esa-sceva/satcom-chunk-collection huggingface dataset:
# SPACNet: A Simulation Platform of an Acoustic Cognitive Network
## 4 Simulation Experiment
### Results and Analysis  
Link quality cognitive routing based on a multiparameter comprehensive evaluation and fixed routing were simulated in the simulation experiment, including the packet delivery rate (PDR), average end-to-end delay (A2D), and average expected transmission count (AETX).  
Figure 8: A time-varying simulation channel in the simulation experiment.  
Figure 7: Topology and initial route of the simulation experiment. (**a**) Network topology. (**b**) Initial route.  
PDR is defined in Equation (20):  
\[PDR=D_{r}/D_{0}\, \tag{20}\]  
where \(D_{0}\) and \(D_{r}\) are the total number of data packets generated in the whole network and the number of data packets received by the sink node, respectively.  
A2D is defined in Equation (21), which is the average of the total time that a data packet is successfully transmitted from one source node to another, including transmitted delay, relay process delay, wait delay, and collision delay.  
\[A2D=\frac{\sum\limits_{d}^{D_{r}}time_{s}(d)-time_{0}(d)}{D_{r}}\, \tag{21}\]  
where \(time_{0}\) and \(time_{s}\) are the absolute times of a data packet generated in a source node and the data packet received in the sink node.  
AETX is defined in Equation (22), which is the average ratio of the total number of data packets transmitted and the data packets received in the sink node.  
\[AETX=\frac{D_{s}}{D_{r}}\, \tag{22}\]  
where \(D_{s}\) is the total number of transmitted data packages.  
Finally, the simulation results are shown in Figures 9 and 10a,b.  
The PDR of cognitive routing was far superior to that of fixed routing, because link failures in fixed routing occurred when nodes 5 and 17 died, while the new relying nodes were selected to transmit packets through cognitive routing. It can be seen that the PDR of both routing protocols increased as the \(\lambda\) decreased, and the decreasing trend was from fast to slow, because the load was the main influencing factor for high \(\lambda\), while the dead nodes were the main factor for low \(\lambda\). It can also be seen that the PDR difference between the routing protocols tended to be zero, rapidly increasing as \(\lambda\) decreased, and then roughly remaining the same for low \(\lambda\). This is because the inevitable P2P transfer time led to cognitive routing that also lost efficacy due to excessive \(\lambda\).  
In view of the A2D and AETX, it can be seen that the value and the tendency were roughly the same for both, and even the fixed route had a slightly superior performance. This is because packets that did not successfully transfer data were not counted in both calculations. In fact, the PDR was the most important network evaluation parameter.  
Figure 9: PDR result of the simulation experiment.  
In this simulation experiment, one can observe that the behavior of the cognitive protocol and the fixed routing protocol could be quantitatively evaluated in SPACNet, demonstrating the effectiveness of the proposed simulation platform.
## 2 Materials and Methods
### Experimental Procedure  
Participants visited the laboratory on five separate days (Figure 1). During the first visit, the maximal load for a single repetition (1 RM, MVC) was determined, starting with ten repetitions and progressively reducing to one [35; 36]. The 1 RM was represented by the heaviest weight a participant could lift once using the correct lifting technique, without any additional movements. Additionally, they familiarized themselves with the testing setup (Squat Jump (S)), Countermovement Jump (CMJ)) and the stimulus procedure with the eccentric half-squat exercise. All the participants performed a sub-maximal (ranging from 50% to 65% of the measured MVC) eccentric half-squat.  
All jumps were performed on a three-dimensional platform (Kistler Type 9281C, Kistler Instruments, Winterthur, Switzerland) with ground reaction forces (GRF) to be recorded [37]. The subsequent four visits were designated as experimental sessions, with two sessions  
Figure 1: The whole study procedure (MVC: Maximum Voluntary Contraction—1 RM, SJ: Squat Jump, CMJ: Counter Movement Jump).  
serving as controls (involving no conditioning activity, equating to rest) and the other two as conditioning sessions (involving post-activation potentiation exercise--PAPE, specifically eccentric half-squat). The sequence of these sessions was randomized. Furthermore, Squat Jumps were assessed during two sessions (one control and one post-activation potentiation exercise), while Countermovement Jumps were evaluated during the other two.  
