esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Behavioral and Quantitative Analysis of Reconfigurable S\({}^{3}\)PR Net
### Computational Complexity  
Algorithm 1 is used to design a reconfigurable S\({}^{3}\)PR net with \(N_{R}=\) ((\(N\), \(M_{o}\)), \(\mathcal{R}\)). In addition, Algorithm 2 computes the control places to a reconfigurable S\({}^{3}\)PR net with \(N_{R}=\) ((\(N\), \(M_{o}\)), \(\mathcal{R}\)).  
**Theorem 10**: _Given a reconfigurable S\({}^{3}\)PR net with \(N_{R}=\) ((\(N\), \(M_{o}\)), \(\mathcal{R}\)), where \(N_{R}\) with \(N_{k}=\) (\(P_{\text{Ck}}\), \(T_{k}\), \(F_{k}\), \(W_{k}\), \(M_{k_{0}}\), \(K_{k}\)), the time complexity of Algorithm 1 is polynomial._  
Let \(N_{k}\) be states in \(N_{R}\) with \(N_{k}=\) (\(P_{\text{Ck}}\), \(T_{k}\), \(F_{k}\), \(W_{k}\), \(M_{k_{0}}\), \(K_{k}\)), \(\mathcal{R}=\{r_{1},r_{2},r_{3},\ldots,r_{k}\}\), \(rr_{k}=\) {\(L_{k}\), \(R_{k}\), \(\varphi_{k}\), \(\varphi_{k}\), \(\varphi_{k}\)}, and net (\(N_{R}\), \(M_{R_{0}}\)) be the obtained reconfigurable S\({}^{3}\)PR net. Let \(x\) be the cardinality of \(\mathcal{R}\), i.e., \(|\mathcal{R}|=x\). The \"While\" loop is executed \(x\) times to design state \(N_{k}\) in a reconfigurable S\({}^{3}\)PR net (\(N_{R}\), \(M_{R_{0}}\)). Therefore, in the worst case, the computational complexity of algorithm 1 is O(\(x\)). Thus, the computational complexity of the Algorithm 1 has polynomial time complexity.  
**Theorem 11**: _Given a reconfigurable S\({}^{3}\)PR net with \(N_{R}=\) ((\(N\), \(M_{o}\)), \(\mathcal{R}\)), where \(N_{R}\) with \(N_{k}=\) (\(P_{\text{Ck}}\), \(T_{k}\), \(F_{k}\), \(W_{k}\), \(M_{k_{0}}\), \(K_{k}\)), the time complexity of Algorithm 2 is polynomial._  
Algorithm 2 is used to design a control place \(V_{S}\) to each minimal siphon \(S\), \(V_{S}\)\(\subsetneq\)\({}^{*}S\) in each state \(N_{k}\) in a reconfigurable S\({}^{3}\)PR net (\(N_{R}\), \(M_{R_{0}}\)) to achieve the liveness of net (\(N_{R}\), \(M_{R_{0}}\)). Obviously, each \(V_{S}\) is associated with the minimal siphon \(S\) in net (\(N_{k}\), \(M_{k_{0}}\)). Let \(x\) be the cardinality of \(\mathcal{R}\), i.e., \(|\mathcal{R}|=x\). Let \(y\) be the number of minimal siphons \(S\) (denoted as \(S^{\prime}\)) that requires \(V_{S}\) i.e., \(|S^{\prime}|=y\). The \"While\" loop is executed \(x\) times to design state \(N_{k}\) in reconfigurable S\({}^{3}\)PR net (\(N_{R}\), \(M_{R_{0}}\)). The \"FOR loop\" loop is executed \(y\) times to design \(V_{S}\) for the \(S^{\prime}\) in (\(N_{k}\), \(M_{k_{0}}\)). Therefore, the computational complexity of Algorithm 2 is O(\(xy\)). Thus, the computational complexity of the Algorithm 2 has polynomial time complexity.
