esa-sceva/satcom-chunk-collection huggingface dataset:
# Enabling Trustworthy Multicast Wireless Services through D2D Communications in 5G Networks
## 6 Conclusions  
In this paper, we have proposed a protocol, called eCMS-SD2D (enhanced CMS with secure D2D communications), to perform a secure transmission of a multicast service while also efficiently utilizing network resources of the forthcoming 5G network. In doing this, we use mechanisms as signature and encryption in order to increase the data protection and the privacy when nearby devices aim at transmitting each other through proximity-based links (e.g., D2D). The outcome of our performance evaluation shows that, with the proposed approach, it is possible to decrease the data loss caused by malicious users (considered in the reference scenario) by guaranteeing, at the same time, a reasonable data-rate to the users within the multicast group during a radio transmission.  
Conceptualization, S.P., C.S. and G.A.; Investigation, S.P. and C.S.; Software, C.S.; Validation, S.P. and G.A.; Writing--original draft, S.P. and C.S.; Writing--review & editing, G.A., L.M., A.O., A.M. and A.I.  
The authors declare no conflict of interest.
## 3 Exploring Speaker-Independent Dysarthric Speech Recognition
### Datasets and Data Split
#### 3.2.3 Dysarthric Speech  
Dysarthric speech from the six dysarthric datasets serves as the dysarthric training data. A five-fold speaker cross-validation is implemented in the training, dividing dysarthric speakers in each dataset into five subsets. For each fold, speakers from four subsets are  
\begin{table}
\begin{tabular}{l l l r r r r r r} \hline \hline \multirow{2}{*}{**Name**} & \multirow{2}{*}{**Language**} & \multirow{2}{*}{**Content**} & \multicolumn{3}{c}{**Dysarthric Part**} & \multicolumn{3}{c}{**Canonical Part**} \\ \cline{3-10}  & & & **Length** & **\#Spk.** & **\#Utt.** & **Length** & **\#Spk.** & **\#Utt.** \\ \hline
**Torgo**[54] & English & Word, Sentence & 3.0 h & 8 & 3.1 k & 4.6 h & 7 & 5.9 k \\
**UASPEECH**[55] & English & Word & 10.2 h & 16 & 12.0 k & 4.9 h & 13 & 9.9 k \\
**Domotica**[56] & Dutch & Sentence & 8.8 h & 17 & 4.2 k & N/A & N/A & N/A \\
**COPAS**[57] & Dutch & Word list, Sentence & N/U & N/U & N/U & 4.5 h & 131 & 1.8 k \\
**PC-GITA**[58] & Spanish & Word, Sentence & 1.0 h & 50 & 1.8 k & 1.0 h & 50 & 1.8 k \\
**EasyCall**[59] & Italian & Word, Phrase & 8.2 h & 31 & 11.3 k & 4.0 h & 24 & 10.1 k \\
**SSNCE**[60] & Tamil & Word, Phrase & 5.5 h & 20 & 7.3 k & 2.9 h & 10 & 3.7 k \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of dysarthric datasets used in the experiment. (N/U: Not used in this work; N/A: not available; #Spk, #Utt: number of speakers, number of utterances.)used as source speakers for training and validation, whereas the remaining subset serves as the target speakers used for evaluation (testing). Training data that included all source speakers across the six datasets are labeled as **D**. If the training data only included source speakers from the same dataset as the target speakers, they are referred to as **D_indm**.  
For the **Torgo** dataset, which contains only eight dysarthric speakers, we conducted a four-fold cross-validation, dividing speakers into four subsets and using three subsets for training and one for evaluation in each fold. When using the training data from multiple dysarthric datasets (**D**), we maintained five folds, but in the fifth fold, the same training speakers as the fourth fold were used for **Torgo**. To avoid evaluation overlap, the remaining **Torgo** speakers were excluded from evaluation in that fold.  
For each source dysarthric speaker, 90% of the recordings were randomly selected for training, while the remaining 10% were used for validation. In the **UASPEECH** dataset, block 1 and 3 were used for training, and block 2 was for validation.
