esa-sceva/satcom-chunk-collection huggingface dataset:
# First-Principles Study for Gas Sensing of Defective SnSe\({}_{2}\) Monolayers
## 2 Method  
The SnSe\({}_{2}\) monolayer structure is based on the experimental lattice parameters of bulk SnSe\({}_{2}\)[16]. The initial lattice constants of SnSe\({}_{2}\) monolayer are \(a=b=3.81\) A and thickness of vacuum is set about 16 A. All calculated structures contain the fully lattice constants and atom positions optimization. After structure optimization, the lattice constants of the SnSe\({}_{2}\) monolayer are \(a=b=3.87\) A and the energy gap is 0.78 eV. The Visualization for Electronic and Structural Analysis (VESTA) software is a 3D visualization program for structural models [17]. We use VESTA to show the crystal structure and the defective single layer in this work. The calculation was implemented in the Vienna Ab initio Simulation Package (VASP) and performed by the projector augmented wave (PAW) method with the Perdew-Burke-Ernzerhofer (PBE) generalized gradient approximation (GGA) [18; 19]. The SnSe\({}_{2}\) monolayer is constructed with a \(3\times 3\times 1\) supercell in order to perform gas molecules adsorption calculation. The energy cutoff for a plane-wave basis was set up to 400 eV within the \(12\times 12\times 1\) Monkhorst-Pack \(k\)-point grid for all study cases. The energy convergence threshold and force convergence criteria were set to \(10^{-5}\) eV per unit cell and 0.01 eV A\({}^{-1}\).  
We also discussed the vacancy and doped SnSe\({}_{2}\) monolayers that adsorb NO\({}_{2}\) and NH\({}_{3}\) gas molecules. First of all, we put the NO\({}_{2}\) and NH\({}_{3}\) molecules on 3 A above the Se site or the dopants. We calculated two orientations of N atom, the N atom of gas molecules toward and backward the defective SnSe\({}_{2}\) monolayers, named N-bottom and N-top, respectively. In order to understand the defect effect of SnSe\({}_{2}\) monolayers adsorbing gas molecule, a Se atom was substituted by a vacancy or a dopant atom (N and O). In this paper, the structures of the SnSe\({}_{2}\) monolayers with gas adsorption were relaxed. The initial configurations for gases adsorption on the defective SnSe\({}_{2}\) monolayers are illustrated in Figures S1 and S2 of the supplementary material.
## 3 Effect of Adversarial Training on Network Performance
### Performance of an Adversarially Trained ResNet18 Network
\begin{table}
\begin{tabular}{l l c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Maximum**} & \multicolumn{4}{c}{**Median**} \\ \cline{3-10}  & & **Clean** & **FGSM** & **JSMA** & **C\&W** & **Clean** & **FGSM** & **JSMA** & **C\&W** \\ \hline \multirow{6}{*}{
\begin{tabular}{} \end{tabular} } & Clean & 99.68 & 70.69 & 80.70 & 93.27 & 99.50 & 65.30 & 76.82 & 91.54 \\ \cline{2-10}  & FGSM & 99.65 & 95.92 & 78.40 & 97.51 & 99.56 & 92.24 & 73.53 & 96.69 \\ \cline{2-10}  & JSMA & 99.66 & 78.71 & 97.78 & 96.13 & 99.56 & 73.85 & 96.80 & 95.42 \\ \cline{2-10}  & C\&W & 99.62 & 88.99 & 81.37 & 98.60 & 99.52 & 85.71 & 76.29 & 98.11 \\ \cline{2-10}  & FGSM + JSMA & 99.65 & 95.34 & 96.34 & 98.05 & 99.54 & 93.80 & 95.34 & 97.53 \\ \cline{2-10}  & FGSM + C\&W & 99.67 & 97.54 & 77.87 & 98.79 & 99.57 & 96.59 & 75.14 & 98.38 \\ \cline{2-10}  & JSMA + C\&W & 99.69 & 89.80 & 97.00 & 98.63 & 99.56 & 86.57 & 95.57 & 98.25 \\ \cline{2-10}  & FGSM + JSMA + C\&W & 99.63 & 97.57 & 96.68 & 98.84 & 99.51 & 96.58 & 95.37 & 98.44 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Test accuracy of ResNet18 measured between 40 and 100 epochs. The network is trained with data augmentation including random rotation, random translation and random erasing.
