esa-sceva/satcom-chunk-collection huggingface dataset:
# Modular Combined DC-DC Autotransformer for Offshore Wind Power Integration with DC Collection
## 3 MCAT Main Circuit Parameters Selection
### Parameter Calculation of DC-Isolation Capacitors and Compensation Inductors in HVM  
In the HVM, \(\Delta U_{\text{c}}\) of each DC-isolation capacitor is almost identical because every DC-isolation capacitor has the same value, and the \(U_{\text{vdc}}\) of MMC\({}_{\text{H},N}\) (and MMC\({}_{\text{H},N+1}\)) is lowest in the HVM. According to (10), \(\alpha_{\text{H},N}\) (or \(\alpha_{\text{H},N+1}\)) is largest in the HVM and supposed to be calculated particularly. \(U_{\text{vacH},N}\), \(U_{\text{vdcH},N}\), and \(P_{\text{MMCH},N}\) are described as [11]:  
\[u_{\text{vH},N}=u_{\text{vacH},N}+U_{\text{vdcH},N} \tag{11}\]  
\[U_{\text{vacH},N}=1.05\frac{U_{\text{dcH},N}}{2} \tag{12}\]  
\[U_{\text{vdcH},N}=\frac{(E_{\text{low}}+U_{\text{dcH},N})}{2} \tag{13}\]  
\[P_{\text{MMCH},N}=\frac{P_{\text{N}}(1-1/k_{\text{dc}})}{2N} \tag{14}\]  
where \(u_{\text{vH},N}\) is the voltage at MMC\({}_{\text{H},N}\) valve side, \(u_{\text{vacH},N}\) and \(U_{\text{vdcH},N}\) are the AC component and DC component of \(u_{\text{vH},N}\). \(U_{\text{vacH},N}\) are the fundamental RMS L-L voltage at MMC\({}_{\text{H},N}\) valve side, \(U_{\text{dcH},N}\) is the DC voltage of MMC\({}_{\text{H},N}\). Substituting these variables into Equation (10), \(C_{\text{H},N}\) is expressed as follows:  
\[\begin{split} C_{\text{H},N}&=\frac{\sqrt{2}(1-1/k_ {\text{dc}})P_{\text{N}}/2N}{\sqrt{3}\times\frac{10}{2}\frac{1}{2}U_{\text{ dotH},N}2\pi/C_{\text{H},N}}\frac{1}{(\frac{U_{\text{ dotH},N}+E_{\text{low}}}{2})}\\ &=0.248\frac{(1-1/k_{\text{dc}})P_{\text{N}}}{  
where \(\alpha_{\text{H},N}\) denotes the rate of fluctuation of \(u_{\text{CH},N}\). \(\alpha_{\text{H},N}\) is an artificial technical index. After specifying \(\alpha_{\text{H},N}\), the corresponding value of \(\text{C}_{\text{H},N}\) could be calculated by (15). The DC-isolation capacitor and the compensation inductor are supposed to occur in series resonance at \(f\) so that they produce no reactive power. For this purpose, the calculation expression of \(L_{\text{H},N}\) is:  
\[L_{\text{H},N}=\frac{1}{(2\pi f)^{2}\text{C}_{\text{H},N}} \tag{16}\]  
After obtaining the values of \(\text{C}_{\text{H},N}\) and \(L_{\text{H},N}\), the value of other DC-isolation capacitors in the HVM could be taken as the \(\text{C}_{\text{H},N}\), so that their \(\alpha\) are smaller than \(\alpha_{\text{H},N}\) and \(\alpha_{\text{H},(N+1)}\), as mentioned above. The values of other DC-isolation capacitors and compensation inductors in the HVM are described as:  
\[\left\{\begin{array}{l}C_{\text{H},1}=\cdots=C_{\text{H},N}=\cdots=C_{\text{ H},2N}\\ L_{\text{H},1}=\cdots=L_{\text{H},N}=\cdots=L_{\text{H},2N}\end{array}\right. \tag{17}\]
# Correcting for Mobile X-Band Weather Radar Tilt Using Solar Interference
## 2 Weather Radar Deployment and Data
The data in question are from a seven-week (2 February 2017 to 21 March 2017) test period during which the radar's signal-to-noise ratio (SNR) required for data storage was reduced. While the reduction led to the retention of a significant amount of background noise, it also captured a greatly increased number of opportune solar hits at a reduced intensity, which have been useful for analysing the NXPol-1 system. In addition to data from NXPol-1, raingauge data from the Environment Agency (EA) and Scottish Environment Protection Agency (SEPA) are used to provide external verification of the tilt corrections. Fifteen-minute accumulation time series are used during the study, with data quality-control and processing discussed in Section 5.2.2.
