esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Distributed Cognition-Based Data Analysis and Results
### The Result of Trace Plots  
These Trace Plots show the iterative convergence process of each path parameter in the Bayesian structural equation model. Analysis shows that the parameter values of the core paths, such as LF\(\rightarrow\)PEU (1.0-1.3), PF\(\rightarrow\)LF (1.0-1.4), and PEU\(\rightarrow\)SA (0.9-1.2), are stable and converge well. The trajectories of the two chains (Chain.1 and Chain.2) highly overlap, indicating that the MCMC sampling has reached a stable state. However, there are significant differences in some paths: The parameter values of LF\(\rightarrow\)WTCTU (\(-\)0.5 to 0.25) and PEU\(\rightarrow\)WTCTU (\(-\)0.5 to 0.5) cross the zero line. The influence direction may change due to the characteristics of the model or data, and their robustness needs to be further verified. Although SA\(\rightarrow\)WTCTU (0.25-0.75) and PE\(\rightarrow\)WTCTU (0.1-0.4) have a positive relationship, their intensities are moderate, and the influence of PU\(\rightarrow\)WTCTU (0.0-0.5) is relatively weak. It should be noted that the data of some prior types (such as weak information) in the EF\(\rightarrow\)PF path is missing, which may affect the integrity of parameter estimation. Overall, the core paths of the moderately informative prior model (such as LF\(\rightarrow\)PEU\(\rightarrow\)SA) have high stability.Figure 4.  
Figure 4: Trace Plots of Four Prior Distributions (n = 500).  
Figure 3: Sensitivity Analysis: Path Coefficients Across Different Priors (n = 500).
###### Abstract  
The capabilities of 6G networks surpass those of existing networks, aiming to enable seamless connectivity between all entities and users at any given time. A critical aspect of achieving enhanced and ubiquitous mobile broadband, as promised by 6G networks, is merging satellite networks with land-based networks, which offers significant potential in terms of coverage area. Advanced routing techniques in next-generation network technologies, particularly when incorporating terrestrial and non-terrestrial networks, are essential for optimizing network efficiency and delivering promised services. However, the dynamic nature of the network, the heterogeneity and complexity of next-generation networks, and the relative distance and mobility of satellite networks all present challenges that traditional routing protocols struggle to address. This paper provides an in-depth analysis of 6G networks, addressing key enablers, technologies, commitments, satellite networks, and routing techniques in the context of 6G and satellite network integration. To ensure 6G fulfills its promises, the paper emphasizes necessary scenarios and investigates potential bottlenecks in routing techniques. Additionally, it explores satellite networks and identifies routing challenges within these systems. The paper highlights routing issues that may arise in the integration of 6G and satellite networks and offers a comprehensive examination of essential approaches, technologies, and visions required for future advancements in this area. 6G and satellite networks are associated with technical terms such as AI/ML, quantum computing, THz communication, beamforming, MIMO technology, ultra-wide band and multi-band antennas, hybrid channel models, and quantum encryption methods. These technologies will be utilized to enhance the performance, security, and sustainability of future networks.  
