esa-sceva/satcom-chunk-collection huggingface dataset:
# Long-Term Occurrence and Fate of Microlastics in WWTPs: A Case Study in Southwest Europe
###### Abstract  
Microlastic (MP) water pollution is a major problem that the world is currently facing, and wastewater treatment plants (WWTPs) represent one of the main alternatives to reduce the MP release to the environment. Several studies have analysed punctual samples taken throughout the wastewater treatment line. However, there are few long-term studies on the evolution of MPs over time in WWTPs. This work analyses the performance of a WWTP sited in Southwest Europe in relation with annual occurrence and fate of MPs. Samples were monthly taken at different points of the facility (influent, secondary effluent, final effluent, and sludge) and MPs were quantified and characterised by means of stereomicroscopy and FTIR spectrophotometry. The majority of MPs found in wastewater and sludge samples were fragments and fibres. Regarding to the chemical composition, in the water samples, polyethylene (PE), polyethylene terephthalate (PET) and polypropylene (PP) stood out, whereas, in the sludge samples, the main polymers were PET, polyamide (PA) and polystyrene (PS). The MPs more easily removed during the wastewater treatment processes were those with sizes greater than 500 \(\upmu\)m. Results showed that the MPs removal was very high during all the period analysed with removal efficiencies between 89% and 95%, so no great variations were found between months. MP concentrations in dry sludge samples ranged between 12 and 39 MPs/g, which represented around 79% of the total MPs removed during the wastewater treatment processes. It is noticeable that a trend between temperature and MPs entrapped in sewage sludge was observed, i.e., higher temperatures entailed higher percentage of retention.  
microlastics; sludge; WWTP; removal efficiency; secondary treatment Article
## 2 Materials and Methods
### Microalgal Cultivation in Swine Wastewater (SW)
#### 2.1.2 Microalgal Isolation and Identification  
_H. rubescens_ KNUA214 was isolated from Dokdo Island (37\({}^{\circ}\)14\({}^{\prime}\)22.2\({}^{\prime\prime}\) N, 131\({}^{\circ}\)52\({}^{\prime}\)11.2\({}^{\prime\prime}\) E) in the East Sea of the Republic of Korea. Samples were transported to the laboratory and inoculated into 100 mL of BG-11 medium supplemented with 100 \(\upmu\)g mL\({}^{-1}\) impenem (JW Pharmaceutical, Seoul, Republic of Korea). The cultures were incubated on an orbital shaker (Vision Scientific, Bucheon, Republic of Korea) at 160 rpm and 25 \({}^{\circ}\)C under approximately 130 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\) with a 16:8 light/dark cycle. Upon visible microalgal growth, 1.5 mL of the culture was centrifuged at 3000\(\times\)\(g\) for 5 min to obtain an algal pellet. A BG-11 agar plate supplemented with 30 \(\upmu\)L of 100 \(\upmu\)g mL\({}^{-1}\) impenem was inoculated with the microalgal pellet and incubated under similar conditions. Single colonies were isolated from the agar plate and successively re-streaked onto BG-11 agar plates without antibiotics to establish pure colonies.  
Genomic DNA was extracted from approximately 200 mg of algal cells using a DNA extraction buffer (100 mM Tris-HCl, pH 9.0-9.5; 1 M KCl; and 10 mM EDTA, pH 8.0) supplemented with RNase A and sterile glass beads. The lysate was vortexed, heated at 65 \({}^{\circ}\)C, and centrifuged at 13,523\(\times\)\(g\) to remove the cellular debris. DNA was purified from the resulting supernatant using the Wizard\({}^{\oplus}\)DNA Clean-Up System (Promega, Madison, WI, USA), yielding high-purity DNA suitable for downstream molecular analysis.  
PCR amplification was performed to target the 185 ribosomal RNA (rRNA) region using the primers NS1 (5\({}^{\prime}\)-GTA GTC ATA TGC TTG TCT C-3\({}^{\prime}\)) and NS8 (5\({}^{\prime}\)-TCC GCA GGT TCA CCT ACG GA-3\({}^{\prime}\)) (1). The internal transcribed spacer (ITS) region was also amplified using the primers ITS1 (5\({}^{\prime}\)-TCC GTA GGT GAA CCT GCG G-3\({}^{\prime}\)) and ITS4 (5\({}^{\prime}\)-TCC TCC GCT TAT TGA TAT GC-3\({}^{\prime}\)) [30].  
