esa-sceva/satcom-chunk-collection huggingface dataset:
# Verdet Constant of Magneto-Active Materials Developed for High-Power Faraday Devices
## 1 Introduction
### Thermal Effects in High-Power Faraday Devices  
One of the main factors limiting the performance of the laser components are the thermal effects arising due to the always-present absorption of a small portion of the incident laser power [9; 12; 13; 14]. Among all of the components of a laser system, the FDs are particularly affected: the absorption of the vast majority of the magneto-active media is much higher (\(\sim\)10\({}^{-3}\) cm\({}^{-1}\)) than that of the common optical elements. A full description of the physical phenomena involved in the heating of magneto-active medium and their impact on the FD performance is a very complex topic. There are, however, several sources in the available literature dealing with the detailed description [15; 16]. Within the scope of this review, we consider only a simplified cause-and-effect diagram of the physical phenomena involved in the absorption-induced thermal effects in magneto-active media (see Figure 1). When a magneto-active medium is exposed to high power laser radiation, a nonuniform temperature distribution is generated as a result of absorption and nonuniform cooling of the medium. This nonuniformity in the temperature results in a nonuniform distribution of the polarization-plane rotation angle over the cross-section in Equation (1) (polarization distortions) due to the temperature dependence of the Verdet constant, as well as due to the induced nonuniform polarization state changes, because of the thermal-stress-induced birefringence (via the photo-elastic effect). Consequently, the performance of a FD strongly deteriorates. As an example, we may mention the case of a Faraday isolator, in which the polarization distortions decrease the isolation ratio, leading to output power losses in the permeable direction, backward power leakage in the opaque direction, and to a beam quality degradation. Furthermore, the nonuniform temperature distribution leads to the thermal lensing, due to the thermal bulging and changes in the refractive index, causing wavefront distortions of the passing laser beam.
## 5 Engineering Application
### Filed Monitoring and Analyzing  
Engineering field monitoring is utilized to analyze the deformation of surrounding rock in the roadway, aiming to verify the scientific reliability of the research results and enhance coal recovery rates. To monitor real-time roadway deformation during the mining of the 1513 working face, a 100 m section of roadway was chosen. The 100 m roadway was equipped with five stations spaced 20 m apart. Monitoring was conducted on the roadway of the mine's 1513 working face. Figure 9 illustrates the location of the specific monitoring station.  
The deformation monitoring of the surrounding rock of the roadway before and after reducing the coal pillar width is illustrated in Figure 10. Reducing the coal pillar width increases the deformation of the working face during mining, with the displacement at Y2 and Y3 stations being the most significant. The deformations at Y2 and Y3 stations on both sides of the roadway are 71 mm and 88 mm, respectively, with average displacements of 62.2 mm and 76 mm. Before reducing the coal pillar, the maximum displacements at Y2 and Y3 were 50 mm and 62 mm, respectively, with average displacements of 45.4 mm and 54.4 mm. Thus, compared to the original coal pillar width, roadway deformation increases  
Figure 8: Roadway support section diagram.  
Figure 9: Roadway measuring point layout.  
by approximately 41-42%, with the narrower coal pillar exhibiting more deformation than the 25 m-wide pillar.  
On-site data analysis reveals that the coal pillar, affected by the \"hinge-hinge\" structure, shows greater deformations at Y2 and Y3 stations in the return airway. Conversely, stations experience lesser deformation owing to reduced stress impact on the coal pillar. These observations suggest that stable coal pillar conditions ensure safe production at the mine.
## 2 Literature Review and Development of Hypotheses
### Existing Methodologies in Quality Assessment  
A multivariate method is used in scientific research to assess and test multivariate causal links in structural equation modeling or SEM. Compared to other modeling techniques, SEM assess the impacts, both direct and indirect, on intended causal connections [44]. SEM is a statistical technique that has evolved over three generations and is around 100 years old. Using path analysis, the first generation of SEM created the causal modeling logic [45; 46; 47]. Factor analysis was subsequently added to SEM by the social sciences. SEM increased its capability with the second generation. The \"structural causal model\" [48], created by Judea Pearl in 2000, marked the beginning of the third generation of SEM, and after that, Bayesian modeling was then included [49].  
