esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Nanoparticles on Algae: Adsorption, Distribution, Ecotoxicity and Fate
## 4 Ecotoxic Effects of NPs on Algae
### Effect of NPs on Intracellular Substrates  
NPs can induce the production of intracellular reactive oxygen species (ROS), such as superoxide radicals (O\({}_{2}\)-), singlet oxygen (\({}^{1}\)O\({}_{2}\)), hydroxyl radicals (HO-) and hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) [56], which induce oxidative stress in cells, resulting in oxidative-reduction imbalance in an organism [46; 47]. Oxidative stress refers to the body in a variety of harmful stimuli, the body of high activity of free radicals and the imbalance of the oxidation system and antioxidant system, which eventually leads to tissue damage [20]. It has been proven that the production of ROS can damage intracellular lipid, carbohydrate, protein, DNA and other biological macromolecules, leading to inflammation and oxidative stress [16]. ROS can interact with the side chain of polyunsaturated fatty acids and nucleic acids and other macromolecules, resulting in lipid peroxidation [45]. Lipid peroxidation is regarded as the most severe form of damage resulting from oxidative stress as it can lead to changes in the cell membrane system, which in turn disrupts the cellular function of the organism [51]. Oxidization of nucleic acids by ROS would also cause mutagenesis [57]. ROS can result in the formation of many lipid decomposition products. Some of these decomposition products are harmless and others may cause disordered cell metabolism and dysfunction [58; 59]. ZnO NPs facilitated the formation of phosphate granules, starch pyrenoid complexes and lipid droplets in _Scenedesmus obliquus_ cells, which was observed by transmission electron microscopy [20]. Similarly, ZnO NPs were also proven to induce phosphate granule formation in _Chlorella pyrenoidosa_ and _S. obliquus_ by [PERSON] et al. [60]. To control ROS level in cells, cells have evolved an ROS scavenging system of enzymatic and nonenzymatic antioxidants, such as ascorbic acid (AsA), glutathione,tocopherol, carotenoid, catalase (CAT), superoxide dismutase (SOD), peroxidase (POD) and ascorbate peroxidase (APX) [61; 62]. It was suggested that ROS production led to enhanced production of antioxidants and increased activity of antioxidant enzymes for the activation of the complex cellular defense mechanisms [51]. These antioxidants were able to neutralize free radicals or their toxic effects, acting at different steps. The intervention of NPs has an impact on the protein and enzyme activity [51]. Antioxidant enzymes such as superoxide dismutase (SOD), catalase (CAT), glutathione S-transferase (GST) and peroxidase (POD), among other enzymes, are mechanisms of antioxidant protection in algal cells. In the presence of less toxic NPs, the activity of these antioxidant enzymes increases to eliminate the toxicity of ROS to algal cells [63], and when the toxicity of NPs in water is too strong, the antioxidant defense system of algae cells is destroyed and the enzyme activity is significantly inhibited [45; 64].
## 5 Experimental Verification
### Measurement Methodology for Different Loss Components  
Accurate quantification of individual loss components in the high-speed magnetic field-modulated motor requires a systematic measurement approach with specialized instrumentation and decoupling techniques. The comprehensive loss measurement methodology employs the segregated loss determination principle, wherein total losses are first measured through an input-output power differential using precision power analyzers with sampling rates exceeding 1 MHz to capture high-frequency harmonics. Core losses are isolated through specialized no-load tests with compensated friction and windage contributions determined via deceleration tests at various vacuum levels. Copper losses are measured using a combination of DC resistance tests and AC impedance analysis that accounts for skin and proximity effects at operating frequencies up to 20 kHz. Mechanical losses, particularly challenging in high-speed applications, are quantified through custom-designed coast-down tests with temperature-controlled environments to ensure reproducibility. Rotor losses in permanent magnets and conducting components are indirectly measured using calorimetric methods with carefully calibrated heat flow sensors that can detect power losses as low as 0.5 W. Additional losses are extracted by subtracting all identified loss components from the total measured loss, with uncertainty analysis conducted through Monte Carlo simulations to establish measurement confidence intervals. This comprehensive methodology enables accurate validation of the theoretical loss models and finite element predictions across the entire operating range of the flywheel energy storage system.
