esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The aryl hydrocarbon receptor (AhR) is a ligand-activated transcription factor that integrates environmental, dietary, microbial, and metabolic cues [18]. Previous studies have demonstrated that AhR plays an important role in the regulation of the immune system and modulation of inflammation, cell proliferation, cell differentiation, and apoptosis [19,20]. As reported, inflammation is a well-described risk factor of cancer due to its contribution to induction of oncogenesis [19,21]. AhR expression has been shown to increase in many cancer subtypes, including lymphocytic leukemia [22], breast cancer [23], lung cancer [24], and gastric cancer [25,26]. It has been reported that AhR overexpression reduces PPARy stability by forming a ubiquitin ligase complex [27]. As shown by [PERSON] et al., AhR-knock-out mouse embryonic fibroblast cells (3T3-L1) were characterized by an increase in the expression of PPARy; conversely, a decrease in endogenous PPARy was observed in AhR-overexpressing 3T3-L1 cells [27]. Moreover, a similar correlation was highlighted in the above-cited study, revealing that blocking of AhR degradation by the proteasome inhibitor carbobenzoxyl-L-leucyl-L-leucyl-L-leucine (MG-132) caused a decrease in the PPARy expression level [27]. Furthermore, [PERSON] et al. have proved that methyl 5-fluoro-3-[2-(4-hydroxyanilineo)-4-oxo-4,5-dihydro-1,3-thiazol-5-ylidenmethyl]-1_H_-2-indolecarboxylate (Les-6166) is able to affect _AhR_ mRNA expression in the 3T3-L1 cell line in vitro [28]. However, the PPARy-AhR-induced anticancer properties of the 4-TZDs, namely Les-4368 and Les-4370, have never been studied in CACO-2 cells in vitro, which is crucial due to the increasing colorectal cancer morbidity. To date, Les-4368 and Les-4370 derivatives were studied only on leukemia cell line (HL-60) [29]. As afore mentioned, it seems to be crucial to study toxicity and mechanism of action of mentioned compounds on other cell types, especially CACO-2 cell line (a common model for gastric cancer or enterocytes in the differentiated state), and compare these data with non-cancerous cells such as normal fibroblasts (well-established model of normal cells). Therefore, the aim of the present study was to evaluate the anticancer properties of two 4-TZD derivatives, Les-4368 and Les-4370, and the engagement of two intracellular receptors (PPARy and AhR) in this phenomenon in BJ and CACO-2 cells in vitro by measuring the metabolic activity, reactive oxygen species (ROS) production, LDH release levels, and expression of certain genes and proteins after treatment of the above-mentioned cells with the tested 4-TZD derivatives.
## 2 Digital Platform--DNS with SWAT Module
### Land Phase  
This phase is the main source of sediment in a majority of basins, which is directly related to rainfall and runoff energy [75; 76], which have a decisive influence on the phenomena of detachment and initiation of soil particle transport. The two most important processes that occur in this phase are surface runoff and erosion (detailed in Chapters 2.1.1 and 2.1.2), with soil type and land use being the most important responders. The SWAT module takes these responses into account by dividing the basin into areas with unique land use, soil, and slopes, creating so-called hydrological response units (HRU). For each HRU, the individual hydrological components of the land phase, such as rainfall, surface runoff, and evapotranspiration, are then simulated. The basis here is the water balance Equation (1), which was described in detail in the theoretical SWAT documentation [77]. Based on the daily components of the hydrological cycle the daily water budget in each HRU is calculated [37; 78; 79; 80; 81].  
\[SW_{t}=SW_{0}+\sum_{i=1}^{t}\Big{(}R_{day}-Q_{surf}-E_{a}-w_{seep}-Q_{gw}\Big{)} \tag{1}\]  
where: \(SW_{t}\) is the final soil water content, \(SW_{0}\) is the initial soil water content, \(R_{day}\) is the amount of precipitation, \(Q_{surf}\) is amount of surface runoff, \(E_{a}\) is the amount of evapotranspiration, \(W_{seep}\) is the amount of water entering the vadose zone from the soil profile, and \(Q_{gw}\) is the amount of return flow (all in mm H\({}_{2}\)O).  
