esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Design and Fabrication
The photovoltaic structures were made of silicon wafers with a diameter of 3 inches, an orientation <100>, type p, resistivity 1-3 \(\Omega\)-cm, and a thickness of 350 \(\upmu\)m. The size of the solar cell structures with a textured surface is 10 \(\times\) 22 mm\({}^{2}\) (the connexion ribbon/front pad/collector was extended from 1 mm to 2 mm in order to provide a larger connection surface). The texturing of the cell surfaces was conducted through a photolithographic process that defined inverse pyramids with sides of 10 \(\upmu\)m that were placed in a network between the collection contacts/electrodes of the photocarriers generated by incident light. By taking into consideration the wing profile as it is presented in Figure 1 that presents a significant curvature, it was obvious that not all of the photovoltaic cells will have the same exposure to the incident Sun light. In order to overcome this disadvantage, it was decided to improve the design of the cells to obtain a much better exposure to the irradiation source. It was determined that the solution that would be used would be to texturize the surface of the cells and thus reduce reflection losses and increase absorption in the near IR spectral range [8]. The total absorption on the textured incident surface increased by 4 times compared to that of the flat surface. One problem specific problem that was encountered during materials selection was the establishment of the right type of epoxy resin to be used for embedding the photovoltaic cells into the wing structure. It is well known that some resins change their optical properties (transparency) if exposed to ultraviolet rays or heat. These tests were conducted on a representative batch of resin-coated cells 10 \(\times\) 10 mm\({}^{2}\), which will be presented further in Figure 3, and mechanical tests were performed (i.e., 100 thermal cycles \(-\)20 \({}^{\circ}\)C +60 \({}^{\circ}\)C and 3 cycles UV exposure 20 min each). In order to establish which epoxy resin would be suitable for use for the main parameters (open circuit voltage (Voc (mV)) and short circuit current (Isc (mA))) of the photovoltaic cells, photovoltaic cells that were covered with 8 different types of epoxy resins were measured before and after the mechano-climatic tests, and the results are pre  
Figure 3: Molds for ensuring the uniformity of coating and the cells coated. sented in Table 1. As one can notice, the PV cells show good stability, even after 100 thermal cycles from \(-20\)\({}^{\circ}\)C to \(+60\)\({}^{\circ}\)C as well as after combined mechano-climatic temperature-UV radiation testing.
## 7 Conclusions
In addition, the project was funded by the Euregio Science Fund 1 st call, project number IFN 10. [PERSON] was funded by Austrian Science Fund (FWF): project number V309-N26. The work of [PERSON] and [PERSON] was funded by the ESA project Glaciers_cci (4000109873/14/1-NB). **Acknowledgments:** Landsat scenes were obtained from USGS ([[http://earthexplorer.usgs.gov/](http://earthexplorer.usgs.gov/)]([http://earthexplorer.usgs.gov/](http://earthexplorer.usgs.gov/))) and the ESA online dissemination platform ([[https://eo-sso-idp.eo.esa.int](https://eo-sso-idp.eo.esa.int)]([https://eo-sso-idp.eo.esa.int](https://eo-sso-idp.eo.esa.int))). The TanDEM-X DEM was obtained through the DLR DEM_GLAC1371 proposal. [PERSON] acknowledges key inputs from [PERSON] - University of Oslo, validation data from Abramov Glacier are kindly provided by [PERSON] and [PERSON] for the provision of the Kesselwandferner photo in Figure 4 (www.christianengl.de). Glaciological data (glacier outlines, DEMs) were provided by the University of Innsbruck and alpS GmbH in Innsbruck, Austria. We acknowledge the University of Innsbruck for their long-term field measurements and the WGMS for distributing the mass balance, ELA and AAR data for HEF, KWF and VNF. **Conflicts of Interest:** The authors declare no conflict of interest. **Abbreviations**  
AAR Accumulation Area Ratio  
ASMAG Automated Snow Mapping on Glaciers  
ASTER Advanced Thermal Emission and Reflection Radiometer  
DEM Digital Elevation Model  
DLR Deutsches Forschungszentrum fur Luft und Raumfahrt (German Aerospace Center)  
DN Digital Number  
ELA Equilibrium Line Altitude
## 3 Eigenvalue Fusion and other Fusion Methods for Detecting General Wideband Signals
### Channel and Signal Models  
Consider a wideband communication system in which the overall bandwidth is partitioned into \(K\) non-overlapping narrow bands, as in [9, 10]. Some of these bands may be vacant during a time interval in a specific geographic region, being available for opportunistic secondary transmissions. The wideband spectrum sensing problem in this case can be cast as a hypothesis test where \(\mathcal{H}_{0,k}\) represents the absence of the primary signal and \(\mathcal{H}_{1,k}\) represents the presence of the primary signal in the \(k\)-th band with \(k=1,\,\ldots,K\).  
