esa-sceva/satcom-chunk-collection huggingface dataset:
## 9 Discussion and Conclusions  
This study presents a comprehensive literature review and analysis to understand the factors influencing environmental emissions from CIPP and ETs. It analyzes and compares the emissions produced from both alternatives for 8-inch-, 10-inch-, and 12-inch-diameter sewer pipes. The comparative analysis highlights the substantial environmental and economic advantages of CIPP over ET. CIPP is more cost-effective and environmentally sustainable, generating 70% fewer ecological impacts, 75% fewer human health risks, and 60% less resource depletion. Moreover, it achieves a 78-100% reduction in carbon emissions compared to ET, which produces 77% more greenhouse gases and 80% more pollutants. The reduced environmental emissions from CIPP will potentially decrease future risks of global warming, environmental pollution, and help in mitigating health risks. Moreover, CIPP consumes less time as compared to ET, due to lower input allocation, less fuel consumption, and minimum surface disruption, thereby decreasing the overall cost of underground infrastructure rehabilitation projects. These benefits make CIPP the greener option for pipeline rehabilitation.  
CIPP offers additional advantages, including better worker safety, improved productivity, and superior structural outcomes. In contrast, ET is better suited for challenging geological conditions but comes with significant drawbacks. It involves extensive de-watering, higher project complexity, and greater surface disturbance. ET also produces more greenhouse gas emissions due to its longer project durations and greater resource consumption, further exacerbating its environmental impact. Despite its advantages, CIPP emits styrene, VOCs, and other hazardous chemicals during the curing process. Potential health risks of these chemicals include headache, dizziness, fatigue, and irritation to the eyes and nose, with styrene being carcinogenic to humans. However, these emissions are generally low, dissipate quickly, and rarely result in long-term environmental harm. Recycling materials used in CIPP can reduce its environmental impacts by approximately 10%, further enhancing its sustainability. Conversely, ET's reliance on large-scale excavation, higher fuel consumption, and significant pollutant emissions leads to greater ecological degradation and a less sustainable profile overall.  
In conclusion, CIPP is the more environmentally preferable method, offering significant reductions in greenhouse gas emissions and resource depletion while minimizing health risks. Meanwhile, ET's higher carbon footprint and environmental costs make it a less sustainable alternative. Improvements in resin technology and emission-reduction  
Figure 6: EIA processes for 12-inch-diameter ET.  
practices can further enhance the ecological performance of CIPP, ensuring its continued relevance in sustainable infrastructure projects.
## 2 Torsional Thrust Balance Description
### Fiber Optic Displacement Sensor  
A small displacement on the order of 10 s micrometers will occur when the desired thrust range is exerted on the torsional thrust balance arm. The Fiber Optic Displacement Sensor (FODS), which is compatible with vacuum, was used to measure the arm deflection displacement. This kind of displacement sensor has been applied in previous torsional balance development, and successfully realized the small displacement measurement [6; 7]. In this work, the Philtec. Model RC32 was adopted; its nominal resolution is 0.25 um in the range of DC-100 Hz. The control unit of the sensor provides an analog voltage output of 0-5 V, which is proportional to the displacement between the measured target and the sensor probe. The manufacturer provides a calibration curve from a range 0.149-3.175 mm, and the linear range is 1.05-1.88 mm with a sensitivity of 2.757 mV/\(\mu\)m. In the experiments of this paper, the distance between the arm and the sensor probe was always kept in the linear measurement range of the sensor.
# A Method for Managing ADS-B Data Based on a 4D Airspace-Temporal Grid (GeoSOT-AS)
## 5 GeoSOT-AS Based Regional and Time Segment ADS-B Data Extraction Algorithm  
The search includes ADS-B and its corresponding attribute data, including flight speed, heading, aircraft type, and so on. ADS-B data extraction is a search for the required data from the aerial database, which in essence is a query for data and attributes of spatial entities according to certain conditions, forming a new subset of spatial data, organizing the dataaccording to the spatial location, and directly extracting multiple attributes corresponding to the spatial location through the grid code. In this process, it is necessary to convert the retrieved spatial range into a set of segmentation elements covering the query area and use the set of codes to directly query the attribute information with the spatiotemporal matching of ADS-B data.  
