esa-sceva/satcom-chunk-collection huggingface dataset:
# W-Band Modular Antenna/Detector Array for the Electron Cyclotron Emission Imaging System in KSTAR
## 3 Antenna/Detector Array Design
### Unit Antenna/Detector Module
#### 3.2.2 Dual-Dipole Antenna/Detector with an Ultra-Wideband Antenna Balun  
A dual-dipole antenna with a Schottky diode and an antenna balun is illustrated in Figure 6. From AA' to BB', the dual-dipole antenna consists of two dipole radiators and a Schottky diode. The dual-dipole antenna is designed to have the center frequency of 90 GHz [23]. The dimensions of the dual-dipole antenna are \(l\) = 0.838 mm, \(d\) = 0.965 mm, \(w_{1}\) = 0.152 mm, and \(w_{2}\) = 0.127 mm. For the detection of W-band ECE signals, a Schottky diode working up to W/F-band is used. The Schottky diode is mounted at the center location of the dual-dipole radiators to generate an IF signal by mixing the ECE (RF) and LO signals. An external bias voltage is supplied to turn on the Schottky diode with the optimum bias condition.  
To feed the dual-dipole antenna, an ultra-wideband balun is connected to the CPS-based antenna feeding line. The antenna balun consists of a CPS taper (BB' to CC') and a CPS-to-MSL transition (CC' to DD'). The CPS linewidth of the dual-dipole antenna feeding point is very narrow (6 mil), resulting in high line impedance (157 \(\Omega\) at BB'). In order to match line impedance for ultra-wideband and low-loss performance, a CPS impedance taper (from 157 \(\Omega\) at BB' to 115 \(\Omega\) at CC') and a CPS-to-MSL transition (from 115 \(\Omega\) at CC' to 50 \(\Omega\) at DD') are used. The CPS-to-MSL transition gradually changes the line impedance and field distribution from CPS to MSL by increasing the line width of the bottom ground plane. The line characteristic property of each s  
Figure 5: Elliptical mini-lens: (**a**) perspective view, (**b**) side view (\(x\)–\(z\) plane).  
Figure 6: Schematic view of the dual-dipole antenna/detector with a Schottky diode and an antenna balun.  
transition is accurately analyzed using the Schwarz-Christoffel transformation with an analytical model [24]. In the analytical model, each section of the CPS-to-MSL transition is divided into three regions, and the capacitances (\(C_{1}\), \(C_{2}\), and \(C_{3}\)) of each region are calculated [25]. Hence, the characteristic line impedance of each section is calculated by Equations (3) and (4).  
\[Z_{0}=\frac{120\pi\varepsilon_{0}}{\sqrt{\varepsilon_{eff}\left(C_{1}+C_{2}/ \varepsilon_{r}+C_{3}\right)}} \tag{3}\]  
\[\varepsilon_{eff}=\frac{C_{1}+C_{2}+C_{3}}{C_{1}+C_{2}/\varepsilon_{r}+C_{3}} \tag{4}\]  
To obtain ultra-wideband performance and to minimize line loss in the presence of a big line impedance difference, the line impedances from the CPS feed (157 \(\Omega\) at BB') to MSL (50 \(\Omega\) at DD') are adjusted to form an optimal Klopfenstein taper [24]. The minimum operating frequency of the transition structure is determined by the length of the transition, and the length of the antenna balun is obtained as 49 mm to have the minimum operating frequency of 2 GHz [26].
# Human Activity Signatures Captured Under Different Directions Using SISO and MIMO Radar Systems
### Related Work  
One crucial challenge for radar-based HAR systems is the direction of motion of certain activity in relation to the illuminating radar. For instance, a person may fall in a direction either parallel or perpendicular to the radar's boresight. Conceivably, a monostatic single-input single-output (SISO) radar will not be able to detect the fall if the fall direction is perpendicular to the boresight of the radar. This is due to the fact that the radar systems are merely sensitive to the changes in the scatterer's radial distance with respect to the radar itself. A scatterer moving perpendicular to the radar's boresight has zero Doppler frequency and thus appears as a stationary object to the radar and can therefore not be distinguished from other stationary objects in an indoor environment by the radar system. Generally, a strong degradation of the classification performance is expected for activities with a greater angle of motion to the radar's boresight. The prior state-of-the-art approaches to alleviate this problem are delineated here along with their limitations.  
