esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Assembly Technology of Dense Small Screws for Flat Panel Parts
## 4 Conclusions  
The mounting plates of electronic equipment, such as radar, require numerous screws to fix various sensors. The screw assembly is characterized by various types of densely distributed small screws. A small screw automatic assembly system was developed to improve the assembly quality and efficiency. The mechanical system adopts a 6R series manipulator equipped with an industrial camera, a laser distance measurer, a torque sensor, and a motor encoder. The system can sense multi-source states, such as image, distance, torque, and angle. First, the position and orientation relationships of the workpiece, pixel, and base coordinate systems are obtained using the three-point positioning method and nine-point calibration method, establishing the foundation for automatic screw assembly. Second, a servo-positioning strategy is designed to locate the threaded hole accurately, and a method for assessing the assembly quality of screws is developed. The angle-torque curves of various small screws are obtained through experiments, and a template is constructed by polynomial fitting. The similarity between the angle-torque curve and template is calculated based on the Frechet distance to determine the assembly quality of the screws. The feasibility of the method is verified by considering a flat plate part with five sizes of threaded holes as an example. Results show that the system can achieve high-precision positioning and assembly for the M2, M3, M3.5, M4, and M5 screws.  
The system developed in this paper realizes automatic screw assembly and can be applied to most industrial scenarios, especially performing excellently in the assembly of dense small screws for electronic devices such as radar installation boards. However, it should be noted that during screw installation, despite successful assembly, minor errors may slightly shift the installation board from its original position. When the error accumulates to a certain extent, it may prevent hole alignment and thus assembly. Therefore, the installation board requires fixture fixation.  
In future work, we will design optimization strategies for screw assembly sequences, enabling the installation board to undergo automated assembly without the need for fixture fixation.  
Conceptualization, R.W.; methodology, R.W. and X.G.; software, X.G.; validation, X.G. and S.L.; formal analysis, R.W.; investigation, R.W.; resources, R.W.; data curation, S.L.; writing--original draft preparation, X.G.; writing--review and editing, R.W.; visualization, X.G. and S.L.; supervision, R.W.; project administration, R.W.; funding acquisition, R.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (51975157).  
Not applicable.  
Figure 26: Visual positioning accuracy.
# Electronic Beam Steering Metamaterial Antenna with Dual-Tuned Mode of Liquid Crystal Material
## 3 Design, Simulation and Analysis of Dual-Tuned CRLH Metamaterial Antenna Based on LC
### Simulation of CRLH Metamaterial Antenna Based on LC
Moreover, the parameters of the proposed antenna are compared with other LC-based antennas in the references in Table 3. As shown in the table, the performance of the LC metamaterial antenna proposed in this paper is relatively balanced in all aspects. In addition, it also has the advantages of flexible and diversified tuning modes. \begin{table}
\begin{tabular}{c c c} \hline
**Bias Voltage State** & **CRLH Unit CellFreq. (\(\beta\)d = 0) (GHz)** & **AntennaFreq. (\(\theta\) = 0) (GHz)** \\ \hline \(V_{\text{DC1}}\) = 0 V, \(V_{\text{DC2}}\) = 0 V & 14.4 & 14.42 \\ \hline \(V_{\text{DC1}}\) = \(V_{\text{max}}\), \(V_{\text{DC2}}\) = 0 V & 14.65 & 14.66 \\ \hline \(V_{\text{DC1}}\) = 0 V, \(V_{\text{DC2}}\) = \(V_{\text{max}}\) & 14.78 & 14.8 \\ \hline \(V_{\text{DC1}}\) = \(V_{\text{max}}\), \(V_{\text{DC2}}\) = \(V_{\text{max}}\) & 15.1 & 15.11 \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison for the zero frequency between the unit cell and the antenna. Figure 7: Radiation of the CRLH metamaterial antenna: (**a**) the realized far-field pattern at 14.4 GHz under dual-tuned mode of LC, (**b**) curve of the main lobe direction varies with frequency under dual-tuned mode of LC and (**c**) radiation efficiency of the antenna.
## 3 Unloading-Damaged Test Procedure
### Mechanical Characteristics of the Unloaded-damaged Samples  
To illustrate the mechanical characteristics of the unloaded-damaged samples, uniaxial loading tests of the unloaded-damaged samples and the original samples were carried out, and the stress-strain curves are shown in Figure 8. Unloaded-damaged samples under different initial confining pressures (10, 15, and 20 MPa) were divided into three groups (groups A, B, and C). The MN section is the stage of microcrack development inside the rock during the loading process (point M is the yield point, and the stress corresponding to point N is the uniaxial strength), and the microcrack development stage of the unloaded-damaged samples is more significant than that of the original samples. The reason is that the microcracks induced by the unloading damage are orthogonal to the confining pressure direction and expand first during the uniaxial loading process, resulting in the yield point shifting down and an accelerated axial strain growth rate and the uniaxial strengths are smaller than those of the original samples. This indicates the feasibility of the above unloading damage test.  
