esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experimental Results
### Results of Channel Pruning  
Channel pruning is an important scheme integrated into Darts_Tiny. In this section, we shall visualize the effect of channel pruning by depicting the variation of F1 through a complete neural architecture search over HRSID and SSDD. Evaluation results are given in Figures 9 and 10. The horizontal axis represents the number of epochs. The vertical axis represents the F1, obtained after each epoch, that reflects the detection accuracy. In the results from HRSID, channel pruning is executed 4 times in epochs 5, 10, 32 and 40 respectively. It is not surprising to see that the F1 validation score falls sharply in these epochs as channel pruning is not differentiable. However, we can see that the F1 validation score recovers quickly after a few epochs of training. Thus, channel pruning does work in restraining overfitting. We can see that after the 41 st epoch, the overfitting is stable. As for the results from SSDD, channel pruning is triggered 3 times. Because the scenarios in SSDD are simpler, overfitting here is not as serious as what was achieved in HRSID. However, channel pruning also works in SSDD and can restrain overfilling under a small threshold. For both datasets, the variation in F1 (especially the F1 validation score) via channel pruning does not show a negative impact on detection accuracy.  
Figure 10: The effect of channel pruning on SSDD: pruning is triggered 3 times. As the scenarios in SSDD are simpler, overfitting is not as serious as what was achieved with HRSID. However, F1 validation score recovers quickly after a few epochs of training.  
Figure 9: The effect of channel pruning on HRSID: pruning is triggered 4 times. After that, overfitting (the gap between two curves) is restrained.
## 3 Results
### Time-Series Characteristics of NPP  
From 2003 to 2020, the NPP in the YRB exhibited a noticeable fluctuating upward trend, with an approximate growth rate of 3.1 gC/(m\({}^{2}\cdot a\)) (Figure 4). In 2010, there was a significant decrease in NPP, followed by a substantial increase in 2013. The data indicate that, in July 2010, the YRB was severely affected by heavy rainfall, leading to a catastrophic rainstorm disaster that caused extensive damage to vegetation and farmland. The annual average NPP in 2003 and 2020 was 521.77 gC/(m\({}^{2}\cdot a\)) and 560.29 gC/(m\({}^{2}\cdot a\)), respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{\(\theta_{ANPP}\)} & \multirow{2}{*}{\(\theta_{HNPP}\)} & \multirow{2}{*}{\(\theta_{PNPP}\)} & \multirow{2}{*}{**Driving Factor**} & \multicolumn{2}{c}{**Relative Contribution/\%**} \\ \cline{5-6}  & & & & **Climate Change** & **Human Activities** \\ \hline \multirow{3}{*}{\(>\)0} & \(>\)0 & \(>\)0 & Double-driven improvement & \(\frac{\theta_{INPP}}{\theta_{INPP}+\theta_{ANPP}}\) & \(\frac{\theta_{ANPP}}{\theta_{INPP}+\theta_{ANPP}}\) \\  & \(>\)0 & \(<\)0 & human-driven improvement & 100 & 0 \\  & \(<\)0 & \(>\)0 & climate-driven improvement & 0 & 100 \\ \hline \multirow{3}{*}{\(<\)0} & \(<\)0 & \(<\)0 & Double-driven degradation & \(\frac{\theta_{INPP}}{\theta_{INPP}+\theta_{ANPP}}\) & \(\frac{\theta_{ANPP}}{\theta_{INPP}+\theta_{ANPP}}\) \\  & \(<\)0 & \(>\)0 & human-driven degradation & 100 & 0 \\ \cline{1-1}  & \(>\)0 & \(<\)0 & climate-driven degradation & 0 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Contribution rate calculation under different driving factors conditions.  
The maximum NPP was recorded in 2015, while the minimum was observed in 2005, with a difference of 68.92 gC/(m\({}^{2}\cdot a\)).  
In the 11 river systems within the YRB (Figure 5), NPP exhibited varying degrees of growth. The Wujiang had the highest NPP value, with annual averages of 742.2 gC/(m\({}^{2}\cdot a\)). The Jinsha River basin had the lowest NPP value, with an annual average of 416.7 gC/(m\({}^{2}\cdot a\)).  
