esa-sceva/satcom-chunk-collection huggingface dataset:
# Optical Chirality Determined from Mueller Matrices
## 4 Results
### Structural Optical Activity in Cellulose Nanocrystals
#### 4.5.2 Method II  
The bianisotropic effects of the CNC film can be quantified by using a differential decomposition with results as shown in Figure 14. The circular dichroism of the CNC film shows a resonance behavior, whereas the corresponding circular birefringence levels out at a close to constant level in the spectral region below the resonance. The linear effects are small and are dominated by \(L_{\text{B}}\) and \(L_{\text{P}}^{\prime}\), whereas \(L_{\text{D}}\) and \(L_{\text{D}}^{\prime}\) are very close to zero.  
Furthermore, \(L_{\text{B}}\) and \(L_{\text{B}}^{\prime}\) are of similar shape and magnitude indicating that the in-plane optic axes of the CNC film are close to \(\pi/8\). The oscillations seen most clearly in \(C_{\text{B}}\) and \(C_{\text{D}}\) are due to film thickness interference effects and are a sign of low absorption confirming the small values of \(L_{\text{D}}\) and \(L_{\text{D}}^{\prime}\). The depolarization of the CNC film is shown in Figure S4 in the Supplementary Materials.  
Figure 13: The transmission Mueller matrix of a 20 \(\upmu\)m cellulose nanocrystal film (solid curves). The dashed curves shows the best model fit obtained with non-linear regression.
### Related Works, Research Gaps, and Questions
#### 1.4.3 Research Questions  
This study aimed to address these research gaps by posing several key questions. First, it explored the impact of continuous versus periodic rebalancing on portfolio performance. This included analyzing the effects of transaction costs and slippage, as well as how different rebalancing frequencies affect the agent's ability to learn optimal policies. Second, the study investigated how different feature extractors, such as CNNs versus MLPs, influence the agent's understanding of the market environment and its ability to make accurate predictions about future asset prices.  
Furthermore, we examined the use of technical indicators compared to OHLC price data as inputs to the RL agent. According to the Efficient Market Hypothesis (EMH) [33], price alone should reflect all available information, but the use of additional indicators may enhance the agent's decision-making in practice [7]. Finally, we sought to determine which RL algorithm--DQN, DDPG, PPO, or SAC--and agent nature--value optimization or policy optimization--are most suitable for portfolio optimization, especially when considering different history windows and their impacts on both performance and convergence. This included evaluating the effectiveness of agents in handling a fixed set of instruments: index funds or ETFs, which track well-known market benchmarks and are widely used in the investment community [2].
## 3 Results
### Designing for Acoustic Wellness  
Designing for acoustic wellness in dental settings requires adapting proven strategies from broader healthcare environments to address the unique psychological and spatial dynamics of outpatient dentistry. Hospital-based research shows that poor acoustic environments contribute to sleep disruption, physiological stress, and delayed recovery, supporting the need to extend similar design principles to dental clinics [8; 19; 62]. To be more specific, dental clinics often evoke anticipatory anxiety, making sound control not only a matter of comfort but of clinical importance. [PERSON] et al. (2024) found that chaotic soundscapes in clinics intensify stress-related responses [4], while [PERSON] et al. (2023) quantitatively recorded noise levels surpassing comfort thresholds, urging the application of sound-absorbing ceiling tiles, wall panels, and spatial zoning [5]. Similarly, [PERSON] et al. (2021) linked poor acoustic design to decreased well-being in healthcare learning spaces, reinforcing its importance in academic and pediatric clinics [5; 51].  
Furthermore, strategic spatial zoning is essential: high-noise areas such as sterilization rooms should be acoustically separated from quiet zones like recovery or consultation rooms [29; 39]. In open-plan clinics, sound-dampening dividers and directional barriers can reduce sound spillover, though private operatories offer the greatest control over the auditory environment [26; 43].  
Emerging research also supports biomedical acoustics, a concept that uses evidence-based layout planning and sound modulation to enhance healing [10]. These principles align with biophilic strategies, where natural acoustic stimuli, like water sounds or birdsong, create calming environments [25; 57; 63]. These can be particularly effective in waiting or reception areas, where patients experience peak anticipatory anxiety [19].  
