esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of BDS-5G Combined Precise Point Positioning
## 1 Introduction
proposed a solution method for hybrid GNSS and 5G positioning, called multiple-rate adaptive Kalman filter (MRAKF), and real-world driving experiments showed that the proposed new method improved positioning accuracy significantly, compared to traditional methods [22]. [PERSON] et al. proposed a model that combined BDS pseudorange with 5G raw observations and studied the influence of the layout of base stations on the positioning effect [23]. Despite the significant contributions of these studies, they are all based on the standard single point positioning (SPP) approach, and no research has been carried out on a combined BDS-5G PPP model. Therefore, in this study, a combined BDS and 5G PPP model is developed by introducing carrier phase observations of BDS and 5G time of arrival (TOA) observations. The impact of different numbers and geometries of 5G base stations on the convergence effect of PPP is analyzed from both static and dynamic perspectives under different numbers of visible satellites.
# Vectored-Bloom Filter for IP Address Lookup: Algorithm and Hardware Architectures
## 3 IP Address Lookup Using a Vectored-Bloom Filter
### Hash Table Implementation  
In implementing the hash table, hash collisions should be carefully considered. In order to reduce the number of collisions, we use two hash indexes and a linked list to store a prefix into the hash table. In other words, storing a prefix can have a maximum of three choices of hash entries. The first hash index has the highest priority. If both entries indicated by the two hash indexes have already been filled by other prefixes, the prefix is stored in the entry indicated by the linked list. Each hash table entry stores a prefix, the length of the prefix, a corresponding output port and a linked list.
# Multi-Body Dynamics Modeling and Simulation of Maglev Satellites
## 3 Design of Three Body Multi Closed Loop Control Law
### Design of Payload-Centered Attitude Pointing Control Law
 \tag{55}\]  
where \(\tau\) is a small enough positive number. In Equation (42), \(\overline{K}(q_{pev})\) is the diagonal matrix and the diagonal element \(\overline{k}(q_{pev,i})(i=1,2,3)\) is:  
\[ \begin{split}\overline{k}(q_{pev,i})&=kr_{2}(a|q_{pev,i }|^{p-1/(k_{2})}+b|q_{pev,i}|^{8-1/(k_{2})})kr_{2}-1\\ &\times(a(p-\frac{1}{k_{2}})|q_{pev,i}|^{p-1/(k_{2})})\\ &+b(g-\frac{1}{k_{2}})|q_{pev,i}|^{8-1/(k_{2})})\end{split}  \tag{56}\]  
For the error dynamics equation, combining observer, sliding surface, and control law, under the influence of unknown disturbance \(\mathbf{D}_{p}\), the error states \(\mathbf{q}_{pev}\) and \(\mathbf{\omega}_{pe}\) converge to the origin within a fixed time \(T\), and the upper bound of \(T\) satisfies:  
\[T\leq T_{o}+T_{a}+T_{s} \tag{57}\]  
where \(T_{o}\) is the convergence time, take \(K=L_{m}\) as the solution according to the equation; \(T_{a}\) is the convergence time, satisfying:  
\[ \begin{split} T_{a}&\leq\frac{2}{3^{1-k_{1}}a^{k_{ 1}}2^{(p_{1}k_{1}+1)/2}(1-p_{1}k_{1})}+\frac{2\tau^{1/(2-1)}}{\rho}\\ &+\frac{2}{3^{1-k_{1}}\beta^{1}2^{(p_{1}k_{1}+1)/2}(g_{1}k_{1}-1 )}\end{split}  \tag{58}\]  
In summary, the satellite platform position tracking control and load attitude control of the load center control method are provided by the electromagnetic force \(\mathbf{F}_{tr}=\mathbf{F}_{tr1}+\mathbf{F}_{ur2}\) of the upper OLFMB, the electromagnetic force \(\mathbf{F}_{ar}\) of the LLFMB, and the electromagnetic force \(\mathbf{F}_{tr}=\mathbf{F}_{lr1}+\mathbf{F}_{lr2}\) of the lower OLFMB. The load attitude control allocated according to the strategy of no additional disturbance will not affect the force situation of the satellite platform.
