esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Expedition Cruising in Svalbard and the Application of ICT  
Svalbard is an archipelago located within 71-81\({}^{\circ}\) Northern Latitude and 10-35\({}^{\circ}\) Eastern Longitude, approximately midway between the North Cape of Norway and the North Pole. The archipelago consists of islands of a total area of approximately 61,022 km\({}^{2}\). Around 65% of Svalbard's terrestrial area and about 86% of its territorial waters is under environmental protection [72], because of the archipelago's rich and diverse wildlife which includes various bird colonies as well as mammals such as reindeer, the Arctic fox, polar bears, walrus, seals and whales. At the same time, Svalbard is one of the most northern populated places on earth with a population of around 2500 inhabitants across the capital of Longyearbyen, the Russian mining settlements of Pyramid and Barensburg, and the research community of Ny-Alesund [73].  
The relative accessibility and developed infrastructure in Svalbard, as well as its abundant wildlife and stunning landscape, make the archipelago appealing for cruising activities [74]. Expedition cruises search for solitude and avoid other ships to ensure a unique Arctic experience in Svalbard. The number of expedition cruise passengers visiting Svalbard increased from 3417 in 2001 to 12,519 in 2014 [75], with Svalbard's visitors exceeding those in neighbouring Greenland, Canada or Russia [74]. However, sailing in such remote areas is challenging due to often rapidly changing or unexpected weather and sea ice conditions.  
Tourism and maritime operations are regulated under the umbrella of the Svalbard multi-jurisdictional legal framework, consisting of the Svalbard Treaty, the Svalbard Environmental Act, international and state regulations, as well as the general regulatory framework of shipping, particularly for operations in polar waters [76]. Considering the nature of expedition cruising and its particular operating environment, the complex multi-jurisdictional framework is regarded as being inadequate and insufficient as a governance tool, as it lacks sector-specific elements [12].  
In addition to governmental regulation, tourism management in Svalbard is supplemented by AECO self-regulation, e.g., through operational guidelines and a range of tailored technologies to facilitate the pursuit of the network's goals, such as dealing with crowding and environmental degradation.
# Shear Strength Analysis and Slope Stability Study of Straight Root Herbaceous Root Soil Composite
## 4 Discussion
### Influence of Test Methods on the USRS Shear Strength Parameters  
Earlier studies typically employed indoor direct shear tests to assess the reinforcement impact of USRS due to challenges in preparing soil samples [38; 39; 40]. These tests aimed to determine the cohesion, internal friction angle, and shear strength of the soil-root composite. However, indoor and outdoor direct shear tests are associated with several limitations in the testing process. In particular, the shear failure surface is artificially fixed and can only be sheared along the horizontal interface between the upper and lower boxes, which is not necessarily the weakest shear plane of the soil sample. In addition, non-uniform shear strain and shear stress distributions can arise inside the specimen. Lastly, the shear surface of the soil sample gradually shrinks during the shearing process, yet shear strength computations are grounded in the original cross-sectional area of the soil sample. Thus, to enhance the testing validity, this study adopted triaxial tests to determine the shear strength parameters for USRS and RUS. This method, more practical in nature, ensures that the shear failure surface is the weakest plane. Consequently, the results gain enhanced credibility, thus providing a scientific basis for evaluating the shallow stability of real-world ecological slope-protection projects.  
Investigating the impact of root content on soil-root composite material strength often involves employing remolded soil tests [41; 42]. However, the evaluation of the influence of the root content on shear strength indicators through indoor shear tests of remolded soil-root composites presents challenges [43]. This arises from the multifaceted nature of shear strength in soil-root composites, influenced not only by plant species and the natural environment but also by numerous factors encompassing soil type, particle shape and gradation, void ratio, interparticle binding material, density, and moisture content. In particular, remolded soil samples fail to capture the original growth morphology and natural distribution of plant roots within the soil. Moreover, they do not fully represent the synergetic characteristics of the soil-root interface during the shearing process. Therefore, investigating shear strength attributes in soil-root composites via remolded soil samples, including determining the optimal root content [44; 45; 46], can only provide a theoretical basis for vegetation selection in ecological slope protection projects. It is almost impossible and unrealistic to determine the optimal root content for USRS.
