esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In this work, in particular, we have focused on and investigated the formal aspects of the rays that describe the spatial development of the electromagnetic energy density in the case of single Gauss-Maxwell beams and interference in the coherent superposition of two Gauss-Maxwell beams. Furthermore, we also consider the limit where the behavior of these vector beams and their superpositions can be described, in a good approximation, by the usual Gaussian beams, hence neglecting the associated polarization state. Particular interest is thus paid to the transverse momentum, which is eventually the observable quantity in an experiment and, therefore, the quantity of interest to take the methodology here proposed to the analysis of real laboratory experiments. Accordingly, this work has been organized as follows. To be self-contained and, at the same time, to offer a wider contextualization of the work, in Section 2 we introduce a general overview of the treatment for standard scalar Gaussian beams, starting from a revision of some general aspects connected to monochromatic scalar fields in vacuum, and how a ray (\"photon\" trajectory) equation can be properly specified for these fields. Then, the case of Gauss-Maxwell beams is analyzed and discussed in Section 3, first for a linearly polarized beam and then extended to any polarization state. Section 4 is devoted to the extension of this methodology to the superposition of two coherent Gauss-Maxwell beams with the same polarization state and also with mutually arbitrary polarization states--a case that may occur if a beam is diffracted by a two slit and, immediately afterwards, each diffracted beam acquires a different polarization state. To conclude, a series of final remarks are summarized in Section 5.
## 5 LCCA and Performance Efficiency
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**SBS-7\%** & \multicolumn{2}{c}{**Millions**} & \multicolumn{2}{c}{**BF**} & \multicolumn{2}{c}{**Millions**} & \multicolumn{2}{c}{**CF**} & \multicolumn{2}{c}{**Millions**} & \multicolumn{2}{c}{**GF**} & \multicolumn{2}{c}{**Millions**} \\ \hline Initial & 0 & 347 & Initial & 0 & 183 & Initial & 0 & 285.5 & Initial & 0 & 214 \\ Construction & & & Construction & & Construction & & Construction & & & & \\ \hline Maintenance free & & & Maintenance free & & & Maintenance free & & Maintenance free & & & \\ period & 1-5 & & period & 1-8 & & period & 1-7 & & period & 1-6 & \\ \hline General & & & General & 9-16 & 1.1 & General & & & & & \\ maintenance & & & maintenance & 9-16 & 1.1 & & & & & & \\ \hline Removal of & & & & & & & & & & \\ Wearing Course & & & & & & & & & & \\ \hline Maintenance free & & & & & & & & & & \\ period & 11-13 & & & & & & & & & \\ period & 11-13 & & & & & & & & & \\ period & 11-13 & & & & & & & & & \\ \hline General & & & & & & & & & & \\ maintenance & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Maintenance intervention schedule for different mixture types. Option 1. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  
Each fiber type has distinct physical and mechanical properties, as well as their interaction with the binder and aggregate matrix. Although both CF and BF exhibit similar fatigue performance, the superior rutting resistance of BF can be linked to its relatively higher surface roughness and better thermal stability, which enhances the interlocking and bonding within the asphalt matrix. BFs possess a higher melting point and excellent resistance to deformation under high temperatures, which contributes to their enhanced performance under loading. In contrast, CFs have a smoother surface and lower strain tolerance which may limit their effectiveness in resisting permanent deformation under repeated loading. GF mixtures demonstrated the weakest performance in both fatigue and rutting resistance due to their lower tensile strength, lower thermal stability, and susceptibility to breakage during mixing and compaction. Additionally, the bond between glass fibers and the asphalt binder is generally weaker, which could result in less effective load transfer and reinforcement.
## 2 Materials and Methods
### Cost Function
#### 2.2.3 Safety  
The last term of the cost function assesses the safety (\(t_{S}\)) with regard to the distance \((d)\) from every segment \((j)\) of the needle to each obstacle \((i)\) of the environment. The term is based on the obstacle distance cost function equation proposed by [PERSON] in Hong (2018). When the mentioned distance is greater than a defined maximum \((e_{d})\), the cost function is not affected; meanwhile, if the distance \(d_{ij}\) is less than the minimum distance allowed to the obstacle \((r_{c})\), the trajectory is discarded.  
\[t_{S}=\frac{1}{N_{o}N_{e}}\sum_{i}^{N_{o}}\frac{N_{e}}{j}\frac{e_{d}-r_{c}}{d_ {ij}-r_{c}} \tag{3}\]  
\[d_{ij}=\begin{cases}d_{ij}&\text{if}\quad r_{c}<d_{ij}\leq e_{d}\\ e_{d}&\text{if}\quad\quad e_{d}<d_{ij}\end{cases} \tag{4}\]  
where \(N_{e}\) and \(N_{o}\) are the number of elements in the flexible needle and the obstacles in the three-dimensional space, respectively.  