The 18S rRNA and ITS sequences obtained, along with closely related sequences retrieved from the National Center for Biotechnology Information (NCBI) database, were analyzed using MEGA X (Molecular Evolutionary Genetics Analysis) software version 10.1.6. [31]. The Kimura 2-parameter (K2) model was determined as the best-fit nucleotide substitution model based on the Bayesian Information Criterion. This model was employed to construct a maximum likelihood (ML) phylogenetic tree with 500 bootstrap replicates to evaluate the robustness of the inferred tree topology [32].
# XOR Binary Gravitational Search Algorithm with Repository: Industry 4.0 Applications
## 2 Gravitational Search Algorithm
### Analysis of Binary Gravitational Search Algorithm  
An analysis of BGSA shows that in certain cases, the algorithm does not behave as expected. Focusing on acceleration term of BGSA as in (7), the following analysis is performed in different cases.  
As the value assigned to parameter \(X_{k}(t),\ k=1,...,N\) in each dimension may only be any binary value of _zero_ and _one_, the result of subtraction \(X_{j}(t)-X_{i}(t)\) in each dimension may be any of following possible conditions. In the following cases, undesirable behaviour of velocity vector, where occurred, is shown under unchanged sign of the velocity vector condition.  
1. In the case when \(X_{i}^{k}(t)=X_{j}^{k}(t)=0\) or \(X_{i}^{k}(t)=X_{j}^{k}(t)=1\). One has \(X_{i}^{k}(t)-X_{j}^{k}(t)=0\) which results in no acceleration for the dimension \(k\) which is reasonable. In this case, the value of bits in the \(k\)th dimension for the corresponding object and the best solutions are exactly the same and no change is required in that dimension.
2. If \(X_{j}^{k}(t)=1,\ X_{i}^{k}(t)=0,\ X_{j}^{k}(t)-X_{i}^{k}(t)=1\) resulting in a positive acceleration term. In this case, if the velocity in \(k\)th dimension is positive, this acceleration term works fine and adds up to the speed. However, if the velocity in \(k\)th dimension is negative, the acceleration term decreases the absolute value of velocity and makes the probability of change smaller which is completely undesirable.
3. If \(X_{j}^{k}(t)=0,\ X_{i}^{k}(t)=1,\ X_{j}^{k}(t)-X_{i}^{k}(t)=-1\) results in a negative acceleration term. In this case, if the velocity in \(k\)th dimension is positive, this acceleration term decreases the velocity and consequently the probability of change. Since \(X_{j}^{k}(t)\) is different from \(X_{i}^{k}(t)\), this behaviour is not desirable. On the other hand, if the velocity in the \(k\)th dimension is negative, the acceleration term increases the absolute value of velocity and makes the probability of change larger, which is desirable.  
Motivated by the cases in which the acceleration term for BGSA does not behave as desired, this algorithm is modified using an XOR operator. It is shown that using such acceleration term, the undesirable conditions will never be met again.
## 4 Processing  
RANSAC [26] is an iterative method to estimate the parameters of a mathematical model from a set of observed data. Therefore, it also can be interpreted as an outlier detection method. Furthermore, it is a non-deterministic algorithm because it produces a good result only with a certain probability, with this probability increasing as more iterations are allowed.  
A simple example is the fitting of a line in two dimensions to a set of observations. If this set contains both inliers, i.e., points that can be approximately fitted to a line, and outliers, points that cannot be fitted to this line, a simple least-square method for line fitting generally produces a line with a bad fit the inliers. The reason is that it is optimally fitted to all points, including the outliers. On the other hand, RANSAC can produce a model that is only computed from the inliers, provided that the probability of choosing only inliers in the selection of data is sufficiently high.  
Figure 15 shows the application of the RANSAC method that aims to recognize the fitted line. Figure 15a shows the data set with many outliers, and Figure 15b shows the fitted line in blue through the RANSAC method so that it is possible to recognize the road lane at the image frame. Therefore, the RANSAC is the simplest method to recognize road lanes even in curvature because it can be segmented into small pieces having an almost straight line with good accuracy and precision. Moreover, it requires fewer computer resources than advanced algorithms to carry out the recognition process. The critical point is that the road lane can be straight or curved, which leads to applying, respectively, Linear RANSAC or Parabolic RANSAC.  
Figure 14: The process of binary buffering in the segmented band.  
Figure 15: (**a**) A data set with many outliers for which a line has to be fitted. (**b**) Fitted line with RANSAC; outliers do not influence the result.
