esa-sceva/satcom-chunk-collection huggingface dataset:
# Graphene-Based Materials for the Separator Functionalization of Lithium-Ion/Metal/Sulfur Batteries
## 2 Preparation Methods of Graphene-Based Materials
### Others
#### 2.3.3 Graphene-Oxide Reduction  
The GO reduction method is also reliable for preparing monolayer graphene with high yield and wide application, whose production is usually accompanied by more defects than others. Initially, graphite is oxidized in a strong acid with a potent oxidizing agent to obtain graphite oxide, having various oxygen-containing groups between the layers and carboxyl/hydroxyl groups at the edges. The process weakened the interlayer van der Waals forces and widened the layer distances. [PERSON] [76] and [PERSON] [77] successively reported the use of KClO\({}_{4}\) as an oxidizing agent in fuming HNO\({}_{3}\) and H\({}_{2}\)SO\({}_{4}\) to oxidize graphite flakes, both of which are time-consuming while producing the toxic and explosive gas ClO\({}_{2}\) during the reaction process. [PERSON] proposed an approach using the H\({}_{2}\)SO\({}_{4}\)-NaNO\({}_{3}\)-KMnO\({}_{4}\) mixture to oxidize graphite, which is the current standard method [78]. External exfoliation, such as ultrasonic exfoliation, can then transform graphite oxide flakes into GO with a single atomic layer thickness (Figure 4C) [79]. Finally, graphene can be obtained by reduction primarily including (1) chemical reduction: the most common scheme, using reducing agents that do not react with water, such as hydrazine [80], hydroquinone [81], etc., to eliminate the oxygen-containing functional group on the surface of graphite oxide for graphene preparation [82]; (2) electrochemical reduction: using linear voltammetric scanning in an electrolytic cell with graphite oxide as the cathode and an inert counter electrode to prepare graphene [83]; and (3) thermal reduction: rapid expansion and exfoliation of graphite oxide occurring at a high temperature and inert atmosphere, while some of the oxygen-containing groups are pyrolyzed and generate CO\({}_{2}\), ultimately obtaining graphene [84; 85]. In addition to changing the temperature, the properties of graphene products can also be controlled by the carbon-oxygen content, the size, and the distribution of pores during the thermal reduction process [86].
## 2 RFM Optimization
### Application to Push-Frame Images  
In this study, we apply the RFM refinement procedure described above to push-frame images from two CubeSat platforms, PlanetScope and SkySat. See Appendix A for technical details on these platforms. With both systems, the images are acquired by several arrays of CCDs as the satellite is moving along its track. The frame rate and the location of the sensors in the focal plane of the telescope were designed to insure some degree of overlap between the different sub-scenes, whether acquired by different sensors at a given time or at successive times by the same sensor. Each sub-scene is provided with its own RFM and is considered as a separate image in our procedure. We use Level L1B products, which contain the images and associated RFMs, which were determined by the provider using a terrain-dependent approach. Prior to RFM refinement, we pre-select image pairs for each type of satellite using a specific procedure.
# Medical Augmented-Reality Visualizer for Surgical Training and Education in Medicine
## 2 Materials and Methods
### Global Calibration  
Initially, each device in the modules uses its local coordinate system (Figure 8), and hence the output data is expressed into a common global coordinate system with the origin on the working table (\(X_{\mathrm{w}}\), \(Y_{\mathrm{w}}\), and \(Z_{\mathrm{w}}\) axes in Figure 8). These modules must be set up and calibrated before operation. First, the PhTM must be calibrated to estimate the internal and external parameters of the projector in the PrM and the camera in the PhTM, using the pinhole method proposed in Ref. [29]. The same algorithm and calibration pattern are used to calibrate the pair of stereo cameras (HdTC\({}_{1}\) and HdTC\({}_{2}\)) in the HdTM. All calculations are then realized in the same global coordinate system.
# Collective Communication Performance Evaluation for Distributed Deep Learning Training
## 6 Experiments in the PyTorch Environment  
Section 6 of the study provides an in-depth analysis of the performance of a collective communication library in PyTorch. Similar to the approach taken in the Linux shell experiments, this section examines both the parameter server and ring all-reduce methods.  
In the parameter server method, the broadcast and gather (including allgather) operations were scrutinized by breaking them down into subroutines. In each experiment, latency was measured while incrementally adding GPUs, allowing for an analysis of the results concerning GPU expansion. These experiments were conducted across various server environments, including bare metal, singularity, single-docker containers, and cross-docker containers. Latency was measured based on the execution time of the backend functions, and the communication libraries MPI, GLOO, and NCCL were utilized in these experiments. The goal was to comprehensively assess and compare the performance of these communication libraries under different conditions and configurations within the PyTorch environment.