## 3 Results
### Use Case of Fault Diagnosis and Testing  
The merging of the proposed methods with AI techniques opens new avenues for enhancing the analysis and optimization of LPTV circuits. The incorporation of AI, particularly machine learning (ML), offers significant advantages in handling the complex and computationally intensive tasks associated with the frequency symbolic method. A special case is fault diagnosis which should be differentiated from typical functional testing. It is possible for the circuit to be in a faulty state but still pass functional test, which is equivalent to behavioral test. In such a case, one or more elements can have parameters, e.g., beyond the tolerance range. This can mark the beginning of a component's degradation process (due to aging or environmental influence) and can affect the circuit (system) lifetime (or Mean-Time Between Failure parameter, MTBF). Successful fault diagnosis can be an important part of predictive maintenance, especially in cases of a component's soft fault (parameter beyond the tolerance range while still operating), as it can be a step before hard fault (malfunction), often leading to circuit (system) malfunction. Exemplary areas are as follows:  
1. Fault Diagnosis in Electrical/Electronic Circuits: * Predictive Maintenance: AI can be used to predict potential failures in circuits by analyzing patterns and anomalies in frequency responses. By training ML models on historical data of circuit performance, it is possible to foresee faults before they occur, allowing for proactive maintenance [13; 17]. * Anomaly Detection: AI systems can be employed to detect anomalies in circuit behavior that may indicate faults. Machine learning models can learn the normal operating conditions of circuits and flag deviations from these norms, thus identifying potential issues in real-time.  
Figure 5: Use case of AI-based assistance for FSM usage.  
2. Testing and Validation: * Automated Testing: AI-driven automated testing procedures can significantly reduce the time and effort required for validating circuit designs. By using reinforcement learning techniques, AI systems can optimize test scenarios to cover a wide range of operating conditions, ensuring robust performance. * Simulation-Based Testing (SBT): AI can enhance simulation-based testing by rapidly generating and evaluating numerous test cases, thus providing comprehensive insights into circuit behavior under various conditions. This approach can help identify edge cases and potential weaknesses in the design. * Parameter Tuning: AI techniques can be used to optimize the parameters of LPTV circuits. Genetic algorithms and other optimization techniques can help find the best parameter values that improve circuit performance while meeting design constraints.  
By integrating AI techniques with the frequency symbolic method, the processes of circuit analysis, fault diagnosis, testing, and optimization can be improved. This integration not only enhances efficiency but also provides a deeper understanding of the complex behaviors of LPTV circuits, paving the way for more reliable electrical and electronic systems.
## 3 Improved TSETMF Algorithm
### Improved Self-Adaptive Median Filter
At this stage, this new current pixel value, as the input of the next pixel, can proceed with the recursion operations until the filtering process has finished. Thus, this improved self-adaptive median filter may retain and deliver non-noise pixel values more effectively. Median replacement can be conducted using the improved self-adaptive median filter, whose central value represents the pending pixel value 255, as seen in Figure 1d. At this stage, after ranking the pixel values in a 5 \(\times\) 5 window, the ranked pixel values are 0, 0, 0, 0, 0, 0, 120, 120, 122, 122, 124, 125, 126, 127, 127, 128, 129, 255, 255, 255, 255, 255, 255, and 255. By adopting the middle value, the replaced central pixel value is 126, which may then participate in the subsequent ranking and replacement. By adopting the recursion method, the improved self-adaptive median filter window can possess larger non-noise pixel values and, thus, turn the ranked middle values into mostly non-noise pixel values. This approach can also be utilized to  
Figure 1: Denoising principle comparisons for the improved self-adaptive median filter. (**a**) A self-adaptive median filtering image after processing 95% salt and pepper noise. (**b**) Partially enlarged details. (**c**) Gray values of self-adaptive median filtering image. (**d**) Gray values of improved median filtering image using recursion method and self-adaptive window. The white central value, 255, as the original value, represents the pending processing pixel point. The blue section represents the processed pixel points. The red section represents unprocessed pixel points. demonstrate the effectiveness of the denoising effects by taking advantage of the improved self-adaptive median filter. Compared with the traditional self-adaptive median filter, the improved self-adaptive median filter, adopting the recursion method and self-adaptive window, can conduct noise judgment using different filtering windows and its ranked values include the previous pixel values after performing the median replacement. This may greatly reduce the adverse effects of high-density noise. Assuming that the signal and noise values are flipped, the accuracy of noise judgment and median replacement can be ensured to output the excellent high-noise removal effects by means of the improved self-adaptive median filter.
