esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Synchronous Generators' Local Mode Eigenvalues in Modern Power Systems
### Limitations and Aims of This Work  
The paper is focused on a detailed investigation of **local oscillation modes**. The presented work is related to the study of oscillations in the Slovenian power system. The Slovenian power system covers a small area (20,271 km\({}^{2}\)) and has strong 400-kV transmissions between the country and neighboring countries. It has 74 synchronous generators with nominal power >1 MVA. The majority of generators are of small nominal power (between 10 and 40 MVA); only two generators are large (813 MVA in the nuclear power plant and 727 MVA in the thermal power plant). Due to most small and well-distributed generators and their direct connections (over block transformers and relatively short transmission lines) to the stiff backbone, the local oscillation modes are pronounced in the Slovenian power system.  
In this paper, we have tried to answer the following questions:  
* What are the frequencies and dampings of local oscillations for different generators? Can we estimate the area where the eigenvalues of local oscillation modes for other generators lie?
* How does the generator's operating point (i.e., its active and reactive power) affect its local mode eigenvalues? Is this effect similar for different generators (size, age, type)? What impact does the connection to the infinite bus have?
* What is the impact of control systems on local mode eigenvalues? The purpose of the obtained findings will be:
* The prognosis (estimation) of the local oscillations without measurement.
* In the case of available measured data of existent oscillations (instantaneous values of produced three-phase powers and rotor speeds of generators), the classification of the measured oscillations in the local oscillations (and exclude them from torsional oscillations, control oscillations, and inter-area oscillations).
* To prevent activities that would cause local mode eigenvalues to become stability-problematic.
* To make these eigenvalues better damped (PSS design does not fall within the scope of this paper).
# Texture Analysis of Near-Infrared Vein Images During Reactive Hyperemia in Healthy Subjects
## 4 Discussion
### Possible Relation with Movement Artifacts  
Although entropy has previously been interpreted as a marker of local structural complexity potentially influenced by venous occlusion, our current findings revealed no statistically significant variation in this parameter across the phases of the protocol. This stability might initially suggest the absence of relevant physiological changes in pixel-level complexity. However, it also invites reflection on potential confounding factors, particularly motion-related artifacts, which are common in superficial imaging of the hand dorsum. In this context, minor involuntary movements or positional adjustments, especially under cuff-induced pressure or transient discomfort, could introduce small displacements of the skin surface. Such movements might affect pixel alignment within the ROIs and generate slight fluctuations in grayscale distribution, which are known to impact texture-derived measures such as entropy [51]. Yet, the absence of significant entropy variation in our results may suggest that any motion-related variability was minimal or effectively neutralized by averaging across frames and subjects. Moreover, supporting the robustness of our measurements, no parallel changes were observed in other texture features known to be sensitive to image disruption, namely contrast, energy, and homogeneity. This pattern strengthens the interpretation that the image texture remained globally stable and that movement was unlikely to have masked substantial entropy dynamics. Nonetheless, the possibility that motion artifacts subtly influenced image consistency cannot be entirely excluded. To further reduce this potential source of noise in future studies, we recommend implementing motion correction algorithms or image registration techniques [52], particularly when working with small ROIs and manually defined regions. Additionally, using high-frame-rate imaging or mechanical stabilization of the hand could enhance the temporal resolution and minimize intra-procedural variability. Ultimately, the lack of significant entropy variation, despite clear changes in vein width and correlation, likely reflects a true physiological decoupling. Alternatively, it may highlight the need for composite indices, such as entropy-to-width ratios, which more effectively capture the dynamic interplay between texture and morphology.
## 3 Results and Discussion
### Conductivity Measurement  
It can be seen from Figure 13 that the resistivity of GO-PANI and PDA is 0.227 \(\Omega\cdot\)cm\({}^{-1}\) and 803 \(\Omega\cdot\)cm\({}^{-1}\), respectively. After polymerization, the resistivity of GO-PAN-PDA is close to that of PDA, which is much larger than that of GO-PANI. This result suggests that the addition of PDA to GO-PANI not only maintains the sheet-like barrier properties of GO-PANI, but also increases the resistivity of the material and reduces the corrosion current, thereby enhancing the anti-corrosion performance of the composite. However, when the amount of doped PDA is small, the resistivity is relatively large. It can be reasonably explained that the excessive addition of PDA in the anti-corrosion performancetest decline the anti-corrosion performance. It is worth noting that although the less PDA is added, the higher the resistivity is, a certain amount of PDA is also required to support the compatibility and adhesion between materials.