The cost \((c)\) for the trajectory will be the sum of each term multiplied by a weight, which depends mainly on the criterion with which to evaluate the importance of effort, \(w_{1}\), safety, \(w_{2}\), and insertion convenience, \(w_{3}\). Based on the weighting proposal in Hong (2018), where the authors prioritize safety (90%) over effort (10%), for the initial simulations, a weight of 70% is assigned to safety, followed by 20% for path length to minimize tissue damage, and finally, 10% for insertion angle convenience. However, in Section 3.2, the results of comparing the variations of the corresponding cost values are detailed.  
\[c=w_{1}t_{A}+w_{2}t_{L}+w_{3}t_{S} \tag{5}\]
# Addressing Text-Dependent Speaker Verification Using Singing Speech
###### Abstract  
The automatic speaker verification (ASV) has achieved significant progress in recent years. However, it is still very challenging to generalize the ASV technologies to new, unknown and spoofing conditions. Most previous studies focused on extracting the speaker information from natural speech. This paper attempts to address the speaker verification from another perspective. The speaker identity information was exploited from singing speech. We first designed and released a new corpus for speaker verification based on singing and normal reading speech. Then, the speaker discrimination was compared and analyzed between natural and singing speech in different feature spaces. Furthermore, the conventional Gaussian mixture model, the dynamic time warping and the state-of-the-art deep neural network were investigated. They were used to build text-dependent ASV systems with different training-test conditions. Experimental results show that the voiceprint information in the singing speech was more distinguishable than the one in the normal speech. More than relative 20% reduction of equal error rate was obtained on both the gender-dependent and independent 1 s-1 s evaluation tasks.  
speaker verification; singing speech; corpus; X-vector +
Footnote †: journal: Accepted: 26 June 2019; Published: 28 June 2019
###### Abstract  
Prior research reveal that low-viscosity resin is able to significantly penetrate initial caries lesions, which leads to their stabilization. The objective of the present report is to assess the shear bond strength (SBS) of orthodontic brackets bonded with different adhesives to demineralized enamel treated with a low-viscosity resin infiltrant. It also aims to compare the achieved bond strengths to those achieved in relation to sound enamel (SE). A total of 48 newly extracted third molars were collected, distributed in four groups (n=12), covered with a nail varnish, with 4 x 4 mm of uncoated area, immersed in Buskes demineralizing solution (14 days, 37 \({}^{\circ}\)C) or remained untreated. Group I: SE + Transbond XT; Group II: demineralized enamel (DE) + ICON + Transbond XT; Group III: DE + ICON + Scotchbond Universal; Group IV: DE + ICON + Assure PLUS. SBS was quantified in megapascals (MPa) and statistically analyzed (ANOVA, \(p\leq 0.05\)). The mode of failure was assessed microscopically (10 x magnification). The highest SBS detected was in Group IV, and the difference was statistically significant (F = 14.37; \(p=0.000\)). Treatment with a resin infiltrant on DE does not impair the shear bond strength when compared to SE, although it does produce a significantly higher strength when combined with Assure PLUS.  
enamel; demineralization; caries infiltration; ICON; shear bond strength; in vitro 2020, 10, 3619; doi:10.3390/app10103619
# A Usable Encryption Solution for File-Based Geospatial Data within a Database File System
## 6 Discussion
### Main Benefits and Limitations
Moreover, spatial files are protected in storage via encryption. However, as part of future research, we will explore a complete _decentralised_ architecture wherein multiple DBFS nodes protect spatial files in-house. In this completely decentralised architecture, there would be no central server and thus no _single point of failure_. A multi-node ECMSDK architecture currently requires a master-slave configuration, for which a dedicated master node must always be online. To this end, the implementation of a complete _peer-to-peer_ (P2P) architecture with multiple ECMSDK nodes can be undertaken, where--similar to _IPFS_--a spatial file uploaded to any node would first be encrypted and then synced with all other ECMSDK nodes. This approach would leverage the DBFS file management features and encryption-decryption model at each node in the P2P architecture within an enterprise GIS. To the best of our knowledge, no such peer-peer architecture based on DBFSs is currently available in the market.