# A Study of Friction Nonlinearity and Compensation for Turntable Servo Systems
## 3 Compensation Based on the Friction Model  
### Feedforward Compensation Based on the Friction Model  
The principle of feedforward compensation is relatively straightforward: following accurate modeling of the system's friction, the friction model is combined with the system's state variables to compute the friction torque, which is then fed forward to the controller for compensation. In the simulation model, friction is added; the effectiveness of the compensation is shown in Figure 4. This method does not affect the stability of the system; however, it is dependent on the accuracy of the model. When parameters change due to environmental factors or operational conditions, the parameters identified from offline experiments may become inaccurate, leading to under-compensation or over-compensation. For example, with \(T_{c^{\prime}}=1.5T_{c}\), over-compensation causes oscillation in the position of the load.
###### Abstract  
Opportunistic networks, an evolution of mobile Ad Hoc networks (MANETs), offer decentralized communication without relying on preinstalled infrastructure, enabling nodes to route packets through different mobile nodes dynamically. However, due to the absence of complete paths and rapidly changing connectivity, routing in opportunistic networks presents unique challenges. This paper proposes a novel probabilistic routing model for opportunistic networks, leveraging nodes' meeting probabilities to route packets towards their destinations. Thismodel dynamically builds routes based on the likelihood of encountering the destination node, considering factors such as the last meeting time and acknowledgment tables to manage network overload. Additionally, an efficient message detection scheme is introduced to alleviate high overhead by selectively deleting messages from buffers during congestion. Furthermore, the proposed model incorporates cross-layer optimization techniques, integrating optimization strategies across multiple protocol layers to maximize energy efficiency, adaptability, and message delivery reliability. Through extensive simulations, the effectiveness of the proposed model is demonstrated, showing improved message delivery probability while maintaining reasonable overhead and latency. This research contributes to the advancement of opportunistic networks, particularly in enhancing connectivity and efficiency for Internet of Things (IoT) applications deployed in challenging environments.  
cross-layer; message delivery; probabilistic routing; history-based algorithm +
Footnote †: journal:  
0000-0001-4512-330X/0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-0000-00000-0000-00000-00000-00000-00000-00000-00000-00000-00000-0  
Furthermore, the model incorporates techniques integrating strategies across multiple protocol layers to maximize energy efficiency, adaptability, and message delivery reliability.  
This paper presents a comprehensive exploration of leveraging opportunistic networks for enhancing Internet of Things (IoT) communication in Section 2. The proposed solution is presented in Section 3, where we introduce a novel framework employing probabilistic routing algorithms and efficient resource utilization mechanisms to optimize message delivery and address connectivity challenges in dynamic environments. Subsequently, Section 4 details the empirical results and performance evaluations of our proposed solution, presenting significant improvements in several performance metrics. Finally, in Section 5, the paper concludes by summarizing the findings, emphasizing the potential of opportunistic networks to enhance IoT communication.
# Comparison of the Sentinel-3A and B SLSTR Tandem Phase Data Using Metrological Principles
## 4 Results--Sea Surface Temperature Comparison
### SST Uncertainties
Figure 13 shows the standard deviation of the SST differences as a function of cloud probability for the difference retrieval algorithms and shows an increase in standard deviation for a higher likelihood of cloud for quality level 5 pixels. This implies that pixels with a higher cloud probability should have a larger associated uncertainty than a fully clear sky pixel. Again, this is a missing component of uncertainty in the current uncertainty budget. Figure 13 also shows that, in terms of the possible extra component of uncertainty, the D3 (three channel, dual view) retrieval seems to be the most sensitive and the N2 (two channel, nadir view) the least sensitive to the extra source of error related to cloud probability. This also matches with the case looking at the SST difference as a function of cloud probability discussed above, where the D3 algorithm also showed the largest SST differences. Figure 11: Theoretical uncertainty (Blue) and associated SSES standard deviation (Orange) for the quality level 5 data for the different retrieval algorithms. Figure 12: Theoretical uncertainty-normalised distributions for quality level 5 pixels for the different retrieval algorithms. Figure 13: Standard deviation of SST differences for quality level 5 data as a function of cloud probability for N2, N3, D2 and D3 algorithms (see Figure 10 for key). The plot has normalised the curves to zero cloud probability.