Specifically, the upper and Jialingjiang River NPP growth rates were 5.42 gC/(m\({}^{2}\cdot a\)) and 6.77 gC/(m\({}^{2}\cdot a\)), respectively, while the Jinsha River and Wujiang River NPP growth rates were 1.77 gC/(m\({}^{2}\cdot a\)) and 1.83 gC/(m\({}^{2}\cdot a\)), respectively. Regions with slower NPP growth included the Mintuo, Dongting Lake, Poyang Lake, and the middle reaches, with growth rates of 2.84, 2.06, 2.25, and 2.83 gC/(m\({}^{2}\cdot a\)), respectively. Growth rates in other river systems ranged from 3.9 gC/(m\({}^{2}\cdot a\)) to 4.32 gC/(m\({}^{2}\cdot a\)).
## 2 Support Vector Machine  
We assume that the training sample set of the interference monitoring is \(T=\{(x_{1},y_{1}),\ldots,(x_{l},y_{l})\}\in(\chi\times\gamma)^{l}\), where \((x_{i},y_{i})\), \(i=1,...,l\) are the n-dimension vectors constituted by the characteristic parameters of the interference monitoring, in which \(x_{i}\in\chi=R^{n}\), and \(y_{i}\in\gamma=(1,-1)\). It is defined that, when \(y_{i}=1\), \(x_{i}\) is the characteristic parameter with interferences and when \(y_{i}=-1\), correspondingly \(x_{i}\) is the characteristic parameter without interferences.  
The SVM aims to build a classification hyperplane \(wx+b=~{}0\), making \(x_{i}w\geqslant 1-b\) for the sample of \(y_{i}=1\), and \(x_{i}w\leqslant-1-b\) for the sample of \(y_{i}=-1\). Therefore, the interference monitoring problem is transformed into the optimization problem by SVM [11] as follows:  
\[\begin{array}{l}\min\limits_{w,b,\xi}\frac{1}{2}\|w\|^{2}+C\sum\limits_{i=1 }^{l}\xi_{i}\\ s.t.y_{i}(wx_{i}+b)\geqslant 1-\xi_{i},\xi_{i}\geqslant 0,i=1,2...,l\end{array} \tag{1}\]  
where \(\xi=(\xi_{1},\xi_{2}\cdots\xi_{n})\) is the relaxation factor and \(C\) is the penalty parameter. The Wolfe dual of Equation (1) is:  
\[\begin{array}{l}\min\limits_{2}\sum\limits_{i=1}^{l}\sum\limits_{j=1}^{l} \alpha_{i}\alpha_{j}y_{j}K\left(x_{i},x_{j}\right)-\sum\limits_{i=1}^{l}\alpha _{i}\\ s.t.0\leqslant\alpha_{i}\leqslant C,\sum\limits_{i=1}^{l}\alpha_{i}y_{i}=0 \end{array} \tag{2}\]  
where each \(a_{i}\) is a Lagrange multiplier, which corresponds to the sample \((x_{i},y_{i})\); \(K(x_{i},x_{j})\) is Gaussian kernel function, which is defined as:  
\[K(x_{i},x_{j})=exp(-\frac{\|x_{i}-x_{j}\|^{2}}{\sigma^{2}}) \tag{3}\]  
Let \(a_{1},\cdots,a_{l}\) is the optimum solution of Equation (2), the classification criterion of GNSS signals to be monitored is:  
\[f\left(x\right)=\mathrm{sgn}\left\{\sum\limits_{i=1}^{l}\alpha_{i}y_{i}K\left( x_{i},x\right)+b\right\} \tag{4}\]As is shown, the classifier decided by Equation (4) only relies on the samples \((x_{i},y_{i})\) when \(a_{i}\
eq\ 0\), and their input \(x_{i}\) is called the Support Vector. The \(b\) in Equation (4) can be solved by:  
\[b=y_{j}-\sum_{i=1}^{l}y_{i}a_{i}K(x_{i}x_{j}) \tag{5}\]  
where the \(a_{j}\) corresponding to \(y_{j}\) should meet the condition of \(0\leq\alpha_{j}\leq C\).  
The common method to solve Equation (2) is the Sequential Minimal Optimization (SMO) algorithm [12]. However, the training time will rapidly increase as the number of training samples increases, making it hard for interference monitoring to be in real-time. Therefore, in this paper the TWSVM technique is adopted to improve the training speed.
# Simplified Derivative-Based Carrierless PPM Using VCO and Monostable Multivibrator
## 6 Discussion and Analysis of Results
### Discussion and Perspectives  
#### 6.2.1 Interpretation of Results  
The experimental validation demonstrates successful implementation of both conventional and proposed derivative-based PPM modulation techniques across multiple demodulation approaches. The MATLAB simulations and hardware implementations provide comprehensive evidence for the feasibility of carrierless PPM systems in practical communication applications.
