esa-sceva/satcom-chunk-collection huggingface dataset:
# Local Data Debiasing for Fairness Based on Generative Adversarial Training
## 5 Experimental Evaluation
### Experimental Setting
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & \multicolumn{2}{c}{**Adult Census**} & \multicolumn{2}{c}{**German Credit**} \\ \hline
**Group** & **Protected (\(S_{s}=S_{w}\) Female)** & **Privileged (\(S_{s}=S_{w}\) Male)** & **Protected (\(S_{s}=S_{w}\) Young)** & **Privileged (\(S_{s}=S_{w}\) Old)** \\ \hline \(Pr(S=S_{s})\) & 36.21\% & 63.79\% & 19\% & 81\% \\ \hline \(Pr(Y=1|S=S_{s})\) & 11.35\% & 31.24\% & 57.89\% & 72.83\% \\ \hline \(Pr(Y=1)\) & 24.78\% & & 70\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Distribution of the different groups with respect to the protected attribute and the decision one for both the Adult Census Income and the German Credit datasets. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Sanitizer** & **Discriminator** \\ \hline Layers & 3 \(\times\) Linear & 5 \(\times\) Linear \\ Learning Rate (LR) & \(2\times 10^{-4}\) & \(2\times 10^{-4}\) \\ Hidden Activation & ReLU & ReLU \\ Output Activation & LeakyReLU & LeakyReLU \\ Losses & VectorLoss & MSE \\ Training rates & 1 & 50 \\ Batch size & 64 & 64 \\ Optimizers & Adam & Adam \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Hyper parameters of neural networks for Adult/German dataset. external classifiers and all epochs, we report the space of achievable points with respect to the fidelity/fairness trade-off. Note that most approaches described in the related work (cf. Section 3) do not validate their results with independent external classifiers trained outside of the sanitization procedure. The fact that we rely on three different families of classifiers is not foolproof, in the sense that it might exist other classifiers that we have not tested that can do better, but it provides higher confidence in the strength of the sanitization than simply relying on the internal discriminator. For each fold and each value of \(\alpha\), we train the sanitizer during 40 epochs. At the end of each epoch, we save the state of the sanitizer and generate a sanitized dataset on which we compute the \(BER\), \(sAcc\) and \(fid\). Afterwards, \(HeuristicA\) is used to select the sanitized dataset that is closest to the \"ideal point\" (\(BER=0.5,fid=1\)). More precisely, \(HeuristicA\) is defined as follows:  
\[Best_{Epoch}=min\{(BER_{min}-\frac{1}{2})^{2}+fid_{e},for\in\{1,\ldots,MaxEpoch \}\}, \tag{6}\]  
with \(BER_{min}\) referring to the minimum value of \(BER\) obtained with the external classifiers. For each value of \(\alpha\in[0,1]\), \(HeuristicA\) selects among the sanitizers saved at the end of each epoch, the one achieving the highest fairness in terms of \(BER\) for the lowest damage. We will use the three families of external classifiers for computing \(yAcc\), \(DemoParity\) and \(EqOddGap\). We also used the same chosen sanitized test set to conduct a detailed analysis of its reconstruction's quality (_diversity_ and quantitative damage on attributes).
## 5 The Potential of Computer Vision Research in UAV Datasets
### Leveraging UAV Datasets for Computer Vision Applications
#### 5.1.8 Multimodal Data Synthesis and UAV Control  
UAV-Assistant facilitates monocular pose estimation by introducing a multimodal dataset featuring exocentric and egocentric views. It enhances 3D pose estimation tasks with a novel smooth silhouette loss function and differentiable rendering techniques. KITE incorporates spoken commands, audio, and images to enhance UAV control systems. It includes commands recorded by 16 speakers, supporting movement, camera-related, and scenario-specific commands with multi-modal approaches.  
Together, these datasets improve a wide range of computer vision applications, including robust vision in difficult conditions, real-time traffic surveillance, emergency response, and human behavior analysis.
## 6 Conclusions and Future Work  
This paper introduced a novel methodology for dynamic UAV task allocation, path planning, and energy management, using the Adaptive Perturbation PSO with Local Search (APSO-LS) algorithm within a rolling horizon framework. The experimental results demonstrate that the approach effectively minimizes makespan and manages energy in dynamic environments. The rolling horizon framework ensures adaptability, and the weighted objective function balances makespan and energy. Key contributions include a unified dynamic framework, enhanced PSO with local search, and validated energy-aware planning for real-world UAV applications.  