## 3 Case of Study  
The platform of analysis in this paper is the SATH(r) concept designed by the Spanish enterprise Saitec (see Figure 2).  
One of the main differences between the SATH(r) platform and the previous concepts of floating wind platforms is the composition of its hull, which is made of concrete instead of steel like previous platforms (WindFloat [32], TLP [17], Hywind [33]).  
A prototype of the SATH platform, called BlueSATH(r) platform, will be installed in spring 2020 in front of the Sardinero beach in Santander, a city situated in the North of Spain, as part of the ARCWIND project. Figure 3 shows the construction of the BlueSATH(r) platform in the workshops.  
Figure 1: Calculation of the economic parameters.  
Figure 2: SATH® platform concept. Source: Figure courtesy of Saitec [31].  
The technical data of the prototype concept are presented in Table 1. BlueSATH(r) is the first offshore wind platform development of the Saitec company of a 1:6 scale prototype of a 10 MW wind turbine using the SATH(r) technology in open sea waters [31]. Its objective is to model the behaviour of SATH(r) concept, \"allowing structural optimization, cost reduction and validating structural turbine integrity\" [31]. It is composed of 3 chain moorings keeping their position using drag anchors [31].  
SATH(r) platform is composed of the following components (see Figure 4):  
* Twin horizontal floaters stiffened with diaphragm walls with conical ends and linked with bar frames.
* Transition piece linking the tower to the platform.
* Heave plates linked to floaters.
* Single point mooring.  
The location selected for the case of study, whose main characteristics are shown in Table 2, is Figueira da Foz (west coast of Portugal, Iberian Peninsula) [35; 36] (see Figure 5).  
Figure 4: SATH® platform main components. Source: It has been adapted from a figure courtesy of Saitec [31].  
Figure 5: Case study Figueira da Foz (west coast of Portugal, Iberian Peninsula) [37].  
The alternatives of the case of study depend on the electric tariff (50 \(\epsilon\)/MWh or 150 \(\epsilon\)/MWh; 56.32 \(\lx@sectionsign\)/MWh or 168.97 \(\lx@sectionsign\)/MWh) and the capital cost (6% and 8%) (Table 3 shows the scenarios analysed). The total power of the farm is 500 MW.
# WEREC: An Unsupervised Method for Educational Concept Extraction Based on Word Embedding Refinement
## 3 Methods
### K-Means Clustering Algorithm  
K-means clustering is an unsupervised learning method that follows a data partition strategy based on Euclidean distance [45]. It is distinguished by an iterative learning process where the center of each cluster (i.e., the centroid, calculated as the mean of data points in the cluster) is continually updated until it meets a convergence criterion. The criterion of inertia abides by the within-cluster sum of squares. K-means aims to minimize a cost function, which is the sum of the squared error on \(K\) clusters. This function is mathematically written as Equation (4), where \(c_{k}\) is the \(k\)-th cluster, \(x_{i}\) is the \(i\)-th data point in cluster \(c_{k}\), and \(\mu_{c_{k}}\) is the centroid of cluster \(c_{k}\).  
\[J(C)=\sum_{k=1}^{K}\sum_{x_{i}\in c_{k}}\left\|x_{i}-\mu_{c_{k}}\right\|_{2}^{2}. \tag{4}\]  
The K-means clustering algorithm has several advantages [45]: (1) relatively high computing efficiency, (2) low time complexity, and (3) good interpretability. It has thus been applied in contexts such as computer vision and image processing, information retrieval, and knowledge extraction. However, the K-means algorithm must randomly select \(K\) data points to initialize cluster centroids and can potentially converge to undesired local minima, which increases the randomness and unsteadiness of cluster membership. Therefore, initially chosen cluster centroids and the number of clusters (i.e., K) warrant careful consideration before implementing this algorithm. We built a Python program with the scikit-learn package and selected initial centroids by measuring their probability of contributing to the overall inertia in each sampling step. No ideal heuristic strategy or mathematical criterion was available to determine the value of \(K\). Therefore, we empirically tested different values and evaluated the cluster results based on an intrinsic metric. For the sake of computational efficiency when dealing with extensive data, the Calinski-Harabasz (CH) score [46] was adopted to estimate clustering performance. The CH score can be calculated as in Equation (5), where \(n_{k}\) is the number of members in cluster \(c_{k}\) and \(\mu\) is the centroid of the entire dataset with given \(N\) seed concept vectors. The higher the value of the CH index, the better the clustering validity; that is, clusters are primely separated from each other and are distinctly preferable.  
