esa-sceva/satcom-chunk-collection huggingface dataset:
# Propospheric Attenuation in GeoSurf Satellite Constellations
###### Abstract  
In GeoSurf satellite constellations, any transmitter/receiver, wherever it is located, is linked to a satellite with zenith paths. We have studied the tropospheric attenuation predicted for some reference sites (Canberra, Holmdel, Pasadena, Robledo, and Spino d'Adda), which also set the meridian along which we have considered sites with latitudes ranging between 60\({}^{\circ}\) N and 60\({}^{\circ}\) S. At the annual probability of 1% of an average year, in the latitude between 30\({}^{\circ}\) N and 30\({}^{\circ}\) S, there are no significant differences between GEO slant paths and GeoSurf zenith paths. On the contrary, at 0.1% and 0.01% annual probabilities, large differences are found for latitudes greater than 30\({}^{\circ}\) N or 30\({}^{\circ}\) S. For comparing the tropospheric attenuation in GeoSurf paths with that expected in LEO highly variable slant paths, we have considered, as reference, a LEO satellite constellation orbiting in circular at 817 km. GeoSurf zenith paths \"gain\" several dBs compared to LEO slant paths. The more static total clear-sky attenuation (water vapor, oxygen, and clouds) in both GEO and LEO slant paths shows larger values than GeoSurf zenith paths. Both for rain and clear-sky attenuations, Northern and Southern Hemispheres show significant differences.  
GEO; LEO; satellite constellations; GeoSurf; rain attenuation; clear-sky attenuation 1
## 3 System Architecture
### PV Generation Model  
RERs like wind, tidal biogas, geothermal, and biomass are costly and are not available everywhere. However, PV energy is less expensive and available everywhere. Therefore, the smart home considered in this study was assumed to have its own rooftop PV generation system.  
This study used an already developed probabilistic model based on a beta distribution for the modeling of solar irradiance and PV pattern generation, as reported in References [44; 45]. Historical irradiance data for a complete year was used for the modeling and generation of PV patterns, and then, these generated patterns were used as the input in this study. The probability density function (PDF) of the solar irradiance \(G\) is described by the following Equation (7):  
\[f\big{(}G\big{)}=\left(\frac{\gamma(\alpha+\beta)}{\gamma(\alpha)\gamma( \beta)}\right)\times G^{(\alpha-1)}\times\left(1-G\right)^{(\beta-1)},\ 0<G(t)<\infty, \tag{7}\]where \(G\) is the solar irradiance in kW/m\({}^{2}\); \(f(G)\) is the beta distribution function of \(G\); and \(\alpha,\beta\) are the parameters of the beta distribution function. These parameters are computed from the standard deviation \(\sigma\) and mean \(\mu\) of the random variable \(G\):  
\[\beta=(1-\mu)\times\left(\frac{\mu\times(1+\mu)}{\sigma^{2}}-1\right), \tag{8}\]  
\[\alpha=\frac{\mu\times\beta}{1-\mu}. \tag{9}\]  
The output power of the PV panel installed in the proposed smart home was calculated using Equations (10)-(12), as reported in [PERSON] et al. [46]:  
\[P_{ac}(h)=0.92\times P_{n}\times G(h)\times\eta_{DC-AC}\times\mathcal{E}/1000, \tag{10}\]  
\[\mathcal{E}=(1-\gamma\times\Delta T_{c}(h)), \tag{11}\]  
\[\Delta T_{c}(h)=\Big{|}25-\left(T_{d}(h)+(NOCT-20)\times G(h)/800\right)\Big{|}, \tag{12}\]  
where \(P_{n}\), \(G(h)\), \(NOCT\), \(\eta_{DC-AC}\), and \(\gamma\) represent the rated power of the PV system (1-100 kW\({}_{\text{p}}\)), solar irradiance, temperature of the cell operating under normal conditions (45 \({}^{\circ}\)C), efficiency of the inverter (95%), and coefficient of the power temperature (0.007), respectively.
## 3 Experiment and Analysis
### Point Cloud Registration Experiment for Simple Target Object  
In this experiment, a cup is used as an example for registration. Figure 3a shows the original point cloud data and Figure 3b shows cup point cloud data after removing the background. Figure 3c shows the registration result obtained by the traditional algorithm while Figure 3d shows the registration result obtained using the registration algorithm proposed in this paper. The red regions in the point cloud represents the source point cloud data while the green and the blue regions represent the target point cloud and the rotated cloud point data, respectively.  
and Z. The rotation angle in three directions and the matching error distance between the source cloud and the transformed point cloud are considered as the evaluation indices. The rotation angle and the registration error distance in this experiment are shown in Table 4.  