Best practices include specifying materials with high NRC and CAC ratings, using insulated doors, noise-controlling HVAC, and rubber flooring in non-clinical areas [27; 50; 68]. Real-time noise monitoring can further optimize these spaces dynamically [10; 58]. Additionally, incorporating feedback from dental professionals is key to ensuring both functionality and sensory comfort [28]. Guidelines from industry sources discuss early-stage acoustic planning to minimize retrofitting costs and disruptions. Ultimately, as dentistry transitions toward a more holistic, preventive model, acoustic wellness must be considered a core design parameter, benefiting not only patient experience but also staff retention, workflow efficiency, and environmental sustainability [2; 13; 36].
## 2 Methodology of the Research
### Surface Measurement by Contactless Method  
The laser profilometer assembly (Figure 4) consists of basic and complementary parts. The basic parts include the support frame from components with vertical positioning of the measuring head and the programmable sample feed in the X and Y axes, the laser beam source, the lens and a CMOS (Complementary Metal Oxide Semiconductor) sensor camera. Additional parts include a computer with service and evaluation software and an image splitter.  
The optical part of the system consists of the AVT Marlin 131B camera and the Tamron 23 FM50 SP 50 mm lens with a visible area of 22 mm \(\times\) 7 mm. The automated sample feed in the X and Y axes is achieved with the Standa 8 MT160-300 stepper motors, up to a maximum of 300 mm in each axis. The system enables measurement of samples weighing no more than 8 kg with a positioning accuracy of 2.5 micrometers per step, each step consisting of 8 micrometers. The sensor resolution is 0.02 mm/pixel.  
To eliminate the impact of vibrations on the measurement and on the quality of the measured data, the samples on the LPM worktop were placed in the plasticine.  
Using the experimental system, it is possible to measure and evaluate the surface roughness parameters of samples according to ISO 11562 (_Rq_, _Rv_, _Rz_, _Ra_, _Rp_). The results of measured profile evaluation can be exported in csv format, suitable for further processing of experiments in the form of raw data.  
The image splitter Matrox TripleHead2 Go-Digital Edition delivers crisp processing and evaluation of the measured data images. This device serves the purpose of splitting one graphic output from the computer into three independent graphic outputs, and in combination with the use of another graphic output and the choice of an extended desktop, four independent images can be obtained by having a different part of the desktop on each monitor.  
The Main Principle of the System  
The system employs laser profilometry through a so-called triangulation principle (Figure 5), in which a laser line is projected onto the measured surface under a certain angle, which is subsequently scanned by a digital camera placed perpendicularly above the scanned surface.  
Figure 4: The laser profilometer assembly.
###### Abstract  
CaF\({}_{2}\):Eu\({}^{3+}\) glass-ceramic sol-gel materials have been examined for reddish-orange photoluminescence applications. The transformation from precursor xerogels to glass-ceramic materials with dispersed fluoride nanocrystals was verified using several experimental methods: differential scanning calorimetry (DSC), thermogravimetric analysis (TG), X-ray diffraction (XRD), transmission electron microscopy (TEM), infrared spectroscopy (IR-ATR), energy dispersive X-ray spectroscopy (EDS) and photoluminescence measurements. Based on luminescence spectra and their decays, the optical behavior of Eu\({}^{3+}\) ions in fabricated glass-ceramics were characterized and compared to those of precursor xerogels. In particular, the determined luminescence lifetime of the \({}^{5}\)D\({}_{0}\) excited state of Eu\({}^{3+}\) ions in nanocrystalline CaF\({}_{2}\):Eu\({}^{3+}\) glass-ceramic materials is significantly prolonged in comparison with prepared xerogels. The integrated intensities of emission bands associated to the \({}^{5}\)D\({}_{0}\)\(\rightarrow\)\({}^{7}\)F\({}_{2}\) electric-dipole transition (ED) and the \({}^{5}\)D\({}_{0}\)\(\rightarrow\)\({}^{7}\)F\({}_{1}\) magnetic-dipole transition (MD) are changed drastically during controlled ceramicization process of xerogels. This implies the efficient migration of Eu\({}^{3+}\) ions from amorphous silicate sol-gel network into low-phonon energy CaF\({}_{2}\) nanocrystals.  
