esa-sceva/satcom-chunk-collection huggingface dataset:
# A Frequency-Reconfigurable Dual-Band RF Crossover Based on Coupled Lines and Open Stubs
## 1 Introduction
Using the same design, instead of one, three varactor diodes were implemented instead of one [28]. As per the conducted investigation, there exists no frequency-reconfigurable crossover operating at two frequencies. Therefore, a dual-band crossover with tuneable frequencies has been analyzed, designed, simulated, fabricated, and measured. Some considerations were followed while conducting the design, such as achieving a planar design that can be easily integrated with other microwave devices. The size of the design should be reasonable. The complexity of the design shall be avoided to simplify the fabrication process and decrease the cost. Using few switching elements since its increase will consequently lead to more soldering required and more resistance. Thus, insertion loss, isolation loss, and return loss can be negatively affected. Also, it will increase the number of biasing circuits needed. The proposed design is accomplished by using four CLs, two open stubs, and two varactor diodes while maintaining the symmetry of the design to reach closed-form equations from the even-odd-mode analysis. The low frequency can be changed from 2.06 GHz to 2.40 GHz with a tuning range of 15.3%. The high frequency can be altered from 5.44 GHz to 5.84 GHz, and the tuning range is 7.1%. The proposed crossover can be used for WiMAX (2.3 GHz and 5.8 GHz) and IEEE 802.11 WLAN (2.4 GHz and 5.725 to 5.82 GHz) applications. In the next section, Section 2, the theoretical analysis of the proposed design, basically the even-odd-mode analysis, crossover conventional specifications, CL constraints, and advanced design system (ADS) [36] results are presented. Section 3 is about simulating the design using high-frequency simulation software (HFSS) [37], where an insight into the covered frequencies and S-parameters of the design can be found. Then, Section 4 is regarding implementation and measurement, where the crossover fabrication, S-parameter measurements, results discussion, and comparison with the aforementioned published designs are stated. Lastly, Section 5 is the conclusion of the proposed design.
## 3 Method
### Automatic Learning Weight Fusion  
In this part, we use a set of weights which are automatically determined by back propagation to fuse the expression features extracted from the two branches, as shown in Figure 2d. The weights \(\gamma_{1}\) and \(\gamma_{2}\) are determined by the Adam optimizer [23] and back propagation, which is the same way in which the parameters w and b in the convolution kernel are updated. The formula is as follows:  
\[\left\{\begin{array}{c}m_{t}=\beta_{1}\cdot m_{t-1}+(1-\beta_{1})\cdot\frac{ \partial L(w,b,\gamma)_{t-1}}{\partial\gamma_{t-1}}\\ v_{t}=\beta_{2}\cdot v_{t-1}+(1-\beta_{2})\cdot\left(\frac{\partial L(w,b, \gamma)_{t-1}}{\partial\gamma_{t-1}}\right)^{2}\\ \widehat{m}_{t}=m_{t}/\left(1-\beta_{1}^{t}\right)\\ \widehat{v}_{t}=v_{t}/\left(1-\beta_{2}^{t}\right)\\ \gamma_{t}=\gamma_{t-1}-\alpha\cdot\hat{m}_{t}/\left(\sqrt{\delta_{t}}+\varepsilon \right)\end{array}\right., \tag{1}\]  
where \(\beta_{1}\) and \(\beta_{2}\) are parameters in the Adam optimizer, commonly represented by 0.9 and 0.999, \(\mathrm{L}(w,\mathrm{b},\gamma)\) is the loss function, and \(\alpha\) is the learning rate. The initial values of \(m_{t}\) and \(v_{t}\) are both 0. The \(\varepsilon\) is a small value, commonly set to \(10^{-8}\).  
For each candidate nodule \(Li\), both branch networks can extract the expression features. We fuse the expression features after the flat layer and before the full connection layer. \(E_{1}(Li)\) represents feature vectors of the branch 1 output (similar to branch 2). The feature vectors \(E_{fusion}(Li)\) are fused by automatic learning weights as follows:  
\[E_{fusion}(Li)=\gamma_{1}E_{1}(Li)+\gamma_{2}E_{2}(Li), \tag{2}\]  
where \(\gamma_{1}\) and \(\gamma_{2}\) are weights which are automatically determined by back propagation from the training process. The feature vectors \(E_{fusion}(Li)\) can get the probability that \(Li\) is a real nodule through the full connection layer and the softmax. In the fully connected layer, except for the final output layer, the middle layer uses ReLU as the activation function and uses dropout with a threshold of 0.5.
