esa-sceva/satcom-chunk-collection huggingface dataset:
# An Accurate Direction Finding Scheme Using Virtual Antenna Array via Smartphones
## 5 Performance Evaluation
### Nonuniform Circular Array  
According to the discussion in Section 4.8, we set some comparative experiments to verify the VUCA system's robustness in a nonuniform circular array circumstance in the underground parking lot. First, we try to rotate our body naturally and stably during sampling, and we believe the angular velocity of our smartphone in these cases is approximately constant. Then, we detect the anchor points in the environment using both the regular VUCA system and the VUCA-gyroscope system. In Figure 13a, it shows the mean error of direction finding using two systems while \(L=5\) m, 10 m, 20 m, 30 m, \(\beta=0^{\circ}\), \(\phi=45^{\circ}\) and \(\theta=0^{\circ}\).  
Theoretically, the VUCA-gyroscope system should be better than the VUCA system by getting rid of the impact of the changing velocity of the smartphone. However, we find that there are not many differences between the performances of these two schemes in normal cases. We believe that adding the gyroscope to our system brings the increase in system calculation quantity. Thus, we need to reduce the number of sampling points to guarantee the response speed of our system. In addition, when we rotate our body, the smartphone itself may slightly spin in our hands. It will cause the output error of the gyroscope.  
After that, we also try to rotate our body in more unstable patterns during the VUCA system and the VUCA-gyroscope system sampling progress. For example, we stop rotating during sampling for a few seconds. Then, we detect the anchor points in the environment using both the VUCA system and the VUCA-gyroscope system. We set the same experimental variables with Figure 13a. However, through the observation of Figure 13b, we find that the accuracy of the VUCA system has been seriously affected under this particular circumstance, while the VUCA-gyroscope performs much better.  
Considering the efficiency and accuracy of the direction finding, we decide to utilize the VUCA-gyroscope system only when the standard deviation of the changing angular velocity during sampling is higher than the threshold we set. We let the experimenter try to rotate his body with the smartphone 50 times in natural patterns. After that, through the outputs of the gyroscope, we calculate the standard deviation of the shifty angular velocity during every sampling process. Finally, we plot the CDF of these results in Figure 14a. According to the extensive experiments, we decide to set the threshold value to 1 rad/s.  
Figure 13: (**a**) mean errors of the VUCA system and the VUCA-gyroscope system with different distance and stable motion patterns; and (**b**) mean errors of the VUCA system and the VUCA-gyroscope system with different distance and unstable motion patterns.
# Research on Scene Classification Method of High-Resolution Remote Sensing Images Based on RFPNet
## 5 Discussion
### Analysis in Relation to Dropout Rate  
In the Dropout strategy, the Dropout rate \(p\) is the probability for one neuron to be discarded, and the probability to be retained is _1-p_. The larger \(p\) is, the more features will be discarded during feature selection. The Dropout rates ranging from 0.2 to 0.8 were trained on the UC Merced dataset and the SIRI-WHU dataset respectively under the condition of keeping other parameters unchanged. The experimental results are shown in Figure 17. It can be seen that the classification accuracy of UC Merced dataset is the highest when the Dropout rate is 0.5. For the SIRI-WHU dataset, the classification accuracy is optimal when the Dropout rate is 0.8. It indicates that the optimal Dropout rate may not be the same when using the same model and different datasets.  
Figure 16: Schematic diagram of the classification accuracy corresponding to different learning rates.  
Figure 17: Schematic diagram of the classification accuracy corresponding to different Dropout rates.
