esa-sceva/satcom-chunk-collection huggingface dataset:
# Beyond the Horizon, Backhaul Connectivity for Offshore IoT Devices
## 3 Results
### Measured Average Pathloss at Various Distances  
Experimental campaign to measure received signal level at regular intervals were conducted and several short-term measurements at six receiver sites were taken. Figure 7, shows the pathloss variations for all five beyond-the-horizon receiver sites, calculated from the measured received signal level. More than 500 sampled values were recorded at a carrier frequency of 5.8 GHz using spectrum analyzer. In all measurements, it was observed that the average path loss increased with the increase in distance. As 10 km distance was at LOS range, its average path loss variations are not shown. The average path loss for 30 km and 40 km were similar, although the path loss was slightly higher at 40 km distance. Average path loss values above 50 km, measured at 55 km and 65 km were at least 10 dB higher than 30 km and 40 km. The average measured path loss at 80 km distance was considerably very high with average value measured at 158 dBm. The measurements are taken for a time period. To know the complete variation of path loss in ED, one either needs long-term measurements with enough signal-to-noise (SNR) or fade margin.  
Figure 6: Measured path loss variations shown on the P.E equation-based simulation of predicted path loss.
# Consolidating ICESat-2 Ocean Wave Characteristics with CryoSat-2 during the CRYO2 ICE Campaign
## 2 Data  
This section contains an overview of the data products from ICESat-2 and CryoSat-2, as well as the corrections made to CryoSat-2, which were determined from a comparison with in situ observations. A comparison between CryoSat-2 and Sentinel-3A/B and SARAL is also done to assess the behavior of CryoSat-2.
# Investigation on Ship Hydroelastic Vibrational Responses in Waves
## 2 Ship Hydroelasticity Theory in Regular and Irregular Waves
### Hydrodynamic Theory in Ship Hydroelasticity
#### 2.3.3. Diffraction Wave Force  
In regular waves, the linear diffraction wave force acting on the wetted elastic hull surface in calm water is expressed as:  
\[F_{D}(t)=-\rho\zeta_{A}\iint\limits_{S(t)}\mathbf{n}\cdot\mathbf{u}_{r}( \mathrm{i}\omega_{e}-U\frac{\partial}{\partial x})\varphi_{D}ds(r=1,\;2, \cdots,m) \tag{10}\]  
For ships advancing in irregular waves, the diffraction wave force is obtained by integrating the contribution of each component regular wave that has its corresponding encounter wave frequency. The time-domain convolution integral method is used to obtain the diffraction wave force induced by irregular waves:  
\[F_{D}(t)=\int_{0}^{t}h_{r}^{D}(t-\tau)\zeta(\tau)d\tau(r=1,2,\cdots,m) \tag{11}\]  
\[h_{r}^{D}(t)=\frac{1}{\pi}\int_{0}^{\infty}H_{r}^{D}(i\omega_{e})\mathrm{e}^{ \mathrm{i}\omega_{e}t}d\omega_{e}(r=1,2,\cdots,m) \tag{12}\]  
where \(h_{r}^{D}(t)\) is the impulse response function of diffraction wave force for \(r\)-th mode of motion, and \(H_{r}^{D}\) (\(\mathrm{i}\omega_{e}\)) is the frequency-domain response amplitude operator of diffraction wave force in regular waves.
## 1 Introduction
With the geometric survey, it is possible to determine the geometric characteristics of the main structural elements, which for the chimney under study are the inner and the outer skin, the 8 meridians, and 11 parallels. It should be highlighted that the chimney is characterized by a height equal to 50 m, considering the presence of a 10 m height basement, which presents a stiffness significantly greater than that of the chimney (Figure 5). For this reason, to define the dynamic behavior of the historical chimney, only the upper slender part is considered. The chimney presents the inner and outer skins from the base to 26.50 m in height. The outer skin is characterized by a tapered section with a diameter ranging between 3.70 m at the base to 2.62 m at the height of 26.50 m. Above the quote of 26.50 m, the chimney has only one skin (the external one) with different thicknesses: 0.51 m from 26.50 m to 28.00 m, 0.37 m from 28.00 m to 28.90 m, 0.33 m from 28.90 m to 29.23 m, and 0.28 m up to the top. Regarding the mechanical property of the masonry, flat jacks tests in different positions of the chimney were executed to evaluate the compression resistance (f\({}_{\text{d}}\)) and Young's modulus (E\({}_{\text{d}}\)) [32]. After the test it was obtained a masonry compressive strength, f\({}_{\text{d}}\) = 4.96 MPa, and a value of [PERSON]'s modulus E\({}_{\text{d}}\) = 7150 MPa. As deeply discussed in [33; 34], it is necessary to reduce the value of the compressive strength obtained from the in situ tests by the following:  
\[\text{f}_{\text{d,red}}=(\text{f}_{\text{d}}/\text{c}_{1})\cdot\text{c}_{2}=3. 55\text{ MPa}, \tag{1}\]  
where c\({}_{1}\) = 1.2 is a coefficient that considers the number of tests carried out, and c\({}_{2}\) = 0.86 is a coefficient that considers the geometrical slenderness of the structure and the eccentricity of the vertical loads acting on the considered structure.
