esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Real Data  
The IVIM maps estimated on an illustrative slice of a HN and a pelvic patient using the three different methods are shown in Figure 5 and Figure 6, respectively. The maps  
Figure 3: Error \(\epsilon\) maps of \(D\) (**a**), \(f\) (**b**), \(D^{*}\) (**c**), computed from a simulated image using the three methods (columns) at different \(SNR\) values (rows).  
estimated with the clustering were almost similar to those estimated with the CAR method without clustering. The most relevant differences lie in the more defined boundaries of contrasted structures (e.g., the external boundary in the HN case, and the bladder wall and cortical bone of the femoral head in the pelvic case) and in the reduced blurred effects.  
\(D\), \(f\) and \(D^{*}\) values estimated by the three methods and averaged over the HN and the pelvic patients are reported in Table 1. As expected, the variations between the original CAR and that with clustering were limited, whilst the Bayesian approach without clustering showed different values, especially for \(D^{*}\).  
Figure 4: Error \(\varepsilon\) (**first row**) and \(CV\) (**second row**) for the simulated images: values for \(D\) (**first column**), \(f\) (**second column**) and \(D^{*}\) (**third column**). Significant differences are indicated by * (\(p<0.05\)) and **(\(p<0.001\))**.  
Figure 5: Parametric maps of \(D\) (**first row**), \(f\) (**second row**) and \(D^{*}\) (**third row**), estimated from a selected slice of a HN patient using the three methods (columns). The tumor, the parotid gland and the masticatory muscle are delineated in red, green and blue, respectively. Red arrows indicate improvement in boundary definition, yellow arrows indicate reduced blurred effects.
# IEEE 802.11 ah: A Technology to Face the IoT Challenge
## 3 Comparative Analysis of IEEE 802.11 ah with Previous IEEE 802.11 Amendments
### Comparison of IEEE 802.11 Amendments Based on MAC Features
#### 3.1.10 Null Data Packet (NDP)  
Null frame is a frame meant to contain no data but flag information. They are widely used in IEEE 802.11 WLANs for control purposes such as power management, channel scanning, and association keeping alive.
# The Impact of GCP Chip Distribution on Kompsat-3A RPC Bias Compensation
## 3 Experimental Results
### Kompsat-3A Error Pattern Analysis  
Utilizing the dense GCP information obtained earlier, we performed an error pattern analysis of the provided RPCs across the entire image. At each GCP location, we compared the RPC-estimated target image coordinates \((l,s)\) to the correct image coordinates \((l^{\prime},s^{\prime})\), derived from robust image matching. Subsequently, we depicted coordinate errors along the column (sample) and row (line) in different colors, as shown in Figure 13.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Kompsat-3A ID** & **Number** \\ \hline
1 & 175 \\
2 & 115 \\
3 & 160 \\
4 & 149 \\
5 & 162 \\
6 & 180 \\
7 & 181 \\
8 & 175 \\ Mean & 162 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of automatically generated GCPs.  
Figure 12: The resulted GCP information for a total of eight Kompsat-3A target images (triangles: GCP).  
Figure 13: Cont.  
All tested images exhibited distinct error patterns, with no uniform error distribution across each image. The errors ranged from \(-28\) to \(31\) pixels, corresponding to approxi  
Figure 13: Error pattern of each Kompsat-3A target image (**left**: column coordinates error, **right**: row coordinates error).  
mately 18-20 m, considering the ground sample distance (GSD) of the Kompsat-3A data. Notably, Images 1, 3, and 8 displayed discernible patterns where the error distinctly increased or decreased with the progression of the image line. In the case of Image 3, these differences reached up to 18 pixels, though Image 2 demonstrated a relatively uniform error distribution across its area compared to the other images.  
Based on this analysis, we concluded that the bias across the entire Kompsat-3A image area is non-uniform, with significant error fluctuations along the image line direction. This analysis indicates that GCPs with good distribution along the image line are required.
