esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Alternative methods of killing microbes have been extensively researched in connection with the widespread appearance of antibiotic resistance among pathogenic bacteria. In this study, we report on in vitro antimicrobial phototoxicity research of cationic phthalocyanine with 2-(4-N-methylmorpholphin-4-ium-4-yl)ethoxy substituents against selected clinical strains of _Pseudomonas aeruginosa_ isolated from the lower respiratory tract and chronic wounds. The microorganisms tested in the research were analyzed in terms of drug resistance and biofilm formation. The photocytotoxic effect of phthalocyanine was determined by the reduction factor of bacteria. The studied cationic phthalocyanine at a concentration of 1.0 \(\times\) 10\({}^{-4}\) M, when activated by light, revealed a significant reduction factor, ranging from nearly 4 to 6 log, of _P. aeruginosa_ cells when compared to the untreated control group. After single irradiation, a decrease in the number of bacteria in biofilm ranging from 1.3 to 4.2 log was observed, whereas the second treatment significantly improved the bacterial reduction factor from 3.4 to 5.5 log. It is worth mentioning that a boosted cell-death response was observed after the third irradiation, with a bacterial reduction factor ranging from 4.6 to 6.4 log. According to the obtained results, the tested photosensitizer can be considered as a potential antimicrobial photodynamic therapy against multidrug-resistant _P. aeruginosa_.  
antimicrobial activity; photodynamic therapy; photosensitizer; phthalocyanine; Pseudomonas aeruginosa; biofilm; lower respiratory tract; chronic wound infections Article
## 2 Results and Discussion
### Wine Analysis
Alternatively linalool oxides are also produced from _Botrytis cinerea_ metabolism, and this may be the defining factor between the Riesling wines [44], although none of the wines chosen were late harvest or styles of Riesling associated with _Botrytis cinerea_. However those wines with higher levels of linalool oxides isomers also contained high levels of rose oxides isomers. Formation of rose oxide isomers is from a different source, namely yeast metabolism. There are two different reductive pathways known in yeast that result in rose oxide formation [45]. The limonene and \(\alpha\)-terpineol found in R3 and R4 is quite interesting as acidic environments, such as in wine, result in the formation of \(\alpha\)-terpineol from limonene [46]. Therefore it was anticipated that wines with higher concentrations of \(\alpha\)-terpineol would have lower concentrations of limonene isomers. But \(\alpha\)-terpineol is also one of the most abundant terpen produced by yeast [47], therefore the terpene content of these two Riesling wines may be due to a combination of factors. The high levels of linalool in R6 may also be due to several factors, such as a high level of linalool-glycos in the grapes and then of release of this into the free form. But it could also be related to the low transformation of linalool after fermentation resulting in retention of more linalool than the other wines. As stated before linalool degrades to linalool oxides in the presence of oxygen, but it may also be degraded by _Botrytis cinerea_[48]. It has also been found that higher levels of caffeic and gallic acid can stop the degradation of linalool in wine [49]. Therefore the nonvolatile composition, which was not investigated beyond sugar content in this study, may be more favorable for linalool retention. Figure 2: PCA plot of 12 white wines on concentration of chiral mono-terpenes. Finally it is of great interest to determine if the differences in the chiral monoterpene content of these wines is also present in sensory perception. We have stated earlier in the paper that all of these compounds are present at levels below their known perception threshold, but current perception thresholds may not be realistic in wine, as the solution they are tested in plays an important role and most perception thresholds are measured in water or a water and ethanol solution [50; 51; 52]. To add to this complexity it is known that the perception threshold of some monoterpenes, specifically linalool, changes when it is in a mixture with other monoterpenes [53]. Therefore further research investigating the sensory impact of these compounds is important. It will be able to determine how the compound impacts aroma individually and then in combination with other chiral terpen.
# Research on Wi-Fi Fingerprint Database Construction Method Based on Environmental Feature Awareness
###### Abstract  
Indoor localization technology is becoming increasingly widespread, but traditional methods for constructing Wi-Fi fingerprint databases face significant challenges, particularly in large, multi-room environments. These methods often suffer from low efficiency and high costs associated with manual data collection. To address these issues, various approaches like crowdsourcing and sparse collection have been introduced, but they still struggle with limitations such as inadequate data accuracy and uneven distribution. In this paper, we present a novel method for constructing Wi-Fi fingerprint databases based on environmental feature awareness. By leveraging deep learning to analyze the relationship between environmental features and Wi-Fi signal strength, our method enables faster and more efficient database construction. Experimental results demonstrate that our environmental feature-aware model significantly outperforms existing methods in prediction accuracy, greatly enhancing both the efficiency and accuracy of Wi-Fi fingerprint database construction. This approach also reduces the need for manual intervention and improves generalization capabilities. Our method proves to be highly practical and adaptable, especially in large-scale structures like nursing homes. It holds a substantial potential for broader application in extensive indoor environments, offering considerable value for widespread adoption.  
