esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This study's findings show that 30 _fs_-laser irradiation can produce defined topological and chemical surface changes in zirconia, LD glass ceramic, and VITA _En_ materials, as shown by EDS analysis and SEM. The EDS results revealed that all irradiated samples exhibited an increase in carbon content and a reduction in oxygen content across all tested _fs_-laser parameters.  
The present findings demonstrate that ultra-short _fs_-laser irradiation induces dose-dependent changes in the chemical composition and surface morphology of zirconia, LD, and VITA _En_ materials. The observed increase in carbon content can be attributed to a combination of surface reactions, adsorption of carbon from the ambient environment, and carbon deposition from laser-induced plasma.  
These findings expand earlier observations of _fs_-laser irradiation phenomena observed for dielectric materials, including metal oxides and carbides. SEM analysis of _fs_-laser irradiated regions revealed progressive controlled melting and recrystallization, with an absence of the pile-up features typically associated with significant thermal damage that accompany laser processing using _ns_- or _ps_-laser treatment processes.  
These results support the use of _fs_-laser treatments on dental ceramic materials, demonstrating high precision and control with minimal substrate damage and no microcrack formation. The _fs_-laser irradiation could facilitate the modification of dental ceramic surface materials to improve bonding with natural teeth, leading to more durable restorations. Additionally, _fs_-laser irradiation may be employed to create microscale textures on ceramic dental implants, potentially enhancing osseointegration.  
Conceptualization, R.G., M.R. and V.L.L.; methodology, V.L.L. R.G., B.A. and A.B.C.; validation, B.A. and I.V.L.; formal analysis, V.L.L.; investigation, V.L.L.; resources, A.B.C., M.R. and I.V.L.; data curation, V.L.L., R.G., M.R. and L.J.W.; writing--original draft preparation, V.L.L.; writing--review and editing, V.L.L., R.G., L.J.W. and M.R.; visualization, V.L.L. and B.A.; supervision, R.G., M.R. and L.J.W.; project administration, V.L.L., B.A., M.R. and R.G. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge the financial support provided by the Australian Dental Research Foundation Inc. (Grant #0309-2022), which made this research possible.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
The authors acknowledge VITA Zahnfabrik H. Rauter GmbH & Co., Bad Sackingen, Germany for providing research samples.  
The authors declare no conflicts of interest.
## 6 Illustrative Case Study and Discussion
### Discussion
This study contributes to the shift in conflict research from a qualitative to a quantitative approach and from post-evaluation to preemptive mitigation. For example, the frequency of each conflict driver can be used to determine its importance and management priority. The illustrative case studies showed that there were symptoms of conflict beforehand, and it gradually intensified. In the Cheonseong Mountain Tunnel case, the frequency of dominant conflict drivers first popped up significantly in the first quarter of 2003, as shown in Figure 4, and it reached the highest level in the first quarter of 2005. If practitioners had noticed the signal of conflict beforehand, they could have seized the golden hour for mitigation. Thereafter, the early mitigation of conflict might decrease the negative impact on the project execution. In addition, there are various textual data representing the conflict phenomenon, such as social network service (SNS) data, although this study used only news articles. Such real-time online data can be used to assess public acceptance immediately and how it is changing. By monitoring the public acceptance of civil infrastructure projects in real-time, practitioners can respond proactively. As a point of departure for data-driven conflict management, this study introduced a framework for automatically identifying conflict drivers from textual data using LLMs. Through this study, the authors confirmed the viability of developing an automated system that monitors the social conflict related to civil infrastructure projects in real-time based on online information. Although this study utilized an existing LLM without any modification, this study, as applied research, is novel in that it presents a framework and illustrative cases for NLP-powered conflict management. Data-driven conflict analysis can help construction managers manage social conflicts by generalizing conflict drivers and events, propagation paths, and response strategies. Consequently, it is expected to reduce not only construction costs but also social costs caused by conflicts.