# A Ku-Band GaAs Multifunction Transmitter and Receiver Chipset
## 3 Circuit Blocks of the Ku-Band Multifunction Chipset
### Medium Power Amplifier (MPA)  
A broadband MPA with high gain and output power is designed using E-mode HEMT to compensate for loss from the digital attenuator and digital phase shifter. Figure 12 illustrates a schematic of the MPA.  
A three-stage common source topology is adopted to obtain more than 20 dB gain over the Ku-band. In the first two stages, a \(2\times 50\)-\(\mu\)m HEMT (M\({}_{1}\), M\({}_{2}\)) is used for high gain and wide bandwidth. The third stage employs an \(8\times 50\)-\(\mu\)m HEMT (M\({}_{3}\)) for high output power and linearity. The input is matched to 50 \(\Omega\) by the L-section network of L\({}_{1}\) and L\({}_{2}\). Two inter-stage matching networks are implemented using L\({}_{3}\)-L\({}_{4}\)-L\({}_{5}\)-L\({}_{6}\) and L\({}_{7}\)-L\({}_{8}\)-C\({}_{5}\), respectively. The matching frequencies of the networks are intentionally set differently from each other, such that wideband operation is obtained. The output network (L\({}_{9}\)) is matched to the load-pull impedance for high output power. A parallel R-C network (R\({}_{1}\) and C\({}_{2}\)) is inserted into the signal path to improve the stability at low frequencies. The MPA test cut, as shown in Figure 12, occupies \(1.9\times 1.0\) mm\({}^{2}\), excluding probing pads. The MPA consumes DC power of 380 mW, with a drain bias voltage of 5 V. The measurement results are shown in Figure 13. As shown in Figure 13a, the MPA exhibits a peak gain of 27.3 dB at 14.2 GHz and more than 22.4 dB gain over the Ku-band. The input and output matching are below \(-6.7\) and \(-10.3\) dB, respectively, over the Ku-band. The output power, power gain and power-added efficiency (PAE) at 15 GHz are shown in Figure 13b. The output P1 dB (OP1 dB) and saturated power (P\({}_{\rm sat}\)) are 18.9 and 19.6 dBm, respectively. The maximum PAE is 25.5%. A discrepancy between the measurement and simulation is presumably due to inaccuracy of the large-signal transistor model.  
Figure 11: Schematics of (**a**) input voltage transformers (IVT), (**b**) output voltage transformers (OVT) for controlling the digital attenuator and phase shifter, (**c**) OVT for D\({}_{\rm out}\).  
Figure 12: Schematic and chip micrograph of the medium power amplifier (MPA).
# Mapping Surface Flow Velocity of Glaciers at Regional Scale Using a Multiple Sensors Approach
## 4 Discussion
These improved maps of glacier surface velocity will certainly be useful for glacier flow modelling, mass conservation techniques or any other applications using glacier surface velocity as input data. For example, it has been shown that knowledge of glacier flow is an effective constraint to calculate the distribution of glacier thickness while preserving the true shape of glacial valleys [7; 8; 42].
# High Power All-Fiber Supercontinuum System Based on Graded-Index Multimode Fibers
###### Abstract  
An all-fiber supercontinuum source based on graded-index multimode fibers is reported. The supercontinuum source is based on a homemade mode-locked oscillator and a three-stage picosecond amplifier, which obtained the supercontinuum by a graded-index multimode fiber. The laser output with a spectral range of 480-2440 nm, an average power of 25 W, and a repetition frequency of 8.27 MHz is obtained. To the best of our knowledge, this is the highest average power for generating a supercontinuum with an all-fiber structure based on the graded-index fiber. The effects of GRIN fiber length and different pump peak powers on the supercontinuum generation are also verified. The results showed that the graded-index multimode fiber can effectively obtain a supercontinuum with high power.  