# A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models
## 5 Fair Editions of Ensemble Models
### GAFairC: Group AdaBoost with Fairness Constraint  
The authors of [27] proposed Group AdaBoost with Fairness Constraint (GAFairC), a modified version of AdaBoost that optimizes a group-aware loss function while enforcing fairness constraints. This study employs equalized odds, a widely used fairness metric, to assess model fairness. The proposed modifications to the classical AdaBoost algorithm include three key components: Rectified Fairness Penalty, which introduces a penalty term in the loss function that activates only when unfairness is detected, preventing unnecessary constraints; Penalty Intensity Selection, a dynamic approach that adjusts the intensity of fairness constraints using a greedy search algorithm; and post-pruning, which ensures that the final ensemble model strictly adheres to the fairness constraint while maximizing accuracy. The authors evaluated their approach on five standard benchmark datasets--Bank, KDD, Compas, Credit, and Adult--and compare its performance against methods such as AdaBoost and AdaFair, among others. The results demonstrate that GAFairC achieves superior fairness (lower equalized odds) while maintaining or improving accuracy compared to existing approaches.
# A Review of Blast Loading in the Urban Environment
## 4 Experimental Methods and Instrumentation  
### Imaging Techniques  
#### 4.1.1 Three-Dimensional Imaging  
[PERSON] and [PERSON] [120] used background-orientated schlieren (BOS) to reconstruct three-dimensional shock waves using multiple high-speed cameras pitched at varying viewing angles. BOS is a detection method that allows for the visualisation of shock waves and is termed a refractive imaging technique. The increased density at a shock wave boundary increases the refractive index of a given fluid at that location. Using a suitable background with high spatial frequency and contrast makes it possible to capture a distortion against the background using a high-speed camera. Therefore, a shock front's subsequent location can be determined by identifying this distortion's location. There are numerous approaches to processing these images; most use a reference image and a \"distorted image\", followed by a type of comparison. BOS is described as an _\"effective method to visualise explosively driven shock waves\"_. Two common methods include image subtraction and image correlation, both of which have been widely used or discussed in other sources, e.g., [121; 122; 123]. Alternatively, optical flow tracking velocimetry methods [124] may be used.  
[PERSON] and [PERSON] collected a series of images using four high-speed cameras using image subtraction processing techniques to form BOS images. The development of an automatic point detection algorithm allowed for the location of shock waves to be determined efficiently. The algorithm's point detection capabilities were tested against a \"careful\" manual detection and were shown to achieve good accuracy. As a result, it was revealed that these techniques could define the three-dimensional shape and size of a given shock wave. Whilst this technique could track both spherical and asymmetric shock waves, its feasibility was only tested within the boundaries of a simple scenario. When used within a scaled experiment for an urban setting, the inherent complexity from numerous shock reflections may prove more challenging to capture. Additionally, the careful placement of cameras would need to be considered to minimise or eliminate potential blind spots.
# Continuous Autonomous Ship Learning Framework for Human Policies on Simulation
## 4 Experiment
### Experiment: Simple Traffic 1(Marine Traffic Environment for Inward Ships)  
After learning the avoidance of dynamic obstacles, two RL algorithms were implemented and compared in the new environment. The new environment had neighboring ships following the right path to comply with human-defined navigation rules in the harbor that were not randomly created. The experiment result is shown in Figure 10.  
Figure 8: (**a**) Learning through two cases of neighboring ships entering harbors from two adjacent coastal areas. Cases are run through the case-based curriculum system. (**b**) As an example of operation in a normal port, incoming neighboring ships enter the port along the right path. (**c**) Convergence of learning from approximately three million steps, stabilizing after five million steps (approximately one learning scenario—no. of learning steps/700).  
Figure 9: _V(s)_ graph evaluated by critic during learning: (**a**) Random RL and (**b**) ILFAS RL. Random RL graph looks sharper because of the long learning time. Nevertheless, the ILFAS graph shows a quick convergence even in a small episode. The graph converging in a short learning time demonstrates that transfer learning is properly implemented. (Unit: value loss/learning steps.).  
The learning results in Section 4.2 were applied to this experiment. Although the successful arrival rates to a destination were similar (90% in 100 episodes), the ILFAS showed a slightly high success rate. The ILFAS learned the intrinsic motivation that the right path was safe; therefore, it avoided neighboring ships encountered. Random RL did not know the navigation rules of neighboring ships entering the harbor using the left path; hence, it selected the right path as the avoidance action. However, both learning methods showed significant differences in controlling autonomous ships. As shown in Figure 11, the ILFAS is stable in controlling the ship; however, the control by the random RL fluctuates substantially.
