & 0.9380 & 10.5956 & 0.8931 & 13.8567 & & \\ \cline{1-1}  & SNV + 1 st Der & 6 & 0.9194 & 11.6972 & 0.8995 & 13.0994 & & \\ \cline{1-1}  & SNV + 2 nd Der & 7 & 0.9461 & 9.4447 & 0.9044 & 12.6364 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: The performance of four prediction models for crude protein content, oil content, erucic acid content, and glucosinolate content.  
Figure 4: The optimal model parameter for crude protein content (**a**), oil content (**b**), erucic acid (**c**), and glucosinolates (**d**). (Blue dots represents the reference values and red dots represents the predicted values).  
evaluate these models, and the results were presented in Table 2. The RMSEPs of the crude protein content, oil content, erucic acid content, and glucosinolate content were 0.5514%, 0.5680%, 2.8113%, and 10.3209 umol/g, respectively. The R\({}^{2}\)p of proteins, fat, erucic acid and glucosinolate were 0.9461, 0.9503, 0.9572, and 0.9335, respectively.
## 2 Role of AI in Dentistry
### Automated Processing Interpretation of Dental Photos  
Automated processing and interpretation of dental photos have revolutionized the field of dentistry, particularly in the accurate detection and identification of anatomical features. Deep learning algorithms have shown remarkable promise in this area, especially in recognizing and labeling teeth using periapical radiographs. These radiographs, which are specialized dental X-rays, capture detailed images of individual teeth and the surrounding bone, allowing for precise identification of various anatomical structures. Beyond simple identification, these algorithms are also being utilized in more complex dental procedures. For instance, in smile design, deep learning can help in planning and visualizing aesthetic outcomes by analyzing the patient's dental and facial structure. In dental implant placements, AI can assist in determining the optimal position and angle for implants, significantly reducing the risk of complications. Furthermore, automated systems are being developed to aid in the surgical extraction of teeth, where they can identify the most appropriate extraction techniques based on the individual patient's anatomy. In the diagnosis and treatment of temporomandibular joint (TMJ) disorders, AI-driven tools are helping clinicians analyze joint movements and detect abnormalities that might not be visible through conventional methods. Similarly, for dental fractures, AI can enhance the detection of microfractures that are often missed during routine examinations. These advancements in automated processing not only improve the accuracy of diagnoses but also streamline dental workflows, ultimately leading to better patient outcomes and more efficient treatment planning.
## 2 WBANs for Life and Safety Critical Applications
### Limitations of Existing WBANs Solutions
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **IEEE 802.11** & **IEEE 802.15.1** & **IEEE 802.15.4** & **IEEE 802.15.4** & **IEEE 802.15.6** \\  & **a/b/g/n (WiFi)** & **(Bluetooth)** & **802.15.4** & **(UWB) [29]** & **(WBANs Standard) [9]** \\  & **[26]** & **[27]** & **(Zigbee) [28]** & & \\ \hline Modes of & _Ad hoc,_ & _Ad hoc_ & _Ad hoc_ & _Ad hoc_ & _Ad hoc_ \\ Operation & Infrastructure & _Ad hoc_ & _Ad hoc_ & _Ad hoc_ & _Ad hoc_ \\ \hline Physical & & & & & Narrowband, Ultra \\ (PHY) Layers & Narrowband & Narrowband & Ultra Wideband & Wideband (UWB), \\  & & & & (UWB) & Human Body \\  & & & & & Communication (HBC) \\ \hline Radio & & & & 75–724, & 402–405, 420–450, & \\ Frequencies & 2400, 5000 & 2400 & 868/915, 2400 & 3128–4859, & 863–870, 902–928, \\  & (MHz) & & & 3000–5000, & 950–956, 2360–2400, & \\ \hline Power & High & Medium & Low & Low & Ultra low (\(\approx\)1 mW at \\ Consumption & (\(\approx\)800 mW) & (\(\approx\)100 mW) & (\(\approx\)50 mW) & (\(<\)50 mW) & 1 m distance) \\ \hline Maximal & Up to & Up to 1 Mb/s & Up to & Up to 27.24 Mb/s & 10 Kb/s to 10 Mb/s \\ Signal Rate & 150 Mb/s & & 250 Kb/s & & \\ \hline Communicatio & Up to 250 m & 100 m (class 1 & & \\ n Range & (802.11 n) & device) & Up to 75 m & Up to 30 m & Up to 10 m \\ \hline Networking & Infrastructure & Ad-hoc very & Ad-hoc, Peer-to-Peer, Star, Mesh & Ad-hoc, Peer-to-Peer, & Intra-WBAN: \\ Topology & based & small networks & Ad-hoc, Peer-to-Peer, Star, Mesh & Ad-hoc, Peer-to-Peer, & Coordinated, \\  & & & Mesh & Star, Mesh & 1/2-hop star. \\  & & & & & Inter-WBANs: \\  & & & & & non-standardized \\ \hline Topology size & 2007 devices & Up to 8 devices & Up to 65536 & Up to 65536 & Up to 256 devices per & Up to 256 devices per & body, and up to 10 \\  & for structured & per Piconet & devices per & devices per & WBANs in a volume of \\  & WiFi BSS & & network & network & 6 \(\times\) 6 \(\times\) 6 m \\ \hline Target & & & Optimized for & Optimized for & Optimized for & Health Monitoring, \\ Applications & Optimized for & Optimized for & sensor, home & short range and & Sports, Disability \\  & Data Networks & Voice Links & automation, & high data rates, & Assistance, Body \\  & & & _etc._ & localization, _etc._ & Centric application, _etc._ \\ \hline \end{tabular}
\end{table} 
Table 3: WBAN-Related Standards.