# Optimized Architecture for Efficient OFDMA Network Design
## 4 Validation and Robustness Study of the Analytical Model
### Traffic Parameters  
In the analytical model, elastic ON/OFF traffic is considered. For the validation study, it is assumed that the ON data volumes and OFF periods follow exponential distributions, aligning with the assumptions of the analytical model. Let \(\overline{x}_{\text{on}}\) represent the average ON data volume (which includes the main page and embedded objects), and \(\overline{t}_{\text{off}}\) denote the average OFF period (representing the reading time).  
However, since the memoryless property of the exponential distribution does not always capture the actual behavior of data traffic, a truncated Pareto distribution was selected for the robustness study to better reflect real-world traffic patterns.  
The traffic parameters in Table 2 are based on the analysis of OFDMA network performance metrics from reference [25], providing a solid foundation for our choices:
## 5 Conclusions  
In this paper, a data-level fusion method is proposed to discriminate physical targets and active false targets in the multistatic radar system. On the basis of the signal model and measurement model of the multistatic system, the velocity vector association discriminator and its deficiency are introduced according to the spatial correlation difference in the physical target and active false target motion states. Aiming at the problem, the coordinate system transformation is performed first to reduce the demand for receivers. Based on the fact that the deception velocity of the physical target obeys the Gaussian distribution with zero mean and the active false target obeys the Gaussian distribution with the mean being its true deception velocity, the deception velocity-based method is proposed to discriminate the active false target and physical target. The proposed method can keep a constant misjudgment probability for physical targets and discriminate targets effectively, especially with large deception velocities. Moreover, the proposed method can be combined with the location information association method to further improve the discrimination performance. The simulation verifies the feasibility and superiority of the proposed discrimination method.  
Conceptualization, Q.L., Y.G. and H.X.; methodology, Q.L., Y.G. and L.Z.; software, Q.L., Z.C. and Y.G.; validation, P.Z., Z.C. and Y.H.; writing--original draft preparation, Q.L. and Y.G., funding acquisition, Q.L., H.X. and Z.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Proof of Concept Foundation of Xidian University Hangzhou Institute of Technology under Grant XJ2023230036, in part by the Opening Project of Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing under Grant GXKL06230203 and in part by the National Natural Science Foundation of China under Grant 62001062 and 62301408.  
The datasets are available from the corresponding author upon reasonable request.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ECCM & electronic counter-countermeasures \\ DRFM & digital radio frequency memory \\ PT & physical target \\ FT & false target \\ \end{tabular}  
Figure 9: Discrimination performance of proposed method versus different locations.
###### Abstract  
Ensuring the reliability and robustness of spacecraft systems remains a key challenge, particularly given the limited feasibility of continuous real-time monitoring during on-orbit operations. In the domain of Fault Detection, Isolation, and Recovery (FDIR), no universal strategy has yet emerged. Traditional approaches often rely on precise, model-based methods executed onboard. This study explores data-driven alternatives for self-diagnosis and fault detection using Machine Learning techniques, focusing on spacecraft Guidance, Navigation, and Control (GNC) subsystems. A high-fidelity functional engineering simulator is employed to generate realistic datasets from typical onboard signals, including sensor and actuator outputs. Fault scenarios are defined based on potential failures in these elements, guiding the data-driven feature extraction and labeling process. Supervised learning algorithms, including Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs), are implemented and benchmarked against a simple threshold-based detection method. Comparative analysis across multiple failure conditions highlights the strengths and limitations of the proposed strategies. Results indicate that Machine Learning techniques are best applied not as replacements for classical methods, but as complementary tools that enhance robustness through higher-level self-diagnostic capabilities. This synergy enables more autonomous and reliable fault management in spacecraft systems.  
fault detection, isolation, and recovery (FDIR); spacecraft autonomy; self-diagnosis; artificial intelligence; data-driven methods; guidance, navigation, and control Article
## 2 Methodology  
This study, by optimizing the vertical angle and relative slope parameters of the visibility analysis algorithm, intuitively and quantitatively displays visibility grid data on the landscape based on tourists' viewpoints and realizes the transformation from making calculations only for the visible area to quantitatively evaluating the quality of visually experiencing the landscape considering parallax. It considers a variety of landscape visual influence factors (visible area, landscape water system distribution, number of landscape resources) to construct an index system for evaluating landscape visual effects. Finally, a set of improved landscape visual evaluation methods is proposed by integrating the analytic hierarchy process (AHP) and an optimization algorithm into the visibility analysis.