## 2 Materials and Methods
### Analytical Methods  
All hydrogen and oxygen isotope ratios of precipitation are denoted as \(\delta^{2}\)H (or \(\delta\)D) and \(\delta^{18}\)O, defined by  
\[{}^{2}\text{H }(\text{or }\delta\text{D})=\left(\frac{(^{2}\text{H}/^{1}\text{H} )_{\text{sample}}}{(^{2}\text{H}/^{1}\text{H})_{\text{V-SMOW}}}-1\right)\cdot 1000 \% \tag{1}\]  
\[\delta^{18}\text{O}=\left(\frac{(^{18}\text{O}/^{16}\text{O})_{\text{sample}}} {(^{18}\text{O}/^{16}\text{O})_{\text{V-SMOW}}}-1\right)\cdot 1000\% \tag{2}\]  
where V-SMOW refers to the Vienna Standard Mean Ocean Water  
The stable isotope (\(\delta^{18}\)O, \(\delta\)D) analysis of snow samples was carried out at the Chemical Analytical Center of IWEP SB RAS. Directly before instrumental analysis, snow samples were placed into the closed specially prepared plastic containers [25,34] and melted at room temperature. Then, 10 mL of melted snow water was filtered through a membrane filter with a pore diameter of 0.45 \(\mu\)m using sterile Minisart\({}^{\text{\textregistered}}\) NML Plus syringe and syringe nozzles to determine the isotopic (\(\delta\)D and \(\delta^{18}\)O) composition. Five parallel samples were taken from the filtrate and analyzed by laser absorption IR spectrometry on a PICARRO L2130-i (WS-CRDS) instrument. The measurement accuracy of \(\delta\)D and \(\delta^{18}\)O (1\(\sigma\), n = 5) was \(\pm\)0.4% and \(\pm\)0.1%, respectively. The secondary parameter d-excess proposed by [PERSON] [57] was calculated as:  
\[\text{d}-\text{excess}=\delta\text{D}-8\cdot\delta^{18}\text{O} \tag{3}\]  
Using the error propagation method [58] and starting from errors of \(\delta^{18}\)O and \(\delta\)D, the calculated accuracy for d-excess was estimated as \(\pm\)0.8%.  
Precipitation-weighted mean values of stable isotope compositions (\(\delta\)D, \(\delta^{18}\)O, and d-excess) in precipitation for the winter period were calculated by the formula:  
\[\text{Cvwm}=\frac{\sum(\text{Cj-Qj})}{\text{Q}} \tag{4}\]  
where Cvwm is the seasonally precipitation-weighted mean values, %; Cj is the value of \(\delta\)D, \(\delta^{18}\)O or d-excess in the j-th precipitation sample, %; Qj is the amount of the j-th precipitation sample, mm weq.; Q is the total precipitation amount for the winter period, mm weq.  
Similarly, using Formula (4), the depth-weighted mean values of \(\delta\)D, \(\delta^{18}\)O, and d-excess for a snow cover of the study area were calculated, where Cj is the value of \(\delta\)D, \(\delta^{18}\)O or dexc in the snowpack of the j-th sampling point; Qj is the height of the snowpack at the j-th point, mm weq.; Q is the sum of the snowpack's depths at all points, mm weq.
# Game-Theoretic Power and Rate Control in IEEE 802.11\(p\) Wireless Networks
## 4 Numerical Results
### Practical Considerations
The Medium Access Control (MAC) layer can be utilised to duty cycle the vehicles for their transmission according to the metrics used in this problem. The packets that are received can be calculated in two ways: firstly by adding a sequence number on the packet to show how many packets have been sent (successfully and unsuccessfully) or by using acknowledgments. As such, we will have the received packets over the sent packets at the receiver. For example, if the sender sent a packet with an \(SN=5\) and the \(RxPhyOk_{i,j}=4\), then the \(PRR=0.8\). In this case, the receiver will send a message to the sender informing it that it missed one packet instead of sending 4 acknowledgment packets for each received message. Note that we use different definitions for PRR, which are based on the PHY and network layer. Specifically, Equation (1) is the general theoretical case of \(PRR_{i,j}\). The equivalent equation of the network layer is the (18). The latter equation is utilised in practical research works, whereby the packets are being counted, since the SINR requires a calculation that needs to include the interference measurement as well, which may be challenging to obtain in practice. For instance, the noise floor may need to be obtained constantly. Hence, we attempted to provide a PHY layer metric that can be acquired in practice: thus, to use Equation (18) further down the stack, which is a function of the SINR as we see in the equivalent Equation (1). The \(RxPhyOk_{i,j}\) is affected by the SINR, in practical scenarios; by increasing the SINR, the PRR increases as well, unless interference or collisions are present. In the next subsection, plots for the utility versus SINR and the utility versus transmission power are presented. According to Equation (3), the utility function is proportional to the PRR. This is also shown in Figure 2, where it moves from the grey area to the maximum value due to the increase in the SINR, which can be done by increasing the transmission power or reducing interference with duty cycling. In the next section of the paper, simulation results and detailed discussions will be given to justify the theoretical analysis.
