esa-sceva/satcom-chunk-collection huggingface dataset:
# Long-Term Occurrence and Fate of Microlastics in WWTPs: A Case Study in Southwest Europe
## 3 Results and Discussion
### Chemical Composition of MPs  
The chemical composition is a relevant characteristic that determines the MP density and therefore, directly influences over the removal efficiency. Over 30 kinds of polymers have been described in wastewater samples of different WWTPs [51]. In this study PE, PP, PS, PA, PET and polyvinyl chloride (PVC) were detected in the wastewater samples (Figure 6). In the influent, on average, PP is the polymer most frequently detected with an abundance of 24.9 \(\pm\) 5.5% (ranges between 15.8-37.4%), followed by PET with 23.2 \(\pm\) 2.9% (27.8-18.1%), PE with 17.3 \(\pm\) 4.2% (13.0-26.0%), PS with 14.5 \(\pm\) 2.7% (10.4-17.3%), PA with 3.9 \(\pm\) 3.4% (9.3-22.4%) and PVC with 6.2 \(\pm\) 3.1% (1.5-10.7%). Different studies reported that most frequent polymers in urban wastewaters are PS (20-90%), PE (5-60%), PP (2-40%),  
Figure 5: Shape variation of microplastics in influent, secondary effluent and final effluent samples during the period studied.  
PET (3-38%), PA (2-35%) [2; 3; 75] and PVC in low abundances [61]. These data are in agreement with those found here, excepting for PS that was detected in percentages lower than values described in previous works. These variations in the abundance of different type of polymers in the influent are determined by the origin of wastewater that arrives to the WWTP (urban, industrial, agricultural) [74]. As the wastewater stream progresses through the different stages of WWTP, polymers less dense than wastewater, such as PP and PE, increased their proportion, being in the final effluent in percentages around 47.4 \(\pm\) 3.6% and 29.6 \(\pm\) 5.0%, respectively. On the contrary, polymers denser than wastewater, such as PS, PA, PET and PVC, decreased in abundance during the treatment processes due to their facility of settling, so they represented in the final effluent around 21%, whereas in the influent their proportion was notably higher (58%). An example of the FTIR spectra for each polymer are shown in Figure S2.  
Figure 6: Composition variation of microplastics in influent, secondary effluent and final effluent samples during the period studied.  
In addition, it has been analysed the relation between the chemical composition and the shape and colour of MPs during the different treatments in WWTP. Linking chemical composition with colour (Table S3), it is noticeable that, more than 90% of PVC microparticles were purple and yellow. Linking chemical composition with shape (Table S4), in the influent samples it has been found that PVC, PP, PET and PE have percentages of fragments of 98%, 77%, 74% and 67%, respectively. Moreover, 90% of the particles that corresponded to PA were fibres. Foams, pellets and films did not represent an abundance higher than 10% for any polymer.
## 3 Constraint-Handling Techniques in Metaheuristic Algorithms
### Constraint-Handling Techniques  
Metaheuristic algorithms have been used to solve complex optimization problems. In their original versions, these have been designed to solve unconstrained optimization problems. However, optimization problems in engineering usually have constraints. Currently, constraint-handling techniques, such as the PF, FR, SR, and \(\epsilon\)C method, have been the most popular techniques to solve constrained optimization problems [44]. Each one of these techniques is described below.