###### Abstract  
(1) Background: Advancements in dental imaging technologies have significantly transformed diagnostic and surgical practices. The integration of cone beam computed tomography (CBCT), artificial intelligence (AI), augmented reality (AR), and virtual reality (VR) is enhancing clinical precision, streamlining workflows, and redefining dental education. This review examines the evolution, applications, and future potential of these technologies in modern dental practice. (2) Methods: A narrative literature review was conducted, synthesizing findings from recent studies on digital radiography, CBCT, AI-assisted diagnostics, 3D imaging, and involving simulation tools (AR/VR). Peer-reviewed journal articles, systematic reviews, and clinical studies were analyzed to explore their impact on diagnosis, treatment planning, surgical execution, and training. (3) Results: Digital and 3D imaging modalities have improved diagnostic accuracy and reduced radiation exposure. AI applications enhance image interpretation, automate clinical tasks, and support treatment simulations. AR and VR technologies provide involved, competency-based surgical training and real-time intraoperative guidance. Integrating 3D printing and portable imaging expands accessibility and personalization in care delivery. (4) Conclusions: The integration of CBCT, AI, AR, and VR represents a paradigm shift in dentistry, elevating precision, efficiency, and patient outcomes. Continued research, standardization, and ethical practice will be essential for widespread adoption and maximizing clinical benefits.  
Keywords:dental imaging; cone beam CT; artificial intelligence; augmented reality; virtual reality; surgical planning; diagnostic precision
## 1 Introduction
This is because soil particle size and mineral composition have similar effects on the overall amplitude of the reflection spectrum. In hilly areas, the mineral composition of sand, clay, and silt usually includes quartz, feldspar, illite, montmorillonite, and other components and is rich in organic matter and water from crystallization [15; 16]. At 900 nm, 1400 nm, and 1900 nm, absorption peaks may appear in different types of soil with similar positions and amplitudes. In addition, the slope, degree of soil weathering, and drainage performance in hilly areas can affect the spectra. The mixing interaction of these factors mask the differences between sand, clay, and silt, making it difficult to distinguish them in the spectra [17; 18; 19]. The mixing effects of the reflection spectra of sand, clay, and silt in hilly areas make it difficult for traditional, empirical, or semi-empirical models to quantitatively detect soil texture content via their sensitive feature bands or their combinations [20; 21]. In recent years, machine learning models have been proven to have the ability to learn complex patterns and object features using a large number of training data for model training, which helps improve the accuracy of classification and recognition. These models have been widely used in the fields of soil organic matter, organic carbon, heavy metals, etc. [22; 23; 24]. However, the mixed spectral unmixing and the prediction of the content of sand, clay, and silt in hilly areas have rarely been reported. CNN is a kind of feedforward neural network machine learning model that utilizes convolution computation and depth structure [25]. It has strong feature learning abilities. Via multi-layer convolution and pooling operations, high-level abstract features can be extracted from the original spectral data. These features can represent the characteristics of different components in the spectra and help to distinguish different components in the mixed spectra [26; 27; 28]. At the same time, via the non-linear mapping ability and model generalization ability of the neural network, a complex spectral mixing relationship can be modelled to reduce the cross-correlation between the mixed spectra of different soil particles and improve the prediction precision of soil texture content [29; 30]. In this study, the laboratory spectra of soil samples and hyperspectral satellite remote sensing images were utilized to explore the performance of VNIR-SWIR hyperspectral data in predicting the content of sand, silt, and clay in soil texture. The aims of this study are as follows: (1) to evaluate the performance of machine learning models in mixed spectral unmixing between sand, silt, and clay in soil texture; (2) to evaluate the performance of laboratory spectra and hyperspectral images in predicting the content of sand, silt, and clay; (3) to evaluate the performance of hyperspectral images in mapping the spatial distribution of soil texture composition [31].
# Gyro Error Compensation in Optoelectronic Platform Based on a Hybrid ARIMA-Elman Model
## 2 Background Knowledge  
### Architecture of the Photovoltaic Platform System  
The photoelectric platform system mainly consists of a host computer, control box, driver board, motor, encoder, and fiber-optic gyroscope, as shown in Figure 1. As the key feedback angular velocity sensor, the output accuracy of the fiber-optic gyroscope seriously affects the pointing accuracy of the entire system platform [8; 9].
