esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The main problem with counting vehicles in rest areas in Europe and America is the overflowing of parking lots in the rest areas. Thus it is impossible to count parked vehicles with traditional, direct methods, which use cameras and Lidar (light detection and ranging) to detect the presence of vehicles in individual parking spots. The solution to this problem may be an indirect method which uses cameras to count the vehicles entering and leaving the rest area and which sorts the vehicles into categories. This article introduces a method for determining car park occupancy in rest areas using indirect measurement and evaluates the uncertainty of this method for determining the occupancy. This indirect method counts the vehicles entering and leaving the car park and sorts the vehicles into categories. The difference between the number of vehicles that entered and the number of vehicles that left in a given time gives the number of vehicles occupying the car park, i.e., the parking space demand for a given time. This demand will vary over time. In order to register the vehicles entering and leaving, mains-free supply video cameras were installed next to the entrance and exit. The counting and categorizing could be conducted organoleptically, semi-automatically, or using an image computer analysis using artificial intelligence networks. Because the difference between the number of vehicles entering and leaving is calculated over a long period, a relative error (regarding car park capacity) might be grave even when the counting errors are minor. The authors will show how the certainty of this indirect counting method can be improved.  
vehicle counting; car park occupancy; mains-free supply; measurement uncertainty; indirect method; AI application
## 3 Results and Discussion
### Effects of Loading Frequency on the Phase Angle  
When the asphalt mixture bears the dynamic load, a phenomenon occurs where the strain response lags behind the loading stress. The degree of this lag can be characterized by phase angle (\(\delta\)) (i.e., \(\delta\) represents the viscoelasticity of asphalt mixture). Similar to the dynamic modulus, the master curves of the phase angle can be also obtained by the shift factors. Figure 8 presents the master curves of phase angle of five groups of samples.  
As is shown in Figure 8, different from the master curves of dynamic modulus, the master curves of phase angle firstly increase with the increase of loading frequency to the highest point and then decrease with the further increase of loading frequency. This is because at low temperature, the asphalt mixture behaves as elastic and is mainly subjected to asphalt binder, so when temperature increases (or frequency decreases), the asphalt binder becomes soft and behaves as viscous, then leading to the increment of phase angle. At high temperature, asphalt mixture mainly behaves as viscous and the influence of the asphalt binder on the mixture becomes weak while the interlocking force between aggregates on mixture becomes distinct. Thus, with the further increase of temperature (or decrease of frequency), the phenomenon where the aggregates skeleton mainly bears the loading stress becomes more obvious, then leading to the decline of the phase angle. Additionally, compared with the dynamic modulus, the phase angle usually exhibits an overall upward or downward trend at one single temperature and mutation point. Therefore, the master curves of phase angle are not as smooth as that of dynamic modulus. Moreover, it can be seen from Figure 8 that the phase angle  
Figure 8: The master curves of phase angle of five groups of samples (reference temperature: 20 \({}^{\circ}\)C).  
of the first group (5.0% ACU and 5.0% ACL) are the smallest, indicating that the asphalt migration has compromised the aggregates skeleton and aggravated strain response lag. With the simulation method of the state of asphalt migration in this paper, this phenomenon can be better understood at the quantitative level.
## 3 Kinematic Parameter Identification Method  
### Identification Model  
In this paper, the actual kinematic parameters of the parallel robot can be identified by utilizing the measured pose data. These actual kinematic parameters include the position coordinates of the twelve Hooke's joints and the initial lengths of the six hydraulic cylinders. To facilitate the following kinematic parameter identification modeling, the nominal solution of the inverse kinematics of the parallel robot can be denoted by \(I_{ci}\) and expressed as follows:  
\[I_{ci}=f_{I}(p_{ci},\rho_{0}) \tag{16}\]  
where \(i\) = 1, 2, \(\ldots\), \(n\) is the selected pose number in the identification process, \(I_{ci}\) is the stroke length vector of the hydraulic cylinder, \(p_{ci}\) is the nominal pose vector of the mobile platform, and \(\rho_{0}\) is the nominal kinematic parameter vector of the parallel robot.  
