esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Network Model and Problem Statement
### Problem Statement  
Mobility modeling is important in wireless and mobile networking research. Different scenarios require different mobility models. Hence being able to design a mobility model which reflects real world characteristics has been a critical requirement. Research results also show that mobility models influence the performance of protocols significantly [20]. It is known that humans' motion behavior has the following unique characteristics: (1) Location preferences, that is, some people are more likely to go to certain locations, and then interact more frequently with some certain nodes than others. (2) Time-variant mobility. Humans' activity changes with respect to time. Different activities occur at different times and locations. However, most widely used models are very simplistic, and they generate traces that show properties very different from those extracted from real scenarios.  
For example, the random waypoint mobility model and the random direction model are the two most widely used mobility models. In the first model, nodes randomly select destination, speed, and pause time at the destination. In the random direction model, nodes randomly select directions. In these two models, nodes move freely and all nodes generate homogeneous behavior. They do not reflect social connections among mobile users nor the possible influence of the connections on motion behavior. However, the network model used in our paper emphasizes the importance of humans' social  
Figure 1: Traveling pattern of the moving node in our network model.  
roles [20], and it combines both the social activities and the geographic movements. Table 1 summarizes the characteristics of some mobility models. Based on these analyses, we propose a novel routing protocol called ARP that use peoples' agendas to carry out routing.
## 3 Results and Discussion
### Surface Roughness
[30] validated only the negative influence of high printing temperature, but the interaction between temperature and layer thickness was not examined. However, this investigation has shown that high temperature, when combined with thin layers, decreases the surface roughness, but the influence of printing temperature on surface roughness in general is only marginal. When optimizing surface roughness, it would be obvious to set the surface inclination angle to 0\({}^{\circ}\), thus horizontal, since the roughness is least there. Nevertheless, it is also interesting to determine how the staircase effect can be minimized through the optimization of other parameters. Therefore, a surface inclination angle close to 0\({}^{\circ}\) (7.3\({}^{\circ}\)) was chosen for both surfaces in X and Y directions. For optimization of surface roughness, an infill percentage of 50%, a layer thickness of 0.1 mm, a printing speed of 92 mm/s, and a printing temperature of 220 \({}^{\circ}\)C were chosen, with a predicted roughness value of 0.094 \(\upmu\)m. After conducting a validation test print with these settings, a roughness of 7.5 \(\upmu\)m was measured. However, it is quite clear that values as low as the predicted ones are not yet achievable with the FFF process. This is due to limitations in the printing technique and printer equipment (used nozzle diameter was 0.2 mm). Moreover, it is obvious that the regression model developed to predict surface roughness is not yet precise enough to consider this physical boundary and needs refinement. Nevertheless, the roughness achieved was an improvement compared to the levels obtained before optimization.
# Performance of Numerically Optimized Tuned Mass Damper with Inerter (TMDI)
## 3 Simulation Procedure
### TMD Optimization  
The classical TMD is computed for the same mass ratio as the TMDI to ensure the same masses. The stiffness and viscous damping coefficients of the classical TMD are designed according to two methods. First, the well-known [PERSON]'s rules are adopted, which minimize the structural displacement response if the primary structure damping ratio is negligible [1]. Second, TMD parameters are numerically optimized using the same optimization functions as for the TMDI in order to obtain equal normalized displacement peaks, even for \(\zeta_{1}=1\%\), and also for the benchmark of the two half TMDs with different natural frequencies and damping ratios.
## 3 Results and Discussions
### Comparison between Fixed and Variable Geometry Turbines  
The potential of utilizing a variable geometry turbine instead of a fixed one is clearly depicted in Figure 8. It can be seen from Figure 8a that turbine efficiency for variable geometry is always higher than with a fixed geometry turbine for the various engine operating points. For a fixed geometry turbine, the mass flow rate is fixed. Therefore, and as explained earlier, the evaporator produces excessive heat which results in extremely superheated fluid. A variable geometry turbine, on the other hand, has the ability to adapt the wasted heat in the engine, which substantially improves the turbine performance. The minimum and maximum improvements in the turbine efficiency with variable geometry in comparison to that with fixed geometry are 20% and 70%, respectively. Similarly, turbine power is significantly higher when integrated with a variable geometry turbine, as shown in Figure 8b. This is because of the variable mass flow rate that directly influences the turbine power as per Equation (18). At P3, for instance, the variable geometry turbine presents 18 kW turbine power, while the fixed geometry turbine presents 14.7 kW. At P1, in fact, the turbine power could be doubled when utilizing a variable geometry turbine compared to a fixed one.  
