esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Reflectance Indices for Remote Sensing of Plants and Revealing Actions of Stressors
## 4 Problems of Measurement and Analysis of Reflectance Indices
### The Atmosphere and Soil Influence on Plant Reflectance  
The atmosphere and soil background can strongly modify vegetation indices (particularly the NDVI, SR, and others), decreasing their informativity. The atmosphere influence is related to the atmospheric water vapor, aerosol depth, clouds, brightness, and other factors [147; 10; 149]. It was shown that the light scattering in the atmosphere influences mainly the transmission of visible light, and the atmosphere light absorption influences mainly the transmission of NIR [147]. The influence of the soil background is related to color, structure and relief, humidity, erosion, and other factors [146; 10]. It should be noted that the influences of vegetation, atmosphere, and soil on RIs can interact with each other [10] because the atmosphere and soil can directly modify the spectrum of reflected light and its spatial distribution.  
It is important that the increasing intensity of the atmospheric influence positively shifts the values of NDVI [10]; in contrast, increasing the soil brightness negatively influences these values. The noise induced by the atmosphere and soil influences increases the error of the RIs measurement up to 30 to 50% [10; 141]. The effective methods of the investigation of the influence of atmosphere and soil parameters on the plant reflectance are the development of radiation transfer models. On the basis of these models, it was shown that sharp changes in RIs are caused by aerosol depth and soil brightness [147]. Using the SAIL model, [PERSON] et al. [146] showed that soil properties (including texture and moisture) influence the sensitivity of the NDVI, SAVI, TSAVI, MSAVI, and GEMI to vegetation. Based on this investigation, the authors proposed a new vegetation index, OSAVI, which is optimized for the minimization of the soil influence on the measurement of RIs. The modifications of OSAVI can be used for all types of soil [64; 98].  
The mathematical modelling is an effective tool of the reflectance data analysis and interpretation of results. However, its application is limited by a long duration of calculations, which is unsuitable for routine measurements. Alternatively, a system analysis of the influence of soil and atmosphere based on statistical methods is widely used. For example, the different components of the influence of atmosphere and soil on RIs can be detected by using the \"principal component analysis\" [10]. Another way is the development of new reflectance indices that can decrease soil- and atmosphere-caused errors. The OSAVI, which was noted above, can exclude the soil influence. The atmospheric effects can be minimized by the correction of the difference between red and blue light in the vegetation index IAVI [70] and its special case ARVI [71]. The empirical vegetation index EVI, which is based on the red, blue, and NIR reflected light [157], includes correction, decreasing the influence of aerosols and soil.
# Advances in Improving Energy Efficiency of Fiber-Wireless Access Networks: A Comprehensive Overview
## 2 Fiber-Wireless Broadband Access Network  
Merging optical fiber and wireless technologies in broadband access networks by leveraging the advantages of both technologies presents a viable solution to support the increasing bandwidth demands of future applications [11; 12]. The concept of FiWi access networks is based on the seamless integration of the mobility and coverage offered by wireless networks and the high bandwidth and better stability offered by optical technology. In comparison with traditional access networks, FiWi access networks can be considered as an alternative or transitional solution that provides high data rates more cost-effectively with the desired quality of service (QoS).
# Image-Based Dedicated Methods of Night Traffic Visibility Estimation
## 3 Proposed Visibility Estimation Methodology
### Feature Fusion and Classification  
Visibility estimation by laser transmission or image analyses can generally produce satisfactory results. However, the results of the two methods do not necessarily match, and there will be differences between them. Therefore, the method of combining the results of the two methods through the MNLR model to generate final image visibility is proposed. The variables describing the visibility are composed of the atmospheric transmissivity obtained by the laser transmission system, the road image transmittance, the target image transmittance, the target image low brightness values, the target image high brightness values, and contrasts obtained by image analyses. The detailed descriptions of the variables are given in Table 1. The MNLR model is first trained by using train set, then tested by using the test set, and compared by using statistical measures of goodness of fit [16].  