# Connecting the Elderly Using VR: A Novel Art-Driven Methodology
## 3 Materials and Methods
### Evaluation Approach
#### 3.3.1 Prototype Questionnaire
\begin{table}
\begin{tabular}{p{14.2 pt} p{14.2 pt} p{14.2 pt}} \hline
**1.** & **Introduction (only for interviews)** & \(\bullet\) Could you tell me a little bit about yourself (where you were born, where you live, your family, your background) \\ \hline
**2.** & **Interaction with art (only for interviews)** & \(\bullet\) What do you expect from an art installation or a work of art? \\  & & \(\bullet\) List three spaces (urban, professional, domestic) that are (in your opinion) focused on art. \\  & & \(\bullet\) How do you think an art installation can make a positive contribution in social terms? \\ \hline
**3.** & **Seniors’ domestic space and art (focus groups and interviews)** & \(\bullet\) What do you think of your social environment? How do you feel in your town? \\  & & \(\bullet\) How to fight against loneliness at home? Could interior design have an influence on the feeling of loneliness? \\  & & \(\bullet\) What is your favourite room in your house and in which room do you spend the most time? (Ex: bedroom, living room, dining room, kitchen, bathroom, terrace, garden) \\  & & \(\bullet\) What types of feelings/sensations do you have when you are at home? \\  & & \(\bullet\) How would you redesign/modify your home to make you feel better and make it more welcoming and possibly age-appropriate? \\  & & \(\bullet\) If we are talking about objects that interact with you and react to your movements/emotions. What support would be interesting to use (ex: TV, radio, fridge etc). \\ \hline
**4.** & **Loneliness and social connections vs. isolation (only focus groups)** & \(\bullet\) What are you doing in your free time? What are your hobbies? \\ \hline \end{tabular}
\end{table} 
Table 1: Questions of the user needs collection phase. regarding art and interior design, the focus groups and, notably, the interviews provided valuable insights into critical facets of seniors' daily lives and mental well-being. Table 2 outlines the survey questions used to assess the experience of senior users while navigating Cap de Ballon either with a VR headset or in the desktop version. The evaluation targeted effectiveness metrics, ease of use, comprehension, system familiarity, and user satisfaction. Responses were quantified using the Likert scale.
## 7 Case Studies of Smart Cities
\begin{table}
\begin{tabular}{c c} \hline \hline
**Smart World** & **Examples of Services** \\ \hline Transportation & Intelligent parking, bicycle management, and ITS (intelligent transport systems) \\ \hline Healthcare & Mobile health/mHealth solutions, smart home care systems, remote monitoring systems for the elderly, chronically ill or disabled, and stay-at-home patient monitoring during a pandemic (i.e., COVID-19) \\ \hline Energy & Smart meters for electricity, gas, and water supply, smart grid services, and decentralized energy systems \\ \hline Building and Housing & Smart homes using sensors for heating, air conditioning, lighting, security \\ Infrastructure & systems, and other applications \\ \hline Education & Mobile learning applications and remote access \\ \hline Waste Disposal & Smart bins in homes, shopping centers, public buildings, and public spaces \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Examples of services for 5G-enabled smart cities.
# A Novel Hybrid-Copy Algorithm for Live Migration of Virtual Machine
###### Abstract  
Live migration of virtual machines is an important approach for dynamic resource scheduling in cloud environment. The hybrid-copy algorithm is an excellent algorithm that combines the pre-copy algorithm with the post-copy algorithm to remedy the defects of the pre-copy algorithm and the post-copy algorithm. Currently, the hybrid-copy algorithm only copies all memory pages once in advance. In a write-intensive workload, copy memory pages once may be enough. However, more iterative copy rounds can significantly reduce the page faults in a read-intensive workload. In this paper, we propose a new parameter to decide the appropriate time to stop the iterative copy phase based on real-time situation. We use a Markov model to forecast the memory access pattern. Based on the predicted results and the analysis of the actual situation, the memory page transfer order would be adjusted to reduce the invalid transfers. The novel hybrid-copy algorithm is implemented on the Xen platform. The experimental results demonstrate that our mechanism has good performance both on read-intensive workloads and write-intensive workloads.  
l 4 June 2017; virtual machine; hybrid-copy; Xen +
Footnote †: journal: Future Internet
# A Novel UWB Positioning Method Based on a Maximum-Correntropy Unscented Kalman Filter
## 3 Proposed Algorithm Based on the Maximum-Crentropy UKF  
In this paper, based on the UKF, we first obtained the predicted state information and the covariance matrix through traceless transformation. Then, the nonlinear regression method based on the maximum cross-correlation entropy criterion was used to reconstruct observation information, which enhanced the robustness of the unscented Kalman filter algorithm for heavy-tailed noise.
# Classification of Hyperspectral Images Based on Supervised Sparse Embedded Preserving Projection
## 4 Experimental Results and Analysis
### Indian Pines Dataset
An SSEPP algorithm was always better than an SPP algorithm because SSEPP indicated the sample label and minimized the same class sample spacing, features that were very helpful for feature extraction. Before 7-dimension, the classification effect of the PCA algorithm was better than SSEPP, but after 7-dimension, the proposed algorithm was always better than PCA. After feature extraction reached 8-dimension, the SSEPP method was always optimal. This verifies the effectiveness of the algorithm.