Figure 8: Comparison between fixed and variable geometry turbines for (**a**) turbine efficiency, (**b**) turbine power, and (**c**) cycle thermal efficiency at different engine operating points.  
Since cycle thermal efficiency is directly related to turbine performance, it presents similar trends, as shown in Figure 8c. A minimum improvement of 4.48% can be achieved when a variable geometry turbine is utilized. At P3, the advantage of utilizing a variable geometry turbine over a fixed one is clear. The cycle efficiency of the variable geometry turbine is 25.45% higher than that of the fixed geometry turbine.  
The main aim of coupling the ORC system to the engine was to explore the feasibility of such a system in improving engine performance. Figure 9 shows that the engine performance is enhanced in terms of power and BSFC after utilizing the ORC system. This enhancement is expected since the ORC power output is added to the engine power as per Equation (1). Similarly, and as per Equation (2), the engine BSFC improves by improving the engine power. As presented in Figure 9a, the engine power could improve by at least 1% when integrated with an ORC system with a variable geometry turbine. At P3, in fact, the engine power may increase by 5.5% when bottomed with an ORC system. Engine BSFC presents a similar trend, as shown in Figure 9b. Figure 9 also depicts the benefits of utilizing a variable geometry turbine instead of a fixed geometry turbine. It is clear that, a variable geometry turbine is favorable over different engine operating points.
# Document Image Shadow Removal Based on Illumination Correction Method
## 4 Experiments
### Experimental Settings and Baseline Methods  
Since the numbers of images in existing real datasets are too small, as shown in Table 1, it is difficult to support the training of the network model. Therefore, we take the synthetic datasets FSDSRD and SDCSRD as the training set to train the models and then test them on the real datasets Adobe, HS, Jung, Kligler, and OSR. The FSDSRD contains 14,420 pairs of triples consisting of ground truth, shadow image, and soft shadow mask. The images in FSDSRD are resized to \(512\times 512\). The images in Jung and Kligler are also resized to \(512\times 512\) for inference; after obtaining the predicted shadow-removed images, they are resized to their original size to calculate the metrics. In this work, \(\lambda_{sha}=0.5\), \(\lambda_{ill}=0.1\), and \(\lambda=0.4\).  
DICNet is compared with ten state-of-the-art methods, including seven heuristic-based methods [14; 15; 17; 28; 29; 30; 31] and three neural network-based methods [22; 25; 27], to illustrate its performance. For the heuristic-based methods, the results are obtained with the code provided by the authors, and for the neural network-based methods, the results are cited from original articles.
## 3 Results
### Identification and Functional Annotation of Differentially Expressed Metabolites  
A total of 345 metabolites (32%) were differentially expressed, where 203 (59%) of these were downregulated and 142 (41%) were upregulated (Table S1). The upregulated metabolites were classified into 10 different classes of compounds, which mostly comprised phenolic acids (42; 30%), flavonoids (22; 15%), organic acids (13; 9%), and amino acids and derivatives (11; 8%). The downregulated metabolites mainly consisted of flavonoids (43; 21%) phenolic acids (41; 20%), and alkaloids (32; 16%), and were grouped into nine different classes. Values for log2 FC of the upregulated metabolites ranged between 1 and 18, with a mean value of 2.59. An average VIP value of 1.31 was observed for all the upregulated metabolites. Capsaicin and related compounds were not differentially expressed. Among the top 10 upregulated metabolites, eight were classified as phenolic acids, and two were flavonoids (Table 1). The average VIP and log2 FC values for the top 10 upregulated metabolites were 1.42 and 9.52, respectively. 1-Phenylethanol, Dicaffeoylshikimic acid, 2-Methoxycinamic acid, 1,3-O-Dicaffeoylquinic Acid (Cynarin), and 4-O-(4'-O-alpha-D-Glucopyranosyl) caffeoylquinic acid, all phenolic acids, were the top five upregulated metabolites, with log2 FC values of 18.04, 16.4, 13.33, 8.06, and 7.52, respectively. For the top ten downregulated metabolites, three were phenolic acids, six were flavonoids, and one was  