The uniaxial strengths of unloaded-damaged samples with different initial confining pressures (10, 15, and 20 MPa) are 37.22, 36.56, and 37.80 MPa, respectively, with approximately the same quantitative values. This indicates that the uniaxial strength of the unloaded-damaged samples is largely independent of the initial confining pressure size, reflecting the reasonableness of the abovementioned unloaded-damage classification criterion by unloading quantity.  
Figure 7: Sample unloading damage stress-strain curve.
# Correspondence between Multilevel Graph Partitions and Tree Decompositions
## 4 Correspondence between Multilevel Graph Partitioning and Tree Decompositions
### Bijection Proof
Both tree decompositions are identical, i.e., \(T=T^{\prime}\). It is clear by construction that the number of bags and cells is always the same. That the root bag and the tree backbone are conserved directly follows from the definition. It remains to show that the contents of the bags are conserved. Denote by \(X\) a bag of \(T\) and \(X^{\prime}\) the corresponding bag of \(T^{\prime}\). Further, denote by \(C\) the intermediate cell. We need to show that \(X=X^{\prime}\). In the first step, we show that the equality holds for all bags except the root bag by showing that \(\forall x:x\in X\implies x\in X^{\prime}\) and \(\forall x:x\in X^{\prime}\implies x\in X\). Afterwards, we show that equality also holds for the root bag. Start with a node \(x\) in \(X\). Either \(x\) is in \(X\cap X_{p}\) or in \(X\setminus X_{p}\). Using Lemmas 3 and 3, we conclude that \(x\) is in \(B_{C}\) and \(S_{C}\), respectively. As \(X^{\prime}\) is defined as \(B_{C}\cup S_{C}\), we conclude that \(x\in X\). Next, we start with a node \(x\in X^{\prime}\). We need to show that \(x\in X\). As \(X^{\prime}\) is defined as \(B_{C}\cup S_{C}\), \(x\) is either in \(B_{C}\) or in \(S_{C}\). If \(x\in B_{C}\), we conclude using Lemma 3 that \(x\in X\cap X_{p}\) from which follows that \(x\in X\). Using Lemma 3, we derive that if \(x\in S_{C}\), \(x\in X\setminus X_{p}\). As \(x\) is either in \(X\cap X_{p}\) or in \(X\setminus X_{p}\), we have shown that \(x\in X\). It remains to be shown that the equality holds for the root bags \(X_{r}\) and \(X^{\prime}_{r}\) with root cell \(V\). By construction, \(X^{\prime}_{r}=S_{V}\). It remains to be shown that \(X_{r}=S_{V}\). We need to show that \(x\in X_{r}\implies x\in S_{V}\) and \(x\in S_{V}\implies x\in X_{r}\). Pick \(x\in X_{r}\). The path requirement implies that for every bag \(X\) that contains \(x\) either \(X\) is the root or the parent \(X_{p}\) of \(X\) also contains \(x\). As consequence, no cell except the root cell contains \(x\). \(x\in S_{V}\) follows directly. Next, pick \(x\in S_{V}\). \(x\) is therefore not part of any cell except the root cell. The node requirement implies that \(x\) must be part of a bag \(X\). If \(X=X_{r}\), then we are finished. Otherwise, \(x\) must also be part of \(X\)'s part bag as \(x\) is not part of \(X\)'s corresponding cell. By applying this argument iteratively, we conclude that \(x\in X_{r}\). We have shown that \(X=X^{\prime}\) for every bag, which concludes the lemma. From Lemmas 3 and 3, the main Theorem 3 directly follows. This concludes all proofs in this paper.