###### Abstract
This is the case with Lassa fever virus, for which _Mastomys natalensis_ is the main reservoir host in Mali [6]. The geographic distribution of the viruses and their remarkably biological adaptation in rodents suggest a co-evolution between the viruses and their murid hosts [7]. The feces of small rodents infected with _Schistosoma mansoni_, _Hymenolepis diminuta_ and _Hymenolepis nana_ that contaminate water are likely to cause intestinal schistosomiasisomiasis due to _S. mansoni_, helminthiasis due to _H. diminuta_ or _H. nana_, or cause gastrointestinal infection induced by the presence of numerous helminiths, in contact with humans [3]. Some small rodent pathogens are transmitted to humans via hematophagous ectoparasitic vectors, including fleas that transmit the plague _bacillus Yersina pestis_[8], whose reservoirs are _Rattus norogeicus_, _Rattus rattus_ and the house mouse _Mus musculus domesticus_, but also to a lesser extent commensal species such as _Arvicanthis niloticus_, _Mastomys erythrolecucus_ and _Cryetonyms gambianus_. Cutaneous leishmaniasis (CL) due to _Leishmania major_, which is more widespread in West Africa, is transmitted to humans by sandflies [9] that live in the burrows of _Gerbilliscus gambianus_, _M. erythrolecucus_ and _A. niloticus_, small rodents that serve as reservoirs. Recent epidemics of cutaneous leishmaniasis with _L. major_ described in urban areas in Burkina Faso [10], Ghana [11] and in rural areas in Mali [12] illustrate a possible risk of emergence of these zoonotic diseases transmissible to humans. _Schistosoma mansoni_ schistosomiasis remains a public health problem in Mali; despite the implementation of control strategies, the animal reservoir has been invoked as a possible factor linked to the persistence of the disease, and small mammals could play a role [13]. Tick-borne boreliosis, widely distributed in West and North Africa, is caused by _Borrelia_ spp. which are transmitted to humans by ticks of the genus _Ornithodoros_ living in the burrows of small rodents that are the primary animal reservoir for Borrelia infections [10; 11; 12; 13; 14]. The risk of vector-borne or direct transmission of bacterial diseases from small rodents to humans is high in Mali, with notably a 35% seroprevalence of boreliosis due to _Borrelia crociduzae_ identified in small rodents in Bandiagara [15]. Most of these small mammal species sampled in our study represent a source of contamination of infectious diseases such as salmonellosis, leptospirosis, tick-borne relapsing fever porreliosis, murine typhus, Lassa fever, Rift Valley fever, Crimean-Congo hemorrhagic fever, viral meningitis, toxoplasmosis and coxiellosis, through ingestion of contaminated food, water or through the bites of infected arthropod vectors (ticks, fleas, mosquitoes) [1; 6; 7; 8; 14]. The emergence of bacteria transmitted by ticks is a threat to livestock keepers in Mali, where 15.8% to 58.3% of ticks carry bacteria such as _Ricketlsia_ spp., _Coxiella burretiti_, _Borrelia_ spp. and _Anapalsmataceae_ such as _Wholbachia_ spp. and _Eltrlichia ruminu
## 3 Materials and Methods
### Modeling of Drying Kinetics  
The drying kinetics of CD, VMD and CPD-VMFD were fitted based on the mass losses of the true lavender samples. For CD, weight losses were monitored every 2 min for the initial 20 min and then every 5 min thereafter until the end of the drying process.  