## 2 Background
### Examples of Applying BIM in FAB Construction Projects  
The semiconductor companies with a global market, such as Intel and TSMC, as shown in Figure 2, have been applying BIM to their FAB construction projects. Intel has introduced BIM to minimize the downtime of production lines when replacing internal production facilities of existing semiconductor plants. It has increased the ratio of pre-production work by means of prefabrication and has eventually reduced the time and cost for installing the facility by setting up the proofs of concept (POCs) based on the 3D models of high consistency [10]. TSMC, a Taiwanese semiconductor company, has introduced and utilized BIM early from the construction stage, envisaging in advance integrated management during the operation management stage. As a result, the operation management platform based on Crystal 5D has been established thus as to flexibly cope with the change of the production lines by building the related libraries [12; 13].  
Figure 1: BIM-based FAB project drawing relationship diagram (adapted from [9; 10; 11]).  
Figure 2: Case study: BIM-based FAB construction projects (adapted from [14; 15]).  
SK Hynix, a world-class memory semiconductor chipmaker and supplier, had built three FAB plants in South Korea: M14 and M16 at Icheon, M15 at Cheongju. The latest M16 was completed in 2021, occupying a total area of 57,000 m\({}^{2}\) with an investment of 3.5 trillion Korean Won. In order to address the limitations of 2D modeling programs based on floor plans and problems associated with a large number of piping, ductwork, and cable tray components installation at accurate locations, these three projects had adopted BIM from the design stage. Implementation of BIM in these FAB construction projects demonstrated that BIM could help in determining optimal conditions for the distance between cleanrooms and electrical rooms, as well as in checking the placement of cables connected to each piece of equipment in the cleanrooms [16; 17].  
Furthermore, Exyte, as a construction engineering company, is widely known for its expertise in planning and designing high-tech production plants. In 2019, Exyte introduced their smart FAB concept where the digital twin acts as the core element and consists of BIM along with real-time data from the facility monitoring and control system. With the implementation of this concept, more enhanced quality and safety standards were achieved, at the same time, optimizing the system's sustainability and increasing energy efficiency throughout the FAB's life cycle [18; 19].
## 2 Materials and Methods  
Before attempting to formulate a day-ahead load prediction methodology, it is useful to present and discuss the current state of the day-ahead prediction of Greece's electricity load curve, as reported to the European Transparency Platform (ENTSO-E), which is responsible for the central collection and publication of Electricity Generation, Transportation, and Consumption Data and Information for the Pan-European Market [35]. As an example, the actual demand values, on an hourly basis, are compared with the day-ahead predictions for January 2022 in Figure 1. The average values and variability, on a monthly basis, of the most important statistical metrics for 2022 are shown in the same Figure to quantify the prediction accuracy. MAPE is 2.61% with a standard deviation of 0.33%, and nRMSE is 0.036 with a standard deviation of 0.005. Finally, the mean bias error (MBE) is 73 MW, which indicates an over-prediction. On a qualitative basis, it is interesting to see in this example of January 2022 (Figure 1) that the most pronounced prediction failures are observed with regard to the morning peaks, and to a much lesser extent, to the late afternoon peak loads. It must be mentioned in this respect that the specific level of prediction accuracy is not attained by a truly day-ahead computation. That is, not all 24 h of the next day are predicted. Instead, the next day's prediction is corrected at noon, based on the--known at that time--actual load data of the first half of the day.  
Figure 1: Greek system load during January 2022, day-ahead predicted versus actual values (MW) [35].  
The effect of this improved prediction may be seen in the example of a typical weekday (20 January 2023) based on the data reported daily by the Greek system operator (IPTO) [34] presented in Figure 2. The modified noon prediction reduces the 24 h MAPE from 3.35% to 2.47% and nRMSE from 0.041 to 0.035. Thus, the forecasts reported in the ENTSO-E database are not true 24-hour-ahead forecasts. This explains the somewhat reduced accuracy in our true 24-hour-ahead forecasts presented in the next section.  
Before proceeding to study the accuracy of the prediction of special events and spot-specific cases of prediction failure, it is important to understand the general typologies of the Greek system load curves in the main seasons of the year.
## 3 Results
### Main Results from Studies  
#### 3.3.1 Radiography Analysis  
[PERSON] et al. [47] used CBCT (Cone Beam Computer Tomography) for this analysis. The dimensions of the alveolar ridge before tooth extraction were 9.68 mm in buccal height, 5.01 mm in lingual height, and 9.69 mm in width in the selected tooth.  