## 4 Methodology
### User Spacecraft  
For a period spanning more than two decades, the FDF has used DOWD measurements for missions equipped with oscillators that are incapable of being made coherent with the carrier uplink. In this study, DOWD analysis has been conducted on four user satellites that the FDF has supported in recent times. All four satellites are in the LEO regime and possess vastly differing orbital configurations, as shown in Figure 5. This diversity of orbital geometries aids in identifying any discrepancies that could occur due to the varying geometry.
# Super-Resolution Multicomponent Joint-Interferometric Fabry-Perot-Based Technique
## 2 Principle Analysis of MJI-HI Spectral Super-Resolution Based on a FPI  
### Principle of the FPI  
The FPI consists of two parallel, semi-reflective mirrors separated by an interval \(d\) with a medium refractive index \(n\), as shown in Figure 1. A parallel beam of wavenumber \(\
u\) is incident on the FPI, with an incident angle \(\theta\). After several reflections and transmissions, the angle of the outgoing light remains unchanged. The OPD between adjacent beams is given by \(\Delta_{FPI}=2 nd\text{cos}\theta\), whereas the phase difference is \(\delta_{FPI}=4\pi nd\
u\text{cos}\theta\).  
The outgoing beams of the FPI converge onto the image plane via the optical system for multi-beam interference. The effect of interference can be seen as generating a transmission spectrum \(T_{FPI}\) as follows:  
\[T_{FPI}(\delta_{FPI})=\frac{(1-R)^{2}}{1+R^{2}-2R\cos\delta_{FPI}}, \tag{1}\]  
where \(R\) is the reflectance of the FPI surface. The [PERSON] expansion of Equation (1) yields [18]  
\[T_{FPI}(\delta_{FPI})=\frac{1-R^{2}}{1+R^{2}}\Big{[}1+\xi\cos\delta_{FPI}+(\xi \cos\delta_{FPI})^{2}+(\xi\cos\delta_{FPI})^{3}+\ldots\Big{]}, \tag{2}\]  
where \(\xi=2R/(1+R^{2})\). Thus, \(T_{FPI}(\delta_{FPI})\) can be rewritten as follows [18]:  
\[T_{FPI}(\
u)=\sum_{m=0}^{\infty}a_{m}\cos(m\delta_{FPI})=\sum_{m=0}^{\infty}a_ {m}\cos(m\cdot 4\pi nd\
u\cos\theta) \tag{3}\]  
Figure 1: Schematic diagram of the FPI principle.  
Thus, \(T_{FPI}(v)\) is composed of a fundamental wave, with a frequency of \(2 nd\)cos\(\theta\) and its higher harmonics. Because \(\xi\) is only related to the FPI's reflectivity \(R\), \(a_{m}\) is also only related to \(R\). From another perspective, \(T_{FPI}(v)\) is a periodic function of \(v\). Any periodic function can be expanded into a Fourier series, and the value of \(a_{m}\) can be calculated directly using the Fourier series expansion formula.