## 3 Experiments
### Model Complexity Analysis  
The proposed method has proven its superior ability in quantitative and qualitative FSR performance based on the previous experiments. In this section, we compare the model performance, size, and execution time of our approach with other state-of-the-art methods. The results are shown in Figure 9. From the figure, it can be observed that our method achieves the best quantitative results while maintaining comparable execution time and model size, which makes it a possible choice for FSR tasks.  
Figure 8: Visual comparison for \(\times 8\) SR on face images with JPEG artifacts; “JPEG HR” represents the original HR images with JPEG artifacts. Please zoom in for better comparison.  
Figure 9: Model complexity scattergram for \(\times 8\) SR on the CelebA test set.
# HERMES CubeSat Payload Thermal Balance Test and Comparison with Finite Volume Thermal Model
## 6 Discussion
For what concerns the structural components, there is a perfect match between the numerical data and the test results. There is a good match with the internal components and the reason of the difference is explained in Section 5.3. Finally, the presented results agree with analogous activities carried out on flying CubeSats: for instance, the temperature discrepancies between numerical and experimental results found in [5] are compatible with the outcome of the present work.
## 4 Results and Discussion
### Coal Basic Information and MIP Results  
The twelve coal samples with different coal ranks varied from lignites to anthracites with R\({}_{\text{o,m}}\) 0.46% to 2.79%. The maceral composition of the coals was dominated by vitrinite and variable exinite content. In addition, the coals have moistures of 0.57-8.63%, ash yields of 5.38-38.08%, volatiles of 3.5-48.97% and fixed carbon of 25.36-75.05% respectively, as shown in Table 1. Six coal samples with different coal ranks were selected for mercury porosimetry, the mercury intrusion curves are showed in Figure 2. The average pore diameter of the selected coals has LL-SL5# (26.12) \(>\) YQ-WK15# (15.22) \(>\) HDG6# (13.80) \(>\) TY-DY6# (11.32) \(>\) LL-DP 10# (10.25) \(>\) BLG6# (8.70) as listed in Table 2. The cumulative intrusion curves can be divided into three types: Type I, for instance LL-SL5#, has small threshold pressure, high cumulative intrusion volume and low mercury withdrawal rate. The mercury intrusion curve can be divided into three sections: the curve increases steadily until the cumulative intrusion volume reaches 35% when the pressure is under 1 MPa; Curve slope increases suddenly and the rate of mercury intrusion slows down significantly when the pressure is higher than 1 MPa; Then, the curve verge to horizontality little by little when the pressure is higher than 10 MPa. The mercury withdrawal curve can be divided into two sections: the curve is similar to horizontality when the pressure is higher than 10 MPa; The slope become larger and the curve is similar to vertical when the pressure is under 10 MPa. Type I reflects that macropore and micropore predominate the pores, which means that strong heterogeneity existed. Type II, for instance HDG6#, has high threshold pressure, high cumulative intrusion volume and low mercury withdrawal rate. The mercury intrusion and withdrawal curves are stable and no inflection exists. Type II reflects that the even-distributed pore structure. Type III, that includes BLG6#, LL-DP 10#, TY-DY6# and YQ-WK15#, has high threshold pressure, low cumulative intrusion volume and high mercury withdrawal rate. Type III reflects that micropore dominates the pores.