# Underwater Acoustic Target Tracking: A Review
## 4 Theory of Target Tracking
### Position Determination  
The process of position determination in visual tracking refers to locate the area of the target in each frame. After a target is correctly detected, the corresponding relationship between adjacent frames should be determined, which is also called target matching. Target matching is completed by the criterion that the center position and size of the same target changes little in consecutive frame images during a small sampling interval [41]. While in non-visual tracking, the position of the target is determined by the location of detecting sensors and the distance of these sensors to the target. The distance information is easily obtained from active sensors. Nevertheless, passive sensors only obtaining bearing information also can be used to determine the position of the target by three bearings from different non-collinear sensors, which is equivalent to using trilateration to compute the position by distance information from three nodes. The position determination algorithms can be classified  
Figure 3: The process of target tracking based on images.  
into range-based and range-free ones. Range-based positioning algorithms determine the distance of a sensor to the target using obtained measurements such as RSSI, TOA, and DOA, then the trilateration, triangulation or multi-lateration are used to estimate the position of the target. Range-free positioning algorithms estimate the position of the target according to the connectivity of tracking networks without range and bearing information. However, the accuracy of positioning is guaranteed by the number of sensing sensors, which means the algorithm is limited for WSNs. The commonly used range-free positioning algorithms are the centroid or weighted centroid algorithms, distance vector hop (DV-hop), approximate point-in-triangulation test (APIT) and convex programming algorithms.  
Continuous positioning at the time sequence can also realize tracking tasks, and the corresponding velocity, as well as acceleration, can be obtained according to the position at several sampling instants. However, the state of the target always varies with time and measurements are corrupted by noise seriously, which results in tracking with continuous positioning having poor performance. Therefore, most of tracking algorithms adopt filtering techniques as the technology of state estimation and reducing the noise interference. Position determination algorithms are usually used to obtain the initial state of the target.
###### Abstract  
The outbreak of COVID-19 resulted in severe pressure on the existing medical infrastructure in China. Several Chinese cities began to construct temporary hospitals for the centralized treatment of COVID-19 patients. The harmful exhaust air from the outlets of these hospitals may have a significant adverse impact on the fresh-air intakes and surrounding environment. Owing to the need to rapidly construct these hospitals within 6-10 days, just a few hours are allowed for the analysis of the impact of this exhaust air on the environment. To overcome this difficulty, a high-efficiency simulation framework is proposed in this study. Based on the open-source computational fluid dynamics software, FDS, the proposed framework is adaptive and incorporates building information with different levels of detail during various design phases of the hospital, and has been applied in the design of the Wuhan Huoshenshan Hospital, the first typical COVID-19 temporary hospital in China. According to the simulation results, neither the fresh-air intakes nor the surrounding buildings would be polluted by the harmful air discharged from the air outlets of the Huoshenshan hospital. The proposed simulation framework can provide a reference for the design and overall planning of similar hospitals in China and other affected countries.  
COVID-19; SARS-CoV-2; pollutant dispersion; FDS; LOD
## 1 Introduction  
Food and medicine are vital to humans everywhere on the planet. However, their transportation is not easy, since most food and medicine spoil or degrade quickly if not refrigerated, with medicine often requiring very specific storing temperatures. This continuous chain from production to the consumer in a controlled temperature environment is called the cold chain. The cold chain is often difficult to maintain, requiring storage, transfer, and delivery, with the minimal or zero exposure of products to warmth. This logistics chain currently requires a lot of energy and resources, a lot of which are wasted in the process. Trucks that are specifically made to accommodate these refrigerated products are generally inefficient with their energy use, emitting significantly more greenhouse gases and air pollutants than their non-refrigerated counterparts. Some further issues associated with road-based cold transport vehicles' poor environmental performance are noise pollution, air pollution, tire microplastics, and the refrigerants used in the cooling process, which all contribute to adverse health effects for both humans and natural ecosystems. On top of that, they are often dependent on the traffic conditions of the local environment, which can be highly unpredictable due to car accidents, road works, and weather conditions. These problems are especially present in cities with much traffic and unoptimized transportation planning. Urban cold chains not only can, but must, be made more efficient. This shift in both will and need is evidenced by the EU instatement of a ban on all but zero-emissionroad vehicles by 2035 [1] and demanding increasing cuts to emissions across all sectors of society [2].  