From Table 3, it can be observed that for the same point cloud sample with the same experimental parameters, the initial registration time using the traditional algorithm is 0.340 s. Because of the combination of FPFH feature description and greedy projection triangulation, the initial registration time obtained using the proposed algorithm is 0.252 s. Table 4 shows a comparison of the two algorithms. The average error distance obtained is 1.58 mm and 1.49 mm using the traditional algorithm and the proposed algorithm, respectively. As shown in Table 2, the point cloud is transformed by the different transformation matrix, and the average error distance obtained is smaller by the proposed algorithm.
## 6 Deflection Measurement
### Optical CMM  
Compared to the laser tracker, optical CMM uses cameras to locate the 3D position of the target referring to the camera coordinate system. There are two types of optical CMMs regarding the target mechanism.  
* A black and white ring is used as the target for position recognition, which is shown as markers in Figure 15. These markers are normally adopted by 3D camera systems. In addition to the stereo cameras, illuminations are provided to offer appropriate light for marker recognition. According to the measurement volume, the measurement accuracy is up to 6 \(\upmu\)m [45].
* An infrared light-emitting diode (LED) is used as the target. As shown in Figure 16, three infrared line scan cameras are used to estimate the 3D position of the LEDs. The product specification of the Nikon Metrology K610 (Nikon, Tokyo, Japan) states a volumetric precision of 60 \(\upmu\)m, depending on the measurement situation (distance and inclination towards the camera, ambient light conditions) [44].  
Figure 14: Layout of four SMRs for 6D pose measuring [125].  
Figure 15: 3D camera measurement system [45].  
Both types of targets can be very convenient to stick to the measuring object. In particular, black and white rings are very suitable for measuring a large number of components. When the 3D/6D deformation of each arm needs to be measured, at least three targets are attached to the arms (Figure 17).  
Both measuring techniques have their advantages and disadvantages. The laser tracker has a larger measuring range and is not affected by ambient light. Due to the size and price of the SMR, a laser tracker is normally used to measure the displacement of the robot EE. Optical CMM is adopted to measure not only the displacement of the robot EE but also the deformation between the arms (joint deformation). When the stiffness modelling is built with the VJM method, the joint stiffness needs to be identified. Direct joint deformation  
Figure 16: All system components of the Nikon Metrology K610 [44].  
Figure 17: 3D/6D deformation measurement: (**a**) black and white rings are stuck on the arms and (**b**) LEDs are stuck on the arms [117].  
measurement provides more precise information for joint stiffness identification. When only robot EE deformation is provided, the joint deformation is estimated by decoupling the robot EE deformation to each joint based on the inverse kinematics of the robot. However, the ambient light and the measuring volume should be considered if the optical CMM is appropriate for the size of the robot. In addition, the targets should be confirmed to be recognized when the robot moves to different positions.
# Optical Sensing Technologies for Cryo-Tank Composite Structural Element Analysis and Maintenance
## 3 Test Article Description
### Optical Technologies
#### 3.1.1 Fiber Bragg Grating Sensor  
The physical principle of the FBG is based on the diffraction occurring from an electromagnetic radiation in accordance with Bragg's law. It describes the condition for constructive interference from several crystallographic planes of the crystalline lattice separated by a distance d:  
\[2 dsin\theta=n\lambda \tag{1}\]  
where \(\theta\) is the incident angle, \(n\) is an integer, and \(\lambda\) is the wavelength. The reflected spectrum is centered on the Bragg wavelength and depends on the effective index of refraction and the period (\(\Lambda\)) of the grating, according to the well-known equation:  
\[\lambda=2n_{\mathit{eff}}\ \Lambda \tag{2}\]  
In the hypothesis of constant temperature, if a local deformation is present, the grating period varies, and the reflected wavelength changes accordingly.  
An FBG is bonded close to the clamped edge of the tested curved plate, where the strain achieves the maximum value. Related strain data are logged along the entire test execution by two different sets of instruments. The quasi-static-strain history is saved by an MOI sm-130 interrogator, while time response is monitored by Redondo FAE sense 400  
Figure 4: Setup view: (**a**) representative structural element from outer tank, 1 m long; (**b**) lateral view of the element mounted in the test rig for bending load application.  
Figure 5: Optical interrogators: (**a**) low-frequency MOI sm-130 for FBG; very-high-frequency Redondo for FBG; (**b**) very low frequency for distributed sensing (OdisiB).  
