esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Spatial Autocorrelation Analysis  
Spatial autocorrelation represents a statistical approach used to measure the distribution characteristics and mutual relations of spatial data. There may be some dependence or similarity between data values in adjacent or close positions, and this dependence or similarity weakens or disappears as the distance increases. From a spatial perspective, global and local Moran's I were used in the paper to analyze the differences in the distribution characteristics of per capita SIUS among different prefecture-level cities in Henan Province. In 1950, Moran introduced the global Moran's I for the first time [31]. This statistic depicts the spatial autocorrelation across the entire research region. The formula for the global Moran's I statistic is given below.  
\[I_{G}=\frac{\sum_{i=1}^{n}\sum_{j=1}^{n}\int_{ij}\left(x_{i}-x\right)\left(x_{ j}-\overline{x}\right)}{S^{2}\sum_{i=1}^{n}\sum_{j=1}^{n}W_{ij}} \tag{2}\]  
where \(I_{G}\) indicates the global Moran's index; \(W_{ij}\) is the value of each element in the spatial weight matrix; \(\overline{x}\) is the average value of the attribute values of all cities; and \(W\) is the sum of the spatial weights.  
The value of Moran's I varies between \(-1\) and \(1\) and is positively spatially correlated when it is greater than \(0\). The greater the value, the more pronounced the spatial correlation. When the value is less than zero, it indicates a spatially negative correlation [32]. The Z-score tests the significance of this index. If the Z-score is close to \(0\), there is no significant clustering. A positive Z-score indicates clusters with high attribute values and vice versa for clusters with low attribute values. However, only when the outcome of the significance test shows \(p<0.05\) does it imply that the value of the research target exhibits significant spatial autocorrelation. In this case, the value of [PERSON]'s I is statistically significant.  
To improve the accuracy of the identified region, [PERSON] (1995) put forward the local Moran's index (\(l_{i}\)), which effectively evaluated the correlation between each area and adjacent areas [33]. The formulas are given below.  
\[I_{i}=\frac{\left(x_{i}-\overline{x}\right)}{S^{2}}\sum_{j}W_{ij}(x_{i}- \overline{x}) \tag{3}\]
# Design and Verification of a New Fixture for Machining of Porous Blocks for Medical CAD/CAM Systems
###### Abstract  
This paper presents a new innovative approach for designing and manufacturing a fixture for locating and clamping porous blocks of biocompatible material, which is required for their machining on CNC machines. Manufacturing porous blocks for their application in medical and/or dental fields is gaining traction. However, limited solutions are available today. In order to address this issue, a new design has been proposed for locating and clamping porous blocks. Finite element analysis was used as a verification tool for the designed fixture with the workpiece, which showed a low concentration of stresses. After the manufacturing, dimensional verification in the form of CAD analysis showed small deviations on the manufactured object with deviations peaking around +0.015 mm, thus validating the adequate locating and clamping of the workpiece.  
Keywords:fixture; design; porous block; CNC machining; FEM +
Footnote †: journal: Applied sciences
# Effect of Different Local Vibration Durations on Knee Extensors' Maximal Isometric Strength
###### Abstract  
The prolonged application (>20 min) of local vibration (LV) on muscles or tendons is known to reduce maximal isometric strength. However, the effect of short vibration durations (\(\leq\)6 min) is still unknown. In fourteen participants, the changes in maximal voluntary isometric contraction (MVIC) were measured after 1, 3, and 6 min of rest (CONT) or local vibration (LV) over the quadricipital tendon (frequency: 100 Hz; amplitude: 0.5 mm). Before and after each condition, the amplitude of the twitch induced by a 100 Hz potentiated electrical doublet (PD\({}_{\rm POT}\)); the relative electromyographic activity of the vastus medialis and rectus femoris muscle during the MVIC (RMS\({}_{\rm MVIC}\).M\({}^{-1}\)); the torque developed 50 ms after the onset of contraction (T\({}_{50}\)); and the voluntary activation level (VAL) were evaluated. None of the three LV durations significantly changed the MVIC compared with the control condition (\(p\) = 0.379). The indices of central (i.e., VAL, T\({}_{50}\), RMS\({}_{\rm MVIC}\).M\({}^{-1}\)) and peripheral (e.g., PD\({}_{\rm POT}\)) fatigue were unaffected (\(p\) > 0.147). In conclusion, a short-duration LV (\(\leq\)6 min) on a voluminous muscle group does not impair maximal force production or induce any central or peripherical fatigue.  