## 5 Performance Evaluation
### Numerical Results
and Random methods. The superiority of AD-SAC stems from its ability to effectively handle the mixed discrete-continuous action space inherent in our problem. In contrast, Hybrid-PPO addresses the discrete-continuous action space by parameterization, because the single network needs to learn two different types of action mappings simultaneously, which is prone to achieving suboptimal performance. In addition, while D3 QN struggles with the continuous power allocation and UAV trajectory optimization, and DDPG faces challenges in discrete offloading decisions, our decoupled approach allows specialized agents to optimize each action type. The discrete agent efficiently learns offloading patterns based on accuracy requirements, while the continuous agent finetunes power allocation and UAV positioning to minimize interference and reduce transmission latency. To validate the effectiveness of our hybrid action space decoupling approach, Figure 8 ablation studies comparing our proposed AD-SAC, AD-SAC-D (discrete agent with continuous actions mapped to discrete space), and AD-SAC-C (continuous agent with discrete actions mapped to continuous space). The results demonstrate that the proposed AD-SAC achieves optimal performance in terms of latency and energy consumption. AD-SAC-D shows a 7.4% latency increase and 24.3% energy increase due to discretization loss in power allocation and trajectory, while AD-SAC-C exhibits the worst latency performance 11.7% increase but better energy efficiency 13.5% increase due to the continuous representation of discrete offloading decisions. This cross-advantage phenomenon validates our theoretical analysis that latency is primarily dominated by offloading decisions, while energy is dominated by power allocation and trajectory. The results conclusively demonstrate that forcing action space conversions leads to significant performance degradation, proving the  
Figure 6: Comparison of latency and energy consumption vs. maximum latency requirement of IoTDs across different methods. (**a**) Latency. (**b**) Energy consumption. Figure 7: Comparison of latency and energy consumption vs. accuracy requirements across different methods. (**a**) Latency. (**b**) Energy consumption. necessity of our decoupling design that preserves the native characteristics of both discrete and continuous actions.
## 2 Functional Electrical Stimulation (FES) of Muscles
### Restoration  
FES has been used to induce contractions of impaired muscles, e.g., in spinal cord injury patients as a rehabilitation technique for improving the performance and physical state of spastic paralyzed muscles and for restoring the locomotor abilities of these patients [33; 34]. Computer controlled portable stimulators have been developed for the functional activation of paraplegic's muscles [35]. Apparatus and systems have been reported for the restoration of standing and walking by combining microcomputer-controlled electrical stimulators and instrumented walkers [36]. When electromyographic (EMG) measurements are required in the presence of FES, the high-magnitude stimulus artefact produced necessitates methods for suppression of this artefact [37].  
Two major issues associated with functional electrical stimulation (FES) of paralyzed muscles [38] are: (i) The mechanism of force generation by means of the recruitment of muscle fibres, and (ii) the variation of the muscle force with time as a result of fatigue under sustained stimulation. These issues determine the quality of performance to a great extent. Objective means of monitoring fatigue are essential in cases of lack of sensation due to paralysis. Measured EMG and phosphometabolites profiles (the latter monitored by Magnetic Resonance Spectroscopy, [PERSON] et al. [39]) have been attributed to the fatiguing and recovery processes of the contractile machinery and of the excitation-contraction coupling mechanism [40]. Musculo-tendon modelling and incorporating the fatigue profiles enabled researchers to predict the muscle force profiles in continuous as well as in intermittent stimulation [41; 42; 43; 44]. Finally, electromagnetic modelling of FES allowed researchers to predict the current density profiles within the muscle [45; 46], effect of stimulating electrode configuration [47; 48] and transmembrane potential distribution along a nerve fibre [49; 50].
## 5 Materials and Method
### T&O Compounds Analysis  
Four T&O compounds including dissolved (_d_-GEO, _d_-MIB, _d_-CYC and _d_-ION) and particulate T&O compounds (_p_-GEO, _p_-MIB, _p_-CYC and _p_-ION) were analyzed using solid-phase microextraction followed by GC-MS which was modified according to the method of [PERSON] et al. [33]. One liter or more water samples were filtered immediately through a Whatman GF/C glass fiber filter and divided into a dissolved and a particle-bound fraction. The filtrate (80 mL) with the dissolved fraction was transferred into 125 mL vials immediately. The filter residue with the particulate fraction was rinsed into a 125 mL vial with 80 mL of Milli-Q water, and then the cells were disrupted using ultrasound in ice bath. The sample with particle-bound T&O compounds were salted out with 25 g NaCl to improve recovery, stirred vigorously, and extracted at 65 \({}^{\circ}\)C for 30 min using a 2 cm long CAR/DVB/PDMS fiber (Supelco). GC-separation was done using HP 6890/5973 GC-MS equipped with a DB-5 MS column. The GC was programmed from 60 \({}^{\circ}\)C (constant temperature for 2 min) to 200 \({}^{\circ}\)C (8 \({}^{\circ}\)C/min) with a 2 min hold, and finally to 260 \({}^{\circ}\)C (15 \({}^{\circ}\)C/min). For the selected ion monitoring mode, \(m/z\) 107 and 95 for MIB, \(m/z\) 112 and 126 for GEO, \(m/z\) 137 and 152 for CYC and \(m/z\) 177 and 91 for ION were used. Quantities of each analyte were compared to the corresponding standard curve. The detection limits were 0.2, 0.2, 0.5 and 0.4 ng/L for GEO, MIB, CYC and ION, respectively.