## 3 Results
### To Convertion to S-ITSK Using a Uniform Transformation Key  
The analysis of geometric inhomogeneities of the Unified Transformation Key (UTK) (v. 2018) was performed on 18 selected lines (Figure 6). The numerical values of geometric deformations in the form of changes in deviation per 1 km and their percentage frequencies are presented in Table 5. DL indicates longitudinal deviation, DQ lateral, DLDQ in horizontal position. The conversion methodology between ETRF2000 and the JTSK national coordinate system is described in [25].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Railway Line**} & \multirow{2}{*}{\(\sigma_{0}\)**Apriori**} & \multirow{2}{*}{\(\sigma_{0}\)**Aposteriori**} & \multicolumn{2}{c}{**Critical \(\sigma_{0}\)**} & \multirow{2}{*}{**RMS \(\sigma_{p}\)**} & \multirow{2}{*}{**Max. \(\sigma_{p}\)**} \\  & & & & **Redundancy \(\
u\))** & **[mm]** & **[mm]** \\ \hline RL 1371 Velká Kras–Vidnava & 1.0 & 0.8505 & 1.1744 (\(\
u=43\)) & 2.8 & 3.3 \\ RL 2302 Bojkovice–Hostetin & 1.0 & 1.3331 & 1.1193 (\(\
u=93\)) & 4.2 & 5.3 \\ RL 1363 Jesenik–Mikulovice & 1.0 & 1.5912 & 1.1326 (\(\
u=75\)) & 4.2 & 6.8 \\ RL 2302 Bzenec railway station & 1.0 & 1.0785 & 1.1669 (\(\
u=47\)) & 4.2 & 5.2 \\ \hline \hline \end{tabular} \(\sigma_{0}\) apriori–unit standard deviation apriori; \(\sigma_{0}\) aposteriori–unit standard deviation aposteriori critical value of \(\sigma_{0}\) for chi-square distribution and redundancy \(\
u\); RMS \(\sigma_{p}\)–root mean square positional standard deviation of the set of determined points of the secondary system; max. \(\sigma_{p}\)–maximal positional standard deviation.  
\end{table}
Table 4: Consistency of accuracy of a terrestrially determined secondary system in relation to a GNSS determined primary system.  
Interval percentages of deformations per 1 km between the results obtained by transformation with the uniform transformation key \(v\). 1202 (S-JTSK) and S-JTSK/05. The magnitudes of the geometric deformations of the UTK reach up to 10 mm/1 km in 90% of the horizontal coordinates and up to 20 mm/1 km in the remaining 8%.  
The presence of residual inhomogeneities adversely affects metrological accuracy in terms of consistency between GNSS and terrestrial measurements.
# The Impact of Distributed Energy Storage on Distribution and Transmission Networks' Power Quality
## 1 Introduction  
Transmission and distribution networks are required in today's power system, among other things, to maintain a balance between energy supply and demand, regardless of the particular characteristics of the resources used in energy generation or fluctuations in consumer energy use [1]. As a direct result of the balancing efforts, there are certainly predictable occasions when demand on these networks peaks and troughs, resulting in undersupply and oversupply of generated electrical energy. Any demand-supply imbalance is manifested in power quality, which is the degree to which the voltage magnitude (and angle) of the supplied electrical energy to consumers deviates from the rated tolerance limits as determined by their Distribution System Operators (DSOs) or Transmission System Operators (TSOs).  
The utilization of Energy Storage Systems (ESSs) in the management of power quality within these networks is not entirely new [2, 3]. What is interesting is the rise in ESS energy density innovation, which portends for greater analytical flexibility [4, 5, 6, 7] in where they are situated within the power system's supply network in order to maximize the benefits of their operational capabilities. The Pumped Hydro Storage System (PHSS) has been used for a long time in ESS technology for the contingent compensation of network electricity supply performance within a centralized framework.  
Other solutions have recently emerged because of their technological properties, which include enhanced tractability in where and how they are connected to electrical networks to improve power quality. Flywheels, compressed air storage, molten salts, battery energy storage, superconducting magnetic storage, and super-capacitors are some of the technologies currently being deployed or demonstrated in the electrical sector [8,9]. Each of these technologies is based on the mechanical, electromechanical, thermal, electric, and chemical properties that they possess.  