Figure 3: (a) Principal component (PC) biplot for the PC1 and PC2 reveal the overall variation between the cultivars in terms of metabolomic profiles. (b) Orthogonal partial least squares discriminant plot for the chile pepper cultivars. Treatment 1: “NuMex LotaLutein’; treatment 2: “NuMex Pumpkin Spice”. Numbers 1, 2, and 3 correspond to the first, second, and third biological replications, respectively. an alkaloid. Mean values for the VIP and log\({}_{2}\)FC for the top 10 downregulated metabolites were 1.42 and -91.68, respectively. Phenolic acids and flavonoids comprised the top five downregulated metabolites; namely, 3,6-Di-O-caffeoyl glucose (log\({}_{2}\)FC value \(=-16.73\)), Rosmarinic acid methyl ester (\(-15.36\)), 2,3-Dimethoxybenzaldehyde(\(-14.93\)) (all three are phenolic acids), and Quercetin-3-O-(2\({}^{\prime\prime}\)-O-Hammosyl)rutinoside-7-O-glucose (\(-11.96\)) and Quercetin-3-O-(6\({}^{\prime\prime}\)-O-acetyl)glucosyl-(1\(\to\)3)-Galactoside (\(-6.01\)) (both flavonoids).
## 2 Related Work
### Handcrafted vs. CNN-Based in FER Applications  
Specifically for FER applications, few comparisons have been made between the two methods' features. Authors in [24] provide an overview of recent advances in emotion recognition using multimodal signals, where both ways of extracting features have been used to recognize emotion by facial expression. In [25], a combination of automatic features learned from CNNs with the VGG architecture with handcrafted features computed by the BoVW model succeeds with a classification accuracy of 75.42% on FER-2013 and 87.76% on the FER+ datasets.  
For FER applications, CNN-based methods dominate. In [26], the ResNet-50 network infrastructure is used to extract features and recognize facial expressions, achieving a classification accuracy of 95% in a dataset created by the authors, consisting of 700 images and seven different categories of emotions. In [27], a method based on CNN and image edge detection is proposed reaching an average recognition rate of 88.56% for the mixture of the Facial Expression Recognition (FER-2013) and Labeled Faces in the Wild (LFW) databases. In [28], CNN are used to recognize facial expression. The authors have created a dataset collection of images from various sets to avoid bias in any set. Image augmentation allowed a validation accuracy of 96.24%. The difficulty of recognizing emotions from facial expressions depicted in images taken in a real-world environment is addressed in [29] by using asymmetric pyramidal networks with multi-scale kernels and adopting stochastic gradient descent with a gradient centralization optimizer. This method achieved a classification accuracy of 74.1% for FER-13, 98.5% for CK+, and 99.8% for the JAFFE database. In [30], face cropping, rotation strategies, and simplification of CNN are proposed, achieving recognition accuracies of 97.38% and 97.18% on the CK+ and JAFFE databases, respectively.  
A fine-grained, scenario-based comparison of handcrafted and CNN-based feature extraction methods based on criteria including (a) classification accuracy, (b) computational resources (in terms of time needed), and (c) robustness (in terms of imposed noise) has not been performed to our knowledge, and this is the main objective of our study.
## 3 System Model
### Energy Consumption Model  
In underwater acoustic communication, the attenuation function within a channel, as defined by the Thorp propagation model, is given by Equation (1), where \(f\) represents the frequency in kHz, and \(d\) is the distance in km.  
\[A(d,f)\ =\ d^{k}\alpha(f)^{d} \tag{1}\]  
Here \(k\) and \(\alpha(f)\) denote the energy scattering factor and the absorption coefficient, respectively. The energy scattering factor is assigned values based on the scattering type: 1 for columnar scattering, 1.5 for real scattering, and 2 for spherical scattering. The absorption coefficient \(\alpha(f)\) is determined by Equation (2) and is measured in dB/km.  