# Degree-Constrained Steiner Problem in Graphs with Capacity Constraints
## 4 Necessary and Sufficient Conditions for the Existence of a Solution
D: \(|V_{1}^{\prime\prime}|>2\) and the nodes in \(V_{1}^{\prime\prime}\) are neighbor nodes of node \(v\) such that \(v\in V_{m}^{\prime\prime},m\geq|V_{1}^{\prime\prime}|\); \(E\): \(|V^{\prime\prime}\setminus V_{1}^{\prime\prime}|\geq 2\) (there are at least two nodes with degree bound greater than one); and \(|V^{\prime\prime}\setminus(V_{2}^{\prime\prime}\cup V_{1})^{\prime\prime}|\geq 1\) (there is at least one node with a degree bound greater than two). A Steiner hierarchy spanning all the nodes of a set \(M\) in a connected graph and respecting non-uniform capacity degree constraints can be found if and only if \(A\wedge B\wedge(C\lor D\lor E)\). Proof. : \(A\) and \(B\) are necessary conditions (cf. Lemmas 1 and 2). Moreover, one of the cases \(C,D\), or \(E\) must be true. Following Lemmas 3, 5, and 6, each of these conditions is sufficient. Expression \((C\lor D\lor E)\) also provides a necessary condition; that is, one of them must be true for the existence. We prove that if none of them are true, there is no [PERSON] hierarchy respecting the constraints. Let us suppose that none of them are true: there are more than two nodes in \(V_{1}^{\prime\prime}\) (\(\
eg C\)), they are not neighbors of a node with sufficiently large degree bound (\(\
eg D\)), and there is no node with degree bound greater than two or another with a degree bound of two (\(\
eg E\)). We start our proof with \(\
eg E\). If there is a single node with a degree bound greater than two, it must be the neighbor node of the nodes in \(V_{1}^{\prime\prime}\), but following \(\
eg D\), its degree bound is not sufficiently large. If there is no node with a degree bound greater than two, there are only nodes in \(V_{1}^{\prime\prime}\) and in \(V_{2}^{\prime\prime}\). Following \(\
eg C\), there are more than two nodes in \(V_{1}^{\prime\prime}\). Therefore, the three conditions cannot be false together (and another fourth condition cannot guarantee the spanning hierarchy) even if \(A\wedge B\) is true.
## 4 Results and Discussion
### TS-InSAR Deformation Monitoring Results  
#### 4.3.1 Sentinel-1A PS/DS-InSAR Deformation Inversion Results  
Figure 11 presents the deformation results derived from the PS-InSAR and DS-InSAR processing of 15 Sentinel-1A scenes using January 18, 2025 as the master image, identifying 265,704 PS points with deformation rates ranging from \(-\)63.10 to 22.18 mm/yr in the study area. Following phase optimization, the interferogram quality and monitoring point density significantly improved, with DS points increasing by a factor of 6.75 to reach 1,794,067 points, showing deformation rates from \(-\)62.74 to 19.14 mm/yr, while achieving point densities of 1544.66 point/km\({}^{2}\) for PS measurements and 10,429.77 point/km\({}^{2}\) for DS measurements. The deformation analysis demonstrates strong consistency between PS-InSAR and DS-InSAR results, particularly in terms of spatiotemporal distribution, with 87.03% agreement for PS-InSAR and 91.34% agreement for DS-InSAR in areas exhibiting deformation rates between \(-\)5 and 5 mm/yr, indicating relative stability in the Jinchuan mining district periphery. However, active mining areas displayed substantial deformation, causing decorrelation and phase unwrapping challenges due to large-scale ground displacement.  
Figure 10: Quality evaluation of interferograms created using Sentinel-1A and “Fucheng-1”/“Shenqi” SAR images. (**a**) Average phase difference (SPD), (**b**) the number of residues or local errors (PSD).  