# Efficient QC-LDPC Encoder for 5G New Radio
## 2 5G NR QC-LDPC Codes
### Introduction to QC-LDPC Codes  
A binary QC-LDPC code can be characterized by the null space of an array of sparse circulants of the same size [7; 23; 24]. Taking into account the implementation, the parity-check matrix \(H\) of a QC-LDPC code can be defined by its base graph and shift coefficients \((P_{i,j})\). Elements 1s and 0s in the base graph are replaced by a circulant permutation matrix and a zero matrix of size \(Z\times Z\), respectively. For two positive integers \(m_{b}\) and \(n_{b}\), with \(m_{b}\leq n_{b}\), consider the QC-LDPC code expressed by the following \(m_{b}\times n_{b}\) array of \(Z\times Z\) circulants over GF(2):  
\[H=\begin{bmatrix}Q(P_{1,1})&Q(P_{1,2})&\cdots&Q(P_{1,n_{b}})\\ Q(P_{2,1})&Q(P_{2,2})&\cdots&Q(P_{2,n_{b}})\\ \vdots&\vdots&\ddots&\vdots\\ Q(P_{m_{b},1})&Q(P_{m_{b},2})&\cdots&Q(P_{m_{b},n_{b}})\end{bmatrix}. \tag{2}\]  
The exponent matrix of \(H\), which is \(E(H)\), has the following form:  
\[E(H)=\begin{bmatrix}P_{1,1}&P_{1,2}&\cdots&P_{1,n_{b}}\\ P_{2,1}&P_{2,2}&\cdots&P_{2,n_{b}}\\ \vdots&\vdots&\ddots&\vdots\\ P_{m_{b},1}&P_{m_{b},2}&\cdots&P_{m_{b},n_{b}}\end{bmatrix}. \tag{3}\]  
Each entry in the matrix \(E\) is referred to as a shift value. It should be noted that the parity check matrix \(H\) in Equation (2) can be constructed by expanding the \(m_{b}\times n_{b}\) exponent matrix \(E(H)\). This procedure is referred to as protograph construction [25].
## 2 Materials and Methods
### Methods
#### 2.2.3 Flexural Strength and Residual Flexural Strength  
Crack mouth opening displacement (CMOD) tests are normally used to study the effect of fibers on the post-cracking behavior of concrete and displays the ability of fibers to redistribute stresses and bridge the cracks formed. The tests were conducted following the BS EN 14651:2005 + A1:2007 [6], which measures the opening of the crack at mid-span using a displacement transducer mounted along the longitudinal axis. This method was previously used to investigate the post-cracking performance of macro-synthetic fibers in concrete [30; 31; 32].  
A total of four beams were cast per concrete batch. The beam dimensions were 700 mm \(\times\) 150 mm \(\times\) 150 mm (Figure 2), with a span length of 500 mm. A notch (25 mm depth and 5 mm width) was cut in the middle of the beam to control the crack initiation and propagation along the initially induced notch. The test was conducted under three-point bending loading conditions, with the load applied at a constant CMOD rate of 0.05 mm/min up to CMOD = 0.1 mm, followed by a constant CMOD rate of 0.2 mm/min. The load (kN) and CMOD (mm) were recorded for the analysis of residual flexural strength at various CMODs. CMOD1 to CMOD4 correspond to CMODs of 0.5 mm, 1.5 mm, 2.5 mm, and 3.5 mm respectively. The loads corresponding to each CMOD were recorded as \(F_{j}\).  
The flexural strength \(f_{L}\) (MPa) was calculated according to BS EN 14651:2005+A1:2007 [6], using Equation (1).  
\[f_{L}=\frac{3F_{L}l}{2 bl_{sp}^{2}} \tag{1}\]  
Figure 2: Setup for the notched three-point bending test according to BS EN 14651:2005.  
where, \(F_{L}\) = the load corresponding to the limit of proportionality, \(l\) = span length, \(b\) = width of the specimen, and \(h_{sp}\) = distance between the tip of the notch and the top of the specimen. \(F_{L}\) was determined by the highest load within the first CMOD interval of 0.05 mm.  
The residual flexural strengths \(f_{Rj}\) (\(j=1,2,3,4\)) were calculated using Equation (2), according to BS EN 14651:2005 + A1:2007 [6].  
\[f_{Rj}=\frac{3F_{j}l}{2 bh_{sp}^{2}} \tag{2}\]  
where, \(F_{j}\) = load corresponding to the \(j\)-th recorded CMOD.