The visibility \(d\) is considered as the dependent variable. The variables in Table 1 are considered as the independent variables. MNLR analysis is carried out using a computing package SPSS (Statistical Package for the Social Sciences). The model developed is given as follow, \(\alpha_{1}\), \(\alpha_{2}\),..., \(\alpha_{6}\), \(b_{1}\), \(b_{2}\),..., \(b_{6}\), c are the parameters:  
\[d=\alpha_{1}e^{\tau+b_{1}}+\alpha_{2}e^{\tau_{k}+b_{2}}+\alpha_{3}e^{\tau_{T}+ b_{3}}+\alpha_{4}e^{\tau_{i}+b_{4}}+\alpha_{5}e^{\tau_{h}+b_{5}}+\alpha_{6}e^{ \tau_{i}+b_{6}}+c \tag{20}\]  
Since the actual night traffic environment includes two different situations, with streetlight (ambient brightness) and without streetlight (non-ambient brightness), the degree of its influence on traffic is different. We divide the MNLR model into two cases: ambient brightness and non-ambient brightness. It is worth noting that the subdivision of the lighting environment and the production of the training set should be processed and optimized empirically on a large number of images.  
Figure 3: Horizontal brightness histograms of the images under different visibility conditions. (**a**) clear daytime; (**b**) foggy daytime; (**c**) clear evening; (**d**) foggy evening; (**e**) clear midnight; (**f**) foggy midnight.
## 1 Introduction
[13] showed a strong correlation between the amount of deposits and the atmospheric concentrations of dust particles for different European locations, so long as they were far from large sources of pollution. The same research suggested that seasonal fluctuations in the concentration of air pollutants were much higher in urban areas than in rural ones. The vast majority of deposit and pollution models in the cited literature are only partially theoretically motivated and chiefly rely on empirical studies. The main reason is that these are complex scientific problems with many variable nonlinear factors, which have so far defied a complete theoretical explanation. Artificial intelligence is currently the most effective way of studying and solving such problems. Our own work in this area started with the intuition that the levels of environmental pollution as defined by STN 33 0405 (on the basis of pollution deposits trapped in deposit collectors) should correlate well with standard indicators of atmospheric pollution, such as PM10, already measured on a regular basis by the Slovak Hydrometeorological Institute (SHMU) [14]. We hypothesized that with sufficient collected data, it would be possible to train reasonably accurate machine learning models to be capable of estimating the levels of pollution deposits from the atmospheric pollution indicators provided by the SHMU. This could potentially save a lot of effort associated with time-consuming field measurements according to STN 33 0405. Our initial attempts in this direction [15], however, ended in failure. Neither nonlinear transformations of atmospheric pollution indicators, nor their joint combinations have shown significant correlations with the levels of pollution deposits from our field measurements. Analysis of these results and their possible causes, which we briefly reiterate in the following sections, led us to search for new sources of information, such as satellite imagery, which we expected would be more tightly related to the levels of pollution deposits measured in the field. We also felt the need to redefine the target attributes and directly estimate the field-measured levels of pollution deposits, rather than their upper bounds, as we had done earlier. As the results presented below demonstrate, the combination of satellite imagery with this new methodology significantly increased the accuracy of models. The rest of this article is structured as follows: Section 2 describes potential improvement directions that we considered before embarking on the course of action reported in this paper. Section 3 describes the input and output data, and also lists possible reasons for our earlier modeling failure. Section 4 provides a brief outline of the new approach. Section 5 details the process of attribute selection and modeling, and also reports the achieved estimation results. Section 6 contextualizes these results and evaluates their statistical significance. Finally, Section 7 summarizes our findings and outlines directions for future work.
## 4 Results and Discussion  
### Case Study and Datasets  
The natural gas consumption datasets that were used in this research work to examine the applicability and effectiveness of the proposed forecast methodology corresponded to five years (2013-2017), as described in Section 3. Following the first step of the methodology, we split our dataset into training, validation, and testing ones. For the convenience of handling properly the dataset, we defined the data of the first three years as the training dataset (1095 days), the data of the fourth year as the validation dataset (365 days), and the remaining data (5 th year) as the testing dataset (365 days), which approximately corresponded to 60%, 20%, and 20%, respectively, as presented in Section 3. Thus, it was easier for our analysis to handle the above values as annual datasets and have a clearer perception of the whole process.  
