esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Density and Hardness  
Figure 7 reports on the density of the raw materials and the rotomolded samples produced by dry blending and melt blending. The GTR density (NRR = 1.169 g/cm\({}^{3}\) and RR = 1.246 g/cm\({}^{3}\)) is higher than the neat LDPE density (0.910 g/cm\({}^{3}\)) and superior to the other tested compounds. However, the density of the GTR/LDPE compounds decreases at first due to bubble formation, interfacial gaps, and voids created between two phases. In the period following, the value increases with increasing GTR content for both types of compounds (dry and melt blending). Nevertheless, it can be observed that the regeneration process did not affect the compound density as the values are close (within experimental uncertainty) for both LDPE/NRR and LDPE/RR compounds. Also, a similar trend for both blending methods is observed  
Hardness data for the rotomolded parts (Shore A and Shore D) are shown in Figure 8 for both the dry blending and melt blending methods. As expected, due to the elastic nature of the rubber particles, it is observed that hardness decreases with increasing GTR content in both methods [28]. Based on Figure 8, the regeneration process can decrease the rigidity of samples due to the presence of oil in the RR structure, but again, a limited difference in hardness is observed here.  
Figure 6: SEM images of the rotomolded samples with different concentrations of nonregenerated rubber (**b–d**) and regenerated rubber (**e–g**) compared with neat LDPE (**a**) for melt-blended samples.  
Figure 7: Density as a function of GTR content. (**a**) dry blending and (**b**) melt blending.
## 2 Experimental Arrangement  
### Vacuum Chamber, Instrument and Procedure  
All experiments have been performed in the cryogenically pumped New Experiments on Electric Thrusters (NExET) vacuum chamber. It is a 1.8 \(\times\) 0.8 m stainless steel vessel. The overall pump stack warrants a background pressure as low as \(2\times 10^{-5}\) mbar-Xe during operation of a 200 W input power plasma source. The grounded vacuum chamber is used as potential reference for all experiment series. A schematic of the experimental set-up is drawn in Figure 1. The Faraday cup is attached to an aluminum structure designed to automatically align the probe with the thruster equatorial plane. The mechanical frame is mounted on a URS1000 BCC motorized rotation stage and controlled by a SMC100 unit provided by Newport. An in-house computer program was used to interface with the controller, record and save data. The pivot point of the rotating structure is aligned with both the thruster axis and exit plane. The system allows for a 180\({}^{\circ}\) scan on the horizontal plane with respect to the thruster. Probe alignment with the EP device is made using a laser system. The thruster centerline is referred to as the 0\({}^{\circ}\) angular position of the probe. The distance between the FC collimator entrance and the thruster exit plane (\(R\)) is here limited by the vacuum chamber diameter. Consequently, \(R\) is fixed at \(27.4\pm 0.2\) cm (\(\sim\)6 thruster mean channel diameters). The far-field plume is usually defined as the region where \(R\) is greater than four thruster diameters [17]. In the far-field plume domain the thruster is assumed to be a point ion source [25; 33; 34]. The whole probe system is electrically connected to the common ground reference.  
A calibrated Keithley 2410 sourcemeter in voltage source mode has been used to measure the ion current collected by the Faraday cup. A TTI-EX752M dual output 300 W power supply that can deliver up to \(\pm\)75 V in single output mode is used to define the desired potential on the collimator. Both instruments are referenced to ground.  
A standard procedure has been followed for each measurement. The different steps are listed in Table 1. For all measurement series, the rotation of the arm that supports the FC always follows the same cycle: start at \(-90^{\circ}\), angular sweep up to \(+90^{\circ}\) and back to \(-90^{\circ}\) the rest position. Here the angle refers to the FC angle. The angular step size is 1\({}^{\circ}\).  
Figure 1: Schematic (not to scale) of the experimental set-up for fare-field plume measurements of the ISCT200 Hall Thruster. The probe is aligned with the thruster centerline at 0\({}^{\circ}\).