Nevertheless, we recognize that the current formulation remains an idealized abstraction and must be refined before field deployment. First, although we assume a constant cruise speed for tractability, real-world UAVs encounter wind gusts, payload shifts, and aerodynamic drag that cause speed fluctuations. Future work will therefore develop a closed-loop flight control module--leveraging model predictive control (MPC) and disturbance observers--to actively compensate for external disturbances and enforce near-constant airspeed while minimizing additional energy consumption. Second, to scale APSO-LS to larger fleets and more complex environments, we will implement a parallelized architecture using GPU-accelerated particle evaluations and asynchronous global-best updates. This will enable real-time replanning for dozens of UAVs across hundreds of tasks. Third, our energy model assumes homogeneous UAVs and fixed power rates. We plan to extend it to heterogeneous platforms by integrating manufacturer flight logs to derive phase-dependent power profiles and battery degradation curves.  
Conceptualization, methodology, formal analysis, visualization, funding acquisition W.G.; software, validation, investigation, resources, data curation, project administration Z.H.; writing--review and editing, W.G. and Z.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 62273263, and in part by the project of the Development of a Training System for Urgently Needed Technical Talent and the Construction Plan for the \"Fuzhou Tongji Vocational Education Faculty Training Base\".  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Remaining Useful Life Prediction of Cutting Tools Using an Inverse Gaussian Process Model
## 2 Methods
### Remaining Useful Life Evaluation Model
According to Equations (7)-(10), the CDF and PDF corresponding to remaining useful life at \(t_{k}\) are as follows:  
\[F_{\rm R_{k}}(r_{k})=\Phi\left(\sqrt{\frac{\lambda}{\omega-y_{k}}}\frac{ \sigma_{\mu}\Lambda^{(t_{k})}(r_{k})-\alpha_{\mu}\sigma_{\mu}(\omega-y_{k})}{ \sqrt{\sigma_{\mu}^{2}+\lambda(\omega-y_{k})}}\right)-\exp\left(2\alpha_{\mu} \lambda\Lambda^{t_{k}}(r_{k})+\frac{2\lambda^{2}\Lambda^{(t_{k})2}(r_{k})}{ \sigma_{\mu}^{2}}\right)\]  
\[\times\Phi\left[-\sqrt{\frac{\lambda}{\omega+y_{k}}}\frac{\left(\sigma_{\mu}^{ 2}+2\lambda(\omega-y_{k})\right)\Lambda^{(t_{k})}(r_{k})+\alpha_{\mu}\sigma_{ \mu}^{2}(\omega-y_{k})}{\sqrt{\sigma_{\mu}^{4}+\lambda(\omega-y_{k})\sigma_{ \mu}^{2}}}\right], \tag{11}\]  
and  
\[f_{\rm R_{k}}(r_{k})=\sqrt{\frac{\lambda}{\omega-y_{k}}}\frac{ \sigma_{\mu}}{\sqrt{\sigma_{\mu}^{2}+\lambda(\omega-y_{k})}}\times\Phi\left[ \sqrt{\frac{\lambda}{\omega-y_{k}}}\frac{\sigma_{\mu}r_{k}-\alpha_{\mu}\sigma_ {\mu}(\omega-y_{k})}{\sqrt{\sigma_{\mu}^{2}+\lambda(\omega-y_{k})}}\right]\]  
\[-\exp\left(2\alpha_{\mu}\lambda r_{k}+\frac{2\lambda^{2}r_{k}^{2}}{\sigma_{ \mu}^{2}}\right)\times\left(2\alpha_{\mu}\lambda r_{k}+\frac{2\lambda^{2}r_{k }^{2}}{\sigma_{\mu}^{2}}\right)\]  
\[\times\Phi\left[-\sqrt{\frac{\lambda}{\omega-y_{k}}}\frac{\left(\sigma_{\mu}^{ 2}+2\lambda(\omega-y_{k})\right)r_{k}+\alpha_{\mu}\sigma_{\mu}^{2}(\omega-y_ {k})}{\sqrt{\sigma_{\mu}^{4}+\lambda(\omega-y_{k})\sigma_{\mu}^{2}}}\right]\]  
\[+\sqrt{\frac{\lambda}{\omega-y_{k}}}\frac{\sigma_{\mu}^{2}+2\lambda(\omega-y_ {k})}{\sqrt{\sigma_{\mu}^{4}+\lambda(\omega-y_{k})\sigma_{\mu}^{2}}}\times \exp\left(2\alpha_{\mu}\lambda r_{k}+\frac{2\lambda^{2}r_{k}^{2}}{\sigma_{\mu} ^{2}}\right)\]  
\[\times\Phi\left[-\sqrt{\frac{\lambda}{\omega-y_{k}}}\frac{\left(\sigma_{\mu}^{ 2}+2\lambda(\omega-y_{k})\right)r_{k}+\alpha_{\mu}\sigma_{\mu}^{2}(\omega-y_{k })}{\sqrt{\sigma_{\mu}^{4}+\lambda(\omega-y_{k})\sigma_{\mu}^{2}}}\right], \tag{12}\]  
It is difficult or even impossible to predetermine a failure threshold in many scenarios. One possible method to tackle the above-mentioned problem is to assume the failure threshold follows a specified distribution [40; 41]. For a given surface roughness requirement, the wear failure threshold \(\omega\) for the wearing process is a random variable in a change interval of [\(\omega_{L}\), \(\omega_{U}\)]. In this study, the \(\omega\) is assumed to obey uniform distribution. Then, the CDF of the remaining useful life can be expressed as follows:  