It has been also observed that \(\kappa_{1}\) exhibits significant fluctuations before or after strong mainshocks [61, 62], which are reflected in its statistical distribution [63, 64]. For the quantification of these fluctuations, the variability \(\beta_{W}\), defined in [61] as the ratio of the standard deviation over the mean value of \(\kappa_{1}\), in an EQ catalog excerpt (i.e., a part of an EQ catalog consisting of \(W\) consecutive EQs) has been employed. It was found that when the size \(W\) of the EQ catalog excerpt is comparable with the number of EQs that usually occurs during the lead time of Seismic Electric Signals (SES) activities \(\beta_{W}\) minimizes before the strongest EQs in California and Greece [65]. We note that SES are low-frequency variations of the electric field of the Earth that precede EQs, see, e.g., References [4, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75]. Moreover, characteristic (local) minima of \(\beta_{W}\) have been identified in California [76], Japan [11], Mexico [77, 78, 79], Eastern Mediterranean [80, 81], and Global seismicity [13, 82] before the strongest EQs, i.e., those in the EQ catalog having a magnitude \(M\) greater than or equal to a target threshold \(M_{t}\), \(M\geq M_{t}\), and these minima are statistically significant precursors [83, 84]. Interestingly, the appearance of these minima has been related [85] to the emission of SES activities and are also simultaneous with changes in the long-range correlations of the EQ magnitude time series [64, 86, 87] (cf. an extensive related review has been published by [PERSON] et al. [88]). Finally, almost a year ago, the combination of the study of the fluctuations \(\beta_{W}\) of the order parameter \(\kappa_{1}\) of seismicity provided an earthquake nowcasting method of estimating the epicenter of a forthcoming strong EQ, which was based on the self-consistent construction of average EPS maps [79, 81]. The present paper focuses on the study of the most recent global seismicity (see Figure 1) by means of NTA when incorporating the advances of the average EPS maps as well as taking advantage of the development of long-range correlations in the EQ magnitude time series upon the appearance of \(\beta_{W}\) minima. We will see that the combination of these methods allows --within a nine-month time window and inside a region covering only 4% of the total studied area--an estimation of the time and place of occurrence of all \(M\geq 8.5\) EQs with only two false alarms during the period from 1 January 1976 to 31 January 2022. The paper is organized as follows: In the next Section 2, the EQ data and the methods that will be used are briefly explained and the results are presented in Section 3. Their discussion follows in Section 4, while our Conclusions are presented in Section 5.
# Heuristic Fuzzy Approach to Traffic Flow Modelling and Control on Urban Networks
## 3 Fuzzy Controller Design
### Fuzzy Controller Design Using Two-Valued Logic for the Inputs and Five-Valued Logic for the Output  
The number of fuzzy rules for the first fuzzy model is eight for the first, second and third junctions, because there are three inputs using two-valued logic.  
The number of fuzzy rules for two-valued logic for the inputs and five-valued logic for the outputs is eight for Junction 1, Junction 2 and Junction 3.  
The following heuristic fuzzy rules, which are based on expert knowledge can be also described as if-then rules below.  
1. If (numbercars1 is short) and (numbercars2 is short), then (greenlight1) is average.
2. If (numbercars1 is short) and (numbercars2 is short) and (numbercars3 is long), then (greenlight1 is short).  
Figure 3: Flow chart of the fuzzy logic controllers.  
* If (numbercars1 is short) and (numbercars2 is long) and (numbercars3 is short), then (greenlight1 is very short).
* If (numbercars1 is short), (numbercars2 is long) and (numbercars3 is long), then (greenlight1 is very short).
* If (numbercars1 is long) and (numbercars2 is short) and (numbercars3 is long) then (greenlight1 is long).
* If (numbercars1 is long) and (numbercars2 is long) and (numbercars3 is short), then (greenlight1 is long).
* If (numbercars1 is long) and (numbercars2 is long) and (numbercars3 is long), then (greenlight1 is average).  
The number of membership functions for the output with five-valued logic is presented below, where 'trim' means the triangular membership function from the MATLAB script.  
MF1 ='very short': 'trim', [0 0 25]  
MF2 ='short': 'trim', [0 25 50]  
MF3 = 'average': 'trim', [25 50 75]  
MF4 = 'long': 'trim', [50 75 100]  
MF5 ='very long': 'trim', [75 100 100]  
The value 100 is measured in seconds, and it is the full cycle of the green light for the two outputs.  
The results for the fuzzy controller computations and the simulation of the Aimsun platform are given in Tables 1-4.  
The number of fuzzy rules for the fourth junction with two inputs and one output is 4, according to fuzzy logic description for the number of rules and topology of Junction 4.  
The following heuristic fuzzy rules, which are based on expert knowledge, can be also described as if-then rules below.  
1. If (number cars1 is short) and (number cars2 is short), then (greenlight1) is long.
2. If (number cars1 is short) and (number cars2 is long), then (greenlight1 is medium).
3. If (number cars1 is long) and (number cars2 is short), then (greenlight1 is very short).
4. If (number cars1 is long) and (number cars2 is long), then (greenlight1 is short).
# Virtual Pseudonym-Changing and Dynamic Grouping Policy for Privacy Preservation in VANETs
## 9 Analysis and Discussion
### Algorithm Complexity  
We analyze the complexity of algorithms used in the proposed solution in terms of time. Time complexity is the time taken by algorithms to perform a specific task. Our proposed solution has three main algorithms, i.e., vehicle grouping, pseudonym-changing protocol, and position-mixing method. The reason to calculate the time complexity of algorithms is to identify the proposed solution's total running time concerning privacy protection. Higher time requirements for privacy scheme certainly affects other services of the VANETs such safety services and infotainment services. The complexity of the algorithms is given below.