In this literature review, we summarize the current state of urban cold transportation from the perspective of environmental performance and efficiency, review the current optimization methods found in the literature and outline future directions for urban cold transportation research. Section 2 discusses the current shortcomings of, and problems within, the cold chain, putting into numbers the economic and environmental damage caused by cold chain failures, as well as the level of pollution in the current cold chain. In Section 3, we identify aspects of the cold chain that can be optimized to decrease its environmental impact and discuss different methods of, and approaches to, optimization. In Section 4, we discuss the future of urban infrastructure and how the phasing out of urban automobility affects the planning and optimization of logistics and cold transportation. Conclusions are drawn in Section 5, followed by a list of references.
## 3 Results
### Cytotoxicity Assessment via NRU Assay in HepG2 Cells with GNPs  
A cytotoxic study was also conducted in control and treated samples of GNPs via NRU assay, as detailed in Section 2.4.3. The NRU assay data are very analogous to the MTT assay data for HepG2 cancer cells and show a similar trend. Like the MTT assay, the NRU assay shows that at the initial concentration of GNPs, viability of cancer cells was not significantly affected, but when the concentration of GNPs rises, cell growth reduces. In the case of GNPs-1 exposed to HepG2 cells, cell viability by NRU assay at 24 h was 100%, 98%, 89%, 77%, 63%, 51%, and 15% (Figure 6) for concentrations of 1 ug/mL, 2 ug/mL, 5 ug/mL, 10 ug/mL, 25 ug/mL, 5 ug/mL, 25 ug/mL, 50 ug/mL, and 100 ug/mL, respectively (\(p<0.05\) for each concentration). In the case of GNPs-2 and HepG-2 cells, cell viability was recorded as 102%, 99%, 91%, 85%, 76%, 57%, and 47% (Figure 6) for concentrations of 1 ug/mL, 2 ug/mL, 5 ug/mL, 10 ug/mL, 25 ug/mL, 50 ug/mL, and 100 ug/mL, respectively (\(p<0.05\) for each concentration).  
Figure 5: Cytotoxicity assessments by MTT assay in HepG2 cells after the exposure of GNPs-1, GNPs-2, and GNPs-3 at different concentrations for 24 h. The values are mean \(\pm\) SE of three independent experiments. * \(p<0.05\), ** \(p<0.01\), and *** \(p<0.001\) vs. control.  
Figure 6: Cytotoxicity assessment by NRU assay in HepG2 cells after exposure to GNPs-1, GNPs-2, and GNPs-3 at different concentrations for 24 h. The values are mean \(\pm\) SE of three independent experiments. * \(p<0.05\), ** \(p<0.01\), *** \(p<0.001\) vs. control.  
In the case of GNPs-3 and HepG-2 cells, cell viability was recorded as 100%, 100%, 98%, 95%, 83%, 66%, and 49% (Figure 6) for concentrations of 1 \(\mu\)g/mL, 2 \(\mu\)g/mL, 5 \(\mu\)g/mL, 10 \(\mu\)g/mL, 25 \(\mu\)g/mL, 50 \(\mu\)g/mL, and 100 \(\mu\)g/mL, respectively (\(p<0.05\) for each concentration). We also provide the comparative anticancer activity of gold NPs against liver cells by MTT (Table 1) and NRU (Table 2) assays.
# Opportunistic In-Flight INS Alignment Using LEO Satellites and a Rotatory IMU Platform
## 2 Positioning Architecture
### Slave EKF-Based Doppler Positioning Model  
The slave EKF model estimated the states of the dynamic receiver defined as attitude quaternion vector, position, velocity, clock bias, and clock drift. In this part, we defined the EKF model and parameters for the Doppler positioning slave block. Moreover, the measurement model of the system was discussed based on the MC-SDR receiver's model.
## 4 Security Proofs
### Secure Authenticated Key Exchange
When \(E\) makes \(Send(\Pi_{MS_{i}V_{j}^{\prime}}^{u},\mathcal{M})\) and \(Send(\Pi_{V_{j}^{\prime}MS_{i}^{\prime}}^{v},\mathcal{M})\) queries, \(\mathcal{S}\) can deal with them and output the corresponding messages by running the protocols of Sections 3.3 and 3.4 with generated public/private keys \((pk_{V_{j}},sk_{V_{j}})\)'s and encryption/decryption oracles \((\mathcal{O}_{E},\mathcal{O}_{D})\). The operations of \(Execute(\Pi_{MS_{i}V_{j}^{\prime}}^{u},\Pi_{V_{j}^{\prime}MS_{i}}^{v})\), \(Test(\Pi_{MS_{i}V_{j}^{\prime}}^{u})\), \( Reveal(T)\), and \( Reveal(\Pi_{MS_{i}V_{j}^{\prime}}^{u})\) queries are depicted in Figure 15.  