\[F_{\rm R_{k}}(r_{k}\left|\rm Ra\right.)=\frac{1}{\omega_{U}-\omega_{L}}\int_{ \omega_{L}}^{\omega_{U}}F_{\rm R_{k}}(r_{k})d\omega, \tag{13}\]  
The average remaining useful life can be calculated as follows:  
\[\Lambda^{(t_{k})}(t)=\Lambda^{(0)}(t+t_{k})-\Lambda^{(0)}(t), \tag{10}\]0  
The method to estimate parameters \(\theta=\)\(\left(\alpha_{\mu},\sigma_{\mu}^{2},\lambda\right)\) in the above model will be introduced in Section 2.3.
###### Abstract  
In this paper, we investigate power domain division-based multiple access (PDMA) to support the base stations (BS) equipped with multiple antennas to serve mobile users. Such a system deploys multiple input single output (MISO)-based wireless transmission and a full-duplex (FD) scheme. Furthermore, such MISO PDMA system consists of BS employing transmit antenna selection to reduce complexity in signal processing at the receivers. We distinguish two kinds of mobile users, device-to-device (D2D) users and traditional users. In such MISO PDMA, there exists a trade-off between outage performance of each PDMA user and power allocation factors. Since the implementation of the FD scheme at PDMA users, bandwidth efficiency will be enhanced despite the existence of self-interference related to such FD. In particular, exact expressions of outage probability are derived to exhibit system performance with respect to D2D users. Finally, valuable results from the simulated parameters together with the analytical results show that MISO PDMA can improve its performance by increasing the number of transmit antennas at the BS.  
full-duplex; multiple input single output; power domain division based multiple access 2019
# Stability Analysis of the Longitudinal Slope Linear Shield Tunnel Excavation Face
## 2 Materials and Methods for Longitudinal Slope Shield Tunneling
### Numerical Simulation Analysis of Longitudinal Tunnel
#### 2.2.1 Numerical Simulation Modeling  
FLAC3D 7.0 (Fast Lagrangian Analysis of Continua in 3 Dimensions) is an explicit finite difference program widely used for geotechnical analysis of soil and rock mechanics problems. It solves the full set of governing equations of motion in a Lagrangian framework, allowing for simulation of a large strain, non-linear material behavior, and progressive failure. Compared to traditional FEM-based software, FLAC3D is more suitable for problems involving localized failure, shear band formation, and material nonlinearity. However,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Setback Amount S (mm)** & \(i\) **(\%)** \\ \hline
1-1 & & \(-15\%\) \\
1-2 & 3 mm & 0\% \\
1-3 & & 15\% \\ \hline
2-1 & & \(-15\%\) \\
2-2 & 6 mm & 0\% \\
2-3 & & 15\% \\ \hline
3-1 & & \(-15\%\) \\
3-2 & 9 mm & 0\% \\
3-3 & & 15\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of test conditions.  
Figure 4: Schematic of conditions.  
as an explicit solver, its time-stepping scheme requires small time increments for stability, which can limit computational efficiency for very large or stiff models. In this study, FLAC3D was selected due to its robustness in capturing the complex three-dimensional failure mechanisms associated with tunnel face instability.  
To clarify the destabilization failure mode of the soil under the limit state of a longitudinal slope shield tunnel, a 3D finite element model with dimensions of 100 m (length) \(\times\) 100 m (width) \(\times\) 80 m (height) was established using FLAC3D software, based on the transparent soil test, as shown in Figure 5. The front, back, left, and right boundaries of the soil model were constrained in the X and Y directions, while the bottom surface was fully constrained in all three directions. The upper surface was set as a free surface at the ground level to ensure that the vertical deformation of the model was not restricted by boundary conditions.