# Blind Image Quality Assessment Based on Multi-Window Method and HSV Color Space
###### Abstract  
To improve the evaluation accuracy of the distorted images with various distortion types, an effective blind image quality assessment (BIQA) algorithm based on the multi-window method and the HSV color space is proposed in this paper. We generate multiple normalized feature maps (NFMs) by using the multi-window method to better characterize image degradation from the receptive fields of different sizes. Specifically, the distribution statistics are first extracted from the multiple NFMs. Then, Pearson linear correlation coefficients between spatially adjacent pixels in the NFMs are utilized to quantify the structural changes of the distorted images. Weibull model is utilized to capture distribution statistics of the differential feature maps between the NFMs to more precisely describe the presence of the distortions. Moreover, the entropy and gradient statistics extracted from the HSV color space are employed as a complement to the gray-scale features. Finally, a support vector regressor is adopted to map the perceptual feature vector to image quality score. Experimental results on five benchmark databases demonstrate that the proposed algorithm achieves higher prediction accuracy and robustness against diverse synthetically and authentically distorted images than the state-of-the-art algorithms while maintaining low computational cost.  
blind image quality assessment; multiple normalized feature maps; spatial correlation; difference map; HSV color space +
Footnote †: journal: Applied Sciences
## 5 Simulated Jamming Results Analysis
### High jamming Scenario
#### 5.2.1 First Trajectory
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{4}{c}{**Horizontal**} & \multicolumn{4}{c}{**Vertical**} \\ \cline{2-7}
**Combination** & **RMSE** & **Mean** & **Max** & **RMSE** & **Mean** & **Max** \\ \hline GPS DF & 45.243 & 10.621 & 344.106 & 132.595 & 25.015 & 1026.897 \\ \hline GPS TF & 3.492 & 3.261 & 28.611 & 5.701 & 2.732 & 94.982 \\ \hline GPS TF + Galileo SF & 4.045 & 3.377 & 48.045 & 10.212 & 3.474 & 158.456 \\ \hline GPS TF + Galileo TF & 1.670 & 1.333 & 8.904 & 2.809 & 1.799 & 13.211 \\ \hline GPS + Galileo TF + GLONASS SF & 2.179 & 1.649 & 18.975 & 4.324 & 2.271 & 47.251 \\ \hline GPS TF + Galileo TF + GLONASS DF & 1.300 & 1.110 & 3.347 & 3.166 & 2.274 & 11.363 \\ \hline \end{tabular}
\end{table} 
Table 4: RMS error, mean, and Max positioning errors with high jamming [first trajectory]. Figure 15: (a) Vertical positioning error for different GNSS signal combinations [first trajectory]. (b) Zoomed view of vertical positioning error for different GNSS signal combinations [first trajectory].
## 3 Results
### Results and Experimental Setup
#### 3.2.3 Molecular Property Prediction  
The evaluation of molecular property prediction was conducted by comparing the performance of the NPVAE model with the state-of-the-art (SOTA) model, UniMol, on the QM9 dataset [59; 63; 64]. The UniMol model is a deep learning model based on the SE(3) Transformer architecture for 3D molecular representation, and the trained 3D molecular representation model has been utilized to perform various downstream tasks, including molecular property prediction [65]. In its original study, the UniMol model was developed to predict molecular properties, including HOMO, LUMO, and the HOMO-LUMO gap. The UniMol model was fine-tuned for 10 epochs on the electrolyte dataset using the pre-trained model released by the authors, and its predictive performance was evaluated on the test set. Likewise, the NPVAE model was also trained separately for HOMO and LUMO, and its predictive performance was evaluated on the test set. The HOMO and LUMO prediction performance of the NPVAE model on the electrolyte dataset was then compared with that of the UniMol model (see Table 3). The HOMO and LUMO values obtained through DFT calculations were used as the ground truth, and the Mean Absolute Error (MAE) of the predictions from both the UniMol and NPVAE models was computed.  