# Prediction of Cutting Parameters of HVOF-Sprayed Stellite 6
## 1 Introduction
Although there are some data on machining Stellite alloys, the available information is still very limited; in particular, information about the adhesion on a substrate or the adhesive forces of the applied material on the substrate were not studied systematically. The surface roughness, along with other parameters of the topography of machined surfaces [16], is of crucial importance to the specificity of coatings technologies [17; 18; 19; 20]. That is why the authors decided to devote deeper attention to this topic in this paper. Because the authors have been dealing with the final surface topography generated by various technologies for a long time, they have new results to try to apply [21; 22; 23; 24; 25]. Our study will be mainly analytical, examining the exact definition and discrete evaluation of the influence of different combinations of main cutting parameters and analogously each of the parameters separately on the final topography of the surface, especially in terms of good adhesion and solid adhesion. These questions and their solutions are covered by the relevant sections below. The paper presents an original method for determination of the main stress-deformation functions and processes, the generation of adhesive forces and the stress between the turned substrate and the coating. Thus, the mechanical and adhesive processes involved in chip formation interact with the machining tool, the final surface condition, and the generation of bonding forces between the turned substrate material and the coating material. A new equation for mechanical equilibrium is derived in stress-strain processes that occur during chip formation. The newly derived equilibrium equation for chip machining enables the acquisition of new exact knowledge and the necessary data. The monitored parameters are analysed on three mechanically different materials. It is a measurement and analytical processing of changes in the continuous and resulting values of the parameters in the cut, taking into account the assumption of subsequent application of the coating layer.
## 4 Results  
### Experimental Setup  
To match the input signal characteristics, we used 360 Hz resampled signals from Lead II across all datasets, as described in the respective dataset subsections. Based on the pre-processing and dataset setup described in Section 3, we conducted a series of experiments to evaluate the robustness and generalizability of the proposed DA-BSM framework.  
The evaluation process was designed to assess the robustness and generalizability of the proposed DA-BSM framework across heterogeneous ECG datasets with varying annotation granularities. Specifically, the framework first involved pretraining a beat-level classifier using all labeled beat segments contained within the 48 ECG recordings of the MITDB. These fine-grained labels enabled the model to effectively learn detailed beat-level characteristics essential for accurate arrhythmia classification.  
In contrast, the SPH and PTB-XL datasets, which contain only coarse-grained annotations at the record level, were partitioned into training (80%), validation (10%), and test (10%) subsets. This strategic partitioning allowed us to rigorously evaluate the model's capability to adapt the fine-grained knowledge acquired from MITDB pretraining to datasets characterized by limited labeling granularity.  
During training, the Adam optimizer with a learning rate of 0.0001 was employed, and early stopping criteria were applied to mitigate overfitting. Training was conducted for a maximum of 50 epochs. Categorical cross-entropy loss was utilized for the arrhythmia classification task, while binary cross-entropy loss was applied during the WSL-based domain adaptation phase. This training procedure ensured that the model effectively transferred learned beat-level patterns to new, coarsely annotated datasets, thus demonstrating enhanced robustness and generalization performance.  
Figure 4: Architecture of the 2D CNN model for arrhythmia classification using DA-BSMs.  
We provided training and validation curves for the rhythm classifier in Figure 5. While minor fluctuations are observed, the validation accuracy exhibits convergence in both datasets. Early stopping based on validation loss was applied to prevent overfitting and ensure stable model training. All models were implemented in Python 3.10, While Python's development started in the Netherlands, the Python Software Foundation, which now maintains the language, is based in the United States, using TensorFlow 2.10 and Keras and trained on a desktop PC equipped with an NVIDIA RTX 3070 GPU.  
Several performance metrics--including accuracy, sensitivity, specificity, and F1 score--were computed on the test subsets of the SPH and PTB-XL datasets to quantitatively evaluate the effectiveness of the proposed framework. This comprehensive experimental setup ensured that the models were thoroughly validated on unseen data, providing reliable results.
# Application of Thermal Image Data to Detect Rebar Corrosion in Concrete Structures
## 2 Research Methodology
### Focus of the Study  
As noted in Section 2.1.2, the cross-sectional area of the rebar decreases as the rebar corrosion progresses, and the resistance of the conductor is proportional to its length and inversely proportional to its area. Therefore, the following hypothesis was established: When a fixed amount of current is applied to the rebar, the distribution of thermography that appears on the concrete's surface varies owing to the differences in heat generation caused by the electrical resistance.  