# Study of Implantation Defects in CVD Graphene by Optical and Electrical Methods
###### Abstract  
A Chemical Vapor Deposition graphene monolayer grown on 6H-SiC (0001) substrates was used for implantation experiments. The graphene samples were irradiated by He\({}^{+}\) and N\({}^{+}\) ions. The Raman spectra and electrical transport parameters were measured as a function of increasing implantation fluence. The defect concentration was determined from intensity ratio of the Raman D and G peaks, while the carrier's concentration was determined from the relations between G and 2D Raman modes energies. It was found that the number of defects generated by one ion is 0.0025 and 0.045 and the mean defect radius about 1.5 and 1.34 nm for He\({}^{+}\) and N\({}^{+}\), respectively. Hole concentration and mobility were determined from van der Pauw measurements. It was found that mobility decreases nearly by three orders of magnitude with increase of defect concentration. The inverse of mobility versus defect concentration is a linear function, which indicates that the main scattering mechanism is related to defects generated by ion implantation. The slope of inverse mobility versus defect concentration provides the value of defect radius responsible for scattering carriers at about 0.75 nm. This estimated defect radius indicates that the scattering centres most likely consist of reconstructed divacancies or larger vacancy complexes.  
graphene; defect; ion beam; Raman spectroscopy; Hall mobility +
Footnote †: journal: Applied Physics Letters  
_Article_  
Study of Implantation Defects in CVD Graphene by Optical and Electrical Methods
# A 60-GHz Ultra-Thin and Flexible Metasurface for Frequency-Selective Wireless Applications
## 3 Results and Discussion
### Parametric Analysis and Optimization of the Results of the 60-GHz Metasurface
#### 3.2.2 Square Loop Length, 'a'  
It has been observed from the simulation analysis that the square loop length 'a' of the dual loop metasurface has a significant influence on the reflection and transmission coefficients profiles with a relatively lesser effect on the reflection phase as shown in Figure 6a. As the value of 'a' increases, the resonant frequency shifts to the left, which indicates a decreasing magnitude of the design frequency as in Figure 6b,c. The possible explanation of this phenomenon is, the increase in the inner square size, 'a' has lengthened the current path of the metasurface, and thus it results in the decrease of the resonant frequency. In this case, the reflection phases are set within the range \(0^{\circ}\) to \(20^{\circ}\) for each iteration of the parametric analysis. The plots show that at the frequency of consideration, i.e., 60 GHz, as the 'a' increases, the reflection phase decreases, however, at 'a' = 1.16 mm, the reflection phase is exactly at \(0^{\circ}\). The bandwidth remain almost constant (9 GHz) from a = 0.96 mm to a = 1.16 mm before decreasing at 'a' = 1.36 mm. While considering the reflection phase and the resonant frequency at 60 GHz, an optimal value of a = 1.16 mm is selected.  
Figure 5: Reflection phase, reflection coefficient and transmission coefficient of the dual loop metasurface at a varied angle of incidence, \(\theta\): (**a**) Reflection phase, (**b**) reflection coefficient and (**c**) transmission coefficient.  
Figure 6: Reflection phase, reflection coefficient and transmission coefficient of the dual loop metasurface at varied square loop length, ‘a’: (**a**) Reflection phase, (**b**) reflection coefficient and (**c**) transmission coefficient.
## 6 Measurement Results and Discussion
A total of seven taps were used in the transmit adaptive FFE and the VCSEL was biased with a bias current of 4.5 mA. Figure 11(a) shows the output eye using the 12 coefficient adaptive FFE in the TX. It has an average optical output power of 1.8 mW. As before, the filter is again able to re-synchronize the eyes and remove skewing. When using 5 additional taps of linear equalization in the receiver (Figure 11(b)), the BER can be improved from \(2.5\cdot 10^{-6}\) to \(1.5\cdot 10^{-7}\). The transmission was also tested using 50 m of OM5 fiber, simulating the use in server-rack environment. Figure 11(c) reveals that the BER does not drop significantly for fiber lengths up to 50 m when using receiver equalization. In comparison to SOTA NRZ VCSEL drivers [6], a significant increase in data rate by a factor of \(\approx\)1.6x is achieved. Although the BER is quite a bit higher than \(10^{-12}\), it is still below the Forward Error Correction (FEC) threshold of \(2.3\cdot 10^{-5}\) for 3 % overhead [25]. Incorporating the FEC overhead, an increase in data rate by a factor of \(\approx\)1.5x is reached.
