esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Impedance Control of a Traveling Wave Ultrasonic Motor with a Spherical Rotor
## 3 Experimental Setup  
In order to understand the behavior of the proposed ultrasonic motor under different external loads and control strategies, a mechanical characteristics testbed was built and is shown in Figure 3. It mainly consisted of the stator, spherical rotor, dynamic torque transducer, encoder, and a brake. Unlike common mechanical characteristics testbeds, a small handle was fixed on the right side of the dynamic torque transducer for conducting human-machine force interactions. The preload between the stator and rotor was applied by a screw and disc spring, and was detected by an internal pressure sensor. A dynamic torque transducer was used to detect the output torque of the ultrasonic motor, as well as the interaction torque between the operator and the device when the brake was set to be free. The encoder was an incremental type and was used to measure the angular displacement and velocity of the rotor. The brake was designed based on the principle of friction and could simulate an external variable load through an adjusting screw. Two excitation signals were generated and amplified using a driving circuit board based on programmable system-on-chip (PSoC) technology. Using the board, the frequency and phase difference of the two-phase voltage could be easily changed.  
A STM32F4-type control board (Yingshi Digital, Guangzhou, China) was used as a slave computer to collect and process the sensor data and control the ultrasonic motor, and the sampling control period was set to be 5 ms. The whole measurement and control system architecture is shown in Figure 4.  
Figure 4: Measurement and control system architecture. PSoC: programmable system-on-chip.  
Figure 3: Testbed of TWUM with spherical rotor.
## 3 Results and Discussion
### Link Budget Simulation
#### 3.7.4 Airtime  
In Figure 22, the airtime vs. the code rate is evaluated against different spreading factors. The set payload length is 25 bytes with a bandwidth of 125 kHz. The lower the spreading factor, the lower the airtime. This means that using a higher spreading factor will provide more airtime for all code rates. This becomes unsustainable at higher payloads, where the higher spreading factors would not be able to transmit the packets  
Figure 19: Sample link budget calculation with SF8 and CR = 4/5.  
Figure 18: Gap duration for SSO and lower inclination orbits.  
successfully. Moreover, increasing the code rate (x-axis) from 4/8 (indicated as 1/2 in the figure below) to 4/5 will decrease the airtime, as it is shown in the figure below. From the previous discussion, we can conclude that the airtime is inversely proportional to the bit rate. Therefore, to get a higher bit rate and faster time on air, it is preferable to select a small SF with a large code rate. However, it is important to strike a balance between selecting SF and ensuring a positive link margin.  
Figure 21: Bit rate for different spreading factors.  
Figure 20: Link margin for different spreading factors.
## 4 Experimental Results
### Data Washing Algorithm
#### 4.1.2 Effectiveness of Data Washing Algorithm against Target Attacks  
Table 4 shows the effects of the FC and CP attacks on the ImageNet [37] and Cifar10 [36] datasets. Table 5 shows the equivalent results for the case where the Data Washing algorithm is applied. It is seen that the false positive rate is reduced to just 1% for the FC attack on the ImageNet dataset and 0% for the FC and CP attacks on the Cifar10 dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Statistic** & **Algorithm** & **FC Attack [6]** & **FC Attack [6]** & **CP Attack [7]** \\  & **ImageNet [37]** & **Cifar10 [36]** & **Cifar10 [36]** \\ \hline Accuracy & 98.00\% & 99.50\% & 93.34\% \\ \hline Target Number & 100 & 200 & 10 \\ \hline Misclassified & 98 & 185 & 9 \\ \hline Target’s False Positive Rate & 98.00\% & 92.50\% & 90\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Effects of FC and CP target attacks without Data Washing algorithm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Statistic** & **Algorithm** & **TensorClog** & **Category Diverse** \\ \hline Accuracy & 0.8316 & 0.6166 & 0.1660 \\ \hline Accuracy after DW & 0.8178 & 0.7446 & 0.7044 \\ \hline Accuracy increment & \(-\)0.0138 & 0.1280 & 0.5384 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effectiveness of Data Washing algorithm in mitigating effects of paralysis attacks on Resnet18 [10].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Statistic** & **Algorithm** & **Original** & **TensorClog** & **Category Diverse** \\ \hline Accuracy & 0.8316 & 0.6166 & 0.1660 \\ \hline Accuracy after DW & 0.8178 & 0.7446 & 0.7044 \\ \hline Accuracy increment & \(-\)0.0138 & 0.1280 & 0.5384 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Effectiveness of Data Washing algorithm and denoising autoencoder in mitigating effects of paralysis attacks on Resnet18 [10].