local vibration; maximal voluntary isometric strength; knee extensor +
Footnote †: journal: Applied Sciences
## 3 Modeling the VSG Control Strategy
### Implementation of VSG Control  
At present, designs for VSG control strategies include implementations that can be categorized into two types that address either the output or topology. Regarding the output type, the VSG implementation can be divided into a controlled current source [11; 21; 22; 23; 24; 25; 26] and a controlled voltage source [12; 13; 27; 28]. The foundation of conventional power systems features synchronous generators as the voltage source. Consequently, they are not able to integrate a large quantity of renewables into the power network as current sources. Regarding the topology type, the VSG implementation can be divided by order, such as a seventh full order [11; 21; 22], a fifth full order, a reduced fourth order [23; 24; 25; 26], a second order [13; 18], and a first order [12; 27], as shown in Table 1.  
Among the numerous research on VSG implementation, the concept based on second-order model, which includes the swing equation and voltage amplitude given by a reactive power controller, is simple in that can be combined with virtually any control scheme for the converter [28]. A higher-order model may bring unnecessary complexity and difficulty for model implementation. It has also been demonstrated that during normal and abnormal operating conditions, low-order VSM algorithms are more stable than high-order algorithms [29]. The basic schematic of a VSG-control-based power converter is shown is Figure 6, where the DC side can be assumed as an ideal source with infinite capacity.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Model Implementation** & **General Comments** \\ \hline \multirow{2}{*}{Seventith order} & Electric, magnetic, swing equation of stator & Full representation of SG dynamics. \\  & and rotor winding & \\ \hline \multirow{2}{*}{Fifth or fourth order} & Electric, magnetic, swing equation of stator winding & Only stator winding is considered \\  & & stator winding & compared to seventh-order model. \\ \hline \multirow{2}{*}{Second order} & Swing equation and voltage amplitude given & Simple scheme for converter \\  & by the reactive power controller & implementation for a close imitation. \\ \hline \multirow{2}{*}{First order} & Inertial emulation by power response & \\  & calculated from grid voltage tracking & Incapable of isolated operation. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification of VSG modeling. SG: synchronous generators.  
Figure 5: Parallel operation for an oncoming generator that needs to compare and adjust its parameters in order to match those of the running system.  
Magnetic fields are the media for mechanical-electric energy conversion in synchronous machines. Considering electromagnetic transient emulation for specific scenarios (self-synchronization), a \"synchroverter\" can be modeled by the combination of equations (1) to (4) [18]. Further studies have modified the basic VSG model for better stability by designing parameters for virtual inductors and capacitors [30], small signal analyzzation [31], and alternative/self-tuning inertia during dynamic response processes [10].
## 2 Materials and Methods
### Data Analysis  
The analysis was carried out after collecting the data in a spreadsheet. SPSS (version 26) was used for the analysis. First, a one-sample Kolmogorov-Smirnov (K-S) test was performed for height and diameter to determine if the variable followed a normal distribution or not. The diameter was found to be normally distributed (\(p\) = 0.200), while the height was not normally distributed (\(p\) = 0.025). To compare the difference between the mean diameter among genders, an unpaired t-test was performed to check if the difference in the mean was statistically significant. In order to check whether the difference in length of the CT is statistically significant, the Mann-Whitney U test was performed. To check whether there was statistical significance in the difference between the diameter of the CT among three age groups (i.e., 29 years and below, 30-39 years, and more than 40 years) one-way ANOVA was used. To compare the length of the CT among these age groups, the Kruskal-Wallis test was performed.
## 3 Development of SoS Metamodels and Ontologies
### Extending SoS Ontologies Based on Flow
#### 3.3.3. SoS Operational Ontology (SoSOO)  
As the SoSCO emphasizes the relationship between capabilities (what), then it is necessary to make a description of the performers (who) can exhibit capabilities and activities (how) that can support capabilities. To this end, the SoS operational ontology (SoSOO) is defined as shown in Figure 6. The SoSOO illustrates the refinement of missions by mapping structure and behavior. The boxes with dashed lines represent the flows.  