# Using Low Salinity Waterflooding to Improve Oil Recovery in Naturally Fractured Reservoirs
###### Abstract  
Low salinity waterflooding is an effective technique to accelerate and boost oil recovery. The impact of this technique has been investigated widely in laboratories for various scales and rock typing, most of which have demonstrated a potential improvement in oil recovery. This improvement has been attributed to several chemical and physical interactions that led to a change in the wettability to become more water-wet, as well as a reduction in the residual oil saturation. Meanwhile, it is rare to find a discussion in the literature about the efficiency of low salinity flooding in naturally fractured reservoirs. Therefore, in this work, we investigate the potential advantages of this method in fractured reservoirs using numerical simulations. A new approach to estimate the weighting factor using a tracer model has been proposed to determine the brine salinity and, hence, its properties in the mixing region. We have also used the relative permeability curves as a proxy for any physical and chemical mechanisms which are not represented explicitly in the model. The simulation outcomes highlighted the advantage of low salinity waterflooding in fractured reservoirs. An increment in oil recovery by 10.7% to 13% of Stock Tank Oil Initially In Place (STOIIP) was obtained using the dual- and single-porosity model, respectively. Therefore, the low salinity waterflooding technique represents a promising low-cost, effective method in fractured reservoirs.  
n
n  
naturally fractured reservoirs; low salinity waterflooding; weighting factor; wettability alteration; residual oil saturation reduction
## 4 Experimental Study of the CAFB Using a Model Bridge
### FE Analysis of Model Bridge  
Prior to performing the response test of the model cable-stayed bridge using the CAFB-based IDAQ system, an eigenvalue analysis was performed through detailed FE modeling. Figure 8 and Table 1 show the model bridge and its detailed specifications used for the FE analysis and response test.  
Figure 8: Design of a unified view of the model bridge.  
In order to analyze the structural dynamic properties of the produced model bridge, in this paper, FE modeling-based numerical analysis was conducted. For this purpose, I-DEAS from UGS, a commercial structural analysis program, was used, and a three-dimensional detailed FE model was organized by applying the physical properties of Table 1. Here, a 1D beam element was considered for the bridge deck and the tower of the structure, the rigid element was considered for the bridge deck boundary conditions, a 1D rod element was considered for the cable; and, as for the floor beam and lumped mass, a 1 kg-size of lumped mass was considered for each of the 39 nodes, which are the positions of the floor beams on the area of the central bridge deck excluding the pylon and both sides. Next, as for the boundary condition, a clamp was considered for the substructure of the pylon, a roller was considered for both sides of the superstructure and the right-side pylon connection, and a pin was considered for the left-side pylon connection. Next, as for the degree of freedom condition of the structure, the DOF was given in the direction of \(y\)-axis with the 39 nodes on the central bridge deck as the base to complete the three-dimensional detailed FE model. Finally, considering that the sensor position can be determined later by the target mode response, an eigenvalue analysis was performed using the Guyan reduction method Guyan (1970), which can analyze the modal characteristics based on the finite DOF. The detailed FE model completed for the model bridge is shown in Figure 9 and the eigenvalues for the three low-level bending modes are shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Characteristic** & **Dimensions** \\ \hline Total/main span length & 4.22 m/2.22 m \\ Superstructure width/Tower heights & 0.17 m/1.00 m \\ Tower boundary conditions & Roller and hinge \\ Bridge end boundary conditions & Roller \\ Bridge material & Structural steel \\ Cable material & Spring (\(k=1.03\) N/mm) \\ Concentrated load & 1 kg (39 EA) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Design details of the model bridge.  
Figure 9: FE modeling of the model bridge.
## 2 Extreme Learning Machine (ELM)-Based Moving Cast Shadow Detection
### Classification Using Extreme Learning Machine  
After feature extraction, the objective of the next step is to assign each pixel to one of two categories: shadow and object. The ELM [43; 44; 45; 46; 47; 48] is adopted for classification, which has been proven to be effective and efficient in addressing a variety of classification problems. Next, the classification procedure using the ELM algorithm is described in detail.  
Suppose that a training set is denoted as \(\{(x_{i},y_{i})\}_{i=1}^{N}\) with \(N\) samples and \(C\) classes, where \(x_{i}=[x_{i1},x_{i2},\cdots,x_{id}]\in\Re^{d}\) is the \(i\)th training sample with \(d\) dimensions. \(y_{i}=[y_{i1},y_{i2},\cdots,y_{iC}]\in\Re^{C}\) is the \(C\)-dimensional label vector of \(x_{i}\). Note that if \(x_{i}\) belongs to the \(j\)th class, then \(y_{ij}=1\). Otherwise, \(y_{ik}=0(k\
eq j)\). The training set \(\{(x_{i},y_{i})\}_{i=1}^{N}\) is utilized to train the single hidden layer feedforward neural network (SLFN). Here, the network contains \(d\) inputs, \(L\) hidden-layer neurons and \(C\) outputs.  