The SWAT module has been successfully used many times around the world to simulate sediment yield transport in the land phase of the basin area, as confirmed by numerous publications (e.g., [82; 83; 84]). Nevertheless, there are some limitations to both the data and the module itself. These include the common problem with the unavailability of data on sediment yield efficiency [85]. In practice, this causes situations where the time step of sediment calibration and validation is different than for hydrology, for which monitoring data are usually more readily available even for smaller basins. This reduces the accuracy of the model in the simulation of the land-phase sediment yield. The problem is also the generalization of the SWAT model, the effect of which is, for example, allowing all eroded  
Figure 1: Methodological diagram (Ch. numbers—related paragraphs in the text.  
soil to reach the riverbed with surface runoff (without taking into account sedimentation still in the land phase), as well as simulation of sediment-bound pollutants, determined by the primary concentration of nutrients in the soil, soil loss, and the soil coefficient enrichment, which is often not applicable in many basins [86]. Another limitation of the model in this phase is the use of numerous empirical and quasi-physical equations developed on the basis of climatic conditions in the USA [87], including simplified sediment routing algorithms for erosion simulation [84; 88; 89].
## 4 Analysis of Relevant Influencing Parameters
### Effect of Delaminations  
The vibration analysis of composite beams with delaminations is another complex issue, which can be generally solved with the use of advanced FE models (i.e., [44; 45; 46; 47]), or with the support of coupled numerical-experimental studies on artificially damaged specimens (see, for example [48; 49; 50]). In both the cases, besides different composite laminates are taken into account, a common outcome of existing literature projects is represented by the high sensitivity of vibration frequencies to delaminations. Otherwise, dedicated studies for delaminated LG structures are still missing.
## 3 Results and Discussion
### Mini-Slump  
As shown in Figure 8a, when the proportion of HCDA was 85%, its initial fluidity (252 mm) was the largest in the group. As the proportion of HCDA gradually decreased from 95% to 85%, the initial fluidity of the slurry slowly increased until it reached the maximum value, while it started to decrease when it continued to decrease to 80%. With the increase in reaction time, the results of the fluidity at 30 min and 60 min show that the ratio of HCDA to GBFS was correlated with the fluidity of the slurry, and the larger the proportion of GBFS, the better the fluidity of the slurry. The initial fluidity of DSPB decreased with increasing HCDA content. This trend is closely related to the water requirement of HCDA [63].  
Figure 8b shows the effect of PCE, with a significant increase in the initial and subsequent fluidity of the slurry with an increase in dosage. When the PCE dosing was 3%, the initial fluidity was the lowest, the loss of fluidity was also very fast, and the fluidity was lost  
Figure 8: Effects of (**a**) HCDA ratio, (**b**) PCE, and (**c**) SS dosing on the mini-slump value of the DSPB slurry.  
after 15 min. With the increase in PCE dosing, the fluidity increased and the loss of fluidity with time decreased. This shows that by adding PCE, polycarboxylic acid molecules can be effectively adsorbed on the surface of HCDA particles due to the surface repulsion of molecules, thus dispersing the agglomerated particles, and free water can be lubricated in the interstices of the particles, which effectively improves the macroscopic mobility and performance of the slurry [74; 75].  
From Figure 8c, it can be seen that the initial fluidity was less than that of the Control group when no activator was doped, as well as when SS was doped at 1% and 1.5%. This may be due to the repulsive bilayer force generated by the adsorption of silicate oligomers on the surface of slag particles in the system, and thus the initial fluidity without activator was lower. On the other hand, when the SS doping was greater than 0.5%, the decrease in the initial fluidity may have been due to the higher viscosity of the SS solution itself, as well as due to more free water being consumed for the dissolution process. As the reaction time grew, the fluidity decreased with the increase in SS doping. The C1-SS0 and Control groups had less loss of fluidity with time than the groups with higher activator dosing due to the incorporation of SS. SS provides the system with a high source of alkalinity and silica, which is a necessary condition for the generation of the C-S-H gel by the hydration reaction [76].
# Opto-Electronic Oscillators for Micro- and Millimeter Wave Signal Generation
## 2 Current Progress of the Common Topologies of the OEO
### Progress of the OEO toward Lower SMSR and Phase Noise
An SMSR of more than \(72.5\) dB was achieved wherein the SSB phase noise was \(-123.6\) dBC/Hz at a \(10\) kHz offset from the \(10\) GHz carrier signal. Figure 3: Typical dual-loop configuration of an OEO. In addition to the typical solutions listed above, there are other novel solutions to improve the phase noise and/or the SMSR characteristics of the OEO. For a millimeter-wave OEO, a high-quality opto-electronic filter was proposed, e.g., a Q-factor of 30,000 at a central frequency of 29.99 GHz [73]. This resulted in an 83 dB SMSR and a \(-113\) dBc/Hz SSB phase noise at a 10 kHz offset. A cascaded microwave filter was presented in [49]. In this configuration, the cascaded filter configuration was implemented with a single passband filter having an opto-electronic filter. With this approach, an SMSR of 125 dB was used to achieve the  
and an SSB phase noise of \(-103\) dBc/Hz for a 10 kHz offset at the 17.33 GHz carrier were achieved. The linewidth of the laser can affect the phase-noise performance of the OEO. A narrowband microcavity laser with a physical side length of 16 \(\upmu\)m is used for the microwave signal generation in the loop of the OEO [74]. An SSB phase noise of \(-116\) dBc/Hz was achieved for a 10 kHz offset from the carrier microwave signal. The microwave signal can be tuned between 1.85 GHz and 10.24 GHz thanks to a tunable optical bandpass filter.