The authors in [35; 36] suggest that a SISO radar positioned on the ceiling can detect fall activities. However, this is not a general solution for direction-independent HAR. For direction-independent HAR, it is understandably compelling to use monostatic beamforming multiple-input multiple-output (MIMO) radars that are capable of measuring the angular information [37]. However, these systems are often limited by their angular resolution, which in turn limits their cross-range resolution significantly. Such monostatic MIMO radars do not perform adequately, especially not for persons relatively far away from the radar, which degrades the overall classification accuracy. The authors in [38] use the phase information of frequency modulated continuous wave (FMCW) monopulse radars to measure the angle of arrival, but the study is limited to hand gesture sensing. The authors in [39] combine the FMCW mode of radars with interferometry to track vital signs and detect position and life activities, but they do not generally address the challenges caused by the direction of human activities. To improve the angular resolution, direction of arrival algorithms such as the \"_estimation of signal parameters via rotational invariance techniques_\" (ESPRIT) method and \"_multiple signal classification_\" (MUSIC) method can also be used [40], but these direction of arrival algorithms usually require a high signal to noise ratio [41]. Although the authors of [18] discuss a multistatic Doppler radar, the study is limited to the detection of armed persons. A bi-static radar configuration is used in [42] to improve the activity classification performance. However, the quality of data can be improved by using a MIMO radar system instead. The performance of another radar configuration is explored in [43] using a MIMO radar system in bi-static configuration, but the study is limited to personnel localization only.
# Galerkin's Spectral Method in the Analysis of Antenna Wall Operation
## 4 Dissipation Radiation Field Analysis of Antenna Array of Limited Dimensions
### Solution to Distraction Problem  
Due to the general form of the formulation of the scattering problem on an unconstrained antenna array--defining the scattering matrix of the grid in the spectral space spanned by the full harmonic system--generalizing, using the above-defined window functions, the solution procedure for the case of an array of constrained dimensions does not pose any major difficulties. Only the initial equations containing the current distribution change:  
\[\underline{l}(x,y)=H_{mn}(x,y)\underline{\sum}_{q}\,\underline{\oint}_{q} \,\underline{\oint}_{q}\,(x,y)=\sum_{k}\overline{l}_{k}\overline{H}_{mnk}e_{ k}(x,y), \tag{161}\]  
\[\underline{E}^{s}(x,y,0)=\left(\underline{G}*\underline{l}*H_{mn}\right)(x,y )=\sum_{k}\overline{\underline{G}}_{k}\overline{l}_{k}\overline{H}_{mnk}e_{ k}(x,y) \tag{162}\]  
where  
\[\overline{\underline{l}}_{k}=\sum_{q}\,\underline{\oint}_{q}\,\underline{ \oint}_{qk} \tag{163}\]  
by multiplying the amplitudes of their spectral representations by the Fourier transform of the window function \(\overline{H}_{mnk}\) corresponding to the selected cell (\(m\),\(n\)). Each cell (\(m\),\(n\)) corresponds to a different form of the window function \(H_{mn}\)(\(x\),\(y\)) due to its location relative to the central point of the window function carrier \(H_{MN}\)(\(x\),\(y\)). For each pair of numbers (\(m\),\(n\)), a different solution is obtained defining the distribution of currents on the surface of the metallized part of the cell (\(m\),\(n\)). From a formal point of view, the formulation and solution of the problem of scattering on an antenna array of limited size should take into account the continuous spectrum of wave number values \(k\) in the spectral distributions of field quantities and currents, i.e., the continuous Fourier transform should be used instead of spectral distributions at points corresponding to discrete values of wave numbers of successive Floquet harmonics. The numerical solution of the problem formulated in this way would also in this case reduce the analyzed problem to a problem with a discrete spectrum, imposed by numerical procedures of the fast Fourier transform (FFT) type. Hence, it seems justified to use spectral distributions of the analyzed quantities with a spectrum determined by harmonics corresponding to the case of an unconstrained periodic structure. Thus, in terms of the formal notation of equations, except for the modification in the above equations by introducing a window function, the formulation and solution of the posed scattering problem on an infinite periodic antenna structure consist of the decomposition of the basic functions, currents and fields into Floquet harmonics, in a way analogous to the unconstrained antenna grid analyzed in the previous chapter.
