esa-sceva/satcom-chunk-collection huggingface dataset:
# Treatment of Biofilm Communities: An Update on New Tools from the Nanosized World
## 5 Classification of Nanoproducts for Antimicrobial Nanotechnology
### Function-Based Classification  
A more function-oriented classification has been recently proposed by [PERSON] [9]. The three proposed groups distinguish between different types of Nanoparticle pharmaceutical Drug Delivery Systems (NDDSs), as the author defines them. This perspective highlights the target of the NDDSs and their multiple functions. The majority of the applications of these systems are not for infectious diseases. However, some examples are present. Types 1 and 2 are drug-loaded NDDSs with at least two different functions (e.g., longevity, targetability, stimuli-sensitivity or cell penetration), the latter differing from  
Figure 5: Damages caused to the bacterial cell by different types of nanoparticles (NPs). Reactive Nitrogen Oxide Species (RNOS). Reactive Oxygen Species (ROS). R-SH schematizes a compound with a thiol (SH) group. RSNO stands for S-nitrosothiol. (With permission, from [84]).  
the former, since it encapsulates more than one drug. Further examples of different multicomponent drug-delivery systems, apart from the ones cited above as combinations of organic and inorganic components that are part of this class if regarded from the point of view of functional classification, are nanoparticles releasing nitric oxide (NO) [95], three-component NPs (silver, chitosan and iodine) [96] and gentamicin-loaded silica hydrogel NPs [97]. The complexity of these nanosystems successfully targeted bacterial pathogens, but the analysis of their effect on biofilm is lacking. The combinations of multiple factors able to tackle bacterial survival results in the substantial merging of some classes belonging to different classification systems. In fact, nanoproducts included in the class in which inorganic and organic components are combined can fit either in type 1 or type 2 NDDSs, based on the number of different components that the nanosystem carries.  
The third group encompasses theranostic NDDSs that conjugate diagnostic and therapeutic purposes. This last group has never been applied to biofilm infection, therefore this group will not be further described.  
The two classifications presented here (composition-based and function-based) are therefore intended to better focus on the nanoproducts characteristics and possible applications and are not necessarily in contrast. Indeed, some points of contact are present.
###### Abstract  
The current study evaluates the performance of three machine learning models--Decision Trees, Random Forest, and Linear Regression--applied to aquaculture data to mitigate risks in aquaculture management. The performances of these models are analyzed and properly demonstrated using metrics including the Mean Squared Error (MSE), R-squared (R\({}^{2}\)), Root Mean Squared Error (RMSE), and Concordance Index (C-index). The Random Forest model achieved the highest prediction accuracy among all machine learning models, followed by Linear Regression and the Decision Trees. The scatter plot for Linear Regression demonstrates good predictive accuracy for mid-range values. However, it shows significant deviations at the extremes, indicating that the model struggles to capture the full range of variability in the data. The bar chart of coefficients pinpoints the variables with the greatest impact on the predictions, providing suggestions for potential areas that can be improved and providing model interpretability. Future work could incorporate more predictive statistics models focusing on improving the models for extreme values by assessing non-linear models, feature engineering methods, and expanding research into less influential variables. The results greatly impact several sections, including aquaculture management, policy-making, and operational strategies, providing valuable insights for stakeholders and decision-makers. Apache Spark was used for data processing and machine learning model implementation; Apache Cassandra was also used for data storage, ensuring efficient large dataset management and SQL tools for structured data handling; Oracle VM VirtualBox for cross-platform virtualization; and Spark Connector was also used.  