All test days started with a 6 min warm-up routine on a static bicycle. Following the warm-up, the baseline of the jumping test (depending on the day) was performed (PRE) and repeated after the conditioning stimulus (POST1). Then, volunteers rested to 2 min (POST2), and they repeated the jumps. The best of three jumps, based on maximum height output, was used for further analysis. The maximum power, maximum work of the eccentric and concentric phases, and lower limb stiffness were analyzed and evaluated.
# Distributed Satellite Relay Cooperative Communication with Optimized Signal Space Dimension
## 5 Conclusions  
In this paper, a signal space reconstruction method was used to study the effect of signal separability from the characteristic extraction and signal distribution characteristics of superimposed signals on different degrees of freedom. We optimized the transmission algorithm through the signal space structure to improve its adaptability to complex interference communication scenarios. Secondly, we used ring planning to exploit the potential of exchange cooperative transmission management. This can realize the joint optimization utilization of space resource optimal management and network coding. The proposed method can improve spectral efficiency and transmission reliability compared with other existing methods. Simulations and experimental data verify the effectiveness of the proposed method.  
Conceptualization, Y.W. and X.W.; methodology, Y.W. and X.W.; writing--original draft preparation, Y.W.; writing--review and editing, Q.L., X.W. and H.L.; supervision, H.L.; funding acquisition, Y.W. and Q.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Key Research and Development Program of China (number 2018 YFB0804103), the National Natural Science Foundation of China (number 61902297), and in part by Shaanxi Intelligent Social Development Strategy Research Center (number QTZX2181).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Problem Description  
To date, 3D-UWSN architecture has largely been designed for efficient data gathering and routing with the aim of extending network lifetime by saving energy at deployed sensor nodes. Still, these contributions resulted in a few problematic issues, which are discussed in this section. In a UWSN, the AUV plays a significant role in collecting data from the network. Energy saving is modeled with clustering and sleep-wakeup scheduling called the AUV-assisted, energy-efficient clustering (AEC) mechanism [43]. The AUV selects the CH on its first traversal and collects data during its second traversal. The awake time for nodes is based on the sensing interval, guard delay, transmission of data packet time and transmission of acknowledgment packet time. CH selection is poor due to the estimation only including energy. Here, the time to wakeup of sensors is predicted from the delay and transmission time, which are the key parameters used to forward the data, since it is essential to measure energy for allotting sleep slots.  
Clustering-based data collection using an AUV is designed to balance the energy consumption [44; 45]. In a location prediction-based data collection scheme (ALP), the AUV selects to visit a node with a smaller collection delay. According to the collection delay, the trajectory path of the AUV is constructed. Then, a bipartite K-means-based clustered network uses an AUV for data collection and prefers data collection based on the residual energy and distance of the node. The target nodes are selected one after the other, i.e., the AUV has the ability to collect data from multiple nodes, but, here, the data from CHs are collected one at a time, which increases the delay in data delivery. Increase in the trajectory length increases the data collection delay; also, the deployment of a single AUV increases tour length.  
Two AUVs are presented for data gathering with an enhanced lawn shower pattern path (ELMPP) [46]. The path planning follows straight line segments and turning line segments. Further, the relay node is established by a communication link with the prediction of the RSSI value. The same sensor is selected as a relay, which tends to drain more energy at the relay nodes, and the RSSI is capable of determining the strength of the communication link but not the strength of the node. The common problems existing in prior research works are:  
* In a UWSN, using a single AUV for data collection cannot collect data in a large-scale environment, which increases delay, and also it fails to deliver emergency data promptly. Pre-defined path planning of a single AUV causes a longer waiting time for the nodes before the AUV reaches a nearer position;
* In a UWSN, continuous sensing of the environment consumes more energy, and transmission in multiple hops towards the sink also consumes more energy across all the nodes in the network, which reduces network lifetime.  
The above-stated problems were overcome in the proposed research by the design of cluster-based delay and energy-aware data gathering in a 3D-UWSN.
# The Effects of Methane Storage Capacity Using Upgraded Activated Carbon by KOH
## 3 Results  
### Upgrading Activated Carbons  
Elemental contents, such as C, H, O, N, and S, were different in the precursors of wood and coconut (Table 1).  
C contents were relatively high in the samples from coconuts, while H and O were relatively more concentrated in the wood samples. Similarly, the level of volatile matter in the wood samples was 2.5 times higher than in the coconut samples. The molar ratio of O/C, which is indicative of oxygenated functional groups (OFGs), of the wood samples was eight times higher than that of coconut samples. OFGs, as electron donors, may accelerate the redox reaction more easily than elemental C when chemical activation with KOH is applied. Thus, pore development on the surface of the wood samples would be more enhanced [32; 33].  
