esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Electronic Behavior of Pd-InN upon Gas Adsorption  
Figure 5 presents the band structures of gas adsorption systems, the molecular DOS of target gases, and the atomic DOS of bonded atoms. Based on our calculations, the bandgap of the Pd-InN monolayer was obtained as 0.748 eV. In the CO system, the band gap was slightly narrowed to 0.712 eV, which attributes the electron redistribution caused by the charge-transfer [37] from the Pd-InN monolayer to the CO molecule. The charge-transfer could activate the orbitals of the CO molecule [38], making the DOS deform accordingly. It could be found that the states at \(-5\), \(-2.8\), and \(0\) eV in isolated CO molecular DOS disappeared, while several small peaks around \(-3\)-\(0\) and \(3\) eV in adsorbed CO molecular DOS emerged, which suggests the activation of molecular orbitals in adsorption. Moreover, the electron hybridization during charge-transfer can support the activation of molecular orbitals as well [39], and it therefore could account for the change in the electronic behavior of the whole system. One can see that there are somewhat hybridization between Pd \(4d\) and C \(2p\) orbitals around \(-3\)-\(0\) and \(3\) eV, which verifies the formation of chemical bonds due to the orbital interaction [40; 41].  
Upon the NO system, from the band structure, we can see that there was one state at the top of the conduction band crossing the Fermi level, forming an n-doping behavior for the Pd-InN monolayer [42]. Accordingly, the system behaved semimetallic in property and the bandgap was calculated as \(0\) eV. In the molecular DOS, the deformations not only included the disappeared magnetic moment of the NO molecule, but also laid in the shift of states after adsorption. Particularly, the state at the Fermi level became unoccupied and altered to the region above the Fermi level. Moreover, the N \(2p\) orbital of this activated NO state was highly hybrid with the Pd \(4d\) orbital in adsorption, verifying the chemical bond of Pd-N given the strong electron hybridization. The stronger binding force of Pd-N in the NO system than in the Pd-C in CO system could also be verified due to the larger overlapped area. In other  
Figure 5: Band structures and DOS distributions of adsorbing systems. (**a1**–**a3**) CO system; (**b1**–**b3**) NO system. The figures from left to right successively represent band structures of gas adsorption systems, the molecular DOS of target gases, and the atomic DOS of bonded atoms. The dash line is the Fermi level.  
words, the change of the electronic behavior of the Pd-InN monolayer in the NO system was more significant than that in CO system, which could result in a larger change in conductivity in the former system. For sensing applications, the Pd-InN monolayer may be more admirable for NO detection.
# GRACE/GFO and Swarm Observation Analysis of the 2023-2024 Extreme Drought in the Amazon River Basin
## 3 Data and Methods  
### Data  
#### 3.1.1 GRACE/GFO Data  
Currently, GRACE/GFO solutions are available in two types: spherical harmonic (SH) coefficients and Mason. Preprocessing is required to obtain \(1^{\circ}\times 1^{\circ}\) TWSC gridded data from the SH solution, which includes steps such as coefficient replacement, filter smoothing, de-striping correction, and glacier isostatic adjustment [15; 34; 35]. Additionally, the scale factor approach is applied to recover signal loss caused by coefficient truncation and error processing [36]. In contrast, the \(1^{\circ}\times 1^{\circ}\) TWSC gridded data from the Mason solution can be directly extracted without any additional processing.  
In our study, we used three SH solutions and two Mason solutions. The three SH solutions were derived from the Center for Space Research at the University of Texas at Austin (CSR), the Helmholtz Centre Potsdam-German Research Centre for Geosciences (GFZ), and the Jet Propulsion Laboratory (JPL), labeled CSR-SH, GFZ-SH, and JPL-SH, respectively. The two Mason solutions were provided by CSR and JPL, labeled CSR-M and JPL-M, respectively.
## 4 Results
### Thermal Stability  
Table 4 shows the maximum thermal-stable temperature and final degradation temperature of the PCMs under study, which are ordered by their maximum thermal-stable temperature.  