## 3 Discussion
### Potential Improvements and Updates to the Algorithm  
During the processing of the SEN4 LST product and analysis of results, some potential improvements to the algorithm have been identified:  
1. SW coefficients were obtained from simulated data using 61 atmospheric profiles representative of different atmospheric conditions. A new more completed atmospheric database has been recently constructed with around 5000 atmospheric profiles, which could be used to compute a new set of algorithm coefficients. It would be also possible to separate between daytime and nighttime atmospheric profiles, thus providing a set of SW coefficients for daytime acquisitions and another set of SW coefficients for nighttime acquisitions.
2. Extension of the SEN4 LST product to nighttime acquisitions: the SEN4 LST product was generated only for daytime acquisitions because the inputs to the algorithm, emissivity and water vapour, are generated from visible and near-infrared (VNIR) data acquired by the MERIS sensor (and VNIR data is only available for daytime acquisitions). SEN4 LST product for nighttime acquisitions could be generated by using the same emissivity maps obtained from the daytime acquisitions, since it could be a good approximation. In the case of the atmospheric water vapour, whose variability is higher during the daily cycle, it would be preferable to use some external auxiliary data (i.e., re-analysis). LST methods based on day/night pairs, using the 3.7 \(\upmu\)m of SLSTR observations could also be taken into account in future research.
3. Synergy between high resolution and low resolution data for surface emissivity retrieval: in the framework of the SEN4 LST project a synergy between S2/MSI and S3/OLCI+SLSTR was proposed. However, during the realization of the project it was accorded that \"operational and near-real-time processing\" should be a main driver to select the best candidate algorithm [34]. For this reason, synergy was focused on OLCI and SLSTR, since both sensors are on board the same platform.
## Recovery
#### 3.3.2 Cell Invasiveness  
In vitro invasion assay was performed using chambers coated with GFR Matrigel. As shown in Figure 7B, exposure to microgravity for 24 h strongly increases cell invasion with respect to 1g conditions (4.26 \(\pm\) 0.66 vs. 1.00 \(\pm\) 0.14; \(p\) < 0.01). To further confirm such behavior, we estimated by confocal microscopy the depth of Matrigel invasion reached by HaCaT cells exposed to microgravity for 24 h. As reported in Figure 8, HaCaT cells maintained at normal gravity form ovoidal structures and invade the matrix up to a depth of 49.71 \(\pm\) 3.91 \(\upmu\)m (Figure 8A-C), while cells growing in microgravity appear scattered and able to invade deeper, reaching a depth of 119.99 \(\pm\) 5.67 \(\upmu\)m (_p_ < 0.001; Figure 8D-F). Overall, such findings confirm that cells exposed to microgravity for short periods dramatically increase their invasiveness capability.  
Figure 7: Effect of microgravity exposure in RPM on migration (**A**) and invasion (**B**) in HaCaT cells. Data expressed as fold increase of control value considered as 1 are means of three independent experiments, each performed in quadruplicate, with S.D. represented by vertical bars (* \(p\) < 0.05; **_p_ < 0.01 versus 1g control; * \(p\) < 0.05; * \(p\) < 0.01 versus the cells exposed to RPM for 24 h by ANOVA followed by Bonferroni post-test). Tables illustrate the number of migrating and invading cells/field, respectively.  
As noticed for cell migration, invasiveness decreases after 48 h of RPM with respect to 24-h RPM treated cells (2.91 \(\pm\) 0.42 vs. 4.26 \(\pm\) 0.66; \(p\) < 0.05). Moreover, after 72 h of microgravity exposition, cell invasion was significantly suppressed when compared to the 24 h RPM treated cells, (1.38 \(\pm\) 0.08 vs. 4.26 \(\pm\) 0.66; \(p\) < 0.01); HaCaT cells almost completely lose their invasive property, reaching values superimposable to those measured in 1g control cells (1.38 \(\pm\) 0.08 vs. 1.00 \(\pm\) 0.14 \(p\) = n.s.). Again, similar results were obtained when cells exposed to microgravity for 24 h were reseeeded into a 1g field for 24 h: cells displayed significantly reduced invasiveness values in respect to the microgravity condition at 24 h (1.88 \(\pm\) 0.31 vs. 4.26 \(\pm\) 0.66 \(p\) < 0.05), the migrating phenotype \"disappears\", and cells recover values similar to those displayed by 1g controls at 48 h (1.88 \(\pm\) 0.31 vs. 1.00 \(\pm\) 0.19; \(p\) = n.s.). In addition, no significant difference was observed with respect to cells maintained for 48 h in RPM condition (Figure 7 panel B and table).