## 4 Analysis of the Origin of Low-Resistivity Oil Reservoirs
### Additional Conductivity of Clay Minerals
This suggests that, under high-formation-water-salinity conditions, the impact of mud's additional conductivity is less pronounced. Therefore, under the conditions of a relatively low formation-water salinity, the significant additional conductivity of clay cannot be overlooked. If the reservoir exhibits lateral variations in lithology, it may lead to complex changes in the resistivity characteristics of the oil and water layers. Figure 10: The \(R_{t}-S_{w}\) relationship for reservoir rocks with different \(Q_{v}\).values (e.g., \(Q_{v}=0.25\)); the impact of clay’s additional electrical conductivity is significant. At 50% water saturation, \(Q_{v}\) varied from 0 to 0.92 meq/mL, resulting in a resistivity decrease from 50.47 ohm.m to 11.9 ohm.m, marking a substantial 76% alteration. This underscores the significant impact of \(Q_{v}\) under conditions of a low porosity, a low salinity, and elevated bound water saturation. With increasing \(Q_{v}\), the resistivity index of mud sandstone reservoirs experiences a continuous decline, albeit at a diminishing rate. Figure 9: The core resistivity measurement results fitted with the Waxman–Smits equation: (**a**) the formation factor versus the core porosity and (**b**) the resistivity index versus the water saturation. (In the figure, \(y\) denotes the expression of the fitted curve and R\({}^{2}\) denotes the correlation coefficient).
# Predicting Road Traffic Collisions Using a Two-Layer Ensemble Machine Learning Algorithm
## 3 Results  
### Results of the Classification before SMOTE  
Since we wanted to predict the occurrence or absence of a road traffic collision, our problem was a binary classification [43]. In this study, the data sample from the driver simulation was split into 30% training data and 70% test data. The model's predictive performance on the test dataset was evaluated by comparing accuracy, precision, and F1 scores. The effectiveness of each algorithm has been determined from the simulation driver data by employing AdaBoost, DT, NB, and k-NN as base models using the same selected feature set, then employing the stacking ensemble method using logistic regression as a meta-classifier to improve the model's accuracy. We performed two scenarios: the first without SMOTE, and the second with SMOTE. Before pruning DT and setting the k value for k-NN, DT achieved the highest accuracy of 87%, followed by the two-layer ensemble with 85%, and Naive Bayes with 83%. AdaBoost and k-NN achieved a similar score of 79% before the SMOTE technique, as illustrated in Table 3.  
The robustness of the ML model is largely assessed and validated using the area under the receiver operator curve (AUC). When the AUC is higher than 0.7, the developed model  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline AdaBoost & 0.79 \(\pm\) 0.11 & 0.76 \(\pm\) 0.13 & 0.71 \(\pm\) 0.12 & 0.72 \(\pm\) 0.14 \\ \hline k-NN & 0.79 \(\pm\) 0.08 & 0.81 \(\pm\) 0.41 & 0.66 \(\pm\) 0.19 & 0.68 \(\pm\) 0.25 \\ \hline DT & 0.85 \(\pm\) 0.12 & 0.87 \(\pm\) 0.27 &is said to have good predictive power. Before SMOTE, the two-layer ensemble had an area of about 0.87%, followed by the NB algorithm at 0.83%, AdaBoost at 0.82%, k-NN at 0.8%, and DT at 0.77%, as shown in Figure 9. The experiment was conducted before implementing pruning on DT and setting the k value on k-NN.
