esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study on Rock-Like Specimens with Single Flaw under Hydro-Mechanical Coupling
## 5 Conclusions  
A series of uniaxial compression tests, conventional triaxial compression test, and hydraulic coupled triaxial test were conducted on cylinder gypsum specimens with a single pre-existing flaw. X-ray computed tomography (CT) and scanning electron microscope (SEM) were employed to determine internal cracks and the crack mechanism, respectively. The experimental research on mechanical properties and cracking behavior under hydro-mechanical coupling produced several results that contribute to its fundamental understanding.  
1. The stress-strain curves of conventional and hydraulic coupling triaxial tests can be divided into two types according to post-peak behavior. The first type gradually declines after the peak, and the failure mode of these specimens were mostly tensile failures. The second type remained a long yield platform after the peak, and failure mode of these specimens were mostly shear failures.
2. Base on detailed macro and micro observation, nine types of cracks under hydro-mechanical coupling with different geometry and mechanisms (tensile, shear, or mixed) that led specimen failure were summarized. Though the results were obtained by macro and micro observation after failure, it still reflects the cracking behavior of jointed rock mass under hydro-mechanical coupling.
3. Specimens preferred to shear failure under high confining pressure, and the results showed confining pressure may inhibit the tensile cracks. The specimens were prone to tensile failure under high water pressure, and the results showed that water pressure may promote the tensile cracks. However, specimens preferred shear failure under high confining pressure and low water pressure, which may be because the lubrication of water reduced the resistance of the potential sliding surface, and the lubrication effect may exceed the expansion effect under that condition.  
Conceptualization, C.Z.; data curation, J.X.; formal analysis, J.X.; funding acquisition, C.Z.; investigation, J.X., S.Y. and C.M.; methodology, C.Z., S.Y. and H.M.; supervision, C.Z.; writing--original draft, J.X.; writing--review and editing, C.Z.  
This research was funded by the National Key R&D Program of China (No. 2017 YFC0805004), the National Natural Science Foundation of China (No. 41572262, No. 41202193, No. 41672265), the Shanghai Rising-Star Program (No. 17 QC1400600), and the Shanghai Committee of Science and Technology (No. 14 YF1403800). The project was supported by the National Key R&D Program of China (No. 2017 YFC0806000) and the Shanghai Municipal Science and Technology Major Project (Grant No. 2017 SHZDZ0X0).  
The authors would like to thank Shaoxing University, Rock Mechanics and Geohazards Center for supporting the experiment.  
The authors declare no conflicts of interest.
## 7 Security Analysis and Cryptographic Guarantees
### Mutual Authentication Guarantees
#### 7.2.3 Forward Secrecy  
The protocol achieves _strong forward secrecy_ since session keys depend on fresh, ephemeral scalars \(\kappa_{\mathcal{A}}\) and \(\kappa_{B}\), randomly chosen for each session. Even in the event of private key compromise, prior session keys remain protected as the ephemeral scalars are not reused or disclosed.
## 2 G-LiHT Design and Instrumentation
### Imaging Spectrometer  
The Hyperspec imaging spectrometer (Headwall Photonics, Fitchburg, MA, USA) enables high spectral and spatial resolution imaging by using f/2.0 telecentric optics and a high efficiency aberration-corrected convex holographic diffraction grating, providing an optical dispersion of 100 nm per mm over a 7.4 mm spatial by 6.0 mm spectral focal plane. The imaging spectrometer is based on the Offner form and is designed to operate in the 400-1,000 nm spectral region with a 50\({}^{\circ}\) full field of view. The Hyperspec imaging spectrometer accepts a C-mount objective lens (Cinegon f/1.4 8 mm, Schneider Optics, Hauppauge, NY, USA) with high optical performance using ultralow dispersion glass and broadband anti-reflection coating designed for the visible to near-IR spectrum. Coupled to  
Figure 5: Irradiance cosine diffuser (**a**) and GPS antenna (**b**) attached to the leading edge of a Cessna 206 wing with a custom-mounting device. The wing-mounted pod containing the G-LiHT instrument package is seen below (**c**).  