# Towards Self-Reinforced PLA Composites for Fused Filament Fabrication
## 3 Methods
### Optimisation of The Filament
In order to counterpart this challenge, the filament needs to be cooled uniformly at a lower rate, thus allowing the shrinkage to happen from the outside to the centre [58]. When the material exits the nozzle, it starts to cool down as the atmospheric temperature is lower than the extruder temperature. Here, it is important to set up a cooling system for regulating temperature of the extruded filament. As hot filaments are soft and thus deformable, the puller mechanism easily flattens the final shape, thus yielding the challenge of an oval shape instead of round filaments. The cooling system consists of a path circulated with a water flow. The filaments, after passing through the cooling bath, lead to the conveyor belt station. Another reason for oval-shaped filaments is often the high polymer crystallinity leading to flat, distorted or oval filaments upon leaving the nozzle. In this study, we used semi-crystalline polymers, and they are known to have high domains of crystals in comparison to amorphous regions, which normally exhibit deformation problems in the form of oval or flat filaments. This formation is a process-related phenomenon. The non-uniform polymer shrinkage causes the filament to become oval or flat. Non-uniform shrinkage is influenced by the molecular orientation, reinforcing fibres and the fibre direction, uniformity in cooling, and melt temperature uniformity. For counteracting the oval or flat or distorted filament shape, the following approaches are employed:  
* _Cleaning the extrusion channel and nozzle of contaminants_--a clean channel helps the molecular orientation to be kept in order, thus reducing the oval shaping effect;
* _Reducing the filament output speed_--to decrease the flow speed difference, the RPM (Rotation Per Minute) of the extruder is decreased, thus reducing the potential difference in molecular orientation and thus the shrinkage;  
Figure 9: Microscopic images of base-matrix material B filament, PLA 6302D. Top: challenge such as voids and unmelted polymer during filament production. Bottom: good quality filament. * _Setting up a more uniform cooling_--a possible external improvement is providing a perfectly uniform cooling as the filament has a tendency to form an oval or distorted shape as a result of the temperature gradient on the surface. Another difficulty of filament production is the filament thickness deviation. The deviation in the thickness is influenced by various factors such as:  
* External factors;
* Temperature settings;
* Extruder RPM;
* Polymer degradation;
* Material contamination;
* Non-uniform cooling;
* Winder mechanism; and
* Holes or bubbles in the filament. For better control over the filament deviation, we report that the right balance between the extruder RPM, the winding speed, and a uniform cooling setup is important.
# Green Strategies for Molecularly Imprinted Polymer Development
## Green Strategies in MIP Development
### Supercritical Fluid Technology
Several imprinted polymers were produced in scCO\({}_{2}\) by FRP, such as poly(DMAEMA-_co_-EGDMA), poly(MMA-_co_-EGDMA), poly(2 VPy-_co_-EGDMA), and poly(HEMA-_co_-EGDMA), towards LA. Poly(DMAEMA-_co_-EGDMA) copolymer exhibited the best performance on the LA purification; when the acidic crude extract containing 15% of LA was passed two times through the column, 34% of LA could be selectively recovered. The results suggested that the choice of the monomer was crucial to the recognition performance of the system. In the same year, [PERSON] et al. [40] reported the production of dibenzothiophene sulfone (DBTSO\({}_{2}\))-imprinted polymer in scCO\({}_{2}\) by FRP, for selective removal of the oxidized products from diesel. MAA-EGDMA copolymer was produced and obtained as an easy-to-handle white dry powder which was easily packed in the glass column to evaluate its performance. A model diesel solution containing DBTSO\({}_{2}\) (23.1 mM) was passed through the MIP-glass column loaded with 200 mg of polymer, and 69.9% of DBTSO\({}_{2}\) was retained. Additionally, the reusability of the columns was also evaluated for three consecutive cycles, and the retained ability increased with the reuse cycle, from 41.8 to 47.5%. Gallic acid (GA)-MIP was reported this year by [PERSON] et al. [41] for selective removal of GA from a natural matrix that has been extensively evaluated for antitumor activity [42]. In this manner, GA-imprinted polymer was synthesized by FRP in scCO\({}_{2}\) using MMA as a third monomer, MAA as a functional monomer, EGDMA as a crosslinker, and THF as a porogen. The effect of crosslinking degree was also studied, and the average particle size of MIP particles prepared ranged 150-200 nm. It was shown that the MIPs' adsorption capacity increased with the addition of up to 6 mol of EGDMA, however, for higher amounts the opposite trend was observed. Recently, [PERSON] et al. [43] described acetamide (ACET)-imprinted polymers for API purification processes. ACET is a pharmaceutical impurity present at low traces in API post-stream reactions [44] and has potential genotoxic behavior, which means that it can induce human diseases [45]. Copolymers were produced by FRP in scCO\({}_{2}\), using two different monomers, MAA and MAM, and the effect of addition of a co-solvent, ACN, in the polymer synthesis was evaluated. Static and dynamic binding and selectivity tests were performed with ACET, benzamide (BENZ), and pivalamide (PIV) solutions. MAM-based MIP revealed the best performance, adsorbing 2.21 mmol g\({}^{-1}\) of ACET, when ACN was used as co-solvent in the polymerization step. MIPs also presented a higher selectivity for ACET than for BENZ and PIV.