## 3 Results and Discussion
### Application of Pectin-Based Electrospun Film in Multilayers
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Multilayer Structure**} & **Water Vapor** & **Limonene** & \multicolumn{2}{c}{**Thickness (\(\upmu\)m)**} \\  & **Permeance \(\times\) 10\({}^{\text{10}}\)** & **Permeance \(\times\) 10\({}^{\text{10}}\)** & **PHBV** & **Pectin** \\  & **(kg\(\cdot\)m\({}^{-2}\cdot\)Pa\({}^{-1}\cdot\)s\({}^{-1}\))** & **(kg\(\cdot\)m\({}^{-2}\cdot\)Pa\({}^{-1}\cdot\)s\({}^{-1}\))** & **Layers** & **Layer** & **Total** \\ \hline PHBV/PHBV & 5.00 \(\pm\) 0.83 \({}^{\text{a}}\) & 3.81 \(\pm\) 0.47 \({}^{\text{a}}\) & 72 \(\pm\) 9 \({}^{\text{a}}\) & - & 72 \(\pm\) 9 \({}^{\text{a}}\) \\ PHBV/electrospun & & & & & \\ pectin/PHBV & 1.75 \(\pm\) 0.14 \({}^{\text{b}}\) & 0.22 \(\pm\) 0.11 \({}^{\text{b}}\) & 73 \(\pm\) 7 \({}^{\text{a}}\) & 25 \(\pm\) 5 \({}^{\text{a}}\) & 98 \(\pm\) 8 \({}^{\text{b}}\) \\ PHBV/solvent-casted & & & & & \\ pectin/PHBV & 3.94 \(\pm\) 0.56 \({}^{\text{c}}\) & 0.22 \(\pm\) 0.08 \({}^{\text{b}}\) & 70 \(\pm\) 5 \({}^{\text{a}}\) & 24 \(\pm\) 5 \({}^{\text{a}}\) & 95 \(\pm\) 5 \({}^{\text{b}}\) \\ \hline \end{tabular}  
* \({}^{\text{a-f}}\) Different letters in the same column indicate a significant difference (\(p<\) 0.05).  
\end{table} 
Table 6: Water vapor and limonene permeance of the multilayer films based on pectin and poly(3-hydroxybutyrate-_co_-3-hydroxyvalerate) (PHBV). Figure 10: Scanning electron microscopy (SEM) images of cross-sections of the multilayer films based on pectin and poly(3-hydroxybutyrate-_co_-3-hydroxyvalerate) (PHBV). (**a**) PHBV/PHBV, (**b**) PHBV/electrospun pectin/PHBV; (**c**) PHBV/solvent-casted pectin/PHBV. Scale markers of 50 \(\upmu\)m.
# Dynamic Path Planning for Vehicles Based on Causal State-Masking Deep Reinforcement Learning
## 5 Comparison of Simulation Results
### Generalization Analyses
This performance advantage of CSM-TD3 is attributed to the causal consistency constraints, which ensure that the agent's strategy focuses on causal features that directly influence control inputs when responding to environmental changes. This reduces the interference of spurious correlations in path planning, enabling the agent to react quickly and make effective adjustments in the presence of dynamic obstacles, achieving optimality and stability of its paths. Upon combining the above analysis with the path step comparisons in Tables 4 and 5, we see that CSM-TD3 exhibits exceptional planning performance in both static and dynamic obstacle environments. Leveraging causal enhancement strategies and causal consistency constraints, CSM-TD3 effectively identifies and utilizes state features that have direct causal impacts on control decisions. This capability allows the agent to maintain optimal, shortest, and smooth paths even in complex dynamic environments.
## 3 Results
### Comparison of Seasonal Evaportranspiration Sums  
Seasonal ET sums for 2019 from each model (STEPS, SCOPE, and TSEB) are also compared with reference datasets (MOD16, GLEAM, and TerraClimate). A spatial correlation was applied to examine the degree of agreement between the models and other products. The chosen period, March to November, captures the growing season and peak ET activity. Spatial variability among ET products is most prominent in summer (Figure 3a) when atmospheric water demand is typically high in the Mediterranean region. This reflects the models' ability to capture the spatial dynamics of ET linked to seasonal variations in water demand. As expected, ET estimates are proportionally higher in summer, ranging from 0 to 350 mm in spring, from 150 to over 650 mm in summer, and declining to 0 to 150 mm in autumn. This seasonal pattern aligns well with daily ET trends reported in many Mediterranean site-scale measurements.  