# Measuring Gas Flaring in Russia with Multispectral VIIRS Nightfire
## 5 Conclusions
On a site-level, we are able to monitor catastrophic events resulting in temporary increases in the flared volumes when the infrastructure fails to utilize the gas (Khanty-Mansi GPP, 2016), as well as to track and confirm the activities to reduce gas flaring (Vankor field, after 2014). An effect of gas composition on the average temperature is reported for oil and gas fields in Russia: flares from oil fields appear larger and hotter compared to the naturalgas fields. This may result not only from gas composition, but also from the technological differences in flare design, combustion efficiency and operation. We compare national-level long-term trends and seasonal variations of the gas flaring in Russia and the US. For the summer months, night-time observations of sub-arctic oil and gas fields are not possible and need to be interpolated or filled in with an alternative daytime fire product. We observe similar summer-winter maxima and minima in the seasonal patterns for both countries. However, gas flaring's response to the COVID-19 economic slowdown is different: in the US, it showed a 50% drop in spring 2020, while in Russia it followed the same pattern as in previous years. The following are available online at [[https://www.mdpi.com/article/10.3390/rs13163078/s1](https://www.mdpi.com/article/10.3390/rs13163078/s1)]([https://www.mdpi.com/article/10.3390/rs13163078/s1](https://www.mdpi.com/article/10.3390/rs13163078/s1)), (References [67; 68; 69; 70; 71; 72; 73; 74; 75; 76] are cited in the supplementary materials). Textual supplementary data to this article contains more detailed description of the gas flare catalog and additional graphical and table materials. Digital supplementary data contains Catalog of gas flares in Russia in 2012-2020 as an MS Excel table and KML file for Google Earth. Conceptualization, M.Z. and C.E. ; methodology, M.Z. and A.M.; software, M.Z. ; validation, A.M., C.E. and M.H. ; resources, F.-C.H. ; da ta curation, T.G. ; writing--original draft preparation, A.M. and M.Z. ; visualization, A.M and M.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This study. Annual Nightfire data are available at [[https://eogdata.mines.edu/download_global_flare.html](https://eogdata.mines.edu/download_global_flare.html)]([https://eogdata.mines.edu/download_global_flare.html](https://eogdata.mines.edu/download_global_flare.html)) (accessed on 27 July 2021; registration required). The authors declare no conflict of interest.
# Generic Methodology for Field Calibration of Nacelle-Based Wind Lidars
## 5 LOS Velocity Calibration and Uncertainties
### Measurement Setup  
The measurement setup of a wind lidar field calibration must replicate as closely as possible the conditions in which the lidar measures. Current power performance standards [14] consider 10-min averages of measurements in the 'free stream', typically at 2.5 rotor diameters upstream the turbine. For nacelle lidars, the calibration measurement range should therefore be of the same order, i.e., \(\sim\)250 m for modern wind turbines.  
Depending on the height of the reference instrument(s), maintaining the beam close to the horizontal may demand installing the lidar on a stiff platform at a similar height. With a relatively small mast, the lidar can be positioned on the ground and its beam tilted up. The tilt angle \(\varphi\) should however be limited to avoid measurement errors due to flow inhomogeneities within the inclined probe volume and caused by vertical shear and veer as well as an eventual sensing range error.  
The measurement range \(D_{conf}\) is defined as the distance between the lidar and the plane orthogonal to the optical centreline (i.e., the symmetry axis of the lidar trajectory, if it exists). \(D_{conf}\) must be configured so that:  
\[D_{conf}=D\cdot\cos\alpha \tag{3}\]  
where \(D\) is the total distance (not the horizontal one) between the lidar and the reference instrument(s), and \(\alpha\) the opening angle between the centreline and the LOS to calibrate. \(D\) must be accurately measured, for instance using a range-finding theodolite (also called 'total station') or high-resolution GPS.  
In the examples of the 5B and ZDM, the calibration was conducted at DTU Wind Energy's test site for large wind turbines, Hovsere, Denmark. The main site characteristics of Hovsere are the terrain flatness and a climate featuring high occurrence of strong western winds [17].  