## 4 Results and Discussion  
### Monitoring Results  
#### 4.1.1 Macrocell Corrosion Current (iCORR,MACRO)  
Macrocell corrosion currents occur when a difference exists in the potential between different rebar zones of the same reinforced concrete element. In RCSs, being exposed to differing environments means that rebars reach distinct free Gibbs energies, which induces potential gradients that produce macrocell currents. Figure 5 shows the results of following up iCORR,MACRO in the three columns: (A) free of aggressive agents; (B) the chloride-contaminated LZ; and (C) the carbonated LZ.  
As revealed, for the column free of aggressive agents (Figure 5A), the intensity of macrocell currents is virtually negligible (below 0.2 uA/cm\({}^{2}\) in all cases). In this case, lack of an aggressive agent favours potential gradients not being produced between the sensors and the column's general rebar. This situation was left for the 4 months that monitoring lasted. Conversely, evolution in the other two columns differed.  
However, differences were observed for the column whose LZ was manufactured with chloride-contaminated concrete (Figure 5B). The sensors placed in the LZ clearly displayed anodic behaviour at all times (values were 0.3-0.7 uA/cm\({}^{2}\)). The chloride ion triggered depassivation with the sensor placed in the LZ, which favoured corrosion by pitting under the thermodynamic conditions of oxygen (O\({}_{2}\)) and humidity (H\({}_{2}\)O) availability. The sensors placed in the UZ displayed cathodic behaviour, but their intensity dropped with time because humidity in the column progressively lowered for being under laboratory conditions (only the LZ was wetted).  
Figure 4: Testing procedure: (**A**) exposure condition; (**B**) macrocell measurement and (C) corrosion rate and double layer capacity measurement.  
Furthermore, differences were also seen for the column whose LZ was carbonated, although evolution was much less uniform over time with changes in polarity during the 4-month study period. In this case, the sample's carbonation favoured generalised corrosion of the sensors placed in the LZ. Nonetheless, as the passive layer was not totally destabilised, the evolution of the macrocell currents showed much more unstable behaviour. The values obtained for such evolution were more stable after 60 days of follow-up. After this time, the sensors in the LZ behaved like net anodes with values between 0.4 and 1.0 mA/cm\({}^{2}\).
# Near-Surface and High-Resolution Satellite Time Series for Detecting Crop Phenology
## 2 Materials and Methods
### Methodology
#### 2.2.4 Accuracy Assessment  
To assess the accuracy of PlanetScope and PhenoCam-retrieved crop phenological transition dates, we conducted visual interpretations of crop emergence and maturity for each site-year using the digital repeat photography of the PhenoCams. Compared to field-based phenological observations, the PhenoCam photos could enhance the temporal resolutions of observations, expand the geographical coverage, and reduce costs. In this study, the crop phenology of the 57 PhenoCam site-year observations was visually interpreted via a consistent protocol. Specifically, the crop emergence was interpreted when the crops were first visible within the defined ROI of the PhenoCam photos. The crop maturity was interpreted when the crop leaves were approximately 50% yellow within the ROI.  
We evaluated the concordance among the PlanetScope, PhenoCam, and visually observed crop phenology using the statistical measures of root mean square error (RMSE), bias, and R square. These statistics quantified the magnitude of differences among the characteristic phenology, the average signed difference, and the degree of the variation in one type of phenological measurement explained by another, respectively.
## 4 Discussion  
The irreversibility flow of the gasification process (4080.21 MJ/kg of product) is much higher than the corresponding value for pyrolysis (48.75 MJ/kg of product). This finding means that the exergy consumption in gasification is almost 84 times higher than pyrolysis. This is a considerable difference; therefore, one can establish that the modeled pyrolysis process is more refined than gasification, from an energy distribution viewpoint. This fact is supported by the obtained exergy efficiency of both processes. However, it is worth mentioning that both processes probably need more refinement, since the obtained exergy efficiencies are rather low (11% vs. 3%).  
Another exergetic performance parameter that was evaluated in this study was the resource exergy intensity, which indicates the current flow of exergy needed to complete 1 kg of product production. Gasification obtains an intensity of 4080 MJ/kg of product and pyrolysis obtains 18.64 MJ/kg of product. The results that are consistent with previous exergy outcomes confirm the better energetic profile of the pyrolysis process. Globally, the evaluated thermochemical technologies are expending too much energy to produce biofuel. The ways forward, to improve the current designs, involve including optimization strategies such as heat integration [51], mass integration [52], or combined approaches (e.g., heat-integrated water recycling networks) [53].  
The sustainability evaluation delivered interesting results, in terms of a broader perspective (beyond exergy assessment). The results described in Tables 6 and 7 indicate an overall better performance of the pyrolysis process than gasification. This difference is more significant in the energy and exergy areas than the other ones, with higher performances from the pyrolysis process. Figure 5 displays a radial chart with percent score results for each indicator, to better illustrate these results.  