The method actually converts the latitude and longitude heights as well as the temporal cross-domain range into a primary key query that is coded instead. Due to the multi-layer profile structure, the index can be defined as the finest level first, and then the index aggregation can be performed using the prefix matching property of the code. The flow chart is shown as Figure 8.
# Nonlinear Vibrations of Low Pressure Turbine Bladed Disks: Tests and Simulations
## 4 Numerical Analysis
### Modal Damping Value  
This value was determined by analyzing the experimental responses of individual blades without UPDs (free condition, reference 'linear' response). Ten responses were selected, focusing on those blades exhibiting a more consistent signal. These responses were then processed using a custom Matlab code designed for fitting the experimental curves with a Single-Degree-of-Freedom (SDOF) forced response. This code identifies the peak amplitude and corresponding frequency within a critical RPM range specified by the user. Subsequently, it generates a fitted curve with minimal deviation from the experimental data, utilizing the least squares criterion, while ensuring that the maximum amplitude and critical frequency remain consistent with the initial parameters. Using an average of the identified values, the modal damping ratio of the fitted curve will be set as the value in the Policontact software for the mode of interest.  
The following formula represents the SDOF response to the harmonic excitations that are used for the fitting procedure:  
\[A=\frac{A_{max}\cdot 2\ \xi}{\sqrt{\left(2\frac{\omega_{0}}{\omega_{0}}\xi \right)^{2}+\left(1-\left(\frac{\omega_{0}}{\omega_{0}}\right)^{2}\right)^{2}}} \tag{3}\]  
Here, the following correspondences hold: \(A\)--Response amplitude, \(A_{max}\)--Peak amplitude, \(\xi\)--Modal damping, \(\omega\)--Frequency of harmonic excitation, and \(\omega_{0}\)--System natural frequency.  
It is evident from the above formula that the only unknown parameter is \(\xi\) and thus it can be calculated. Figure 10 represents the output of the MATLAB code for one blade, where the blue curve represents the response of a generic blade that is fitted, with the red curve, defined as an SDOF forced response, characterized by the same peak amplitude and corresponding frequency value. The final value of modal damping is evaluated as the average of the estimated value identified on the 10 blades and set to 0.0032. This value is further used for simulations.  
Figure 10: Example of one blade-response fit with an SDOF forced response.
## 1 Introduction
The cloud computing feature has reduced computational time and enhanced observation efficiency; in addition, a programming interface is provided for access to data processing features and ML algorithms. Various lithological formations in the Kohat-Karak regions of Khyber Pakhtunkhwa of Pakistan contain distinctive alluvium, limestone, Jatta Gypsum, and six other lithologies (Figure 1 and Figure S1 [38]). These six lithological formations in the Kohat Plateau, Pakistan, are outlined in Table A1 (Appendix A). Subtle compositional differences in these formations comprise various proportions of sandstone, shale, and mudstone [39], resulting from Himalayan molasse depositions. However, the lithologies of this region are difficult to map through conventional mapping practices due to the complexly deformed, inaccessible, and rugged terrain. For example, a previous study in the same area used conventional remote sensing (RS) techniques for mapping the gypsum lithological unit only [19]. The freely available 10 m spatial resolution blue (B2), green (B3), red (B4), and NIR (B8), and 20 m SWIR1 (B11) and SWIR2 (B12) bands of Sentinel-2 data have rarely been explored for mapping lithologies in such terrain. Recently, [PERSON] [4] reported 85% accuracy from SVM classification of 10 lithological classes using Landsat 8 OLI with digital elevation model (DEM) and geomorphometric attributes of ALOS/PALSAR. Some studies reported SVM 83.16% [22] accuracies using Sentinel-2 applied to fused Sentinel-2+ASTER data. The RF algorithm has also been considered as the best model with reported accuracies of 91% [40] after application to Sentinel-2 and 85.75% [41] with application to a fusion of Sentinel-2+ASTER+DEM data. In other case studies, the maximum likelihood classifier (MLC) has been reported as the best, with accuracies of 70% [42] and 76% [43] applied to Sentinel-2 data. Therefore, different ML algorithms perform better in different lithological formations for a diverse choice of satellite data. Additionally, the computational cost for such studies has rarely been reported previously. This paper aims to present a lithological mapping solution with the following objectives:  
1. Apply better means of data annotation to obtain higher map accuracy than previous ML lithological mapping solutions at lower computational cost and higher spatial resolution. 2. Present a novel system architecture to improve the existing geologic map of the Kohat Plateau using Sentinel-2 MSI datasets by (1) extracting training data from PCA, MNF, and previous maps for better annotation, and (2) comparison of SVM vs. ANN ML lithological classification. 3. Obtain a medium spatial resolution (1:30,000), high-accuracy ML map for the region with subtle compositional differences in the region as a prospecting tool for further mineral exploration.
