esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The main topic of the presented work is the evaluation of the in-line viscometer (VIS) operation installed on the industrial line for polyethylene terephthalalate (PET) foil production. The tests were performed during the regular operation of the machine, which results from the need to maintain production continuity. Polymer viscosity control is of particular importance in the production of degradable materials and recycled polymers. The processing of PET film is, therefore, a particularly difficult issue due to the presence of both of these problems at the same time. The conducted research had a two-pronged character: assessment of the correct operation of the viscosty measurement system and testing of the effectiveness of reactive additives during the extrusion process. Measurements were carried out with the use of several types of input materials, including recycled blends. The key tests were carried out with the addition of viscosity modifiers. Measurements conducted during the extrusion process confirmed the effectiveness and high sensitivity of the in-line system (VIS), while clear changes in the polymer flow characteristics were observed only after the addition of chain extenders. The in-line measurements revealed that the addition of 1% of the reactive compound increased the viscosity from the initial 150 Pa-s to over 350 Pa-s. The most significant increase in viscosity for the additive based on pyromellitic dihanhydride (PMDA) confirms the effectiveness of the reactive extrusion method and the suitability of the used measuring. During further analysis, the obtained films were also tested. The results showed no negative effects of the reactive extrusion on the mechanical performance of the foil; however, for recycled materials, the average values of tensile strength and elongation at break have deteriorated. A positive aspect of the use of reactive additives was the greater uniformity of mechanical properties. For some materials, there was a significant increase in the haze factor (transparency), which should be considered a disadvantage.  
polyethylene terephthalalate; foil manufacturing; reactive extrusion; in-line rheology +
Footnote †: journal: _Sciences_
## 3 Test Methods
### Measurement Point Layout  
In this study, six measurement points were arranged above the tunnel of the Taizhou S1 Line underground section, with one point placed every 10 m (see Figure 1). The selection  
Figure 1: Schematic diagram of the location of tunnel and ground measuring points.  
Figure 2: Tunnel section and elevation.  
of six measurement points with a 10 m interval was based on the following reasons. The six points cover various locations above the tunnel, ensuring a comprehensive capture of the vibration propagation characteristics at different distances and directions. The 10 m interval was chosen to balance measurement density with data management complexity, allowing for detailed recording of vibration wave propagation while avoiding excessive density that could lead to data redundancy [26]. The measurement points were evenly distributed along the tunnel's exterior, and measurements of vibration acceleration were taken in the x, y, and z directions to ensure a thorough analysis of vibration propagation characteristics in all directions.  
The measurement points from P2 to P6 were selected in an area with an open spatial layout, where surrounding buildings and other potential sources of interference are minimal. This choice effectively reduces external vibration disturbances, ensuring that the measured vibration data primarily reflects the vibrations generated by the train passing through the tunnel. The measurement points are symmetrically and evenly distributed along the tunnel's exterior, allowing for the capture of vibration propagation characteristics in various directions and providing comprehensive vibration propagation data.
###### Abstract  
This study aims to develop a real-time assessing methodology for a power systems voltage stability. The proposed algorithm is based on the Thevenin equivalent (TE) impedance estimation method, which applies the phasor measurement unit technology practically. To present an accurate analysis of the real-time situations of a power system, the developed voltage stability index can be used as useful information for system operators to establish appropriate countermeasures. Moreover, by considering the results of voltage stability margin calculation within the Korean power system, the effect of voltage stability on the dynamic behavior of the system is presented. Furthermore, to increase the accuracy, load model parameter estimation is introduced in this algorithm. The load model might be used for calculating the stability margin more accurately. The power-voltage curve is drawn in theory using the TE voltages and impedances. To validate the case study of the proposed method, simulations were executed using the Matlab software. The simulations demonstrated the effectiveness of the proposed method and detected voltage stability/instability under severe contingency scenarios.  
