esa-sceva/satcom-chunk-collection huggingface dataset:
# Layered Graphs: Applications and Algorithms
## 3 Algorithm
### MDS and MCD
* In \(S\), for any two triples with the same mask, we need only to retain the triples with the smallest size. The other triples cannot lead to an optimal solution. Figure 5: MDS: The vertices of a minimum dominating set are \(\{a,d,f,h\}\) from \(G_{1}\) and \(\{c,i\}\) from \(G_{2}\). The cardinality of any MDS of \(G\) is \(6\). The vertices of MDS are shown in larger bold font. Figure 6: MCD: The vertices of a minimum connected dominating set are \(\{c,d,e,g,h\}\) from \(G_{1}\) and \(\{b,c,d,e,f,i\}\) from \(G_{2}\). The cardinality of any MCD of \(G\) is \(11\). The vertices of MCD are shown in larger bold font. * If two triples have the same mask and the minimum size, then they can be combined into one triple where the respective counts are added. * Thus, only \(2^{k}\) triples suffice for a chosen mask for layer \(i\) which implies \(2^{2k}\) triples suffice \(\forall_{j}\,cs_{i,j}\). The information of only two layers is stored. Thus, the algorithm needs \(O(k2^{2k})\) space. This is in addition to the space required by the input graph, which is \(O(nk)\). For \(k=O(\log n)\), \(O(k2^{2k})\) is the dominating term, so the space complexity is \(O(k2^{2k})\). * Thus, for a chosen mask for layer \(i\), potentially \(2^{2k}\) triples of the previous layer must be processed. That is, for all masks of layer \(i\), a total of \(2^{3k}\) triples must be processed. * Consider mask \(j\) in layer \(i\) and mask \(l\) in layer \(i-1\). Recall that there are \(2^{k}\) triples stored corresponding to mask \(l\) in layer \(i-1\). All the vertices that are covered by the combination of \(j\) and \(l\) in layer \(i-1\), say \(A\), and not covered in layer \(i\), say \(B\), can be computed in \(O(k^{2})\). This needs to be computed only once. Subsequently, for each of the triples stored corresponding to \(l\) in layer \(i-1\), we need only to check if the undominated vertices are a subset of \(B\) in \(O(k)\) time. Thus, \(O(k2^{k})\) is the dominating term in the time complexity, yielding \(O(k2^{2k})\) for all masks in the previous layer. So, for all masks in the current layer, the time complexity is \(O(k2^{3k})\). Thus, the time complexity of the algorithm is \(O(\frac{n}{k}k2^{3k})=O(n2^{3k})\). Similar constraints hold for MCD. We carry forward the existing connected components, and eventually, when the final layer is processed, all the components must be connected. The MCD algorithm is explained in detail in Theorem 4 along with time and space complexity analyses. The current layer in the following functions is \(i\). * \(feasible(j)\): Any \(j\) is valid. * \(compatible(j,l)\): The union must dominate all vertices of \(V_{i-1}\). For MCD, all components of \(l\) must have edges with vertices in \(j\). If \(i=q\), then \(V^{*}\) must be one component and it must dominate \(V_{q}\) also. * Extension: Performed as per the observations listed above. * Summarize: Let \(opt\gets min(\forall_{j}\forall_{d}size_{q,j,d})\); \(count\gets 0\); \(\forall_{j}\forall_{d}\) if \((size_{q,j,d}=opt)\) then \(count\gets count+count_{q,j,d}\); return \((opt,count)\).
# A Review of Test Stimulus Compression Methods for Ultra-Large-Scale Integrated Circuits
## 3 Encoding-Based Test Stimulus Compression Methods
### Fixed-Length to Variable-Length  
The encoding method from fixed-length to variable-length divides the original test dataset into equal-length data blocks and then selects variable-length code words to replace each data block. The length of the code words needs to be chosen based on the data characteristics of the data blocks, which directly affects the data compression efficiency of this compression method. Typical technologies for this type of encoding include Huffman encoding [42], followed by the development of selected Huffman encoding [43], optimal selected Huffman encoding [44], and other improved Huffman encoding [45].  
As a data block partition-based encoding technique, Huffman encoding first divides the original test dataset into fixed equal-length data blocks, then counts the occurrences of  
Figure 3: Dictionary encoding for testing [17].  
each data block. Finally, it encodes the data blocks with higher occurrences with shorter code words and those with fewer occurrences with longer code words. The decoder used in Huffman encoding technology is based on a finite state machine. The complexity of the decoder is directly proportional to the number of data block types. When there are more data block types in the original test set, the number of states in the state machine increases, and the decoder structure becomes more complex. To reduce the complexity of the decoder, selected Huffman encoding only encodes data blocks with higher occurrence counts and does not compress other data blocks. It adds information bits to mark whether each code word is used for encoding. This technology can effectively reduce the design complexity of the decoder and also reduce the hardware resource occupation of the compression technology. [PERSON] proposed optimal selected Huffman encoding for information bit selection optimization.  