# Flexible UHF RFID Tag for Blood Tubes Monitoring
## 4 Fabrication and Experimental Characterization of the Proposed Tag Antenna
### Radiation Patterns  
The last parameter measured for the characterization of the proposed tag antenna is the radiation diagram shown in Figure 14. The radiation patterns were obtained for the proposed tag antenna attached to the tube full of blood at the center frequency \(f_{c}\) = 867 MHz of the UHF band. By placing the tag in horizontal or vertical positions, the H-plane or the E-plane cuts of the radiation patterns can be measured. As it can be seen in Figure 14, the simulated and measured results are in good agreement for both E-plane and H-plane. The radiation patterns are essentially quasi-omnidirectional at \(f_{c}\) = 867 MHz, and with linear polarization. The maximum radiation in the H-plane (Figure 14b) is not directed at broadside, but it appears at 330\({}^{\circ}\). In addition to this tilt in the pointing angle, we observe that the H-plane radiation pattern is asymmetric. This is due to the effect of the feeding point of the antenna and to the presence of the blood tube along the back-side direction.  
It was observed that the simulated total efficiency (radiation efficiency \(\times\) \(\tau\)) of the tag when it is mounted on an empty tube reaches a peak of 81 % out of the UHF band at \(f\)0 = 1.2 GHz. This shows that the designed tag has quite high efficiency due to low losses in the materials used for manufacturing. However, when the tag operates attached to a tube full of blood the total efficiency  
Figure 14: Simulated (red-dash) and measured (blue-solid) radiation patterns for the proposed tag attached to a tube full of blood (scales of the graphs are given in dB). (**a**) E-plane; (**b**) H-plane.  
Figure 13: Measured differential radar cross section (ARCS) for the proposed tag attached to full and empty blood tubes.  
reduces to 3.5% at the center frequency of the UHF band (\(f\)0 = 867 MHz). This is due to extra absorption of the electromagnetic power by the blood, and it is the major factor that limits the reading distance in our design.
# Soft Error-Tolerant and Highly Stable Low-Power SRAM for Satellite Applications
## 3 Simulation Results and Analysis  
This section presents the simulation results for the RHSP16T cell. The performance of the proposed RHSP16T cell was evaluated using various design metrics with 90 nm CMOS technology, and the bsim3V3 model was used for the transistors. To demonstrate the superiority of the RHSP16T cell, the simulated design metrics were compared with those of the QUCCE12T, WE-QUATRO, RHBD10T, RHD12T, and RSP14T cells. To enhance the read stability of the RHSP16T cell, the widths of transistors N2, N7, P3, and P7 were set to 1.7 times that of the access transistors N1 and P1. For improved write speed and stability, the widths of transistors P6 and P2 were set to 0.34 times those of their respective access transistors, while the widths of transistors N3, N5, P4, and P5 were set to 0.5 times those of their respective access transistors. Additionally, the transistor sizes specified in the respective papers were used for simulating the other comparison cells.