VMD samples were monitored every 2, 3 and 4 min for 480, 360 and 240 W. Different drying time intervals were applied in order to ensure a similar energy input between subsequent measurements regardless of the microwave power level.  
The moisture ratio (MR) of lavender leaves during drying experiments was calculated using the following equation:  
\[MR=\frac{M_{(t)}-M_{e}}{M_{o}-M_{e}} \tag{1}\]  
where M\({}_{(t)}\) is the moisture content at time \(\tau\). M\({}_{o}\) is the initial moisture content, and M\({}_{e}\) is the equilibrium moisture content (kg water/kg dry matter). The values of M\({}_{e}\) are relatively small comparing to those of M\({}_{(t)}\) or M\({}_{o}\). The error due to the simplification is negligible [46; 47; 48], thus the moisture ratio was calculated as follows:  
\[MR=\frac{M_{(t)}}{M_{0}} \tag{2}\]  
Table Curve 2D Windows v2.03 was used to fit the basic drying models to the measured MR determined accordingly to Equation (2). There are several drying models in the literature that can be used to describe the kinetics of drying plant materials. For drying model selection, drying curves were fitted to five well known thin drying models, including the modified Page model. Henderson-Pabis, logarithmic, Midilli-Kucuk, and Weibull ones. The best fit was determined using two parameters: the value of the coefficient of determination (R\({}^{2}\)) and root-mean squared error (RMSE). A model fits better if the value of R\({}^{2}\) is closer to 1, and the RMSE value is closer to 0, using the following equations:  
\[R^{2}=\frac{\sum\limits_{i=1}^{N}{(MR_{pre_{i}}-\overline{MR}_{\text{exp}})}} {\sum\limits_{i=1}^{N}{(MR_{\text{exp}_{i}}-\overline{MR}_{\text{exp}})}} \tag{3}\]  
\[RMSE=\sqrt{\frac{1}{N}\cdot\sum\limits_{i=1}^{N}{(MR_{\text{exp}_{i}}-MR_{pre_ {i}})^{2}}} \tag{4}\]where \(MR\) is moisture ratio, \(\overline{MR}\) is the mean value of moisture ratio, \"_pre_\" and \"exp\" indicate predicted and experimental values, respectively, while \"_i_\" indicates subsequent experimental data and \(N\) is the number of observations.  
Tests conducted in this study proved that the best fitting was obtained for the modified Page model:  
\[MR=A\exp(-k\tau^{n}) \tag{5}\]  
where \(A\), \(n\), and \(k\) are constants.
## 4 Application to Earthquake Monitoring  
### PPP Prior to the Earthquake  
In order to assess the accuracy of GNSS PPP, 1 Hz BDS and GPS data at station LASA over a 12 h time span (from 10:00 to 22:00) on 24 April 2015 (prior to the earthquake event, and the station is stationary) was processed. Dual-frequency, ionosphere-free, pseudo-range, and carrier phase combinations with an elevation angle cutoff of 7\({}^{\circ}\) were used. BDS satellite orbits and clocks were held fixed to the WHU orbit and WHU 1 s clock, while the IGS final orbit and WHU 1 s clock were fixed for GPS. Epoch-wise station coordinates and clock, tropospheric zenith path delay, as well as float ambiguities are estimated. The observation models, dynamical models, and estimated parameters are also given in Table 2.  
The visible number of satellites and results from BDS PPP and GPS PPP solutions are shown in Figure 4. Average numbers of observed satellites of GPS and BDS are almost the same, which is 8.1 and 8.4, respectively. RMS of BDS PPP and GPS PPP solution errors over the period from 14:00 to 22:00 (after the convergence) have been calculated (Table 6). For BDS solutions, the horizontal RMS is better than 1 cm and consistent with GPS, while the vertical reaches up to 5 cm, somewhat worse than GPS. The somewhat worse vertical performance of BDS PPP is mostly due to the slightly worse orbit and clock precision of the BDS GEO satellites.  