# Remediation of Metal/Metalloid-Polluted Soils: A Short Review
## 4 Removal Techniques
### Chemical Processes
#### 4.2.4 Chemical Soil Washing  
Chemical soil washing permits the elimination of heavy metals/metalloids from contaminated soils by extraction of the pollutants followed by a reaction to produce insoluble compounds. Leaching needs an extractant solution, which can be water, chelators, inorganic and organic acids, or surfactants, also containing the reactant that will permit the production of metal hydroxides, sulfides, carbonates, and phosphates. The derived solid particles can be separated via sedimentation or filtration at the end of the process. The description of the chemical soil washing method is reported, for example, by [PERSON] et al. [131], who underlined how this technique permits to reduce the most mobile metals. The drawback is the chance to destabilize some strongly bound fractions, compromising the effectiveness of the process itself.  
Several agents have been studied. The most-used chelator is ethylenediaminetetraacetic acid (EDTA), which is excellent for the process since it has a strong extraction capacity, but it may cause secondary pollution [83]. EDTA is poorly biodegradable and persistent in soil, and is damaging to soil functions. For this reason, more environmentally friendly agents have been tested.  
[PERSON] et al. [83] used ethylenediamine tetra (methylene phosphonic acid) (EDTMP) and polyacrylic acid (PAA) for soil washing, and tried to optimize operative conditions and reduce ecological risks and toxicity. The same was done by [PERSON] et al. [84] who studied four types of acids that are less toxic and more biodegradable than EDTA, namely iminodisuccinic acid (ISA), glutamate-N,N-diacetic acid (GLDA), glucomonocarbon acid (GCA) and polyaspartic acid (PASP). They demonstrated that these acids are more biodegradable but less effective than EDTA. [PERSON] et al. [85] coupled soil washing with soil stabilization to improve results. They tested the addition of some reactants (lime, biochar, and carbon black) to reduce the bioavailability of residual metals, and these agents caused less damage to soil microbial communities.  
Chemical soil washing is an efficient, rather fast, and widely used method. The main drawbacks are damage to soil properties and the creation of secondary pollution due to the presence of chemical agents.
## Methods
### Simulation
#### 3.4.3 Synthetic Tree Detections  
Simulated ALS point clouds were generated to derive synthetic tree detections. Like [PERSON] et al. [7], we assumed vegetation surfaces impervious to the laser beam, a nadir orientation, and an infinitely small footprint. The point clouds (Figure 3) were created by modeling the light crown of each simulated tree according to relationships published by [PERSON] [41].  
A tree crown is defined by the tree species-specific parameters \(b\) and \(c\) (see Table 2), the tree height \(h\), crown base height \(cbl\), and its crown radius \(r\) (all provided by the _Waldplaner 2.0_ software, [38]). The height above ground \(e_{i}\) of a point with distance \(d_{i}\) to the tree location less than or equal to \(r\) is calculated by Equation (2). Please note that since the parameters of the Douglas fir were not given by [PERSON] [41], these were assumed to be similar to the parameters of the Norway spruce.  
\[e_{i}=h-c\cdot(h-cbl)\cdot\left(\frac{d_{i}}{r}\right)^{\frac{1}{b}} \tag{2}\]  
For each plot, we generated uniquely-distributed xy-coordinates with the same pulse density as the original point cloud. For each point, the z-coordinate was calculated by applying Equation (2) for all trees and adding the value of the DTM (of the original ALS point cloud) at the tree location. Finally the point is linked to the tree which resulted in the maximum z-coordinate and--for a subsequent evaluation--it is labeled with the ID of that tree.  
Since the use of crowns shape functions might result in well-shaped crowns, which would allow an unambiguous co-registration, we simulated irregularities by adding residuals. After a coarse manual optimization (with the aim of realistic tree detection characteristics), we chose normally-distributed horizontal residuals with a standard deviation of 1.5 m and gamma-distributed vertical residuals with a shape of 3 and a scale of 0.3 m.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Species** & \(\mathbf{c}\) & \(\mathbf{b}\) \\ \hline _Quercus petraea_ (sensile oak) & 0.50 & 0.50 \\ _Fagus silvatica_ (European beech) & 0.40 & 0.33 \\ _Abies alba_ (European fir) & 0.50 & 0.50 \\ _Alnus gluttinosa_ (European alder) & 0.56 & 0.50 \\ _Pica abies_ (Norway spruce) & 0.66 & 1.00 \\ _Pseudotsuga menziesisi_ (Douglas fir) & _0.66_ & _1.00_ \\ _Pinus silvestris_ (Scots pine) & 0.64 & 0.50 \\ _Larix decidua_ (European larch) & 0.80 & 0.45 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Tree species-specific light crown model parameters according to [PERSON] [41]. Values indicated in italics were based on assumption, since they were not given by [PERSON] [41]. Reproduced with permission from Eugen Ulmer Verlag, 2017.  