\[\text{CH}=\frac{\left(\sum_{k=1}^{K}n_{k}\left\|\mu_{c_{k}}-\mu\right\|_{2}^{ 2}\right)\left(N-K\right)}{\left(\sum_{k=1}^{K}\sum_{i=1}^{n_{k}}\left\|x_{i}- \mu_{c_{k}}\right\|_{2}^{2}\right)\left(K-1\right)}. \tag{5}\]
###### Abstract  
Revealing the interlayer pressure distribution in multilayer insulation (MLI) for cryogen (e.g., liquid hydrogen) containers is very important to improve the insulation-performance-predicting quality. This paper proposed an inversion method to reconstruct the interlayer pressure of multilayer insulations on the basis of experimentally measuring the reflectors' temperatures. The layer-by-layer (LBL) model was modified by considering the interlayer pressure distribution in MLIs to calculate the reflectors' temperatures. Groups of pre-given interlayer pressure distributions and the corresponding temperature distributions calculated by the LBL model were used to train an extreme learning machine (ELM) algorithm. Finally, the interlayer pressure distribution of the MLI was reconstructed by the trained ELM algorithm based on the measured reflectors' temperatures. The method was validated by four additional testing cases. The results showed that the proposed algorithm was accurate in reconstructing the interlayer pressures. Published experimentally measured temperature distributions of a 60-layer MLI were used as input data. The abovementioned inversion method was adopted, and a reasonable interlayer pressure distribution was obtained. Moreover, the thermal insulation performance of the MLI was calculated by the LBL model considering the reconstructed interlayer pressure distribution. We found that the predicted heat flux of the MLI deviated from the experimental results by only 2.77%, while the error of the classical LBL model ignoring the non-ideal vacuum condition was as high as 89%. Meanwhile, the predicted corresponding temperature distribution deviated from the tested value by less than 1.13 K. The proposed method can be applied to assess the interlayer pressure distribution of industrial cryogen containers and precisely predict the thermal insulation performance of a practical multilayer insulation structure.
# A Graph Convolutional Network Based on Sentiment Support for Aspect-Level Sentiment Analysis
## 4 Experimental Analysis
### Experimental Results  
As shown in Table 3, compared with the baseline models, we found that the accuracy improvement range of the SSGCN on _Restaurant_ was 0.54-5.27%, and the improvement range of the macro F1 was 0.75-11.68%. The accuracy improvement range on _Laptop_ was from 0.95% to 6.70%. The accuracy improvement range on _Twitter_ was 0.10-3.43%, and its improvement range for the macro F1 was 0.32-4.04%. In summary, on all three datasets, the SSGCN showed better accuracy compared with the models based on attention structures or GCNs. In the _Restaurant_ and _Twitter_ datasets, the evaluated macro F1 results illustrate that the SSGCN was the best model. Our model achieved only a sub-optimal effect in terms of the macro F1 value on the _Laptop_ dataset. This was because the syntax structure of the _Laptop_ dataset was relatively chaotic, which increased the difficulty of obtaining syntactic information for certain samples. The experimental results demonstrate that our model achieved higher accuracy by incorporating the semantic and grammatical information of aspect words with SSWs.