Similarly, the nominal solution of the forward kinematics of the parallel robot can be denoted by \(p_{ci}\) and simplified as follows:  
\[p_{ci}=f_{F}(I_{ci},\rho_{0}) \tag{17}\]  
When the kinematic parameter errors produced by the manufacturing and installation processes are taken into account, the actual kinematic parameter vector of the parallel robot is denoted by \(\rho\). Hence, the calculated pose of the parallel robot is expressed as follows:  
\[p_{i}=f_{F}(f_{I}(p_{ci},\rho_{0}),\rho) \tag{18}\]  
The pose error of the parallel robot can be given by the following expression:  
\[e_{i}=p_{mi}-p_{i} \tag{19}\]  
where \(p_{mi}\) is the vector of the measured pose with a coordinate measuring machine.  
Measurement error is inevitable in the actual pose measuring experiment. Let \(p_{i}\)' be the nominal pose vector and \(\varepsilon_{i}\) be the measurement error vector for the \(i\)th measured pose. Thus, the vector of the measured pose can be expressed as follows:  
\[p_{mi}=p_{i}^{\prime}+\varepsilon_{i} \tag{20}\]  
By substituting Equations (18) and (20) into Equation (19), the pose error of the parallel robot can be expressed as follows:  
\[e_{i}=\left(p_{i}^{\prime}+\varepsilon_{i}\right)-f_{F}(f_{I}(p_{ci},\rho_{0} ),\rho) \tag{21}\]  
It is well known that the measurement error vector \(\varepsilon_{i}\) is uncertain. In addition, \(\varepsilon_{i}\) is changeable in each measuring process. For n = 7, although an approximate solution of the actual kinematic parameters of the parallel robot can be calculated, the solution only decreases local pose error and is sensitive to the measurement error. In order to improve the global pose accuracy of the parallel robot and reduce the sensitivity of the pose measurement error, the poses of more than 7 should be measured in the kinematic parameter identification process. Thus, the optimization method can be used to identify the actual kinematic parameters of the parallel robot. Let \(L\) be the optimization objective function, and take the minimum of the sum of squares of \(e_{i}\) as the objective function. The objective function \(L\) can be expressed as follows:  
\[L=minE=min\left\{\sum_{i=1}^{n}e_{i}^{T}e_{i}\right\} \tag{22}\]
###### Abstract  
This paper analyses mechanical property prediction through Machine Learning for continuous fiber-reinforced polymer matrix composites printed using the novel Material Extrusion Additive Manufacturing technique. The composite is formed by a nylon-based matrix and continuous fiber (carbon, Kevlar, or fiberglass). From the literature, the elastic modulus and tensile strength were taken along with printing parameters like fiber content, fiber fill type, matrix lattice, matrix fill density, matrix deposition angle, and fiber deposition angle. Such data were fed to several supervised learning algorithms: Ridge Regression, Bayesian Ridge Regression, Lasso Regression, K-Nearest Neighbor Regression, CatBoost Regression, Decision Tree Regression, Random Forest Regression, and Support Vector Regression. The Machine Learning analysis confirmed that fiber content is the most influential parameter in elasticity (E) and strength (\(\sigma\)). The results show that the K-Nearest Neighbors and CatBoost provided the closest predictions for E and \(\sigma\) compared to the other models, and the tree-based model presented the narrowest error distribution. The computational metrics point to a size versus prediction time tradeoff between these two best predictors, and adopting the prediction time as the most relevant criterion leads to the conclusion that the CatBoost model can be considered, when compared to the others tested, the most appropriate solution to work as a predictor in the task at hand.  
additive manufacturing; mechanical properties; composites; machine learning +
Footnote †: journal: Applied
# DPS Clustering: New Results
## 4 Results: Third Stage of DPS
### Logic and Action Plan  
A collection of spots \(C\subset\mathrm{DPS}_{\mathbb{C}}(X)\) is considered by the expert as a whole if the degree of advantage in the closeness of internal transitions (paths) between any of its spots over external transitions from \(C\) to \(\tilde{C}\), allows the expert to conclude that \(C\) is non-random.  
Thus, the cluster of spots \(C\) must have a tighter internal connection between them in the general background of all spots from \(\mathrm{DPS}_{\mathbb{C}}(X)\). There are no other constraints on \(C\), in particular on shape.  