the spectrometer is the RA1000 m/D high speed rugged megapixel focal plane array that allows 50 progressive frames per second to be acquired through an EPIX (Buffalo Grove, IL, USA) PIXCI ECB1 PCI Express CameraLink interface. The camera uses a 1,004 \(\times\) 1,004 pixel 2/3-inch format interline CCD with 7.4 \(\upmu\)m square pixels and 12-bit radiometric resolution. Other camera features include a digital fine gain for adjustable camera sensitivity over a 60 dB dynamic range, electronic shuttering and low smear characteristics. The camera is controlled with the serial communication channel of the CameraLink interface. Each image frame is coded with a computer timestamp synchronized with a Time Tools (Dudley, UK) LC2750 GPS Timing Receiver for post-processing geolocation using data from the GPS-INS detailed in Section 2.2 (Figure 6).
# Deep Learning Forecasting for Supporting Terminal Operators in Port Business Development
## 3 Materials and Methods
### Methods
#### 3.1.2 Embedding Neural Networks  
Sometimes, information is available in the form of categorical variables. In the statistical literature, the standard procedures for dealing with categorical variables are one-hot and dummy encoding. Nevertheless, these coding schemes produce high-dimensional sparse vectors, which could present potential reliability problems in the obtained estimations when there are many categorical features or one of the variables presents many levels. Embedding is an innovative technique to analyse categorical variables that appeared for the first time in the natural language processing context [49]. With embedding, the ability of the neural network to learn representations has been used to encode the levels ofcategorical variables into numerical vectors. The coordinates of the levels of the categorical variables in the new space are the network's parameters learned during the training process. Furthermore, the Euclidean distance in the new real-valued space of the levels indicates that they are similar concerning the target variable.  
Let \(\mathcal{B}=\{b_{1},b_{2},\ldots,b_{n_{b}}\}\) be a discrete set of categories. An embedding layer is a function  
\[z_{b}:\mathcal{B}\mapsto\mathbb{R}^{q_{b}}\]  
where \(q_{b}\in\mathbb{N}\) is a hyperparameter defined by the modeller describing the size of each embedding layer. This function is equivalent to applying different \(q_{b}\)-dimensional FCN layers to the levels of the categorical variable after one-hot encoding. In this setting, the output of each neuron will be equal to the weight multiplied by the only element equal to one. Consequently, they can be trained in the same way as the other network parameters related to the other layers [50]. The output of these layers can be seen as the coordinates of the levels of the categorical variables in the new \(q_{b}\)-dimensional space. Furthermore, the Euclidean distance in the new space of the levels indicates that they are similar concerning the target variable.  
Denoting with \(n_{b}=|\mathcal{B}|\) the number of levels of the categorical feature, the number of weights that must be learned in the embedding layer during training is \(n_{b}\times q_{b}\).
## 2 Materials and Methods
### Measurement Positions  
In total, 22 measurement positions were selected, 20 of which were at ground level in line-of-sight (LOS) of an NR BS, and two of which were on the roof of a building (both in LOS) at a height of 19 m above ground level (excluding the 1.5 m height of the measurement antenna). Since the BS configurations in the considered network were limited to azimuthal beam steering, the measurement probe and the UE were positioned along the same line with respect to the BS, under the assumption that this would direct the PDSCH beam towards the measurement probe while servicing the UE. The separation distance between the UE and the probe ranged from 10 m to 172 m (on average, 53 m).