As shown in Figure 12, DS-InSAR demonstrates a significant improvement in monitoring point density within the mining areas compared to PS-InSAR. However, due to the relatively long temporal baseline of Sentinel-1A and the limitations of C-band satellites in rapid deformation monitoring, which leads to large deformation during the revisiting period, the P4 mining area experiences complete coherence loss, resulting in there being no deformation monitoring points in this area. In the P1 and P5 areas, the deformation gradually increases from the mining boundary toward the mine center, but no monitoring points are found at the center of the mining area. For P3, the deformation near the mine center is large enough to cause phase unwrapping errors, manifesting as apparent uplift errors in the central regions. This indicates that the relatively longer revisiting period and coarser resolution of Sentinel-1A lead to limitations in monitoring mining areas. These limitations cause complete decorrelation or phase unwrapping errors in the mining center, highlighting the challenges in accurately monitoring mining regions using Sentinel-1A data.
## 5 Development of Multi-Core Hardware Platform for Energy Conservation
### Performance Evaluation  
To evaluate the energy conservation performance, the multi-core node EMWSN is used (Figure 14). EMWSN consists of three cores: the 8-bit low power AVR core (ATmega1281), the 32-bit low power ARM core (AT91 SAM7 Sx) and the good leading-time ultra-low power iGLOO FPGA [36]. The iGLOO FPGA acts as the management core. It can configure the EMWSN to work in different modes without the change of the wired connection.  
According to the experimental results in the previous Table 10, the lifetime of different WSN nodes can be calculated. If the node is powered by a pair of AA batteries and the temperature and light sensors sample every 3 min, the theoretical lifetimes of the multi-core EMWSN node, the single-core AVR node, the single-core ARM node and the single-core TelosB node will respectively be 1270 days, 825 days, 382 days and 945 days. From this result, it can be concluded that the multi-core platform is more efficient in the energy consumption.  
\begin{table}
\begin{tabular}{|c|c|c|c|c|c|c|} \hline \multirow{2}{*}{**Working Modes**} & \multicolumn{2}{c|}{**Different Core Status**} & \multirow{2}{*}{**Strong Points of This Mode**} & **Run-Time** & **Equipment** \\  & & & & **Contexts to Be** & **That Is** \\  & **NanoRisc** & **AVR** & **SoC** & **Applied** & **Available in** \\  & & & & & **This Mode** \\ \hline AVR-core & Active & Active & Inactive & More energy-efficient in transmitting small-sized, low-rate messages and in processing the simple tasks & When executing the scalar WSN tasks & Sensors, IEEE 802.15.4 medium, _etc._ \\ \hline SoC-core & Active & Inactive & Active & More energy efficient in transmitting large-sized, high-rate messages and in processing complicated tasks. & When executing the WSN multimedia tasks & IEEE 802.11 \\ \hline Hybrid & Active & Active & Active & Can self-adapt to different contexts. Highly context aware & Mix of the above two & Both of above \\ \hline Sleep & Active & Inactive & Inactive & Ultra low energy consumption & When node are idle, to conserve the energy & N/A \\ \hline \end{tabular}
\end{table}
Table 11: Different working modes of MiLive.  
Figure 14: **(a)** Block diagram of the multi-core node EMWSN. **(b)** Prototype board of the multi-core node EMWSN.
## 3 Laboratory Model Test
### Settlement of Piled Rafh from Model Test  
Piled-raft settlement due to tunneling in the laboratory model test is shown in Figure 3. Figure 3a shows the settlement of the piled raft as O\({}_{\text{H}}\) increased when O\({}_{\text{V}}\) = 0.5. When the P\({}_{\text{N}}\) was 1, settlement of 0.19 mm occurred at 0.0 D, and this decreased to 0.16 and 0.07 mm when the offsets increased to 1.0 and 2.0 D. When P\({}_{\text{N}}\) was 2, as O\({}_{\text{H}}\) increased from 0.0 to 1.0 and 2.0 D, settlement of the piled raft was 0.31, 0.12, and 0.1 mm, respectively. When P\({}_{\text{N}}\) was 3, there was settlement of 0.36 mm at O\({}_{\text{H}}\) of 0.0 D, 0.20 mm at 1.0 D, and 0.11 mm at 2.0 D.  