Out of the three years of the defined training dataset, we used the first two as the initial training dataset, while the third (3 rd) year was used as a dataset reservoir for the bootstrapping procedure. This year was properly selected to be part of the initial dataset, as for each value of \(k\) (the bootstrapping step), a corresponding number of days/weeks/months was additionally needed to be included in the training dataset during the bootstrapping process, thus, avoiding any possible data shortage and/or deterioration that would lead to inaccurate results.  
The proposed forecast combination approach, presented in Section 3, offered generalization capabilities and, thus, it could be applied in various time-series datasets, for a different number of \(k\), according to daily, weekly, or monthly prediction. Taking as an example the case of a month-ahead prediction, for each bootstrapping step \(k\), the training dataset shifted one month ahead, getting one additional month each time from the reserved third year of the initial training dataset. In this case, \(k\) more months in total were needed for implementing efficiently this approach. If we considered \(k=12\), then \(12\) additional months of the initial dataset needed to be added and reserved. This approach justified our case where one year (i.e., the third year) was added to the initial training dataset and was further reserved for serving the purposes of the proposed methodology. Different values of \(k\) were also examined without noticing significant divergences in forecasting, compared to the selected \(k\) value.  
In the next step, the validation procedure (comprising one year of data) was implemented to calculate the in-sample forecasting errors (MSE and MAE) for each ensemble forecasting algorithm (ensemble ANN, ensemble hybrid, ensemble RCGA-FCM, and ensemble SOGA-FCM). The same process was followed for the testing procedure by considering the data of the last year. The two examined ensemble forecasting methods, i.e., the simple average (AVG) and the error-based (EB), were then applied in the calculated validation and testing vectors (Yvd) for each one of the forecast combined methodology (Yvd-ANN, Yvd-Hybrid, Yvd-RCGA, Yvd-SOGA).
## 5 Results
### Real Scenario with MVDS Interference  
This section calculates a real-life satellite signal scenario. We introduce various interference types--adjacent satellite interference (ASI), adjacent channel interference (ACI), carrier-to-intermodulation (C/IM), and carrier-to-cross-polarization (C/XP). In our case, the self-interference values at the corresponding operating frequencies are summarized in Table 5.  
\[\left[\frac{\mathrm{C}}{\mathrm{N}_{0}}\right]^{-1}_{\mathrm{Total}}=\left[ \frac{\mathrm{C}}{\mathrm{N}_{0}}\right]^{-1}_{\mathrm{up}}+\left[\frac{ \mathrm{C}}{\mathrm{N}_{0}}\right]^{-1}_{\mathrm{Down}}+\left[\frac{\mathrm{C }}{\mathrm{ASI}}\right]^{-1}+\left[\frac{\mathrm{C}}{\mathrm{IM}}\right]^{-1} +\left[\frac{\mathrm{C}}{\mathrm{ACI}}\right]^{-1}+\left[\frac{\mathrm{C}}{ \mathrm{XP}}\right]^{-1}+\left[\frac{\mathrm{C}}{\mathrm{MVDS}}\right]^{-1} \tag{28}\]  
We consider different power levels of the MVDS broadcaster (\(-10\) dBW to \(10\) dBW). Table 6 presents a comprehensive analysis of the MVDS side lobe interference effect on the GEO satellite system in a realistic scenario. This table quantifies the impact of increasing MVDS transmitted power on key performance metrics at the satellite receiver.  
Table 6 clearly demonstrates an inverse relationship between the transmitted power of the MVDS system and the quality of the signal received at the GEO satellite. As the MVDS power increases from \(-10\) dBW to \(10\) dBW, we observe a consistent decrease in \(\left[\frac{\mathrm{C}}{\mathrm{N}_{0}}\right]_{\mathrm{total}}\left[\frac{ \mathrm{C}}{\mathrm{N}}\right]\), and \(\left[\frac{\mathrm{F}_{\mathrm{h}}}{\mathrm{N}_{0}}\right]\). This degradation in the carrier-to-noise ratios directly translates to a decline in the perceived signal quality.  
At \(-5\) dBW, the quality is \"fair,\" and it further degrades to \"critical\" when the MVDS power reaches \(0\) dBW. This critical point corresponds to a \(\left[\frac{\mathrm{F}_{\mathrm{h}}}{\mathrm{N}_{0}}\right]\) of \(-3.93\) dB, indicating that the interference power is approaching or exceeding the carrier power.  