Because of the \(\alpha\)-perfectness of the \(cX(\alpha)(k)\) spots, it is natural to consider the minimum distance between the closest points in them as the distance between them:  
\[d(cX(\alpha)(k),cX(\alpha)(\bar{k}))=\min\{d(y,\bar{y}):y\in cX(\alpha)(k), \bar{y}\in cX(\alpha)(\bar{k})\}. \tag{13}\]  
It will no longer be a metric since there are no triangle inequalities for \(d\) (13), as the example below shows:  
**Example 2**.: _Consider subsets of the real line: \(A=[1,1]\), \(B=[2,3]\), \(C=[4,4]\). It is obvious \(d(A,B)+d(C,B)<d(A,C)\), \(d(A,B)=|2-1|\), \(d(B,C)=|4-3|\), \(d(A,C)=|4-1|\)._  
Based on the distance \(d\), the formalization of the expert's logic for connecting spots is constructed: in each cluster of spots \(C\), any two spots can be connected by a chain of spots whose distance \(d\) between neighboring links will be smaller than the distance \(d\) from \(C\) to \(\tilde{C}\). This is the \(d\)-clustering condition necessary for \(C\). Its formal analysis will be the first part of the functioning program to algorithmise the third stage of the DPS. Note that \(d\)-clustering and DPS-clustering are different interpretations of clustering: the first is based on connectivity, and the second on limiting.  
The second part of this program consists of constructing, based on the initial distance \(d\) in the original space \(X\), measures of proximity and distance between spots expressing in this the expert. Based on these, only those spots are selected from the \(d\)-clusters of spots in which the proximity advantage of the inner transitions over the range of the outer ones will be non-random in the expert's opinion.
# A Few-Shot Learning-Based Material Recognition Scheme Using Smartphones
## 5 Performance Evaluation
### Performance Evaluation of FSMR
#### 5.2.1 Few-Shot Learning Evaluation
In contrast, with more data in the 10-way 15-shot configuration, the model achieved a decent classification accuracy of 68.15%. A particularly noteworthy result was observed for the five-way configuration. Figure 9: Material recognition accuracy of FSMR using different N-way K-shot configurations. Despite the moderate number of classes to classify, the average classification accuracy for all combinations of 10-shot and 15-shot exceeded 80%. This indicates that a five-way configuration is well suited for setting up the training dataset in few-shot learning. In the previous experiment, the maximum number of evaluation classes was 10. For the 3-way, 5-way, and 8-way configurations, experiments were conducted by randomly selecting 3, 5, and 8 classes from the 10 total classes, respectively. The results showed variations in accuracy depending on the specific combinations of randomly selected classes. Table 3 presents the classification accuracy achieved with different class combinations for the 5-way 10-shot configuration. Among the 252 possible combinations of selecting 5 classes out of 10, the best-performing combination achieved a high accuracy of 93.49%, while the worst-performing combination achieved a low accuracy of 61.07%. Objects such as Bluetooth speakers showed a lower accuracy in combinations that included them, as their small surface area caused significant waveform variations when the smartphone was rotated in place. Additionally, objects with soft, stable surfaces (e.g., blankets, sofas) and objects with hard, unstable surfaces (e.g., metal drawers) exhibited distinct waveform patterns. Combinations of objects with different material properties generally resulted in higher accuracy. Developing methods to minimize the performance differences caused by these class combinations could further improve the overall classification accuracy.
## 3 Results and Discussion
### Microopree Characteristics  
Figure 9 plots the differences in pore-size distribution among the specimens. In all specimens using silica fume and quartz powder, the maximum pore size tended to decrease as the C/S molar ratio increased, with pores in the radius range of 10 to 100 nm being the most frequently measured in all specimens. The QP specimens showed a clear tendency of decreasing pore diameter measured as peak values as the C/S molar ratio increased, reflecting the matrix-filling effect of the hydration products due to the pozzolanic reaction [31]. Figure 10 plots the cumulative pore volume of the specimens. In all specimens using silica fume and quartz powder, the pore volume tended to decrease as the C/S molar ratio increased. This result is similar to that of the strength characteristics of the specimens according to the C/S molar ratio.  