###### Abstract  
(1) Background: A simple approach to map irrigated landcover has been introduced by using measures derived from the optical spectral range as an alternative to the thermal range. It has been demonstrated that substituting surface temperature (Ts, 'thermal approach') with SWIR-transformed reflectance (STR, 'optical approach') to detect surface moisture is feasible with comparable results. (2) Methods: Using an iterative thresholding procedure to minimize within-class variance, the bilevel segmentation of variables derived from Landsat-8 representing surface moisture and vegetation cover was achieved for the 2020-2021 summer for a key irrigation district in Australia. (3) Results: The results of irrigated landcover by the optical approach were found to be comparable with those obtained by the thermal approach. The classification accuracy was assessed using water delivery records at the farm level. Although the overall accuracy was high in both cases, the optical approach (97.6%) performed slightly better than the thermal approach (93.9%). (4) Conclusions: The feasibility of using STR to map irrigated landcover has been confirmed by a high-level overall accuracy assessment. This has broader implications in terms of irrigated landcover assessment, as the use of satellite imagery in these applications may not necessarily be limited to microwave or thermal sensors.  
irrigated landcover; thresholding method; Central Goulburn District 2024 13 633 1
## 3 Results and Discussion
### Effect of Inlet Velocity  
To study the impact of the average inlet velocity on the response of both biosensor models, Figure 10 shows the evolution of the slope at the origin of time related to the association phase versus the average inlet velocity with and without electrothermal field. In absence of the electrothermal effect (i.e., V\({}_{\rm rms}\) = 0 V), increasing the flow velocity improves the convective mass transport of the analyte towards the sensitive surface and subsequently enhances the reaction rate. In Figure 10, the initial slope related to the association stage with electrothermal effect (V\({}_{\rm rms}\) = 15 V) for the first model of the biosensor is presented for \(\alpha=\theta=160^{\circ}\).  
This figure exhibits two regions: a linear region for low inlet velocities and an almost saturation region for larger inlet velocities. In the first region, the reaction rate is greatly accelerated for any value of the applied voltage because the analyte in the bulk has time to diffuse to the reaction surface. In the second region, the enhancement factor of the binding reaction decreases because the amount of analyte carried by the flow is large [23]. We can conclude that the electrothermal effect is dominant for low inlet velocities and will be negligible for the large flow rates.  
For comparison, the initial slope of the association phase for both biosensor models without and with electrothermal field versus the average inlet velocity reveals that the second model of biosensor is more efficient than the first one without and with an applied voltage.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Applied Voltage (V)** & **0** & **5** & **10** & **15** \\ \hline Temperature rise (K) & 0 & 0.3 & 1.3 & 3.0 \\ Enhancement factor for association & - & 1.05 & 1.88 & 3.84 \\ Enhancement factor for dissociation & - & 1.01 & 1.58 & 2.25 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Enhancement factor for association and dissociation phases and temperature rise.  
Figure 9: Initial slope related to association and dissociation phases of CRP complex versus the applied voltage. The average inlet velocity is fixed at 100 \(\upmu\)m/s. The value of initial slope is calculated by choosing few of the initial points from the curves of Figure 8 then a linear interpolation by the least square method is used to compute the slope.