# A Real Option Pricing Decision of Construction Project under Group Bidding Environment
## 2 A Real Option Pricing Decision Model under Group Bidding Environments
### The Winning Probability and the Weighted Option Value Calculation  
#### 2.3.1 The Winning Probability Calculation  
To enhance the external environmental impact of the bidding price and to form a universally applicable winning probability prediction model, in this research, the winning probability is correlated with the average group bidding price, which is regarded as the maximum benchmark price of the probability. If the bidding price deviates from the group characteristics, the winning probability will be reduced. The enterprise will predict the number of competitors and their prices by analyzing published bidding data and assuming that independent competitors will continue the same bidding strategy when bidding in groups. At the same time, due to the differences in the characteristics of the bidding projects, the market information asymmetry, the liquidity of market resources and the scientific differentiation of bidding decisions among enterprises, the bidding environment is divided into the independent and the coupled. In an independent environment, it is assumed that each price is not based on other prices, the bidder can deduce each competitor's price separately and calculate their maximum value and optimal price jointly. Additionally, in the coupled environment, it is assumed that each competitor also improves its winning probability by predicting others in the group and incorporating its decision basis. The average is taken as the arithmetic mean, and the ratio of the sum of the group bidding price to the total number of bidders is taken. The winning probability formula of the \(i\)th (\(i\) = 1,2,..., \(N\)) competitor is as follows:  
\[W_{i}(P_{i},\ C_{i})=u\times e^{-b(|P_{i}-AP_{i}|/AP_{i})^{n}} \tag{9}\]  
\[AP_{i}=\frac{\sum_{i=1}^{N}P_{i}}{N} \tag{10}\]  
where the \(C_{i}\) and \(W_{i}\) are the estimated cost and winning probability of the \(i\)th competitor, respectively. \(N\) is the number of competitors; \(P_{i}\) is the bidding price of the \(i\)-th competitor; the \(AP_{i}\) is the average group bidding price of the \(i\)th competitor; \(n\), \(b\) and \(u\) are the constant calibration parameters, connection the bidding price and the winning probability; and \(u\) represents the winning probability when the \(AP_{i}\) is equal to the \(P_{i}\).
# A Hybrid Mapping Method with Position and Stiffness for Manipulator Teleoperation
## 6 Conclusions  
Humans have the ability to actively regulate their arm position and impedance to obtain a versatile and stable behavior while interacting with dynamic uncertain environments, and transferring the versatile skills of human behavior to teleoperate manipulators is a major concern in robotics. This paper proposes a hybrid mapping method with position and stiffness for manipulator teleoperation through the exoskeleton master of CyberForce and sEMG sensors. Firstly, the robot workspace is redefined into the free space, fine space and contact space. Then, the fixed scale mapping in free space and virtual impedance mapping in fine space are proposed for position teleoperation. Moreover, the stiffness at the human arm endpoint is predicted by sEMG signals, and the stiffness mapping method is utilized to adjust the stiffness of the slave manipulator. Finally, three typical experiments of trajectory tracking in free space, obstacle avoidance in fine space and human robot interaction in contact space are accomplished, and the experimental results show the effectiveness of the proposed method.  
Conceptualization, Z.J. and F.Y.; Funding acquisition, Z.J. and H.L.; Software, C.L. and F.Y.; Supervision, F.N.; Writing-original draft, Z.J.; Writing--review & editing, F.N. and D.Y. This research was funded by the National Natural Science Foundation of China, grant number 91848202 and grant number 61803124, and the Post-Doctor Research Startup Foundation of Heilongjiang Province.  
The authors declare no conflict of interest.  
Figure 17: Experimental results of torque and stiffness.
## 2 Theory, Requirements and Synthesis
### Practical Implementations
#### 2.4.2 PIN-FET  
As an inexpensive photodetector we employed a PIN-FET module FUJITSU FRM3Z121 KT/LT consisting of an InGaAs PIN photodiode and a GaAs IC preamplifier. We designed an additional circuit (Figure 8) to supply it with electricity as well as to filter and further amplify the received signal. The gain is set to nearly 40 dB.  
Figure 6: A block diagram for the proposed CLC system comprising an input buffer, a differentiator, a mixer, an integrator, a summing amplifier, a triangle- and square-wave generator, an adjustable phase shifter.  
Figure 7: CLC schematic with building blocks (yellow) corresponding to those in the block diagram of Figure 6. The orange building block represents a single-to-dual voltage source adapter.