Figure 3b shows the settlement of 1-, 2-, and 3-piled rafts as the O\({}_{\text{V}}\) increased from 0.5 to 1.0 and 1.5D when O\({}_{\text{H}}\) was 0.0 D. When P\({}_{\text{N}}\) was 1, piled-raft settlement of about 0.19 mm occurred at O\({}_{\text{V}}\) of 0.5 D, and it decreased to 0.15 and 0.14 mm as it increased to 1.0 and 1.5 D. When P\({}_{\text{N}}\) was 2, settlement of 0.31 mm occurred at 0.5 D, 0.19 mm at 1.0 D, and 0.18 mm at 1.5 D; settlement of 0.36, 0.30, and 0.27 mm occurred at 0.5, 1.0, and 1.5 D for the 3-piled raft, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**P\({}_{\text{N}}\)**} & \multicolumn{2}{c}{**Offset between Pile Toe and**} & \multirow{2}{*}{**Relative**} & \multirow{2}{*}{**Note**} \\ \cline{2-2} \cline{4-4}  & & **Tunnel Crown** & & **Density** \\ \cline{2-2} \cline{4-4}  & **Horizontal** & & **Vertical** & \\ \hline \multirow{4}{*}{1} & & 0.5 D & L & 1\_0.0H\_0.5V\_L \\  & 0.0 D & 1.0 D & L & 1\_0.0H\_1.0V\_L \\  & & 1.5 D & L & 1\_0.0H\_1.5V\_L \\ \cline{2-2} \cline{4-4}  & 1.0 D & 0.5 D & L & 1\_1.0H\_0.5V\_L \\  & 2.0 D & 0.5 D & L & 1\_2.0H\_0.5V\_L \\ \hline \multirow{4}{*}{2} & & 0.5 D & L & 2\_0.0H\_0.5V\_L \\  & 0.0 D & 1.0 D & L & 2\_0.0H\_1.0V\_L \\  & & 1.5 D & L & 2\_0.0H\_1.5V\_L \\ \cline{2-2} \cline{4-4}  & 1.0 D & 0.5 D & L & 2\_1.0H\_0.5V\_L \\  & 2.0 D & 0.5 D & L & 2\_2.0H\_0.5V\_L \\ \hline \multirow{4}{*}{3} & & 0.5 D & L & 3\_0.0H\_0.5V\_L \\  & 0.0 D & 1.0 D & L & 3\_0.0H\_1.0V\_L \\  & & 1.5 D & L & 3\_0.0H\_1.5V\_L \\ \cline{2-2} \cline{4-4}  & 1.0 D & 0.5 D & L & 3\_1.0H\_0.5V\_L \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of cases for laboratory model test.
# An Effective ELECTRA-Based Pipeline for Sentiment Analysis of Tourist Attraction Reviews
## 4 Experimental Design
### Ablation Studies  
For further evaluation, an ablation study was performed. This involved firstly discarding the pre-processing procedure, then only enabling one pre-processing sub-procedure, then enabling any two sub-procedures and disabling one pre-processing sub-procedure, and finally retaining other sub-procedures. The results of ablation studies are detailed in Table 3. A primary goal of this work is to identify the most effective sub-procedure for PLMs for sentiment analysis. Observing the results of the individual sub-procedure on the SenTARev dataset, it is worth noting that even a single pre-processing sub-procedure can bring improvements. Among these four pre-processing sub-procedures, negation substitution appears to be the most effective, verifying its importance in sentiment classification. Then we find redundancy deletion and special character removal also contribute to improvement. Stopwords removal had minimal impact. Performance with negation pre-processing is generally better than without it when the same number of pre-processing sub-procedures is included. Although it does not present a multiplicative or proportional relationship, performance becomes better as the number of pre-processing sub-procedure increases. We note that the best performance comes from combining all the pre-processing procedures.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & \(F_{Neu}\) & \(F_{Neg}\) & \(F_{Pos}\) & \(m\_avg_{F}\) & \(w\_avg_{F}\) \\ \hline TextCNN & 0.4074 & 0.7945 & 0.9759 & 0.7259 & 0.9443 \\ TextRNN & 0.4010 & 0.8229 & 0.9798 & 0.7345 & 0.9494 \\ TextRCNN & 0.4802 & 0.8571 & 0.9832 & 0.7735 & 0.9574 \\ TextRNN-Att & 0.4504 & 0.8722 & 0.9822 & 0.7683 & 0.9565 \\ BERT & 0.5410 & 0.8903 & 0.9851 & 0.7756 & 0.9594 \\ RoBERTa & 0.5155 & 0.8781 & 0.9761 & 0.7894 & 0.9418 \\ ELECTRA & 0.5818 * & 0.9062 * & 0.9864 * & 0.8246 * & 0.9663 * \\ \hline \hline \end{tabular}  
* Denotes significance at \(p\leq 0.05\).  
\end{table}
Table 2: Experimental results of performance comparison.