Routing techniques; sixth generation (6G) networks; satellite networks; mobility; routing protocols; THz communication; VLC communication 202513245
## 2 Materials and Methods
### Images and Statistical Analysis
Parcellation and segmentation images by MRI contrast are presented in individual spaces overlaid on the MRI, as shown in Figure 2. To extract the SUV values, brain PET, parcellation, and segment images were co-registered using SPM. For parcellation images extracted by the PMOD algorithm, the SUV was calculated in the region of the AAL atlas, and parcellation images extracted by the FreeSurfer algorithm were used to find the SUV in the region of the Desikan-Killany atlas. These two atlases may differ slightly in the cortical and subcortical regions. Figure 2 illustrates the regions of the AAL atlas in the PMOD viewer, and the regions of the Desikan-Killany atlas in Freeview. Figure 1: The negative (**A**) and positive (**B**) FBB brain PET images. Cropped T1-weighted MRI (**top**), cropped PET in MRI space (**bottom**) are shown. Figure 2: Representation of the parcellation and segmentation images using PMOD (**A**), cortical parcellation, and sub-cortical segmentation images using FreeSurfer (**B**), AAL atlas (**C**), and Desikan-Killany atlas (**D**). As shown in Figure 2, cropping was performed using PMOD for the PET images and T1-weighted MRI to reduce the computation time. After re-slicing the individual images of the subject using SPM, co-registration was performed on cropped T1-weighted MRI. The re-slicing of brain PET, parcellation, and segmentation were performed to match the resolution of T1-weighted MRI. After the T1-weighted MRI was processed for skull stripping and intensity normalization, the regions were determined by registration to the atlas. SUV were extracted from regions where brain parcellation and segment images in the MRI space overlapped the brain atlas. For the brain atlas, SUV data were extracted using the AAL atlas in PMOD and Desikan-Killiany atlas in FreeSurfer. For a comparative analysis, 36 FBB PET, 78 FMM PET, and 65 FPN PET images were used for classification. In the PMOD and FreeSurfer analysis process, images that could not be analyzed normally and images in which spatial normalization was difficult to perform were visually evaluated and not selected for analysis. Assuming that there is a confounding effect of age and sex, the images were randomly divided into four groups: \(\geq\)70 years old, <70 years old, male, and female. To increase the legitimacy of statistical comparisons of SUVr, images of the subjects were balanced; however, selection bias may still have occurred.
# Multi-Scale Feature Learning for Language Identification of Overlapped Speech
## 5 Conclusions and Future Work  
In this paper, we propose an improved SE-Res2 Net-CBAM-BiLSTM method that can extract multi-scale features from speech signals. We evaluated our method on two datasets: AP17-OLR, a public Oriental language dataset, and a multilingual cocktail party dataset that we constructed with different energy ratios and speaker numbers. The experimental results show that the language identification accuracy of the improved SE-Res2-Net-CBAM-BiLSTM network on the AP17-OLR dataset was improved by about 3% compared to the baseline model CNN-CBAM-BiLSTM. On a multilingual cocktail party dataset of a 100% overlapped scenario, with a target language weight of 1.2 over the non-target language weight of 1, the accuracy of the proposed model was improved by 11% compared to the baseline model. In addition, the comparative experiments show that the accuracy, recall, and F1 values of the model in this paper were improved over the other three models, and the stable model performance indicates better robustness. Finally, different loss functions were also compared, and the focal loss method produced better results.  
In the future, we will design new model frameworks to improve the performance of language identification networks. In addition, we will variously simulate multi-language cocktail party scenarios and conduct joint training experiments on source separation and language identification tasks.  
Conceptualization, Z.A. and M.A; methodology, Z.A. and A.H.; software, Z.A.; validation, M.A. and A.H.; formal analysis, Z.A.; investigation, Z.A. and M.A.; resources, M.A. and A.H.; data curation, M.A.; writing--original draft preparation, Z.A.; writing--review and editing, M.A. and A.H.; visualization, Z.A. and M.A. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Strengthening Plan of the National Defense Science and Technology Foundation of China (grant number: 2021-JCJQ-JJ-0059) and the Natural Science Foundation of China (grant number: U2003207).  
Not applicable.  
The dataset supporting the conclusions of this article is available at [[https://www.speechocean.com](https://www.speechocean.com)]([https://www.speechocean.com](https://www.speechocean.com)), accessed on 11 March 2020.  
The authors declare no conflict of interest.
## 5 Results from Experiments Using the Platform  
We now turn to the use of the platform and present results from an extensive set of experiments targeting the performance comparison of all implemented routing protocols, namely: the forward action-oriented MFR, the carry action-oriented MoVe, and the two versions of MAD (original and improved, the latter using a more refined estimation of the retaining time; see Section 3.2.4).
## 3 Selection and Construction of Thin Chip and Cape Seal Surfacings
### Binder Selection
#### 3.2.4 Binder Selection: Slurry for the Construction of the Cape Seal  
Cape seals [25] are normally constructed using a slurry consisting of a bitumen emulsion, crusher dust, sand and a cement filler. The slurry provides additional protection and acts as an adhesive, keeping the stone in place where the turning movement of vehicle tyres can potentially result in stone loss. Practical experience has shown that a well  
Figure 3: Working principles as applicable to a standard pen bitumen, 1.5% SASOBIT-M®-modified bitumen and 3% SASOBIT®-modified bitumen binder.  
constructed Cape seal can be expected to have an advantage in terms of maintenance-free surfaced life [26] comparable to that of an asphalt layer at considerably less costs.  