# CMAG: A Mission to Study and Monitor the Inner Corona Magnetic Field
## 1 Introduction  
### State-of-the-Art in Coronal Science  
The solar corona, extending from the transition region into the heliosphere, exhibits fascinating characteristics that challenge our current understanding. The temperature of the corona rises rapidly in its lower layers, reaching temperatures of a few million Kelvin, while the gas pressure decreases sharply through the transition region and then decreases smoothly upward. Within the inner corona, complex magnetic fields give rise to highly structured, bright features known as streamers and coronal loops. These structures, influenced by the solar dynamo and activity cycle, remain enigmatic in terms of their magnetic properties.  
Coronal loops, in particular, are hotter and denser than the surrounding plasma, yet the mechanisms responsible for their heating and filling remain elusive [1]. The overall high temperature of the global corona itself also poses a significant mystery. Unraveling the processes of coronal heating is considered a paramount challenge in solar physics. The lack of knowledge about the magnetism of the inner corona further complicates our understanding of these phenomena. This region of the corona hosts intricate magnetic field reconnection processes, leading to the generation of flares and energetic particle events across a broad range of scales.  
Flares emit radiation across various wavelengths, from radio waves to hard X-rays and even gamma rays. Many large flares are accompanied by coronal mass ejections (CMEs), which are the primary sources of space weather effects on Earth, potentially triggering severe geomagnetic storms [2]. CMEs also generate powerful shock waves as the plasma expands during their formation and propagation through the corona. Additionally, a multitude of magnetohydrodynamic (MHD) waves can be observed propagating upward into the heliosphere within the inner corona [3; 4]. It is evident that magnetic fields play a crucial role in governing all aspects of the inner solar corona, driving the solar wind and influencing space weather phenomena. However, the detailed properties of these magnetic fields remain largely unexplored [5].  
The CMAG mission offers an opportunity to address key questions related to the coronal heating problem, flare and CME formation, and the generation and energy transport of waves. By providing fundamental data on the vector magnetic field and velocity measurements of the inner corona at unprecedented spatial and temporal resolutions, CMAG would enable substantial advancements in coronal models beyond existing empirical approximations. Moreover, it would contribute to more accurate space weather predictions, ultimately enhancing our understanding of the Sun and its influence on the heliosphere, the Earth's environment, and our technological systems and society.
## 3 System Model
### User Type Probability
\begin{array}{l}\text{Prob}\left(n_{a}r_{m}<r_{p}<n_{b}r_{m}\right)=\text{ Prob}\left(r_{p}>n_{a}r_{m}\right)-\text{Prob}\left(r_{p}>n_{b}r_{m}\right)\\ =\frac{\lambda_{m}}{\lambda_{m}+n_{b}^{2}\lambda_{p}}-\frac{\lambda_{m}}{ \lambda_{m}+n_{b}^{2}\lambda_{p}},\end{array}  \tag{4}\]  
_where \(\lambda_{m}\) and \(\lambda_{p}\) are the MBS density and PBS density._  
Proof. : The proof of Lemma 1 is presented in Appendix A. Then, the probabilities of the PSF MUEs and the USF PUEs can be calculated similarly based on Lemma 1 and can be expressed by \(\text{Prob}\left(r_{p}>n_{a}r_{m}\right)=\frac{\lambda_{m}}{\lambda_{m}+n_{b}^ {2}\lambda_{p}}\) and \(\text{Prob}\left(r_{p}<n_{b}r_{m}\right)=1-\text{Prob}\left(r_{p}>n_{b}r_{m} \right)=1-\frac{\lambda_{m}}{\lambda_{m}+n_{b}^{2}\lambda_{p}}\), respectively. Combining Equation (4) with the user association strategy in Equation (3), the probability of this typical UE belonging to the user type \(l\) can be defined as \(A_{l}=\text{Prob}(l\in\mathbf{L})\), which is expressed as:  