Keywords:CaF\({}_{2}\) nanocrystals; glass-ceramics; Eu\({}^{3+}\) luminescence; sol-gel synthesis +
Footnote †: journal: Applied Physics Letters
# Investigating Anti-Evasion Malware Triggers Using Automated Sandbox Reconfiguration Techniques
## 2 Related Works
### Malware Analysis Platforms  
There are a number of open-source and commercial malware analysis platforms available, including Cuckoo Sandbox [5], PyREBox [15], Joe Sandbox [6] and HookMe [7]. Such tools have been used in broader malware analysis for academic research; however, they are not without their limitations, including complicated configuration requirements [5], lack of GUI [15] and subscription costs [6]. [PERSON] et al. [16] used Cuckoo Sandbox to gather features for a recurrent neural network model for malware detection. Such malware analysis platforms can be very effective for extracting useful properties about malware process execution, and related system processes; however, there is often substantial preprocessing of data required before the outputs from the analysis platform are suitable for machine learning systems, as described by [PERSON] [14]. In their work, a total of 70,518 possible features were extracted, which were then reduced down to 306 features through a process of feature selection methods, resulting in additional time and effort required for the analyst. This suggests that there is scope for further tools to reduce the time and effort requirements of feature engineering for malware analysis. [PERSON] et al. [17] also describe the challenge of reproducability between malware analysis platforms, as reporting may varying in terms of the inputs, analysis, output formats or options used. This further supports the argument that there is a need for better tooling that can offer greater consistency and sharing for analysts, reducing time and effort requirements.  
A key requirement of a malware analysis platform is the ability to hide from the testing environment, and to be able to analyse malware samples that may be _context aware_. Context-aware malware will check the environment that it is situated within, and will only execute if a specific set of conditions are satisfied [4]. There are multiple analysis \"fingerprints\" that malware may use to determine whether it is executing inside a test environment, such as a virtual machine. These can include environmental artefacts, process introspection artefacts and reverse Turing tests amongst others [4]. This level of sophistication has become more common over the years as malware developers have continued the arms race against malware analysts to evade detection. As stated in [4] \"differences unrelated to the evasion techniques being tested should be minimised (e.g., file system contents, time, settings, hardware resources, etc., should all be the same) so that spurious differences in execution are not conflated with evasion\". Quite clearly, it is crucial that the analysis process needs to be able to assess different environment configurations in a efficient and timely manner to satisfy that malware has not evaded detection through any context-aware checks. Our work addresses this need so that virtual environments can be quickly reconfigured, or tested under different anti-evasion techniques, to determine the malware triggers effectively.
## 2 System Description
### Multi-Dimensional Video Distribution Systems (MVDS)  
Multi-dimensional video distribution systems (MVDS) operate primarily in the Ku-band (10-13.5 GHz) [4; 8]. In Lebanon, MVDS efficiently distributes digital television and broadband internet services for point-to-multipoint or point-to-point transmission over large areas using high-power Ku-band transmitters operating in the range of 13.75-14.75 GHz [14]. With higher-frequency bands, MVDS achieves greater bandwidth capacity than earlier systems [8], enabling the delivery of numerous channels and interactive services [4]. Its architecture allows for signal retransmission without regeneration, simplifying deployment. Modern digital modulation techniques enhance spectral efficiency [8].
## 4 Experiments and Results
### Data
#### 4.1.3 Data Preprocessing  
The differences in the measurement unit of the features could affect the performance of some models. In particular, LR, SVMs and NNs are sensitive to feature scaling [27]. Therefore, the data are normalized, as shown in the following equation.  
\[x_{i}=\frac{X_{i}-\min(X_{tr})}{\max(X_{tr})-\min(X_{tr})} \tag{3}\]  
where \(x_{i}\) is the normalized value for \(X_{i}\), \(X_{tr}\) is the original training set and max() and min() mean the maximum and minimum values for a given dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter Type** & **Parameter** & **Average** & **Standard Deviation** & **Minimum** & **Maximum** \\ \hline \multirow{6}{*}{Input variables} & Gross floor area (m\({}^{2}\)) & 8135.8 & 5342.9 & 325 & 36,699.0 \\  & Building area (m\({}^{2}\)) & 2628.3 & 1791.7 & 122.8 & 11,392.40 \\ \cline{1-1}  & Building height (m) & 20.5 & 7.9 & 5.2 & 70 \\ \cline{1-1}  & Number of floors & 4.1 & 1.9 & 1 & 20 \\ \cline{1-1}  & Number of basement floors & 0.9 & 0.6 & 0 & 4 \\ \cline{1-1}  & Number of parking spaces & 94.8 & 147.2 & 3 & 1830 \\ \hline \multirow{2}{*}{Target variables} & Construction cost & \multirow{2}{*}{10,260.3} & \multirow{2}{*}{6113.2} & \multirow{2}{*}{251.0} & \multirow{2}{*}{28,622.0} \\  & (million W) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters and attributes for the input and target variables.  