Assume a multipath frequency-selective fading channel, where \(h_{\ell}\), \(\ell=0,1,\ldots,L-1\), represents its discrete-time impulse response with \(L\) resolvable paths between a single primary transmitter and a CR. Omitting the CR index, without loss of generality, the signal received by a CR can be written as  
\[y_{t}=\sum_{\ell=0}^{L-1}h_{\ell}s_{t-\ell}+v_{t},\quad t=1,2,\,\ldots,2L-1 \tag{7}\]  
where \(s_{t}\) is the primary transmitted signal samples and \(v_{t}\) is the complex additive white Gaussian noise (AWGN) samples with zero mean and variance \(\sigma^{2}\), _i.e._, \(v_{t}\sim\mathbb{C}\mathcal{N}(0,\sigma^{2})\). We assume that noise samples are independent of signal samples.  
In a multipath fading channel, a wideband signal undergoes frequency-selective fading. The frequency-selective behavior of the channel in a given observation instant (snapshot) can be unveiled from its frequency response, which can be obtained by a \(K\)-point FFT of the channel impulse response, _i.e._,  
\[H_{k}=\frac{1}{\sqrt{K}}\sum_{i=0}^{L-1}h_{i}e^{-j2\pi ik/K}\,,\quad k=1,\, \ldots,K \tag{8}\]  
Analogously, a snapshot of the received signal can be represented in the frequency domain by the \(K\)-point FFT of \(y_{t}\), _i.e._,  
\[\begin{split} Y_{k}&=\frac{1}{\sqrt{K}}\sum_{i=0}^ {K-1}y_{i}e^{-j2\pi ik/K}\\ &=H_{k}X_{k}+V_{k},\qquad k=1,\,\ldots,K\end{split} \tag{9}\]  
where \(X_{k}\) is the primary signal in the frequency domain and \(V_{k}\) is the thermal noise in the frequency domain at the \(k\)-th band. These frequency-domain noise samples will be Gaussian with zero mean andvariance \(\sigma^{2}\), as the FFT operation is a linear transformation and the samples of a Gaussian noise in the time domain are Gaussian distributed. We also model the primary signal samples as a Gaussian random signal, since most of the modulated signals, especially OFDM signals, can be accurately modeled as having Gaussian distributed amplitudes. We also assume a slow fading channel, meaning that the channel frequency response is constant during a sensing period, but independent from one period to another.
## 2 Experimental Program
### Methods
#### 2.3.3 Variable Relative Humidity  
The relative humidity of the environment is very important for the carbonation reaction of concrete because it will affect the content of concrete pore water. CO\({}_{2}\) diffusion dominates the carbonation progress, and the reaction will slow down when the relative humidity becomes large. When the pores in concrete are saturated with water, carbon dioxide scarcely reacts with the concrete because CO\({}_{2}\) hardly diffuses in water. Moreover, the carbonation reaction also becomes slow when the humidity is too low. CO\({}_{2}\) cannot dissolve into the thin layer of water covering the pores even if CO2 diffuses into the capillary pores. Therefore, there is a suitable range of relative humidity for the carbonation reaction of concrete. [PERSON] [34] found that the optimal range for carbonation is 50-70%. In this paper, keeping the temperature and CO2 concentration unchanged, and altering the humidity to simulate the effect of GNP in different climate changes on the carbonation depth of concrete. We set the experimental temperature at 25 \({}^{\circ}\)C, the humidity was 50-85%, and the CO2 concentration was 6%. According to the formula of [PERSON] [21], the relationship between humidity and carbonation time is as follows:  
\[RH(t)=50+(35/35)t \tag{3}\]  
where \(RH(t)\) is the experimental humidity (%) and \(t\) is the carbonation time (days).  
By measuring the carbonation depth of GC0 and GC0.05 at 7, 14, 21, 28, and 35 days, the influence of humidity change on the carbonation depth of concrete was investigated.