**Keywords: machine learning; data mining; algorithms assessment; decision trees; random forest; linear regression; Mediterranean-farmed fish; fish mortality; aquaculture performance metrics; Apache Spark**
## 2 Materials and Methods  
### Experiments  
The description of the lab-scale apparatus utilized for performing the tests (Figure 1) can be found elsewhere [34]. In all the tests, the feed solution was charged in the crystallizer tank and recirculated through the membrane modules. The solvent evaporated at the interface of microporous hydrophobic membranes on the warm (retentate/feed) side, diffused through the pores, and condensed on the opposite (permeate/distillate) side (Figure 1). A pump ensured the counter-current recycle also of the cold permeate stream (constituted by distilled water), the function of which was to condense the water vapor passing across the membrane pores. The driving force of the process was a vapor pressure difference across the membrane which was imposed by a temperature difference across the membrane. Therefore, the feed/retentate line was heated by an ISCO GTR 2000 heater whilst the permeate/distillate side was cooled by an NESLAB RTE 17 refrigerated bath. A crucial requirement for an MCr is to prevent crystal deposition inside the membrane module and on the membrane surface. The recirculation of the feed solution helps in removing crystals eventually deposited and/or grown on the membrane surface.  
In Table 1, the characteristics of the utilized membranes can be found. In both cases, experiments were performed at the same temperature of the feed (34 \(\pm\) 1 \({}^{\circ}\)C).  
Since the crystals appeared every 30 min, a suspension sample of them was extracted carefully and with alacrity. For each test, three different samples were analyzed (the first one at the onset of crystallization, the second one after 30 min, and the third one after 1 h). Particles were extracted, examined visually with an optic microscope (model Axiovert 25, ZEISS, Oberkochen, Germany), and pictures recorded with a video camera module VISIOSCOPE Modular System equipped with an optical head (10/100X) in order to determine the crystal growth rate (\(G\)), which was determined according to the following equations [18]:  
\[\ln(n)=\frac{-L}{Gt}+ln(n^{0}) \tag{2}\]  
and  
\[B^{0}=n^{0}G \tag{3}\]  
where \(n\) is the crystal population density, \(L\) is the crystal size, \(t\) is the retention time, and \(n^{0}\) is the population density at \(L\) equal to zero.
## 4 Conclusions  
This study comprehensively investigates the tribological behavior of 45 steel after intensive quenching and high-temperature tempering treatment, and analyzes in detail the correlation between microstructural and mechanical properties and friction and wear performance. By comparing the conventional CS_QAT process with the new CS_IQAT process, the following conclusions are drawn:  
1. The microstructure of 45 steel obtained by the CS_IQAT process is more detailed and homogeneous, and its hardness and yield strength are significantly increased. The impact toughness is enhanced by nearly 1.9 times compared with the traditional CS_QAT specimens. This improvement significantly optimizes the overall mechanical properties of the material and is expected to significantly extend the service life of the workpiece.
2. In terms of wear resistance, specimens from the CS_IQAT process show lower average coefficients of friction and relative wear rates than those from CS_QAT, especially at high frequencies, where the relative wear rate is only 58 to 72 percent of that of CS_QAT specimens, and the wear surface is much flatter. This finding indicates that the CS_IQAT process can effectively replace the conventional process and significantly improve the wear resistance of the workpiece.
3. Comparison of the two processes revealed that the main wear mechanism shifted from adhesive wear to abrasive wear, and that the effect of abrasive wear on part life became more pronounced as the frequency of testing increased. The revelation of this mechanism is of great significance for the optimization of process parameters and improvement of part design.  
Conceptualization, S.X. and X.Z.; methodology, S.X. and X.Z.; validation, X.Z., Y.F. and Y.L.; formal analysis, X.S.; investigation, S.X.; resources, S.X.; data curation, S.X.; writing--original draft preparation, S.X.; writing--review and editing, X.Z., Y.F. and Y.L.; visualization, X.S.; supervision, Y.F.; project administration, S.X.; funding acquisition, S.X. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Tianchi Program for High-level Talents of Xinjiang Uygur Autonomous Region (100400018).  
Not applicable.  
The datasets generated for this study are available on request to the corresponding author.  
The authors declare no conflicts of interest.
# A Component-Based Model for Novel Modular Connections with Inbuild Component
## 3 Component-Based Model  
### Component Identification  
According to the parameter analysis, the significant factors affecting the mechanical properties of modular connections were the geometric dimension of twin beams and admissible construction errors reserved between the inbuild component and columns. In reference to the classical studies on the component method and the three-dimensional macro model on [PERSON] et al. [29; 30], the model for novel modular connections was built (shown in Figure 30).  