Keywords:phasor measurement units; thevenin equivalent circuit; thevenin impedance; voltage instability index; voltage stability margin; wide area monitoring system +
Footnote †: journal: Applied
## 2 Risk Modeling Techniques
### Statistical Risk Models
#### 2.1.1 Statistical Risk Models for Renewable Energies  
Risk assessment in renewable power plants involves addressing various technical, environmental, and financial uncertainties. Renewable generation variability can affect energy production predictability, creating uncertainties in power supply. Therefore, a new concept of energy-based availability has been introduced.  
Renewable generation availability modeling can be based on either operation time or energy production.  
Time-based availability is the percentage of time a system is available compared to the total possible time that it could be available. On the other hand, production-based availability builds on this by also considering the loss of production during downtimes. It measures the actual production in a given time period compared to the maximum possible production if the system operated at full capacity the entire time. Time-based availability is simpler to compute but may not fully capture the risk of energy losses. Production-based availability offers a more accurate representation of these risks if energy values are precise. The time-based availability can be calculated as follows [6]:  
\[A_{T}=\frac{A\textit{available time}[\text{hour}]}{\textit{Total studied time}[\text{hour}]}=1-\frac{\textit{Unavailable time}[\text{hour}]}{\Big{(}\textit{Available time}\Big{)}[\text{hour}]} \tag{7}\]  
where \(A_{T}\) is the time-based availability.  
The production-based availability method evaluates energy production as a fraction of expected energy, offering a clearer view of operational risks. It can be modeled as follows [7]:  
\[A_{P}=\frac{Actual\ energy\ production[\text{kWh}]}{Expected\ energy\ production[\text{kWh}]}=1-\frac{\ Lost\ production[\text{kWh}]}{\left(\begin{matrix}Actual\ energy\ production\\ +\ Lost\ production\end{matrix}\right)\ [\text{kWh}]} \tag{8}\]  
where \(A_{P}\) is the production-based availability.  
Risk management in renewable energies' operations involves advanced predictive maintenance, robust design, and operational strategies adapted to specific local conditions. Using sensors and real-time data analytics helps preemptively identify and address potential failures, while designing turbines to withstand local environments and employing flexible operational tactics can mitigate various risks associated with renewable energy production.  
Full-time availability of renewable power plants is the ratio of hours deemed available as a fraction of the full period, such as the month or the year. The advantage of full-time availability is considering the effect of maintenance on the availability of renewable power plants.  
The full-time availability can be calculated as follows:  
\[A_{F}=\frac{Number\ of\ available\ hours\ [\text{hour}]}{Total\ period\ [\text{hour}]} \tag{9}\]  
where the total period is 8760 h in a year.
# MPFM-VC: A Voice Conversion Algorithm Based on Multi-Dimensional Perception Flow Matching
## 5 Results
### Experimental Results
Nevertheless, its overall performance was slightly lower than MPFM-VC, particularly in preserving speaker-specific features during more complex singing phrases, as reflected in the SECS and WER scores. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Evaluated Models** & **MOS (\(\uparrow\))** & **MCD (\(\downarrow\))** & **WER (\(\downarrow\))** & **SMOS (\(\uparrow\))** & **SECS (\(\uparrow\))** \\ \hline GT & \(3.55\pm 0.03\) & - & 21.21\% & \(3.73\pm 0.06\) & 0.82 \\ Free-VC & \(3.07\pm 0.03\) & 7.24 & 25.82\% & \(3.21\pm 0.04\) & 0.62 \\ Diff-VC & \(2.49\pm 0.06\) & 8.85 & 35.20\% & \(2.21\pm 0.05\) & 0.52 \\ DDDM-VC & \(2.87\pm 0.06\) & 8.42 & 32.61\% & \(2.89\pm 0.05\) & 0.59 \\ Voiceflow-VC & \(3.12\pm 0.04\) & 7.33 & 22.61\% & \(3.34\pm 0.05\) & 0.69 \\ \hline MPFM-VC (ours) & \(3.28\pm 0.03\) & 6.89 & 15.37\% & \(3.53\pm 0.05\) & 0.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of voice conversion under low-quality conditions. MPFM-VC maintained relatively high speech quality and speaker similarity under low-quality input conditions. It yielded the highest scores across the reported evaluation metrics--including MOS, MCD, WER, and SMOS--and its spectrograms retained structural clarity. This outcome may be related to the integration of multi-dimensional feature-aware flow matching, which enables more detailed modeling of speech under varying noise conditions. Additionally, the content perturbation-based training augmentation may improve the model's robustness to missing or degraded input features, and the adversarial training on speaker embeddings may contribute to better timbre preservation under noisy conditions.