OperationalPerformer is a logical entity that executes OperationalActivities to manipulate resources. ConceptRole denotes a role played by an asset or location in an SoS operational context [29]. OperationalContext provides a visual connection between high-level operational concepts. Although linking the operational structure with capabilities is straightforward, such as OperationalPerformer exhibits Capability, such mappings are too simplified to reflect the underlying relationships between operational ontologies. Hence, OperationalActivity is used to refine missions in the operational domain and support capabilities (mapsToCapability). Since capabilities specify their expected behaviors (OperationalActivity), and structures (OperationalPerformer) realize capabilities through the behaviors they can perform, matching the behaviors improves the mapping between capabilities and operational structure. The proposed flow-based representation introduces various flows in the SoS operational architecture to bridge structure and behavior. OperationalExchange asserts the flow's existence between OperationalPerformers, which can be classified as follows: OperationalActivityEdge denotes the flow of resources (objects/information) between OperationalActivity; Transition indicates the state change of a single OperationalPerformer under the influence of the OperationalExchange with other OperationalPerformers and the internal OperationalActivityEdge; OperationalMessage shows the execution sequence of OperationalExchange among multiple OperationalPerformers in operational interaction scenarios.
## 5 Conclusions  
The problem of identifying the presence or the absence of residual brain tumor in iUS image data was addressed in this work. Our hypothesis is: (1) residual tumorous tissue is most of the time located beyond the borders of the resection cavity, which is well visible in B-mode modality and (2) tumor tissue is highlighted in B-mode and CEUS modalities. Firstly, the approach consists of extracting relevant information from the iUS image data. Moreover, secondly, it allows keeping possible tumor remnants using image fusion techniques. Two kinds of evaluation were performed, i.e., in terms of region localization containing the tumor residuals and in terms of the voxel being correctly classified. The experiment showed that the method was able to successfully localize brain regions, which possibly include tumor residuals for 15 out of 19 patients (Set A). Average values of the accuracy, the area under the ROC curve and the error rate were 0.9507, 0.7351 and 0.0493, respectively. A better characterization of the tumor residuals including texture descriptors, for example, and additional intraoperative ultrasound modalities should improve the performance of the new automatic approaches. Our approach represents a considerable advance in the computer-assisted surgery field for automatic detection of residual brain tumors. Nevertheless, at this stage, it is important to note that the method was tested \"offline\", and it is still far from clinical application. Future works will focus on method improvements and on its validation of a large patient database.  
This work has been supported by the National Council of Science and Technology of Mexico (CONACYT) under Grant Number 493442. The authors would like to thank the department of neurosurgery, University Hospital Leipzig, for the clinical study and data collection in the context of a previous research project funded by the German Research Society (Deutsche Forschungsgemeinschaft). The University of Guanajuato, Engineering Division, Campus Irapuato-Salamanca, is recognized for providing the necessary funds for covering the costs to publish in open access.  
[PERSON] and [PERSON] designed the project. [PERSON] performed the data acquisition on patients during brain tumor surgeries. [PERSON], [PERSON], [PERSON] and [PERSON] developed and implemented the image processing and visualization tools to address the problem. [PERSON] and [PERSON] contributed equally to the paper writing.  
The authors declare no conflict of interest.
## 3 Results  
### Evaluation of Estimation Precision  
At present, there are two kinds of accuracy evaluation methods: one is based on field observation data; the other is comparative analysis and evaluation based on the research results recognized by peer experts. It is difficult to verify the field observation data of _NPP_ or _NEP_ with a large range and multiple vegetation types due to the large scope of the study area. The acquisition cycle of field observation data is long, and the acquisition time is difficult to match the model simulation time. The two are not comparable to a certain extent due to the influence of climate change and other factors. Based on the above considerations and the analysis of relevant literature within the research area, the ZGS model with apartially overlapping research area and verified accuracy was selected. Comparing the _NEP_ value converted by the MOD17A3 product and ZGS model, it is found that the _NEP_ value converted by the MOD17A3 product is smaller than that produced by the ZGS model. The fitting results are shown in Figure 2. There is a good correlation between the product data value and the model data value, with the determination coefficient \(R^{2}\) = 0.8479, and the fitting curve is significantly correlated (\(p\) < 0.01), indicating that the _NEP_ data converted by MOD17A3 product is reliable.