Furthermore, many researchers have further improved and optimized Huffman encoding. [PERSON] [46] combined linear feedback shift register (LFSR) with selected Huffman encoding. It first encodes the data blocks with LFSR; then, it encodes the data blocks that cannot be encoded with LFSR using selected Huffman encoding [47]. In addition, [PERSON] further divided the data blocks that cannot be selected for Huffman encoding into smaller data blocks and then performed secondary selected Huffman encoding on the smaller data blocks [48]. These improved technologies have significantly increased the compression efficiency of Huffman encoding and the design complexity of the decoder. For ATE, [PERSON] proposed optimizations and improvements for the hardware implementation of selected Huffman encoding to reduce test power consumption and test time [49]. Recently, Tenentes improved Huffman encoding's detection capability for non-modeled faults for ATE test interfaces [50].
# XOR Binary Gravitational Search Algorithm with Repository: Industry 4.0 Applications
## 2 Gravitational Search Algorithm
### Binary Gravitational Search Algorithm  
The modification made to GSA to solve binary optimization problems is similar to BPSO [9]. In this case, the velocity vector in BGSA changes its meaning to the probability of change. Hence, a large absolute value for velocity in a dimension results in high possibility of change in such direction. Based on such a description, the function \(P(V_{i}^{j})\), the probability of change in \(j\)th bit of \(i\) th object, is considered to be a hyperbolic tangent of the velocity vector as follows [16].  
\[P(V_{i}^{j}(t))=tanh\left(\left|V_{i}^{j}(t)\right|\right),\ i=1,...,N,\ j=1,...,d \tag{10}\]  
where \(tanh(.)\) represents the hyperbolic tangent function as follows:  
\[tanh\ (x)=\frac{1{-}e^{-2x}}{1{+}e^{-2x}} \tag{11}\]  
Calculating the \(P(V_{i}^{j})\), the probability of change in every dimension, the next state in each dimension is calculated as follows [16].  
\[x_{i}^{j}(t{+}1)=\left\{\begin{array}{ll}\overline{x}_{i}^{j}(t)&if\ \ r_{i}^{j}{ \in}P(V_{i}^{j}(t+1))\\ x_{i}^{j}(t)&Otherwise\end{array}\right. \tag{12}\]  
where \(\overline{x}_{i}^{j}(t)\) represents the complement of \(x_{i}^{j}(t)\) and \(r_{i}^{j}{\in}[0,\ \ 1]\) has a uniform random value.
###### Abstract  
Carbon fiber reinforced plastic (CFRP) is typically hard to process, because it is easy for it to generate processing damage such as burrs, tears, delamination, and so on in the machining process. Consequently, this restricts its wide spread application. This paper conducted a comparative experiment on the cutting performance of the two different-structure milling cutters, with a helical staggered edge and a rhombic edge, in milling carbon fiber composites; analyzed the wear morphologies of the two cutting tools; and thus acquired the effect of the tool structure on the machined surface quality and cutting force. The results indicated that in the whole cutting, the rhombic milling cutter with a segmented cutting edge showed better wear resistance and a more stable machined surface quality. It was not until a large area of coating shedding occurred, along with chip clogging, that the surface quality decreased significantly. At the stage of coating wear, the helical staggered milling cutter with an alternately arranged continuous cutting edge showed better machined surface quality, but when the coating fell off, its machined surface quality began to reveal damage such as groove, tear, and fiber pullout. Meanwhile, burrs occurred at the edge and the cutting force obviously increased. By contrast, for the rhombic milling cutter, both the surface roughness and cutting force increased relatively slowly.  
carbon fiber reinforced plastic; milling cutter; wear morphology; machined surface quality; cutting force
###### Abstract  
A small zero-order resonant antenna based on the composite right-left-handed (CRLH) principle is designed and fabricated without metallic vias at 30 GHz to have patch-like radiation. The mirror images of two CRLH structures are connected to design the antenna without via holes. The equivalent circuit, parameter extraction, and dispersion diagram are studied to analyze the characteristics of the CRLH antenna. The antenna was fabricated and experimentally verified. The measured realized gain of the antenna is 5.35 dBi at 30 GHz. The designed antenna is free of spurious resonance over a band width of 10 GHz. A passive beamforming array is designed using the proposed CRLH antenna and the Butler matrix. A substrate integrated waveguide is used to implement the Butler matrix. The CRLH antennas are connected to four outputs of a \(4\times 4\) Butler matrix. The scanning angles are \(12^{\circ}\), \(-68^{\circ}\), \(64^{\circ}\), and \(-11^{\circ}\) for excitations from port 1 to port 4 of the \(4\times 4\) Butler matrix feeding the CRLH antenna.  