## 3 Results and Discussion  
### Powder Characterizations  
The XRD patterns of the LSCF-SDCC composite cathode with varying SDCC composite electrolyte contents are shown in Figure 3. The XRD pattern of the LSCF-SDCC composite cathode comprised LSCF and ceria peaks for all the samples and did not exhibit any phase change or formation of any secondary impurity phase upon mixing and calcination. The standard peaks of the perovskite-structured LSCF (space group R-3C [167], JPCDS PDF# 01-081-9113) and cubic fluorite SDC (space group Fm-3m [225], JPCDS PDF# 00-034-0394) were identified. The crystallite size (\(D_{\text{XRD}}\)) of the calcined powders were determined using the Scherrer equation [31]. Table 1 shows the \(D_{\text{XRD}}\) of the prepared composite cathode powders. The \(D_{\text{XRD}}\) values of the LSCF and SDCC starting powders were 31 and 63 nm, respectively. The XRD results show that the \(D_{\text{XRD}}\) of the LSCF-SDCC composite cathode increased. This increase indicates the occurrence of LSCF and SDCC crystallization during calcination at 680 \({}^{\circ}\)C for 1 h. However, the addition of SDCC composite electrolytes to the LSCF powders did not reveal any change in their phase structure, thus confirming the purity of the prepared LSCF-SDCC composite cathode powder [28]. However, the intensity of SDC diffraction peaks increased with SDCC composite electrolyte content, whereas that of the LSCF peaks decreased accordingly. This result can be considered as evidence of the increased SDCC nominal content in the composite cathode.  
Figure 2: Cross-section FESEM images of (**a**) uniaxially pressed single-cell solid oxide fuel cell (SOFC), (**b**) SDCC composite electrolyte, (**c**) LSCF–SDCC composite cathode, and (**d**) NiO–SDCC composite anode.  
Specific surface area (\(S_{\text{BET}}\)) of the pure LSCF and SDCC powders were 11.8 and 4.24 m\({}^{2}\)/g, respectively. A considerable difference was observed in the \(S_{\text{BET}}\) of the LSCF powders after mixing SDCC composite, due to increase in different powder particle size. In fact, the mean particle sizes of the pure LSCF and LSCF-50 SDCC composite powders were 78 and 642 nm, respectively. This difference can be attributed to the presence of the macro-sized carbonates in the SDCC composite electrolyte and the formation of loose agglomerated particles upon synthesis and thermal treatment. The \(S_{\text{BET}}\) of the LSCF-SDCC composite cathode also gradually decreased with the increase in SDCC content. This result corresponded with the large particle size that resulted in small \(S_{\text{BET}}\)[34]. Table 1 shows that the average particle size of the LSCF-SDCC composite cathode gradually increased with the SDCC composite electrolyte content. Therefore, the SDCC composite electrolyte content influenced the \(S_{\text{BET}}\) and the particle size of the LSCF-SDCC composite cathode significantly more than the ball milling process and calcined temperature. The obtained \(S_{\text{BET}}\) of the LSCF-SDCC composite cathode is comparable with that in the literature [28].
## 2 Architecture and Operation of Proposed RO PUF
### Reliability  
The reliability represents how reproducible the CRPs generated by a PUF are, under variable operating conditions, e.g., different temperature, supply voltage, ambient noises, etc. The intra-die HD is a way to estimate the reliability. Given an input challenge \(C\), each chip \(i\) produces an \(n\)-bit reference response \(R_{i}\) under the normal operating condition. It is then measured \(k\) times with the same set of challenges under varying operation environments. The produced responses are \(R_{i,j(j=1,2,\ldots,k)}\). The assessing of reliability for chip \(i\) can be written as [17]:  
\[S=1-BER=1-\frac{1}{k}\sum_{j=1}^{k}\frac{HD(R_{i},R_{i,j})}{n}\times 100\% \tag{11}\]  
where \(BER\) stands for the bit error rate. With 1000 CRPs produced by the PUF under various temperatures and supply voltages, the reliability is measured. Figure 8a,b show the fabricated RO PUF's reliability with the temperature and voltage variations, respectively. The worst-case reliability is observed to be 98.30% at \(-40\)\({}^{\circ}\)C and 1.22 V. The proposed PUF is characterized with a high temperature reliability over a long temperature range. This is because the delay cell we implemented  
Figure 7: The measured inter-die HD distribution for the proposed RO PUF.  
in each RO is the current starved inverter. The oscillation frequency of the RO using the current starved inverter is much less sensitive to the temperature variations [18].