(Redondo Optics, Inc., Corona, CA, USA), characterized by a dynamic bandwidth of tens of kHz. The two data sets are correlated but refer to distinct phenomena: the modification of the structural elastic behavior following an internal crisis, and the structure-borne sound caused by flaws initiation, respectively.
## 3 Results
The boundaries for the two parameters, cost and gamma, were extended to [0.25, 512] and [0, 10], respectively, following the suggestion in [22], where the fine grid search had cost = [2, 32] and gamma = [2\({}^{\circ}\)(-7), 2\({}^{\circ}\)(-3)]. For the imbalanced data, we set the boundary for class.weight from 0.99 to 0.01 through a decrease of 0.01, which was equal to the class weight changing from [0.99, 0.01] to [0.01, 0.99]. For an imbalanced classification problem, the minority class is challenging to predict because there are few examples of this class. In our example, the minority class was the MCI and AD group and the majority class was the normal control group, which was about 3.5 times larger than the minority class (1328/377 = 3.52). Without tuning of the three hyperparameters, gamma, cost, and class.weight, the testing set in the 10 times repeated fivefold cross-validation failed to predict the characteristics of examples from the MCI and AD group with a sensitivity of 0 and specificity of 100% (Table 3). The positive predictive value and the negative predictive value at base rate 12% were, respectively, NaN% and 88.00%, which would definitely not be acceptable for clinical applications. After hyperparameter tuning, we successfully improved the differentiation of examples from the minority class (MCI and AD group) from the majority class (normal control group) with a sensitivity of 70.67% and specificity of 50.94% for the testing set in the 10 times repeated fivefold cross-validation (Table 4). The positive predictive value and the negative predictive value at base rate 12% were, respectively, 16.42% and 92.72%, which are acceptable for current clinical applications. Our results show that the hyperparameter tuning workflow with high-performance computing machine-learning for imbalanced Alzheimer's disease data that we have presented can significantly reduce computational time while correcting imbalances. Figure 2: (**a**) Computational time vs. number of cores and (**b**) speedup vs. number of cores for imbalanced data. We also used mlr.tuneParams in the mlr package [23] with parallel hyperparameter tuning in the Lonestar6 HPC system for comparison with our results. The grid search ranges for the three hyperparameters, cost, gamma, and class.weight, were the same. The 10 times repeated fivefold cross-validation was also performed. After 2.76 h running in a single-node 128 core parallel setup, the optimal hyperparameters were found at gamma = 0.02, cost = 32, and class.weight = [0.81, 0.19] and the final performance for the testing set exhibited the following values: sensitivity = 60.00%, specificity = 57.36%, PPV12 = 16.10%, and NPV12 = 91.32%). Compared to the paralleled mlr.tuneParams method, our hyperparameter tuning workflow could not only better adjust the imbalance bias with higher sensitivity, a higher positive predictive value and negative predictive value at base rate 12%, and slightly lower specificity, but also ran 18.1% faster.
## 3 Results and Discussion
### Principal Component Analysis
heptasiloxan, N.N-dimethylpyridyn-4-amine, and 2-furancarboxaldehyde were a group of compounds that either did not differentiate or weakly discriminated the origin of the coffee beans and the roasting process type. Their percentage share in the coffee bean profiles was similar in all analyzed cases (Table 1). Another important finding is that the compounds on the negative and positive sides of the PC2 principal component discriminated between the CC- and CCR-roasted coffee beans, respectively (Figure 2b). A particularly high amount of pyridine, acetic acid ethenyl ester, and 2-furylmethyl acetate is characteristic of the CC roasting process [38]. Figure 2: Projection of variables (main volatile compounds characterizing the CC and CCR coffee roasting process) on the PC1 and PC2 factor plane—(**a**); projection of cases (coffee beans from Brazil, Ethiopia, Guatem, Costa Rica, and Peru roasted in the CC and CCR roasters) on the PC1 and PC2 factor plane—(**b**). Figure 3a shows the projection of the variables (electronic nose response \(\Delta\)R/Rmax) on planes PC1 (66.75%) and PC2 (24.93%). The analysis revealed a strong positive correlation between the responses of sensors AS-MLV-P2, TGS2600, TGS2602, TGS2610, TGS2611, and TGS2612. There was also a positive correlation between sensors TGS2603 and TGS2620. However, no correlation was found between these two groups (Figure 3a). All Agrinose responses \(\Delta\)R/Rmax for AS-MLV-P2, TGS2600, TGS2602, TGS2603, TGS2610, TGS2611, TGS2612, and TGS2620 located in the area delimited by two circles have a strong effect on the ability to distinguish the country of origin of the coffee beans and the roasting process, depending on the roaster type used [21]. The first principal component  
Figure 3: Projection of loadings (maximum responses of sensors characterizing the CC and CCR coffee roasting process) on the PC1 and PC2 factor plane—(**a**); projection of cases (coffee beans from Brazil, Ethiopia, Guatem, Costa Rica, and Peru roasted in CC and CCR roasters) on the PC1 and PC2 factor plane—(**b**). PC1 differentiates the volatile compound profile obtained in the roasting process in the CC roaster from that yielded by the CCR roasting process in as much as 66.75%. Sensors AS-MLV-P2, TGS2600, TGS2611, TGS2610, TGS2602, and TGS12 most accurately characterized and described the CC roasting process's generation of undesirable aromatic compounds associated with the influence of coal tar, which is a defect in this type of roasting process [13].