Globally, there is a parity in terms of renewability and global warming potential quantitative values. This is an expected result, since the nature of these processes is similar, and they use bio-based feedstock (second-generation). Consequently, very high percent scores for the renewability material index were obtained in both cases. Conversely, this is not the case for the energy and exergy efficiency indexes. The results reflected that the gasification process requires significant improvements to reach higher efficiencies that make this process more sustainable. An important fact is that more energy is consumed in the process than is obtained in the product. This is a clear sign that the process is not efficient enough to generate cleaner energy. From these outcomes, the need to include optimization strategies that might lead to a better performance of these technologies is evident. The addition of a water regeneration system and using internal heat streams to meet energy requirements are suitable alternatives to accomplish that goal.  
Figure 5: Radial chart of percent score for sustainability indicators.
###### Abstract  
The circular Halbach array is of great application value in electromagnetic energy conversion. The Halbach array can be formed with multiple magnets according to a specific method. The magnetic flux intensity of the magnet array is concentrated and enhanced in a certain area. The concentrated magnetic flux intensity could lead to a higher rate of change in the magnetic flux when the magnet and the coil move relatively. The spatial arrangement, structural dimensions, and residual flux density of the Halbach array are the main factors affecting its performance. Some areas with almost no magnetic induction in the Halbach array affect the performance of electromagnetic energy conversion. This paper conducted a comparative study of the Halbach array and the two-directional magnet array in terms of energy conversion. The root mean square (RMS) value and phase of the induced voltage in the coil were analyzed by a finite element method. Simulation shows that the RMS value of the induced voltage in the coil is equal when the gap is 1.55 mm and the REMEH adopts either of the two magnet arrays. The two-directional array has advantages in the energy harvesting performance when the gap is less than 1.55 mm, while the Halbach array has advantages in the energy harvesting performance when the gap is greater than 1.55 mm. The experimental verification of the designed prototype was implemented in this study. The experimental voltage is in good agreement with the simulation voltage. The threshold value of the gap in the experiment is about 2 mm, which is slightly higher than the simulation value. This characteristic has a certain reference value for the application of the Halbach array in the electromagnetic field.  
Halbach array; two directional magnet array; magnetic flux density comparison; voltage comparison +
Footnote †: journal: Applied sciences
## 2 Related Work
### Convolutional Neural Network in HAR  
The convolutional neural network (CNN) is one of the deep learning architectures which are widely implemented for applications such as image classification, speech recognition, and natural language processing. It stands out from three important ideas, which are the following: sparse interactions, parameter sharing, and equivariant representations [15]. There are several outstanding architectures in CNN, for example LeNet [19], AlexNet [20], GoogLeNet [21], VGGNet [22] and ResNet [23]. These architectures have greatly improved CNN's performance.  
The existing works [24; 25; 26; 27; 28] have shown that CNN can classify HAR with high accuracy. A CNN-based feature extraction method was adopted to extract the local dependency and scale-invariant characteristics in time series data. A CNN approach [24] was used to extract the feature representation of activity and the variation of activities can be analyzed by the model. They also conducted test on three public datasets which are SKODA, OPPORTUNITY, and Actitracker. The model consists of a pair of convolution layers and one max-pooling layer, followed by two fully connected layers.  
In [25], tri-axial acceleration data were collected by the single accelerometer to perform the activity classification using CNN. The model was trained in eight different classes of activities that include falling, running, jumping, walking, walking quickly, step walking, walking upstairs, and walking downstairs.  
Actigraphy sensors were used in [26] to collect actigraphy data during sleep to study sleep and physical activity patterns. Physical activity data were collected during the awake time and used to assess the sleep quality of the subject in poor or good sleep efficiency. It showed that the CNN model has the highest specificity and sensitivity compared to all other methods used in the experiments.  
In [27], they compared performance between the deep learning model and machine learning model. In their experiments, the CNN model outperforms the entire machine learning model in HAR in both the public datasets that are the OPPORTUNITY dataset [29] and Hand Gesture dataset. The transfer learning in CNN [28] was performed to evaluate the effectiveness of the CNN's structure framework in adapting data collected by different types of sensors. A study has been conducted on transfer learning for sensor data between the different users, application domain, sensor modality, and sensor location. In their results, CNN can be used to conduct training and adapt to different datasets effectively and reduce training time.