The 'Test Molecules' column represents the number of molecules in the test set for each dataset. These generated molecules (\"Generated) were sampled from the latent space of the trained NPVAE model. Since \"Generated did not have HOMO and LUMO values, DFT calculations were conducted to obtain the HOMO/LUMO ground truth values. The  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Metric** & **Value** \\ \hline PBValid & 0.995 \\ Validity & 1.000 \\ Novelty & 1.000 \\ Frag Similarity & 0.445 \\ Scaffold Similarity & 0.239 \\ IntDiv & 0.869 \\ SA Score & 0.4253 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation metrics of the generated molecules.  
Figure 6: Histogram of the HOMO, LUMO, and molecular weight distributions of the generated molecules.  
NPVAE model trained on the electrolyte dataset predicted the molecular properties of the *Generated, and these predictions were compared with the DFT-derived HOMO/LUMO ground truth values to compute the Mean Absolute Error (MAE).  
Table 3 presents the results of molecular property prediction, where the NPVAE model, fine-tuned on the electrolyte additive dataset, showed the lowest performance in predicting LUMO properties with a Mean Absolute Error (MAE) of 0.00158 for 1000 molecules in the test set. Furthermore, excluding the generated molecules, the molecules with high reliability that were already present in the dataset demonstrated relatively lower error rates for LUMO properties compared to HOMO properties. This also suggests that, as observed in Figures 4 and 5, the latent space for LUMO is better clustered compared to HOMO.
# Wireless Caching: Making Radio Access Networks More than Bit-Pipelines
## 5 Pricing: Creating Incentive for Caching
### Pricing Radio Resources, Memory, and Privacy  
To fully unlock caching gains, VNOs should be allowed to control the physical layer directly. In particular, a RAN sells its radio resources to VNOs and lets a VNO decide how to use its bought power and spectrum, etc. In this case, VNOs have more freedom and incentive to optimize the SE or EE. A VNO may reduce its energy cost by lazy or opportunistic scheduling, or utilize cheap or even free harvested energy. Physical-layer multicasting can be exploited to push popular files to multiple users without additional spectrum and energy costs.  
The memory cost should be considered in pricing. Intuitively, the hit ratio is increased if a user allocates more memory for caching, but more memory means a higher device cost paid by this user. Accordingly, users who are willing to contribute more memory to cache more data should be rewarded e.g., by offering them some discount, as noted previously. Pricing also holds the promise of advocating data sharing that is required by the request prediction. Sharing a user's historical requests or social connections, however, is risky in terms of sacrificing her or his privacy. Being concerned with one's own privacy, a user is usually unwilling to share personal data for request prediction. As a result, users who agree to upload private data should be rewarded. Moreover, serving different users may incur different costs. It is easier to predict the request of a user with low demand uncertainty, which means greater caching gain. Further, users with highly common interests are more likely to be served by multicast pushing. Hence a VNO will tends to reward those users who are interested in commonly popular content.  
Figure 5: Pricing for Coded Caching: An Effective Throughput Analysis [39].  
Sharing the infrastructure, each VNO will announces its own pricing and reward policy. Each user then will chooses her or his favorite VNO based on the willingness of sharing private data, memory allocation, and her or his own preferences. In this context, mechanism design needs more quantitative study from a game-theoretic perspective.
# Design of a Device Based on an LED Matrix for Water Sterilization
## 3 Results
### The Influence of the Different LED Types on E. coli Viability  
Under static conditions, all tested devices demonstrated the ability to completely sterilize the material, albeit with varying efficiencies. LED 1 required 180 min for the total elimination of viable bacteria, while LEDs 4, 5, 7, and 9 required 40 min and LED 3 required 20 min. LEDs 2, 6, and 8 exhibited the highest effectiveness, achieving sterilization in 10, 5, and 7 min, respectively (Figure 2). When tests were conducted under stirring conditions, the efficiency of the devices improved across the board. LED 1 achieved 100% mortality in 120 min, LED 4 in 40 min, and LED 2 in 2 min. LEDs 3, 5, 6, 7, 8, and 9 accomplished sterilization in less than 1 min (Figure 2). Based on its exceptional antibacterial activity under both static and stirring conditions, LED 2 was selected for further studies. Additionally, its relatively low cost (EUR 2.22/unit) positioned it as a highly competitive option for application in prototypes designed for disinfecting large volumes of water.  