Figure 4: Number of observed satellites and the times series of PPP results with BDS and GPS (LASA station, from 10:00:00 UTC, 24 April 2015).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Types** & **WHU 1 s vs. COM** & **WHU 1 s vs. GBM** & **WHU 1 s vs. WHU 30 s** \\ \hline GEO & & 0.63 & 0.05 \\ IGSO & 0.17 & 0.26 & 0.07 \\ MEO & 0.10 & 0.08 & 0.08 \\ \hline \end{tabular}
\end{table}
Table 5: Statistic RMS values of BDS WHU 1 s clocks with respect to COM, GBM, and WHU 30 s clocks in term of three satellite types of GEO, IGSO, and MEO (unit: ns).
# GPU Algorithms for Structured Sparse Matrix Multiplication with Diagonal Storage Schemes +
## 6 Algorithms
### Banded Matrix-Matrix Multiplication
```
__global__ void sparseBandedMatMulDiag (Matrix A, Matrix B, Matrix C, int* BdiagDense) { __shared__ entry daS[T][T];  int myX = blockIdx.x * T + threadIdx.x;  int myT = blockIdx.y * T + threadIdx.y;  entry result = 0;  int maxLoop = ceil(A diagonal size/ T);  for (int j = 0; j < maxLoop; ++j){  Store the [myY[]- th entry from the (T*j+threadIdx.x)-th diagonal  of A to  daS[threadIdx.x][threadIdx.y]  __syncthreads();  for (int j = 0; j < T; j++) {  update []result[] by multiplying  between one element from  daS[0...T-1][threadIdx.y] and  the corresponding element from B  if both exist } }  C[myX][myY]=result; }
```  
**Algorithm 1**CUDA Algorithm for Banded Matrix-Matrix Multiplication  
We observe that for a thread block, in one iteration, in general, all threads access the shared memory \(T\) times. That means that each entry of \(A\) will be used \(T\) times. This gives us an advantage in terms of the \"data reuse\" of shared memory. The number of entries from \(B\) that need to be accessed for these multiplications is more than \(T^{2}\). But, to know the exact number, we need to specify the diagonal indices from both \(A\) and \(B\). During an iteration of the algorithm, to compute an entry from block \(C[i_{1}\ldots i_{2}][j_{1}\ldots j_{2}]\) of \(C\), where \(i_{2}-i_{1}=j_{2}-j_{1}=T\), we bring a block of size \(T\times T\) from \(A\) (denoted by \(A[k_{1}\ldots k_{2}][j_{1}\ldots j_{2}]\), \(k_{2}-k_{1}=T\)) into the shared memory. So, the indices of the diagonals of matrix \(B\) from which the other entries come are in the set \(\{i_{1}-k_{2}\ldots i_{2}-k_{1}\}\). For simplicity, we assume that all diagonals whose indices are given in the set exist in \(B\). From the above information, we can compute the number of entries of matrix \(B\) which need to be accessed to perform \(T^{3}\) multiplications. Unlike the entries of \(A\), we do not bring the entries of \(B\) into the shared memory during multiplication to avoid high memory traffic. To see this, assume the entries from matrix \(B\) are to be brought into the shared memory for \(T^{3}\) multiplications. As the total number of diagonals in \(B\) required in any iteration for a thread block is \(2T-1\) and the maximum number of entries in any of those diagonals is \(2T-1\), it is a good idea to bring a block of size \((2T-1)\times(2T-1)\approx 4T^{2}\) of \(B\) into the shared memory to avoid code divergence in performing these \(T^{3}\) multiplications. So, the average number of times an entry of \(B\) will be reused (if brought into shared memory) is \(T/4\). As \(T\) is small (ranging from 4 to 16), \(T/4\) is also small. This observation is true for dense matrix multiplications, where the matrices are stored in the CDM storage scheme.