The implementation of ESS, such as BESS, is an important avenue for increasing a distribution network's energy efficiency. The purpose of this study is to give an overview of the best BESS placement and functioning that will improve the overall network's performance. There are three goals to this study. The first is an assessment of the impact of ESS integration in transmission and distribution networks on power quality. The second is a comparison of power quality performance resulting from the adopted ESS integration design (centralized versus distributed integration) inside the transmission and distribution network. Finally, an examination of which ESS integration topology (distribution network versus transmission network) has the greatest impact on power quality performance. NEPLAN electric power system simulation and modeling software [10] were used to assess the power quality of a typical interconnected transmission and distribution network.
# Real-Time Filtering Non-Intentional Bid Request on Demand-Side Platform
## 4 Research Methodology
### Building Features  
Since access behavior is concerned with the important characteristics for recognizing an abnormal audience, we focus on analyzing the potential attributes to extract the features that represent the corresponding abnormal access behavior. To study the associated access behavior of a bid request, it can best be treated under three headings: identifier, measuring, and observation period.  
**Identifier:** the advertising fraud traffic can be identified under two categories of source: audience and publisher. Most of the prior works have focused on publishers, such as [1; 7; 9; 11]; however, we also can discover suspicious audiences which used to generate advertising fraud traffics. In particular, the associated identifiers of audiences include:  
* Audienceld: identifies the browser via third-party cookies. For browsers, the audienceId is renewed if the cookie is wiped;
* (IP, UA): the combination of IP address (IP) and user agency. IP is the IP address of the user machine. User agency is used to identify the kind of browser, browser version, OS, and OS version. This combination is best in identifying a user since one IP address can be used by multiple users in some cases. Similarly, associated identifiers of publisher comprise three attributes:
* URL: the link accessed by the bid request;
* domain: the domain of the URL;
* publisher: is unique ID given for each publisher;  
**Measuring:** according to the previous works [9; 12; 14], we summed up three characteristic manners of non-intentional access behavior to estimate and measure non-intentional access behavior.  
* Traffic volume: the extraordinarily large number of bid requests from a specific identifier indicates non-intentional access behavior;
* Behavior frequency: an audience accessing a small number of publishers or a publisher accessed by a small number of audiences also implies non-intentional access behavior;
* Behavior regularity: the traffic from an audience or publisher with regularity also assumes non-intentional behavior. For example, botnet behavior.  
**Observation period:** the access behavior of non-intentional bid requests can be observed under four observation periods: second, minute, hour, and day.  
In this research, we focus on analyzing and observing in detail the access behaviors of audiences with reference to audienceld and (IP, UA).  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Category** & \\ \hline bid request & 12,490,828 \\ bid response & 6,350,292 \\ audienceId & 9,107,971 \\ (IP address, user agency) & 12,411,316 \\ click & 4187 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The basic statistics of dataset.
## 3 Experimental Results
### Characterization of Single-Gain Lasers
The tuning map for laser 4 is shown in Figure 7. This laser uses the same tuning mechanism as laser 3. As shown, laser 4 achieves a tuning range of more than 70 nm. As a result, the generated LO signals can cover frequencies from the microwave to the terahertz range. The electrical linewidth of the generated LO signal in an optical heterodyne system is directly linked to the optical linewidth of each laser employed. To assess the lasers' suitability for LO signal generation, their optical linewidths were measured using the delayed self-heterodyne setup described in the previous subsection. Under free-running conditions, and with only the SOA section of the lasers biased, optical linewidths below 40 kHz were achieved. Figure 8a illustrates the self-heterodyne spectrum for laser 2, centered at 40 MHz. The phase noise of the beat signal was measured using the ESA's built-in phase noise measurement function. The results, presented in Figure 8b, indicate a  
Figure 6: The wavelength tuning map for laser L3 was obtained by varying the voltage applied to the micro-ring resonators in the external cavity. The laser exhibited a tuning range of around 80 nm. During these measurements, the SOA current was held constant at 100 mA. phase noise below \(-60\) dBc/Hz at a 100 kHz offset, obtained under the laser's free-running operation regime.