As shown in Section 2.1.1, as the corrosion progresses, corrosion by-products are created. These products surround the concrete in the form of a film of oxidized steel, based on the difference in the corrosion ratio, and infiltrate in the concrete through pores. The following hypothesis was established: The heat transfer coefficients of the rebar and concrete vary according to the degree of corrosion; when current is applied to the rebar and it is heated, the speed at which heat is transferred to the concrete surface varies.  
This study was performed based on the two aforementioned hypotheses, which follow physical phenomena. The cross-sectional area of the rebar decreases as the corrosion progresses in the rebar, and there is a difference in the heat generated in the rebar owing to the difference in electrical resistance when electricity is applied to the rebar and it is heated. When the rebar within the concrete corrodes, corrosion by-products, such as oxidized steel, are created. The by-products with high thermal conductivity fill the pores in the concrete. As a result, the process of heat transfer to the concrete surface varies for each corrosion ratio. Therefore, an infrared camera was used to capture the differences in temperature distribution on the surface of the concrete, and the data were stored in a database.
## 4 HCR-1 Protocol Description
### Cluster Formation in \(\mathcal{R}(1)\)  
The CMs in \(\mathcal{R}(1)\) have to decide which cluster to join with the cluster formation algorithm. In related works, the CM selects its CH with the consideration of the distance or the residual energy separately. For example, in [10], the CM chooses its CH with minimum transmission range. In [19], the CM selects its CH with the highest residual energy within the transmission range. However, pursuing the energy efficiency of CM in cluster formation may increase the traffic load of CH. On the other hand, considering the residual energy of CH in cluster formation may reduce the energy efficiency of CMs.  
Therefore, in HCR-1, we propose a cluster formation algorithm with joint consideration of the transmission efficiency of CM and the residual energy of CH. Given a CM \(j\) and a set of CHs in its JOIN list, the CM \(j\) uses the clustering cost function to select its CH.  
\[C_{form}(i,j)=\beta\times\frac{d(i,j)}{R_{c}}+(1-\beta)\times(\frac{E_{MAX}-E_{ r}(i)}{E_{MAX}}),i\in JOIN \tag{8}\]  
where \(d(i,j)\) is the distance between CH \(i\) and CM \(j\) and \(E_{r}(i)\) is the residual energy of the CH \(i\). \(R_{c}\) is the clustering range, and \(E_{MAX}\) is the initial energy of each node. The parameter \(\beta\) is the clustering factor for the tradeoff between CM and CH.  
The CH \(i\) with minimum \(C_{form}(i,j)\) will be selected as the CH for CM \(j\). The impacts of \(\beta\) on the network lifetime will be evaluated in Section 5.1.  
```
1:Node \(i\) receives \(E_{ave}(1)\) from the sink;
2:if\(E_{r}(i)\geqslant E_{ave}(1)\)then
3:\(NEXT(i)\gets 1\);
4:else
5:\(NEXT(i)\gets 0\);
6:endif
7: Sets up random backoff timer \(T_{b}(i)\) (Equation (5));
8:repeat
9:if Receives AC message from CH \(j\)then
10: Records CH \(j\) to JOIN list;
11:endif
12:if Receives AR message from CH \(j\) AND \(k(j)=2\)then
13:if\(NEXT(j)=0\)then
14: Records CH \(j\) to SERV list;
15:else if\(NEXT(j)=1\) AND CH \(j\) is in SERV list then
16: Deletes CH \(j\) from SERV list;
17:endif
18:else if Receives AR message from CH \(j\) AND \(k(j)=1\)then
19:if\(NEXT(j)=1\) AND \(d(i,j)<d(i,0)\)then
20: Records CH \(j\) to RELAY list;
21:endif
22:endif
23:until\(T_{b}\) terminates
24:if\(NEXT(i)=1\)then
25:\(status\gets CH\);
26: Forwards the data to the sink directly;
27: Broadcasts AC and AR message;
28:else if SERV is not empty or JOIN is empty then
29:\(status\gets CH\);
30:if RELAY is empty then
31: Forwards the data to the sink directly;
32:else
33: Selects the relay with minimum \(C_{route}\) from RELAY list (Equation (7));
34:endif
35: Broadcasts AC and AR message;
36:else
37:\(status\gets CM\);
38: Selects its CH with minimum \(C_{form}\) from JOIN list (Equation (8));