# Evolution of System Embedded Optical Interconnect in Sub-Top-of-Rack Data Center Systems
## 3 LightningValley--First-Generation Optically Enabled Data Center System
### Polymer Interconnect Measurement Set-Up and Results  
Insertion loss measurements were carried out on the polymer waveguide flex using an 850 nm VCSEL test light source. The reason for this is that all commercial midboard transceivers deployed in the reported systems use 850 nm VCSELs, which are high-volume commodity items. The 850 nm VCSEL test source was connected to a 5 m multimode graded-index optical fiber of category OM3 with a core size of 50 \(\upmu\)m. The fiber was wound 10 times across a 50 mm diameter mandrel to produce a normalized modal launch condition. The full optical link path shown in Figure 5 is described as follows:  
1. An optical signal generated in the BOA module on the first controller daughtercard couples into the 2 \(\times\) 12 MT compliant ferrule of the first S-curved polymer waveguide flexible cable.
2. The signal propagates along the first S-curved flexible polymer waveguide cable.
3. At the card edge, the optical signal couples into the 2 \(\times\) 12 MT compliant ferrule of the straight midplane polymer waveguide flex through the Huber + Suhner FiberGate connector.
4. The signal propagates along the midplane polymer waveguide flex from the first daughtercard location to the second controller daughtercard location.  
Figure 5: LightningValley optically enabled data storage switch controller daughters with polymer waveguide flexible ribbon and Huber + Suhner FiberGate optical backplane connector.  
* The signal couples into the 2 \(\times\) 12 MT compliant ferrule of the second S-curved flexible polymer waveguide cable through the FiberGate connector on the second controlled daughtercard.
* The signal propagates along the second S-curved flexible polymer waveguide cable.
* The signal couples into the optical interface of the BOA module on the second controller daughtercard.  
The total transmitted power was measured across each of the 24 channels and compared to reference measurements in order to evaluate the insertion loss. The results are shown in Figure 6. The result for channel 24 was discarded as the channel was damaged. An average insertion loss of 6.76 dB was measured with a large standard deviation of 3.15 dB with 87% of waveguide links falling below the estimated BOA transceiver link budget of 8 dB. The size and variation in polymer waveguide insertion loss were too large to be considered for future variants of on-board interconnect, and so optical fiber flexplanes were adopted in the subsequent ThunderValley2 and Pegasus systems. It should be noted that since deployment in this early prototype, optical polymer formulations have continued to improve with polysiloxanes from Dow Corning [16] and Ormocer formulations such as \"Sunconnect\" by Nissan Chemicals [17].
# Fast Performance Modeling across Different Database Versions Using Partitioned Co-Kriging
## 5 Experimental Evaluation
### Experimental Settings
Cassandra: The physical server is equipped with two 4-core Intel(R) Xeon(R) CPU E5-2683 V3 @2.00 GHz processors, 32 GB RAM, 86 GB disk, and running CentOS 6.5 and Java 1.8.0_211. **Baseline Algorithms**. To evaluate the performance of PCK approach, we compare it with six state-of-the-art algorithms: DeepPerf [10], CART [3], Finetune [46], DDC [47], Model-shift [33], and Ottertune [2]. We provide a brief description for each algorithm as follows. DeepPerf and CART establish performance prediction models in target domain directly. They consider the performance prediction problem as a nonlinear regression problem and apply different machine learning methods, namely Deep Neural Network (DNN) and the Classification and Regression Trees (CART) technique, to find this nonlinear model. Finetune and DDC are two widely used transfer learning schemes. Finetune is a network-based transfer learning method. It freezes the partial network that pre-trained in the source domain, and transfers it to be a part of DNN which used in target domain. DDC is a mapping-based transfer learning, which maps instances from the source and target into a new data space. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Subject Database** & **Category** & **Subject Versions** & **Benchmark** & **\# of Selected Parameters** & **Performance** \\ \hline MySQL & RDBMS & 5.5, 5.7, 8.0 & sysbench & 10 & Latency (ms) \\ PostgreSQL & ORDBMS & 9.3, 11.0 & pgbench & 9 & Transactions per second \\ SQLite & Embedded DB & 3.31, 3.36.0 & Customized & 8 & Transactions per second \\ Redis & In-memory DB & 4.0.1, 5.0.0, 6.0.5 & Redis-Bench & 9 & Requests per second \\ Cassandra & NoSQL DB & 2.1, 0.3, 11.6 & YCSB & 28 & Throughput (MB/s) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Subject databases and versions, benchmarks, parameters, and performance metrics. Model-shift approach shifts the model that has been learned in the source to predict the system performance in the target using linear regression models. CART is applied to build performance prediction models in source domain. Ottertune is a transfer learning approach that exploits source samples to learn a performance model in the target. The Gaussian Process (GP) model is used to learn a performance model that can predict unobserved response values.