The Naive Bayes algorithm, which solves classification problems, is a supervised learning algorithm based on Bayes theorem. The primary use for it is in-text classification. The two terms Naive and Bayes, which together make up the Naive Bayes algorithm, are defined as follows: Naive: It receives its name from the naive assumption that one feature's occurrence stands alone from the occurrence of other features. For instance, if some fruit is defined based on its color, shape, and taste, then an apple is identified as a red, spherical, sweet fruit. Thus, each characteristic works independently of the others to help distinguish that it is an apple. Bayes: The reason it is named Bayes is that it relies on Bayes' Theorem [50].
# An Intelligent Control Method for Servo Motor Based on Reinforcement Learning
## 4 The Proposed Intelligent Control Method  
### The Structure of the Control Method  
As shown in Figure 3, reinforcement learning based on the intelligent control method consists of four main components: (1) strategy network, which is used to control the running state of the motor and output the specific duty cycle; (2) value network, which is used to evaluate the output value of each duty ratio of the policy network under a specific state and target trajectory; (3) temperature coefficient, which is used to balance the proportional relationship between the duty ratio value and the strategy in the loss function, affecting the degree of emphasis on the exploration and utilization of the algorithm; (4) experience buffer, which is used to store interactive data between the reinforcement learning agent and servo motor control environment as training data for strategy improvement.
# Benefits and Lessons Learned from the Sentinel-3 Tandem Phase
## 9 Geophysical Signal and Geometric Differences
### SRAL Waveform Consistency over Land Ice  
In this section, we provide an initial investigation of the consistency of S3A and S3B acquisitions over land ice. Because this is an overview paper, we focused on a high-level comparison of the waveforms acquired by each altimeter at the same point in space and time; a more detailed quantitative analysis is provided in a companion paper [13], which is dedicated purely to the analysis of tandem phase data over ice sheets. To assess the consistency of waveforms acquired by the Sentinel-3A and Sentinel-3B SRAL instruments, we focused on the ice surface above subglacial Lake Vostok in East Antarctica. This is an established validation site for new altimetry missions [31; 32; 33; 34] due to its low surface gradients and relatively uniform topography. Specifically, we compared cotemporaneous, co-located (within 150 m) acquisitions made during Sentinel-3A's cycle 34 and Sentinel-3B's cycle 11 along a ground track crossing the central part of the lake (Figure 13). Because tandem phase acquisitions represent co-located, cotemporaneous, and co-orientated acquisitions, they provide a unique opportunity to isolate the instrument performance, and therefore to check interoperability between the A and B units. Our analysis demonstrates that S3A and S3B are operating with an extremely high level of consistency, with close similarity of waveforms, both in terms of their magnitude and the distribution of backscattered power (i.e., waveform amplitude and shape). This consistency applies both to the waveform leading edge (corresponding to the surface return from the point of closest approach) and the trailing edge (corresponding to any subsurface and off-nadir contribution). This indicates the performance of both  
Figure 11: **Left**: Spectral analysis of 80-Hz sea-level anomaly showing an energy bump in the power spectral density (PSD), high magnitude squared coherence (MSC) value, and decreasing phase shift around the swell wavelength. **Right**: WAVEWATCHIII wave spectrum showing strong swell partition with 16.4 s period and 200\({}^{\circ}\) angle wrt track. Reprinted from [16].  
Figure 12: S3-A and S3-B radargrams exhibiting distorted waveforms and SLA oscillations due to aliasing. Reprinted from [16].  
instruments in a near-identical manner and, additionally, that small (\(\sim\)100 m) shifts in the imaging location do not substantially impact upon the received echo shape over flat ice surfaces (Figure 13).
## 1 Introduction
### Research Contributions  
Based on a literature review of thermal comfort research, it can be concluded that the recent studies related to thermal comfort have been focused on the development of different sensing techniques to build individual thermal models and apply such models into occupant-responsive HVAC system [12; 13; 14; 15; 16; 17; 18; 19; 20; 24; 25; 26]. Compared to sensing approaches like wearable devices, contactless vision-based occupancy sensors such as IR camera are much less intrusive and have drawn much more attention than before [24; 25; 26]. However, most recent studies using IR camera for thermal comfort use regression analysis with only skin temperature, which may not have high performance when predicting occupant thermal comfort. Therefore, taking both environment-related and occupant-related factors into account, this research aims to develop adaptive thermal models with three different feature sets trained with RF and SVM. In order to collect occupant data, a six day experimental study was conducted in an open-plan office in Shanghai with the non-intrusive sensing system consisting of indoor air temperature and relative humidity sensor and an IR camera. The study contributes to the field of the personalized thermal comfort by proposing new feature sets and prediction algorithms to develop adaptive thermal models, which could further be integrated into personalized system control as well as task ambient conditioning system in open-plan offices. The rest of the paper consists of methodology, including the experimental setup and the development of thermal models, result analysis and discussions about the prediction performances as well as the conclusion and future work.