Finally, the tree detection method presented in Section 3.3 was applied to each synthetic point cloud. To allow an accuracy assessment, each detected tree was labeled with its tree ID taken from the point cloud. If a tree was detected multiple times, only the detection with the tallest z-coordinate was labeled.
## 4 Experimental Evaluation
### Results and Analysis
#### 4.3.2 Effect of Each Component  
Compared to the previous work, our approach incorporates self-training to leverage unlabeled data and feature extractor to extract information in a pseudo-lexicon. Thus, we do ablation studies on the 10% training set in order to confirm the validity of these alterations on the final results. Separate comparisons were conducted for the results obtained after removing different modules, as illustrated in Table 5.  
**Self-training.** We use the self-training framework to leverage both labeled data and unlabeled data. The result shows the F1-score, F1-score (all geological words), and F1-score (complex geological words) drops of 0.13%, 3.3%, and 8.32%, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **F1-Score** & \
\begin{tabular}{c} **F1-Score** \\ **(All Geological Word)** \\ \end{tabular} &
\begin{tabular}{c} **F1-Score** \\ **(Complex Geological Word)** \\ \end{tabular} \\ \hline Conv-SEC & 80.63 & 66.70 & 63.11 \\ BERT & 90.67 & 82.02 & 81.23 \\ ZEN & 90.45 & 79.84 & 80.44 \\ WMSEG & 91.1 & 81.91 & 78.78 \\ WeiDC & 90.94 & 80.52 & 78.18 \\ GeoCWS & **91.13** & **85.57** & **90.32** \\ \hline \hline \end{tabular}
\end{table}
Table 4: The experimental results [%] of comparing baselines and our method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **F1-Score** & \
\begin{tabular}{c} **F1-Score** \\ **(All Geological Word)** \\ \end{tabular} &
\begin{tabular}{c} **F1-Score** \\ **(Complex Geological Word)** \\ \end{tabular} \\ \hline GeoCWS & 91.13 & 85.57 & 90.32 \\ w/o self-training & 91.00 & 82.27 & 82.00 \\ w/o pseudo-label & 91.07 & 83.30 & 85.38 \\ selection & 90.34 & 85.34 & 88.80 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The effectiveness of our model after deleting certain components.  
**Reliable pseudo-label selection.** The reliable pseudo-label selection is designed to reduce the noise present in pseudo-labels. When removing the reliable pseudo-label selection, the performance of the F1-score, F1-score (all geological word), and F1-score (complex geological word) declines by 0.06%, 2.27%, and 4.94%.  
**Feature extractor.** We use the key-value memory networks as the feature extractor to improve CWS performers by encoding pseudo-lexicon information. As seen in the results, the F1-score, F1-score (all geological words), and F1-score (complex geological words) decrease by 0.79%, 0.23%, and 1.52% when we use the output of the encoder directly.  
The results demonstrate that the elimination of any module has an impact on the model's performance.
## 1 Introduction  
High proton concentrations, which define acidic environments, present a particular challenge for unicellular organisms since the protonation of biological molecules can affect their charge, structure, and function, which have potentially damaging consequences for the cell. Therefore, bacterial cells usually have homeostatic and protective mechanisms in place to counteract the inhibitory effects of low pH. Understanding how microbes sense and respond to acid stress is an important goal if we are to successfully control them, prevent infections, and to fully exploit them for biotechnological applications [1]. The goal of this review was to survey the molecular mechanisms that two unrelated well-studied bacteria use to combat the challenges presented by acidic pH.  