## 3 Results and Discussion
### Zinc Solubility at Various pH Values  
Zinc solubility is one of the significant components that can affect zinc absorption and availability [16]. Figure 3 presents the experimental data on the zinc solubility of the three peptide fractions-zinc complexes at various pH values. The three peptide-zinc complexes exhibited higher zinc-releasing capacity at pH 2-6. Higher solubility of metal ions in acidic pH increases their absorption in the duodenum and proximal jejunum. When the pH was increased to 7 and 8, the amount of zinc released was dramatically diminished, and the zinc absorption rate was markedly reduced in the distal small intestine. In weakly alkaline conditions, a portion of Zn\({}^{2+}\) could combine with -OH to form  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Functional Groups** & **P1** & **P1-Zn** & **P2** & **P2-Zn** & **P3** & **P3-Zn** \\ \hline N–H & 3316 & 3403 & 3388 & 3415 & 3419 & 3426 \\ Amide I & 1651 & 1650 & 1651 & 1652 & 1652 & 1647 \\ Amide II & 1542 & 1546 & 1542 & 1545 & 1541 & 1545 \\ COO- & 1400 & 1402 & 1399 & 1402 & 1401 & 1403 \\ Amide III & 1334 & 1336 & 1334 & 1337 & 1334 & 1337 \\ Amide VI & 658 & 591 & 659 & 570 & 659 & 597 \\ \hline \end{tabular}
\end{table}
Table 3: Wavenumber (cm\({}^{-1}\)) of band shifts observed in the FTIR spectra of three molecular weight peptide fractions and their zinc complexes.  
precipitates [15], which decreases the zinc content in the supernatant. The pH in the human intestinal tract is approximately 7.2, and the P1-Zn complex exhibited higher zinc solubility in the weakly basic conditions, potentially because low molecular weight peptide fraction with highly exposed side chains and higher surface charges provide more binding sites for peptide-zinc complexes formation, which could decrease zinc precipitation and interactions with other metals [36]. Furthermore, zinc, which chelates amino acids and loosely binds to intracellular proteins, is the available forms of zinc mineral inside enterocytes or intestinal absorptive cells. Therefore, P1 has higher zinc chelation capacity than other peptide fractions and can enhance the solubility of zinc at neutral pH conditions.
## 7 Simulation Results  
Figure 9 shows the results of the modification protocols: LEACH-KNN, SEP-KNN, TEEN-KNN, and DEC-KNN. When comparing the simulation results between original and modified protocols, we notice that the proposed approach performs better in network clustering. The distance between CHs and the base station is shorter than the original protocol, and there is only one cluster with no nodes using the SEP-KNN approach. The network does not contain dead cluster heads, and there are no CHs on the network's edge.
## 3 Materials and Methods
### Artificial Neural Networks (ANNs)  
ANN is a sequence of fully connected neuron layers that convert an input data \(x\) to a distribution of probability to predict the output class variable \(y\). As a result, the ANN serves as a function that performs the mapping for the probability distribution \(p(y|x)\) for which the ANN was trained. This function is mapped by an ANN utilizing \(I\) hidden layers followed by an output layer. The weighted edges connect the nodes in each layer to all the nodes in the succeeding layer. These weights can be seen as a weight matrix. Each layer of the network also has a bias vector \(b\). A non-linear function is used as part of the calculation in all hidden layers. Each neuron in the model has an activation function associated with it. This function sets the output value of each neuron in a range of \(-1\) to 1, or of 0 to 1. In earlier publications, the activation function was often a softmax function.  
\[\text{softmax}(x_{i})=\frac{e^{(x_{i})}}{\sum_{j}e^{(x_{i})}} \tag{1}\]  
where \(x\) is a single value output.  