Four months after tooth extraction and grafting with AMD, the alveolar ridge measured 11.38 mm in buccal height, 7.13 mm in lingual height, and 11.33 mm in width. These measurements were also taken 6 months after implant placement and showed alveolar ridge dimensions of 11.30 mm in buccal height, 6.49 mm in lingual height, and 10.26 mm in width.  
[PERSON] et al. [48] used CBCT for this analysis. The baseline dimensions for the test group (AMD) were 7.86 \(\pm\) 1.16 mm in width, 9.25 \(\pm\) 1.9 mm in buccal height, and 9.53 \(\pm\) 176 mm in lingual height. As for the control group (ADD), the baseline dimensions were 8.11 \(\pm\) 1.3 mm in width, 8.95 \(\pm\) 1.6 mm in buccal height, and 8.86 \(\pm\) 1.54 mm in lingual height. The differences in baselines between both groups were not statistically significant (\(p\) > 0.05).  
After 6 months, the mean loss in terms of buccolingual ridge width was 0.85 \(\pm\) 0.38 mm for the AMD group and 1.02 \(\pm\) 0.45 mm for the test group (\(p\) = 0.36). The buccal height had a mean loss of 0.61 \(\pm\) 0.20 mm in the AMD group and 0.72 \(\pm\) 0.27 mm in the test group (\(p\) = 0.31). The lingual height had a mean loss of 0.66 \(\pm\) 0.31 mm in the AMD group and 0.56 \(\pm\) 0.24 mm in the test group (\(p\) = 0.43). The mean losses were higher in the test group regarding the width and buccal height, although none of the differences were statistically significant.
## 5 Conclusions  
In this paper, a technique for recovering signals from received OAM beams based on pseudo-Doppler effect in the frequency domain was developed. Whereas reference [10] developed a method for detecting individual OAM modes based on an interpolation technique involving a minimum of two receiving antennas positioned tangentially on the peak region of OAM beams in the far field, this paper advanced the method to the point of recovering information carried on multiple OAM beams simultaneously, amenable to real-time implementation. Preliminary simulation results confirmed the ability of this technique to recover the modulation signals from several OAM beams in accordance with the mathematical least-squares type of signal-processing carried out in the frequency domain, based on the effect that the pseudo-Doppler technique imparts a different fractional pseudo-Doppler frequency shift to each received OAM mode. This technique was motivated by the observations that current spatial techniques for receiving and recovering the OAM modes in unguided radio-frequency applications are limited by the conical shapes of the (non-zero-order) OAM beams, which result in poor SNR, impractically large receiving antennas, short link distances, applicability to only very short wavelengths or guided propagation, or a combination thereof. These limitations are a consequence of attempting to receive and recover the OAM modes by the reverse of the spatial technique of transmitting them using co-axially situated circular antennas, motivated by increasing spectral efficiency of wireless links via spatial multiplexing. Here the spatial multiplexing applies at the transmitter, but the receiving technique essentially transforms the recovery problem into the frequency domain inside the receiver, keeping the radiated bandwidth the same. Other efforts at increasing data throughput of wireless links are directed at exploiting wide spectral bandwidth where available, using ultra wide-band (UWB) techniques, specifically UWB antennas [20]. Such efforts could be combined with the present OAM technique to further increase transmission capacity in the future.  
The author wishes to thank Huawei Canada R&D Center, Ottawa, for the support of this work-in-progress.  
The authors declare no conflicts of interest.
## 5 Numerical Studies
### Effect of Thermal Deformation (TD) on Transmission Error (TE)  
In this example, LTCA is conducted on a spur gear pair with thermal deformation under unloaded and loaded conditions to study the transmission error. To compare the influence of thermal deformation on the transmission error between the spur gear pairs with different materials, TCA was conducted on steel and plastic, and the mechanical uncertainty was not considered.  