\(V_{ref}\) was obtained using a cup (type _Thies First Class Advanced_) and a sonic (type _Gill R3_) anemometer--providing calibrated measurements of \(V_{hor}\) and \(\theta\) respectively. They were top-mounted at \(H_{ref}=8.9\) m agl. (above ground level) on two masts separated by 5 m.  
The two nacelle lidars were placed on the ground - on its legs for ZDM, on a special structure for 5B. The distance from the masts was \(\sim\)260 m. As a result, the physical tilt inclination of the beam from the horizontal was \(\varphi\approx 1.6^{\circ}\)--which is different from the reading of the lidar inclinometers. The effective probe lengths at 'half-width half-max' were estimated to 25 m for 5B (constant with the range) and 45 m for ZDM (increase proportionally to the square of the range).  
Figure 5 shows the measurement setup in Hovsere. The beam position was detected using hard target methods and adjusted until its height agl. was the same as the reference instruments. We estimated the standard uncertainty on the beam height to be 10 cm. The lidar beam was positioned close to the reference instrument (Figure 6), with a horizontal separation of \(\sim\)1-2 m.
## 1 Introduction
### Research Scope  
The aim of this paper is to analyse potential relations between spatial properties of a town (expressed by spatial indicators) and lighting levels of a road in an urban context (expressed by luminance and illuminance values). Furthermore, it is essential to analyse how relations between spatial properties and lighting levels varies when the energy efficiency of the lighting systems increase or how relations varies, passing from roads that are in compliance with minimum lighting requirements, to roads that are not in compliance with such requirements.  
A relation between the spatial properties and the lighting levels is expected for roads in compliance with minimum lighting requirements. They are established as a function of a lighting class selection procedure that is based on the intensity of traffic volumes. According to the standards, higher luminance and illuminance levels are expected in more trafficked and central roads, where there are more restrictive lighting requirements; lower values are expected in peripheral and less trafficked roads. On the other hand, a lack of correlation can certainly mean that the lighting levels do not correspond to the actual levels of use of the roads, but it could also mean that the roads do not meet the minimum lighting requirements or that the lighting systems are not energetically efficient. Having lighting levels corresponding to the actual use of roads, it is possible to optimize the energy consumption of the lighting systems, avoiding energy waste and oversized lighting systems.  
In the paper, the analysis of the possible relations between spatial properties and lighting levels has been structured in three main phases, as shown in Figure 1. Specifically, the first phase (Section 2) represents the application of the space syntax analysis to an urban context, throughout a spatial indicators (i.e., integration and choice indices) assessment. The second phase (Section 3) represents the analysis of lighting levels for an urban context, throughout a lighting parameters (i.e., luminance and illuminance values) evaluation. The third phase (Section 4) represents the research of correlations between spatial indicators and lighting parameters. The analysis has been applied to the case study of a medium sized town in central Italy.
# Review on Quality Control Methods in Metal Additive Manufacturing
## 4 Experimental Quality Monitoring Methods in Metal AM and Welding
### Discussion on Experimental Quality Monitoring Methods in Metal AM and Welding
\begin{table}
\begin{tabular}{c c c c c} \hline
**Process** & **Data Types** & & **Outcome** & **Reference** \\ \hline \multirow{8}{*}{Metal AM} & \multirow{2}{*}{Image} & - & Identified the melt pool, plume, spatter, and keyhole & \multirow{2}{*}{[59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 86]} & \\  & & - & Extended quality-related features from image data & \\  & & - & Identified dimensional errors of each layer & \\  & & - & Quantified the elemental composition & \\  & & - & Identified the width of cladding tracks & \\  & & - & Developed spectrum data acquisition system with & \\  & & & wider concentration range & \\  & Acoustic signal & - & Identified the thermal properties of the product & \\  & & - & Predicted porosity & \\ \hline \multirow{8}{*}{Welding} & \multirow{2}{*}{Plasma spectrum} & \multirow{2}{*}{Image} & - & Identified the melt pool and keyhole & \multirow{2}{*}{[54; 87; 88; 89; 90; 91; 94; 95]} & \\  & & - & Identified distortion of the cladding track & \\ \cline{1-1}  & & - & Quantified the elemental composition & \\ \cline{1-1}  & & - & Identified the keyhole occurrence & \\ \cline{1-1}  & & - & Identified the keyhole and spatter & \\ \cline{1-1}  & Acoustic signal & - & Monitored the process temperature & \\ \cline{1-1}  & & - & Identified deformation, crack, and discontinuities & \\ \hline \end{tabular}
\end{table} 
Table 2: Classification for experimental quality control studies of metal AM and welding by utilized data. In general, existing experimental studies on both metal AM and welding have focused mainly on integrating multiple sensors to improve the detection accuracy. Anomalies and defects can be detected by various types of monitoring data, thus, integrating multiple sensors can lead to robust quality monitoring. Several studies on metal AM have reported monitoring systems that integrate spectrum sensors and image sensors, however, few cases of integrating acoustic sensors have been reported. Because acoustic signals have a better penetration capability than do optical or spectrum signals, they are appropriate for finding internal defects in the product [99]. Incorporating acoustic signals into an integrated monitoring system can be a promising experimental quality monitoring method for metal AM.