During summer, when precipitation is minimal, GLEAM exhibited the lowest cumulative ET (<350 mm) compared to other models across all seasons. Other products showed some ET variability over the Balkans and France. Spatial autocorrelation analysis provided further insights into model agreement. STEPS showed a strong correlation with TSEB, MOD16A2, and GLEAM, with the strongest correlation observed betweenSTEPS, TSEB, and GLEAM in summer. In contrast, SCOPE exhibited a generally lower correlation with other products, indicating that its spatial behavior is distinct. As expected, SCOPE also showed relatively high ET levels (>200-350 mm) over the Nile region, where ET is negligible in GLEAM's estimates throughout the year (consistent with annual findings). On the other hand, STEPS, TSEB, and MOD16A2 exhibited good agreement across all seasons.  
The likely reason for these spatial differences is how models are trained on closing water and energy balance when imbalances are caused by the radiation budget, latent heat flux, ground and sensible heat fluxes, or soil moisture dynamics. Additionally, differences in model parameterizations have influenced their outputs. The high correlation observed among STEPS, TSEB, and GLEAM in summer, and with MOD16A2 in spring, highlights their consistency and potential for intercomparison, particularly during the growing season.  
Figure 3: **(a) Seasonal evapotranspiration (ET) sums for the period March to November 2019 from STEPS, SCOPE, TSEB, MOD16A2, and GLEAM (b) refer to their corresponding correlation matrix. The highest correlations were observed between STEPS and GLEAM in spring and summer with TSEB in summer, and STEPS and GLEAM in autumn. Conversely, the lowest correlations were found in the case of SCOPE with other products. Seasonal demarcations for this comparison adhere to reference [46].**
## 5 Simulation Results
### Network System Performance
The number of SBSs to be turned on changes with the demand of network traffic in different time periods, and the number of SBSs to be turned on and the traffic demand curves of both schemes keep the same trend. The number of SBSs to be turned on in the SSC-UA scheme and BS-on-UA scheme is basically the same, and the number of SBSs to be turned on in the BS-on-UA scheme is slightly larger than that in the SSC-UA scheme in some cases. Comparing the average delay and energy consumption of the SSC-UA scheme and BS-on-UA scheme, the energy consumption of the SSC-UA scheme is higher than that of the BS-on-UA scheme in all time periods, while the average delay is lower for the SSC-UA scheme than the BS-on-UA scheme, which is consistent with the analysis results in Figure 10.  
Figure 11: Comparison of time delay CDF of three schemes. Figure 12: Network Performance of SSC-UA Scheme. Figure 14 shows the number of SBSs that are in On, Standby, and Sleep states at different times of the day in the SSC-UA scheme. From Figure 14, it can be seen that when the number of SBSs turned on is relatively small, it means that the current traffic demand of the network is relatively low, and the ratio of the number of SBS in the Sleep state to the number of SBS in the Standby state is larger accordingly. When the number of On SBS increases, the ratio of the number of Sleep state SBSs to the number of Standby state SBSs decreases. This is because when the traffic demand is small, there will be many regional SBSs with low traffic load, and more SBSs can be turned off through the objective function, and these SBSs will not be turned on in a short period of time, which in turn will not have a large impact on the wake-up delay. While when the traffic demand is large, the system will put more switched-off SBSs in the Standby state by weighing the impact of energy consumption and delay, so that they can be woken up quickly in the next period which reduces the average wake-up delay of the network.
# Non-Terrestrial Networks with UAVs: A Projection on Flying Ad-Hoc Networks
## 3 Applications and Use Cases of FANETs
### Emergency Situations  
A possible application of a FANET is in disaster-struck areas (e.g., bush fire affected or flooded areas) where a swarm of unmanned UAVs is deployed to video capture and search the area for survival, or to inspect the damaged condition of the area, or form an NTN for use by survivors for a certain period. The area could be spread over tens or even hundreds of kilometers, and camera-equipped automated drones can capture footage of particular areas and stream (in almost real-time) the video to ground stations or to human rescuers in a helicopter, which can respond quickly as needed. Hence, the source of the video data is the drones themselves, and the video can be streamed from UAV to UAV (via the NTN) to reach a manned helicopter or ground stations. The NTN can also be used by survivors on the ground, who can connect to nearby UAVs (and so use the NTN) to send messages back to human rescuers, and even for survivors to be temporarily connected (to each other) where required. Moreover, more UAVs can be deployed if needed, and the NTN can be spread out further, reshaped, and resized accordingly as needed, providing flexible and dynamic on-demand customization of a network. Due to the limited battery power on each UAV, new UAVs can replace existing UAVs (nodes) in the NTN to maintain the NTN in certain configurations for the required time.  