## 4 5G and V2X Deployment Projects
### Live Trial of 5G-Connected Car Concept To Launch in Turin, Italy  
On 2 December 2021, a live connected car trial took place in Turin, Italy, showcasing new roaming technology for traffic safety. It was organized by the 5G Automotive Association (5 GAA) in collaboration with eight member companies representing leading technology firms from around the world. The objective was to demonstrate the near-real-time notification of roadway hazards through 5G edge networks, enabling improved driver and pedestrian safety. The trial highlighted the potential of 5G and edge technology in delivering connected services that can transform daily life and contribute to the digital transformation of smart cities. With high-speed 5G transmission and edge servers equipped with AI capabilities, smart city technologies, such as real-time traffic management, became feasible. The connected car concept leveraged this technology to communicate traffic hazards to drivers and pedestrians via user-authorized mobile apps, facilitating safer and more efficient navigation.  
The live trial specifically focused on addressing the technical challenge of enabling seamless communication between different network operators as vehicles cross borders. Roaming services, which allow uninterrupted communication regardless of the network, were successfully demonstrated. This trial was the first of its kind in Europe and aimed to prove the effectiveness of the connected car concept in a roaming scenario. The trial involved multiple objectives, including testing interworking between different network operators, ensuring global operational availability for OEMs, and seamless V2X service transfer in a multi-network operator roaming scenario. The collaborative effort involved a large ecosystem of stakeholders, including car manufacturers, mobile network operators, and technology providers, highlighting the importance of public-private sector cooperation in adopting and advancing 5G-connected car technology.  
The trial provided valuable insights and learnings for various players in the industry, paving the way for future development of 5G-connected car services at the edge. The City of Turin, along with its Smart Road infrastructure and technical support from 5T, played a significant role in facilitating the trial. The success of the trial showcased the transformative potential of 5G and edge technology in revolutionizing the automotive industry and creating safer and more connected transportation systems [98].
# English-Welsh Cross-Lingual Embeddings
## 5 Results
### Quantitative Evaluation  
The task of bilingual dictionary induction, a natural byproduct of learning bilingual mappings, and which we have introduced in Section 4, is a good proxy for evaluating the quality of cross-lingual mappings.  
We thus report results of appplying the VecMap method. However, we also experimented with Meemi, but since the results were slightly lower across most configurations, we only report VecMap performance. Table 2 shows the top 20 configurations in terms of accuracy. As we can see, _fastText_ consistently performs best when compared to _word2 vec_, and CSLS clearly outperforms inverted softmax and cosine similarity in terms of retrieval metrics. On the other hand, the threshold for minimum frequency and context windows seem to be less relevant, as there is high variability among the best configurations. Regarding the overall scores, note that these are in line with what previous work has found when dealing with language pairs involving English and a low-resource language. For example, [PERSON] et al. [49] report P@1 scores for their best models of 24.8 for English-Finnish, 21.5 for English-Farsi, or 19.3 for English-Russian, and [PERSON] et al. [50] report roughly similar or worse results for dictionary induction experiments involving, e.g., Turkish (9.96) or Latvian (13.53). Note that theirs is an unsupervised approach.  
\begin{table}
\begin{tabular}{c c c c c} \hline Model & Dim. & MF & CW & Retrieval & Acc. \\ \hline fastText & 500 & 6 & 6 & CSLS & 22.92 \\ \hline fastText & 500 & 6 & 4 & CSLS & 21.85 \\ \hline fastText & 500 & 6 & 8 & CSLS & 21.75 \\ \hline word2 vec & 300 & 6 & 4 & CSLS & 21.75 \\ \hline word2 vec & 500 & 6 & 8 & CSLS & 21.46 \\ \hline word2 vec & 300 & 6 & 6 & CSLS & 21.46 \\ \hline word2 vec & 500 & 6 & 4 & CSLS & 21.46 \\ \hline word2 vec & 300 & 6 & 8 & CSLS & 21.36 \\ \hline word2 vec & 500 & 6 & 6 & CSLS & 21.36 \\ \hline fastText & 500 & 3 & 4 & CSLS & 20.46 \\ \hline fastText & 500 & 3 & 8 & CSLS & 19.75 \\ \hline fastText & 500 & 3 & 6 & CSLS & 19.36 \\ \hline word2 vec & 300 & 3 & 8 & CSLS & 19.22 \\ \hline word2 vec & 500 & 3 & 6 & CSLS & 19.22 \\ \hline word2 vec & 500 & 3 & 8 & CSLS & 19.18 \\ \hline word2 vec & 300 & 3 & 6 & CSLS & 18.83 \\ \hline fastText & 300 & 6 & 4 & CSLS & 18.57 \\ \hline word2 vec & 300 & 6 & 4 & invsoftmax & 18.48 \\ \hline word2 vec & 300 & 6 & 8 & invsoftmax & 18.48 \\ \hline word2 vec & 300 & 6 & 8 & NN & 18.43 \\ \hline \end{tabular}
\end{table}
Table 2: Top 20 configurations (ranked in descending order) in terms of accuracy (Acc.) for the bilingual dictionary induction task _when using VecMap_. We compare different monolingual embedding models (Model), vector size (Dim.), minimum frequency threshold (MF), context window (CW), and neighbor retrieval method (Retrieval, cf. Section 3).