supercontinuum generation; nonlinear effect; graded-index fiber; mode-locked laser; all-fiber +
Footnote †: journal: Applied Physics Letters  
0000-0002-3091-5564-2000][PERSON]X. [PERSON]  
0000-0002-3195-5640][PERSON]-Z. [PERSON]
## 2 Materials and Methods
### Inoculum and Incubation  
The trial animals were cared and handled following the protocols approved by the Animal Welfare and Ethics Committee of China Agricultural University. Two cannulated Angus bulls (three years old) were used as the donors of rumen fluid and were fed a total mixed ration (TMR) that consist of (% of dry matter--DM basis) a concentrate mixture (30%), grass hay (20%), and corn silage (50%). The animals were fed twice daily at 05:00 and 17:00, respectively. Approximately 300 mL of rumen fluid were obtained from each cattle and incorporated into a 500 mL bottle, at about 3 h after morning feeding. The rumen fluid was filtered through four layers of gauze after mixing, then taken to the laboratory immediately. The fresh rumen fluid prepared was used as the inoculum of the in vitro cultures. The buffered medium for incubation was prepared according to the description of the previous study [16], which included a trace-element solution, a carbonic acid buffer solution, a phosphate buffer solution, a sodium sulfide reducing solution (Na\({}_{2}\)S) and a resazurin indicator. All the feeds were smashed and sieved through 1.0-mm sieve as the substrate samples. Ground feed substrate (200 mg) was accurately weighed and put into each glass syringe (calibrated volume of 100 mL), which contained 30 mL of the rumen fluid-buffer mixture (ratio = 1:2). The rumen fluid-buffer mixture was then continuously gassed with CO\({}_{2}\). There was no air in the syringes, whose heads were sealed. Subsequently, the syringes were incubated in a water bath with intermittent shaking at 39 \({}^{\circ}\)C for 24 h. Five syringes were used as the repetition of each treatment, and additional three ones without substrate were used as the blanks for incubation. Each sample measurement was repeated twice.
# Fusing Ground-Penetrating Radar Images for Improving Image Characteristics Fidelity
###### Abstract  
The analysis of ground-penetrating radar (GPR) data is of vital importance for detecting various subsurface features that might manifest as hyperbolic peaks, which are indicators of a buried object or grayscale variation in the case of contrast in the soil texture. This method focuses on identifying exaggerated patterns through a series of image-processing steps. Two GPR images are initially read and preprocessed by extracting channels, flipping, and resizing. Then, specific regions of interest (ROIs) are cropped, and the Fourier transform is further applied to turn them into the frequency domain. With the help of their frequency signatures, these patterns are extracted from the images, and binary masks are constructed to obtain features of interest. These masked images were reconstructed and merged to make hyperbolic features visible. Finally, Local Binary Pattern (LBP) analysis is used to emphasize these hyperbolic peaks, thereby facilitating their recognition across the whole image. The proposed approach improves the detection of performance subsurface features in GPR data; hence, it is an important tool for geophysical surveys and other related applications. The results prove the high performance of the proposed procedure in improving GPR image characteristics.  