\[\alpha(f)\ =\ 10^{\alpha/10} \tag{2}\]  
\[\alpha\ =\ \frac{0.11f^{2}}{1+f^{2}}+\frac{44f^{2}}{4100+f^{2}}+2.75\times 10^{-4 }f^{2}+0.003 \tag{3}\]  
When a node transmits \(m\) bits of data over a distance \(d\), the model for node transmission energy consumption [29] can be expressed as follows:  
\[E_{\mathrm{S}}\ =\ P_{\mathrm{R}}A(d,f)T_{\mathrm{S}} \tag{4}\]  
The energy consumption by the receiver to receive \(k\) bits of data can be calculated by the following:  
\[E_{\mathrm{R}}\ =\ P_{\mathrm{R}}\times T_{\mathrm{R}} \tag{5}\]  
where \(P_{\mathrm{R}}\) is the received power, and \(T_{\mathrm{R}}\) is the time taken to receive the \(m\) bits of data.
## 5 Discussion
### Limitations and Potentials:  
We also acknowledge limitations in our current reward design. The reward function includes format verification, answer similarity, and partial correctness of extracted key-values and relationships. While effective to some degree, performance on Dataset 2 remains limited in terms of execution accuracy. This is likely due to the complexity of its schemas. As shown in Table 10, Dataset 2 includes schemas with a higher number of node and relationship types, along with longer and denser descriptions. This structural richness increases the difficulty of accurately encoding the schema context and generating correct query logic, especially for small models. This reveals a current limitation of our approach and a promising direction for future research: enhancing the model's schema comprehension through better schema-aware prompt construction or exploring more sophisticated reward mechanisms, such as graph-structural matching, semantic equivalence evaluation, or reward signals derived from execution correctness (e.g., output results matching ground truth answers).  
While our work focuses on Cypher, the support tasks we designed--like extracting key-value pairs and relationship triples--can also be applied to other graph query languages. With some adjustments, our reinforcement learning approach could be used for SPARQL, Gremlin, or GSQL. This makes it possible to develop a more general Text-to-GraphQuery system that works across different types of graph databases.
# Cyber Resilience Limitations in Space Systems Design Process: Insights from Space Designers
## 4 Results and Findings
### Cybersecurity and Resilience Awareness  
_\"Slightly familiar. Absolutely a gap in our current setup.\"--Systems Architect, interview participant_  
This interview section focused on understanding the awareness and integration of cybersecurity and resilience within space projects. Two main themes emerged: \"External Threats and DDoS Concerns [100], Backdoor Vulnerabilities\" and \"External Threats and Cyber Resilience Gaps\". We asked questions related to participants' \"familiarity with the concept of cybersecurity and resilience\", \"understanding of critical cyber vulnerabilities and risks specific to the industry\", and \" current level of cybersecurity and resilience in ground stations used for operations\". Our thematic analysis revealed a severe concern regarding DDoS attacks, which can disrupt mission-critical operations by overwhelming system resources. Participants underscored the necessity of robust defence mechanisms to mitigate such threats, highlighting strategies like network redundancy, advanced threat detection systems, and response protocols to ensure system resilience against DDoS incidents [101].  
Our discussion on backdoor vulnerabilities highlighted the substantial risks posed by unauthorised access points that can be exploited to infiltrate systems. The experts underscored the importance of rigorous access controls, continuous monitoring, and thorough security audits to detect and eliminate potential backdoors. If not addressed promptly, these vulnerabilities can compromise the entire system, leading to data breaches and operational disruptions. When exploring cyber resilience gaps, it became evident that external threats and the dynamic nature of the cyber threat landscape necessitate the continuous adaptation and improvement of security measures. Participants pointed out that existing cybersecurity frameworks often fall short in addressing the rapidly evolving threats, leading to potential gaps in resilience. They advocated for a more proactive approach, incorporating continuous risk assessment and adaptive security measures to bridge these gaps effectively.  
The additional themes identified during our analysis were the lack of understanding of cybersecurity and how to achieve it (\"Familiarity with Cybersecurity\"), which has been reported to be an afterthought that needs to be addressed by providing comprehensive training.  
The inductive code frequency in Figure 4 illustrates the prevalence of various themes identified in the dataset related to cybersecurity resilience in space systems design. Each bar represents a specific question (Q1 to Q15) with the length reflecting the frequency of codes linked to that issue.