Notice that there are only five PCMs, potassium thiocynate, HDPE, dulcitol, adipic acid, and D-mannitol, with a maximum thermal-stable temperature of 200 \({}^{\circ}\)C or higher. The most stable material over this temperature is potassium thiocynate, which presents 540 \({}^{\circ}\)C as a maximum thermal-stable temperature, followed by HDPE and dulcitol with 309 \({}^{\circ}\)C and 293 \({}^{\circ}\)C, respectively. Moreover, there are two additional PCMs, benzanilide and DMPA, which have a maximum thermal-stable temperature within the upper limit of the requested operating temperatures, and therefore they could be considered to be suitable for thermal processes within the temperature range of 120-200 \({}^{\circ}\)C without degradation. On the other hand, there are two materials, benzoic acid and sebacic acid, whose maximum thermal-stable temperature is 121 \({}^{\circ}\)C or lower and therefore should be discarded for the operation in this temperature range. Finally, there are seven other PCMs, hydroquinone, urea, maleic acid, benzamide, salicylic acid, phtalic anhydride, and dimethyl terephthalate, whose suitability within this temperature range will strongly depend on the operating conditions (heat source temperature) of the thermal process for which they are planned to be implemented. However, it should be made clear that the TGAs were performed assuming the worst-case scenario, which is air continuously flowing around the material, but in real thermal applications, it is difficult to achieve such disadvantage conditions.  
Notice that there are six PCMs, benzoic acid, sebacic acid, phtalic anhydride, dimethyl terephthalate, hydroquinone, and salicylic acid, which start the degradation before or during the phase change. The reason lies in the fact that TGAs were performed by applying a constant 50 mL/min air flow to an open crucible. Therefore, the thermal degradation conditions were not exactly the same as those used for thermal cycling of the sample under study.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Material**} & **Maximum Thermal-Stable** & **Final Degradation** \\  & **Temperature** (\({}^{\circ}\)**C) & **Temperature** (\({}^{\circ}\)**C) \\ \hline Potassium thiocynate & 540 & \textgreater{}600 \\ HDPE & 309 & 540 \\ Dulcitol & 293 & 481 \\ D-mannitol & 259 & 424 \\ Adipic acid & 203 & 379 \\ Benzanilide & 196 & 315 \\ DMPA & 190 & 431 \\ Hydroquinone & 157 & 240 \\ Urea & 148 & 500 \\ Maleic acid & 141 & 212 \\ Benzamide & 138 & 225 \\ Salycilic acid & 133 & 203 \\ Phtalic anhydride & 129 & 210 \\ Dimethyl terephthalate & 128 & 265 \\ Benzoic acid & 121 & 195 \\ Sebacic acid & 118 & 201 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Maximum thermal-stable temperature, final degradation temperature of the PCMs under study.
## 3 Results  
### Characterization of GaMBN  
The morphology of the sol-gel samples was observed like a typical heterogeneous glass surface. Based on the EDX results for MBN, O and Si were the predominant elements, followed by P and Ca. For GaMBN, O and Si still made up the largest fraction, followed by Ga, then P and Ca ions. These results confirm that each of the expected ions were present in the glass matrix. MBN and GaMBN molecules were 7-15 \(\upmu\)m in diameter (Figure 2). In the FTIR spectra of MBN and GaMBN each had a PO\({}_{4}\) peak at 464 cm\({}^{-1}\), Si-O-Si asymmetric stretching peaks at 797 and 1067 cm\({}^{-1}\), and a peak near 1647 cm\({}^{-1}\) attributed to the absorption of water by the Si-OH surface. The XRD spectra of MBN and GaMBN were similar (Figure 3); however, MBN had an amorphous state, which indicates the presence of a glass.  
Figure 2: SEM images and EDX results of the (**a**) mesoporous bioactive glass nanoparticles (MBN) and (**b**) Ga-doped mesoporous bioactive glass nanoparticles (GaMBN). Red box indicates the region of interest.  