## 3 Results and Discussion
### Charge Transport within Microporous vs. Compact Tungsten Oxide
However, \(R_{\rm ct}\) of the microporous film decreased to around 38 \(\Omega\) at a potential of \(-\)0.75 V and then to about 11 \(\Omega\) at \(-\)1.0 V. Hence, the values of \(R_{\rm ct}\) at \(-\)1.0 V are quite similar for both types of films indicating a similar facile electron transport within the films at this potential and allowing fast intercalation of Li\({}^{+}\). For both types of films, the impedance values at 1.0 V (Figure 12c,d) are much higher than at \(-\)1.0 V representing a high barrier for intercalation of the ions in the bleached state as already observed in earlier reports [75,76]. The Warburg coefficient \(A_{\rm w}\) obtained from fits of the impedance spectra allows to obtain an independent measure of Li\({}^{+}\) diffusion by the ion diffusion coefficient \(D_{\rm I}\) in the films with the active surface area \(A\), the molar gas constant \(R\) and the temperature \(T\)[77]:  
\[D_{\rm I}=\frac{R^{2}T^{2}}{2A^{2}n^{4}F^{4}C^{2}A_{\rm w}^{2}} \tag{3}\]  
Figure 12: Nyquist plots of a WO\({}_{3\,|}\)comp film (**a**,**c**) and a WO\({}_{3\,|}\)up600 film (**b**,**d**) at different negative applied potentials and at the bleached state (1 V). The insets show the impedance data at high frequencies (**a**,**b**) and the equivalent circuit (**a**) used for fitting the data with the resistances \(R_{1}\) and \(R_{\rm ct}\), the Warburg impedance \(Z_{\rm w}\), the constant phase element \(CPE_{\rm dl}\) and the capacitance \(C_{\rm L}\). The WO3\(|\)comp film (Table S2) provided generally smaller \(D_{\rm I}\) compared to the WO3\(|\)up600 film, which is well in line with the tendency observed from the effective diffusion coefficients obtained from the CV measurements. However, \(D_{\rm I}\) in the range of \(10^{-13}\)-\(10^{-11}\) cm\({}^{2}\) s\({}^{-1}\) were found around two orders of magnitude smaller than \(D_{\rm c,CV}\) and \(D_{\rm a,CV}\). This is a consequence of the fact that \(D_{\rm CV}\) are effective diffusion coefficients describing the combined diffusion of electrons and ions while \(D_{\rm I}\) is characteristic for the diffusion of ions only. Since CV was performed across the whole range of potentials and since the fits of the impedance spectra at \(-1.0\) V needed a dedicated Warburg element, values at intermediate potentials (\(-0.25\) V to \(-0.75\) V) are used for comparison purposes. Throughout this range, \(D_{\rm I}\) was found significantly larger for WO3\(|\)up600 than for WO3\(|\)comp, well in line with larger \(D_{\rm CV}\) for WO3\(|\)up600, confirming faster ion transport in WO3\(|\)up600. The difference in \(D_{\rm CV}\), however, was found smaller than that in \(D_{\rm I}\) presumably caused by slower electron transport in WO3\(|\)up600 which also is considered by the effective diffusion coefficient \(D_{\rm CV}\).
## 3 Results and Discussion  
### Impregnation Methodology  
The performance of the impregnation method depends on several parameters that can affect the yield of TiO\({}_{2}\) impregnated over the Leca particles. The parameters considered in this study were the cleaning procedure of Leca particles, the amount of Leca in contact with the TiO\({}_{2}\) suspension, the contact method between Leca and TiO\({}_{2}\) suspension, the solvent type for TiO\({}_{2}\) suspension, the drying temperature, and the TiO\({}_{2}\) suspension load. These effects of different conditions were analyzed as TiO\({}_{2}\) impregnated weight, and some were submitted to SEM-EDX analysis. Moreover, the effect of the TiO\({}_{2}\) suspension load impregnated on Leca particles was also evaluated in terms of photocatalytic oxidation efficiency over SMX degradation.