Figure 3: Scatter plot showing the relationship between each independent variable and the construction cost.  
There are several ways to deal with missing data in the collected dataset, but we used the method of replacing the missing data with an average value, which is a commonly used method.
# Contextual Coefficients Excitation Feature: Focal Visual Representation for Relationship Detection
## 4 Results
### Evaluation on Visual Relationship Dataset  
Visual Relationship Detection (VRD) dataset [8] is used to evaluate the proposed methods. This dataset contains 5000 images with 100 object categories and 70 predicate categories. In total, there are 37,993 relationship instances with 6672 relationship types and 24.25 predicates per object label. The train/test split is the same as [8], where 4000 training images containing 30,355 relationships with 6672 types and 1000 test images containing 7638 relationships with 2747 types. Note that 1169 relationships with 1029 types are only in the test data.  
Our approach are evaluated on three tasks [8]: **Predicate detection**: with an image and a set of ground truth object bounding boxes, this task is to predict a set of possible predicates between pairs of objects. Since relationship between the pair of objects is critical, this indicator can reflect the performance of the model intuitively, ignoring the error of object detection. **Phrase detection**: given an input image, this task is to output the triplets \(\langle\)subject, predicate, object\(\rangle\) and localize the entire relationships as one bounding box. **Relationship detection**: with an input image, it should output the triplets \(\langle\)subject, predicate, object\(\rangle\) and localize the subject and object bounding boxes. Both phrase and relationship bounding boxes should have at least 0.5 overlap with their ground truth bounding boxes. Obviously, the performance of phrase and relationship detection is affected by the result of object detection due to the pipeline of separate detection. In order to compare the performance of relationship models fairly, the object detection results (both bounding boxes and corresponding detection scores) provided by [8] are used for Phrase detection and Relationship detection. More details in [8].  
Following the original paper [8], the Recall@50 (R@50) and Recall@100 (R@100) are used as our evaluation metrics. Recall@X computes the rate of the correct relationship on the top X prediction. The reason of using Recall@x instead of the mean average precision (mAP) is that mAP would penalize the correct detection if dataset don't have particular ground truth. Note that only the predicate with highest confidence for each pair of objects is considered for predicate, where the prediction score is the product of predicate score and the confidence scores of both subject and object for phrase detection or relationship detection, while prediction score is the predicate score for predicate detection task.
## 5 Experiments
### Performance Comparison of CRGPPO-TKL  
In this section, we evaluate the effectiveness and generalizability of the proposed CRGPPO-TKL algorithm through comparative experiments conducted under various test scenarios. The comparison includes 40 composite production-logistics scheduling rules designed for the FJSP, two DRL-based scheduling algorithms (including DQN-based and PPO-based methods), as well as a randomized version of CRGPPO-TKL (denoted as RN), in which the agent randomly selects actions at each decision level. Each method was executed over 20 independent runs on each test instance.