The N\({}_{2}\) adsorption-desorption isotherms revealed that MBN and GaMBN both exhibited the type IV isotherm of mesoporous materials (IUPAC classification). The type IV isotherm is the result of capillary condensation caused by the pore structure, and exhibits characteristics of a type-H1 hysteresis loop. The results of specific surface area (S\({}_{\text{BET}}\)), pore volume (V\({}_{\text{P}}\)), and pore diameter (D\({}_{\text{P}}\)) measurements demonstrated that when the MBN was doped with Ga, the S\({}_{
## 4 Model Simulations with FEM-3D
### Simulation Results with Motion
Inside this region, the behavior of the levitation force is given by Equation (22) and followed the same law as in the standstill conditions. Besides, at slip equal to one, model 1, model 2, and model 3 developed the maximum levitation force around 404.04, 204.16, and 65.38 N, respectively: \[\overset{\rightarrow}{F}_{z\_model\_4}<\overset{\rightarrow}{F}_{z\_model\_3}< \overset{\rightarrow}{F}_{z\_model\_2}<\overset{\rightarrow}{F}_{z\_model\_1}\] (22) It is important to remark the relevance of this force in the TFLIM. The highest values of levitation and attraction forces were obtained in model 1 (around 404.04 N for slip equal to one and \(-\)747.63 N, with velocities near to the synchronous velocity or slip equal to zero). Region II: \(0<s<0.5\leftrightarrow 10\frac{m}{s}<v_{moving\_part}<20\frac{m}{s}\). In this zone, model 2, model 3, and model 4 continued the evolution from region I (see Equation (23)). These forces did not continue decreasing with the slip as occurred with the levitation force from model 1, \(\overset{\rightarrow}{F}_{z\_model\_1}\), that decreased with the slip. \[\overset{\rightarrow}{F}_{z\_model\_4}<\overset{\rightarrow}{F}_{z\_model\_3}< \overset{\rightarrow}{F}_{z\_model\_2}\] (23)  
For each model, the relationship between the electric currents followed Equation (17). Besides, model 3 was the configuration that presented a low electric current consumed by each phase across the range of velocities. So, with a slip of 0.5, phase A and phase C in model 3 only consumed 66.14 and 70.60 A, respectively. Phase B reached its minimum value with a slip value of 0.3 (around to 75.55 A). If we compare the obtained results considering the whole range of slips from Figures 30-34, we have available a great amount of information about the convenience to include the proposed changes into the geometry of the TFLIM, in order to achieve a more efficient motor. We can conclude that with slip values above 0.5, model 4 developed the highest thrust force. It supposes that the changes included in the geometric parameters are useful to increase the thrust force inside the range of velocities between 0 and 10 m/s. However, model 4 always operated under attraction force condition, so an increment in the thrust force implied a reduction in the levitation force. In this way, the thrust force developed by model 3 was near to model 4 and it operated inside the levitation zone for low velocities. Therefore, we can say that model 3 is an optimum model to operate with velocities below 10 m/s. However, for slip values below 0.5, model 1 presents the best behavior. In conclusion, the proposed changes are irrelevant for velocities from 10 to 20 m/s and model 3 is the most efficient configuration for all range of velocities in comparison with model 1, that presents in phase B, the highest electric current consumed, around 95 A at the synchronous velocity.
## 3 Wearable Sensors in Healthcare
### Design of Biosensors  
According to [PERSON], et al. [49], a biosensor is an analytical integrated functional device capable of analyzing particular quantitative or semi-quantitative data using a biological recognition component. The device broadly consists of three main components. First is the biorecognition component often called a bioreceptor. This is the component that uses biomolecules from receptors or organisms modelled after biological systems to interact with an analyte of interest. Subsequently, a transducer will measure the interaction and outputs a quantifiable signal that is proportional to the target analyte present in the sample.  
Figure 2: A conceptual picture of a remote surveillance system [45].  
The overall aim of the biosensor design is to enable convenient and quick testing at the point of care or concern where the sample was acquired [50; 51; 52]. Second is the transducer, which is an electronic device that converts energy into an electrical current or voltage signal for transmission. The third is the signal amplifier, which simply refers to an electrical circuit that uses electrical power to increase the amplitude of an incoming current or voltage signal and outputs the higher amplitude version at its output terminals.  