# Dynamic Stability of Orthotropic Viscoelastic Rectangular Plate of an Arbitrarily Varying Thickness
## 3 Results and Discussion
### Proposed Numerical Solution
\begin{array}{c}\sum\limits_{n=1}^{N}\sum\limits_{m=1}^{M}c_{klnm}w_{nm}= \sum\limits_{n=1}^{N}\sum\limits_{m=1}^{M}c_{klnm}(w_{0 nm}+\hat{w}_{0 nm}t)-\eta _{3}\int\limits_{0}^{t}(t-\tau)\left\{\sum\limits_{n=1}^{N}\sum\limits_{m=1}^{M} p_{klmn}^{2}(1-2\mu_{klnm}\cos\Theta t)w_{nm}-\right.\\ \hskip 14.226378 pt-\left.\sum\limits_{n=1}^{N}\sum\limits_{m=1}^{M}\left\{\left[ \Gamma_{11}^{*}f_{5 klnm}+\Gamma_{12}^{*}f_{6 klnm}+\Gamma_{22}^{*}f_{7 klnm}+ \Gamma_{21}^{*}f_{8 klnm}+\Gamma_{21}^{*}f_{8 klnm}\right]w_{0 nm}\right\}-\\ \hskip 14.226378 pt-\left.\sum\limits_{n,j=1}^{N}\sum\limits_{m,j=1}^{M}w_{nm} \left\{\left[\left(1-\Gamma_{11}^{*}\right)\zeta_{1 klmnij}+\left(1-\Gamma_{21}^{*} \right)\zeta_{2 klmnij}+\right.\\ \hskip 14.226378 pt+(1-\Gamma^{*})\xi_{3 klmnij}\right]u_{ij}+\left[(1-\Gamma_{22}^{*} )\xi_{4 klmnij}+\left(1-\Gamma_{12}^{*}\right)\xi_{5 klmnij}+(1-\Gamma^{*}) \xi_{6 klmnij}\right]v_{ij}\right\}-\\ \hskip 14.226378 pt-\left.\sum\limits_{n,j,r=1}^{N}\sum\limits_{m,j,s=1}^{M}w_{nm} \left\{\left(1-\Gamma_{11}^{*}\right)\xi_{5 klmnijs}+\left(1-\Gamma_{12}^{*} \right)\xi_{6 klmnijs}+(1-\Gamma_{22}^{*})\xi_{7 klmnijs}+\right.\\ \hskip 14.226378 pt+\left.(1-\Gamma_{21}^{*})\xi_{8 klmnijs}+(1-\Gamma^{*})g_{9 klmnijs }\right\}\left(w_{ij}w_{rs}-w_{0 ij}w_{0 rs}\right)\right\}d\tau\\ \end{array}  \tag{110}\]  
According to the method described in Section 2.2, the next stage of the numerical method is the regularization of the system of nonlinear integrodifferential Equations (108)-(110) with singular Koltunov-Rzhanitsyn kernels [40; 41] of the form (54)-(55). By changing variables \(t-\tau=z^{\frac{1}{\delta}}\), \(0\leq z\leq t^{\alpha}\), (\(0<\alpha<1\)), the integrals over the Koltunov-Rzhanitsyn kernels with a singularity of the following form  
\[A\int\limits_{0}^{t}(t-\tau)^{\alpha-1}\exp[-\beta(t-\tau)]w(\tau)d\tau \tag{111}\]  
take the form  
\[\frac{A}{\alpha}\int\limits_{0}^{t^{\alpha}}\exp\Bigl{(}-\beta z^{\frac{1}{ \delta}}\Bigr{)}w\Bigl{(}t-z^{\frac{1}{\delta}}\Bigr{)}dz \tag{112}\]  
Note that after the change of variables, the integrands of the system (108)-(110) with respect to \(z\) become regular. Then, assuming \(t=t_{i}\), \(t_{i}=i\Delta t\), \(i=1,2,\ldots\) (where \(\Delta t\) is the integration step) and replacing the integrals with quadrature trapezoidal formulas to calculate the unknowns \(w_{imm}=w_{imm}(t_{i})\), \(u_{imm}=u_{imm}(t_{i})\), and \(v_{imm}=v_{imm}(t_{i})\), a system of recurrent formulas is obtained.