However, rectified linear units can outperform uncorrected linear units in many ANN classification tasks. Dropout is a deep learning-specific regularization approach that is commonly utilized. In each repetition, it randomly disables certain neurons. It simply implies that neurons are \"fallen out\" at random. When certain neurons are turned off, we are training a separate model that only employs a subset of neurons in each iteration. As a result, neurons can learn characteristics individually, without relying on other neurons. The dropped neurons are deleted during the forward phase, and no weight update is applied to them during the backward propagation stage. Typically, dropout occurs in fully linked layers because they have the most parameters, and hence are more likely to co-adapt excessively, resulting in overfitting.
## 3 Methods
### New Measures of Permutation Distance
#### 3.2.3 K-Cycle Distance
The triangle inequality is violated if \(k\geq 5\). Consider this case with \(k=6\):  
\[p_{1} =[0,1,2,3,4,5] \tag{36}\] \[p_{2} =[1,0,3,2,5,4]\] \[p_{3} =[0,3,2,5,4,1].\]  
The 6-cycle distance allows cycle operations of length up to six. Note that \(\delta_{6}(p_{1},p_{2})=3\) since there are three cycles of length two; \(\delta_{6}(p_{1},p_{3})=1\) since the even-numbered elements are fixed points, and the odd-numbered elements form a single cycle of length three; and \(\delta_{6}(p_{3},p_{2})=1\), with a single cycle of length six (i.e., cycle the elements of \(p_{3}\) one position to the right to obtain \(p_{2}\)). Thus, \(\delta_{6}(p_{1},p_{3})+\delta_{6}(p_{3},p_{2})=1+1=2<\delta_{6}(p_{1},p_{2})\). Therefore, 6-cycle distance is only a semi-metric, as is \(k\)-cycle distance for any other \(k\geq 6\). We can produce a similar example for the case of \(k=5\) as follows:  
\[p_{1} =[0,1,2,3,4,5] \tag{37}\] \[p_{2} =[1,0,3,2,5,4]\] \[p_{3} =[0,3,2,5,1,4].\]  
As in the previous example, \(\delta_{5}(p_{1},p_{2})=3\). Note that \(\delta_{5}(p_{1},p_{3})=1\) since \(\{0,2\}\) are fixed points, and there is a single cycle of the elements \(\{1,3,5,4\}\); and \(\delta_{5}(p_{3},p_{2})=1\), with a fixed point for element 4, and a single cycle of the remaining five elements. In this example, \(\delta_{5}(p_{1},p_{3})+\delta_{5}(p_{3},p_{2})<\delta_{5}(p_{1},p_{2})\). Thus, 5-cycle distance is also only a semi-metric. In general, as many as \(k\) non-singleton cycles can be merged into a single larger cycle by cycling \(k\) elements, where the cycle edit includes at least one element of each of the merged cycles. This is also true when \(k<5\), but for \(k<5\) the resulting merged cycle is larger than \(k\), requiring multiple cycle edits to transform into all fixed points. When \(k\leq 4\), the \(k\)-cycle distance satisfies the triangle inequality, and is a metric; but when \(k\geq 5\), it is only a semi-metric. Indeed, when \(k\leq 4\), the \(k\)-cycle distance is equivalent to an edit distance with cycles of length up to \(k\) as the edit operations. For example, 2-cycle distance is equivalent to an existing distance metric known as interchange distance, which is an edit distance with swap as its edit operation, which is a metric. The diameter of the space of permutations for \(k\)-cycle distance depends upon the permutation length \(n\) in relation to \(k\). When \(k\) is high, distance is maximized by maximizing the number of permutation cycles, which occurs when there are \([n/2]\) cycles of length 2. When \(k\) is low, distance is maximized when there is a single cycle of length \(n\), which requires \([(n-1)/(k-1)]\) cycle operations to transform to \(n\) fixed points. Thus, the diameter is  
\[D(n,\delta_{k})=\max\left\{\left[\frac{n}{2}\right],\,\left[\frac{n-1}{k-1} \right]\right\}. \tag{38}\]
## 2 Materials and Methods
### Finite Element Analysis  
To perform the finite element simulations, the Simcenter Femap software was selected. Its solver offers a simple linear elastic material model and a multilinear material model as well. This model is defined by the input of measured stress-strain data points [37].  