## 5 Conclusions  
In this paper, we propose a multi-decision-based transformer model that utilizes the representations at different layers in pre-trained models to answer more types of MRC questions. To the best of our knowledge, this idea is not explored in previous work. In order to prevent the information diversity in different layers from being damaged during fine-tuning, we also propose a learning rate decaying method to weaken the influence of the supervisory signals on lower block parameters. Experimental results on five public datasets show that our model speeds up the inference procedure with considerable accuracy. Moreover, we also find that the low block and middle block of our model are good at solving MRC problems requiring shallow information and the top block is skilled in solving deep reasoning problems.  
**Author Contributions:** Conceptualization, Z.B. and X.W.; methodology, Z.B.; software, Z.B. and J.L.; validation, Z.B. and M.W.; formal analysis, Z.B. and J.L.; investigation, Z.B.; resources, X.W. and C.Y.; writing--original draft preparation, Z.B.; writing--review and editing, X.W., C.Y. and M.W.; visualization, Z.B.; supervision, X.W.; funding acquisition, X.W. and C.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by MoE-CMCC \"Artificial Intelligence\" Project (No. MCM20190701) and the Major Research 426 Plan of National Natural Science Foundation of China (Grant No. 92067202).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 7: The similarity of a 1-block baseline and two 3-block models on multiple datasets. “w/o LrD” indicates a 3-block model without learning rate decaying. For all models, we use the “\([CLS]\)” representations in 4 th, 8 th and 12 th blocks to calculate the similarity.
# Formation Control and Distributed Goal Assignment for Multi-Agent Non-Holonomic Systems
## 6 Stability of the System with Target Assignment
This leads to the conclusion that as \(V_{p1}>V_{p2}\), goal exchange results in a rapid decrease (discontinuous) of the Lyapunov-like function. All other properties of the \(V\) still hold including \(\dot{V}\leq 0\) if the condition given by Equation (27) is fulfilled. **Proposition 3**. : _The procedure given by Equation (33) results in a decrease of the sum of the position errors squared._Proof. : As \(V_{a}\) and \(V_{\theta}\) in Equation (39) are invariant under the goal assignment and the sum of all other terms represent the sum of the position errors squared (omitting the constant multiplier \(\frac{1}{2}\)), the proof of Proposition 3 comes directly from Proposition 2. Notice that the sum of the position errors squared can be easily expressed in the global coordinate frame using equality \(e_{ix}^{2}+e_{iy}^{2}=||[e_{ix}\;\;e_{iy}]^{T}||^{2}=||[p_{ix}\;\;p_{iy}]^{T}||^ {2}=p_{ix}^{2}+p_{iy}^{2}\) (refer to Equation (6)) to transform the position error of each robot in (39). The tracking algorithm presented in Section 2 together with the goal exchange procedure resulted in time intervals continuous algorithm with discrete optimization that uses the Lyapunov-like function as a criterion to be minimized. The discontinuities occur in two situations: when the robot is in the collision avoidance region (the reference trajectory is temporarily frozen and then unfrozen) and when a pair of agents exchange the goals. The set of these discontinuity points was of zero measure. Note that including goal exchange in the control reinforces fulfilling condition (27) because it supports reduction of the component \(\sum_{i=1}^{N}\frac{1}{2}k_{3}(e_{ix}^{2}+e_{iy}^{2})\). The presented algorithm does not guarantee optimal solutions but each goal exchange improves the quality of the resulting motion. The total improvement depends significantly on the initial state of the system. In extreme cases, there is a situation in which the initial coordinates are close to optimal. The procedure may lead to no goal exchange, and thus no improvement, even though communication costs have been incurred. On the other hand, if the initial coordinates are not special, benefits of using the procedure are usually considerable.
# Wind Turbines Optimal Operation at Time Variable Wind Speeds
## 5 PMSG's Optimal Power Control Based on the PID-type Regulator  
In this paragraph, another control method for ensuring the PMSG's power follows the PMSG's optimal power, (\(P_{PMSG}(t)\to P_{PMSG\mathit{optim}}(t)\)) or (\(\omega(t)\to\omega_{optim}(t)\)), is proposed, and consists of implementing a control based on a PID-type regulator.  