# Detection of Arc Faults in Transformer Windings via Transient Signal Analysis
## 4 Findings  
### Realization of Systems and Simulations  
The 15 MVA transformer model, designed in the Ansys@Maxwell environment, is combined with the arc models created in the Matlab@Simulink environment within Ansys@Simplorer. While Maxwell conducts the transformer magnetic analysis, the current and voltage information from the arcing event is simultaneously extracted via Simulink.  
Figure 2 illustrates the structure of the arc modeled in Matlab@Simulink, adapted to the magnetic model in Ansys. In addition, MDL1 in Figure 2 illustrates the Matlab@Simulink model, while FEA1 depicts the Ansys@Maxwell model. Other circuit elements are components of Ansys@Simplorer, which is used to link Matlab and Ansys. In the diagram, E1, E2, and E3 represent the source voltages; VM indicates the voltmeters; AM denotes the amperemeters; R1, R2, and R3 are the source internal resistances; R11, R12, and R13 are the primary winding resistances; R4, R7, and R9 are the secondary winding resistances; and R5, R6, and R8 represent the nominal load resistances; and R10 signifies the nonlinear arc resistance. When the simulation is initiated in the Ansys@Simplorer environment, Matlab-Simulink--where the mathematical model is embedded--runs simultaneously. The current and voltage components are transmitted to the nonlinear resistance R10 via Simulink, allowing the analyses to be performed.  
In the simulations, arc faults were generated at five different points on the phase-B of the HV winding, with the assumption that the faults occurred across subsequent disks. The arc length and the identified arc points were kept constant during the simulation. After coupling the system, the program execution began in MATLAB. From the MATLAB environment, the simulation was transitioned to the Ansys@Simplorer environment using the AnsoftSFunction block diagram in Simulink. By running both MATLAB@Simulink and Ansys@Maxwell in ANSYS Twin Builder (Simplorer), data can be processed simultaneously between these platforms and saved immediately. The structure is illustrated in Figure 3.  
Figure 2: Matlab-compatible ANSYS model developed for arc analysis.  
Fault arcs were created by removing the leads from the turns for five different cases in phase B of the HV winding. The analysis was repeated for each fault point, with the arc length kept constant. In Figure 4, the turns affected by the fault are indicated in red, while the removed leads (fault points) are indicated in blue.
# Dynamic Line Rating--An Effective Method to Increase the Safety of Power Lines
## 2 Problem Identification--Materials and Methods
### Data of the Line Monitoring Sensor  
The applied sensor is an OTLM (Overhead Transmission Line Monitoring) manufactured superior line monitoring device, which measures the temperature, sag changes, and current load of the wire in real-time [29]. There is also a built-in camera that can also detect the ice layer deposited on the wire. The sensor has more than 20 different IEC (International Electrotechnical Commission) and MIL (Military) standards. The device is powered by a built-in AC adapter that starts operating at a minimum current of 65 A. During the on-the-spot measurements, its most important feature is the conductor temperature measuring, which has a \(\pm\)2 \({}^{\circ}\)C deviation in the range from \(-\)40 \({}^{\circ}\)C to 125 \({}^{\circ}\)C. The conductor temperature resolution is 0.5 \({}^{\circ}\)C.
# Listeners' Spectral Reallocation Preferences for Speech in Noise
## 3 Expt. 1: Spectral Reallocation Preferences for Full-Bandwidth Speech
### Results
#### 3.5.4 Relationship between Preference and Intelligibility  
Figure 3 shows the distribution of listener preferences alongside the percentage of keywords identified correctly as a result of that choice. Listeners exhibited clear preferences when intelligibility was at (or close to) ceiling in quiet and 0 dB SNR conditions in particular. Two-sample Kolmogorov-Smirnov tests using function ks_2 samp in scipy.stats of Python [74] confirmed that listeners' preference distributions were non-uniform for all combinations of feature and SNR (all \(p<0.01\)).  
In some conditions, listeners did not adopt extreme preferences, even though intelligibility was not compromised at those extreme adjustment values. This is especially evident in the quiet condition but also prevails over most of the adjustment range for the 0 dB condition. However, as conditions became more adverse, preferences and intelligibility outcomes were more closely related, and listeners appeared more willing to choose the extremes of the adjustment range. This is clearly seen for the low- and high-frequency bands in the \(-6\) dB SNR condition, where the modal value of listeners' choices occupies one or the other extreme of the range.