# Optimized Architecture for Efficient OFDMA Network Design
###### Abstract
In this section, the assumptions of the model and the derivation of performance parameters are discussed in detail. Section 4 explores the validation and robustness of the analytical model using a custom-built simulator. The simulator is used to compare performance parameters under more realistic conditions versus those predicted by the analytical model. Section 5 presents the results of the simulation, where, due to server capacity limitations, simulations were conducted with a fixed number of 10 stations. Finally, Section 6 concludes the paper, offering insights into the findings and potential directions for future research.
## 4 Dense Sequential Networks
### Forecast Performance
The shape of the latter is obvious, as the MAE is largest at approximately 180 days (forecast issue time and forecast valid time are in exactly opposing seasons) and smallest at roughly 1 year (same seasons). The MAE of forecasts performed with Setups X, Y and Q is largest at 90 days and roughly 270 days, whereas the MAE of persistence forecast is largest at approximately 180 days (half a year). This behavior can be explained by the fact that the NN in Setup X, Y or Q does not know about the current season (or about the season at forecast valid time) and can only indirectly infer this information from available input data (e.g., the temperature profile). The 180-day forecast is thus rather straightforward: if the input temperatures are high (likely summer), then the output temperatures after 180 days will likely be low (winter). Similarly, if the input temperatures are moderate (likely spring or autumn), the output temperatures will remain moderate (likely autumn or spring). However, for the 90-day forecast, the moderate input temperatures (likely spring or autumn) can lead to either high temperatures (likely summer) or low temperatures (likely winter) after 90 days, which results in a larger forecast error. Figure 5: The evolution of the test set mean absolute error (MAE) for forecast using various NN setups. The setups are described in Table 3. The time index \(t\) refers to the day of the radiosonde measurement, with \(t-1\) referring to the previous day, and \(t+i\) to the \(i\)-th day in the future. The legend is the same for all subfigures as long as \(T_{\text{max}}\) in (**a**,**b**) is replaced with \(T_{\text{min}}\) in (**c**,**d**). For the forecasts made by various NN setups, the lines represent the average MAE values in the ensemble while the color shaded regions indicate the interval between the 10 th and 90 th percentile. Taking longer time series of past data as a predictor and using recurrent NNs would likely improve the forecasts, as such NNs are able to grasp the temporal trends. Even a simple predictor such as the day of the year can improve prediction for the long-term forecasts (not shown). The prediction skill could also be enhanced by taking the time of the radiosonde measurement as the predictor. In particular, the minimum temperature prediction is likely sensitive to the time difference between the observation and the occurrence of the daily minimum (typically in the early morning). Another solution is to include the climatological extremes \(T_{\rm clim}\) at the forecast valid time \(t+i\) as predictors (Setups Z and R). Using such setups, the NN can constrain the long-range forecast MAE to the climatological forecast error (Figure 5). Experimenting with additional options is beyond the scope of this study.
# Modeling the Response of Magnetorheological Fluid Dampers under Seismic Conditions
## 3 Numerical Model
### Boundary Conditions  
The boundary conditions applied in this study are shown in Figure 6. The top and bottom surfaces of the computational domain were defined as non-slip stationary walls to realistically represent the surfaces of the piston and housing of the MR fluid damper.  
The inlet and outlet of the domain were defined by specifying the pressure at these locations. The outlet pressure was set to be the gauge pressure for all the simulations conducted in this study, while three different cases of inlet pressure were investigated, as shown in Figure 7. The first case was a constant pressure of \(P_{inlet}\) = 500 Pa. The second case involved the application of an oscillating sinusoidal pressure with respect to time:  
\[P_{inlet}(t)=500+250\sin(\omega t) \tag{21}\]  
where \(\omega=2\pi f\) and \(f=10\) Hz.  
Figure 5: Mesh convergence study.  
Figure 6: Boundary conditions applied to the computational domain.  