The use of a cement or lime filler as part of the slurry mix may result in a brittle mix, reducing the crack-free life of the slurry seal on a highly flexible pavement structure. In this case, it was decided to test a slurry using an anionic nano-silane-modified emulsion with no cement or lime filler. The nano-silane will replace the normal cement or lime filler, potentially increasing the crack-free life of the seal. The influence of nano-silane modifications has been covered in detail by several publications (e.g., [2; 7; 13]). The main advantages of nano-silane modifications of a binder include that it:  
* acts effectively as an aggregate adhesive that permanently binds the bitumen to the aggregate;
* chemically alters the surface of the aggregate to become hydrophobic and repels the water from the mix; and
* assists in the stability and better distribution of the bitumen particles, effectively reducing the percentage of binder required to achieve the same engineering properties in terms of tensile and compressive strengths.  
It follows that the Cape seal (20 mm stone with hand-mixed and hand-placed slurry) was constructed using a combination of previously untested binders (SASOBIT-M(r) tack and an anionic NME binder for the slurry with no cement or lime filler). This seal is representative of a thin asphaltic layer, with the chips held firmly in place by the slurry mix. The deformation characteristics of this thin surfacing were considered of particular interest in terms of their comparison with those of an asphalt surfacing [15]. APT on the Cape seal was included as a good indication of possible punching of the large stone size into the anionic NME-stabilised base layer, which could result in an associated over-exposure of the slurry with severe bleeding of the bitumen binder visible on the surfacing.
# Load Frequency Optimal Active Disturbance Rejection Control of Hybrid Power System
###### Abstract  
The widespread adoption of the power grid has led to increased attention to load frequency control (LFC) in power systems. The LFC strategy of multi-source hybrid power systems, including hydroelectric generators, Wind Turbine Generators (WTGs), and Photovoltaic Generators (PVGs), with thermal generators is more challenging. Existing methods for LFC tasks pose challenges in achieving satisfactory outcomes in hybrid power systems. In this paper, a novel method for the multi-source hybrid power system LFC task by using an optimal active disturbance rejection control (ADRC) strategy is proposed, which is based on the combination of the improved linear quadratic regulator (LQR) and the ADRC controller. Firstly, an established model of a hybrid power system is presented, which incorporates multiple regions and multiple sources. Secondly, utilizing the state space representation, a novel control strategy is developed by integrating improved LQR and ARDC. Finally, a series of comparative simulation experiments has been conducted using the Simulink model. Compared with the LQR with ESO, the maximum relative error of the maximum peaks of frequency deviation and tie-line exchanged power of the hybrid power system is reduced by 96% and 83%, respectively, by using the proposed strategy. The experimental results demonstrate that the strategy proposed in this paper exhibits a substantial enhancement in control performance.  
load frequency control; state space; hybrid power system; linear quadratic regulator; active disturbance rejection control
## 2 Materials and Methods  
GO synthesized by the Hummers method was dissolved and dispersed in ultrapure water to a final concentration of 0.1 mg/mL. Peptide (>90% purity, JPT Company, Berlin, Germany) was dissolved in ultrapure water to a final concentration of 0.2 mg/mL. The typical process of peptide-mediated synthesis of GO-peptide-AgNPs nanohybrids is shown in Figure 1. Firstly, 5 mL GO and 1 mL peptide solutions were mixed under stirring, and then 4 mL ultrapure water was added into the mixed solution to keep the final volume at 10 mL. Secondly, after two hours, the mixed solution was centrifuged at a speed of 13 K to remove the unbound peptide, and the product was dissolved with ultrapure water to 5 mL. Then, 0.1 mL AgNO\({}_{3}\) with a concentration of 0.1 M was added to the GO-peptide solution under stirring and the pH of the mixed solution was adjusted to 2.0. Thirdly, after another two hours, the mixed solution was centrifuged again to remove the excessive Ag\({}^{+}\) and ultrapure water was added to keep the volume at 5 mL. Then, freshly prepared NaBH\({}_{4}\) (1%, \(m/m\)) was added drop by drop into the mixed solution to synthesize GO-peptide-AgNPs nanohybrids. After 10 min, the product was centrifuged and dissolved with water to a final volume of 2 mL for further characterizations and electrochemical tests.  