## 4 Experimental Setups and Data Description
### Evaluation Metrics  
Four evaluation metrics, including Accuracy, Precision, Recall, and F\({}_{1}\)-sore, are selected to evaluate the proposed method and compared algorithms. The above four metrics are defined as follows:  
\[F_{1}=2\times\frac{Pr\times Re}{Pr+Re} \tag{10}\]  
\[\text{Recall}(y,\hat{y})=\frac{TP}{TP+FN} \tag{11}\]  
\[\text{Precision}(y,\hat{y})=\frac{TP}{TP+F\hat{P}} \tag{12}\]  
\[\text{Accuracy}(y,\hat{y})=\frac{TP+TN}{TP+FP+TN+FN} \tag{13}\]  
where \(y\) and \(\hat{y}\) represent the true labels and predicted labels. \(TP\) and \(FP\) are the number of true positives and are the number of false positives. \(TN\) and \(FN\) are true negatives and false negatives, respectively.
# Learning-Assisted Rain Attenuation Prediction Models
## 2 Preliminaries
### Critical Challenges of LARA Models  
The critical challenge of predicting the rain attenuation model compared to the other time-varying model is that there are at least reported 17 input features (Figure 2) that can influence rain attenuation. Although few of these features are a fixed quantity in a particular infrastructure setup, such as station height, azimuth, latitude, polarization, or elevation angle, many of the features have statistical variant behavior, such as rainfall intensity, wind velocity, pressure, raindrop temperature, and raindrop size. Many of these features change over time to time, which may be called statistical variations. The adjustment of these statistical variations may lead to undesirable nonlinear properties in univariate time series. Many of the statistical variation components and nonvariational components are not independent, and thus not separable [9].  
Figure 2: A general illustration of the LARA model’s input and output parameters.
## 3 Experiment Results
### Experiment Results for SUT1
As you can see, for a larger number of \(k\), the largest share of coverage falls into the group from 70% to 80%. Only for \(k=10\), the largest share falls into the 81-99% group. Further, it can be noted that for \(k=20\) and \(k=50\), in general, the distribution worsens compared to \(k=10\) (although the share of variants with 100% coverage is higher, the share with low coverage also increases). Therefore, there is a certain value of \(k\) (in our case, it is \(k=10\)), at which a balance is achieved between the terms \(F_{1}\) and \(F_{2}\) in the sum (6). If \(F_{1}\) outweighs, then the resulting population is not diverse enough to cover the entire code. If \(F_{2}\) outweighs, then excessive attention is paid to the diversity of the population to the detriment of the quality of coverage of each individual.
# Research on O2O Takeout Delivery Route Optimization Problem Based on Improved Genetic Algorithm
## 3 Establishment and Handling of Mathematical Model
### Rider Reward Function  
The rider's reward also mainly depends on the delivery time of the order. [PERSON] et al. [24] summarized four types of incentive functions, and this paper adopts the linear reward mechanism among them. Let \(R_{i}\) represent the reward for the rider completing order \(i\), and its function is illustrated in Figure 2. Here, \(\beta\) denotes the maximum reward per order for each rider. When \(e_{i}\leq t_{ik}\leq l_{i}\), \(R_{i}\) takes the value of \(\beta\); when \(l_{i}<t_{ik}\leq L_{i}\), it decreases linearly with time, and its expression is \(\beta\frac{L_{i}-t_{ik}}{L_{i}-l_{i}}\); when \(t_{ik}>L_{i}\), \(R_{i}\) takes the value of 0.
# A Traffic-Based Method to Predict and Map Urban Air Quality
## 2 Materials and Methods  
### Study Site  
The present study was conducted in a central part of Quito, Ecuador (see Figure 1). The world's highest capital, housing 2.2 million people, is positioned at an average elevation of 2835 m above sea level (m.a.s.l.) [27]. This complex terrain city, although characterized by microclimates, has two well-defined seasons. The dry season that lasts from June to August has low precipitation (14 mm/month) and high winds, while the rainy season, from September to May, is characterized by convective precipitation during the afternoon hours (59 mm/month) [28]. Due to its nestling in the Andes cordillera, morning temperature inversions are commonly demonstrated by dark grey smog episodes during morning rush hours. As a result, the city deals with a decade-long PM\({}_{2.5}\) pollution problem, not only violating World Health Organization (WHO) recommendations for air quality but even less strict limits of national standards [10].