## 4 Experiments
### Experimental Evaluation
#### 4.2.2 Optical Flow Evaluation  
Only one of prior works include optical flow into depth and ego-motion estimation as a 3D geometric understanding scheme, thus we conduct the similar evaluation to make a fair comparison. On the KITTI flow 2015 dataset we evaluated our optical flow component. We adopted the training images as a testing set. We compared our method with previous state-of-the-art deep learning single task methods, including handcrafted EpicFlow [33], supervised FlowNet5 [14] and FlowNet2 [20], unsupervised DSTFlow [34], UnFlow-C [23] and the joint learning method GeoNet [19]. By looking at the EPE of pixels over non-occluded area, we can see that our method beats most deep learning approaches including carefully designed supervised learning method FlowNet2, and comparable to UnFlow-C when trained on KITTI. As demonstrated in Figure 6 and Table 6, our system achieves significant improvement in non-occluded regions against other baselines due to the bidirectional flow occlusion mask and regularities extracted from different tasks.  
By avoiding any improper constraint effecting on occluded regions, the predictions over these regions tend to be primitive and not refined. However, leveraging our 3D alignment loss and mutually supervised term, our method still performs comparable to other baselines in overall regions.  
Figure 4: Comparison of monocular depth estimation between failed cases of [PERSON] et al. [15] and same cases of ours. Our method preserves considerable predictions in vast open scenes and objects close to the front of the camera.  
Figure 5: Comparison of monocular depth estimation between [PERSON] et al. [15] and ours on Make3D [29] dataset. The model is trained on KITTI + Cityscapes and tested on Make3D [29] directly. Our method captures the global scene layout reasonably well and preserves better thin structures.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method** & **Supervision** & **Abs Rel** & **Sq Rel** & **RMSE** & **RMSE Log** \\ \hline Train set mean & Depth & 0.876 & 13.98 & 12.27 & 0.307 \\ [PERSON] [30] & Depth & 0.428 & 5.079 & 8.389 & 0.149 \\ [PERSON] [31] & Depth & 0.475 & 6.562 & 10.05 & 0.165 \\ [PERSON] [32] & Depth & 0.204 & 1.840 & 5.683 & 0.084 \\ [PERSON] [11] & Pose & 0.544 & 10.94 & 11.76 & 0.193 \\ [PERSON] [15] & No & 0.383 & 5.321 & 10.47 & 0.478 \\ [PERSON] & No & 0.406 & 4.071 & 9.624 & 0.385 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Depth estimation results on the Make3D dataset.
## 4 Experimental Validation and Discussion
### Fault Severity Classification  
To realize automatic classification of the GLCM-based texture features and evaluate the performance of the sparse TFI texture feature extraction method proposed in this paper, the 1-nearest neighbor classifier (1 NNC) [54] and least squares support vector machine (LS-SVM) [55; 56; 57] were employed. Both these classifiers are commonly used for pattern recognition, and have stable classification performance and high efficiency. The LS-SVM was performed using LS-SVMlab1.8 [58], and the cross-validation method was used to optimize the parameters. Moreover, the linear kernel function was selected as the kernel function of the LS-SVM.  
As mentioned above, 80 samples were chosen for each test. The dataset of each test was divided into two groups: 40 samples (20 samples for each severity degree) were used as the training dataset  
Figure 12: Values of 12 features of samples in Test 1.  
and the remaining 40 samples as the testing dataset. The process of random grouping, training, and classification was conducted 10 times to obtain more robust results. The average classification accuracy was used as a performance evaluation index for the proposed method.  
Table 2 lists the results of the bearing fault severity recognition. The performance of the STFA-PD was the best of the four time-frequency analysis methods, because it had higher time-frequency concentration and resolution than the others, which made the texture features extracted from the sparse TFIs more accurate for representing differences between the samples with different fault severity degrees.  
Moreover, the results of the first two tests were better than those of the third one, because the third test was on the ball fault severity recognition. The vibration signals of ball faults were more complex than those of outer race and inner race faults, making the TFIs more complex. Consequently, it was more difficult to use texture features to characterize the signals and obtain better classification accuracy in Test 3 compared with Tests 1 and 2. Nevertheless, the classification accuracy of LS-SVM using the STFA-PD-based texture features was 96.50% in Test 3. The WT-based texture features also yielded satisfactory results; however, they were not always as good as those obtained using the STFA-PD-based features.