# Roco Proteins: GTPases with a Baroque Structure and Mechanism
## 7 Proposal of a New Working Mechanism for Roco Proteins  
Based on the above discussed in vitro and in vivo results, we can hypothesize on a new working model for the mechanism of Roco proteins in general, and LRRK2 in particular (Figure 5). GTP-bound monomeric LRRK2 is uniformly distributed in the cytosol. A state that is stabilized by 14-3-3 proteins which bind to LRRK2 upon phosphorylation of its N-terminally located serine residues and prevent LRRK2 aggregation in cytosolic inclusion pools [132; 133; 134]. In this stabilized monomeric state, both the GTPase and kinase presumably have only low basal activity [82].  
In addition, inactive GDP-bound Rab proteins are maintained in the cytosol by binding to GDP dissociation inhibitors (GDIs). Interaction with a Rab-escort protein (REP), facilitates prenylation of these Rab proteins by Rab GGTases, which results in detachment of GDIs and membrane-binding [80; 82; 83]. Here, the aid of GEFs results in a rapid exchange of GDP for GTP, resulting in GTP-bound Rab proteins at the membrane in their active state. These GTP-bound Rab proteins, recruit GTP-bound LRRK2 to the membrane, by binding to the N-terminus of LRRK2. This recruitment has for example been demonstrated for Rab29, which binds the Ankyrin domain of LRRK2 and in this way, recruits the protein to the Golgi apparatus. Upon membrane-binding, the LRRK2 kinase is activated by so far not completely understood mechanisms and phosphorylates prenylated, GTP-bound Rab proteins, as well as its own serine 1292 residue. This Rab phosphorylation hinders interaction with RabGAPs, and as such, slows down GTP hydrolysis of Rab proteins [83; 84].  
Meanwhile or prior to the activation of the kinase domain, membrane-association of LRRK2 also induces LRRK2 GTP hydrolysis, which could, for example, be triggered by the release of 14-3-3 proteins, conformational changes caused by lipid binding, a local higher protein concentration or by binding of a membrane-bound GAP. This step is followed by protein dimerization, resulting in a dimeric, GDP-bound conformation of LRRK2 at the membrane. So far it remains to be determined if GTP binding itself is sufficient to trigger monomerization and detachment from the cell membrane or that additional co-factors are necessary to regulate this process [19].  
Several studies have reported that PD mutations in the Roc and COR domain result in decreased GTPase activity. However the underlying defects in the activation mechanism were not well understood [12; 56; 104; 105; 129; 135; 136; 137; 138; 139]. Our recent work with the CfRoco protein provides a link between PD-mutations, decreased GTPase activity and changes in the monomer/dimer equilibrium [19]. A recent study by [PERSON] et al. links this decreased GTPase activity in R1441G/C and Y1699C mutant LRRK2 to an increased kinase activity, since mutant LRRK2 stays longer in its kinase active, membrane-bound conformation [84].
# Study on the Influence of Dust Removal Fan Structure Parameters on Dust Removal Performance
## 3 Numerical Simulation Scheme of Dust Removal Fan
### Grid Division and Independence Verification  
The Meshing module of the ANSYS Workbench 2023R1 software was used to conduct unstructured meshing of the fluid domain. To ensure the accuracy of numerical simulation and the convergence of calculations, the grid was refined in the rotation region where the driving impeller and the driven impeller were located. The meshing results are shown in Figure 4.  
Before the formal numerical simulation, it is necessary to conduct grid independence verification. This involves changing the size of the minimum cell of the grid at the same ratio to obtain four sets of grid models, with grid numbers of 1,894,317; 2,027,718; 2,225,859; and 2,536,249. The quality of the four sets of grids is shown in Table 2.  
Figure 4: Dust removal fan fluid domain grid model. (**a**) Global grid model; (**b**) grid model cross-section.  
The driving impeller speed was set to 2900 r/min, and the inlet speed was set to 16.405 m/s. Subsequently, the four grid models were numerically simulated. The numerical simulation results of the dust removal fan model with four grid numbers are presented in Table 3.  
The total pressure obtained from the numerical simulation of the model with four grid numbers was used as the evaluation index to verify grid independence. The relationship between the total pressure obtained from the numerical simulation of the model with four different grid numbers and the grid number is shown in Figure 5.  
According to the numerical simulation results, when the number of grids reaches 2,225,859, the simulated total pressure value is basically stable. Additionally, it can be observed from Table 2 that when the number of grids reaches 2,225,859, increasing the number of grids has little impact on improving grid quality. Therefore, the grid division scheme with a grid number of 2,225,859 is selected.