## 4 Engineering Case Calculation
### Initial Stability Analysis of Tunnel Face  
According to the geological conditions of the left line tunnel and mechanical parameters of the surrounding rock, the initial stability analysis of tunnel face was discussed by tunnel face stability discrimination and the support optimization system. According to the above description, the geological conditions and tunnel parameters of the left line tunnel are determined as follows: the buried depth (\(H\)) is 16.13 m, the calculated height (\(D_{0}\)) is equal to the height of upper step at 3.71 m. The equivalent width (\(l_{0}\)) is 5.80 m, and the length of the unsupported section (\(L_{\text{e}}\)) is 0.50 m. The cohesion (\(c\)), internal friction angle (\(\varphi\)), and porosity (\(n\)) are 15 kPa, 20\({}^{\circ}\) and 50%, respectively. The density (\(\gamma\)) and lateral stress coefficient (\(\lambda\)) of the soil are 17 kN/m\({}^{3}\) and 0.357, respectively. The above parameters are input into the tunnel face stability discrimination and support optimization system developed based on the calculation method described in Section 2 to calculate the initial state of stability coefficient of left line tunnel under the original design support. The calculation interface is shown in Figure 13.  
The calculation results of the initial stability coefficient of the left line tunnel show that the supporting stress when the tunnel surface reaches the steady state is 289.90 kN, and the initial stability coefficient (\(K_{0}\)) is 0.733, which is only 61.83% of the critical stability coefficient. Therefore, the tunnel face cannot reach a stable state without reinforcement measures. The tunnel in this study section adopts a double-layer pipe roofing support and reserved core soil to support the tunnel face according to original design scheme. The calculated supporting stress required for the tunnel face to reach stability is 0 kN, and the stability coefficient is 1.13, which is slightly less than the critical stability coefficient of 1.20. The calculation results of the stability coefficient show that, although the original design scheme can initially stabilize the tunnel face, it cannot guarantee safe construction due to insufficient safety reserves, and there is a great construction risk. On-site monitoring shows that the ground surface of the auxiliary road of the left line occurred in an over-limit settlement on 7 October 2019 when the original design scheme was used for construction, which can further confirm the reliability of the calculation results. The original support scheme of tunnel face needs to be properly optimized.
# Interaction in eXtended Reality Applications for Cultural Heritage
## 4 Interaction in Digital Storytelling Applications
### Interaction in VR Applications
#### 4.3.4 Interaction in Virtual Reality Gameplays  
Some Virtual Reality applications use gameplay in order to make history more interesting and appealing to wider groups of people. Thus, Virtual Reality is used to present cultural and historical heritage and to engage users actively in exploration.  
In the Battle on Neretva VR Application [50], gameplay is used to enable the users to experience a famous battle from the WWII. The user has to help his comrades to win the battle and save the wounded. To do so, the user needs to detonate an explosive and destroy the bridge in order to deceive enemy troops. To complete the first assignment, the user has to pick up the reel with a cable and move it to the detonator. Picking up and holding the cable was implemented through a combination of the grab button and controller movement near the reel, but this way of interacting appeared to confuse players as they were unable to pick it up in the first few attempts. To make it easier for users who are inexperienced in Virtual Reality, we simplified the picking up by just moving the controller near the reel.  
The second task on the Battle on Neretva VR Application is the destruction of enemy air crafts while the comrades are trying to build the improvised bridge. Users need to target air crafts with a head movement since the target is fixed in the center of their view, as can be seen in Figure 4; subsequently, the users have to shoot them with a trigger click on the controller. While most users did not find any difficulties in targeting and shooting, some users found it strange that the target was moving, unrelated to the hand and controller movements. Hence, one of the ideas for improvement was to bind the target to the controller movement instead of the head movement.  
The gameplay on the Crvena Stijena VR features a deer hunt in prehistory. To catch the deer, it is necessary to pick up and throw a spear in front of it. The action of picking up  
Figure 4: Target for aircraft shooting and their contribution to immersion on the Battle on Neretva VR Application.  
the spear is the same as picking up the exhibit objects. More specifically, the user needs to hover their hand above the spear and press and hold the corresponding grip button. There is one difference in the mechanics--the throwing of the spear. When the user releases the grip button, the spear does not just drop down to the ground. Instead, it calculates the velocity of the movement of the controller in the hand of the user and adds force to the spear. The amount of force added depends on the velocity of the controller. The force is added to the direction in which the spear is being thrown.