# GW-DC: A Deep Clustering Model Leveraging Two-Dimensional Image Transformation and Enhancement
## 2 Methodology
### Image Conversion Stage
#### 2.1.3 Conversion from Time-Series into MTF Images  
The MTF method transformed one-dimensional time-series into two-dimensional images by constructing the discrete quantile of the Markov matrix and encoding the transition probability field. The MTF was obtained by adding the time position related to the first-order Markov chain. It provided an inverse operation to map the images back to the original signals, making the images easy to realize the visual representation.  
The calculation process of the MTF could be divided into the following three steps.  
Step 1. The data signals were discretized. First, the original time-series \(x\) with length \(c\) were divided into \(Q\) bins, and each data point belonged to a unique \(q_{i}(i\in\{1,2,\ \ldots,\ Q\})\).  
Figure 3: The effect of converting time-series into Recurrence Plot.  
Step 2. The Markov transition matrix \(\mathbf{W}\) with the size of \(Q\times Q\) was constructed. \(W_{i,j}\) was determined by the adjacent frequency of a point between two quantiles \(q_{i}\) and \(q_{j}\), and its calculation formula is  
\[W_{i,j}=\sum_{\forall x\in q_{i},\forall y\in q_{j},x+1=y}\frac{1}{\sum_{j=1}^{ Q}W_{i,j}} \tag{10}\]  
Step 3. The time dependence was added to the transition probability matrix \(\mathbf{W}\), and the Markov transition field \(\mathbf{M_{\theta}}\) with the size of \(c\times c\) was constructed. \(M_{\theta}[i,j]\) represents the transition probability from \(q_{i}\) to \(q_{j}\), and the calculation of \(M_{\theta}[i,j]\) is  
\[\mathbf{M_{\theta}}=\left[\begin{array}{ccc}W_{i,j}|x_{1}\in q_{i},x_{1}\in q_{ j}&\cdots&W_{i,j}|x_{1}\in q_{i},x_{n}\in q_{j}\\ \vdots&\ddots&\vdots\\ W_{i,j}|x_{n}\in q_{j},x_{1}\in q_{j}&\cdots&W_{i,j}|x_{n}\in q_{i},x_{n}\in q _{j}\end{array}\right] \tag{11}\]  
To better manage the graph and improve the operation efficiency, the principle of PAA was applied to this stage to reduce the size of the MTF matrix, and \(\mathbf{M_{\theta}}\) was gridded and averaged.  
The effect of converting the time-series into MTF images is described in Figure 4.  
The MTF images intuitively show the change law through the variation of colors. And the trends in MTF images are similar to grayscale images and RP images.
# Grapevine Branch Recognition and Pruning Point Localization Technology Based on Image Processing
## 2 Materials and Methods
### Pruning Point Localization Algorithm
#### 2.9.2 Pixel Coordinates to Spatial Coordinates  
The process of using depth information to obtain spatial coordinates and spatial distance of pixel coordinates is: (1) Pixel coordinates (\(a\), \(b\)) are converted into camera coordinates (\(x\), \(y\), \(z\)):  
\[\begin{bmatrix}x\\ y\\ z\end{bmatrix}=K^{-1}\begin{bmatrix}a\\ b\\ 1\end{bmatrix}\cdot Z_{d},K=\begin{bmatrix}f_{x}&0&c_{x}\\ 0&f_{y}&c_{y}\\ 0&0&1\end{bmatrix} \tag{8}\]  
where \(K\) is the internal reference matrix of the depth camera, \(f_{x}\), \(f_{y}\) is the camera focal length, \((c_{x},c_{y})\) is the principal point location and \(Z_{d}\) is the depth information.  
(2) Camera coordinates (\(x,y,z\)) are converted into world coordinates (\(X,Y,Z\))  
\[\begin{bmatrix}X\\ Y\\ Z\end{bmatrix}=E\begin{bmatrix}x\\ y\\ z\\ 1\end{bmatrix},E=\begin{bmatrix}R&t\\ 0&1\end{bmatrix} \tag{9}\]  
where \(E\) is the depth camera's external reference matrix, \(R\) is the rotation matrix and \(t\) is the translation vector.  