We focus on _Escherichia coli_, perhaps the best studied of all bacterial species, which is a Gram-negative rod that belongs to the Gammaproteobacteria, and which typically occurs in nature as a commensal of the gastrointestinal (GI) tracts of mammals, birds, and reptiles. It is a highly diverse species that includes strains capable of causing infections in the GI tract and urinary tract, the latter being frequently associated with medical devices. Recent studies indicate that _E. coli_ can also inhabit soil environments [2; 3; 4; 5]. We also review the acid tolerance mechanisms of _Listeria monocytogenes_,a Gram-positive rod that belongs to the division Firmicutes, which is very well studied for its role as an intracellular food-borne pathogen of humans and cattle. In nature, _L. monocytogenes_ is a saprophyte and is characterized by its tolerance to stresses such as low pH, elevated osmolarity, and bile salts and its ability to grow at refrigeration temperatures [6; 7; 8]. While these bacteria are phylogenetically unrelated, they share the ability to enter the host via contaminated food or water, and therefore both can encounter the gastrointestinal environment, including the extreme acid conditions prevailing in the stomach, as part of their life cycles. Several excellent reviews on the topic of acid tolerance have focused on one species or a number of closely related species [9; 10; 11]. Here we sought to compare and contrast the mechanisms used by these unrelated bacteria with the goal of highlighting common themes as well as individual strategies.
## 3 Results
#### 3.2.2 Measured Data Experiment  
This subsection also uses the measured data to verify the difference between the layover separation method based on separable approximate sparse reconstruction and the L1 norm compressive sensing method based on multi-look. The measured data are from the airborne millimeter-wave dual-baseline InSAR system, as shown in Figure 3, and the scattering points' distribution is shown in Figure 4. Now, the 700 th pixel in the azimuth direction with a distance of 600-800 pixels is intercepted and processed using two methods. The normalized amplitude result after processing is shown in Figure 8, and the height of the separated scatterers is shown in Figure 9. It should be explained that the processing mode for real data in this subsection is the same as that in Section 3.1.2, and Figures 5b and 8a are equivalent, while Figures 6b and 9a are also equivalent. In addition, according to the analysis of the results in Section 3.1.2, the traditional compressive sensing method loses sparsity in the small number of baselines in the airborne InSAR system case, and this subsection mainly discusses the differences between the two methods in terms of processing results between the multi-look compressive sensing method and the multi-look compressive sensing method based on separable approximate sparse reconstruction, while the comparative analysis between the traditional compressive sensing method and the multi-look compressive sensing method based on separable approximate sparse reconstruction can be seen in Section 4.2.  
Figure 8: Normalized amplitude diagram. (**a**) Multi-look compressive sensing method (**b**) Separable approximate sparse reconstruction algorithm. The horizontal axes are heights in the elevation direction with the unit m, and the vertical axes represent pixels from 600 to 800 in the slant range.  
From the results of the two methods, the difference in height of the separated scatterers with the two methods is lower than the theoretical resolution of the airborne InSAR system which shows that the two methods both have certain high resolutions in height direction estimation of layover areas. Additionally, it can be seen from Figure 8 that the multi-look L1-norm compressed sensing method is slightly more sparse than the compressed sensing method based on separable approximate sparse reconstruction. Moreover, compared with the traditional L1 norm compressed sensing method, the scatterer height distribution solved by the algorithm proposed in this subsection is not significantly different, as seen in Figure 9. However, in terms of operation time, the multi-look compressed sensing method takes 684.3 s, while the compressed sensing method based on separable approximate sparse reconstruction takes 128.6 s. It can be seen that the method based on separable approximate sparse reconstruction can effectively reduce the operation time while maintaining the resolution ability of scatterers in layover areas and the accuracy of height direction estimation.
# Pharmaceuticals Removal from Wastewater with Microalgae: A Pilot Study
###### Abstract
Additionally, microalgal biomass is produced, which can be valorized to produce biofuels reducing the costs of the wastewater treatment process and helping to mitigate fuel shortage in Europe [14; 16]. [PERSON] and [PERSON] reviewed the removal efficiencies of pharmaceuticals at the laboratory scale by microalgae [17]. They concluded that the operation of batch reactors at long hydraulic retention time (HRT) is still considered uncertain, and a challenge exists for validating the removal efficiencies of emerging contaminants obtained at laboratory conditions into larger systems using real wastewater. Therefore, this work aimed to assess, for the first time, the removal of pharmaceuticals during the tertiary treatment of urban wastewater using microalgae. The experiments were conducted in a pilot installation at a WWTP under realistic environmental conditions. Different conditions known to affect microalgal productivity and that could thus affect pharmaceutical removal by these microorganisms were tested. These included nutrient concentrations (e.g., nitrogen and phosphorus) and environment temperature and light intensity by performing the experiments in summer and autumn conditions.