## 4 Modeling of the Filling Phase
Similarly, the Cross-WLF model also demonstrates good performance, with an RMSE of 8.217 and an \(R^{2}\) value of 0.9991, establishing it as a leading model for predicting viscosity of polymer melts. Other models, including Herschel-Bulkley, Carreau, Carreau Winter, and Cross, also demonstrate good predictive accuracy, with RMSE values of approximately 80.7 and \(R^{2}\) values of approximately 0.917. While these models are reliable, they do not show the same exceptional performance as the Second-Order and Cross-WLF models. In contrast, the Power-Law, Carreau-WLF Winter, and Bird-Carreau models exhibit higher RMSE values and lower \(R^{2}\) values compared to the top-performing models, indicating less accuracy and fit. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Property** & **Property Symbol** & **Value** \\ \hline \multirow{4}{*}{Air} & Density & \(\rho_{\text{air}}\) & 1.185 kg/m\({}^{3}\) \\  & Dynamic viscosity & \(\eta_{\text{air}}\) & \(1.831\cdot 10^{-5}\) kg/(m s) \\  & Thermal conductivity & \(\kappa_{\text{air}}\) & \(2.61\cdot 10^{-2}\) W/(m K) \\  & Specific heat capacity & \(c_{p,\text{air}}\) & 1004.4J/(kg K) \\ \hline \multirow{4}{*}{Polymer} & Density (melt) & \(\rho_{\text{melt}}\) & 860 kg/m\({}^{3}\) \\  & Density (solid) & \(\rho_{\text{solid}}\) & 1060 kg/m\({}^{3}\) \\ \cline{1-1}  & Specific heat capacity & \(c_{p,\text{polymer}}\) & 1700 J/(kg K) \\ \cline{1-1}  & Thermal conductivity & \(\kappa_{\text{polymer}}\) & 0.28 W/(m K) \\ \cline{1-1}  & Thermal diffusivity & \(\alpha\) & 0.19 mm\({}^{2}\)/s \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of material properties of air and Schulamid 66 SK 1000 used for simulation. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Temperature** & **Shear Rate** & **Viscosity** \\ \hline \multirow{8}{*}{280 \({}^{\circ}\)C} & 10 & 1051.87 Pa s \\  & 100 & 567.852 Pa s \\  & 500 & 277.94 Pa s \\  & 1000 & 198.751 Pa s \\  & 5000 & 89.4346 Pa s \\  & 10,000 & 63.1994 Pa s \\ \hline \multirow{8}{*}{290 \({}^{\circ}\)C} & 10 & 801.363 Pa s \\  & 100 & 468.946 Pa s \\  & 500 & 236.128 Pa s \\  & 1000 & 169.62 Pa s \\  & 5000 & 76.6202 Pa s \\  & 10,000 & 54.1708 Pa s \\ \hline \multirow{8}{*}{300 \({}^{\circ}\)C} & 10 & 618.301 Pa s \\  & 100 & 389.41 Pa s \\ \cline{1-1}  & 500 & 202.446 Pa s \\ \cline{1-1}  & 1000 & 146.226 Pa s \\ \cline{1-1}  & 5000 & 66.366 Pa s \\ \cline{1-1}  & 10,000 & 46.95 Pa s \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overview of the data points for the rheological properties of Schulamid 66 SK 1000.
# Binaural Rendering with Measured Room Responses: First-Order Ambisonic Microphone vs. Dummy Head
## 3 Experiment II: Dummy-Head MOBRIR vs. ARIR  
In _Experiment II_, we evaluate and compare the perceptual aspects of MOBRIR (LISHPh) and ARIR-based dynamic rendering (ASDM). The concept of ARIR-based rendering and the relevant signal processing involved to accomplish upscaling are described in Section 3.1. A description of the listening experiment, the implementation, and the corresponding discussions are presented in Sections 3.2-3.4, respectively. Appendix B shows the _MATLAB_ implementation of the ASDM upscaling. Audio examples ([[https://phaidra.kug.ac.at/view/o:77319](https://phaidra.kug.ac.at/view/o:77319)]([https://phaidra.kug.ac.at/view/o:77319](https://phaidra.kug.ac.at/view/o:77319))) of the material used in the listening experiment as well as its response data ([[https://opendata.iem.at/projects/listening_experiment_data/](https://opendata.iem.at/projects/listening_experiment_data/)]([https://opendata.iem.at/projects/listening_experiment_data/](https://opendata.iem.at/projects/listening_experiment_data/))) are available for download. The examples include renderings of static head-orientations and of emulated continuous head-orientations.
# Understanding and Classifying Permanent Denial-of-Service Attacks
## 5 Discussion
### Potential Applications per Class
#### 5.2.1 Damage Mechanism  
In-depth analysis of the damage mechanism in PDoS attacks is pivotal for an effective system assessment, as it significantly influences the subsequent steps in incident management and recovery. A comprehensive understanding of the damage mechanism, whether it involves bootloader corruption, data encryption/deletion, or data-flow manipulation, directly shapes the strategic approaches used for system restoration and resilience. For instance, in cases where the bootloader is corrupted, the focus shifts to firmware-level interventions, which may include reflashing or reconfiguring firmware settings. On the other hand, data encryption or deletion necessitates a different set of recovery actions, potentially involving data recovery tools and techniques, or, in severe cases, resorting to backup systems for data restoration.  