## 2 Preliminaries
### \(\ell_{p}\) Norm and Ball  
**Definition 1**.: _The \(\ell_{p}\)**norm** of a vector \(\mathbf{v}\in\mathbb{R}^{n}\) is defined by_  
\[\|\mathbf{v}\|_{p}=\left(\sum_{i=1}^{n}|v_{i}|^{p}\right)^{1/p}\text{ for }1\leq p<\infty\text{ and }\|\mathbf{v}\|_{\infty}=\max\{|v_{i}|:i=1,\ldots n\}\text{ for }p=\infty.\]  
**Fact 1**.: _For \(\mathbf{x}\in\mathbb{R}^{n}\quad\|\mathbf{x}\|_{p}\leq\|\mathbf{x}\|_{2}\leq \sqrt{n}\|\mathbf{x}\|_{p}\) for \(p\geq 2\) and \(\frac{1}{\sqrt{n}}\|\mathbf{x}\|_{p}\leq\|\mathbf{x}\|_{2}\leq\|\mathbf{x}\|_ {p}\) for \(1\leq p<2\)._  
**Definition 2**.: _A **ball** is the set of all points within a fixed distance or radius (defined by a metric) from a fixed point or center. More precisely, we define the (closed) ball centered at \(\mathbf{x}\in\mathbb{R}^{n}\) with radius \(r\) as_  
\[B_{n}^{(p)}(\mathbf{x},r)=\{\mathbf{y}\in\mathbb{R}^{n}:\|\mathbf{y}-\mathbf{x} \|_{p}\leq r\}.\]  
The boundary of \(B_{n}^{(p)}(\mathbf{x},r)\) is the set  
\[\text{bd}(B_{n}^{(p)}(\mathbf{x},r))=\{\mathbf{y}\in\mathbb{R}^{n}:\|\mathbf{ y}-\mathbf{x}\|_{p}=r\}.\]  
We may drop the first argument when the ball is centered at the origin \(\mathbf{0}\) and drop both arguments for a unit ball centered at the origin. Let  
\(B_{n}^{(p)}(\mathbf{x},r_{1},r_{2})=B_{n}^{(p)}(\mathbf{x},r_{2})\setminus B _{n}^{(p)}(\mathbf{x},r_{1})=\{\mathbf{y}\in\mathbb{R}^{n}:r_{1}<\|\mathbf{y}- \mathbf{x}\|_{p}\leq r_{2}\}\). We drop the first argument if the spherical shell or corona is centered at the origin.  
**Fact 2**.: \(|B_{n}^{(p)}(\mathbf{x},c\cdot r)|=c^{n}\cdot|B_{n}^{(p)}(\mathbf{x},r)|\) _for all_ \(c>0\)_._
**Fact 3**.: \(vol(B_{n}^{(p)}(R))=\frac{\left(2r\left(\frac{1}{p}+1\right)R\right)^{n}}{ \Gamma\left(\frac{n}{p}+1\right)}\)_. Specifically \(vol(B_{n}^{(\infty)}(R))=(2R)^{n}\)._  
The algorithm of [PERSON], [PERSON], and [PERSON] [62] almost uniformly selects a point in any convex body in polynomial time if a membership oracle is given [63]. For the sake of simplicity, we ignore the implementation detail and assume that we are able to uniformly select a point in \(B_{n}^{(p)}(\mathbf{x},r)\) in polynomial time.