Figure 7: Velocity profile applied at the inlet of the annular orifice: (**a**) constant pressure of 500 Pa, (**b**) sinusoidal pressure profile, and (**c**) seismic pressure profile based on the Northridge Earthquake in 1994.  
\[P_{inlet}(t)=\begin{cases}0\,\text{Pa}&0\leq t\leq 3.4375\\ 936.414\sin[\omega(t-3.4375)]\,\text{Pa}&3.4375\leq t\leq 4.375\\ -1560.69\sin[\omega(t-4.375)]\,\text{Pa}&4.375\leq t\leq 6.25\\ -1248.552\sin[\omega(t-6.25)]\,\text{Pa}&6.25\leq t\leq 7.5\\ 936.414\sin[\omega(t-7.5)]\,\text{Pa}&7.5\leq t\leq 9.84\\ 936.414e^{0.2907}\sin[\omega(t-9.84)]\,\text{Pa}&9.84\leq t\leq 25\\ \end{cases} \tag{22}\]  
The third case is based on the Northridge earthquake in 1994 [46]. According to the displacement measured during the earthquake, the inlet pressure was scaled and approximated using a piecewise function, as shown in Equation (22).
# Experimental Research on the Load Transfer Mechanism of Tie Plates for 400 km/h High-Speed Turnouts
## 1 Introduction
Based on the aforementioned stiffness optimization scheme and considering the existing problems, such as the cracking of the encapsulation of the elastic iron tie plate and rubber washer failure under the cover plate, over many years of the operation of high-speed turnouts, the research group proposes a series of optimizations for the structure of the elastic iron tie plate. Changes in the tie-plate structure cause a change in the bolt preload transfer mechanism, likely affecting the vibration and the stiffness characteristics of the tie plate. Therefore, a series of experiments are carried out to quantitatively compare and analyze the overall stiffness characteristics of different tie-plate structures and the differences in bolt force transmission and distribution laws. The novelty of this work mainly includes the following aspects. Firstly, optimized structure of elastic tie plate for 400 km/h high-speed turnout will be put forward innovatively. In addition, the overall stiffness characteristics and load transfer mechanism of the new type of elastic tie plate will first be revealed.
# Application of the KINEROS 2 Model to Natural Basin for Estimation of Erosion
## 5 Conclusions  
Erosion and sediment transport models offer an illustrative idea about natural processes. The development and utilization of computational tools that use numerical methods to solve mathematical formulations have increased our capacity to calculate results by allowing the incorporation of more parameters. These new physically based models have allowed the user to consider spatial and temporary variability of processes and parameters.  
As it is demonstrated in this research, to have observed data of the modeled basin is essential to do an exact calibration. In absence of observed data, we have had to match the results of several models, which for some rainfall events can give differing results. Nevertheless, in the comparison of results, the soil loss estimation per hectare and per year are within the same order of magnitude. The first conclusion of this study is that we are approaching a valid result with KINEROS 2.  
The RUSLE method is long and complex. Many researchers have participated in its formulation, contributing modifications to one or more of its parameters. To obtain a solid material mass value using RUSLE, it is necessary to calculate specific parameters, between intermediate and definitive calculations. However, if it is done with caution, better results are obtained from the RUSLE method than USLE method.  
While the USLE method provides less realistic results than RUSLE, it is simpler to use. Furthermore, it was the first soil loss estimation formula, therefore it is now recommended to use more recent versions such as RUSLE or MUSLE. MUSLE obtains results that are closer to the results obtained from a physically based model, but it is necessary to obtain elsewhere the previous peak flow data for rainfall events.  
KINEROS 2 is a semi-distributed model because it does not discretize the basin by cells but by sub-basins (composites by plane rectangular elements) that discharge into channels. If the user works with low resolution, average slopes will increase, enlarging flow velocity. In addition, sub-basins are represented by rectangular elements that shorten flow lengths, increasing runoff and flow times. In conclusion, without an optimal discretization, the calculated eroded sediment volume from the basin increases.  
Conceptualization, J.F.I. and M.G.V.; methodology, J.F.I. and M.G.V.; software, J.F.I.; validation, J.F.I., M.G.V. and D.J.; investigation, J.F.I.; writing--original draft preparation, J.F.I.; writing--review and editing, J.F.I. and D.J.; supervision, M.G.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.