# Multipath Error Fusion Modeling Methods for Multi-GNSS
## 1 Introduction
In order to ensure the possibility of interoperability among these GNSSs, observation signals with relatively similar frequencies have been set up [14; 15]. It is expected that the GLONASS-KM satellites launched in 2025 will begin to broadcast an L1 (1575.42 MHz) signal, which will be consistent with L1C after GPS modernization, E1 of Galileo, and B1C of BDS3 [16]. In summary, satellite navigation and positioning technology is in the development stage, where multiple navigation systems coexist, and there are observation signals of the same or similar frequencies between the different systems. Multipath error fusion modeling methods are essential for multi-GNSS data processing. SF is the most widely used and effective multipath error correction method at present. This method uses the periodic repetition of satellite orbit to model the multipath error in the coordinate domain or observation domain, which is mainly used for static observation platforms. Compared with the traditional constellation composed of Medium Earth orbit (MEO) satellites only, BDS consists of three kinds of satellites--MEO, GEO, and inclined geosynchronous orbit (IGSO). However the orbit repeat periods are still quite different for the satellites in the same MEO constellation [17]. Different GNSSs need to be modeled independently when using SF, which is cumbersome and inefficient in application. In addition, the effect of multipath error on satellite signals in the same direction should theoretically be the same when using the same frequency observations of different navigation satellite systems for the same observation platform [8]. However, the formation mechanism of the multipath error has not been fully considered in the SF method. Therefore, to avoid the above-mentioned disadvantages of the SF method, using observations of multiple GNSSs and constellations in the space domain can achieve the purpose of making full use of the modeling data to finely describe the multipath error sources. Based on the existing SF method, we first evaluated the orbit repeat period of satellites from GPS, BDS, and Galileo in different periods, and then proposed a data processing scheme that is suitable for the above three GNSSs for SF. In addition, a comprehensive modeling method in the space domain, called the multi-point hemispherical grid model (MHGM) for multipath error sources, was modified considering the characteristics of multi-GNSS with the same or similar frequency observations. Afterward, we compared the performance of the modified MHGM method and the SF method from a multi-GNSS point of view. Finally, the established MHGM using the data of multi orbit periods was compared with that of only one orbit period to verify the improvement of the model accuracy.
## 2 Materials and Methods
The duration of the intervals during which the voltage at the output of the HF generator is equal to zero depends on the phase shift \(\chi\) between the pairs of transistors located on the left and right bridge legs. The transistors are controlled by the microcontroller which generates two direct control signals Q\({}_{1}\) and Q\({}_{2}\) for the upper transistors, and two inverse signals Q\({}_{2}\) and Q\({}_{4}\) for the lower transistors. A measuring circuit was developed (Figure 3), containing a measuring coil L\({}_{3}\), which is inductively coupled to the inductor L\({}_{1}\) and electrically isolated from it, a current-limiting resistor R\({}_{1}\), a level limiter assembled on diodes D\({}_{5}\) and D\({}_{6}\), an integrated operational amplifier INA240 [56], R\({}_{2}\)-R\({}_{7}\) resistors, R\({}_{\text{eq}}\)--ohmic resistivity of the load (inductor L\({}_{1}\) with a nozzle) and D\({}_{1}\), D\({}_{2}\), D\({}_{3}\), D\({}_{4}\)--intrinsic antiparallel diodes. Measuring coil L\({}_{3}\), was wound with 17 turns of copper wire with d\({}_{3}\) = 0.35 mm in two layers. The output of the measuring circuit is connected to the analog input of the microcontroller, where the results of measurements of the nozzle temperature are processed. Nozzle resistance of 0.98 \(\Omega\) was measured by placing a lightweight nozzle in an inductor and then measuring the resistance of the inductor circuit in an unloaded (no nozzle) and then loaded state at a frequency of 100 kHz using an RLC meter. Figure 2: Block diagram of the nozzle TACS: (**1**) inductor; (**2**) nozzle; (**3**) HF generator; (**4**) measuring coil; (**5**) signal registration and processing unit; (**6**) PID controller. The calibration of temperature readings was carried out using control equipment, consisting of a two-channel temperature meter UT-325 with K and J type thermocouples, as well as a reference device on platinum resistors. Modeling of electrical circuits was carried out with the Multisim software package. To determine the TACS parameters in the process of FDM, it is necessary to identify an inductor and a lightweight nozzle as a plant with their subsequent integration into the simulated model of a TACS in the Simulink application of the MATLAB package.