## 3 Seismic Response of the Super-Long-Span Suspension Bridge Exposed to Near/Far-Fault Earthquakes  
Two far- and near-field ground motions were selected for this study. These were the El Centro (Imperial Valley earthquake in 1940, NS component at station El Centro, shown in Figure 6) and Chi-Chi earthquake records (Chi-Chi earthquake in 1999, EW component at station TCU052, shown in Figure 7), respectively. In the previous studies on seismic response analysis of long-span bridges under near-fault earthquake excitations, Chi-Chi earthquake records in 1999 were often selected and adopted [28; 37]. Among the Chi-Chi earthquake records in 1999 from all stations, the EW component seismograms from station TCU052 and TCU068 contained the most typical near-field earthquake characteristics, such as velocity impulsive waveform and large slip displacement [38]. The peak ground acceleration (PGA) of each input ground motion was scaled to 400 gal and 800 gal, respectively. The frequency components of the selected earthquake records were not changed.  
Figure 5: Mode shapes of the finite element model: (**a**) first vibration mode (0.055 Hz); (**b**) second vibration mode (0.089 Hz).  
Figure 6: Imperial Valley earthquake record (station El Centro, NS), May 1940.  
Figure 7: Chi-Chi earthquake record (station TCU052, EW), September 1999.  
Based on the finite element model in Section 2, the seismic responses of the bridge exposed to the different earthquake excitations were calculated and analyzed. The maximum displacement and acceleration responses to the El Centro and Chi-Chi earthquakes are shown in Tables 1 and 2.  
When exposed to a far-fault earthquake, the transverse displacement at the top of the tower was 0.343 m and the lateral horizontal displacement angle was approximately 1/1122, indicating that the super-long-span suspension bridge was in an elastic state. When exposed to a near-fault earthquake, the horizontal displacement angle was approximately 1/295, indicating that a plastic state had appeared in the tower of the super-long-span suspension bridge.  
To explain the reason why the seismic responses to the near-field earthquake were greater than those for a far-field earthquake, a time-frequency analysis of displacement at the tower top was performed, as shown in Figures 8 and 9.  
The results demonstrated that the main frequency of the tower exposed to a far-field earthquake was 0.35 Hz; when exposed to a near-field earthquake, the frequency was 0.15 Hz. Usually, the predominant period of a near-fault earthquake is longer than that of a far-fault earthquake, which was closer to the period of the super-long-span suspension bridge. Thus, exposure to a near-fault earthquake significantly increased the seismic response of the super-long-span suspension bridge.