## 2 EPIC Earthquake Early Warning System
### Estimation of Rapid Earthquake Magnitude  
Earthquake magnitude estimation is not only an important but also difficult part in EEW systems. Initial testing of the default system after implementation showed that the system worked well except for large discrepancies in magnitudes. A major focus of the manuscript is thus on describing the derivation of improved regression results for magnitude estimation. The magnitude, a measure of the energy released during an earthquake, is estimated using empirically derived linear relationships [31]:  
\[M=5.39+1.23\ log_{10}(Pd)+1.38\ log_{10}(R) \tag{1}\]  
where \(Pd\) is the peak displacement (in cm) and R is epicentral distance (in km). The equation parameters, optimized for California, were derived using a global dataset (Northern and Southern California, Japan, and real-time data from California). The relationship had a mean magnitude error of 0.01, standard deviation of 0.31, and a correlation of 0.95 [31].  
[PERSON] et al. (2007), using the data set from northern California, found the relationship between magnitude with \(T_{p}^{max}\):  
\[M=5.22+6.66\ log_{10}\Big{(}T_{p}^{max}\Big{)} \tag{2}\]
## 3 Results, Analysis, and Discussions
### Experimental Dam-Break Test of the IBS Frame
#### 3.2.2 IBS Frame Test 2 for 2 m Reservoir Water Level  
The second test on the same IBS frame was conducted according to the first test. The reservoir water height of 2 m was used for furthering the investigation to acquire more information about the impact of hydrodynamic force on the frame. In the first 3 s of the test, the hydrodynamic force reached the structure, and the hydraulic jump spread to the whole front face of the frame. After 6 s of the test, the hydrodynamic force increased against the frame, and it deflected backward due to the increases in the hydrodynamic forces. Figure 16a,b show the structures after 6 s from the start of the test. The height of the water reached 50 cm on the frame, and the total volume that impacted the structure was around 3.94 m\({}^{3}\).  
After the dissipation of the impounded water from the surrounding area of the frame, the damages to the brick walls were clearly visible. It can be seen that the 2 m impounded  
Figure 16: Dam-break test 2 for 2 m reservoir water level: (**a**) front of the frame; (**b**) back of the IBS frame.  
Figure 15: Graph of 1 m reservoir water level test of the frame.  
water depth caused a higher impact on the structure than the 1 m impounded water depth. Figure 17 displays the damaged areas on the brick walls.  
During this test of 2 m impoundment water depth, all the pressure meters detected the impact of hydrodynamic forces on the structure. PM5 detected the highest impact (58.01 kN) after 8 s, followed by PM9, with a recorded value of 37.41 kN after 6 s of the test. PM9 was used to detect the vibration of the bolt during the deflection of the structure. The structure was deflected because of the high impact of hydrodynamic forces. Figure 18 illustrates the graph of the test results.
# An Overview of Cooperative Driving in the European Union: Policies and Practices
## 4 Current State of the Art of C-ITS Systems
### C-Roads Pilot Sites
#### 4.1.9. Spain  
Similar to Portugal, C-Roods Spain develops through five pilot sites:  
* DGT 3.0--located along the overall road network in Spain, with an extension of approximately 12,270 km. It will be deployed by adopting cellular-based communication technologies (3G and 4G/LTE).