CRLH antenna; mmWave antenna; resonant mode; radiation; patch antenna +
Footnote †: journal: Sensors
###### Abstract  
To maximize the advantages of being low-cost, highly mobile, and having a high flexibility, aerial recovery technology is important for unmanned aerial vehicle (UAV) swarms. In particular, the operation mode of \"launch-recovery-relaunch\" will greatly improve the efficiency of a UAV swarm. However, it is difficult to realize large-scale aerial recovery of UAV swarms because this process involves complex multi-UAV recovery scheduling, path planning, rendezvous, and acquisition problems. In this study, the recovery problem of a UAV swarm by a mother aircraft has been investigated. To solve the problem, a recovery planning framework is proposed to establish the coupling mechanism between the scheduling and path planning of a multi-UAV aerial recovery. A genetic algorithm is employed to realize efficient and precise scheduling. A homotopic path planning approach is proposed to cover the paths with an expected length for long-range aerial recovery missions. Simulations in representative scenarios validate the effectiveness of the recovery planning framework and the proposed methods. It can be concluded that the recovery planning framework can achieve a high performance in dealing with the aerial recovery problem.  
aerial recovery; UAV swarm; mother aircraft; recovery planning framework; genetic algorithm; homotopic approach +
Footnote †: journal: Applied Sciences
# Deep Learning-Based Methods for Prostate Segmentation in Magnetic Resonance Imaging
## 2 Materials and Methods
### Deep Learning Models  
Three different deep learning models including UNet [45], ENet [43], and ERFNet [44] were investigated to account for accurate prostate segmentation, fast training time, low hardware requirements for inference, and low training data requirements. Specifically, UNet was modified to improve segmentation accuracy, as reported in [48; 49]. Briefly, (i) 3 \(\times\) 3 convolutions were replaced by 5 \(\times\) 5 convolutions, (ii) zero padding was used to ensure that the size of the output feature maps was the same as the input size, and (iii) an input size of 512 \(\times\) 512 with 32 filters was used on the first contraction path layer, with doubling of feature maps after each max pool and stopping at 256 feature maps and 2D size of 64 \(\times\) 64.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & **Repetition Time** & **Echo Time** & **Flip Angle** & **Signal** & **Signal-to-Noise** \\  & **(ms)** & **(ms)** & **(Degrees)** & **Averages** & **Ratio** \\ \hline T2w TSE & 3091 & 100 & 90 & 3 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter of MRI protocol.  
Concerning ENet and ERFNet (see Table 1 in [43] and Table 2 in [44] for the description of their architecture), they were mainly applied in self-driving cars to compensate for limited hardware availability maintaining high accuracy and successfully used in two biomedical segmentation issues [48; 49], that is, in the segmentation of high resolution computed tomography (HRCT) images characterized by a slice thickness much lower than that of the T2 weighted images of the prostate studies. This means that the number of slices of each patients' study was much greater than in this study.
# Novel Sparse-Coded Ambient Backscatter Communication for Massive IoT Connectivity
## 5 Simulation Results
### Applicability to Energy Harvesting  
Figure 11 shows the effect of the reflection coefficient \(\alpha\) for the average sum throughput. The magnitude of reflection coefficient \(\alpha\) balances the incident power arrived at tag antenna in Equation (5) between energy harvesting and information decoding. The energy-information trade-off in AmBC determines the average sum throughput in various modulators and detectors [37]. However, in the conventional TD-AmBC, the throughput is only marginally improved as it does not exploit MAI and ISI by assuming orthogonality for the modulation and detection, as shown in Figure 11a.  
Figure 11: Effect of the reflection coefficient \(\alpha\) (\(L^{+}=3\)).  
Figure 10: Effect of the number of time slots \(K\) (\(M=2\), \(L^{+}=3\)).  