# A Review of Air Pollution Monitoring Technology for Ports
## 3 Air Pollutant Monitoring Systems for Ports
### Port Air Pollutant Monitoring
#### 3.1.2 Discontinuous Monitoring  
On the other hand, the online monitoring equipment designed for port air pollutants is not consistent with the manual analysis method combined with the laboratory in terms of analysis principles, working conditions, and sampling methods. However, the criteria for determining the accurate and stable operation of the online monitoring equipment are comparing monitoring with the manual analysis method in the laboratory [46], so it is also very important to conduct regular manual monitoring of port air pollutants. While the prerequisite for manual analysis in the laboratory is the sampling of air pollutants, the development trend of air samplers is currently intelligent, integrated, and multi-functional. To reduce the complexity of the work of the port staff, the use of the combined sampler of air and particulate matter designed by [PERSON] [47] can provide a certain convenience for the collection of air samples. The sampler samples SO\({}_{2}\), NO\({}_{\mathbf{\chi}}\), PM, and other air pollutants through four independent channels, and its temperature control PID algorithm and rotameter error analysis in use allow the system to significantly improve the control accuracy and achieve the expected sampling results. In this way, only one style of the sampler is needed to complete the work in the face of the diverse air pollutants in the port, greatly saving the work's tediousness.  
[PERSON] et al. [48] then designed a wireless sensor multipoint network group air sampler based on a learning wireless remote sensor, which can achieve flexible and variable sampling of air pollution monitoring and provide effective samples for air pollutant monitoring. The sampler, through the learning function of the learning wireless remote control sensor, achieved a multi-sampler synchronous sampling and delayed sampling flexible control, while using PWM (Pulse Width Modulation) to adjust the sampling speed of the sampler, sampling speed 0.1-0.8 L/min adjustable, to achieve reliable automatic sampling of the air. When the sampler is used, the workload of port staff can be reduced, making the construction of smart ports a step further.
# TARS: A Novel Mechanism for Truly Autonomous Resource Selection in LTE-V2V Mode 4
###### Abstract
The mechanism of specifying a counter value for resource usage and probability of reselecting the same resource for communication is purely based on the uniform random selection probability [12]. It is argued that the uniform random selection probability for counter selection and reselection of the same resources is an unfair approach, as it can circumvent some vehicles during transmission in congested vehicular networks. While the periodic transmission of vehicle safety messages and the broadcast nature cannot be given up, the mechanisms for resource selection can be deployed such that the collisions in ARS are reduced and the resource selection is fair towards all vehicles in congested vehicular networks. To this end, the paper presents a Truly Autonomous Resource Selection (TARS) mechanism for vehicular networks. TARS considers resource allocation as a problem of locally detecting the selected resources at neighbor vehicles. To ensure the effect of the random uniform selection probability, TARS employs the crypto-hash function. To observe the behavior of resource selection and collision, this paper presents a Discrete Time Markov Chain (DTMC) model for the counter selection and resource reselection. Subsequently, insights from the model are used to specify the mechanism for fair counter selection, as well as the resource reselection policy. The rest of the paper is divided into the following sections. In Section 2, an overview of the related work is presented. Preliminaries regarding the Device-to-Device (D2D)  
Figure 1: LTE-V2V Mode 3 and Mode 4.  
communication are given in Section 3. Section 4 explains the system model and proposed TARS mechanism. The performance of the proposed technique is evaluated in Section 5. Lastly, the paper is concluded in Section 6.
# Modal Decomposition of the Precessing Vortex Core in a Hydro Turbine Model
## Post-Processing Routines  
### Spatial Fourier Mode Decomposition  
The pressure signals are used to characterize the amplitude and frequency of the PVC in the draft tube cone. In the following, the pressure fluctuations are non-dimensionalized by defining the pressure coefficient \(C_{p}\) as  
\[C_{p_{j}}=\frac{p_{j}}{V_{0}^{2}}, \tag{1}\]  
where \(p_{j}\) is the discrete pressure signal obtained from one microphone, and \(V_{0}=4Q/\pi D^{2}\) is the bulk velocity in the considered section.  