* SISCOGA Extended--including the extension of an existing test site infrastructure in the city of Vigo and its metropolitan area, which is already prepared to test ITS-G5 communication technology. It will cover 150 km.
* Madrid Calle 30--located along the road 'Calle 30' in Madrid, approximately 32 km long. C-ITS services will be deployed by using a hybrid G5/cellular approach.
* Cantabrian pilot--deployed along approximately 75 km in northern Spain, by using hybrid communications.
* Mediterranean pilot--deployed along approximately 125 km at selected road sections in Catalonia and Andalucia using hybrid technologies  
Such areas present a high level of heterogeneity, thus allowing a wide spectrum of use-cases to be tested involving almost all Day-1 applications, except for TJW services, and various Day-1.5 functions, namely VRU, CCN, and infotainment tasks. Moreover, cross-border tests with Portugal are also planned within the SISCOGA Extended pilot site.  
A large number of partners are involved, including public authorities (Direccion General de Trafico, Direccion General de Carreteras, and Madrid Calle 30 SA), associations (ITS Spain, MLC ITS EUSKADI), private companies (Transport Simulation Systems SL, Indra Systemas SA, Ingartek Consulting SL, and SenseFields SL), Universities (Universidad Politecnica de Madrid, Universitat Politecnica de Catalunya, and Universitat de Valencia) and research centres (Asciacion Centro Tecnologico Ceit-IK4, CTAG). According to the planned timeline, the programme will be rolled out during 2019 in two phases, while 2020 will be devoted to evaluation.
## 3 Results  
In order to investigate the potential of using _Csd_ alleles as a marker for patriline identification in honeybee colonies and compare its usefulness with sets of commonly used microsatellite markers, we analyzed worker bees from five colonies headed by naturally inseminated one-year-old queens. We analyzed five microsatellite _loci_ (A113, Ap43, Ap55, B124, and A7); the number of markers used was similar to the numbers of microsatellite loci analyzed in other studies detecting patrilines in bee colonies [4; 5; 29; 30; 31; 32]. Additionally, we amplified the region of the _Csd_ gene, encoding the potential-specifying domain (Figure 1). In the case of the _Csd_ fragment, the amplification product was digested with the _Asel_ restriction enzyme. The amplification products were analyzed using capillary electrophoresis.  
The number of distinct _Csd_ alleles identified in each colony was determined by the number of combinations of the lengths of 5\({}^{\prime}\)- and 3\({}^{\prime}\)-fragments and compared to the number of distinct microsatellite markers (Table 1).  
The results show that the number of different _Csd_ alleles was greater than any of the other marker _loci_ in all the analyzed colonies. The mean number of identified _Csd_ alleles was 17, whereas, in the case of other markers, the mean number was within the range of 4 to 8.8. To identify the number of patrilines in each colony, we reconstructed the paternal genotypes of the analyzed worker bees. To undertake this task, we characterized the maternal _loci_ by genotyping the drones and then identifying the paternal alleles in the worker bees as those that were not the maternal alleles. All worker bees analyzed in our study carried one of the two alleles which were carried by the drones present in the colony, and therefore, those two alleles were identified as the maternal alleles.  
The results show that using _Csd_ genotyping alone, we were able to identify more patrilines than when using the combined set of microsatellite markers. However, using both microsatellite and _Csd_ genotyping enabled us to discover substantially more (from \(\times\) 1.1 to \(\times\) 1.6) patrilines than _Csd_ alone (Table 2).
# Industrial Image Anomaly Detection via Self-Supervised Learning with Feature Enhancement Assistance
## 2 Related Work
### Reconstruction-Based Approach  
One of the unsupervised anomaly detection methods is reconstruction-based method, where the basic assumption of the reconstruction model is that the model performs better for reconstructing normal images, whereas for anomalous images, there will be a large reconstruction error, which serves as an indicator for anomaly detection. Most reconstruction-based methods train the network for reconstruction using Auto-Encoder (AE) [12], Variable Auto-Encoder (VAE) [13], or Generative Adversarial Networks (GAN) [14]. However, due to the strong generalization of the performance of such neural networks, the anomalous regions are also reconstructed well, which seriously affects the anomaly detection performance.  