Knowledge of the damage mechanism also plays a crucial role in risk management and resource allocation during the recovery phase. By accurately gauging the impact and scope of the damage, organizations can prioritize their efforts and allocate resources more efficiently, ensuring a quicker return to normal operations. For critical infrastructure or high-value targets, this understanding is essential to minimizing operational disruptions and financial losses.
## 1 Introduction
The reporting requirement includes physical (recording and reproduction techniques) and perceptual data collections both on-site (e.g., soundwalk) and off-site (e.g., laboratory experiments). In particular, recording and reproducing techniques play a critical role to achieve high ecological validity of soundscape research conducted in the virtual acoustic environment. This is because soundscape is the human perception of the reproduced acoustic environment, and the perception is dependent on the perceptual accuracy of the recording and reproduction techniques. Perceptual accuracy is the basis of auralization [18], and is largely influenced by the spatial properties of sounds such as source distance, source location, and reverberation [19; 20]. Several laboratory experiments on ecological validation of the reproduction systems have been conducted based on the psycholinguistic measure, such as semantic differential method [16; 21; 22]. Those studies compared the subjective responses of verbal descriptors on soundscape among in situ acoustic environments and the reproduced acoustic environments created by different techniques in laboratory conditions to explore the ecological validity of reproduction systems. Recently, virtual reality (VR) [23; 24; 25] and augmented reality (AR) [26; 27; 28] technologies have been increasingly adopted in soundscape studies due to their potential in creating a perceptually accurate audio-visual scene. To create virtual acoustic environments with high ecological validity, it is essential to have a holistic understanding of recording and reproducing techniques with respect to soundscape evaluation methodologies. Even though more studies on the standardization of soundscape recording and reproduction in an ecologically valid way that reflect context of real acoustic environment are necessary, relatively few studies have been done on these technical aspects of soundscape research. This paper attempts to formulate a framework for the appropriate use of audio recording and reproduction techniques in soundscape design. Sections 2 and 3 systematically reviews the acoustic recording and reproduction techniques that can be adopted in soundscape research, respectively. In Section 4, application of spatial audio in virtual and augmented Reality for soundscape design is discussed. Lastly, the degree of perceptual accuracy of the recording and reproduction techniques in representing acoustic environment is addressed in the discussion.
# A Bayesian Nonparametric Learning Approach to Ensemble Models Using the Proper Bayesian Bootstrap
## 2 Bayesian Nonparametric Learning Using the Dirichlet Process  
The aim of the Bayesian nonparametric learning, introduced by [12; 20], is to estimate the data generative process without limiting the family of involved probability density functions to the one with finite-dimensional parameters. Different prior distributions are available in literature both parametrics and nonparametrics; in our approach the Dirichlet process ([21]) is adopted.  
A Dirichlet process prior with parameter \(\alpha=kF_{0}\) is described by two quantities: a baseline distribution function \(F_{0}\), which defines the \"center\" of the prior distribution, and a non negative scaling precision parameter \(k\), which determines how the prior is concentrated around \(F_{0}\). It is well known that the Dirichlet process is conjugate ([21]): given a random sample \(x_{1},\ldots,x_{n}\) from \(F\sim DP(\alpha)\), the posterior distribution is again a Dirichlet process:  
\[F|X\sim DP((k+n)G_{n}) \tag{1}\]with  
\[G_{n}=\frac{k}{k+n}F_{0}+\frac{n}{k+n}F_{n} \tag{2}\]  
The parameter of the Dirichlet process, given the data, is then a convex combination of the prior guess \(F_{0}\) and the empirical distribution function \(F_{n}\). If \(k\to 0\) then a non informative prior on \(F_{0}\) is considered. If \(k\to\infty\) the parameter of the posterior Dirichlet Process is reduced to \(F_{0}\).  
In this case it is easy to compute the posterior distribution, by simple updating the parameters of the prior distribution. This important property allows to derive easily Bayesian nonparametric estimation of different functional of F, such as the mean, median and other quantiles.  
It has to be noticed that infinite computational time is required to sample from the posterior distribution. Since \(F_{0}\) is continuous, in order to approximate the posterior distribution, bootstrap techniques can be applied as explained in the next Section.