The output function of the ELM [43] is expressed as:  
\[f_{L}(x)=\sum_{i=1}^{L}w_{i}h_{i}(x)=h(x)w \tag{12}\]  
where \(w=[w_{1},w_{2},\ldots,w_{L}]^{T}\) is the output weight vector connecting the hidden layer nodes and the output nodes \(h(x)=[h_{1}(x),h_{2}(x),\ldots,h_{L}(x)]\) is the output vector of the hidden layer for the input \(x_{i}\) which is also called ELM non-linear feature mapping [43].  
To obtain the weights \(W\in\Re^{L\times C}\) connecting the hidden layer nodes and the output layer nodes, the sum of squared loss for the prediction errors is minimized by:  
\[\min_{W\in\Re^{L\times C}}\lVert HW-Y\rVert^{2} \tag{13}\]  
where \(\lVert\cdot\rVert\) represents the Frobenius norm and \(H=[h(x_{1});h(x_{2});\ldots;h(x_{N})]\in R^{N\times L}\) is the output matrix of the hidden layer and \(Y=[y_{1},y_{2},\ldots,y_{N}]^{T}\in R^{N\times C}\) is the target matrix of the training set (i.e., labels of the training samples) [43]. Consequently, the output weights \(W\) can be rewritten as follows:  
\[W=H^{\dagger}Y \tag{14}\]  
where \(H^{\dagger}={(HH^{T})}^{-1}H\) is the Moore-Penrose generalized inverse of matrix \(H\).  
Given a new sample \(x_{N+1}\in\Re^{d}\), its network response \(o_{N+1}\) is generated by the obtained network output weights W.  
\[o_{N+1}=W^{T}\varphi(x_{N+1}) \tag{15}\]  
where \(\varphi(x_{N+1})\) is the network hidden layer output for \(x_{N+1}\).  
For the binary classification applications, the decision function of ELM is:  
\[y_{N+1}^{*}=\operatorname*{arg\,\,}_{x_{N+1}\in\{1,2,\cdots C\}}\max(o_{N+1}),C=2. \tag{16}\]
## 3 Results
### Measurements with PSV Ventilation Mode  
In Figure 5, the measurement of interactions between simulated spontaneous breathing and the PSV mode of the mechanical ventilator are depicted. Similarly to the V/A-C mode, the mechanical ventilator is not fully in phase with the lung simulator. The inspiratory time of the lung simulator does not surpass the set maximum inspiratory time threshold of the PSV mode, leading to an intended interaction for all phases during the measurement. However, the delivered peak volume during the active spontaneous sinusoidal breathing Phases 1 increases significantly (\(917.09\pm 45.74\) vs. \(468.40\pm 31.79\) mL, \(p<0.01\)) compared to apnea Phase 2. With a total amount of 12 asynchronies during the observed time span, the asynchrony index for both phases in PSV mode is 50%. The effect of asynchronies can be seen in the increased inspiratory flow of both the lung simulator and mechanical ventilator.  
Figure 5: Pressure, flow and volume tracings during simulated apnea (SA) phase. The xPULM(tm) does not generate any airflow in this phase, and the mechanical ventilator BellaVista(tm) is overtaking the entire ventilation process using the V/A-C mode (**left**) and the PSV mode (**right**).