## 3 Advantages, Characteristics and Types of Digital Twin
### Classification of Digital Twin
#### 3.3.2 Level of Integration  
Based on the integration level of DTs, [PERSON] et al. [2] divided them into three subcategories (Figure 5):  
* _Digital Model:_ In this type of DT, the data between the physical and digital object are exchanged manually, due to which any changes in the state of the physical object are not reflected in the digital one directly, and vice versa.
* _Digital Shadow:_ The data from the physical object flow to the digital automatically, but this is still manual the other way around. As a result, any change in the physical object can be seen in its digital copy, but not vice versa.
* _Digital Twin:_ In this type of DT, there is an automatic bidirectional flow of data between the physical and digital object. Therefore, the changes in either object, physical or digital, directly lead to changes in the other.  
Centre for Digital Built Britain [77] describes Digital Shadow as 'Static Digital Model' and Digital Twin as 'Dynamic Digital Model' as the data in the dynamic one are fed live, which can be used for a real-time control mechanism, whereas in the static model, data are added with time, making it more suitable for strategic planning.
## 2 Results
### Determination of DVB-co-GMA-TETA Sorption Capacity  
The amounts of AV1, AG16 and AR18 sorbed by DVB-_co_-GMA-TETA microspheres at equilibrium, commonly known as the sorption capacities (\(q_{e}\)), were calculated from Equation (1):  
\[q_{e}=\ \frac{(C_{0}-\ C_{e})}{m}\ V \tag{1}\]  
where \(C_{0}\) and \(C_{e}\) (mg/L) are acid dye concentrations in the solution before adsorption and at equilibrium, respectively; \(V\) (L) is volume of the dye solution and \(m\) (g) is mass of DVB-_co_-GMA-TETA. The [PERSON], Freundlich, Temkin and Dubinin-Radushkevich isotherm models (Table 2, Equations (2)-(5)) were selected to illustrate the relationship between the residual concentration of AV1, AG16 and AR18 dyes in aqueous phase at equilibrium and the sorption capacity of DVB-_co_-GMA-TETA at a constant room temperature. Characteristic parameters of selcected isotherm models as well as calculated Marquardt's percent standard deviation (MPSD), determination coefficient (\(R^{2}\)) and adjusted R-squared (\(R_{adj}^{2}\)), which are essential for the evaluation of the best fitting model in a given sorption system, are presented in Table 3[20]. Figure 4: ATR-FT-IR spectra of DVB-_co_-GMA-TETA before and after dye sorption (**a**) as well as (**b**) photos of the microspheres before and after the sorption process. The maximum values of sorption capacities determined experimentally (\(Q_{e\textit{exp}}\)) were found to be 142.4 mg/g for AG16, 172.0 mg/g for AV1 and 216.3 mg/g for AR18 (Figure 5). Based on obtained data shown in Table 3 and Figure 5, the highest \(R^{2}\) and \(R^{2}_{adj}\) values--among four applied models--were obtained using the Freundlich isotherm model. The \(R^{2}\) values were found to be 0.935, 0.934 and 0.951 for AV1, AG16 and AR18, respectively, while \(R^{2}_{adj}\) was equal to 0.925 for AV1 and AG16 and 0.944 for AR18. Parameters 1/\(n\) were in the range of 0.33-0.59 and signify that the sorption of acid dyes is favorable because the value of 1/\(n\)\(<\) 1. The above parameters also designate that the adsorption is multilayer and heterogeneity of adsorption sites is observed. The highest value of \(k_{F}\) was calculated for AR18 dye (36.3 mg1-1/n L1/n/g). Comparing obtained results with published ones, [PERSON] et al. [21] confirmed that AG16 adsorption onto natural and modified clay obeys the Freundlich model (\(R^{2}\) = 0.9299-0.9892, 1/\(n\) \(<\) 1, \(k_{F}\) = 0.0811-0.1475 mg1-1/n L1/n/g). The same conclusions were reported by [PERSON] et al. [22], investigating AR18 adsorption on activated charcoal prepared from almond shell. [PERSON] et al. [23] described AR18 retention by Fe\({}_{3}\)O\({}_{4}\)/MRCS (composite of melamine-formaldehyde resin with chitosan) at various temperatures and found it favorable (\(R^{2}\) values calculated from Freundlich
###### Abstract  
Non-cooperative space object pose estimation is a key technique for spatial on-orbit servicing, where pose estimation algorithms based on low-quality, low-power monocular sensors provide a practical solution for spaceborne applications. The current pose estimation methods for non-cooperative space objects using monocular vision generally consist of three stages: object detection, landmark regression, and perspective-n-point (PnP) solver. However, there are drawbacks, such as low detection efficiency and the need for prior knowledge. To solve the above problems, an end-to-end non-cooperative space object pose estimation learning algorithm based on dual-channel transformer is proposed, a feature extraction backbone network based on EfficientNet is established, and two pose estimation subnetworks based on transformer are also established. A quaternion SoftMax-like activation function is designed to improve the precision of orientation error estimating. The method only uses RGB images, eliminating the need for a CAD model of the satellite, and simplifying the detection process by using an end-to-end network to directly detect satellite pose information. Experiments are carried out on the SPEED dataset provided by the European Space Agency (ESA). The results show that the proposed algorithm can successfully predict the satellite pose information and effectively decouple the spatial translation information and orientation information, which significantly improves the recognition efficiency compared with other methods.  