The proposed SC-AmBC, on the other hand, is designed to exploit these interferences in a non-orthogonal manner. Figure 11b demonstrates the benefits of sparse coding in the throughput. Compared to 5.15 Mbps throughput in TD-AmBC, the maximum throughput of SC-AmBC is 8.26 Mbps, achieving an additional 60% gain. In addition, the energy-information trade-off can be flexibly adapted by controlling \(K\), \(M\), as well as \(\alpha\) in SC-AmBC. In conclusion, the proposed SC-AmBC is an energy-efficient solution for passive RF devices based on energy harvesting.
## 3 Methods
### Parallel Interactive Attention Network
#### 3.4.2. Feature Interaction  
In stage 2, the feature information in \(D_{1}\) and \(M_{1}\) is further extracted separately, and then the extracted feature information is used interactively to improve the feature utilization, which is helpful to improve the short-term OD prediction accuracy. The specific process is as follows.  
\(D_{1}\) and \(M_{1}\) are fed into the DCABs for feature extraction to obtain \(D_{4}\in\mathbb{R}^{\lambda\times\phi\times\phi}\) and \(D_{5}\in\mathbb{R}^{\lambda\times\phi\times\phi}\), respectively, where \(D_{4}\) and \(D_{5}\) contain the feature information of the historical OD matrices of previous days and the historical OD matrices of the same day, respectively. In order to improve the feature utilization and provide richer feature expression, two different feature fusion operations are performed on \(D_{4}\) and \(D_{5}\) to obtain \(E_{1}\in\mathbb{R}^{2\lambda\times\phi\times\phi}\) and \(A_{2}\in\mathbb{R}^{\lambda\times\phi\times\phi}\), respectively, which can be expressed as:  
\[E_{1}=[D_{4},D_{5}], \tag{15}\] \[A_{2}=D_{4}\oplus D_{5}. \tag{16}\]  
In order to further improve the feature expression, the feature interaction is performed again. Specifically, \(E_{1}\) and \(A_{2}\) are fed into the DCABs to generate \(D_{6}\in\mathbb{R}^{\lambda\times\phi\times\phi}\) and  
\begin{table}
\begin{tabular}{c} \hline Input: The historical OD matrices \(M\) of previous days, the historical inflow series \(N\) of the same day. \\ \hline Stage 1: Generation of the Historical OD Matrices of The Same Day \\ 1: \(D_{1}=\) DCAB (\(M_{2}\)); \(D_{2}=\) DCAB (\(D_{1}\)); \\ 2: \(B=N_{\text{unsqueeze}}\) (\(-1\)); repeat (\(1\), \(1\), \(B_{1}\)); \\ 3: \(M_{1}=B_{1}\odot\) epoch softmax(\(D_{2}\)) (\(+\) DCAB (\(B_{1}\))); \\ \hline Stage 2: Feature Interaction \\ 4: \(D_{2}=\) DCAB (\(D_{1}\)); \(D_{3}=\) DCAB (\(M_{1}\)); \\ 5: \(E_{1}=\) thresh.concat (\(D_{1}\)); \(A_{2}=D_{4}+D_{5}\); \\ 6: \(D_{5}=\) DCAB (\(E_{1}\)); \(D_{7}=\) DCAB (\(A_{2}\)); \\ 7: \(A_{3}=D_{4}+D_{5}\); \(E_{2}=\) thresh.concat (\(D_{D_{5}}\)); \\ 8: \(D_{5}=\) DCAB (\(A_{3}\)); \(D_{7}=\) DCAB (\(E_{2}\)); \\ \hline Stage 3: Generation of The Predicted OD Matrix \\ 9: \(E_{3}=\) thresh.concat(\(D_{3}\), \(D_{3}\)); \\ 10: \(O=\) DCB (\(E_{3}\)); \\ \hline Output: The predicted OD matrix \(O\). \\ \hline \end{tabular}
\end{table}
Table 1: PyTorch-like pseudocode for the forward process of the proposed PIANet.  
\(D_{7}\in\mathbb{R}^{\lambda\times\phi\times\phi}\), respectively. Then, the adding operation and concatenation operation are performed on \(D_{6}\) and \(D_{7}\) to obtain \(A_{3}\in\mathbb{R}^{\lambda\times\phi\times\phi}\) and \(E_{2}\in\mathbb{R}^{2\lambda\times\phi\times\phi}\), respectively, which can be formulated as:  
\[A_{3} =D_{6}\oplus D_{7}, \tag{17}\] \[E_{2} =[D_{6},D_{7}]. \tag{18}\]  
After that, \(A_{3}\) and \(E_{2}\) are fed into the DCABs for deeper feature extraction to obtain \(D_{8}\in\mathbb{R}^{\lambda\times\phi\times\phi}\) and \(D_{9}\in\mathbb{R}^{\lambda\times\phi\times\phi}\), respectively.