N\({}_{2}\) adsorption and desorption isotherms of ACs followed the type I isotherms regardless of activation with KOH, as shown in Figure 1a,b.  
As shown in Figure 1a,b and Table 2, the surface area and micropore volume of wood based ACs (AC-WR and AC-WA) were higher than coconut based ACs (AC-CR and AC-CA). After activation, the surface area of upgraded ACs was over two times higher than that of raw-graded ACs. In the adsorption and desorption isotherms of the adsorbents, the hysteresis loop in the relative pressure  
Figure 1: Nitrogen ad-/de-sorption isotherms and pore size distribution for coconut (**a**,**c**) and wood (**b**,**d**) based ACs (activated carbons). These ACs were classified into low and upgraded without/with KOH treatment.  
(P/P\({}_{0}\)) range of 0.4-1.0 was indicative of the mesoporosity of ACs. In the case of AC-WA, the fraction of mesoporosity was rapidly increased compared to AC-WR. However, in the case of AC-C, the 24.5% fraction of mesoporosity was increased to 47.5% after upgrading. The pore size distribution of the adsorbents clearly showed that the mesopores had a broad pore size distribution (Figure 1c,d). All samples dominantly developed micropores, but the mesopore fractions increased after KOH treatment. The surface areas (\(S_{BET}\)) of the upgraded samples (AC-WA and AC-CA) increased significantly, and were over two times greater than those of the precursors (Table 2 and Figure 1).  
We observed the adsorbents with an FE-SEM to investigate their morphologies, as shown in Figure 2. SEM images of the activated adsorbents (AC-WA and AC-CA) revealed rough surfaces caused by pore generation.
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 3 Classification and Evolution of Interference Management Strategies in B5G Networks
### Comparative Summary of B5G Interference Mitigation Techniques  
To complement the taxonomy presented in Section 3.5, this subsection provides a comparative summary of the IMI techniques considered in this study. Table 7 highlights the key strategies, strengths, limitations, and representative techniques for each interference type addressed in Sections 3.1-3.4.
# A Closer Look at Few-Shot Classification with Many Novel Classes
## 5 Experiments
### FSL-MNC Performance
Notably, SHA-Pipeline consistently provides higher classification accuracy than other methods across all tested category settings, including scenarios with fewer categories. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Method**} & \multicolumn{1}{c}{**Shot**} & \multicolumn{1}{c}{**Way**} & \multicolumn{1}{c}{**Average**} \\ \hline  & & 5 & 10 & 20 & 40 & 80 & 160 & 320 & 640 & 1000 & 1280 & 2560 \\ ProtoNet & 1 & 86.21 & 78.74 & 70.74 & 61.38 & 52.43 & 43.73 & 35.86 & 28.78 & 24.78 & 22.68 & 17.56 & 47.54 \\  & 5 & 95.30 & 91.55 & 86.75 & 80.83 & 74.08 & 66.45 & 58.60 & 50.68 & 45.65 & 42.85 & 35.69 & 66.22 \\ ProtoNet-Fix & 1 & 86.21 & 78.44 & 70.19 & 61.27 & 52.17 & 43.42 & 35.50 & 28.355 & 24.41 & 22.35 & 17.26 & 47.23 \\  & 5 & 95.30 & 91.55 & 86.64 & 80.68 & 73.90 & 66.25 & 58.37 & 50.41 & 45.34 & 42.57 & 35.43 & 66.04 \\ SimpleShot & 1 & 85.34 & 70.77 & 68.76 & 59.87 & 50.95 & 42.36 & 34.65 & 27.68 & 23.80 & 21.84 & 16.91 & 46.29 \\  & 5 & 95.02 & 91.39 & 86.52 & 80.50 & 73.71 & 66.02 & 58.09 & 50.49 & 45.22 & 25.31 & 65.81 \\ few-shot-baseline & 1 & 86.21 & 78.99 & 70.47 & 61.32 & 52.30 & 43.57 & 35.77 & 28.68 & 24.68 & 22.60 & 17.49 & 47.43 \\  & 5 & 95.30 & 91.55 & 86.70 & 80.75 & 73.99 & 66.35 & 85.54 & 50.61 & 45.57 & 42.78 & 35.62 & 66.16 \\  & 1 & 86.82 & 79.91 & 72.13 & 62.81 & 53.83 & 45.02 & 37.02 & 29.83 & 25.71 & 23.48 & 11.88 & 48.61 \\  & 5 & 95.78 & 92.49 & 87.86 & 81.97 & 75.20 & 67.49 & 59.53 & 51.52 & 46.40 & 43.49 & 36.18 & 67.08 \\ \hline  & 1 & 88.24 & 81.59 & 73.60 & 64.25 & 55.31 & 46.38 & 38.23 & 30.98 & 26.83 & 24.40 & 19.02 & **48.99** \\  & 5 & 97.06 & 94.00 & 89.18 & 83.27 & 76.54 & 68.71 & 60.62 & 52.55 & 47.41 & 44.32 & 36.93 & 68.24 \\  & 1 & 89.74 & 82.01 & 73.49 & 63.35 & 53.82 & 44.75 & 36.80 & 29.88 & 26.08 & 23.85 & 18.91 & 49.33 \\  & 5 & 97.63 & 94.30 & 89.29 & 83.26 & 76.68 & 69.03 & 61.21 & 52.88 & 47.90 & 44.89 & 37.25 & **68.57** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance on ImageNet-MNC (under different numbers of ways) in comparison with other FSL algorithms.