[PERSON] et al. emphasized the importance of pore-structure identification because the durability of hardened concrete is affected more by pore-size distribution than by total porosity [32; 33]. Figure 11 shows the pore-volume fractions of the specimens, with pore sizes classified into meso-pores (<10 nm, 10-50 nm), macro-pores (50-100 nm), and capillary pores (>100 nm) [33; 34]. In all specimens, meso-pores increased and capillary pores decreased as the C/S molar ratio increased, presumably due to a decrease in pores, with the pores having been filled by the C-S-H hydrates generated by the hydration reaction as the C/S molar ratio increased [35].  
Figure 10: Cumulative pore volumes of the specimens.  
Figure 9: Differential pore size distributions of the specimens.  
Figure 12 shows the threshold pore diameter of the specimens according to the C/S molar ratio. The threshold pore diameter, which is defined as the pore size at which mercury begins to penetrate a specimen during MIP, is the minimum continuous pore diameter throughout the interior of the specimen [36]. It is associated with durability-related properties of cementitious materials such as porosity, specific surface area, and pore volume [37; 38]. The threshold pore diameter tended to decrease with an increase in the C/S molar ratio, with values distributed within the range of 20 to 100 nm. The threshold pore diameter decreased as the C/S molar ratio increased due to the increase in the C-S-H hydrates, which filled the internal pores.  
The above results were combined, and regression analysis was performed to examine the relationship between C/S molar ratio and porosity, as shown in Figure 13. The result is inversely proportional to the compressive strength result, consistent with the result of a previous study [39].  
Figure 11: Pore volume fractions of the specimens.  
Figure 12: Threshold pore diameters of the specimens.  
Figure 13: Porosities as a functions of C/S molar ratio.
# The Availability of Space Service for Inter-Satellite Links in Navigation Constellations
## 2 Relative Movement of Satellites in a Navigation Constellation  
Most navigation constellations consist of MEO satellites located in orbit around 20,000 km. Because of the complex high-speed relative motion of the satellites, it is necessary to introduce the characteristics of the ISL signal before analyzing its availability. For example, the BeiDou constellation includes five GEO satellites, 27 MEO satellites, and three inclined geosynchronous orbit (IGSO) satellites. The altitude of the GEO satellites is 35,786 km, and they are located at 58.75\({}^{\circ}\) E, 80\({}^{\circ}\) E, 110.5\({}^{\circ}\) E, 140\({}^{\circ}\) E and 160\({}^{\circ}\) E. The altitude of the MEO satellites is 21,528 km, the inclination is 55\({}^{\circ}\), and they form a 24/3/1 walker constellation with three backup satellites. The altitude of the IGSO satellites is 35,786 km and the inclination is 55\({}^{\circ}\). Using this information, the range and rangerate of two arbitrary satellites can be simulated using the Satellite Tool Kit (STK).  
Figure 1 shows the line of sight (LOS) range and LOS rangerate from MEO11 to MEO21, MEO31, GEO1, and IGSO1, where MEO\(ij\) is the \(j\)th (1,2,3,4,5,6,7,8) satellite in the \(i\)th (1,2,3) plane, GEO\(m\) is the \(m\)th (1,2,3,4,5) satellite from west to east, IGSO\(n\) is the \(n\)th (1,2,3) satellite with an initial phase increase. The gaps between GEO1 and IGSO1 are sheltered by the Earth. The range varies from 18,160 km to 68,847 km, and the rangerate varies from -3.33 km/s to 3.33 km/s. Hence, the effective SSV of the ISL is at least 68,847 km, which is the distance between the MEO to GEO. That is to say, the ISL signal can be directly used within the geosynchronous orbit, and achieve improved observation geometry.
# Flexible Antennas for a Sub-6 GHz 5G Band:
## 4 Flexible Antennas for a Sub-6 GHz 5G Band
### Flexible Array Antennas for a Sub-6 GHz 5G Band
#### 4.2.3 Transparent Flexible Antenna  
An optically transparent bowtie-slot array antenna for mid-band 5G applications is proposed in [66], where the antenna consists of a gallium-zinc oxide (GZO) conductive film embedded on a transparent Rogers 6006 substrate, as shown in Figure 14a. Four antenna elements are designed, and the elements of the array antenna are placed at 26 mm to provide better isolation between the elements. The array antenna could improve the gain to 0.34 dB from \(-10\) dBi gain achieved with a single antenna element. Compared with the fabricated copper antenna counterpart, the optical array antenna contributes to a smaller gain value due to the low conductivity values of the transparent conductive materials. The array antenna is also fabricated with a copper film as the conductive material to compare the performance of the array with the GZO array. It is observed that the proposed antenna contributed to an enhancement of the bandwidth ranging from 4.22 to 10 GHz, as shown in Figure 14b. The array antenna provides a good optical transparency of 90%.