# Quantum Dual Signature with Coherent States Based on Chained Phase-Controlled Operations
## 2 Methods
### Quantum Dual Signature Scheme
#### 2.3.1 Initial Phase  
In this phase, the participants share the necessary keys and [PERSON] blinds her messages based on agreed principles. The procedures are described as below.  
1. [PERSON] has two classical messages, i.e., the order message \(M_{O}=\otimes_{s=1}^{x}m_{O}^{s}\) and the payment message \(M_{P}=\otimes_{t=1}^{y}m_{P}^{t}\), where \(m_{O}^{s}\), \(m_{P}^{t}\in\{0,1\}\). \(x\) and \(y\) separately stand for the lengths of \(M_{O}\) and \(M_{P}\).
2. Based on QKD protocols, the original keys \(K_{AB}^{ori}\), \(K_{AT}^{ori}\) and \(K_{BT}^{ori}\) are shared between [PERSON] and [PERSON], [PERSON] and [PERSON], and [PERSON] and [PERSON], respectively. \(K_{AB}^{ori}\) has \(x+y\) bits. The lengths of \(K_{AT}^{ori}\) and \(K_{BT}^{ori}\) are \(y\) bits.
3. According to the method mentioned in the section of preparation of the key, the final keys \(K_{AB}\), \(K_{AT}\) and \(K_{BT}\) are obtained. Each of them is a disorderly permutation of \((1,2,\ldots n)\).  
Figure 1: Schematic representation of the quantum dual signature scheme. PCOs and DPCOs separately stand for encryption and decryption based on CPCO. \(||\) represents the connection operation.  
[PERSON] selects a random \(x\)-bit binary sequence \(K_{RO}\). According to each \(m_{O}^{s}K_{RO}^{s}\), [PERSON] prepares the relevant coherent state. The corresponding relationships are shown in Table 1. Therefore, the order information is blinded into \(|M\rangle_{O}^{\prime}=\otimes_{s=1}^{x}|M\rangle_{O}^{s}\).
5. Similarly, [PERSON] generates another \(y\)-bit binary sequence \(K_{RP}\). Based on each \(m_{P}^{t}K_{RP}^{t}\), she acquires blind payment message \(|M\rangle_{P}^{t}=\otimes_{t=1}^{y}|M\rangle_{P}^{t}\). The corresponding relations are shown in Table 2.
## 3 Methodology  
### Sam  
Frequency domain filtering involves eliminating unimportant frequency components while preserving or enhancing the significant ones, which aligns with the concept of the attention mechanism. SAM is an attention mechanism that acts on the spectrum. Its workflow is shown in Figure 3. Specifically, it incorporates a trainable parameter mask that matches the dimension of the input signal. This mask signifies the weight assigned to each frequency component, initially set to 1. During the training process, these weights are adjusted to achieve adaptive filtering and enhance feature extraction. In addition, SAM uses DCT instead of traditional DFT to implement frequency domain transformation, which has the following main benefits: (1) The coefficients of DCT are real numbers, while the coefficients of DFT are complex numbers. This feature makes DCT more suitable for the optimization algorithm based on gradient descent. (2) DCT performs better when processing signals with trends. In practical applications, time series often contain trend components, and DFT may be troubled by the problem of \"frequency leakage\" when representing simple trends. (3) When the adjacent values are highly correlated, DCT can achieve better energy concentration than DFT. In time series, adjacent data points usually have a certain correlation, which makes DCT more advantageous in extracting key features. The forward propagation of this layer is outlined in Algorithm 1.  
```
0: Captured signal sequence \(x^{n}\)
0:\(x^{n}_{filtered}\)
1: Initialize: All-ones learnable array mask\({}^{n}\)
2: # Transform the input series into frequency domain \(sp^{n}\gets DCT(x^{n})\)
3: # Element-wise multiply spectrum by mask mask\(\_sp^{n}\gets sp^{n}\cdot\textit{mask}^{n}\)
4: # Transform the spectrum back into the time domain \(x^{n}_{filtered}\gets IDCT(masked\_sp^{n})\)
```  
**Algorithm 1** Spectrum Attention Mechanism (SAM)  
SAM is a simple attention mechanism, but it has an inherent defect. It relies on the entire signal spectrum, failing to capture the phase information inherent in the original signal. To preserve some of this crucial phase data, we employ segmented SAM (SSAM). SSAM works by utilizing a sliding window to split the original sequence into K equal-length segments, subsequently applying SAM to each segment individually. The resulting SAM outputs from these segments are then concatenated along the channel dimension to form the output features. The core algorithm is outlined in Algorithm 2.  