## 3 Results and Discussion
### Correlation Analysis
* The NDWI shows contrasting patterns: negative in SCS 1 and 3, but strongly positive in SCS 2 (r = 0.519) and moderately positive in SCS 4 (r = 0.108). These results may indicate that, depending on the zone, surface moisture either contributes to cooling through evaporation or to heat retention due to local humidity dynamics. * The SVF also varies notably. It is negatively correlated with LST in SCS 1, 3, and 4, consistent with the idea that lower sky exposure (e.g., in dense urban canyons) traps more heat. In contrast, SCS 2 shows a positive correlation (r = 0.415), which may reflect different urban morphologies or radiative behaviors in that climate zone. These diverse patterns confirm that the influence of each parameter on nighttime temperature is strongly conditioned by climatic context. As a result, a single global model would not adequately capture these differences. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Parameter** & **SCS 1** & **SCS 2** & **SCS 3** & **SCS 4** \\ \hline Log (WC * + 1) & 0.068 & 0.023 & 0.133 & 0.065 \\ Altitude & 0.451 & \(-\)0.700 & \(-\)0.250 & \(-\)0.155 \\ NDVI & \(-\)0.495 & \(-\)0.117 & \(-\)0.057 & 0.124 \\ NDWI & \(-\)0.303 & 0.519 & \(-\)0.103 & 0.108 \\ SVF & \(-\)0.205 & 0.415 & \(-\)0.146 & \(-\)0.127 \\ \hline \hline \end{tabular}  
* WC = Wind Corridor.  
\end{table} 
Table 6: Correlation coefficients between LST and the characterization parameters across each SCS zone. All correlations are statistically significant (p < 0.003; FPR < 5%).
## 3 Results and Discussion
### Electrochemical Study  
CV was performed to test the polymers' electroactivity. Figure 4 shows the CV curves of PPA, POA, POA@TiO\({}_{2}\), and PPA@TiO\({}_{2}\) materials obtained in HClO\({}_{4}\) (1M) at 50 mV.s\({}^{-1}\) scan rate. Regarding POA, three overlapping redox processes were observed. The first occurred at 0.45/0.20 V and resulted in a potential peak separation (E\({}_{\text{p}}\)) of 250 mV. Another redox pair at 0.86/0.74 V presented an E\({}_{\text{p}}\) close to 120 mV. This redox process was assigned to the leucoemeraldine/emeraldine and emeraldine/pernigraniline transitions, respectively [23; 30].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Materials** & \multicolumn{4}{c}{**Redox Peak (V)**} & \multicolumn{4}{c}{**Absorption Band (nm)**} \\ \hline  & **E\({}_{\text{p41}}\)** & **E\({}_{\text{p41}}\)** & & **E\({}_{\text{p42}}\)** & **E\({}_{\text{p52}}\)** & & & \\ \hline PPA & 0.40 & 0.36 & 0.04 & 0.74 & 0.71 & 0.03 & 328 & 503 \\ POA & 0.41 & 0.30 & 0.11 & 0.67 & 0.51 & 0.16 & 298 & 530 \\ POA@TiO\({}_{2}\) & 0.45 & 0.28 & 0.17 & 0.67 & 0.56 & 0.11 & 314 & 512 \\ PPA@TiO\({}_{2}\) & 0.43 & 0.33 & 0.10 & / & / & / & 318 & 452 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Absorption bands and redox peaks of prepared materials.  
The POA had similar CV shapes and peak potentials to the POA@TiO\({}_{2}\) nanocomposite; however, the peak pairs were shifted to higher potentials and lower intensity. The CV profile of the PPA@TiO\({}_{2}\) nanocomposite shows a unique redox process corresponding to 0.43/0.33 V and E\({}_{\text{p}}\) = 100 mV, which corresponds to the leucoemeraldine/pernigraniline reaction [15]. Therefore, CV results may have been due to an earlier (lower) POA oxidation potential, i.e., higher POA reactivity than PPA [39]. Additionally, the anodic and cathodic peaks of PPA were symmetrical (E\({}_{\text{P/2C}}\) = E\({}_{\text{P/2a}}\)), indicating that the relevant redox events were highly reversible [40]. This reversible system corresponds to the p-doping of the polymer (oxidized) and n-doping of the polymer (reduced) represented by the oxidation wave and reduction wave, respectively. These findings show that the electrochemical properties of anisidine isomers depend on functional groups' (-OCH\({}_{3}\)) relative position to one another, their arrangement in the polymer chain, and the existence of metal oxides in the polymer matrix.