# Movable Platform-Based Topology Detection for a Geographic Routing Wireless Sensor Network
## 6 Conclusions  
This paper brings forth a method to recover the topology of WSN (wireless sensors network) based on a movable platform (only applicable to WSNs using location-based routing protocol). It is superior to the previous work thanks to its simple arrangement, reusability, higher accuracy of larger scale and applicability to non-cooperative network topology. This method captures data packets to recover the position of target network node couples, seeks for network station through data packet transmission direction and completes acquiring nodes and data packets of the whole network based on clarified station position; constructs cost equations and calculates cost parameters using the detected node couples, and infers network topology reversely. At the same time, in the process of iterating the cost parameters, the error of the platform in the sampling will also be reduced with the iteration. Among the 30 independent simulation experiments, the platform can complete rebuilding topology within a short time with accuracy rate above 90%.  
Conceptualization, R.L. and J.W.; methodology, R.L. and J.W.; software, R.L.; validation, R.L.; formal analysis, J.C.; investigation, R.L.; resources, R.L.; data curation, R.L.; writing--original draft preparation, R.L.; writing--review and editing, J.C.; visualization, J.C.; supervision, J.W.; project administration, J.W.; funding acquisition, J.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 61702540, and in part by the Hunan Provincial Natural Science Foundation of China under Grant 2018 JJ3615.  
The authors declare no conflict of interest.
# Tribological Analysis of PETG Fire-Retardant Samples Manufactured by FFF
## 4 Conclusions  
This study analyzed the tribological behavior of parts manufactured via Fused Filament Fabrication (FFF) using PETG and its flame-retardant variant (PETG FP). The results obtained allow the following conclusions to be drawn:  
The incorporation of flame-retardant additives into PETG results in an increase in both the coefficient of friction (CoF) and surface wear, indicating a less favorable tribological behavior compared to conventional PETG. However, PETG FP demonstrated greater dimensional and thermal stability during the manufacturing process, which may be advantageous in specific applications.  
Layer height is the most influential parameter in determining surface roughness and tribological behavior. As layer height increases, there is a general trend toward increased wear and CoF, particularly for PETG. In contrast, PETG FP shows greater consistency with increasing extrusion temperature, suggesting improved flow behavior and interlayer bonding.  
Although visual defects such as gaps or stringing were observed, they did not result in significant differences in the Ra roughness values between the materials. However, the Rz parameter proved more sensitive, particularly in relation to the material type and layer height.  
ANOVA analyses confirmed the significant influence of the material type and layer height on tribological behavior, while the extrusion temperature had a more limited effect. This was especially evident in the case of conventional PETG, where no clear trends could be identified.  
Despite its inferior tribological performance, PETG FP remains a viable option for applications where fire resistance is a priority and the frictional demands are moderate or controlled. The proper selection of manufacturing parameters can partially mitigate its disadvantages.  
Conceptualization, J.M.V.-M. and M.B.; project administration, G.R.-G. and J.G.-F.; experimental methodology, J.M.V.-M. and M.B.; investigation, F.d.P.R.-G., J.M.V.-M., and M.B.; data processing, F.d  
Cadiz. The authors want to acknowledge the support from the Spanish Government (SCIENCE AND INNOVATION MINISTRY/FEDER, Grant Project EQC2018-005131-P) from the 2018 State Program for Research Infrastructures and Scientific/Technical Equipment. The authors want to report special thanks to the research group TEP-027 \"Engineering and Technology of Materials and Manufacturing\" of the University of Cadiz.  
The authors declare no conflicts of interest.