Wi-Fi fingerprint database; deep learning; indoor localization Article Research on Wi-Fi Fingerprint Based on Environmental Feature Awareness
## 1 Introduction
As a result of this optimization, as well as increasing demands in terms of products quality, issues such as waste, number of defective parts, need for inspection during production, need for recycling, and production time were also decreased. By using machine learning methods, [PERSON] et al. [35] were able to accurately predict the weight and thickness of the manufactured part. [PERSON] et al. [36] used process parameters optimization instead of adding additives to improve the mechanical properties in a part produced with recycled plastic. Clearly, considerable research efforts have been focused recently to optimize injection molding parts characteristics and minimize several types of defects. However, very limited research has investigated the factors affecting the blush defect. At least, one study can be indicated to have addressed the influence of process parameters on blush: results by [PERSON] et al. [37] indicated that injection flow rate and melt temperature are the most effective parameters. Blush defect not only deteriorates the appearance of the part but also reduces the lifetime and strength of the product. Therefore, due to its importance and the limited research performed so far, there is a clear need for further investigation on the parameters affecting the incidence and exacerbation of this defect. Despite the importance of preventing the incidence of this defect in plastic injection, so far, only a few studies have investigated its causes and impacts. The present research has inspected the effects of eight injection molding factors (flow rate, melt temperature, holding pressure, mold temperature, runner diameter, gate diameter, gate angle, included angle) on the blush. The novelty of the present study is the relatively number of investigated parameters, including both process and design factors. By examining a large variety of parameters, the study results in a comprehensive view of the causes of this defect. What is more, contrary to previous research, the interaction effects of design parameters on the incidence of the defect have also been studied in the current study. In addition, for the first time, different types of ANNs have been used to predict the values of blush defect, and a GA is utilized to optimize the levels of effective factors to achieve the lowest probable blush defect. The research method of this study began with the creation of a CAD model of the injection molded component (plastic bushing). Then, as screening step, the fractional factorial DOE with two levels and the eight design parameters has been conducted. Afterperforming FEA, the results were included in an ANOVA routine to identify effective and ineffective factors. Thereafter, for a more detailed study of the effect of the most relevant parameters, a second design of experiment of CCD (Central Composite Design) type has been performed with five levels on the effective factors.
## 4 Proposed Approach  
### AURORA MINDS' Approach  
In order to deliver a holistic secure solution for healthcare applications, we identify two main modules, the identity management module and the data pipeline for sharing, training, and modeling.  
For the identity management module, our approach proposes a blockchain-based system moving from a centralized to a distributed identity provider model. This approach is based on the following research works [64; 65]. Multiple entities share the task of managing identities on behalf of a public-facing organization. Specifically, we distribute the role of the identity provider between several entities, called virtual Identity Providers (vIdPs). These virtual entities run on distinct physical machines, at different physical locations, with different system admins and operating systems, and include three main modules to enable authentication, credential issuance, and token management. The distributed issuance can be accomplished in two ways: using tokens or attribute-based credentials (ABCs). This approach eliminates the vulnerabilities of centralized identity management systems, like single-point-of-failure attacks and third-party control; enhances security and privacy; and empowers users with control over their own identities through decentralized identifiers and verifiable credentials.  
For the data pipeline module, in healthcare applications, data generated from user interactions is used for training models, with the results applied for clinical purposes. Thus, to ensure data privacy, it must be carefully aggregated, transmitted, and trained. In our approach, Federated Learning addresses potential privacy risks by keeping raw data on the client's device. However, new challenges arise when data is stored locally on devices, as it can be lost if a device is changed or misplaced and can be worsened in scenarios where multiple users share a single device. Therefore, we propose to secure data with encryption mechanisms, like Differential Privacy. This approach ensures data availability, allowing users to grant access to authorized individuals while maintaining both security and accessibility.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**App**} & **Targets** & \multirow{2}{*}{**Gamification**} & **Clinician** & **Parent** & **Privacy** & **Federated** \\  & **ADHD** & & **Tools** & **Tools** & **Controls** & **Learning** \\ \hline