39:endif
```  
**Algorithm 1** Clustering and routing algorithm in \(\mathcal{R}(1)\).
## 2 Materials and Methods
### Remote Sensing Imagery Data  
The imageries used were taken from the PlanetScope CubeSat platform, which has satellites characterized as 3U CubeSats (10 \(\times\) 10 \(\times\) 30 cm) with a mass of approximately 4 kg. PlanetScope CubeSats consist of an optical sensor satellite constellation with high resolution. Currently, there are approximately 170 satellites in solar orbit, which can collect imageries daily from anywhere on the globe with a resolution of 3-5 m. With the official launch of a new generation of PlanetScope eight-band products, on the one hand, Planet has further optimized and improved the key processing of imageries, which is reflected in reducing the number of pixels with poor quality, and improving the registration accuracy between bands. On the other hand, in addition to the existing red (650-680 nm), green (513-549 nm), blue (465-515 nm) and near-infrared (845-885 nm) bands, the coastal blue (431-452 nm), red-edge (697-713 nm), yellow (600-620 nm) and second green (513-549 nm) bands have been added, which are widely used in coastal zones, surface type identification, crop growth assessment, yield estimation, environmental monitoring and so on [41; 42]. Five cloud-free satellite imageries were obtained of the modern agricultural demonstration base throughout the grain-filling stage from the Planet Labs platform. All satellite imageries were downloaded as Surface Reflectance (SR) products from www.planet.com accessed on 17 September 2022. The characteristics of the imageries are shown in Table 1.
# Recent Advancement in Assessment and Control of Structures under Multi-Hazard
## 6 Multi-Hazard Assessment and Control of Wind Turbines  
The tall and slender geometry of wind turbine towers and the large top mass of the turbine and the rotors make wind turbines sensitive to both wind and seismic excitation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Hazards** & **Structure** & **Control System** & **Main Contribution/Conclusion** \\ \hline [PERSON] et al. [146] & Wind, Blast and Earthquake & 5- and 39-story benchmark buildings & Semi-active friction damper & A new controller called input space dependent controller (ISDC) which is more effective than sliding mode controller. \\ [PERSON] and [PERSON] [145] & Wind and Earthquake & 7- and 10-story steel frame buildings. & Sliding floor isolation. & A modified covariance matrix adaptation evolution strategy (CMA-ES) algorithm \\ [PERSON] and [PERSON] [147] & Wind and Earthquake & 16-story steel frame building. & Passive Energy Dissipation (PED). & Methodology for optimal life-cycle cost estimation, and optimal design of retrofitting \\ \hline [PERSON] et al. [148] & Wind and Earthquake & 76-storey building. & Multiple Tuned Mass Dampers (MTMDs) & Life-cycle cost-based optimization framework \\ \hline [PERSON] and [PERSON] [149,150] & Wind and Earthquake & 9-, 20-, and 25-story steel frame buildings. & PED & Optimal retrofits for earthquakes result in undesirable effects on wind response, and vice versa; damper performance is sensitive to site-specific hazard \\ \hline [PERSON] and [PERSON] [151] & Wind and Earthquake & 76-story building & Viscous Fluid Dampers (VFDs) & VPDs are effective in multi-hazard control \\ \hline [PERSON] and [PERSON] [152] & Wind and Earthquake & 76-story and 20-story building & TMD & Optimally placed and tuned TMDs are effective in multi-hazard control \\ \hline [PERSON] et al. [153] & Wind and Earthquake & 76-story building & MTMDs & MTMDs with equal stiffness are better than those with equal masses. \\ \hline [PERSON] and [PERSON] [144] & Earthquake and blast & 4-storey steel frame building & Glass curtain walls as passive TMD & Glass curtain walls can be utilized as distributed TMD for vibration mitigation \\ \hline [PERSON] [154] & Wind and Earthquake & 5-,9-, and 20-story buildings & Variable Friction Cladding Connection (VFCC) & Experimental and analytical evidence demonstrating effectiveness of VFCC. \\ \hline \hline \end{tabular}
\end{table}
Table 2: A summary of published works on vibration control of the building subjected to multi-hazard.  
Wind and seismic loading have been the two most common environmental actions considered for research on the performance assessment of wind turbine towers. For offshore turbines, wave loading is also an important factor.