## 4 Full Dimensioning of V2G Energy Resource
### Environmental Dimension  
As V2G is a technology that will occupy an existing space (within the installed establishment) and be just a technology with a share in the flow of energy (loading and unloading electrical energy from the grid to the vehicle), some of these environmental impacts will not take place. Considering the impact of the availability of energy to be added to the network, one can have some impacts upstream (generating), thus taking into account studies that indicate some related data [23; 24; 25].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**Decrease—V2G**} & \multirow{2}{*}{**ICE**} \\ \cline{2-3} \cline{5-5}  & & **EV** & & **PHEV** \\ \hline Battery Proportion (kW) & 99 & 10 & n/a \\ \hline \multicolumn{3}{c}{Charger Proportion (kW)} & 19.2 & 19.2 & n/a \\ \hline SOC (\%) & 30 & 30 & n/a \\ \hline \multicolumn{3}{c}{Regulation Cycle} & 20:00-8:00 & 20:00-8:00 & n/a \\ \hline \multirow{3}{*}{10 years} & Price & USD 32,529,037 & USD 32,723,614 & USD 33,198,377 \\ \cline{2-5}  & V2G Income & USD 2,268,780 & USD 1,758,834 & - \\ \cline{2-5}  & Final Expense & USD 30,260,257 & USD 30,964,780 & USD 33,198,377 \\ \hline \multirow{3}{*}{Per Mile} & Price & USD 0.735 & USD 0.739 & USD 0.750 \\ \cline{2-5}  & V2G Income & USD 0.051 & USD 0.04 & USD - \\ \cline{2-5}  & Final Expense & USD 0.683 & USD 0.699 & USD 0.750 \\ \hline \multicolumn{3}{c}{V2G income per vehicle/year} & USD 907.51 & USD 703.53 & USD - \\ \hline \multicolumn{3}{c}{Reduction on fee from V2G} & 7.0\% & 5.4\% & 0.0\% \\ \hline \multicolumn{3}{c}{Savings vs. ICE} & 8.9\% & 6.7\% & n/a \\ \hline \hline \end{tabular} Source: the authors, assembly based on [18]. n/a—Not applied.  
\end{table}
Table 3: Ramp down best combination results—comparison of costs.  
* Terrestrial
* Solids: During the V2G operation, there is no production of solid pollutants.
* Liquids: During the operation of the V2G, there is no production of liquid pollutants.
* Occupied space: During V2G operation, the space occupied is minimal, and therefore not considered.
* Aquatic
* Change in pH. During the operation of the V2G, there is no production of pollutants to alter the pH.
* Change in water temperature. During the operation of the V2G, there is no production of pollutants to alter the water temperature.
* Emission of pollutants. For this factor, we do not consider any impact from the technology.
* Aerial
* Particulate and others: During the operation of the V2G, there is no production of particulate pollutants.
* Greenhouse gases: Reduction of 0.08 MtCO\({}_{2}\)/year according to the Brazilian electrical matrix. Taking into account the Santos immersion, it has a proportion of 0.15% of the Santos emissions. Yet the major part would be the reduction in the fleet of cars combustion by electric cars. This is not considered because the electric cars themselves are not being considered, but rather the V2G intelligent charging technology.
## 3 Results and Discussion
### Pushover Analysis  
To carry out the pushover analysis, first, the reference node is fixed, as illustrated in Figure 3. Then, the direction of displacement is established, which varies depending on the considered action. It is crucial to use enough time for the analysis, 100 in this case. Additionally, the vibration mode corresponding to the specified direction is selected. As the assigned loads increase at each time step, the formation of plastic hinges in the FEM is observed. Upon solving the case, the relevant results are obtained.  
For this analysis, a pushover based on the results obtained from earlier analyses is performed. It was determined that the maximum displacement, according to the non-linear dynamic analysis of the Lorca earthquake, was 5.22 cm. In the inverse approach employed, a sufficiently large displacement was applied to evaluate the gradual formation of plastic hinges. In the present case, a displacement of 25 cm was imposed for time step 100. However, it was observed that by step 22, approximately 100 plastic hinges had already formed (Figure 11A), spanning across all floors of the building.  
With the presence of over 100 plastic hinges, it is difficult not to contemplate the imminent collapse of the structure. The impact of an earthquake like that of Lorca on the analysed structure would have been devastating. The pushover analysis, as observed, offers numerous possibilities. It allows for the progressive formation of plastic hinges to be detected as loads increase at each time step. Additionally, it provides valuable information  
Figure 10: Non-linear dynamic analysis during the Lorca Earthquake in North-South Direction: Flexure in Z.  
about structural stresses. For example, even after the structure is assumed to have collapsed, in step 63, numerous plasticisations are evident (Figure 11B).  
In step 63, collapse seems inevitable. The entire structure is fully yielded, with the bases of the pillars affected and plastic hinges collapsed between floors 2 and 3. However, there is doubt as to whether the structure would have reached this point without collapsing previously. It is worth noting that this step corresponds to an imposed deformation of 15.75 cm.