**QbTest**[58] & ✓ & ✗ & ✓ & ✗ & ✗ & ✗ \\
**Cogmed**[59] & ✗ & ✓ & ✓ & ✗ & ✗ & ✗ \\
**ADDitude Magazine**[60] & ✗ & ✗ & ✗ & ✓ & ✗ & ✗ \\
**CADDRA eChecklist**[61] & ✓ & ✗ & ✓ & ✗ & ✗ & ✗ \\
**ADHD Rating Scale-5**[62] & ✓ & ✗ & ✓ & ✗ & ✗ & ✗ \\
**MyADHD**[63] & ✗ & ✓ & ✗ & ✓ & ✗ & ✗ \\
**AURORA MINDS** & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 3: Competitors analysis table.
# Enhancing Neural Network Interpretability Through Deep Prior-Guided Expected Gradients
## 2 Related Work  
Explaining model decisions has become a cornerstone of research in XAI, particularly as machine learning models are increasingly deployed in high-stakes domains that demand transparency and trustworthiness, while recent advances like Expected Grad-CAM [28] and TCAV [29] have improved gradient-based attribution through kernel smoothing and concept activation vectors, respectively, critical limitations persist: heuristic baseline selection in gradient path integration [28], manual concept engineering requirements [29], noise amplification in perturbation methods [30], and performance degradation in constrained training [31]. Our work fundamentally advances this landscape by introducing deep prior-guided baselines that eliminate manual concept annotation through CNN feature space learning, mathematically align baselines with domain-specific missingness via probabilistic modeling (MGM/BGMM), and preserve model fidelity through post hoc explanation refinement. The following subsections detail the technical evolution of gradient-based interpretation methods that underpin our innovations.  
Gradient-based attribution methods are pivotal for interpreting DNNs, quantifying feature importance via output-input sensitivity. While traditional gradient methods (e.g., saliency maps) suffer from vanishing gradients and arbitrary baselines, integrated gradients improves robustness through path integration. Expected gradients further refines this by averaging integrated gradients over multiple baselines, though baseline selection remains heuristic--a gap our prior-guided framework addresses. Here, we focus on EG's strengths and limitations, contextualizing our contribution.
# A 60-GHz Ultra-Thin and Flexible Metasurface for Frequency-Selective Wireless Applications
###### Abstract  
This paper demonstrates a design, fabrication and performance evaluation of a frequency selective 60-GHz flexible metasurface in order to facilitate an unobstructed, secure, and low-loss high-frequency signal propagation. The high-attenuation 60 GHz millimeter-wave (MMW) band is selected for the design of a broadband metasurface. The symmetric metasurface unit-cell structure comprises of an assembly of a circular metallic ring embedded with a square-shaped ring. The screen printing process is deployed to perform fabrication of the metasurface on flexible films by means of highly-conductive nanoparticle copper pastes. Electromagnetic (EM) characteristics of the proposed structure are examined by using the quasi-optic bench testing facility in order to validate its potential contribution in the high data-rate communication links. The proposed ultra-thin and flexible metasurface can be integrated on walls and windows as wallpaper to enhance the wireless signal propagation in highly-dense indoor scenarios.  
flexible; frequency selective; metasurface; wireless Article
## 3 Meta-PPO Anti-Jamming Intelligent Decision Algorithm  
Meta-learning enables agents to have the ability to learn how to learn. The focus of meta-learning is on how to introduce prior knowledge into the model and optimize external memory during training, so as to learn faster and more accurately when training new tasks. Unlike other deep learning algorithms, MAML does not aim to find the optimal parameters for a specific task, but rather seeks to find initial parameters \(\eta\) through training a series of task-related meta-tasks, which enable the model to quickly reach the optimum when faced with new tasks. \(\eta\) has the sensitive characteristic to the learning domain distribution of new tasks, which allows certain features inside the trained model to be more easily transferred among various tasks. Optimal model network parameters can be obtained after a few steps of updating. The gradient descent process of MAML is shown in Figure 2. The \(\eta\) in Figure 2 represents the initial parameters obtained after MAML pre-training; \(L_{1},L_{2},L_{3}\), respectively, represent the loss functions of the new task; \(\
abla\) represents the gradient operator; \(\eta_{1}\),\(\eta_{2}\),\(\eta_{3}\) indicate the optimal updating directions under the new task.  
The proposed intelligent communication anti-jamming algorithm is based on meta deep reinforcement learning combined with the meta-learner and base learner defined in the meta-learning-based MAML algorithm. It takes anti-jamming tasks with different processes but belonging to similar types as independent base learners. Then, the base learners transmit the knowledge they have learned to the meta-learner for collection and summarization, and through which the initial network parameters of the model can be obtained with fast convergence, strong robustness, and better generalization abilities. Although the base learners are independent from each other, they perform intelligent anti-jamming tasks of similar types. Therefore, they are based on the same model. The proposed intelligent communication anti-jamming algorithm is presented in Figure 3.  