# Internet Video Delivery Improved by Super-Resolution with GAN
## 6 Video Quality Assessment Metrics
### Perceptual Quality Assessment
#### 6.2.1 Learned Perceptual Image Patch Similar  
The LPIPS [49] assesses the perceptual distance between the distorted and original videos. Zero-distance means that two videos are perceptually equivalent. This distance assessment uses a space established by high-level video features. For building this space, deep neural networks, conceived to image classification, are used to identify and extract those features, namely: VGG [59], SqueezeNet [62], and AlexNet [63].  
In this work, we built the LPIPS feature space using SqueezeNet due to its reduced computational cost and similar outcomes compared to VGG and AlexNet as shown in [49]. In the same work, the authors showed that these three networks learned world representations related to perceptual judgments. Therefore, the LPIPS strongly correlates with human perceptual metrics, such as JND. Moreover, the LPIPS generalizes different distortions, including those streamed by the SR algorithms.
# The Impact of Spatial Resolutions on Nature-Based Solution Suitability Mapping for Europe
## 6 Conclusions  
This study was performed to support the development of an innovative toolkit for decision-support on Nature-Based Solutions across Europe. The NBS Toolkit is based on open-source datasets. For many of the NBS suitability criteria, there is only one dataset available at the European scale, but for the land use and land cover, several datasets are openly available at different spatial resolutions. Several studies in other research fields are discussing the influence of spatial resolution on the outcome, but this was not tested in context of the suitability mapping of Nature-Based Solutions. To gain a better understanding on the impact of the resolution on the final NBS suitability map, this study compares the outcomes of three European land use and land cover layers to different spatial resolutions in a test area in Germany. The results of this study suggest that for rural areas and larger scale NBS, coarser input layers can be sufficient, while for urban and small-scale NBS, finer resolution may be required. These results were integrated into the development of the NBS Toolkit and, therefore, the suitability mapping method could be upscaled to Europe. Furthermore, the method will be replicated in other regions, such as in Africa. Overall, this study extended the existing scholarship for NBS suitability mapping.  
Conceptualisation, J.O., J.N. and M.K.; methodology, J.O.; software, J.O., J.N. and S.V.; validation, J.O., M.K., J.N. and H.L.C.; formal analysis, J.O.; data curation, J.O.; writing--original draft preparation, J.O.; writing--review and editing, J.O., J.N., S.V., M.K. and H.L.C.; visualisation, J.O.; supervision, J.N., S.V., M.K., L.S.L., S.D.S. and H.L.C.; project administration, L.S.L. and S.D.S.; funding acquisition, S.D.S. All authors have read and agreed to the published version of the manuscript.  
This work was funded by and carried out within the framework of the OPERANDUM project supported by the European Union's (EU) Horizon2020 research and innovation program (Grant No. 776848), and the ALBATROSS project supported by the European Union (G.A. No. 101137895). [PERSON] and [PERSON] were supported by the LANDWISE project funded by the Natural Environment Research Council (NERC) (Grant No. NE/R004668/1).  
The data that support the findings of this study are available from the corresponding author upon reasonable request. In case of a request, the authors will provide a citation along with the requested dataset.  
Authors [PERSON], [PERSON] and [PERSON] were employed by the company KAJO s.r.o. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 7 Experiments and Results Analysis
### Parameter Analysis
#### 7.2.2 The Analysis of Limit  
Set \(SN\) = 30, _Cycle_ = 50. When the _Limit_ is 5, 30, 50, respectively, the fitness of the path generated by the OEABC is shown in Figure 6.  