# A Novel Approach for Cognitive Clustering of Parkinsonisms through Affinity Propagation
## 3 Results
### Cluster Analysis
The exemplars' demographic values and eleven MMSE subscale scores (as a vector: [TO, OS, Reg, AC, Rec, N, SR, P, W, CE, D]) were:  
* Cluster #1: CTRL, female, age 59, education 16, MMSE subscales = [5, 5, 3, 5, 3, 2, 1, 3, 1, 1, 1];
* Cluster #2: PD, female, age 61, education 8, MMSE subscales = [5, 5, 3, 5, 3, 2, 1, 3, 1, 1];
* Cluster #3: PD, female, age 70, education 5, MMSE subscales = [1, 2, 3, 1, 1, 2, 1, 3, 0, 0, 0];
* Cluster #4: PSP, male, age 78, education 8, MMSE subscales = [4, 4, 3, 1, 1, 2, 1, 3, 0, 1, 0]. Cluster #1 was comprised of 68 participants (33 females), Cluster #2 of 30 (14 females), Cluster #3 of 8 (5 females) and Cluster #4 of 34 (17 females). Figure 5 depicted the distribution of each of the four cluster in terms of the number of CTRL participants, PD and PSP patients. The demographic and cognitive characteristics of each cluster were shown in Table 3, together with the results of statistical analyses and pairwise post-hoc results. The four clusters did not differ in the sex distribution, while Cluster #1 was younger and with the highest level of education than the other three clusters and comprised the highest number of CTRL (32). Moreover, Cluster #3 and #4 did not include any CTRL participant, but only PD and PSP patients. Figure 4: Radar plots of the mean of the eleven MMSE subscales per cluster of Progressive Supranuclear Palsy (PSP) patients. In gray and dotted lines the maximum value that each MMSE subscale could reach: Max(TO) = 5, Max(OS) = 5, Max(Reg) = 3, Max(AC) = 5, Max(Rec) = 3, Max(N) = 2, Max(SR) = 1, Max(P) = 3, Max(CW) = 1, Max(CE) = 1, Max(D) = 1. Abbreviations: TO = temporal orientation; OS = orientation in space, Reg = registration of three words; AC = attention and calculation; Rec = recall of three words; N = object naming; SR = sentence repetition; P = praxis; W = writing a sentence; CE = reading a sentence and close your eyes; D = copy a drawing. Figure 5: Distribution of diagnoses among the four clusters found by affinity propagation. Cluster #1 consisted of 32 CTRL, 19 PD and 17 PSP. Cluster #2 consisted of 12 CTRL, 10 PD and 8 PSP. Cluster #3 consisted of 2 PD and 6 PSP. Cluster #4 consisted of 17 PD and 17 PSP. Abbreviations: CTRL = healthy control; PD = Parkinson’s disease patients; PSP = Progressive Supranuclear Palsy patients. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Cluster \#1 (68)** & **Cluster \#2 (30)** & **Cluster \#3 (8)** & **Cluster \#4 (34)** & \(p\)**-Value** & **Post-Hoc** \\ \hline Age & \(62.4\pm 10.1\) & \(71.3\pm 9.94\) & \(70.4\pm 7.93\) & \(69.8\pm 7.64\) & \(<\)0.001  & \(1<2.4\)2  \\ Female, n & \(33\) & \(14\) & \(5\) & \(17\) & N.S. 3  & N.A.
# The Effect of Blade Angle Deviation on Mixed Inflow Turbine Performances
###### Abstract
[38] tested seven designs of inflow mixed turbines with different numbers of blades from 3 up to 15 that have been considered for investigation and obtained porch results at [PERSON]'s work. [PERSON] et al. [39] based on one-dimensional analysis and empirical energy loss models to determine the optimal mixed inflow turbine rotor dimensions. [PERSON] et al. [40] established a specific procedure for mixed flow turbine rotor design. [PERSON] et al. [41] optimized the hub and shroud by changing the position coefficient introduced into the Bezier polynomial to obtain better performance. [PERSON] and [PERSON] [35] developed a relationship (1) between cone angle, inlet blade angle, and camber angle. \[\tan(\beta_{2b})=\cos(\lambda)\;\tan g(\phi) \tag{1}\]  
[PERSON] et al. [42] studied the effect of cone and inlet blade angles on mixed inflow turbine performance in two cases, the first for a fixed outlet section volume and the second for a variable outlet section volume which was parallel to the rotor leading edge, some geometric parameters were fixed to keep the same rotor casing. Authors [43] analyzed the three-dimensional accelerating flow in a mixed turbine rotor. The studies [44,45] include research to ensure high-quality mechanical machining of turbine blades for regular operation. Thus, the analysis of the current state of the issue shows a high interest in the research and manufacture of mixed-flow turbines due to their great promise. However, the complexity of the ongoing processes does not yet allow one to unambiguously determine the physical, technological and design parameters of products. In particular, the relationship between the deviation blade angle and the shape of the rotor, and the performance of mixed-flow turbines has not been sufficiently studied. However, the importance of this parameter has been emphasized in many works. Therefore, the search for optimal and rational parameters of turbines must be carried out at the design stage since the manufacture of turbines is an expensive process. This task can be accomplished by modeling the processes and structures of the developed turbines on digital models. Therefore, the present work aims to create a digital model of a mixed flow turbine and to analyze the effect of the deviation blade angle on the rotor shape and on the performance of a mixed flow turbine at various rotation speeds, which provide 50%, 75% and 100% performance. The main goal of the work is to increase the turbine's efficiency by increasing power and reducing the weight of its rotor. The body parameters remain unchanged.