Figure 2: Influence of the different LEDs tested on _E. coli_ viability. (**A–I**) Results of LEDs 1 to 9, respectively. Tests were conducted under static conditions (black, solid lines) or with stirring (gray, dashed lines). All tests were performed in triplicate.
# An Ensemble Stacked Convolutional Neural Network Model for Environmental Event Sound Recognition
## 3 DS-CNN: Sound Recognition Model Based on CNN and DS Evidence Theory
### Network Architectures  
#### 3.1.1 Feature Learning Module in RawNet  
The feature learning module in RawNet (Figure 1a) contains two convolutional layers (before the pooling layer) to extract features from the raw waveform input with two dimensions. Each convolutional layer with filters of small receptive fields is functionally similar to a bandpass filter bank. Each layer has 40 filters, generating a two-dimensional vector, which can be thought of as the components that we extract from a log-scaled mel-spectrogram input. It covers the audible frequency range (0-22,050 Hz) of the sound segment (about 1 s). Here, each filter corresponds to a frequency characteristic. The receptive field is set as (1,8) in both convolution layers, and the stride is set as (1,1) in time series. The small filter size is set according to the experimental results of EnvNet [5], which demonstrated that the CNN model can extract local features of various time scales hierarchically by using multiconvolutional layers with small receptive fields. The input shape and the parameters of the first two convolutional layers are as in Table 1.  
The output of these two convolutional layers is noted as the \"time-frequency\" feature representation. We then apply no-overlapping max pooling to the output of the convolutional layers with a pooling size of 128. Then, the output of the pool matrix is of size 1 \(\times\) 160 \(\times\) 40 (frequency \(\times\) time \(\times\) channel), which is then reshaped to 40 \(\times\) 160 \(\times\) 1 in order to convolve them in both frequency and time. Finally, the three-dimensional matrix 40 \(\times\) 160 \(\times\) 1 is fed into the convolutional layer \"conv3\" to do classification.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Layer** & **Input Shape** & **Filter** & **Kernel Size** & **Stride** & **Output Shape** \\ \hline Conv1 & [batch,1,20480,1] & 40 & (1,8) & (1,1) & [batch,1,20480,40] \\ Conv2 & [batch,1,20480,40] & 40 & (1,8) & (1,1) & [batch,1,20480,40] \\ Pool & [batch,1,20480,40] & 40 & (1,128) & (1,128) & [batch,1,160,40] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the feature learning module in RawNet.  
Figure 1: The overall framework of the Dempster-Shafer sound recognition system. (**a**) shows the RawNet feature learning module; (**b**) shows the MelNet log-mel feature extraction module; (**c1**) shows the recognition module in MelNet; (**c2**) shows the recognition module in RawNet.
# _remote sensing_
## 4 Satellite Architecture and System Budgets
### Power Budget
As obvious, each peculiar case shall be considered separately, by taking into account the satellite power consumption before planning the actual operations, in order to reduce the depth of discharge to preserve an extended lifetime of the spacecraft EPS.
## 5 Current Licensed Vaccines for Viral Diseases
### Current Licensed Vaccines for Parasitic Diseases  
Parasitic diseases can have devastating effects on the aquaculture industry, and these effects are not limited to monetary and economic losses. If not handled properly and eliminated, the consumption of raw diseased fish can cause infections in other organisms, and even humans.  
There is only one commercially licensed vaccine to treat a parasitic infection (a vaccine against sea lice manufactured by Aquatec) [153; 154]. This is because very little is understood about the immune response to parasites. Parasitic diseases invade host cells and feed off of the host to obtain nutrients for growth, reproduction and transmission. Additionally, parasites have mechanisms in place to prevent and cope with the piscine immune response.  