The specimen geometries were created in the finite element software as two-dimensional models. The gripping length of 50 mm was removed from both ends of all the models, so the loads and constraints can be added to the curve. The constraints and loads of the specimen were defined to mimic the ones induced by the testing machine. After creating the models as accurately as possible, the material models were also defined. The models were meshed using linear four-node quadrilateral plane stress elements, and then a mesh convergence analysis was performed to investigate the mesh independence of the calculated results. The analysis has shown that the 0.25 mm average element size is optimal for this simulation. Using these element attributes, the simple plate specimen was meshed by 60,000 elements, the middle-weakened plate by 52,929 elements, and the side-weakened plate by 52,900 elements. Finally, the simulations were run with the selected element size.  
The results from all the finite element simulations and the laboratory experiments have been post-processed and compared. To ensure the most accurate comparison between the two methods, a smaller (Zone 1) and a higher strain zone (Zone 2) was defined on the specimen, see Figure 6. These zones were used to calculate the average strain values. On these zones, relative errors have been calculated based on the experimental results.  
Figure 5: Measurement set up for the validation of the material models using the GOM Aramis.
# Multi-Phase Adaptive Recoding: An Analogue of Partial Retransmission in Batched Network Coding +
## Appendix B Proof of Theorem 4  
Once the batch reaches 0 innovative rank, the innovative rank in the later phases will all be 0. Hence, \(\Pr(\tilde{R}_{k}=0)\) is the probability that the batch reaches 0 innovative rank after the transmission of some phases before entering the \(k\)-th systematic phase. Thus, \(\Pr(U=k)=\Pr(\tilde{R}_{k+1}=0)-\Pr(\tilde{R}_{k}=0)\) for \(k\geq 1\). A special case is \(U=0\), which means that the batch has 0 rank at the very beginning, so we have \(\Pr(U=0)=\Pr(R_{1}=0)\).  
For \(k\geq 1\), by (2), we have \(\Pr(\tilde{R}_{k+1}=0)\) equals  
\[\sum_{r_{1},r_{2}\ldots r_{k}}\Pr(r_{1})\left(\prod_{j=1}^{k-1} \operatorname{[PERSON]}(r_{j},p;r_{j}-r_{j+1})\right)\operatorname{[PERSON]}(r_{k},p;r_ {k}-0)\] \[=\sum_{r_{1},r_{2}\ldots r_{k-1}}\Pr(r_{1})\left(\prod_{j=1}^{k-2 }\operatorname{[PERSON]}(r_{j},p;r_{j}-r_{j+1})\right)\sum_{r_{k}=0}^{r_{k-1}} \binom{r_{k-1}}{r_{k}}(1-p)^{r_{k-1}-r_{k}}p^{r_{k}}(1-p)^{r_{k}}\] \[=\sum_{r_{1},r_{2}\ldots r_{k-1}}\Pr(r_{1})\left(\prod_{j=1}^{k-2 }\operatorname{[PERSON]}(r_{j},p;r_{j}-r_{j+1})\right)(1-p)^{r_{k-1}}(1+p)^{r_{k-1}}\] \[=\sum_{r_{1},r_{2}\ldots r_{k-2}}\Pr(r_{1})\left(\prod_{j=1}^{k-3 }\operatorname{[PERSON]}(r_{j},p;r_{j}-r_{j+1})\right)\sum_{r_{k-1}=0}^{r_{k-2}} \binom{r_{k-2}}{r_{k-1}}(1-p)^{r_{k-2}}p^{r_{k-1}}\bigg{(}\frac{1-p^{2}}{1-p} \bigg{)}^{r_{k-1}}\] \[=\sum_{r_{1},r_{2}\ldots r_{k-2}}\Pr(r_{1})\left(\prod_{j=1}^{k-3 }\operatorname{[PERSON]}(r_{j},p;r_{j}-r_{j+1})\right)(1-p)^{r_{k-2}}\bigg{(}\frac{ 1-p^{3}}{1-p}\bigg{)}^{r_{k-2}}\] \[=\cdots\] \[=\sum_{r_{1}}\Pr(r_{1})(1-p)^{r_{1}}\Bigg{(}\frac{1-p^{k}}{1-p} \bigg{)}^{r_{1}}\] \[=\sum_{r_{1}}\Pr(r_{1})(1-p^{k})^{r_{1}}.\]  