# Geographically Weighted Flow Cross K-Function for Network-Constrained Flow Data
###### Abstract  
Network-constrained spatial flows are usually used to describe movements between two spatial places on a road network. The analysis of the spatial associations between different types of network-constrained spatial flows plays a key role in understanding the spatial relationships among different movements. However, existing studies usually do not consider the effect of distance decay, which may reduce the effectiveness of the detected bivariate spatial flow patterns. Moreover, most existing studies are based on the planar space assumption, which is not suitable for network-constrained spatial flows. To overcome these problems, this study proposed a new statistical method, the Geographically Weighted Network Flow Cross K-function, which improves the Flow Cross K-Function method by taking the distance decay effect and the constraints of road networks into account. Both global and local versions are extended in this study: the global version measures the overall spatial association and the local version identifies the exact locations where a spatial association occurs. The experiments on simulated datasets show that the proposed method can identify predefined bivariate flow patterns. In a case study, the proposed method is also applied to flow data comprising Xiamen taxi and ride-hailing datasets. The results demonstrate that the proposed method effectively identifies the competitive relationships between taxi and ride-hailing services.  
spatial association; spatial flow; human mobility; distance decay effect Article
# Galileo: The Added Value for Integrity in
## 5 Experimental Results
### Positioning Results
The statistical parameters of the velocity errors considering all of the solutions are summarized in Table 7. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Configuration** & **Max H (m/s)** & **Max up (m/s)** & **SD H (m/s)** & **SD up (m/s)** & **Mean H (m/s)** & **Mean up (m/s)** \\ \hline GPS noRAIM & 1.686 & 2.548 & 0.037 & 0.069 & 0.025 & \(-0.001\) \\ GG noRAIM & 1.680 & 2.566 & 0.043 & 0.065 & 0.023 & \(-0.001\) \\ GPS RAIM & 0.252 & 0.411 & 0.017 & 0.048 & 0.024 & 0.003 \\ GG RAIM & 0.256 & 0.244 & 0.015 & 0.043 & 0.021 & 0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Velocity error statistics considering all epochs. Figure 16: Number of pseudorange-rate observables excluded from the navigation solution as a function of time. Figure 17: Horizontal velocity errors as a function of time. Upper box: configurations without RAIM; lower box: configurations with RAIM. The vertical velocity errors are plotted as a function of time in Figure 18: the configurations without RAIM application are nearly coincident with a maximum error that exceeds 2 m/s. The RAIM algorithm reduces all of the error parameters, in particular the maximum errors are reduced by about five times. In the vertical case, the benefit of the Galileo inclusion is more evident than in the horizontal case. In order to have a fair comparison among the considered configurations, the CDFs of the horizontal and vertical velocity errors are shown in Figures 19 and 20, respectively. As for the position domain, three different time frames have been identified and used to compute the CDFs. In particular, the CDFs computed using all of the available epochs are shown in the upper boxes of the figures. In the central boxes, the CDFs using only reliable epochs are shown, and finally, in the lower boxes, the CDFs using only the common reliable epochs are provided. The figures show that all of the configurations have similar performance in the velocity domain: only a slight improvement can be appreciated using the GPS/Galileo multi-constellation solution together with the application of the RAIM algorithm. Statistical error parameters are computed using only reliable epochs in Table 8: the main advantage of RAIM application is the reduction of the maximum errors in both the horizontal and vertical channel. The maximum horizontal velocity error passes from 0.45 m/s, in the GPS-only case without RAIM, to 0.16 m/s applying the RAIM algorithm. The improvements in the multi-constellation case are even more evident: the maximum error is reduced almost six times, passing from 1.68 m/s to 0.24 m/s. Table 9 further investigates the velocity error statistics where only reliable epochs common to both GPS-only and GPS/Galileo configurations are considered. The results support the previous findings. Moreover, it is shown that, in certain cases, the inclusion of Galileo measurements can degrade the velocity solution. RAIM is required to screen out bad measurements possibly introduced by an additional constellation.