The transmission error of the spur gear pair under the unloaded condition was generated by the geometric error caused by thermal deformation. The difference in transmission error between steel and plastic gear pairs results from the difference in material properties. From Figure 13, it is found that the plastic spur gear pair is more susceptible to thermal deformation than the steel spur gear pair. When \(\eta\) increases, the increase in the standard deviation of the plastic spur gear pair is larger than that of the steel spur gear pair. This difference becomes larger under the loaded condition. The standard deviation of the steel spur gear pair under the loaded condition is similar to that under the unloaded condition, whereas that of the plastic spur gear pair increases by approximately two times, as shown in Figure 14. The PPTE generated by thermal deformation is similar regardless of the size of \(\eta\). As \(\eta\) increases, the standard deviation of the PPTE of the steel and plastic spur gear pairs tends to increase; however, it is not as large as the standard deviation of the  
Figure 11: (**a**) Mean and (**b**) standard deviation (std) of transmission error under loaded condition. When the machining error (\(e\)) increases, there is no thermal deformation or pitch error (\(\eta=0,e_{p}=0\)).  
Figure 12: Peak-to-peak transmission error of (**a**) steel and (**b**) plastic gear with regard to change in \(e\). There is no thermal deformation or pitch error (\(\eta=0,e_{p}=0\)).  
machining error, as shown in Figure 15. The more precise the spur gear pairs with smaller machining errors, the greater is the transmission error generated by thermal deformation. When we design a spur gear pair with a material that is susceptible to thermal deformation, considering the range of the transmission error expected during operation will be useful for a safer and more precise design.  
Figure 14: **(a)** Mean and **(b)** standard deviation (std) of transmission error under loaded condition. When the uncertainty of the material properties (\(\eta\)) increases, there is no machining error or pitch error (\(\epsilon=0,e_{p}=0\)).  
Figure 13: **(a)** Mean and **(b)** standard deviation (std) of transmission error under unloaded condition. When the uncertainty of the material properties (\(\eta\)) increases, there is no machining error or pitch error (\(\epsilon=0,e_{p}=0\)).
# Detecting Malicious Devices in IPSEC Traffic with IPv4 Steganography
## 2 Network Steganography  
At a time when image steganography was the most used and recognizable form of steganography, a new type of steganography, using network protocols as information carriers, was gaining popularity. Network steganography is a generic term that may describe all information concealment techniques used to communicate in telecommunication networks. This nomenclature was proposed for the first time in [17]. The author introduceda novel network steganography method named the Hidden Communication System for Corrupted Networks (HICCUPS) for wireless local area networks (WLANs). Since then, steganography has developed into an essential field of study for data concealment and safe communication in computer communications.
# A Brief Review of Multipath TCP for Vehicular Networks
###### Abstract  
Multipath TCP (MPTTCP) is one of the most important extensions to TCP that enables the use of multiple paths in data transmissions for a TCP connection. In MPTCP, the end hosts transmit data across a number of TCP subflows simultaneously on one connection. MPTCP can sufficiently utilize the bandwidth resources to improve the transmission efficiency while providing TCP fairness to other TCP connections. Meanwhile, it also offers resilience due to multipath data transfers. MPTCP attracts tremendous attention from the academic and industry field due to the explosive data growth in recent times and limited network bandwidth for each single available communication interface. The vehicular Internet-of-Things systems, such as cooperative autonomous driving, require reliable high speed data transmission and robustness. MPTCP could be a promising approach to solve these challenges. In this paper, we first conduct a brief survey of existing MPTCP studies and give a brief overview to multipath routing. Then we discuss the significance technical challenges in applying MPTCP for vehicular networks and point out future research directions.  
MPTCP; multipath routing; vehicular networks +
Footnote †: journal: Physics Letters B  
1
# New Space Engineering Design: Characterization of Key Drivers
## 2 Methodology
### Keywords Definition  
The bibliographic search was primarily conducted between September 2024 and June 2025. Initially, a comprehensive literature database was compiled using relevant keywords and their combinations. The initial set of keywords was defined before the bibliographic search, based on a preliminary analysis of project objectives, sector terminology, and recurring concepts identified in early scoping documents. Examples include terms related to New Space, technological trends, and innovative space systems. Boolean logic was applied to enhance precision and reduce the range of irrelevant results (e.g., AND, OR, NOT). For instance, combinations such as \"New Space\" AND \"engineering design\", \"spacesystems\" AND \"technological trends\", or \"innovation\" AND \"satellites\" NOT \"astronomy\" were employed to identify relevant publications by querying the database. As the research progressed, keywords were gradually refined to focus on more specific topics, enabling a deeper exploration of specific aspects.