# SPinDP: A High-Speed Distributed Processing Platform for Sampling and Filtering Data Streams
## 2 Background and Related Work  
In this section, we discuss the background technologies and related studies of the proposed platform. First, we outline the operational structure and characteristics of Apache Storm and Apache Kafka as stream processing frameworks, which are the bases for implementing SPinDP. Next, we describe InfiniBand, which is used for high-speed internal communication in the proposed system. Finally, we introduce the refinement algorithms that are the core of SPinDP and the characteristics of each algorithm.
## 6 Discussion  
Nonlinear guidance, originally designed for UAV trajectory tracking, has been adopted and used for USV trajectory tracking. This method allows us to keep a low XTE during all sounding profiles' tracking; more importantly, the USV course changes that keep to the track are very gentle, and do not cause significant disturbances in hydrographic measurements. The trajectory tracking and the USV response for the calculated course inputs depend on the correct PID tuning. As stated above, PID tuning was carried out during extensive field tests. The configuration employed did not show any oscillations, the course changes although robust were gentle, allowing us to keep track within specified limits for the unit to weather conditions and wind direction. The correct PID tuning is very important for course and track keeping. As mentioned elsewhere [28], the USV used in this research showed a regular oscillation for track following, caused by not having ideal PID tuning, and this can significantly affect the unit's endurance and the quality of hydrographic measurement.  
The position registered by the navigation GPS is not used directly by the USV for navigation. The pure GPS position readings are filtered using EKF and the final navigation position is calculated using all data available to the autopilot internal sensors. This technique is used widely within the robotics community to estimate a robot's position. Consequently, the position used for navigation, as the results show, has good accuracy. The profile planned by the hydrographers, was followed based on GPS position. If we take into account the antenna position (in the center of the unit) and the offsets between the GPS antenna and the multibeam sonar (main hydrographic sensor) this causes quite significant differences between the real unit track and the sonar antenna track. The displacement differs and depends on the wind speed and direction. Practically, the profile is registered by the RTK system with offsets, therefore, from the hydrographic point of view, all data are registered correctly, however this difference can be diminished.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Profile No.** & \(\sigma_{\mathbf{x}}\) & \(\sigma_{\mathbf{y}}\) & **DRMS** & **2 DRMS** & **CEP** & **R95** & **Mean XTE** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 6: Results for all profiles.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \(\sigma_{\mathbf{x}}\) & \(\sigma_{\mathbf{y}}\) & **DRMS** & **2 DRMS** & **CEP** & **R95** & **Mean Unit XTE** \\ \hline Mean & 0.2170 & 0.2166 & 0.3226 & 0.6452 & 0.2558 & 0.5321 & 0.3058 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Mean accuracy results for USV configuration.
## 6 Proximal Humerus Meshing Properties  
The meshing process plays an important role in FEA: the time consumption of the simulation and accuracy of results will depend on the meshing quality and technique used. The mesh itself functions as a visual representation of the object of study, and shows its response to external loads. There is no standard configuration for the mesh--it will vary according to the investigator's resources and knowledge, and the study's aims. The mesh is a computational grid composed of polyhedral elements; nowadays, the types of elements most used are the 8-noded hexahedra and 4-noded tetrahedra [64]. To sum up, the quality of the mesh and the time needed will depend on the aspect ratio of the element. The smaller the element size, the higher the quality, but also the machine/human time will increase [19; 64].  
Meshing a bone is specifically difficult due to its imperfect geometry; thus, some experience and skill may be required in some cases. Sometimes, a combination of techniques is the best option. To find the most used properties to mesh the proximal humerus, the following parameters will be taken as a reference: mesh type, element type, number of nodes, and element edge length.  
As seen in Table 5, the literature [3; 20; 27; 28; 31; 33; 42; 65; 66] exhibits the great preference for tetrahedral mesh (C3D10) over hexahedral mesh, and the quadratic element over the linear element. Studies tend not to specify the number of nodes for each element, but the number of total mesh elements, although some studies report the use of 8- and 10-nodes. The element edge length is usually about 0.85-5 mm, and fine mesh is preferable for accurate results.