non-cooperative space objects; pose estimation; monocular sensor; dual-channel transformer; quaternion SoftMax-like activation function +
Footnote †: journal: remote sensing  
0202315278. [[https://doi.org/10.3390/rs15225278](https://doi.org/10.3390/rs15225278)]([https://doi.org/10.3390/rs15225278](https://doi.org/10.3390/rs15225278))
###### Abstract
Therefore, a novel threshold value classification method with high accuracy is required. As a spectral water index from optical imagery, the modified normalized difference water index (MNDWI) is an effective way to extract a water body [2; 27; 28; 29; 30]. Generally, a pixel is classified into water if its MNDWI value is more than a certain threshold value [2; 30]. Therefore, the technique process of this threshold-categorized method is simple and efficient. However, the MNDWI value is based on optical remote sensing data that are easily influenced by clouds. Therefore, there is a need to monitor water surface areas dynamically using an easy-to-operate index, such as MNDWI, based on SAR data. To address the problems mentioned above, the aims of the present study were: (1) to propose a MNDWI-like index based on S1A data for dynamic monitoring of water surface areas; and (2) to monitor the water areas of Poyang Lake using the MNDWI-like index based on S1A data.
# Real-Time Behaviour of Dredged Slurry Treated by Air-Booster Vacuum Consolidation
## 6 Correspondence between Model Test and Engineering Practice  
As mentioned in Section 3.1, the PVD was horizontally laid in the model box, and it only aimed to homogeneously consolidate the slurry around the booster plate. Obviously, the width of model box (26 cm) will not affect the consolidating effect of the test slurry. The box width was controlled such that the planar dimension (i.e., the plane of box height and length) was approximately five times the half width. Half of the box width was considered, because the PVD that was horizontally laid across the box width created two equally strengthened zones, and the booster plate was within one of them. Moreover, silicone grease was smeared onto the inner walls of the model box to minimise the boundary frictions. In this way, the tractions at the front-wall/slurry and back-wall/slurry boundaries can be negligible when compared to the magnitude of air pressurisation within the height-length plane. In viewing of the large dimension ratio (i.e., the height/length over the width) and the negligible boundary frictions, the slurry confined between the booster plate and the box walls would approach the one-dimensional deformation condition, which resembles the soil compression in a horizontally laid oedometer. The one-dimensional deformation condition directs our observations only to the height-length plane, while the variations across the width can be reasonably neglected.  
As for the length of model box, it is an important design consideration, because the pressurised air leaves the booster plate and propagates along the length direction. In engineering practice, the spacing between the booster plate and neighbouring PVDs is 50\(\sim\)100 cm; correspondingly, the length of model box was set to a representative value of 70 cm.