###### Abstract  
This study presents a dual-frequency microchip laser with a thermo-optically and electro-optically tuned frequency difference. The dual-frequency microchip cavity is formed by bonding a Lithium tantalite (LiTaO3, LTO) crystal chip and a neodymium-doped yttrium aluminum garnet (Nd:YAG) crystal chip. A single longitudinal mode is generated by the Nd:YAG crystal and split into two frequencies with perpendicular polarizations due to birefringent effect in the LTO chip. Furthermore, continuous beat frequency tuning at different scales is realized by adjusting the temperature and voltage applied to the LTO crystal. A maximum beat frequency of up to 27 GHz is obtained, and the frequency difference lock-in phenomenon is observed below the frequency difference of 405 MHz.  
microwave photonics; tunable dual frequency; microchip laser; electro-optic effect; thermo-optic effect Article
## Appendix A Mathematical Proofs
### Proof of Theorem 2
Thus, if \(\dfrac{\beta_{n}}{\beta_{g}}\) is small enough, then \(\phi(X)>g(X),X\in(0,X^{c})\), and hence \(\phi(X)=g(X)\) has no solution in \((0,X^{c})\). * When \(2\delta_{g}=\delta_{n}\), we have \({\phi_{+}}^{\prime}(0)=\left(\dfrac{\beta_{n}}{\beta_{g}}\right)^{-\frac{1}{ \delta_{g}}}\). Since \(\phi(X)\) is increasing in \([0,X^{c})\subset[0,X_{1})\) from zero to infinity while \(g(X)\) is increasing in \([0,X_{1})\) from zero to infinity, we can get that \(\phi(X)=g(X)\) has at least one positive solution in \((0,X^{c})\) if \({\phi_{+}}^{\prime}(0)<g_{+}^{\prime}(0)\), i.e., \(\dfrac{\beta_{n}}{\beta_{g}}>\left(\dfrac{e}{2d_{w}}\right)^{-\dfrac{1}{ \delta_{g}}}\). If \({\phi_{+}}^{\prime}(0)>g_{+}^{\prime}(0)\), then similar to the arguments for case \(2\delta_{g}<\delta_{n}\), we can prove that \(\phi(X)=g(X)\) has no solution in \((0,X^{c})\) if \(\dfrac{\beta_{n}}{\beta_{g}}\) is small enough. If \(\phi(X^{*})=g(X^{*})\) has a positive solution \(X^{*}\in(0,1)\), then system (9)-(10) has an interior equilibrium \((N^{*},N^{*}_{g})\) with \(X^{*}=\dfrac{N^{*}_{g}}{N^{*}}\) and \(N^{*}=g(X^{*})\). This has been shown by the discussion prior to Theorem 2. Thus the statement of Theorem 2 holds.
## 2 Related Work
These modes include time slotted channel hopping (TSCH), deterministic and synchronous multi-channel extension (DSME) and low-latency deterministic networks (LLDN). TSCH is considered as a new efficient MAC protocol that
## 5 Results Analysis and Discussion
### Results Analysis
#### 5.1.2 Comfort Analysis  
In this study, we initially acquired a set of 12 safe working conditions, which were subsequently categorized into three levels based on four distinct factors. Through orthogonal experiments (as detailed in Table 7), we successfully identified nine combined working conditions. These nine conditions were considered safe and deemed sufficient for further analysis. Subsequently, a 30-person driving simulation was conducted for each working condition, allowing us to collect pertinent features and data. The application of semi-supervised k-mean clustering enabled the determination of safe and comfortable conditions. The results of the clustering, after employing PCA dimensionality reduction, are depicted in Figure 11.  
According to Figure 12, the working conditions are categorized as comfortable and safe when more than 50% of individuals consider them to be so. Among the working conditions characterized by clustering analysis, II-2, II-3, II-5, and II-9 were identified. Out of these conditions, II-9 received the highest rating for comfort, with 26 individuals (86.7%) feeling comfortable in this setting.