## 3 Results
### Frequency Dependence of the Angular Scan Gradient
In the high-frequency regime (above 17.5 kHz, for the data set presented in Figure 8) the secondary field has purely surface character, i.e., it is orthogonal to the surface as shown in Figure 1. Our results confirm that the signal phase generated by materials with high electric conductivity, e.g., copper, aluminium, brass is the same, although still significantly different from that produced by objects with high magnetic permeability, e.g., ferrite as in Figure 6. The phase measurement provides the best discrimination between different object compositions in the low-frequency region, where it shows the highest sensitivity to electric conductivity and magnetic permeability of the object. Figure 4a shows that the gradient of the inductive signal angular dependence decreases with lift-off. For each lift-off, the same frequency dependence of the angular scan, as shown in Figure 8, was recorded. Based on these results, we extract the gradient of linear frequency dependencies as a function of the lift-off value. Figure 9a shows that these gradient values decrease with the increasing lift-off in the same way for all materials (some of the points overlap). The red dotted curve is an exponential fit to the copper data describing the decay in the primary field at the object with lift-off. The dependence as represented in Figure 9a could be used to calibrate the detection system, and subsequently, the gradient of the linear part of the angular scans frequency dependence shown in Figure 8 could be used as an estimation of lift-off. While the gradient of the high-frequency part of the frequency dependence provides an estimation of the actual distance between the object and the primary rf field source, the offset of this dependence is indicative of the skin depth to object thickness ratio, Figure 9b. It is worth pointing out that, as a consequence of the decreasing signal amplitude, the dynamic range of the offset variation decreases with increasing lift-off. To summarise this part, object composition identification with arbitrary relative orientation and lift-off would require initial calibration of the system, i.e., measurement of the angular gradient's frequency dependence, Figure 9a, and calibration of the offsets for a few lift-off values, Figure 9b. Actual testing of the object through local measurement of the frequency dependence of angular gradient might be combined with phase measurement at a low operating frequency. Changing the operating frequency of the atomic magnetometer through adjustment of the bias field is relatively straightforward and was already demonstrated in the case of continuous and selective tuning [4].
## 6 Performance Evaluations
### Simulation Results
#### 6.3.4 Average End-to-End Delay Evaluation  
A further series of experiments are carried out in this part to evaluate the suggested technique in terms of end-to-end latency. Comparisons are made between the proposed method and SMRP [15] and EERP [23] for the above scenarios under different traffic rates and a number of sink nodes.  
Figure 14: Influence of increasing average traffic rate on network lifetime under the second scenario.
## 2 Materials and Methods
### Image Analysis
#### 2.3.3 Application in Pathological Cases  
The validation of the proposed metric and quantitative diagnostic scale was carried out by applying the algorithm to the cases of the second (CTEPH) and third (respiratory pathologies) group. The ventilation and perfusion volumes were extracted based on the previously defined optimal segmentation thresholds and their difference was calculated. Following the scale shown in Table 1, each case was automatically classified based on its \(V_{V\text{-}P}\):  
* Volume difference values between ventilation and perfusion, which were above the \(Mean(V_{V\text{-}P,\text{ }r\text{/}r\text{/}})\) value, indicated a positive diagnosis;
* Volume difference values between ventilation and perfusion, which were below the \(Mean(V_{V\text{-}P,\text{ }r\text{/}})\) value and above the \(Min(V_{V\text{-}P,\text{ }r\text{/}})\) value, indicated suspicion of CTEPH or respiratory pathology, whose diagnosis could not be ruled out, but required a complementary visual analysis to confirm the positive diagnosis of either CTEPH or a respiratory pathology;
* Volume difference values between ventilation and perfusion, which were below the \(Min(V_{V\text{-}P,\text{ }r\text{/}})\) value, indicated a negative diagnosis.  