fusion; GPR; local binary pattern; hyperbolic peaks; Fourier transform Article
# Tensor Rank Regularization with Bias Compensation for Millimeter Wave Channel Estimation
## 3 Signal Model
Then substituting the frequency domain channel matrix \(\mathbf{H}_{k}\) (3) into \(\mathbf{Y}_{k}\) (4), we obtain  
\[\mathbf{Y}_{k} =\sum_{r=1}^{R}\alpha_{r}\mathrm{e}^{-\mathrm{j}2\pi\frac{kf_{s}} {N_{0}}\tau_{r}}\mathbf{W}_{A}^{\mathrm{T}}\mathbf{a}_{MS}(\varphi_{r}) \mathbf{a}_{BS}^{\mathrm{T}}(\theta_{r})\mathbf{F}_{A}+\mathbf{N}_{k}\] \[=\sum_{r=1}^{R}\alpha_{r}\mathrm{e}^{-\mathrm{j}2\pi\frac{kf_{s}} {N_{0}}\tau_{r}}\tilde{\mathbf{a}}_{MS}(\varphi_{r})\tilde{\mathbf{a}}_{BS}^{ \mathrm{T}}+\mathbf{N}_{k}, \tag{5}\]  
where \(\tilde{\mathbf{a}}_{MS}(\varphi_{r})\triangleq\mathbf{W}_{A}^{\mathrm{T}} \mathbf{a}_{MS}(\varphi_{r})\in\mathbb{C}^{M_{ms}}\) and \(\tilde{\mathbf{a}}_{BS}(\theta_{r})\triangleq\mathbf{F}_{A}^{\mathrm{T}} \mathbf{a}_{MS}(\theta_{r})\in\mathbb{C}^{M_{bs}}\) are the equivalent array response vectors. By combining \(\mathbf{Y}_{k}\) of \(K\) selected subcarriers, the received signals can be expressed by a third-order tensor \(\mathcal{Y}\in\mathbb{C}^{M_{ms}\times M_{bs}\times K}\). We notice that each frontal slice \(\mathbf{Y}_{k}\) of the tensor \(\mathcal{Y}\) is a weighted sum of a common set of rank-one outer products, which makes the tensor \(\mathcal{Y}\) admit the definition of the CP model as  
\[\mathcal{Y}=\sum_{r=1}^{R}\tilde{\mathbf{a}}_{\mathrm{MS}}(\varphi_{r})\circ \tilde{\mathbf{a}}_{\mathrm{BS}}(\theta_{r})\circ(\alpha_{r}\mathbf{g}(\tau_{ r}))+\mathcal{N}, \tag{6}\]  
where we assume subcarriers {1, 2, \(\cdots\), \(K\)} are assigned for the training process for simplicity and \(\mathbf{g}(\tau_{r})\triangleq\left[e^{-\mathrm{j}2\pi\frac{kf_{s}}{N_{0}} \tau_{r}},\cdots,e^{-\mathrm{j}2\pi\frac{kf_{s}}{N_{0}}\tau_{r}}\right]^{T} \in\mathbb{C}^{K}\) is the phase vector caused by delay. \(\mathcal{N}\in\mathbb{C}^{M_{ms}\times M_{bs}\times K}\) is the equivalent noise tensor. Due to the sparse nature of mmWave channels, the number of paths \(R\) is typically small relative to the dimension of the tensor \(\mathcal{Y}\)[15, 16]. Therefore, \(\mathcal{Y}\) inherently has a low-rank nature, which ensures that the CP decomposition is unique up to scaling and permutation ambiguities. An estimation of channel parameters \(\{\varphi_{r}\), \(\theta_{r}\), \(\tau_{r}\), \(\alpha_{r}\}\) can be obtained by processing the CP decomposition results from \(\mathcal{Y}\). Define  
\[\mathbf{A} \triangleq\left[\tilde{\mathbf{a}}_{MS}(\varphi_{1}),\cdots \tilde{\mathbf{a}}_{MS}(\varphi_{R})\right]\in\mathbb{C}^{M_{ms}\times R}, \tag{7}\] \[\mathbf{B} \triangleq\left[\tilde{\mathbf{a}}_{BS}(\theta_{1}),\cdots\tilde{ \mathbf{a}}_{BS}(\theta_{R})\right]\in\mathbb{C}^{M_{bs}\times R}\,\] (8) \[\mathbf{C} \triangleq\left[\alpha_{1}\mathbf{g}(\tau_{1}),\cdots\alpha_{R} \mathbf{g}(\tau_{R})\right]\in\mathbb{C}^{K\times R}\, \tag{9}\]  
These three component matrices \(\mathbf{A}\), \(\mathbf{B}\), and \(\mathbf{C}\) are associated with a noiseless version of \(\mathcal{Y}\).
# Detecting the Structural Hole for Social Communities Based on Conductance-Degree
## 3 Conductance-Degree Structural Hole Detection Algorithm
### Conductance and Degree Score  
The larger the conductance value, the more relations exist among the neighbor community and other groups, and the more nodes have an association with the vertex and the more information per path. In real social networks, if those vertices with large conductance are lying on the edge of the communities, then they are more likely to be structural hole spanners.  