As shown in Figure 3, the Meta-PPO algorithm consists of a base learner and a meta-learner. The base learner learns from multiple similar anti-jamming tasks, where it gains the network parameters learned from different tasks, which represent the knowledge of each task's characteristics. The meta-learner is responsible for integrating all the task characteristics and guiding the base learner, allowing the base learner to adapt to new tasks faster, thus solving new problems with better performance. The meta-learner represents the common knowledge of all tasks. Meta-testing uses the initial network parameters obtained by the meta-learner to learn the anti-jamming model and evaluates the parameters with new tasks not included in the meta-training set.  
The modules of the Meta-PPO algorithm are described in detail as follows:  
Figure 2: MAML gradient descent process.
## 3 Results and Discussion
### Changes in Film Wettability and Surface Free Energy--Effectiveness of Activation
Moreover, for all materials regardless of the type of gas used for activation, the polar component of SFE increased significantly (110-378%), while the dispersion component of SFE decreased (4-13%). At the same time, regardless of the type of gas used, the largest change in the dispersive component of SFE was observed for RCP films, while the polar component of SFE was observed for TCL films. In addition, a significant change in surface polarity was observed for all films as a result of activation--the polar component of SFE was increased at least twofold (NTSS) and at most fourfold (TCL with oxygen plasma).
## 3 Simulation Setups and Results
### Results
As shown in Figure 2, the area of the enhanced or reduced magnetic field is dependent on applied frequency and material properties. The location of receivers also needs to be considered to investigate the area of the enhanced field in the future. Figure 6: The G values for different conductivities of inside and outside boxes at 50 Hz magnetic fields. (**a–c**) Different conductivities of the outer shell for PEC inside box. (**d**) Different conductivities of the inside box through an outer shell that has \(10^{6}\) S/m conductivity. The relative permeability of all cases is 1.
## 4 Results
### Environmental Load Scenarios
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Environmental Scenarios**} & **E-1** & **E-2** & **E-3** & **E-4** \\ \hline \multirow{3}{*}{Transition piece} & Simplified Model & 2.47 & 3.04 & 5.92 & 1.84 \\  & OpenFAST & 3.27 & 3.35 & 3.09 & 1.89 \\  & Difference (\%) & 24.56 & 9.39 & \(-\)91.72 & 2.62 \\ \hline \multirow{3}{*}{Mudline level} & Simplified Model & 8.43 & 17.93 & 19.70 & 16.76 \\  & OpenFAST & 11.23 & 21.01 & 16.53 & 19.70 \\ \cline{1-1}  & Difference (\%) & 24.90 & 14.69 & \(-\)19.16 & 14.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Maximum shear forces in MN at transition piece and mudline level due to environmental load scenarios. force of 5.92 MN for scenario E-3, which is a stark contrast to the 3.09 MN predicted by OpenFAST, resulting in a \(-\)91.72% difference. Similarly, at the mudline level, the simplified model's prediction of 19.70 MN is higher than OpenFAST's 16.53 MN, with a difference of \(-\)19.16%. For the rest of the environmental load scenarios, the simplified model underestimates the shear forces obtained by OpenFAST with a relative difference of around 20% in normal operating conditions (E-1) and up to 15% in the E-2 and E-4 scenarios. Regarding the maximum bending moments at the mudline level due to environmental load scenarios, the simplified model is found to overestimate their values for all scenarios except for normal operating conditions. A good agreement is obtained for the E-1 and E-2 scenarios, with relative differences below 10%, and an acceptable difference is found for the E-4 scenario (25%). However, for the E-3 scenario, the simplified model is extremely conservative, leading to a maximum value that is 145% higher with respect to the OpenFAST simulation. Analyzing the OpenFAST results, it is found that the E-2 scenario is the most unfavorable one, presenting a maximum shear force of 21 MN and a maximum bending moment of 699 MN at the mudline level. For this load case, the simplified model is able to estimate \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Environmental Scenarios**} & **E-1** & **E-2** & **E-3** & **E-4** \\ \hline \multirow{3}{*}{Transition piece} & Simplified Model & 316.63 & 390.10 & 764.38 & 214.29 \\  & OpenFAST & 355.90 & 358.69 & 402.25 & 270.06 \\  & Difference (\%) & 11.04 & \(-\)8.76 & \(-\)90.03 & 20.65 \\ \hline \multirow{3}{*}{Mudline level} & Simplified Model & 521.15 & 763.87 & 1249.93 & 535.81 \\  & OpenFAST & 543.95 & 699.00 & 508.97 & 424.35 \\ \cline{1-1}  & Difference (\%) & 4.19 & \(-\)9.28 & \(-\)145.58 & \(-\)26.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Maximum bending moments in MN m at transition piece and mudline level due to environmental load scenarios. Figure 6: Maximum shear forces and bending moments at transition piece and mudline level due to environmental load scenarios. these values with differences of around 15% and \(-9\%\), respectively. Thus, the use of this model with negligible computational costs for a preliminary design stage is justified.