Beyond \(0\) dBW, the values become significantly negative, resulting in a \"bad\" signal quality. This signifies a severe level of interference where the desired signal is likely overwhelmed by the noise and interference from the MVDS side lobes, rendering the communication link unreliable.  
These findings underscore that even the side lobe emissions from MVDS transmitters can have a substantial impact on satellite link performance, particularly as the MVDS power increases.  
In the following section, we investigate the impact of the MVDS interference for different power levels when the signal bandwidth is varied.
# Simulating Slosh Induced Damping, with Application to Aircraft Wing-like Structures
## 1 Introduction
Several benchmark cases were carried out that showed the method provides accurate and reliable results and is able to improve the accuracy of free-surface capturing. Within the SLOW project itself, VoF-based solvers have been successfully employed for the simulation of violent slosh-induced energy dissipation [24], slosh loads [25] and also coupled with a structural solver on fluid-structure-interaction-based slosh damping simulations [26]. The SLOW project also considers other simulation methods, including the use of an SPH model: [PERSON] et al. [27] investigated the added damping of the spring-mass-based vertical sloshing tank, as well as the creation of reduced order models [28; 29; 30]. As an integral part of the overall SLOW project, the present study simulates the slosh-induced damping of partially filled water tanks on an aircraft-wing-like structure under free decay oscillations, employing open-source software. It involves the development of a new, fully coupled, fluid-structure interaction solution for the modelling of the interaction between the sloshing tank and the structure. In these results, the impact of the excitation amplitude of the structure and the number of baffled regions within the sloshing tank are investigated. Results are taken over the first ten free decay oscillation cycles and the resultant slosh-induced damping is explored. The simulations provide numerical insight into the damping behaviour of the sloshing system, as well as the corresponding slosh patterns in different excitation conditions and using different compartment numbers. The results obtained compare favourably with independent experimental results also generated within the SLOW project and will be useful to the community by providing an in-depth understanding of the slosh-induced damping in the aviation field, with outputs being used directly as guidance for the design of next-generation wing technology. This paper is organised as follows: the problem description and numerical setup used are presented in Sections 2 and 3, results and discussions on free decay of an oscillated aircraft-wing-like structure including sloshing tank simulation are presented in Section 4 and a summary of conclusions and future works of this study is presented in Section 5.
## 3 The Proposed Model
### Crowd Density Classification Module  
Once the feature vectors are created, the next stage is to classify the crowd density with the WELM model. WELM is an enhanced version of ELM that manages imbalanced class distribution data [28]. The WELM approach presented the weighted matrix \(W\) similar to how the original ELM model works to balance the data distribution that weakens the majority class and strengthens the minority class. The \(W\) represents a matrix of misclassification values according to class distribution. The \(W\) in (5) signifies diagonal matrixes with \(N\times N\) dimensional in which \(N\) indicates the overall dataset, and \(\#(t_{i})\) denotes the overall amount of samples that belongings to class \(t_{i}\).  
\[W_{ii}=\frac{1}{\#(t_{i})} \tag{5}\]  
The WELM is based on the standardized ELM that minimizes the error vector \(\xi\), as well as minimizes the output weight norm \(\beta\) to have improved generalization outcomes. In the following, the mathematical expression of the minimization problem is given.  
\[\begin{split}& minimize:L_{P_{EM}}=\frac{1}{2}\|\beta\|^{2}+ \frac{1}{2}CW\sum_{i=1}^{N}\|\xi_{i}\|^{2}\\ & subject\ to:h(x)\beta=t_{i}^{T}-\xi_{i}^{T},i=1,\ldots,N\end{split} \tag{6}\]  
The solution of \(\beta\) is obtained from (6) on the basis of KKT condition into (7) if \(N\) is smaller and (8) if \(N\) is larger.  
\[\beta=H^{T}\bigg{(}\frac{I}{\mathcal{C}}+WHH^{T}\bigg{)}^{-1}WT \tag{7}\]  
\[\beta=\bigg{(}-\frac{I}{\mathcal{C}}+WHH^{T}\bigg{)}^{-1}H^{T}WT \tag{8}\]For multi-class classification with \(m\) class, all the labels are mapped into a vector of \([-1,1]\) through length of \(m\); for example, a dataset that is categorized into the 2 nd classes from three classes are \([-1,1,-1]\). The output \(f(x)=HB\) in multi-class classification is a vector \(f(x)=[f_{1}(x)\), \(f_{2}(x)\), \(f_{m}(x)]\) and the class label is evaluated by the following equation. Figure 3 showcases the framework of WELM.  