To evaluate the accuracy of the algorithm, a 3-class confusion matrix was generated and the overall accuracy, as well as the sensitivity and specificity of each class were calculated. A case was considered correctly classified if the algorithm confirmed or could not rule out a positive diagnosis in one of the two lungs of the patients.  
Finally, the volumetric analysis of SPECT images was applied to the evaluation of treatment response in CTEPH. Given that the treatment aims to correct the obstructed segments and improve perfusion, the volume after the treatment (\(V_{post}\)) should be higher than before (\(V_{pre}\)). Therefore, the difference of perfusion volumes (\(V_{post\text{-}pre}\)) was calculated and compared to the patients' response.
# Study on Band Gap and Sound Insulation Characteristics of an Adjustable Helmholtz Resonator
## 3 Modeling and Analysis of Low-Frequency Band Gap Formation Mechanism
### Establishment of Equivalent Model
In the above equivalent model, the expressions of the lower limits of the first and second band gaps\(f_{\text{down1}}\) and \(f_{\text{down2}}\) are:  
\[f_{\text{down1}}=\frac{1}{2\pi}\sqrt{\frac{k_{2}}{m_{2}}}=\frac{1}{2\pi}\sqrt {\frac{\rho_{air}c^{2}S^{2}}{m_{2}V_{2}}} \tag{12}\]  
\[f_{\text{down2}}=\frac{1}{2\pi}\sqrt{\frac{k_{1}}{m_{1}}}=\frac{1}{2\pi}\sqrt {\frac{\rho_{air}c^{2}S^{2}}{m_{1}V_{1}}} \tag{13}\]  
For point B and point D, as shown in Figure 7b,d, the sound pressure field is distributed in the outer cavity, cavity II, the tube of cavity II, cavity I and the tube of cavity I. According to the method of electro-mechanical-acoustic analogy, the air in the cavity I, cavity II and outer cavity can be equivalent to air springs, and the air in the tubes can be equivalent to oscillators. The corresponding \"spring-oscillator\" model is shown in Figure 9.  
According to the above model, the stiffness matrix expression is as follows:  
\[K_{B,D}=\left[ \begin{array}{cc}k_{2}+k_{3}&-k_{3}\\ -k_{3}&k_{1}+k_{3}\end{array} \right] \tag{14}\]  
The mass matrix of the oscillator is:  
\[M=\left[ \begin{array}{cc}m_{1}&0\\ 0&m_{2}\end{array} \right] \tag{15}\]  
Figure 8: (a) The equivalent model of point A; (b) the equivalent model of point C.  
Figure 9: The equivalent model of point B and point D.  
The equivalent model is a two-degree-of-freedom system. According to the vibration theory of a multi-degree-of-freedom system, the upper limit of the first and second band gaps \(f_{up1}\) and \(f_{up2}\) can be expressed as:  
\[\left(2\pi f_{up1,2}\right)^{2}=\frac{\left(K_{11}m_{2}+K_{22}m_{1}\right)\pm \sqrt{\left(K_{11}m_{2}+K_{22}m_{1}\right)^{2}-4m_{1}m_{2}(K_{11}K_{22}-K_{12} K_{21})}}{2m_{1}m_{2}} \tag{16}\]  
where \(K_{ij}\) is the element in the stiffness matrix \(K_{B,D}\). The results obtained by equivalent model calculation and simulation experiment are shown in Table 2 (the error is calculated by regarding the results of FEM as the real value). The correctness of the method has been verified in previous laboratory work [23; 24; 25]. It can be seen that the errors of the results of the equivalent model are small, which proves the correctness of the method. The main reasons for the errors of the two methods were analyzed: In the simulation experiment, when the length of the telescopic screw \(d\) was 25 mm, the volume of the cavity I was small, and the tube of cavity I was no longer a thin tube with a volume much smaller than the cavity I, which weakens the hypothesis of electro-mechanical-acoustic analogy of the Helmholtz resonator. Secondly, the effect of air compressibility in labyrinth tubes should be considered, which makes the calculation results of equivalent model appear to be errors. Finally, because of the folding and winding of labyrinth tubes, the width and length of the labyrinth tube are difficult to measure accurately, and the damping effect of labyrinth tubes on air also causes some errors in calculation.