Figure 3: Spectrum Attention Mechanism.  
Compared to existing filters, the SAM filter has the following advantages: (1) SAM achieves adaptive filtering by assigning appropriate weights to each frequency component. This mechanism can be dynamically adjusted based on the characteristics of the data, which helps reduce the impact of noise and improve the robustness of the model. (2) Traditional attention mechanisms may overlook temporal information. The SAM filter introduces SSAM to segment raw data using a rolling window and apply SAM on each segment, thereby preserving temporal information and improving classification accuracy.
## 3 Cable Terminal-Sheath Structure Electric Field Simulation Research
#### 3.2.2 Air Gap between the Cable Terminals and Sheath, with No Interfacial Wetting
It can be seen that under normal working voltage, there is no dirt on the outer surface of the cable terminal, and the water vapor does not penetrate the inner surface of the insulating sheath. Even when there is an air gap at the interface, this does not lead to partial discharges within the air gap. Figure 12: The electric field cloud image on the interface when the air gap position is different. (**a**) No air gap; (**b**) air gap position 1; (**c**) air gap position 2; (**d**) air gap position 3; (**e**) air gap position 4; (**f**) air gap position 5. 2. Effect of different air gap lengths on electric field distribution in cable termination-sheath structures  
To fix the air gap at position 2, we keep the thickness of the air gap unchanged at 1 mm; the lengths of the air gaps are 1 mm, 3 mm, and 5 mm, respectively, to analyze the influence of different air gap lengths on the interface electric field. As shown in Figure 14, with the increase in air gap length, the maximum electric field and average electric field inside the air gap increase. When the air gap length is 1 mm, the maximum distorted electric field is 7 \(\times\) 10\({}^{4}\) V/m. When the air gap length is 3 mm, the maximum distorted electric field is 7.9 \(\times\) 10\({}^{4}\) V/m. When the air gap length is 5 mm, the maximum distorted electric field is 8.1 \(\times\) 10\({}^{4}\) V/m. It can be seen that with the increase in air gap length, the maximum electric field near the air gap gradually increases, but the increasing range gradually slows down. In general, the size of the air gap has little effect on the electric field distortion at the sub-interface. Therefore, in the subsequent simulation analysis, when considering the air gap on the interface, the length of the air gap is 1 mm, and the thickness of the air gap is 1 mm.
# Application of Artificial Neural Networks in Assessing Mining Subsidence Risk
## 4 Analysis and Discussion  
The analysis of this study consisted of two stages: analysis A and analysis B. Analysis A sought to predict whether or not subsidence would occur using an ANN. Analysis B established a mine subsidence risk (MSR) index using an interaction matrix to indicate the extent of risk for subsidence at any given location. The results of both analyses were integrated into a single classification system referred to as the subsidence risk grade, which categorizes the location based on risk level.
## 5 Results and Discussion  
This section presents the evaluation of the proposed techniques using MATLAB R2022a simulations, chosen for its ability to accurately model the complex dynamics of energy-efficient communication in heterogeneous WSNs and IoT environments. MATLAB provides robust numerical computation capabilities, which are essential for implementing the proposed cluster-head and relay-node rotation algorithms in a precise and controlled manner. Moreover, MATLAB's advanced visualisation tools offer a clear means for comparing the performance of different quality of service (QoS) metrics, such as energy consumption, network lifetime, and throughput--metrics that are crucial for assessing the effectiveness of the proposed methods.  
The choice of MATLAB as the simulation environment is not arbitrary but is instead keeping in view its wide use for evaluating state-of-the-art protocols, such as RLEACH, CRPFCM, and EERPMS. This consistency across studies facilitated a fair and reliable comparison between the proposed methods and existing techniques, ensuring that the novel contributions of this research are accurately evaluated against established approaches.  
The evaluation metrics used in this study, i.e., energy consumption, network lifetime, and throughput--are central to the performance assessment of WSN-based IoT systems, particularly in the context of heterogeneous sensor networks. These metrics were chosen due to their direct correlation with the main objectives of the study, i.e., improving energy efficiency, extending the operational lifespan of the network, and maximising the amount of data successfully transmitted to the base station.