Figure 28: Stress distribution of twin beams with different height.  
Figure 30: Three-dimensional component model for the novel modular connection.  
Figure 29: Mises stress distribution of connections with different height.  
The advantage of the model was that it can distinguish the deformation of intramodule connections and intermodule connections that are shown in Figure 31. The skeleton (a) consists of several horizontal rigid rods and five vertical rigid rods. The plane composed of horizontal rods defines the deformation location of the converging section component (b) deformations. The space formed by vertical rods defines the region of intramodule connections. Converged section components (b) consist of the compression and tension deformations in column main face and side walls. It covers the interaction mechanism between columns and the inbuild component as well. Diagonal springs (c) located between the pair of converged section components represent the shear deformation of the intrajoint panel. Ceiling beams or floor beams were connected to a pair of converged section components through a rigid bar (d). The beam element (e) connected the geometrical center of the upper and lower skeleton to represent the behavior of intermodule connection. It can be concluded from the model that the mechanical properties of global modular connection could be obtained through superposing that of intramodule connections and intermodule connections. Moreover, for each intramodule connection, the key component was the joint panel and converged section component. The model needs to be based on the following assumptions:  
1. The rigid body assumption was made to beam end plate.
2. The allowable construction errors and relative dislocation between the upper and lower column were considered as the mechanical properties of intramodule connections (will be discussed in Section 3.2.3).
3. Based on assumption (2), only rotational deformation will be considered in intermodule connections.
4. The enhancement stiffeners' effect on connections was neglected.  
Figure 31: The component deformation mode of novel modular connections: (**a**) The skeleton; (**b**) Converging section component; (**c**) Diagonal springs; (**d**) Beam connection; (**e**) The beam element (intermodule connection).
# Simulation of Nuclear Quantum Effects in Condensed Matter Systems via Quantum Baths
## 2 The Langevin Equation as a Thermal Bath
### Generalized Langevin Equation (GLE)  
The standard (Markovian) Langevin Equation (4) can be generalized in the following manner:  
\[m\ddot{x}=-\frac{dV}{dx}-\int_{-\infty}^{t}ds\ K(t-s)\ \dot{x}(s)+R(t) \tag{6}\]  
This equation is non-Markovian since the friction force does not only depend on the velocity \(\dot{x}\) at time \(t\), but is rather expressed as an integral over past values of \(\dot{x}\) through the memory kernel \(K(\tau)\), which determines the actual dependence. In order to enforce the canonical ensemble statistics and the equipartition of energy, the random force should then be related to \(K(\tau)\) by the following relation:  
\[\langle R(t)R(t+\tau)\rangle=k_{B}T\ K(\tau) \tag{7}\]  
which reduces to (5) in the Markovian limit \(K(\tau)=2m\gamma\delta(\tau)\). As the bath is supposed to be at equilibrium, the time correlation function \(\langle R(t)R(t+\tau)\rangle\) does not depend on the arbitrary time origin \(t\). Defining the Fourier transformed quantities (here, we modify the memory kernel, which is in principle defined for non-negative times, by symmetrization as \(K(-\tau)=K(\tau)\)):  
\[\tilde{K}(\omega) = \int_{-\infty}^{\infty}d\tau\ K(\tau)\ \mathrm{e}^{-i\omega\tau}\] \[C_{RR}(\omega) = \int_{-\infty}^{\infty}d\tau\ \langle R(t)R(t+\tau)\rangle\ \mathrm{e}^{-i\omega\tau},\]  
yields the following relation:  
\[C_{RR}(\omega)=k_{B}T\ \tilde{K}(\omega). \tag{8}\]  
Equation (8)--or its time-domain version, (7)--follows from the equilibrium between the system and the thermal bath, and it is generally referred to as a fluctuation-dissipation theorem (FDT), similarly to Equation (1). The latter, however, is a general result of linear response theory that involves only intrinsic observables of the system (its velocity auto-correlation and its linear response function) whereas Equations (7) and (8), characterize the system-bath interactions via the friction and random forces in the particular context of the generalized Langevin dynamics. As pointed out by [PERSON], [PERSON] and [PERSON] [20],Equation (1) can be regarded as more fundamental, and following these authors, we will refer to it as the first-kind FDT, while Equations (7) and (8) (that can be derived from it as a corollary when the potential \(V(x)\) is harmonic) will be designated as second-kind FDT.