As can be seen from Figure 6, when _Limit_ = 5, the overall level of the generated solution is low, and the average fitness of the path is only 46. This is because the _Limit_ value is small, meaning that it will be possible to release the scout bee multiple times to generate random paths. Randomness enhancement of the algorithm generation solution and the probability of finding the optimal solution decreases obviously because the experience obtained before cannot be fully utilized to search for a new solution.  
When _Limit_ = 30, although the _Limit_ is not the largest, the overall level of the fitness curve is the highest, and because of the reasonable value of the _Limit_, the algorithm does not fall into a local optimum, nor is it completely dependent on stochastic generation, giving full play to the advantages of both local and global optimization of the ABC algorithm.  
When _Limit_ = 50, the _Limit_ becomes larger, but the fitness of the generation path decreases. When the value of _Limit_ is larger, the generated path can easily to fall into a local optimum, and one cannot achieve global optimization, which leads to a low level of fitness of the path, so a bigger _Limit_ is not the best one, and there is a suitable _Limit_ size to get a better path fitness. The size of the _Limit_ needs to be proportional to the \(SN\), the _Cycle_ and other parameters of OEABC to avoid both complete stochasticity and local optimization.
## 3 Study Area and Data
### Landslide Predisposing Factors
#### 3.3.3 Hydrological and Surface Cover Factors  
The scouring and erosion effects of rivers can cause damage to the feet of slopes [5]. Moreover, seasonal changes in the river water level can lead to periodic changes in the hydrological conditions of the slope, weakening the slope structure and making it less stable. The river density (Figure 5h) is often used to reflect the ability of rivers to erode the slope [20]. Past studies have shown that vegetation is beneficial for the stability of the slope, and its related factors are also often used to evaluate the landslide hazard [55]. Reflecting the state and biomass of the surface vegetation, the NDVI (Figure 5i) was selected to analyze the impact of vegetation coverage on landslide occurrence in this study. Generally speaking, the higher the NDVI value, the higher the vegetation coverage. Table 3 shows that landslides are more likely to occur when the NDVI is lower. In addition, the melting snow and rain infiltration brought by precipitation are not conducive to the stability of the slope and often trigger landslides [56]. Therefore, the annual precipitation (Figure 5j) was used to analyze the relationship between precipitation and landslide occurrence. About seventy-eight percent of the study area has annual precipitation of no more than 100 mm. As shown in Table 3, slope units with annual average precipitation of 71-89 mm are more prone to landslides.
# A Multisensor Approach to Satellite Monitoring of Trends in Lake Area, Water Level, and Volume
## 2 Materials and Methods
### Lake Surface Area
As shown in Figure 6, the eFrac time series (derived from ELI rather than NDLI) had generally smaller residuals relative to its LOESS model. Thus, eFrac was used as the definitive lake surface area dataset for all subsequent analyses. The final step in the lake surface area process involved validation of area measurements by comparison to higher-resolution Landsat images. Two Landsat path/row combinations were needed to cover the area (paths 175 and 176, row 44) with Lakes 1 and 2 in path 175 and the remaining lakes in path 176. For each Landsat image, the areas of each lake were compared to the corresponding daily LOESS-interpolated AVHRR/MODIS eFrac lake area measurement. Because each Landsat pixel covers an area only 1/69 th as large as a 250 m resolution MODIS pixel, the Landsat images provided a more precise estimate of lake area.
## 4 Pointing Errors Caused by Thermal Gradient
### Inclinometer Measurement Compared with Astronomical Observation
The same observation was conducted over the following two days, with the antenna pointing errors pre-compensated based on results from the experiment on 14 February. The pointing error comparison between the two astronomical observations is illustrated in Figure 17.  
It is evident from Figure 17 that the pointing accuracy has significantly improved after compensation, particularly during the period from 14:00 to 17:00 when thermal gradients have the greatest impact on antenna performance. Table 4 presents a comparison of RMS values for pointing errors before and after compensation.