These include parasitic migration, wherein the parasite migrates to host sites where the immune response has not reached or is weak, anti-immune mechanisms, which allow them to resist innate humoral factors, and immunodepression, where parasites induce apoptosis of the host's leukocytes [155]. However, modern science has made advances in understanding parasitic infections. Neutrophils have been found to play a crucial role in defending against parasites, MC degranulation has been observed in several parasitic infections, and eosinophilic granule cells have been found to release various tryptages, lysosomes and antimicrobial peptides at the site of parasitic invasion [144; 156; 157; 158]. The present literature review focuses on investigating ectoparasites and their proteomic expression to determine vaccination mechanisms and methods. For instance, in _Tetracapsuloides bryosalmonae_, known to cause polycystic kidney disease, the activation of a micro-exon gene Tb-MEG1 has been found to avert immune subversion [159; 160]. An anti-Tb-MEG1-based DNA vaccine has been shown to activate IgM responses against the infection [153]. Additionally, the injectable, commercially available vaccine produced by Aquatec utilizes a synthetic subunit antigen along with the keyhole limpet hemocyanin (KLH) to elicit protection against sea lice [161; 162]. Studies investigating _I. multifilis_ have found successful expression of a DNA vaccine using lag52B in rainbow trout and channel catfish, and the recurrent theme in vaccine trials against this parasite seems to be the use of 48 kDa immobilization antigens [163; 164].  
Overall, parasitic vaccines rely on DNA and subunit-based vaccines through intraperitoneal injection [153]. It is important to note the discrepancy in the number of vaccine trials carried out on ectoparasitic diseases in comparison to endoparasitic diseases. Little is known about the genetic composition and protein expression of myxozoans, which play a role in pathogenicity of the fish host. An analysis of their microbiology and DNA composition will facilitate the isolation of proteins directly involved in pathogenesis and aid in developing vaccines.
## 4 Empirical Study
### Goal  
Software defect prediction using machine learning approaches has been investigated in previous studies with a focus on using the default hyperparameter configuration. However, recent studies have reported the impact of hyperparameter optimization on the performances of the classifiers. [PERSON] et al. [5] have pointed out the need for exploring hyperparameter optimization in ensemble learning. The main objective of our experiment was to investigate to what extent hyperparameter optimization would impact the prediction performances of ensemble models.  
Using the Goal-Question-Metric (GQM) template [33], we defined the goal of this experiment as: **evaluating** fine-tuned tree-based ensembles and stacking ensemble models of fine-tuned tree-based ensembles made for the **purpose** of software defect prediction, with **respect** to their detection performance measures (F-measure, AUC and Brier), and comparing them with tree-based ensembles with default hyperparameters from the **perspectives** of researchers and practitioners within the **context** of 21 software defect datasets obtained from different domains. The following research questions were formulated to attain our objectives:  
* **RQ 1.** To what extent does hyperparameter optimization increase the prediction performance of an ensemble?
* **RQ 2.** To what extent does stacking generalization of fine-tuned tree-based ensembles increase defect prediction?  
An overview of the steps to conduct the experiment is shown in Figure 1. First, we selected defect datasets. Then, we used an optimization method to optimize the hyperparameters of the tree-based ensembles. Next, we constructed the ensemble models (tree-based and stacking ensembles). After that, we validated each model and measured the prediction performance. Finally, we estimated the effect size and ranked the ensembles based on their performances. The following subsections describe each step.
# Potential and Limitations of the New European Ground Motion Service in Landslides at a Local Scale
## 4 Discussion
### Potential and Limitations of the EGMS
This approach would provide a group of suitable PS to detect and monitor ground motion related to landslide activity [75; 76; 77]. Finally, another monitoring way for future research on La Miera and similar landslides could be the application of a global navigation satellite system [78] or conventional topographic techniques [79]. These techniques can be useful to determine point displacements in the surrounding and into the landslide area and, therefore, to improve the characterization of the activity of the La Miera landslide, with all its implications. Finally, this work shows that, in those areas where EGMS data indicate deformation, but no in situ instrumental recording is available, the geomorphological approach, together with rainfall records and damage inventory, can be a good alternative to confirm or reject the EGMS data. The geomorphological techniques and building damage inventory would provide spatial validation of the movement, while the precipitation record would provide temporal validation.