## 3 Data and Methods
### The Robust Satellite Techniques (RST) Approach
In its previous application to flooded area detection (e.g., [22; 23]) RST-FLOOD used the ratio and/or the difference between the signals measured in the RED and NIR channels (i.e., RED/NIR and/or RED-NIR) of AVHRR (channel 1: 0.58-0.68 \(\upmu\)m and channel 2: 0.725-1.00 \(\upmu\)m, both at 1 km of spatial resolution) and MODIS (channel 1: 0.62-0.67 \(\upmu\)m and channel 2: 0.841-0.876 \(\upmu\)m, both at 250 m of spatial resolution) as the reference signal (i.e., V(_x,y,t_) in Equation (1)). In particular, the ratio index demonstrated to be more robust respect to false positives, while the difference RED-NIR resulted more sensitive to the presence of flooded area [23]. Concerning VIIRS Imagery data, in addition to the indexes above defined [14], flooded area can be detected in different single bands and/or exploiting their combination, due to the specific spectral response of water in the VNIR region [6; 11; 54]. To better highlight such a behaviour within the local-scale conditions, the spectral profile for different signals along the red transect shown in Figure 1, has been investigated (Figure 6). Reflectance in both single (i.e., RED, NIR and SWIR, Figure 6a) and combined (i.e., RED \(-\) SWIR, RED/SWIR, NDSI-Normalized Difference Snow Index, Figure 6b) VIIRS I-bands were analysed. Considering the reflectance trends along the selected transect at wavelengths between 0.6 \(\upmu\)m and 1.64 \(\upmu\)m (Figure 6a), a clear signal change is observable in correspondence of the areas perturbed by the water presence. In detail, the presence of turbid waters (i.e., rich in sediment) causes an II signal increase that is no more detectable in the two channels at higher wavelength, where the water absorption properties produce a signal decrease. The relative variation between probably flooded/no-flooded area is relatively higher for I3 channel (77%) than I2 one (57%), suggesting better sensitivity of I3 towards these features. According to these outcomes, within the band combination analysis (Figure 6b), we did not consider the NIR (I2) channel anymore. Plots shown in Figure 5b highlight the good sensitivity of the signal band combinations (i.e., I1-I3 or I1/I3) for flooded area detection, with the ratio providing the best score in terms of signal variation. In addition, also their combination in terms of normalized difference, namely the NDSI (NDSI = RED - SWIR/RED + SWIR [55]), proves to be an index suitable for water detection purposes [56] thanks to the high water reflectance contrast at these two wavelengths [11; 15]. On the basis of the above discussed analysis, the ALICE index (Equation (1)) was preliminarily computed on the following five signals: II, I3, II \(-\)I3, II1/I3 and NDSI = (I1 \(-\) I3/I1 + I3), in order to select the best suitable metric (single band or combination) for flooded area detection, in terms of both reliability and sensitivity. It is worth noting that only the ALICE\({}_{\text{SWIR}}\) index should show low negative values in presence of flooded area, while, for all the others, high positive values have to be expected.
# SACT: A New Model of Covert Communication Based on SDN
## 5 Experimental Results
### False Alarm Rate  
False alarm rate is also an important standard to measure watermark tracking scheme. If the controller attempts to extract the watermark information from the data stream without any watermark information, it is possible to cause some false positives. For the data stream without watermark information, the controller extracts the watermark and judges the similarity with the expected watermark information. If it matches the threshold parameter of confidence interval, it is considered that the watermark information is embedded in the detected data stream and leads to misjudgment. Otherwise, it is determined that the current detected data stream is not embedded with watermark information. The experiment of false alarm of our system is shown in Figure 11.  