# Influences of Temperature and Substrate Particle Content on Granular Sludge Bed Anaerobic Digestion
## 1 Introduction
### Effect of Temperature Change in Anaerobic Digestion  
Temperature has a strong influence on anaerobic digestion. It plays a major role in microbial growth, enzymatic activity, kinetics, and conversion processes and, consequently, in biogas yield and composition. All the steps of anaerobic digestion are directly or indirectly affected by digestion temperature. Temperature variation within anaerobic digesters is typically not recommended due to difficulties associated with adaptation of the microbial community and overall stability of the digestion process. The microbial community comprises various types of bacteria and archaea. They have different optimum temperatures for growth rate and activity. For example, the activity and growth rate of hydrolytic bacteria increase with increasing temperature (well beyond the mesophilic range). This is considered a benefit because hydrolysis is often the rate-limiting step; however, this benefit is negated by a decline in the growth rate and activity of methanogens (archae) leading to the accumulation of volatile fatty acids (VFAs) and process failure. an increase in temperature could also affect the digestion of specific types of substrates. It was shown that digestion of substrates rich with protein, such as cattle waste, is negatively affected due to increased ammonia inhibition [21]. Free ammonia concentration (NH\({}_{3}\)) increases due to a shift in NH\({}_{3}\)/NH\({}_{4}\)\({}^{+}\) equilibrium in its favor as temperature rises. There are indications that temperature change within a given range affects microbial activity and biogas production. The activity and growth rate of microorganisms increase by up to 50% for every 10 \({}^{\circ}\)C increase in temperature within the mesophilic range [22]. Change in temperature also affects the physical and chemical properties of produced biogas, as well as other components in the reactor. The solubility of biogas components, especially CH\({}_{4}\), in the reactor liquid mixture is an important aspect. Biogas plants that run at low temperature release effluents with a higher content of dissolved CH\({}_{4}\) compared to biogas plants that run at high temperature. The dissolved CH\({}_{4}\) in the effluent is then released into the atmosphere as a greenhouse gas [23; 24], which is a bad outcome from an environmental and economic standpoint. In this article, we investigate the effect of temperature variation on anaerobic digestion of particle-rich and particle-\"free\" substrates by varying temperature between 25 \({}^{\circ}\)C and 35 \({}^{\circ}\)C. This enables us to understand how biogas production and reactor stability is affected by temperature variation. In addition, it provides insight into how particulate disintegration and hydrolysis are affected by temperature.
# A Flexible FPGA-Based Channel Emulator for Non-Stationary MIMO Fading Channels
## 2 Discrete Non-Stationary MIMO Channel Model  
Considering a MIMO channel with \(S\) transmitting antennas and \(U\) receiving antennas, the channel can be defined by a complex channel matrix. Moreover, the input-output relationship in the discrete time domain can be expressed by a convolution operation as  
\[\mathbf{y}(l)=\mathbf{H}(l,\zeta)\otimes\mathbf{x}(l) \tag{1}\]  
where \(\mathbf{x}(l)=[x_{1}(l),x_{2}(l),\cdots,x_{S}(l)]^{T}\) is the transmitted signal vector; \(\mathbf{y}(l)=[y_{1}(l),y_{2}(l),\cdots,y_{U}(l)]^{T}\) is the received signal vector; \(l\) and \(\zeta\) are the discrete time indexes in the time domain and delay domain,respectively; and \((\cdot)^{T}\) denotes the transpose operator of a matrix or vector. In (1), the channel matrix \(\mathbf{H}(l,\zeta)\) can be further defined as  
\[\mathbf{H}_{U\times S}\left(l,\zeta\right)=\left[\begin{array}{ccc}h_{1,1} \left(l,\zeta\right)h_{1,2}\left(l,\zeta\right)&\cdots&h_{1,S}\left(l,\zeta \right)\\ h_{2,1}\left(l,\zeta\right)h_{2,2}\left(l,\zeta\right)&\cdots&h_{2,S}\left(l, \zeta\right)\\ \vdots&\vdots&\ddots&\vdots\\ h_{U,1}\left(l,\zeta\right)h_{U,2}\left(l,\zeta\right)&\cdots&h_{U,S}\left(l, \zeta\right)\end{array}\right] \tag{2}\]  
where \(h_{u,s}(l,\zeta)\) denotes the channel impulse response (CIR) of the sub-channel between the \(u\)th \((u=1,2,\cdots,U)\) receiving antenna and the \(s\)th \((s=1,2,\cdots,S)\) transmitting antenna, and it can be modeled in the discrete time domain as [20]  
\[h_{u,s}(l,\zeta)=\sum\limits_{n=1}^{N(l)}\sqrt{P_{n}\left(l\right)}\tilde{h}_{ u,s,n}(l)\delta(\zeta-\left\lfloor\tau_{n}(l)\right\rfloor_{\texttt{TS}}) \tag{3}\]  
where \(P_{n}(l)\) and \(N(l)\) are the path power and valid path number at time instant \(l\), respectively; \(\tilde{h}_{u,s,n}(l)\) is the channel coefficient with the normalized power; \(T_{s}\) is the sampling interval; and \(\left\lfloor\tau_{n}(l)\right\rfloor_{\texttt{TS}}\) denotes the discrete time delay. It should be noticed that the channel parameters in (3), such as \(P_{n}(l)\), \(N(l)\), \(\left\lfloor\tau_{n}(l)\right\rfloor_{\texttt{TS}}\), and \(\tilde{h}_{u,s,n}(l)\), are all time-variant, which can take into account the non-stationary aspects of real MIMO channels.