For the preparation of GO-peptide-AgNPs nanohybrids modified GCE, 0.1 mL GO-peptide-AgNPs nanohybrids solution was directly transferred onto the GCE surface and dried for cyclic voltammograms (CV) and amperometric response tests. All the electrochemical tests were carried out on an electrochemical workstation (CHI760D, Chenhua Company, Shanghai, China) with the conventional three-electrode system.  
Atomic force microscopy (AFM) was conducted with a NanoWizard 3 NanoScience atomic force microscope (JPK Instruments AG, Berlin, Germany) at taping mode. Transmission electron microscope  
Figure 1: Schematic presentation on the peptide-mediated biomimetic synthesis of GO–peptide–AgNPs nanohybrids.  
(TEM) characterization was carried out with a Tecnai G220 transmission electron microscope (FEI, Beijing, China) with an accelerating voltage of 200 kV. The chemical state of GO-AgNPs nanohybrids was measured by X-ray photoelectron spectroscopy (XPS, ThermoVG ESCALAB 250, Waltham, MA, USA).
###### Abstract
A uniform distribution of many points in the space on the ship is generally impossible; hence, forced geometrical configurations of the distribution of points are used, and the number of connection points between the measurement stations is generally lower and falls within the range of 3-6. In addition, when the object is on the water, it is not possible to precisely level the Total Station, which forces the use of conformal 3D transformations to combine the measurements into a cloud of points. Measurements on unstable (movable) surfaces are applied using the measurement technology of a completely free position, without leveling the instrument, in a local and random coordinate system related to the Total Station [16; 30]. The use of ANN to solve the problem of coordinate transformation in these specific conditions, due to the very small set of data that can be used to train the network (usually 3-6 common points between measurement stations), seems very difficult or impossible. Therefore, a general question can be raised: is it possible to solve the problem of coordinate transformation using machine learning (ANN) in the case of fewer geodetic networks, with only three common points? Can it be done with better or equal accuracy in comparison to the novel (gradient) and traditionally used methods of coordinate transformation in all kinds of scenarios? In this publication, the authors put forward the thesis that it is possible, and, despite the described difficulties, they set a goal to solve the problem of using ANN for the transformation of coordinates for a minimum number of points in a satisfactory way from the point of view of accuracy. Therefore, the specific objectives are: finding and applying a simple method of densifying the number of points in less geodetic networks, verifying whether it is possible to use ANN to solve the problem of coordinatetransformation for offshore purposes with the help of a point cloud generated in this way, and verification of whether the applied approach enables the achievement of equal or higher accuracy of 3D transformation in relation to novel (gradient) and traditionally used computational methods.
## 2 Fundamental Concepts  
In this section, we provide insights into the general concepts and main preliminaries that are associated with PHY security. First, we provide a brief overview of the basic system model for PLS that is related to eavesdropping problems, as illustrated in Figure 1. Then, we consider and discuss the hurdles of PLS from the perspective of the adversary model that could prevent it from succeeding. The last part of this section presents several wiretap channel models that adopt the common notations and scenarios that are considered in the rest of the discussion.
## 2 Methodology  
### Instrument and Data Description  
Two Leosphere WindCube 200S Doppler lidars are in use in Iceland; both were equipped with a depolarization module during this study period. One is located at KeflavikInternational airport, while the other is on a mobile trailer, ready for rapid deployment during volcanic eruptions. During this study, the mobile lidar was located in Reykjavik, the capital city of Iceland. The basic specifications can be found in Table 1. The lidars were scheduled with two scan strategies: (1) Velocity-Azimuth-Display (VAD) scans, the conic-shaped scans with fixed elevation angles for calibration and wind monitoring, and (2) vertical stare for aerosol monitoring. In this study, we only used vertical stare measurements [20]. Here, data from the lidar located in Reykjavik are used to train and test the models, and subsequently, data from the lidar located at Keflavik airport for testing the trained models. In the following section, if not specified otherwise, the lidar observations were collected by the mobile lidar in Reykjavik.  