# Numerical Simulation Study on the Volumetric Efficiency Loss of CO2 Swash Plate Axial Piston Pumps
## 2 Geometric Model  
A swashplate axial piston pump is composed of a drive shaft, port plate, oil cylinder, plunger, slipper, swashplate, and other key components. The motion characteristics of the piston pump mainly depend on three precision friction pairs, namely, the cylinder/port plate (port pair), plunger cavity/plunger (plunger pair), and swashplate/slipper (slipper pair). These three friction pairs work together to realize the conversion of mechanical energy and hydraulic energy. Although the plunger and slipper are connected by a ball joint pair, the leakage loss and friction loss of the ball joint pair are relatively small compared with those of the above three friction pairs and can be ignored [26]. In the process of pump movement, the relative motion of the friction pairs affects the performance of the piston pump. In order to optimize the friction characteristics, we must ensure the existence of a micron-thickness liquid film between the friction pairs. The existence of this liquid film can effectively reduce friction loss but forms a gap flow channel, triggering leakage volumetric loss. In addition, the compressibility of the work material affects the volumetric efficiency. The CO\({}_{2}\) work material is discharged under pressure in the piston pump and undergoes a phase change from a liquid to a dense phase during the working interval. An accurate characterization of its physical parameters (density, viscosity, etc.) is crucial for any numerical simulation of a piston pump's volumetric efficiency. In order to prevent the plunger and the plunger chamber from touching the mold, there must be a bottom gap between the plunger and the plunger chamber. The CO\({}_{2}\) in this part of the bottom gap cannot be discharged, resulting in a loss of CO\({}_{2}\) compression stagnation volume.  
The 250 CY14-1B plunger pump was selected as the research object for this study, this type of plunger pump was designed and manufactured in China and can be purchased in most manufacturers and cities at present. Three-dimensional modeling software was used to establish a geometric model of the plunger pump fluid domain and sub-film for the three friction pairs. The geometric model is shown in Figure 1. As shown in the figure, the plunger pump fluid domain was established for the plunger cavity, a gap was reserved for plunger movement to the dead point in the plunger cavity, and the mating basin and import and export pipeline linkages were also established. A liquid film model was established for the flow distribution sub-film, plunger sub-film, and sliding shoe sub-film, and the thickness of the liquid film was taken as 10 \(\upmu\)m [27]. The geometrical parameters of the plunger pump fluid domain are shown in Table 1.
# Adaptive Energy Management in 5G Network Slicing: Requirements, Architecture, and Strategies
## 3 Energy Management Proposal for 5G Network Slicing
### Energy Management Strategies for Adaptive Consumption
#### 3.2.1 Prioritization of Services in Network Slices  
In the energy management proposal, the MNO can establish priority levels to differentiate the services, as shown in Figure 1a. With this information, the MNO can prioritize the resource allocation (e.g., energy) for the configuration of a service or a set of services. The prioritization schema can be established automatically by the MNO or can be agreed with customers through contractual terms, and it may depend on several factors such as: (i) environment of applicability of services (e.g., allocation of a higher level for emergency services, services associated with search and rescue operations in disaster, or services for public safety); (ii) characteristics of the services (e.g., the number of end-users, location, average consumption, etc.); or (iii) specific requirements from customers.
# Thermodynamic Formalism in Neuronal Dynamics and Spike Train Statistics
## 2 Mathematical Setting of Thermodynamic Formalism
### Finite Range Potentials  
Equation (9) can be used to find the unique Markov measure that is associated with a finite range potential. As an example, consider a potential \(U\) of range two representing the pairwise interactions in a graph with incidence matrix \(I\). The entries \(I(y,x)=1\) represent the allowed transitions between symbols \(y\to x\) and \(I(y,x)=0\) the forbidden. We introduce the finite \(\mid A\mid\times\mid A\mid\) transfer matrix \(\mathcal{L}_{U}\), which corresponds to the RPF \"operator\" (8) restricted to a finite space.  
\[\mathcal{L}_{U}(y,x)=I(y,x)e^{U(y)},\quad y,x\in A,y\in\sigma^{-1}x \tag{15}\]  
As anticipated in Section 2.3.2, calling \(\rho\) the unique maximal positive eigenvalue of \(\mathcal{L}_{U}\) guaranteed by the Perron-Frobenius theorem, and \(R(x)\) and \(L(x)\) the \(x\)-th entry in the right and left eigenvectors associated with \(\rho\), respectively, we define a normalized potential \(\phi(y,x)=U(y)+\log R(y)-\log R(x)-\log\rho\), such that the matrix  
\[P(y,x)=I(y,x)e^{\phi(y,x)}=\frac{I(y,x)R(y)e^{U(y)}}{\rho R(x)} \tag{16}\]  
is stochastic, i.e., \(\sum_{x}P(y,x)=1\), and represents the transition probabilities of a Markov chain \(P(y\to x)=P(x\mid y)\). The invariant measure \(p\) associated to the matrix \(P\) satisfying \(pP=p\) is  
\[p(x)=\frac{R(x)L(x)}{\langle R,L\rangle}, \tag{17}\]  
where \(\langle R,L\rangle=\sum_{x}R(x)L(x)\). Note that normalisation is done without defining a partition function. The Markov measure \(\mu(p,P)\) of a block is given by \(\mu\left[x_{0,n}\right]=p(x_{0})P\left(x_{1},x_{2}\right)\cdots P\left(x_{n-1},x_{n}\right)\) for \(x_{k}\in A,k=0,..,n\). Here, we have a nice way to show that this measure satisfies the Gibbs property while using the Markov property \(\mu\left[x_{1,n}\mid x_{0}\right]=e^{\sum_{k=1}^{n}\log P(x_{k-1},x_{k})}\) where we see that the conditioning upon the first time is similar to left boundary conditions in statistical physics.  