Hence, for \(k\geq 1\), we have  
\[\Pr(U=k)=\sum_{r_{1}}\Pr(r_{1})\Big{[}(1-p^{k})^{r_{1}}-(1-p^{k-1})^{r_{1}} \Big{]}.\]  
To complete the proof, notice that  
\[\mathbb{E}[U]=\sum_{r_{1}}\Pr(r_{1})\sum_{k=1}^{\infty}k\Big{[}(1-p^{k})^{r_{ 1}}-(1-p^{k-1})^{r_{1}}\Big{]}\]\[=\sum_{r_{1}}\Pr(r_{1})\sum_{k=1}^{\infty}k((1-p^{k})-(1-p^{k-1})) \left(\sum_{i=0}^{r_{1}-1}(1-p^{k})^{i}(1-p^{k-1})^{r_{1}-1-i}\right)\] \[<\sum_{r_{1}}\Pr(r_{1})\sum_{k=1}^{\infty}kr_{1}(p^{k-1}-p^{k})(1-p ^{k})^{r_{1}-1}\] \[<\sum_{r_{1}}\Pr(r_{1})r_{1}(1-p)\sum_{k=1}^{\infty}kp^{k-1}\] \[=\frac{\mathbb{E}[R_{1}]}{1-p}.\]
###### Abstract  
Typical satellite component detection is an application-valuable and challenging research field. Currently, there are many algorithms for detecting typical satellite components, but due to the limited storage space and computational resources in the space environment, these algorithms generally have the problem of excessive parameter count and computational load, which hinders their effective application in space environments. Furthermore, the scale of datasets used by these algorithms is not large enough to train the algorithm models well. To address the above issues, this paper first applies YOLOv8 to the detection of typical satellite components and proposes a Lightweight Typical Satellite Components Detection algorithm based on improved YOLOv8 (LTSCD-YOLO). Firstly, it adopts the lightweight network EfficientNet-B0 as the backbone network to reduce the model's parameter count and computational load; secondly, it uses a Cross-Scale Feature-Fusion Module (CCFM) at the Neck to enhance the model's adaptability to scale changes; then, it integrates Partial Convolution (PConv) into the C2f (Faster Implementation of CSP Bottleneck with two convolutions) module and Re-parameterized Convolution (RepConv) into the detection head to further achieve model lightweighting; finally, the Focal-Efficient Intersection over Union (Focal-E1 oU) is used as the loss function to enhance the model's detection accuracy and detection speed. Additionally, a larger-scale Typical Satellite Components Dataset (TSC-Dataset) is also constructed. Our experimental results show that LTSCD-YOLO can maintain high detection accuracy with minimal parameter count and computational load. Compared to YOLOv8, LTSCD-YOLO improved the mean average precision (mAP50) by 1.50% on the TSC-Dataset, reaching 94.5%. Meanwhile, the model's parameter count decreased by 78.4%, the computational load decreased by 65.97%, and the detection speed increased by 17.66%. This algorithm achieves a balance between accuracy and light weight, and its generalization ability has been validated on real images, making it effectively applicable to detection tasks of typical satellite components in space environments.  
typeical satellite component detection; typical satellite components dataset; YOLOv8; lightweight network 22 August 2024