# A New Method for Large-Scale Landslide Classification from Satellite Radar
## 2 Materials and Methods
### Case Study: The 2015 Gorkha Earthquake  
#### 2.2.1 Validation Data  
We analysed the classification ability of the three methods presented in Section 2--absolute coherence, ARIA and Bx-S--using the inventory of landslides triggered by the 2015 Gorkha earthquake that was compiled by [PERSON] et al. [2]. The inventory consists of 24,915 landslides mapped as polygons that include both scar and runout. The majority of landslides were mapped using pre- and post-event imagery from DigitalGlobe Worldview-2 and -3 with some landslides mapped using Pleiades and Google Earth imagery. Due to restrictions on the SAR imagery available for the event, we use a subsection of this area containing 16,539 landslides, the extent of which is shown on Figure 1 (solid red line).
## 4 Discussion
### Limitations and Future Directions  
At present, there are numerous spectral indices available in addition to the EVI used in this study. Although EVI alone showed a high accuracy, incorporating additional indices could potentially enhance the robustness of the results. For example, the land surface water index may improve the differentiation of cropping cycles [23]. Furthermore, the cropping intensity generation method in this study relies on the shape characteristics of the EVI time series, which can be effectively extracted using deep learning techniques [8]. Applying deep learning to automate the feature extraction process could significantly improve the efficiency of cropping intensity assessments. In summary, while this study demonstrates the effectiveness of EVI in mapping cropping intensity, future research should consider integrating multiple spectral indices and deep learning techniques to provide more robust and reliable assessments.
## 3 Characterizing Public Transportation System
### Practical Use Cases
#### 3.4.1 Event Detection  
The analysis of anomalous registers in either time and/or location data of ticket sales can help authorities to identify special events taking place in the city. The image in Figure 11 presents an aggregated visualization combining spatial and temporal data of smart card transactions for a neighborhood of Montevideo. A small cluster of red pixels (highlighted with a white circle in the figure) is detected on the map. This cluster corresponds to bus ticket sales occurring at midnight, representing a clear outlier from the rest of ticket sales in the dataset. Taking in consideration the location of those ticket sale records (near an outdoor venue), it is reasonable to assume that the transactions correspond to a special social event (e.g., a concert) held at night in that venue. In case the situation repeats periodically, specific action can be taken to satisfy that mobility demand. This relevant case exemplifies how city authorities can take advantage of a methodology using urban data analysis to detect periodical special events in the city and plan the transportation services in response to those events.  
Figure 10: Choropleth map of smart card transactions in the evening.
# Illuminant Adaptive Wideband Image Synthesis Using Separated Base-Detail Layer Fusion Maps
## 3 Proposed method  
Figure 2 shows the visible and near-infrared light image synthesis algorithm proposed in this study. The proposed method is composed of an image pair acquisition system, base-detail decomposition, base layer synthesis, detail layer synthesis, and color compensation. Image acquisition describes a method for acquiring visible and near-infrared image pairs, and decomposes each image into a base layer and a detail layer during the base-detail decomposition process. After that, each layer goes through the synthesis process independently, which is conducted in the base layer synthesis and the detail layer synthesis. The base layer is synthesized by adopting a different synthesis method for the day and night images using the joint bilateral filter and the near-infrared light penetration characteristics according to day- and nighttime images. Detail layer is synthesized using weight map generated by the image variance. Then, the fused luminance image is obtained by adding the synthesized base layer and detail layer. Finally, the final result can be derived through a color compensation step according to the luminance channel value changed in the synthesis process. In Figure 2, each subpart of Section 3 is separated by blocks of different colors.
# Prototype of Augmented Reality Technology for Orthodontic Bracket Positioning: An In Vivo Study
## 3 Results
### The Effect of AR-Assisted Bracket Navigation System  
The results revealed that after implementing the AR system, the vertical error was reduced by approximately 0.29 (45%) mm, whereas the horizontal error was reduced by only approximately 0.06 (18%) mm because the features between the teeth were inconspicuous. Furthermore, the novice orthodontist using the AR system achieved the near accuracy as the expert orthodontist using the Boone gauge.