The equation of the PID-type regulator is  
\[P_{PMSG\_PID}(t)-P_{PMSG\mathit{optim}}(t)=k_{p}\Big{(}\omega(t)-\omega_{ optm}(t)\Big{)}+k_{i}\int\!\Big{(}\omega(t)-\omega_{optm}(t)\Big{)}dt+k_{d}\frac{d \big{(}\omega(t)-\omega_{optm}(t)\big{)}}{dt} \tag{28}\]  
where \(P_{PMSG\_PID}\) is the reference value of the PMSG's power in the case of a PID-type based control, \(P_{PMSG\mathit{optim}}\) is the computed/measured value of the PMSG's power, \(k_{p}\) is the proportional constant, \(k_{i}\) is the integral constant and \(k_{d}\) is the derivative constant of the PID regulator.  
Derivation of Equation (28) yields to  
\[\frac{dP_{PMSG\_PID}(t)}{dt}=k_{p}\frac{d\big{(}\omega(t)-\omega_{optm}(t) \big{)}}{dt}+k_{i}\Big{(}\omega(t)-\omega_{optm}(t)\Big{)}+k_{d}\frac{d^{2} \Big{(}\omega(t)-\omega_{optm}(t)\Big{)}}{dt^{2}} \tag{29}\]  
Then, replacing Equation (3) in Equation (29), it results to  
\[\frac{dP_{PMSG\_PID}(t)}{dt}=k_{p}\frac{d\omega(t)}{dt}-k_{p}k_{1}\frac{dS_{w }(t)}{dt}+k_{i}\omega(t)-k_{i}k_{1}S_{w}(t)+k_{d}\frac{d^{2}\omega(t)}{dt^{2}} -k_{d}k_{1}\frac{d^{2}S_{w}(t)}{dt^{2}} \tag{30}\]  
The PMSG's power in (30), \(P_{PMSG\_PID}\), can be determined from the kinetic motion equation in Equation (21), written as below:  
\[J\frac{d\omega(t)}{dt}\omega(t)=k_{2}S_{w}^{3}(t)-P_{PMSG\_PID}(t) \tag{31}\]  
Since, in Equation (30), there is a second order derivative of the time-varying wind speed curve, \(S_{w}(t)\), the derivation of the kinetic motion equation from Equation (31) is imposed, obtaining  
\[J\frac{d\varepsilon(t)}{dt}\omega(t)+J\varepsilon^{2}(t)=k_{2}\frac{dS_{w}^{3 }(t)}{dt}-\frac{dP_{PMSG\_PID}(t)}{dt} \tag{32}\]  
where the following notation has been made: \(\varepsilon(t)=\frac{d\omega(t)}{dt}\).  
The derived kinetic motion Equation (32), together with the PID-type regulator equation in Equation (30), defines the below system of differential equations used in the simulation environment for determining the PMSG's power curve and the PMSG's angular speed:  
\[\left\{\begin{array}{c}J\frac{d\varepsilon(t)}{dt}\omega(t)+J\varepsilon^{ 2}(t)=k_{2}\frac{dS_{w}^{3}(t)}{dt}-\frac{dP_{PMSG\_PID}(t)}{dt}\\ \\ \frac{dP_{PMSG\_PID}(t)}{dt}=k_{p}\frac{d\omega(t)}{dt}-k_{p}k_{1}\frac{dS_{w }(t)}{dt}+k_{i}\omega(t)-k_{i}k_{1}S_{w}(t)+k_{d}\frac{d^{2}\omega(t)}{dt^{2}} -k_{d}k_{1}\frac{d^{2}S_{w}(t)}{dt^{2}}\end{array}\right. \tag{33}\]
## 3 Test Network Model, Case Study and Optimization Settings Description
### Case Study Description  
The proposed multi-stage OPF-based model is evaluated over several case studies presented in Table 1. The base case includes 55 single-phase connected PVs with a rated power of 3.68 kW and without any control mechanism. Case study 1 uses OLTC as a control mechanism over a single day (1 h resolution) to minimize voltage deviation, while transformer tap settings are constrained to a maximum of five changes over a single-day period. In this case study, stage 1 is performed separately. PV RPC capability is used as a control mechanism in case study 2 to minimize power losses. Case study 2 performs stage 2 separately. OPF-based multi-stage optimization model that combines case study 1 and case study 2 is evaluated in case study 3. Case study 1 is performed with a 1 h resolution, and case study 2 is performed with an intra-hourly (15 min) resolution over a single day.  
Figure 4: Solar irradiance profile for a summer day.  
Figure 3: One-line diagram of the test network.