(3) The actual distance \(d\) between two points is calculated using the Euclidean distance formula:  
\[d=\sqrt{(X_{2}-X_{1})^{2}+(Y_{2}-Y_{1})^{2}+(Z_{2}-Z_{1})^{2}} \tag{10}\]  
The unit is mm, which corresponds to the unit of depth information.
# A Classification Method for Academic Resources Based on a Graph Attention Network
## 5 Experiment
### Datasets  
In this paper, we used a self-built scholar cooperative network dataset, SIG, and the public citation network datasets Cora and Citeseer [30] to verify the classification effect of our model on academic resources. The basic situations of these three data networks are as follows.  
SIG is composed of research scholars of special interest groups on the ACM website, including information retrieval (IR), the Ada programming language (DA), information technology education (ITE), computer graphics (GRAPH), accessibility and computing (AC-CESS), bioinformatics and computational biology (BIO), knowledge discovery in data (KDD), and artificial intelligence (AI)--a total of eight categories. We selected 3669 scholars who published at least four papers in various fields for research. According to their published academic papers, we obtained the cooperation relationships between scholars, selected the keywords with word frequencies greater than 10 as the feature dimension, and constructed a scholar cooperation network. The scholar cooperation network contained 3669 nodes and 10,399 edges, and each node had 3664 feature dimensions.  
Cora is composed of machine learning papers, which are divided into seven categories: case-based, genetic algorithms, neural networks, probabilistic methods, reinforcement learning, rule learning, and theory. In the final corpus, each paper was cited by at least one other paper. There were 2708 papers in the whole corpus. There were 2708 nodes and 5429 edges in the citation network of the Cora dataset, and each node had 1433 feature dimensions.  
Citeseer contains 3312 scientific publications, which are divided into six categories: agents, artificial intelligence (AI), database (DB), information retrieval (IR), machine learning (ML), and human-computer interaction (HCI). The dataset contains 3312 nodes and 4732 edges, and each node has 3708 feature dimensions.  
The specific information of the three datasets is shown in Table 2.  
We implemented the ACGAT based on the PyTorch framework. All the three datasets were split into training, validation, and test subsets with a ratio of 8:1:1. In all experiments, two layers of the ACGAT were used, and Adam was used as the optimizer. The learning rate was 0.005. For input features and normalized attention coefficients, we used a dropout rate of 0.6 and L2 regularization with a full attenuation of 0.0005.
## 2 Preliminaries and Basic Concepts
### Haar Wavelet  
Wavelet analysis is the tool to construct an orthonormal basis for \(L^{2}[\mathbb{R}]\). One of the common wavelet bases is the Haar basis. In fact, for any \(i=2,3,4,...\) there are unique \(j\geq 0\) and \(0\leq k\leq 2^{j}-1\) such that \(i=2^{j}+k+1\). Hence, we define the family \(\{h_{i}\}\) by  
\[h_{1}(x)=\begin{cases}1&,\quad 0\leq x<1\\ 0&,\quad\text{elsewhere}\end{cases}. \tag{5}\]  
and  
\[h_{i}(x)=\psi_{j,k}(x)=2^{j/2}\psi(2^{j}x-k),\quad for\;\;i=2,3,4,...\]  
where  
\[\psi(x)=\begin{cases}1&,\quad 0\leq x<\frac{1}{2}\\ -1&,\quad\frac{1}{2}\leq x<1\\ 0&,\quad\text{elsewhere}\end{cases}.\]  
The family \(\{h_{i}\}\) defines an orthonormal basis for \(L^{2}[0,1]\)--that is, any \(f(x)\) in \(L^{2}[0,1]\) has the expansion \(f(x)=\sum_{i=1}^{\infty}a_{i}h_{i}(x)\). For approximation purposes, the resolution level \(J\) is the maximum value for \(j\) in \(i=2^{j}+k+1\). Thus, for given resolution level \(J\), we define the truncated series expansion of \(f(x)\) by  
\[f(x)\approx\sum_{i=1}^{2^{j}+1}a_{i}h_{i}(x)\]  
where \(i=2^{j}+k+1\), \(j=0,1,2,3,...,J\), and \(0\leq k\leq 2^{j}-1\). In Table 1, we compute explicitly the corresponding values of \(j\), \(k\), and \(i\) for resolution level \(J=3\).  