## 3 Results and Discussion
### Tree-Based Algorithms: Random Forest and Gradient-Boosted Tree Models
#### 3.3.1 Random Forests
Increasing the cases required for parent and child nodes produced underfitting (see Figure 14d,e). Table 9 displays several goodness-of-fit measures for the models after the validation process. The parameters used to obtain these models were the same as those for model (c) in Table 8. In addition, various percentages of samples were used to train each base estimator, achieving a mean RMSE of between 3.6303 and 3.6784, a MAE of between 2.7808 and 2.8207, and a MAPE of between 15.5416% and 15.7916%. As expected, RF outperformed the tree models described in [19] (see Table 3), which achieved a MAPE of approximately 16.22%, an RMSE of 3.808 and an MAE of 2.928 with the best model. In addition, the efficiency of RF was higher than that of both the KNN models (with a best mean RMSE of 3.9694) and the MLP neural networks (with a best mean RMSE of 3.6765); see Tables 6 and 7, respectively. Figure 11: Behaviour of RF models for varying proportions of samples for training each base estimator; maximum depth \(=5\), minimum of 20 cases for parent nodes and 10 for child nodes, number of trees \(=100\). (a) RMSE, training dataset; (b) RMSE, test dataset; (c) MAE, training dataset; (d) MAE, test dataset. Figure 12: Behaviour of RF models for varying numbers of trees; maximum depth \(=5\), minimum of 20 cases for parent nodes and 10 for child nodes, proportion of samples for training each base estimator \(=0.75\). (a) RMSE, number of trees between 10 and 1000; (b) RMSE, number of trees between two and 1000.
# Bit Rate Adaptation Using Linear Quadratic Optimization for Mobile Video Streaming
## 6 Trace-Driven Simulations
Although BBA changes bit rates more often than Tian, BBA suffers from longer rebufferings than Tian while LQE does shorter rebufferings as shown in Figure 6b. This shows that BBA switches bit rate at incorrect timing for mitigating rebufferings while LQE does at appropriate moments that can shorten rebuffering duration. Figure 6: Comparison of ABRs. In sum, by proactively switching bit rates, LQE exhibits up to 22% shorter average rebuffering duration than the other ABRs while achieving up to 8% lower average bit rate; this is an inevitable bit rate degradation to successfully mitigate rebufferings in the experiment scenarios. Figure 7 exhibits example traces showing how Tian and LQE select bit rates according to available throughput in a selected scenario. In this figure, we compare two extreme cases in terms of the number of bit rate changes: Tian with the least number of changes and LQE with the largest number of changes. As we can expect in Figure 6c, LQE changes bit rates more adaptively than Tian: for example, LQE switches bit rates more often during the period between 1100 and 1500 s while Tian tends to stick with one selected bit rate. In particular, during the periods of 1150-1200 and 1400-1450, Tian preserves the higher bit rate than the available throughput, which can result in rebufferings, while LQE accordingly changes bit rates.