The general concept of deployment here is that an NTN (with particular functions (e.g., normal, thermal, or other specialized cameras) and characteristics such as certain levels of reliability, bandwidth, etc) can be spawned ad hoc as needed for a fixed period of time in order to serve particular areas, in contrast to satellite networks with (relatively expensive and limited bandwidth applications). Hence, such dynamic FANETs can complement satellite-based networks, where available.  
Edge computing can be employed in such an application at different levels, from limited on-board (on-UAV) processing of some of the videos captured to processing in some more powerful UAVs or sending to nearby large helicopters (or airships) with more powerful computers (and more energy) for processing, and ground stations, before the processed video is sent to the cloud.  
The above NTN using camera-equipped automated UAVs can also be used for non-disaster settings, e.g., an inspection of large-scale infrastructures such as power lines, pipelines, and roads to check their conditions, for security in large gatherings or events; and so on, where an ad hoc transient network that is flexible and reconfigurable would be useful. Such an NTN can be deployed for military purposes and for outdoor expeditions as well, to support operations and explorations in difficult-to-reach areas for a certain period of time. Note that an NTN can be made secured forming a private network for use over an area for a fixed period of time, e.g., the police or fire brigade can deploy and use such an NTN to support operations on an ad hoc basis.
# Autonomous Mutual Authentication Protocol in the Edge Networks
## 3 Methodology
### Authentication Phase
Thus, the hash calculation of the session key \(K_{R,S}\) achieves the challenge 1 confirmation. Challenge 2 is initiated by the next value by the previous challenge in the first octet, and then collects the value incremented every time in the consecutive octets, as shown by green in Figure 2. The transition value obtained by XOR is \(\lambda_{S}\). Successively, the session key \(K_{S,R}=\left(\lambda_{S}\oplus\ T_{S_{2}}\right)\) with the current timestamp is calculated and sent to the receiver as the challenge 2 with device address. It is encrypted by the public key of receiver \(PK_{R}\). This message has to be responded to before the time-to-live \(ttl\) given by the receiver. \[S\to R:\ E_{PK_{R}}\left(K_{S,R}\parallel\ \lambda_{S}\parallel T_{S_{2}}\parallel a_{c}\right)\] (4) The receiver decrypts the received encrypted message by his private key \(D_{SK_{R}}\) and obtains the challenge 2 response by the sender. The decrypted message is confirmed to be correct by calculating the transition value \(\lambda_{S}\) and by the hash value of the session key \(K_{S,R}\). The device address \(a_{c}\) received is kept for the purpose of device identification. The final session key is \(E_{PK_{S}}{}^{\prime}=H(K_{R,S}\oplus K_{S,R})\). This session-key-combining process is already known by the sender; when he or she decrypts the final message with a symmetric key to know about authentication from the receiver, then the mutual authentication is succeeded. \[R\ \to S:\ E_{PK_{S}}^{\prime}(``Authenticated\")\]
## 3 Results and Discussion
### Interface Binding Energy  
Interface binding energy is a measure of the interaction energy between different systems at the interface, which can be used to measure the strong and weak interaction between PC molecular chains and nickel mold inserts. The binding energy between the two systems is defined as the negative number of the interaction energy:  
\[E_{bind}=-E_{inter}=-[E_{PC/Ni}-(E_{PC}+E_{Ni})] \tag{1}\]  
where \(E_{bind}\) is the interface binding energy of the system, \(E_{inter}\) is the interface interaction energy of the system, \(E_{PC/Ni}\) is the total energy of the system, \(E_{PC}\) is the energy of the polymer, and \(E_{Ni}\) is the energy of the mold insert.  
Figure 5 shows the change of interface binding energy between PC molecular chains and nickel mold inserts with time during the injection molding process. The binding energy grows slowly in the filling stage and rapidly in the packing stage, then tends to be flat in the later packing stage. As shown in Table 1, the interaction energy between the PC system and Ni system is equal to the nonbond interaction energy between the two systems, which shows that interface atoms interact with each other through nonbond interaction. Furthermore, the electrostatic force of the interface system is zero, so the contribution of the atomic binding energy comes from the van der Waals force.  