## 7 Numerical and Results
### Average End-to-End Transmit Energy Consumption \(\mathrm{Y}\)  
In Figure 7a,b, we can observe that \(\mathrm{Y}\) of NN is considerably smaller compared to FN. As \(\beta\) increases, \(\mathrm{Y}\) decreases for FN, while that for NN increases. Hence, the gap between the two routing schemes becomes smaller, as \(\beta\) increases. In addition, the theory-simulation gap is smaller for \(L=250\) m compared to \(L=125\) m because the error between the two decreases as the end-to-end separation \(L\) increases.
## 2 VSG-Based Frequency Control  
The system frequency indicates the overall power balance regulation between the generation and the consumption at any instantaneous time. In the traditional SG-dominated power system, the active power-frequency control is a series of multiple temporal stages, which can be divided into four parts [32], as shown in Table 1.  
The concept of VSG is to mimic the dynamic properties of SGs to let both behave with approximately synchronous responses to load changes. Therefore, the emulations of virtual inertia and the virtual speed governor are indispensable. For PEI-based renewable generation, the external characteristics are largely determined by the supplementary controller. In theoate studies, among the VSG implementations in different orders, the simplest second-order model is with better stability in transients [33] and can be combined with virtually any VSI control strategies based on a cascade structure [34]. By the modification of the supplementary controller, the VSG algorithm enables the PEIs to function in the same way as SGs with the capability to perform frequency response and frequency regulation in multiple temporal stages.
# A NavCom Signal Authentication Scheme Based on Twice Two-Way Satellite Time Transfer
## 3 Performance Analysis
### Authentication Efficiency  
Since the time to complete once TWSTT is much shorter than the time interval between twice TWSTT, the authentication time of the present authentication scheme may be equal to the time interval between twice TWSTT. Based on the aforementioned analysis, it is evident  
Figure 10: Detection probability of authentication scheme at different spreading code rates.  
Figure 9: Detection probability of the authentication scheme at different satellite altitudes.  
that the optimal authentication elevation angles for the user terminal are determined to be 30\({}^{\circ}\) and 90\({}^{\circ}\). Consequently, the authentication time can be estimated as the duration when the elevation angles of both the satellite and the user terminal change by 60\({}^{\circ}\). Additionally, it should be noted that the movement speed of the satellite is closely linked to its altitude in orbit, which further affects the authentication time. Assuming that the altitude of the user terminal is 0 m and the orbital altitude of the LEO satellites is 500-1500 km, Figure 11 shows the authentication time with different satellite orbital altitudes  
Figure 11 clearly illustrates that there is a positive correlation between satellite orbital altitude and the required authentication time, resulting in longer authentication times at higher altitudes. The authentication times vary between approximately 2 to 6 min.
# Steering Dynamic and Hybrid Steering Control of a Novel Micro-Autonomous Railway Inspection Car
## 2 Mathematical Model of the Lateral Dynamic System  
The structure of the proposed autonomous inspection vehicle was designed to enable easy installation or moving off the track by humans or robots; its size and weight were minimized to the greatest extent possible, as illustrated in Figure 1a. The car was 1360 mm long, 1602 mm wide, and 340 mm high, with a wheel profile that followed International Union of Railways code 510-2 to fit a typical track gauge (EN-60 rails) of 1435 mm. The vehicle had a total mass of 200 kg and operated at a speed of 30 km/h.  
The vehicle was compact, lightweight, integrated, self-driving, and stable, with rapid detection and a cutting-edge intelligent inspection system involving camera and laser technology. Figure 1a illustrates the layout of the vehicle components and systems. Additionally, a gauge sensor was used to determine the rails' offset, and artificial intelligence deep learning techniques were applied to increase the accuracy of recognition of rail wear and fastener fault. The vehicle adopted a magneto-rheological mount system (MR mount) designed to reduce vertical vibration during vehicle operation, increasing stability to meet the inspection system's working requirements [11].  
Figure 1: (**a**) The micro-autonomous railway inspection car model and (**b**) steering system of the car. 1. Left DC motor. 2. MR mount. 3. Floor. 4. Battery. 5. CPU. 6. Braking system. 7. Inspection system (laser, camera, light). 8. Laser. 9. Frame. 10. Right DC motor. 11. Rail.  
DC motors on the two front wheels were used to steer the vehicle, as presented in Figure 1b. To reduce vehicle weight, the wheels were made of aluminum and covered with polyurethane to prevent damage during operation. Speed control of the two DC motors was used to control the transmission and guide the vehicle.