The pressure coefficients \(C_{p_{j}}\) obtained from four microphones are decomposed into spatial (azimuthal) and temporal Fourier modes. A similar decomposition is applied in the paper of [45] to calculate the contribution of the PVC to the pressure pulsations in a swirl flame. Accordingly, the 2D Discrete Fourier transform reads  
\[\hat{C}_{p_{j,m}}=\frac{1}{N_{j}N_{k}}\sum_{j=0}^{N_{j}-1}\sum_{k=0}^{N_{k}-1}e ^{-2\pi i(\frac{f}{N_{j}}j+\frac{m}{N_{k}}k)}C_{p_{j+1,k+1}} \tag{2}\]  
where \(C_{p_{j,k}}\) is a matrix of pressure coefficients of the \(k\)-th sensors and \(j\) is the index of the recorded discrete time intervals (\(N_{j}\) is a length of the time series). With four microphones arranged in a circumferential direction (\(N_{k}\) = 4), azimuthal modes with wave numbers \(m\) = 0, \(-\)1, 1, \(\pm\)2 can be identified. The matrix \(\hat{C}_{p_{j,m}}\) contains the complex-valued Fourier coefficients at frequency \(f\) and azimuthal mode \(m\).  
To analyze the spectrum of the flow dynamics, we use the [PERSON]'s overlapped segment averaging spectral estimation with a segment length of \(2^{18}\) sampling points and an overlap of 50%. The power spectral density (PSD) of \(\hat{C}_{p_{j,m}}\) is generated for different operating regimes by varying the parameter \(Q/Q_{c}\). To allow relative comparisons, all values are normalized with respect to an overall maximum value. The frequencies are normalized with respect to the runner speed \(n_{c}\).  
Referring to previous studies [1; 39; 46], the PVC dynamics are characterized by a co-rotating counter winding mode. In our nomenclature, this refers to modes with positive azimuthal wave numbers \(m\) and positive frequency. In this regard, we focus our analysis on the spectra of the \(m=1\) and \(m=2\) modes only. It should be noted that pressure fluctuations of wave number \(m=0\) are not considered in the study, as it is quite difficult to separate the hydrodynamic-induced pressure pulsations from the planar acoustic noise that is present in the experimental rig.
## 3 Results
The lower values of the test statistics indicated the fact that the fitted theoretical distribution described the empirical data better, and therefore the observed theoretical distribution had a better rank. The standard notations \(f(x)\) and \(F(x)\) for PDF and CDF, respectively [53], of the considered theoretical distributions were used in the following sections. For the sake of clarity and easier presentation of the results, to these labels were also added labels related to the seawater environment, the ordinal number of the NiTi alloy, as well as the abbreviated name of the considered theoretical distribution, resulting in terms \(f_{yz}^{(w)}(x)\) and \(F_{yz}^{(w)}(x)\), respectively. The above notation \((w)\) is an abbreviation for the considered theoretical distribution name. The notation \(y\) below refers to the seawater environment (air, tide, sea) and can take one of the values from the set \(\{a,\ t,\ s\}\), while the index \(z\) takes the value from the set \(\{1,2\}\), and represents the ordinal number of the observed alloy. The empirical PDF of the alloy corrosion rate is displayed as a histogram with bins of equal width. Each bin represents the number of corrosion rate data whose values are between values \(a\) and \(b\), divided by the sample size. The fitted theoretical PDF can be expressed as the integral between given points \(a\) and \(b\): \(\int_{a}^{b}f(x)dx=P(a\leq X\leq b)\) where \(P(\cdot)\) is the probability function, \(a\) and \(b\) are any real numbers such that \(a<b\). These probability values are scaled (multiplied) by the interval width. More precisely, probabilities are scaled by \(\frac{M-m}{\pi}\), where \(M\) and \(m\) are the maximal and minimal values of the observed corrosion rate, while \(n\) is the number of bins. In this paper, \(n\) is equal to 10 in all figures containing histograms. It is well known [53; 54] that CDF (denoted as \(F(x)\)) of any theoretical distribution satisfies the following expression:  
\[P(a\leq X\leq b)\approx F(b)-F(a).\]Based on Equations (8) and (9), it is possible to determine an approximate estimate of the probability of corrosion wear \(d(t)\) of the observed NiTi alloy in any seawater environment:  
Figure 9:0  
where \(t>0\) represents the elapsed time (expressed in months) from the beginning of the alloy's exposure to the seawater environment.