As described in the concentration profile, only a few atoms near the interface are absorbed into the cavity under van der Waals attraction in the filling stage. Thus, the binding energy is lower. Driven by the packing pressure in RHCM, the long-chain PC polymer fills into the cavity, and the binding  
Figure 4: Concentration profile of PC atoms in CIM and RHCM at the end of the packing stage. The distance between 2.2 and 3.4 nm is the depth of mold cavity. C and R in graphs indicate CIM and RHCM, respectively.  
energy is large. Especially when the packing pressure is increased, the atomic concentration at the nanostructures increases accordingly. This means that more PC atoms are close to Ni atoms, so the van der Waals attraction increases, and the interface binding energy increases greatly. However, the binding energy of the long-chain PC system in CIM is very low, which indicates that the polymer has a fast cooling rate at room temperature for the mold. Thus, the viscosity increases, and the resistance of the filled cavity increases also. For the short-chain system with low molecular weight, the filling effect is good and the interface binding energy is large, which is due to its low viscosity and good fluidity.
## 2 Experimental Apparatus
The phase of the clock laser for clock transition detection is referenced to the reflector of the lattice beam by partially reflecting the 698 nm beam for a phase-noise-cancelled system. After clock transition detection, the excitation fraction is measured using the \"electronic shelved\" method [26]. For suppressing the fluctuation of excitation caused by the changing of the number of total atoms, three 461 nm laser pulses with a duration of 2 ms are used to detect the numbers of atoms in the ground state (\(N_{\mathrm{g}}\)), the excited state (\(N_{\mathrm{e}}\)), and the background noise (\(N_{\mathrm{bac}}\)), respectively. The normalized excitation fraction is expressed by \((N_{\mathrm{e}}-N_{\mathrm{bac}})/(N_{\mathrm{e}}+N_{\mathrm{g}}-2N_{\mathrm{ bac}})\). The FE is realized in OLC by modulating the lattice laser frequency via applying a sinusoidal voltage signal to the piezoelectric (PZT) of the 813 nm external cavity diode laser. In a previous work, we experimentally demonstrated that the Floquet quasi-energy bands are generated by the dynamical periodic modulation of the incident lattice laser frequency [10]. As the FE is applied, the lattice frequency can be expressed by \(\omega_{\mathrm{L}}=\overline{\omega}_{\mathrm{L}}+\omega_{\mathrm{a}}\sin( \omega_{\mathrm{s}}t)\), where \(\overline{\omega}_{\mathrm{L}}\) is the average frequency of lattice light, \(\omega_{\mathrm{s}}\) indicates the modulation frequency, and \(\omega_{\mathrm{a}}\) represents the modulation amplitude. With FE, the ground and excited states will split to a series of Floquet quasi-levels with a frequency gap of \(\omega_{\mathrm{s}}\). The number and intensity of Floquet sidebands are controlled by the normalized driving amplitude \(A=\omega_{\mathrm{a}}\omega_{\mathrm{P}}L/2\overline{\omega}_{\mathrm{L}}c\), where \(\omega_{\mathrm{p}}\) is the frequency of clock laser and the distance between cooled atoms and reflecting-mirror of lattice beam is \(L\approx 30\) cm [10].
## 4 Results and Discussion
### Lining Behavior and Earth Pressure Acting on Lining  
#### 4.1.1 Lining Displacement  
From the lining displacement in Figures 7a and 8a, the following was found:  
1. Generally, the lining deformation shape is flat in the horizontal direction and moves upward. This is due to the followings: (1) the initial effective earth pressure in the vertical direction is larger than that in the horizontal direction because \(K_{\rm h0}=0.5\) in this analysis, and (2) the buoyancy is larger than the lining self-weight.
2. As the tangential spring constant, \(k_{\rm t}\), increases, the lining shape becomes more circular; that is, the lining deformation becomes smaller. This is because the tangential springs reduce the lining displacement in the tangential direction, and their restraint increases as \(k_{\rm t}\) increases.
3. Compared with load model type 0 (initial shear stress \(\tau_{0}=0\)) the lining at load model type 1 deforms more in the horizontal direction. This is because \(\tau_{0}\) compresses the lining in the vertical direction and extends it to the horizontal direction at \(K_{\rm h0}=0.5\).
4. As the coefficient of the subgrade reaction, \(k_{\rm n}\), increases, the lining shape becomes more circular. This is because the ground reaction force restricts the lining deformation in the horizontal outward direction and increases with larger \(k_{\rm n}\).
5. Compared with the shallow tunnel, the lining in the deep tunnel deforms more in the horizontal direction, and the diameter of the lining decreases. This is because as the overburden depth increases, (1) the larger difference between the initial vertical effective earth pressure and the initial horizontal effective earth pressure requires more lining deformation to redistribute the effective earth pressure, and (2) the increase in water pressure causes more shrinkage of the lining.