## 4 Model Studies
### Results of the Simulated Rainfall Infiltration
#### 4.4.3 The Effect of Initial Saturation on Rainfall Infiltration  
This study researched the infiltration intensity for a stable period in order to compare the relationship between infiltration intensity and initial saturation. During this period, pore gas pressure tended to be stable, and the support effect of gas on the gravity gradient also stopped. The magnitude of infiltration was affected by matric suction, water, and gas permeability characteristics, and the support for gravity gradient could be ignored. In this research, the rainfall infiltration magnitude was described by the relative infiltration magnitude, which was a dimensionless variable. It was defined as the ratio of the rainwater infiltration rate to the saturated permeability coefficient of soil.  
Figure 12: Saturation distribution in depth over time at various initial saturation degrees.  
At the onset of rainfall infiltration, the magnitude of infiltration was mainly affected by the matric suction on the soil surface; the pore water pressure gradient rose because of the higher matric suction. Therefore, the magnitude of infiltration was relatively large at this time because the saturation of the soil surface was low (Figure 13). When infiltration tended to be stable, infiltration was mainly affected by the matric suction and the water relative permeability coefficient at the location of the wetting front. As shown in Figure 14, the stable magnitude of infiltration first declined, then grew with the increase of saturation. The maximum value of stable magnitude for infiltration occurred at the residual saturation (effective saturation = 0) or fully saturated status (effective saturation = 1). The minimum value of stable magnitude for infiltration occurred at the unsaturated status. Initially, as initial saturation was lower, the infiltrated water was driven by matric suction. When the initial saturation increased, although matric suction continued to decrease, the water relative permeability coefficient increased rapidly, and as a result, rainfall infiltration occurred at a minimum value. With the increase of initial saturation, the effect of matric suction on infiltration continued to decrease, and the water relative coefficient of permeability tended to be one (Figure 1), so the water was infiltrated by the pore channels. During this period, the infiltration magnitude was mainly affected by the water relative permeability coefficient. Therefore, the magnitude of infiltration increased with the increase of initial saturation during this period. The maximum value of stable magnitude infiltration for silty sand occurred at the residual saturation, while the minimum value for it occurred at a saturation between 80% and 90%.
## 2 Modeling of the Poloidal Field Coil System on HL-3
### Mathematical Model
#### 2.1.2 Power Supply Model  
The CS/PF power supply system utilizes a four-quadrant controlled thyristor rectifier bridge structure with an internal circulating current to energize the coils. The triggering angle of the thyristors is controlled by the Plasma Control System (PCS) to adjust the terminal voltage of the PF coils. In the control model, the power supply can be regarded as an inertial element with delay [25]. The input is the output voltage of the power supply requested by the PCS, and the output is the actual output voltage value. Assuming that the proportional coefficient is 1, the response time constant is \(\tau_{ps}\), and the power supply delay time is \(\tau\), and the transfer function of the power supply system can be expressed as follows [25]:  
\[G(s)=\frac{e^{-\tau\mathrm{S}}}{s\tau_{ps}+1} \tag{6}\]
## 4 Results and Discussion
### Comparison with Previous Studies  
Table 6 summarizes the results from this study, where the units were standardized for comparison with data from previous studies in Table 7. Since the permeance is a performance indicator that is independent of the membrane thickness, the permeance was multiplied by the average membrane thickness (75 \(\upmu\)m) to convert the results into permeability. Table 6 shows the experimental results of the current study in comparison to the previous studies for water vapor separation from air under various experimental conditions. The corresponding relationships between H\({}_{2}\)O permeability and H\({}_{2}\)O/N\({}_{2}\) selectivity for the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Method** & \begin{tabular}{c} **RH** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **H\({}_{2}\)O Permeability** \\ **(Barrer)** \\ \end{tabular} & \
\begin{tabular}{c} **N\({}_{2}\) Permeability** \\ **(Barrer)** \\ \end{tabular} &
\begin{tabular}{c} **Selectivity (-)** \\ \end{tabular} \\ \hline
**Cup method** & 70 & 11,390 & - & - \\
**Reverse permeation of N\({}_{2}\)** & 50 & 15,610 & 18.93 & - \\  & 30 & 21,820 & 30.08 & 725.6 \\
**Test result** & 50 & 24,430 & 31.16 & 783.8 \\  & 70 & 26,870 & 32.04 & 838.5 \\
**Extrapolation** & 0 & 18,070 & 28.64 & 631.0 \\  & 100 & 30,680 & 33.56 & 914.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Membrane performance factors from this study.