## 1 Introduction
### Research Aims  
The research presented in this manuscript tries to answer two needs and two academic domains.  
The first is providing UX evaluators and experts in user studies with a tool (and a **referencing conceptual framework**) able to assist them during the various phases of analysis of digital cultural products both in the laboratory and when on public display. The second is providing content designers and communication scientists with valuable answers to questions related to the concept, design, development, and overall experience of innovative museum solutions such as the holographic showcase, which are as follows:  
* Do visitors appreciate how the holographic showcase is conceived and realized?
* Do visitors appreciate the subjects, how the story is told, and its duration? What do they understand and remember?
* Do visitors appreciate the appearance of the showcase in terms of the holographic effect, visual grammar, and adopted 3D technique?
* How comfortable is it for visitors to access the contents?
* Do the museum's environmental conditions favor the enjoyment of the holographic showcase?
* What does the evaluator notice about the users' behavior?  
In trying to answer such questions, we structured our evaluation model while taking into account differences in audiences, different technological adaptations and set-ups, the diversity in contents, and the role of the evaluator. Moreover, the results of previously conducted UX evaluations were also used in order to obtain a more general overview on lessons learned about the holographic experience in museums.
# Combined kNN Classification and Hierarchical Similarity Hash for Fast Malware Detection
## 4 Proposed Method
### Rapid Search Method for a Similarity Hash  
Even if similarity hash-based malware detection is faster than the deep learning-based method, it still takes a long time when there are many data. For example, there are about 1 billion malicious files [1]. When we find the kNN of a new file, we need to improve the speed of the kNN search of the similarity hash.  
Therefore, in this section, we propose a kNN search method for the similarity hash using a VP tree. The purpose of the VP tree is to reduce as much as possible the number of distance comparisonsneeded to find the kNN. When there are \(n\) existing files and a new file is given, we can find the kNN of the new file using a brute-force search method. Its complexity is O(\(n\)). However, by using a VP tree, we can reduce the number of distance comparisons. Then, its complexity is O(\(\log n\)).  
The body of the TLSH is given as follows.  
\[\{v_{o},v_{1},\ldots,v_{127}\}\]  
We define the distance between two files \(F_{i}\) and \(F_{j}\) as follows.  
\[\text{Dist}\Big{(}F_{i},F_{j}\Big{)}=\sum_{k=0}^{127}\Bigg{|}F_{i}.v_{k}-F_{j}. v_{k}\Bigg{|}\]  
Note that originally TLSH difference is computed by the sum of the difference of the header and the difference of the body of the TLSH. However, because TLSH does not guarantee triangle inequality [31], we modify the distance between the two files as the difference of only the body of the TLSH.  
Then, we generate a VP tree using the training data, as follows. First, we randomly select a vantage point and compute the distances between the vantage point and the other points. Then, we compute a vantage-point radius that classifies the other points into two groups: inner and outer. Second, the points of the inner group are assigned to the left subtree of the vantage point and the points of the outer group are assigned to the right subtree of the vantage point. Third, we repeat the first and second steps recursively.  
Next, we search the kNN of a new file using the VP tree, as follows. First, if the following condition is satisfied, the left subtree is pruned.  
\[\text{Dist}(\text{VP},\text{Q})>rad(\text{VP})+\text{kNN\_Dist}\]  
In this case, the points of the VP's left subtree are guaranteed to not be contained in the kNN of the query point, as shown in Figure 6. Otherwise, we should search the left subtree.  
Second, if the following condition is satisfied, the right subtree is pruned.  
\[\text{Dist}(\text{VP},\text{Q})<\text{rad}(\text{VP})-\text{kNN\_Dist}\]  
In this case, the points of the VP's right subtree are guaranteed to not be contained in the kNN of the query point, as shown in Figure 7. Otherwise, we should search the right subtree.  
Figure 6: Pruning the left subtree.  
Therefore, we can reduce the kNN search time for the similarity hash using a VP tree. In Section 5, we will show the kNN search time using a VP tree.
## 4 Gaofen-3 Interferometry Analysis and Refinement
### Gaofen-3 Interferometric Analysis
Figure 4: The statistics on pixel coherence for the Gaofen-3 SAR images (marigold color) and Sentinel-1 SAR images (green color).