It follows from (9) and (17) that \(\mu\left[x_{0,n}\right]\) obeys the variational principle and satisfies Equation (1), where \(F(U)=\log\rho\). The Gibbs measure \(\mu\left[x_{0,n}\right]\) gives an exponential weight to each cylinder set, depending on the \"energy\" depending on smaller blocks.
# A Novel Metasurface Lens Design for Synthesizing Plane Waves in Millimeter-Wave Bands
## 2 Geometry of Metasurface Lens  
The metasurface lens, as shown in Figure 1, is composed of a resonant metal layer and a dielectric substrate layer. The resonant metal layer is an FSS structure printed on top of the intermediate dielectric substrate, which plays the role of phase shifting. The various dimensions of the resonant metal are designed to achieve different phase shifts forming several annular zones. For one circular annular zone, the resonant metal structure is of the same dimension needed to achieve the same phase delay. To maximize the phase uniformity of illumination over the metasurface lens, the feeding-antenna array is designed to be composed of a 2 \(\times\) 2 planar dual-polarized patch antenna. A spherical wave from the feeding-antenna array is converted into a plane wave in the vertical and horizontal directions by the metasurface-lens array, which largely reduces the propagation distance and improves the electric-field intensity of the QZ.  
In terms of fabrication, the designed metasurface lens was manufactured by employing a chemical-etching facility based in laser direct-imaging (LDI) technology. A copper layer with a thickness of 35 um was employed in this work as the resonant-metal material due to its promising conductivity and ease of fabrication. A PCB layer with a Rogers RO4003C material was chosen as a dielectric substrate layer based on its low absorption and electromagnetic stability in millimeter-wave bands. Moreover, the resolution of fabrication was 0.05 mm for the copper layer and 0.1 mm for the PCB layer, which was important to ensure the QZ quality of the designed area.
# Effective Privacy-Preserving Collection of Health Data from a User's Wearable Device
###### Abstract
Recently, local differential privacy (LDP), which is a variant of DP, has now attracted attention as a promising way of guaranteeing individual privacy during the process of data collection [3; 4; 5]. Unlike DP, LDP does not require the existence of the trusted data aggregator. In an LDP-based data collection scenario, each data owner, who does not fully trust a data aggregator, perturbs his/her original data in a manner that satisfies DP before sending it to the data aggregator. With its strong privacy guarantees, several successful LDP-based deployments have been implemented in industry by major technology companies, including Google [3], Samsung [6], Apple [7; 8], and Microsoft [9]. In this study, we leverage LDP to collect sensitive health data from wearable devices (e.g., smart band, fitness band, smartwatch, etc.) in a privacy-preserving manner such that a data  
Figure 1: An example of the change in daily cumulative step-count per hour that is measured by a wearable device. owner's original data is not revealed at all (i.e., wearable device user's original health data is not exposed). In particular, our contributions can be summarized as follows:  
* We propose an effective way to aggregate health data acquired from wearable devices under LDP. To mitigate the error incurred by the perturbation mechanism of LDP, the proposed method extracts (samples) a small amount of salient data from given health data and reports the identified salient data to the data collection server under LDP, instead of reporting all the health data. Then, the data collection server reconstructs the health data based on the small amount of salient data received from a wearable device. * Through experiments with a real dataset, we demonstrate that the developed method can effectively collect health data from wearable devices, while preserving privacy. The remainder of this paper is structured as follows. We describe related work in the next section. Section 3 formalizes the problem. Section 4 presents the proposed method for collecting sensitive health data from wearable devices in a privacy-preserving manner. Section 5 contains details of the experimental evaluation of the proposed approach, and in Section 6, we conclude the paper.