Figure 11: Effect of watermarking redundancy times.  
Figure 10: Effect of watermarking redundancy times.  
In this experiment, we send the watermark sequence with step \(\Delta=20\) ms between the arrival delay of adjacent packets, and the minimum interval arrival delay is \(T_{\min}=30\) ms. We set the confidence interval threshold parameter of cosine similarity determination to \([0.8,1]\). At the same time, we simulate the network jitter distribution with mean value of \(\mu=10\) and standard deviation of \(\sigma=2\). The experimental results show that if the length of embedded watermark sequence is 1 L (unit length), we detect and determine the watermark when the controller does not embed any watermark, the average similarity is close to the lower boundary of the confidence interval threshold, which leads to a high error rate.  
With the increase of the length of the watermark sequence, the detection similarity of the watermark sequence decreases rapidly. When the length of the watermark sequence is larger than 2 L, the detection similarity is much lower than the lower boundary of the confidence interval threshold, and the false alarm rate is low. Therefore, SACT has a low false positive rate and can be used in anonymous communication tracking.
## 1 Introduction  
### Kitchen Waste Generation  
The current economic strategy heavily relies on non-renewable raw materials, resulting in hazardous waste generation and various environmental issues [1]. In response, innovative concepts of minimizing waste production and alternative feedstock materials have emerged, aligning with circular economy and green production principles. These systems, particularly developed in the EU, aim to address environmental concerns, mitigate the depletion of non-renewable resources, and facilitate a transition from linear to circular material flows [2].  
In Poland, approximately 12.5 million tons of municipal waste is generated annually, with around 3.27 million tons recycled (26%) and about 1.01 million tons composted or digested. In 2018, the average waste production per person was 325 kg, with significant variations between urban areas (up to 384 kg) and rural areas (under 200 kg). Compared to the EU average of 480 kg, Poland has one of the lowest waste production rates, second only to Romania (280 kg). Notably, the potential for innovative management of biodegradable waste is significant, particularly since it constitutes 28% of domestic waste (approximately 26 kg per inhabitant), which can be effectively utilized for fertilizing green areas. The organic fraction of municipal solid waste (MSW), primarily kitchen waste, is a nitrogen-rich resource often composted by municipal companies, yet it often holds low economic value.  
Kitchen waste (KW) should be managed according to a sustainable way of waste management. KW is defined as organic residues from home kitchens and food waste (FW) is defined as food (plate) waste--that food which has not been completely eaten or spoilt food. Both kitchen waste and food waste contain a high level of organic matter and both macro- and micro-nutrients: nitrogen (N) 0.5-2.5%, phosphorus (P) 0.2-1.2%, potassium (K) 0.4-2.0%, calcium (Ca) 0.5-3.5%, magnesium (Mg) 0.1-0.5%, and organic carbon (C) 40-60% with moisture contents 70-80% and the carbon-to-nitrogen (C/N) ratio from 15:1 to 25:1 [3; 4; 5]. For this reason, it is worth valorizing fertilizers. However, the high water content, heterogeneity, and variability of the composition are problematic. KW should be processed so that, when discharged directly into the soil, it has no negative impact on the environment (emissions, leaching into groundwater), and fertilizer nutrients are fully assailable.
# Recurrent Neural Network-Based Hybrid Localization for Worker Tracking in an Offshore Environment
## 4 Results
### Tracking Accuracy Test
Therefore, the results of the Viterbi experiment show that the initial accuracy and the accuracy after localization convergence rarely differed; however, in the RHLS, it can be seen that the proposed method showed an accuracy improvement of approximately 16% over the course of the sequence. On the A deck, where it was difficult to estimate the heading estimation effect of the PDR as a straight-line test trace, it was also found that the proposed method shows superior accuracy improvement compared to the existing Viterbi method. Because there was no direction change, it took a long time to converge compared to other decks, but the accuracy was continuously improved, resulting in an accuracy improvement of approximately 29%.