\[label\ of\ x=\arg max(x) \tag{9}\]
# Speaker Diarization: A Review of Objectives and Methods
## 8 Summary of Speaker Diarization Methods
### Modular SD  
Classical SD uses a sequence of steps: (1) a speaker embedding extractor for each segment, (2) a clustering algorithm that merges segments based on the speaker embedding similarity and assigns a speaker label to each segment, and optionally, (3) some compensation algorithm such as variational Bayesian refinement can calibrate the clustering. In such modular SD, several components must be optimized using separate criteria, and overlapped speech is difficult to handle, as unsupervised clustering often assumes that each segment is assigned to only one speaker.
## 2 Clinical Data Collection  
This study involves the collection of patient data (Figure 2a) in a clinic; surface temperature data was collected using a non-invasive infrared imaging camera and the scanned geometry of the breast was taken using a 3D scanner. The study was approved by Nazarbayev University Institutional Research Ethics Committee (identification number is 294/17062020).  
Figure 2: Clinical office for collecting patient data (**a**), the 3D Scanner Sense 2 (**b**), and IR camera IRTIS-2000 ME (**c**).  
The geometry of the breast was scanned by a smart 3D scanner called Sense 2 from 3D Systems (Figure 2b), which is a very light and easy-to-use scanner with excellent resolution, used for scanning objects from 200 to 2000 mm, and with a scanning accuracy of 0.9 mm. The front panel of the device is equipped with two cameras and an IR sensor. The scanner generates 3D models of the subjects being scanned by using image processing, while the IR sensors are used to measure the distance between the scanner and the patient. Temperature distributions on the breast skin surfaces were recorded by the thermal camera IRTIS-2000 ME (Figure 2c), which is used for medical research and in the diagnosis of a wide range of illnesses, including oncological diseases. Its temperature resolution for the entire field of view is 0.02 \({}^{\circ}\)C and its temperature measurement accuracy is 0.1 \({}^{\circ}\)C.  
The collected information was used to generate 3D solid breast models to serve as input in the proposed diagnostic method for tumor diagnosis. The collection of data was conducted in the Oncology Center in Nur-Sultan city, Kazakhstan.  
The 3D scanner exports data as surfaces in the STL format rather than full-sized 3D solid models. Therefore, the geometric data had to be manually processed in order to fully construct 3D solid breast models in a CAD system. A scanned model is shown in Figure 3 as an example.  
The scanned surface model was then processed and manipulated in a CAD program, converting it into a full-fledged 3D solid model from which FEM meshes could be generated for FEA simulation and reverse thermal modeling (Figure 4).  
Figure 4: Full-fledged 3D model of the breast.  
Figure 3: A 3D scanned model of the breast.
No physical impact was detected in one protocol using extended daily exposures of 22 hours per day from GD 1 for 19 days at 970 MHz and with a SAR of 0.07 and 2.4 W/kg [21]. Overall, the published data do not converge to a simple correlation between rodents' RF-EMF exposure SAR or duration and their physical development. One may hypothesize lower physiological and developmental health protection of ICNIRP limits in vulnerable populations, i.e., day-long working, and non-working pregnant women, and young children. Here, we aimed to assess the hypothesis that when mother rats receive a wbSAR at the occupational (Oc) limit of the ICNIRP for humans (0.4 W/kg), their descendants will show impairments on growth and development, with less effect at public (Pu) limit (0.08 W/kg). The wbSAR was estimated for mean dam rat body weight of 330 g: 0.4 W/kg to mimic working mothers (OcM exposure, n = 8) and 0.08 W/kg for non-working mothers, i.e., public (PuM exposure, n = 8). [PERSON] et al. [22] highlight the need to assess long duration of exposure in toxicological studies to observe the impact on growth as for RF-EMF during the perinatal period. The descendants' body weights and lengths were examined from birth until adolescence, on pups, sex and stillbirth ratio, and on age at pinna ear detachment, incisor eruption, and eye opening.