## 4 Results and Discussion
### Implementation of AQI Forecasting Model
The predictions here are based on the real scheme, where the best models of them in each category of prediction were reused again by incorporating the actual values from 24 features. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Without Imputation**} & \multicolumn{3}{c}{**With Imputation**} \\ \cline{2-7}  & **RMSE** & **MAE** & \(\mathbf{R^{2}}\) & **RMSE** & **MAE** & \(\mathbf{R^{2}}\) \\ \hline SVM-Polynomial & 35.203 & 28.025 & 0.357 & 35.330 & 28.140 & 0.353 \\ SVM-RBF & 37.696 & 30.300 & 0.263 & 35.368 & 28.485 & 0.351 \\ SVM-Linear & 35.954 & 28.520 & 0.329 & 36.511 & 28.763 & 0.309 \\ Random Forest & 23.388 & 17.476 & 0.716 & 23.384 & 17.485 & 0.716 \\ AdaBoost-Square & 22.935 & 15.932 & 0.727 & 22.927 & 15.939 & 0.727 \\ AdaBoost-Linear & 22.663 & 15.743 & 0.734 & 22.654 & 15.753 & 0.734 \\ AdaBoost-Exponential & 22.708 & 15.777 & 0.733 & 22.723 & 15.790 & 0.732 \\ ANN & 27.008 & 20.542 & 0.622 & 26.882 & 20.416 & 0.625 \\ Stacking Ensemble & 22.872 & 16.372 & 0.729 & 22.618 & 16.105 & 0.735 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Results of ML Algorithms for Fengshan F24-AQI Prediction. Figure 8: Forecast of AQI: (**a**) on 28 May 2019, 07:00; (**b**) on 4 February 2019, 03:00.
## 2 Materials and Methods
The drain current and transfer characteristics of the HEMTs were measured at room temperature using a Yokogawa GS200 and Keithley 2410 DC bias measurement systems in a probe station. Equation (3) describes the threshold voltage (V\({}_{\text{th}}\)) in the modeling as:  
\[\text{V}_{\text{th}}=\varnothing_{\text{b}}-\Delta\text{E}_{\text{c}}-\text{V} _{\text{AlGaN}}=\varnothing_{\text{b}}-\Delta\text{E}_{\text{c}}-\frac{\text{ qN}_{\text{s}}d_{\text{AlGaN}}}{\varepsilon_{0}\varepsilon_{\text{AlGaN}}} \tag{3}\]  
where \(\varnothing_{\text{b}}\), \(\Delta\text{E}_{\text{c}}\), q, N\({}_{\text{s}}\), d\({}_{\text{AlGaN}}\), and \(\varepsilon_{\text{AlGaN}}\) are Schottky barrier height, conduction band difference between GaN and AlGaN, electron charge, 2-DEG density, AlGaN layer thickness, and AlGaN relative dielectric constant, respectively. Hence, it is inferred that the work function of the gate material, the 2-DEG concentration controlled by Al mole fraction, and the AlGaN thickness are important parameters to control the V\({}_{\text{th}}\) of the device. Both simulated and experimental devices show typical normally-on HEMT characteristics, with a negative V\({}_{\text{th}}\) and similar tendencies. The channel region disappears when the gate voltage is reduced below V\({}_{\text{th}}\). A linear fitting method was applied to determine V\({}_{\text{th}}\) in Figure 3b. The simulated V\({}_{\text{th}}\) value was \(-\)2.683 V, with a subthreshold current of 2.504 mA/mm, which almost matched the experimental value of \(-\)2.643 V, with a subthreshold current of 4.457 mA/mm. The measured and simulated values of peak g\({}_{\text{m}}\) were 57 and 58 mS/mm at \(-\)1.7 and \(-\)2.15 V, respectively. Figure 3c shows the drain leakage current as a function of the gate voltage in log scale. The GaN buffer layer doping profile directly controls the subthreshold swing and threshold voltage of the device. Thus, the GaN buffer layer doping profile was judiciously adjusted for better device characteristics. The subthreshold current of the simulated device was well matched with the experimental device. The drain-source saturation current (I\({}_{\text{dss}}\)) is shown in Figure 3d. The gate voltages were 2, 0, and \(-\)2 V from the top drain-current curve, and the drain voltage step was 1 V. The knee voltage of the fabricated device was higher than that of the simulated one, as shown in Figure 3d. Also, thermal degradation was found when the drain voltage was higher than 15 V. The thermal parameters were not considered for the simulated devices. Hence, the I\({}_{\text{dss}}\) appears almost parallel to the \(x\)-axis. Nevertheless, the measured and simulated current values were almost similar and found in accordance to each other. All the simulated discussions were progressed from the device with the aforementioned electrical characteristics. Figure 2: (**a**) Substrate acceptor trap concentration and (**b**) conduction band energy level of the simulated device as a function of depth. The traps are located 0.8 eV below the conduction band edge.