## 2 Materials and Methods
### Geo-Positional Transformer-CNN
#### 2.5.1 GPTransformer Branch
The Multi-Head Attention layer is the most essential component of the Transformer encoder module, and its structure is illustrated in Figure 4. First, the queries (Q), keys (K), and values (V) are linearly projected into spaces of dimensions \(d_{k}\), \(d_{k}\), and \(d_{v}\), respectively, where \(d_{k}\) denotes the dimensionality of the query and key vectors, and \(d_{v}\) denotes the dimensionality of the value vectors. These projections are then repeated \(h\) times to generate \(h\) distinct projected versions (\(h\) represents the number of heads in the Multi-Head Attention mechanism). Each projection performs a Scaled Dot-Product Attention operation in parallel, yielding an output of dimension \(d_{v}\). The outputs from all attention heads are then concatenated and passed through a linear transformation matrix \(W^{O}\) to obtain the final output representation. The computation is defined as follows:  
\[\text{head}_{i}=\text{Attention}(QW^{O}_{i},KW^{K}_{i},VW^{V}_{i})\quad(i=1,2,\dots,h) \tag{6}\]  
\[\text{MultiHead}(Q,K,V)=\text{Concat}(\text{head}_{1},\dots,\text{head}_{h}) W^{O} \tag{7}\]  
The dimensions of the projection matrices are as follows: \(W^{O}_{i}\in\mathbb{R}^{d_{\text{model}}\times d_{k}},W^{K}_{i}\in\mathbb{R}^{ d_{\text{model}}\times d_{k}},W^{V}_{i}\in\mathbb{R}^{d_{\text{model}}\times d_{k}},W^{O }\in\mathbb{R}^{hd_{v}\times d_{\text{model}}}\). The input of Scaled Dot-Product Attention consists of queries and keys of dimension \(d_{k}\), and values of dimension \(d_{v}\) (\(d_{k}=d_{v}=d_{model}/h=32/16=2\)). First, the dot product between the query vector and all key vectors is computed and then scaled by dividing by \(\sqrt{d_{k}}\). A softmax function is subsequently applied to obtain attention weights over the value vectors. Finally, the output is obtained by performing a weighted sum of the value vectors based on these attention weights. The computation is formulated as follows:  
\[\text{Attention}(Q,K,V)=\text{softmax}\left(\frac{QK^{\top}}{\sqrt{d_{k}}} \right)V \tag{8}\]  
The LayerNorm layer is a commonly used normalization technique in deep neural networks, which normalizes across the feature dimensions of each individual sample. This normalization approach effectively mitigates the problem of internal covariate shift, thereby improving the stability of model training, accelerating convergence, and enhancing overall performance. The fully connected layer performs the weighted integration of multidimensional features, enabling the exploration of interactions among different features and generating output with the same dimensionality as the target variable. This layer not only accomplishes the final mapping from features to output but also plays a vital role in information compression, feature fusion, and nonlinear representation. It serves as one of the core structures in the output stage of the model.
## 3 Results
### Case III: Healthy vs. Interictal vs. Ictal States  
In this case, we extended our analysis to a more intricate scenario of distinguishing between healthy states (Z and O), interictal states (N and F), and ictal states (S), separately. The CNN performance metrics for this case are shown in Figure 9. In this comprehensive case, the CNN model yielded an Acc of 90%. Furthermore, the precision, recall, and F1-score metrics all reached an impressive 96%. These metrics emphasize the model's ability to make accurate predictions. The model also demonstrated an effective approach to managing false positives and false negatives, maintaining a harmonious balance.  
The CNN's training and validation metric curves, as depicted in Figure 10a, provide a visual representation of the model's performance in the scenario of (Z+O) vs. (N+F) vs. S. In this specific case, we observed a unique pattern compared to previous scenarios. The learning curves for both NTA and NVA exhibited fluctuations, with NTA reaching 1.0 by the 12 th epoch and NVA at 0.85 by the 27 th epoch. On the other hand, NTL and NVL consistently decreased, highlighting the ability of the model to determine complex differences within these state combinations. The fluctuating patterns reflect the adaptability of the model even in scenarios where accuracy and loss metrics occasionally vary.  
Figure 8: The (**a**) training and validation metrics and (**b**) normalized confusion matrix of CNN in classifying various combinations of healthy vs. epileptic subjects.  
In the multi-class classification scenario with Z+O, N+F, and S, as shown in Figure 10b, the CNN model exhibited significant performance. It yielded a 97% TPR for accurately categorizing Z+O, demonstrating a high degree of accuracy in classifying this state. Notably, Z+O was rarely misclassified as other states, as indicated by the absence of FPs. The model also demonstrated an 87% TPR for N+F, with a 9% FNR, suggesting reasonable identification of N+F. However, N+F had a 4% FPR, indicating rare misclassification as S. For the ictal state, the model achieved a significant 83% TPR, accompanied by a 6% FNR. Additionally, there was an 11% FPR for S, signifying few misclassifications as other states. Overall, the model showcased its capability to differentiate between these states, maintaining a good balance of accuracy and false positive/negative rates in this complex multi-class classification scenario.