It is easy to determine the degree of each node when we load the vertices and edges into memory. The greater the degree, the more importance and influence the node has on the networks. However, not all of the nodes with large degrees are structural hole spanners. Some of them are the core of the communities. In this paper, we computed a CD-score that refers to both the conductance and degree. We denoted \(\alpha\) and \(\beta\) as the regulatory factors, and we indicated that \(\alpha+\beta=1\). The bigger the \(\alpha\), the more influence the detected nodes have, and the bigger the \(\beta\), the more accuracy the detected  
Figure 2: Case of greater conductance. The node in the dark color’s neighbor community has greater conductance than its neighbors.  
nodes have. In our experiments, the \(\alpha\) was 0.3 and the \(\beta\) was 0.7. The larger the conductance and the degree, the greater is the CD-score:  
\[\text{s}=\alpha\cdot\frac{d(v)}{g}+\beta\cdot\phi(v) \tag{4}\]  
where s is the CD-score, d(v) is the degree of node v, \(\phi(v)\) is the conductance of node v's neighbor community, and \(\alpha\) and \(\beta\) are the regulatory factors.  
Algorithm 1 provides a method to compute the conductance. It takes \(O(n)\) time. Computing the degree of a node to detect structural holes uses different approaches but achieves equally satisfactory results in [PERSON]'s [10] and [PERSON]'s [21] work. Although they describe the node's message passing ability, the degree is easier to compute, and we improve the method with conductance. However, because the core of the communities has a large conductance and degree, we need more information regarding the relative position of the vertex in the communities.  
```
0: Network (Nodes and edges)
0:\(\phi(\text{S})\)
1: Initialize node List
2: For each node in List do
3: if node is in v-neighbor do
4: d_sum(S) += 2;
5: edges(S)++;
6: Else
7: d_sum(S)++;
8: cut(S)++;
9: End if
10: End for
11: \(\phi(\text{S})=\frac{cut(S)}{\min(d\_sum(S)\_d\_sum(S\_bar{w}))}\)
12: Return
```  
Figure 3: Illustration of the Conductance–Degree structural hole detection algorithm (CD-SHA). CD-SHA includes 5 steps: (1) Compute the conductance and degree; (2) Calculate CD-score; (3) Identify the local minimum community (LMC) structure in the network; (4) Detect communities by the label propagation algorithm based on conductance (C-LPA); (5) Identify the structural hole spanners according to their CD-scores after filtering.
## 4 Results and Analysis
### Grid Virtual Division Scheme Performance
#### 4.2.3 Impact of Different Numbers of LED  
As mentioned earlier, the number of transmitters projected in a frame affects the extraction efficiency of the RoI to a certain extent. Therefore, in this subsection, we explore the performance of the traditional and proposed schemes under variable LED numbers. We still configure the transmission frequency at 5 kbps and fix the receiver 80 cm from the transmitter to measure the processing time and BER of both schemes. Specifically, we increase the number of transmitters of the same shape and size from one to four in turn. The experimental results are presented in Figures 11 and 12 in the form of a box plot and bar chart, respectively. Intuitively, the number of LEDs affects the extraction time because the proposed scheme requires the stitching of the adjacent blocks. This phenomenon is also demonstrated by the median times of the proposed scheme increasing from 0.23 ms to 0.59 ms with the rise in the number of LEDs, as shown in Figure 11. However, the grid virtual division scheme still has lower median times than the baseline ranging from 1.02 ms to 1.51 ms under the same circumstances, which strongly demonstrates the effectiveness of the proposed scheme.  