Figure 14: The proof model of Theorem 6. Figure 15: The oracles in the proof model of Theorem 6. In Line 3 of \(Reveal(\Pi_{MS_{i},V_{j}}^{u})\) in Figure 15, \(\mathcal{S}\) will abort the simulation because it cannot return the established session key. If the simulation is aborted, \(\mathcal{S}\) will randomly guess \(b^{\prime}\in\{0,1\}\). Otherwise, after \(E\) performs \(Test(\Pi_{MS_{i},V_{j}}^{u})\) and outputs \(b^{\prime\prime}\), \(\mathcal{S}\) will set \(b^{\prime}=b^{\prime\prime}\). Thus, \(\mathcal{S}\) has probability \(\epsilon^{\prime}\) in outputting a correct bit \(b^{\prime}\) (\(=b\)) to \(\mathcal{C}\) where \(\epsilon^{\prime}\geq\frac{\epsilon+\frac{1}{2}}{q2q3}+\frac{q_{2}q3-1}{2q2q3}= \frac{2\epsilon+1+q_{2}q3-1}{2q2q3}=\frac{\epsilon}{q2q3}+\frac{1}{2}\). Hence, if \(\epsilon\) is non-negligible, \(Adv_{\mathcal{F}}^{IND-CCA}(k)=\epsilon^{\prime}-\frac{1}{2}\geq\frac{ \epsilon}{q2q3}\) is also non-negligible.
## 3 The Proposed Data Integration Approach for Agricultural Open Data Platforms
### Data Cleansing Engine (DCE)  
Missing value problems frequently occur within the sensors and it is accepted quite widely in WSNs. There exist several problems which cause missing value in WSNs, such as synchronization issues, sensor errors or transmission issues [46]. DCE is in charge of handling missing sensor measurements and anomalous values. It can be very crucial to estimate missing sensor measurement values and detect anomalies in terms of critical systems. In case of anomaly detection in WSNs, statistical, supportvector machine (SVM) and cluster analysis techniques play a significant role [47]. These techniques can be applied to the sensor data stream to provide solutions for estimating missing values and detecting anomaly.
# Impact of Using Chevrons Nozzle on the Acoustics and Performances of a Micro Turbojet Engine
## 6 Conclusions  
The main conclusion of this paper is related to the overall noise reduction of 2-3 dB for the configurations with chevron nozzles, which is obtained only at high-speed regimes. The noise reduction of the chevrons is obtained through a better mixture film between the jet gases and the air from the atmosphere. The FFT analysis of the acoustic signals highlighted that the noise reduction generated by the chevrons has broadband characteristics. The vibration spectra highlighted that the main vibration source corresponds to the engine speed for each regime, and there was not any identified rule of the vibration variation related to the engine speed. The replacement of the reference nozzle with chevron nozzles does not produce any significant increase in the vibration levels. During the chevrons' testing, the vibration levels were slightly increased but without putting the engine function in danger.  
Regarding the engine performances, modification of the nozzle, by cutting the chevrons, leads to a reduction in the propulsion force, especially for a nozzle with 16 chevrons.  
In terms of engine traction force, the engine suffered losses up to 6% for regime 3 and, for the other regimes, around 4%. The losses were slightly higher for the configuration with N = 16 compared to N = 8. Regarding fuel consumption, a 6-7% decrease at the maximum regime was recorded.  
The main conclusion is that the noise reduction of almost 3 dB comes with a penalty to the engine performance.  
The future research will focus on optimization of the chevrons' design by finding optimum concepts in terms of noise reduction and performances losses without modifying the work line of the compressor and by testing the new optimized chevron designs in an anechoic room with multiple microphones in polar configuration.  
G.C.: Conceptualization, M.D. Acoustic and vibration evaluation, G.C. and D.-E.C. Microturbojet engine performance evaluation. All authors have read and agreed to the published version of the manuscript.  
This work has been funding by University POLITEHNICA of Bucharest through its programs of excellence. The first results of this study were obtained through the \"Excellence Research Grants\" Program, UPB--GEX. Identifier: UPB--EXCELENTA--2016 Research project title: \"Study and research regarding aviation engines jet noise reduction using chevrons\", Contract number: 81/26.09.2016.  
Not applicable.  
The datasets used and analyzed during the current study are available from the corresponding author on request.  
The authors declare no conflict of interest.