To solve this problem, RIAD [15] transforms anomaly detection into an image repair problem by introducing a random mask and attempting to use the mask to cover anomalous regions, making anomalies invisible to the autoencoder. InTra [16] further formulates anomaly detection as a patch sequence inpainting problem and presents a solution through a deep Transformer network architecture solely comprising stacked multi-headed self-attention blocks, where convolutional operations are entirely excluded. The literature [17] utilized a Vision Transformer (ViT) [18] as the backbone network to comprehend the global context among image patches, while concurrently introducing a memory module to alleviate the issue of reduced detection accuracy caused by abnormal generalization. SCGAN [19] uses a CopyPaste image enhancement module to simulate anomalies and assist the network in semantic reconstruction. FEGAN [20] employs a feature extraction network to capture the essential characteristics of images, while an Improved Generative Adversarial Network (IGAN) structured on an asymmetric encoder-decoder framework performs the task of the normal reconstruction of abnormal regions under the condition of insensitivity to distinguishing positive and negative class images. DSR [21] generates feature-level anomalies by sampling the learned quantized feature space and uses a dual-decoder architecture to improve reconstruction accuracy. DRAEM [22], by generating anomalies, employs a reconstruction sub-network and a discriminative sub-network to convert the anomaly detection problem into a discriminative problem. SSPCAB [23] employs the concept of masked autoencoders to reconstruct feature maps, thereby learning the distribution of normal images.
###### Abstract
They used underwater target highlighting structure features to obtain underwater target orientation heading features using quadratic least-squares fitting. The experimental simulation results show that the target scale can be recognized by the target orientation heading features. However, when encountering strong interference, such as acoustic decoys with virtual scale, the active sonar makes a significant error in estimating the target scale. It is difficult to accurately identify the target by echo broadening and apparent angle (azimuthal orientation). In this paper, we first simulate the echo signals received by torpedoes from submarines, suspended acoustic decoys, and mobile acoustic decoys for virtual-scale simulation. Secondly, we estimate the parameter of each target echo signal to obtain their echo broadening and apparent angles, which are converted into apparent scales, respectively. Then, we investigate the correlation between the echo broadening and the apparent angle of different targets. We thus propose a method to recognize the true and false targets based on the correlation between the target's echo broadening and apparent angle, which solves the problem that torpedoes are facing in recognizing the scale features of acoustic decoys simulating virtual scales. In addition, the method improves the ability of active sonar to perform target scale recognition with strong interference. Figure 1: The torpedo attack posture.
# Recent Progress on Irradiation-Induced Defect Engineering of Two-Dimensional 2H-MoS2 Few Layers
## 6 Irradiation-Induced Properties
### Band Structures  
Bulk MoS\({}_{2}\) is a semiconductor with an indirect band gap of 1.23 eV [36; 170]. The band gap of the material depends on thickness [171], as shown in Figure 2. Figure 32a shows the band structure of MoS\({}_{2}\) mono-layers without vacancies. MoS\({}_{2}\) mono-layers have a direct band gap of 1.8 eV, resulting in strong absorption, PL bands [38; 39], and electroluminescence [172] near 680 nm.  
Defects affect the band gap of MoS\({}_{2}\) layers significantly. Figure 32b-c shows calculated band structures of MoS\({}_{2}\) mono-layers with defects. Negative (S vacancies) and positive (Mo vacancies) charges transferred to the orbitals of nearest neighbor Mo or S atoms around the vacancies, resulting in additional states at the bonding level and at mid-gap of the band structures. The _K_-point direct band gap remained intact for MoS\({}_{2}\) mono-layers with S vacancies, while the gap value was reduced. The direct band gap at _K_-point was reduced too for MoS\({}_{2}\) mono-layers with Mo vacancies.