Since we are dealing with both of \(u(x)\) and its derivative \(u^{\prime}(x)\) in differential equations, we will introduce the integrating Haar functions [5] by  
\[p_{i}(x)=\int_{0}^{x}h_{i}(x)dx=\begin{cases}x-\frac{k}{2^{j}}&,\quad\frac{k} {2^{j}}\leq x<\frac{k+0.5}{2^{j}}\\ \frac{k+1}{2^{j}}-x&,\quad\frac{k+0.5}{2^{j}}\leq x<\frac{k+1}{2^{j}}\\ 0&,\quad\text{elsewhere}\end{cases}. \tag{6}\]  
It follows that, if we approximate \(u^{\prime}(x)\approx\sum_{i=1}^{2^{j}+1}a_{i}h_{i}(x)\), then we have \(u(x)-u(0)\approx\sum_{i=1}^{2^{j}+1}a_{i}p_{i}(x)\)
## 3 Threat Model and Privacy Protection Framework
### Privacy Protection Framework  
Our goal in this paper was to protect the location privacy of workers in SC. We designed a framework (Figure 2) where workers report noisy locations to the SC-server based on local differential privacy. Different from the SC model shown in Figure 1, we added an essential step (step 0) where workers add noises to their real locations by themselves based on a local differential privacy algorithm. Therefore, the SC-server can obtain the noisy locations of workers rather than the real locations during task assignment.
# Wire-Grid and Sparse MoM Antennas: Past Evolution, Present Implementation, and Future Possibilities
## 4 Reconfigurable and Smart Antennas
### An Example of the Current Problem  
As we know, antennas are an important part of REDs, so it is important to improve their radioengineering and mass-size characteristics, which are especially relevant for on-board equipment. For example, Figure 1 shows spacecraft of the \"retranslation\" category [256]. Note that their mass decreases with each release, while their complexity increases. At the same time the antenna weight remains considerable (Figure 2) [257]. This is important because the cost of 1 kg cargo delivery to orbit can reach $40,000. Therefore, it is required to optimally design antennas.  
To solve this problem, engineers use composite materials (Figure 3) [258] and transition to a grid of wires (Figure 4) [259]. For example, at the Institute of Computer Technologies, one of their projects studied the properties of orbital and ground antennas from composite materials working in the bands of 18.2-21.2 GHz and 42.5-45.5 GHz. Antennas of such frequency ranges should possess simultaneously high geometrical stability, stability to temperature impacts, raised rigidity, and small mass. Computer simulation played a very important role at every stage in this project and allowed for parametric optimization of antenna designs. Modeling and optimization of these types of antennas, including those composed of thin meshes, remains a non-trivial task. In this case, the best simulation results can be achieved only using electrodynamic modeling in the range of a large number of parameters, which is often hampered by high computational cost requirements. Therefore,  
Figure 1: “Retranslation” category spacecrafts: Potok (2300 kg) 1982 (a); Luch (2400 kg) 1985 (b); Luch-2 (2420 kg) 1995 (c); Luch-5A (1150 kg) 2011 (d); Luch-5B (1350 kg) 2012 (e); Luch-5V (1150 kg) 2014 (f) [256].  
Figure 2: Examples of antennas: Precision antenna Ka-band (6.3 kg) (a); double-mirror parabolic Ka-band antenna (10.5 kg) (b); broadband antenna-feeder device (55 kg) (c) [257].  
it is important to identify and study new approaches to improving the efficiency of such antenna modeling.