Figure 3 illustrates the biosensor's basic parts with their working mechanisms. Normally, the analyte is a biomolecule recognizable by a highly specific biorecognition element. Different transduction platforms are used for the reaction, which generates signals that are detectable by transducers and are converted to displayable data.
## 3 Results and Discussion
### Rapid Chloride Migration
#### 3.2.1 Influence of the Amount of Portland Cement Replacement by GGBS
These results indicate that, for concrete older than 28 days, an increase in the GGBS content does not result in a very significant increase in the chloride resistance. Globally, these results show that the addition of GGBS is very effective in reducing the chloride penetrability in concrete, when compared to Portland cement concrete, even for replacement levels as low as 25% of the cement content, as long as sufficient hydration of the GGBS has taken place when chloride attack occurs. For earlier ages (at least for 28 days of age), higher percentages of GGBS are necessary to obtain significant reduction in the chloride ingress. Moreover, even with the slower hydration of GGBS, the results show that the chloride migration coefficient is always lower for slag concrete than for Portland cement concrete of the same age, cured according to the same procedure, even at ages as early as 28 days, and for percentages of replacement as high as 100% of the cement content. These results indicate that, as long as proper curing is provided during the early days, the effect of the addition of GGBS in reducing the chloride ingress in concrete is able to offset the effect of the lower hydration rate and consequent higher diffusivity of the slag concrete at early ages.
## 3 Methods
### Structure of RT DETR Algorithm  
RT DETR is a real-time object detection model that combines two classic object detection methods: Transformer [35] and DETR [20] (Detection Transformer). The Transformer is a neural network architecture used for sequence modeling, initially applied in natural language processing but proven effective in computer vision. DETR is an end-to-end object detection model that transforms the object detection task into an object query problem and uses the Transformer to solve it. RT DETR adopts the DETR structure and optimizes it for real-time object detection. It comprises three main components: backbone, Encoder, and Decoder. The backbone network, which uses a CNN network, extracts image features. The CNN network's powerful feature extraction capability is widely used in image processing tasks, providing necessary image representations for subsequent object detection tasks. The Encoder, based on the Transformer structure, processes the feature maps output by the backbone and captures global dependencies within the image through the self-attention mechanism. Compared to the traditional DETR model, RT DETR optimizes the Encoder design to reduce computational complexity and improve processing speed. These optimizations include reducing the number of self-attention layers and using more efficient attention calculation methods, aiming to maintain high precision while achieving fast inference. Each self-attention layer in the Encoder includes three main components: multi-head self-attention, feed-forward network, and residual connections with layer normalization. This design allows the model to effectively capture long-distance dependencies within the image, enhancing the understanding of complex scenes. The Decoder component receives the Encoder's output and predefined object queries, combining them through cross-attention and self-attention layers to progressively refine predictions of each object's location and category. Prediction heads at the end of the model perform object classification and bounding box regression based on the Decoder's output. These heads are simple and efficient, designed to quickly generate accurate detection results. Classification heads typically use linear layers, while bounding-box regression heads may include more complex network structures to accurately predict object locations.