## 4 Proposed Modulation Recognition Scheme
### CDRNN Model Design  
As depicted in Figure 2a, our proposed scheme primarily comprises two parts: time-frequency processing and image feature extraction. The IQ signal obtained through simulation data was transformed into TFIs by means of STFT. Since we mainly focused on the frequency over time features, the time-frequency image was binarized to obtain the inputs for the CNN model. Figure 2b shows the block diagram of the CDRNN model proposed in this paper.  
Residual connection, an innovative network architecture design, introduces a mechanism skipping intermediate convolutional layers [35]. It adds a constant mapping to the network, bypassing intermediate layers' complex non-linear transformations and enabling information to flow directly through the network's deep structure. This strategy effectively alleviates the gradient vanishing problem in DNN training and enhances the network's ability to learn residual mappings between inputs and outputs, allowing it to capture and utilize input data features more flexibly and achieve efficient feature reuse. As shown in Figure 3, the residual connection is achieved by combining the outputs of two consecutive layers. The equation of the skip connection is as follows.  
\[y=\mathcal{F}(x,\{W_{i}\})+x \tag{14}\]  
Figure 2: (**a**) Algorithm flowchart. The received IQ signals are processed by STFT to obtain TFIs. Subsequently, the TFIs are binarized and then fed into CNN for feature extraction and recognition. (**b**) Schematic of proposed CDRNN model. In the shallow layer, a large convolutional kernel extracts the overall feature information of TFIs. In the deep layer, a dense block is used with a reduced convolutional kernel size to extract local subtle features. Transition layer transitions between dense blocks and compresses the model.  
where \(y\) is the output submap of the layers considered. The function \(F(x,\{W_{i}\})\) represents the residual mapping to be learned.
# Multi-Axis Laser Interferometer Not Affected by Installation Errors Based on Nonlinear Computation
###### Abstract  
Most multi-axis laser interferometers require accurate installation of the lasers and detectors since the position and orientation installation errors of lasers and detectors bring measurement error to the interferometer. In this paper, the multi-axis interferometer based on nonlinear computation is proposed, which avoids the measurement error caused by installation errors by taking the laser position and orientation as unknowns into the measurement model and discussing the solution component uniqueness of a nonlinear equation system. The simulation results show that even when the installation errors are close to 1 \(\upmu\)m and 1 \(\upmu\)rad, the proposed interferometer can still measure the multi-degree-of-freedom displacement accurately, and the root mean standard error (RMSE) of displacement is 1.884 nm and 5.871 \(\times\) 10\({}^{-7}\) mrad under a reading noise level of 0.1 nm.  
multi-axis laser interferometer; installation errors; solution component uniqueness +
Footnote †: journal: Applied
# Estimation of Structural Deformed Configuration for Bridges Using Multi-Response Measurement Data
## 2 Estimation Algorithm
### Step 2: Measurement Data  
In this step, the measured data matrices for each response \(MD_{\omega}\), \(MD_{\varnothing}\), and \(MD_{\epsilon}\), as shown in Equations (10)-(12), are constructed by using on-site measured displacement data \(\omega\), slope data \(\varnothing\), and strain data \(\epsilon\), respectively. Owing to the limitation of the measured point number, each response data can be obtained only at finite points. \(N_{md,\omega}\), \(N_{md,\varnothing}\), and \(N_{md,\epsilon}\) represent the number of measured data for each response, respectively. The total measured data matrix \(MD\), as shown in Equation (13), is an \(N_{md}\ \times\ 1\) matrix that can be derived by combining the measured data for each response. The total number of measured data \(N_{md}\) is the sum of \(N_{md,\omega}\), \(N_{md,\varnothing}\), and \(N_{md,\epsilon}\).  
\[{MD_{\omega}}^{T} = \left[\omega_{1}\ \omega_{2}\ \omega_{3}\ \omega_{4}\ \cdots\ \omega_{N_{md,\omega}}\right] \tag{10}\] \[{MD_{\varnothing}}^{T} = \left[\varnothing_{1}\ \varnothing_{2}\ \varnothing_{3}\ \varnothing_{4}\ \cdots\ \omega_{N_{md,\varnothing}}\right]\] (11) \[{MD_{\epsilon}}^{T} = \left[\epsilon_{1}\ \epsilon_{2}\ \epsilon_{3}\ \epsilon_{4}\ \cdots\ \omega_{N_{md,\epsilon}}\right]\] (12) \[{MD} = \left\{\begin{array}{c}MD_{\omega}\\ MD_{\varnothing}\\ MD_{\epsilon}\end{array}\right\}(N_{md}\ \times\ 1). \tag{13}\]