As shown in Figure 12, we then calculate the BERs obtained by the receiver when sequentially changing the number of transmitters from one to four. It is evident from the figure that the BER increases slightly as the number of transmitters increases. To be precise, the variation range of the BER at the baseline is 0.16% to 0.28%, while the variation range of the proposed scheme is 0.20% to 0.32%. Intuitively, if our proposed method is highly consistent with the regions extracted by the baseline scheme, the BER results will not fluctuate much. The statistical results are consistent with our expectations and remain within a reasonable threshold, which are sufficient to demonstrate the feasibility and effectiveness of the proposed scheme. It also shows that the increase in the number of transmitters has less of an impact on BER.  
Figure 11: RoI extraction performance under variable LED number.  
Figure 10: BER with variable LED shape and size.
# The Role of Low Temperature Waste Heat Recovery in Achieving 2050 Goals: A Policy Positioning Paper
## 3 Results
### Key Differences between High and Low Temperature Heat Recovery  
Low temperature heat recovery is relatively untested compared with its high temperature counterpart. By now, high temperature recovery, e.g., from factories and power stations, should be standard as the concept is well developed. There are several key technical differences between low and high temperature heat recovery and these have an impact on policy decisions. First, whilst low temperature heat is directly suitable for meeting hot water demand and for use in space heating, the heat needs to be upgraded before it is able to be used in a district heating network. This requires the use of a heat pump adding potentially significant expense and exposing heat recovery projects to the risk of increases in electricity prices. Large differences between the temperature of the waste heat and that required for the district heating network can create significant expense. Variation in the temperature of the waste heat is also a risk but, to some extent, this can be mitigated with storage, ensuring that waste heat is collected when it is of a high temperature.  
Despite the additional costs associated with the use of heat pumps, there are often financial benefits for low temperature heat recovery. Low temperature sources tend to be close to areas of heat demand, reducing transmission costs, and the technology required to recover the heat may be cheaper. Decisions regarding the size of the heat pump often hinge on cost versus efficiency tradeoffs [32].
# Weed Detection and Classification with Computer Vision Using a Limited Image Dataset
## 3 Results
### Texture Features  
The texture features, which comprised three different types of features (HOG, GLCM, and LBP combination), achieved a recognition rate of 80.5% with 160 training images. The results are listed in Table 12.
## 3 Methodology
### Collaborative Management of Meso-Macro Information
#### 3.2.1 Model Analysis  
With the development of modern digital technology, the integrated use of BIM and GIS has been examined. The information model in BIM supports a small space range and focuses on the details of building facilities, which cannot simulate macroscopic topographic information. Therefore, in the process of the collaborative management of meso and macro models, high-quality and high-precision 3D GIS model is introduced to build the surrounding environment of information models. In this way, a visual model co-existing with the information model is constructed. The government units can further understand and master the regional planning situation through the visual presentation of the combined model of BIM and GIS, so as to improve the overall management degree of the regional government units on the mesoscale and macroscale model information.  
In the current practice, three methods have been established that could combine the information model in BIM and the information model in GIS, namely file format conversion, data standard extension, and ontology [30]. The method proposed in this study is based on the unified information model file format in mesoscale and adopts the method of file format conversion so as to realize the analysis of information model files and import them to the platform. Then, the model is regenerated in the platform so that the information model in BIM and the information model in GIS are integrated.  
When integrating BIM and GIS models, it is necessary to parse the spatial location of the models and convert the local coordinate system into the global coordinate system in order to ensure that the spatial positions of the two models are consistent, thus achieving an integrated effect. The implementation process is as follows:  
1. Establishing the spatial relationship between the models: import the BIM and GIS models into the same platform and accurately represent their spatial relationship within the platform.
2. Defining and using a unified coordinate system: define a common coordinate system for the BIM and GIS models and convert the model's original coordinate system to this coordinate system using algorithms such as Bursa-Wolf or seven-parameter transformation for coordinate or projection transformation of each point within the model.  
3. Fine-tuning and verifying the model's spatial location: after completing the coordinate conversion, use platform commands to make appropriate adjustments to the BIM or GIS model's position (such as translation, rotation, etc.), and check the matching effect of the model position.