The primary output of the lidar is the two-dimensional CNR. The relative backscatter coefficients (\(\beta\)) and depolarization ratio (\(\delta\)) can be retrieved from CNR. The detailed methods to retrieve \(\beta\) and \(\delta\) are described by [PERSON] et al. [20].  
The data we use to train the models were collected from four days in 2019: 14 June, 15 June, 31 July and 1 August. During these days, two dust storms were observed by weather observers, as well as other weather conditions, including low clouds, high clouds and rainfall. A detailed description of these two dust events and the lidar measurements can be found in [PERSON] et al.'s study [20].  
A complete lidar profile contains 139 range gates with a resolution of 100 m each. The time difference between the two profiles varied, from around 1.6 to 6 s. To shorten the processing time, 10% of the data were randomly selected for model training.
# Risk Factors for Distortion Product Otoacoustic Emissions in Young Adults
## Results
### Multiple Risk Factors and DPOAEs  
The combination of these risk factors was also evaluated. The scatterplot for all participants for DPM and preferred listening level is shown in Figure 4. The DPM axis was offset to \(-\)5 DPM so as to be able to visualize those participants in the No DPM category. There is a slight positive association (regression line shown) between reported DPM and preferred listening level. However, for the sex-specific data, there are differing associations. For women, there was a statistically significant positive association between DPM and preferred listening level (F[1,158] = 2.13, \(p\) < 0.05) (Figure 5a), but for men, there was a non-statistically significant negative association between DPM and preferred listening level (F[1,52] = 0.49, \(p\) > 0.05) (Figure 5b). In the mixed ANOVA with both preferred listening  
Figure 3: (**a**) The means and standard deviations are shown for DPOAE levels across the frequencies tested in women only by self-reported drinking categories; (**b**) The means and standard deviations are shown for DPOAE levels across the frequencies tested in men only by self-reported drinking categories.  
level, as a continuous variable, and DPM, there were no statistically significant interactions or main effects on DPOAEs. Further, when the LEVEL variable was used in the mixed ANOVA, the results were still not statistically significant.  
Figure 4: The scatterplot for drinks per month (DPM) and preferred listening level (L\({}_{\text{Aeq}}\)) is shown for all 216 participants. The linear regression line is also shown.  
Figure 5: (a) The scatterplot for drinks per month (DPM) and preferred listening level (L\({}_{\text{Aeq}}\)) is shown for women only (\(n=161\)). The linear regression line is also shown; (b) The scatterplot for drinks per month (DPM) and preferred listening level (L\({}_{\text{Aeq}}\)) is shown for men only (\(n=55\)). The linear regression line is also shown.  
In an additional analysis, a new variable was defined in an effort to evaluate the extreme categories of these risk factors. The RISK variable was defined as follows: No Risk--those who reported No DPM and listened at \(\leq\)73.7 dBA; and High Risk those who reported Heavy DPM and \(>\)73.7 dBA. There were 77 participants in this subset analysis; 30 participants (25 women and 5 men) in the No Risk group and 47 participants (34 women and 13 men) in the High-Risk group. After adjusting for age and test ear, the Sex-by-RISK-by-Frequency interaction was statistically significant (F[16; 364] = 1.89, \(p<0.05\)). For the sex-specific mixed ANOVAs, the RISK-by-Frequency was not statistically significant for women (F[5,285] = 1.08, \(p>0.05\)) or men (F[5,80] = 1.29, \(p>0.05\)). In Figure 5(a), the High-Risk women had slightly higher DPOAEs from 1 through 4 kHz compared to the No Risk women; whereas for the men, High Risk had poorer DPOAEs from 1 through 3 kHz compared to the No Risk men (Figure 5(b)). The results on this additional analysis should be interpreted cautiously given the smaller sample sizes, particularly in men.