# On Data Leakage Prevention Maturity: Adapting the C2M2 Framework
## 2 Materials and Methods
### Concepts
#### 2.4.2 Adopting the C2M2
This involved tailoring each level (MIL1 to MIL3) to reflect the DLP context. The process included supplementing additional points to ensure a DLP-specific focus, especially in domains like ARCHITECTURE, where technical solutions are paramount. In this endeavor, among others, ISACAA's best practices for DLP [52] were used as a reference, supplemented by professional judgment, to determine the appropriate placement of these enhancements within the MIL structure. These adaptations can be found in Appendix A.1. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Domain** & **Analytical Justification for DLP Relevance** & **DLP Related** \\ \hline \multirow{2}{*}{ASSET} & Asset management’s crucial role in identifying and protecting sensitive data-bearing assets & Yes \\  & forms a foundational component of comprehensive DLP strategies. & \\ \hline \multirow{2}{*}{THREAT} & This domain, while pivotal in threat and vulnerability identification, does not engage & No \\  & directly with DLP’s specific operational methodologies. & \\ \hline \multirow{2}{*}{RISK} & Integral for establishing a risk mitigation framework, it indirectly supports DLP by & Yes \\  & identifying and assessing risks pertinent to data leakage. & \\ \hline \multirow{2}{*}{ACCESS} & In the context of policy breach via data exfiltration, the domain’s direct involvement in & \\  & operational DLP processes is considered minimal. & No \\ \hline \multirow{2}{*}{SITUATION} & This domain’s emphasis on operational security and threat intelligence monitoring is vital & Yes \\  & for the early detection of potential data exfiltration, aligning with DLP objectives. & \\ \hline \multirow{2}{*}{RESPONSE} & Its focus on responding to cybersecurity incidents, including data breaches, aligns this & \\  & domain closely with the reactive component of DLP. & \\ \hline \multirow{4}{*}{THIRD PARTIES} & The management of third-party risk is essential for comprehensive data security but does & \\  & not intrinsically involve the operational specifics of data leakage prevention. This premise is & \\ \cline{1-1}  & based on the assumption that policies of an organization are uniformly applicable to both & No \\ \cline{1-1}  & internal and external systems. & \\ \hline \multirow{2}{*}{WORKFORCE} & The development of a cybersecurity-aware culture and skilled personnel supports DLP & Yes \\ \cline{1-1}  & through enhanced adherence to data security protocols. & \\ \hline \multirow{2}{*}{ARCHITECTURE} & This domain is directly aligned with DLP, encompassing the implementation of specific & Yes \\ \cline{1-1}  & controls, including data exfiltration prevention mechanisms. & \\ \hline \multirow{2}{*}{PROGRAM} & Provides strategic oversight and governance for cybersecurity initiatives, including DLP, & Yes \\ \cline{1-1}  & ensuring comprehensive management of cybersecurity risks. & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation of C2M2 domains for applicability to the DLP Maturity Model.
# Development of a New Test-Rig for Wheel-Rail Contact Experiments under Large Slip Conditions
## 4 Experimental Research
### Experiments of Different Speeds  
Wheel-rail contact experiments under large slip conditions with different running speeds are carried out utilizing the test-rig. The target creepage is sinusoidal, varying from 0 to 0.3 with a period of 12 s, while the target vehicle velocity is constant at 20 km/h, 40 km/h, 60 km/h and 80 km/h. The applied axle load is 14 t (818.4 MPa) and the sprinkler flow is 4 L/min. Measured slip-adhesion curves are shown in Figure 13.  
Figure 12: Validations of decoupling theory and slip control accuracy. (**a**) Target creepage is constant at 0.05 with two sprays of water; (**b**) target creepage is sinusoidal, varying from 0.25 to 0.3.  
Figure 13: Measured slip–adhesion curves in wheel–rail contact experiments under large slip conditions with running speed of (**a**) 20 km/h; (**b**) 40 km/h; (**c**) 60 km/h; and (**d**) 80 km/h.  
The consistency is observed in Figure 13: two peak points occur in slip-adhesion curves, and the first maximum of adhesion coefficient takes place when the creepage rises from 0 to about 0.02. As the creepage continues to rise, the adhesion coefficient first decreases and then increases. When the creepage reaches 0.3 in the large slip stage, the adhesion coefficient takes the second maximum (a)-(c). In the falling process of creepage, the adhesion coefficient is above that in the rising process, and the adhesion coefficient takes the second maximum (d) in the large slip stage.  
It is obvious that with the increase in running speed, the adhesion coefficient of the second maximum increases. Thus, adhesion recovery presents significant positive correlation with running speed, which is observed in other experimental investigations about adhesion recovery under wet surface conditions [18; 21]. The explanation for this phenomenon may be that with the higher running speed, the water film thickness between wheel and rail decreases due to removal of the surface layer and water evaporation caused by temperature rise, changing the lubrication state, leading to an increase in adhesion recovery.