\[A_{l}=\left\{ \begin{array}{l}\frac{\lambda_{m}}{\lambda_{m}+k_{c}^{2}\lambda _{p}},\text{if }l=pm\\ \frac{\lambda_{m}\lambda_{p}\left(k_{c}^{2}-k_{p}^{2}\right)}{\left(\lambda_{m }+k_{p}^{2}\lambda_{p}\right)\left(\lambda_{m}+k_{c}^{2}\lambda_{p}\right)}, \text{if }l=pp\\ \frac{\lambda_{m}\lambda_{p}\left(k_{c}^{2}-k_{c}^{2}\right)}{\left(\lambda_{m }+k_{c}^{2}\lambda_{p}\right)\left(\lambda_{m}+k_{c}^{2}\lambda_{p}\right)}, \text{if }l=um\\ \frac{k_{p}^{2}\lambda_{p}}{\lambda_{m}+k_{p}^{2}\lambda_{p}},\text{if }l= up\end{array} \right.. \tag{5}\]  
In particular, if \(\rho=0\), then \(k_{c}=\infty\) and \(A_{pm}=0\), which means that the PSFs are zero power ABS. The association probabilities of USF MUEs and PSF MUEs versus \(\rho\) with different \(B_{p}\) are simulated according to Equation (5) in Figure 3. As shown in Figure 3, with the increase of power reduction factor \(\rho\), the transmission power of MBSs over PSF will increase, resulting in the association probability of USF MUEs decreasing and that of PSF MUEs increasing. It can also be found that the sum of \(A_{pm}\) and \(A_{um}\) will decrease with the growth of \(B_{p}\), because more UEs will be offloaded into pico-cells with larger \(B_{p}\).
## 4 Experimental Results and Analysis  
To verify the effectiveness of extracted behavior characteristics for AIS data ship classification and anomaly detection, we selected the classical machine learning algorithm support vector machine (SVM) [39] and random forest (RF) [40] as the implementation method. Both the algorithms have been widely used in classification tasks. All experiments in this section were programmed and implemented in the Python 3.8 environment under Windows 10. We spliced the preprocessed AIS messages into long-term ship behavior sequences based on MMSI codes, and 18,200 samples of five types of ships were finally formed through feature extraction, as described in Section 3.3.1. During the experiment, 200 AIS data samples of each type of ship were selected as test samples, and the rest were used as training samples. The AIS data used in this paper were all from the HY-1C and HY-2B satellites.  
In this paper, the common classifier evaluation metrics are used to evaluate the proposed method, including _Accuracy_, _Precision_, _Recall_, and _F\({}_{1}\)-Score_. The relevant calculation is shown in Equation (7):  
\[\begin{split} Accuracy=\frac{TP+TN}{TP+TN+FP+FN}\\ Precision=\frac{TP}{TP+FP}\\ Recall=\frac{TP}{TP+FN}\\ F_{1}-Score=\frac{2\times Precision\times Recall}{Precision+Recall} \end{split} \tag{7}\]  
where, _TP_, _FP_, _FN_, _TN_ represent True Positives, False Positives, False Negatives, and True Negatives, respectively.
## 6 Conclusions  
The off-design model of the SASCHCS that is exact and can be solved easily was built. It was based on the combination of the characteristic equation in the absorption subsystem and the lumped parameter model of the compression subsystem. The outstanding advantage of this model is the reduced solving time and reliable convergence as the operating condition deviates from the design condition significantly. A prototype and the corresponding experiment system were developed to verify the model. The maximum deviation of the theoretical result and experimental data was lower than 4%.  
The off-design model of the hybrid system was employed to examine the performance in the variable conditions. The COP of the absorption subsystem goes up by 11.55%, 1.31% and 5.43% when the inlet temperatures of hot water and chilled water rise by 35 \({}^{\circ}\)C and 6 \({}^{\circ}\)C and the flow rate of cooling water in the compression subsystem falls by 80%, respectively. Besides, it is found that the COP of the absorption subsystem comes down by 4.02%, 2.52%, 3.45% and 19.58% as the cooling water temperature rises by 12 \({}^{\circ}\)C and the hot water flow rate, chilled water flow rate and compressor speed drop by 80%, respectively. For the compression subsystem, its COP enhances by 9.01%, 12.1% and 40.8% when the inlet temperatures of the hot water and chilled water increases by 35 \({}^{\circ}\)C and 6 \({}^{\circ}\)C and the compressor speed goes down by 80%, respectively. In addition, the COP of the compression  
Figure 12: Performance with the flow rate of circulation water in subcooler.  
Figure 13: Performance with the speed of the compressor.  
subsystem decreases by 36.48%, 1.89%, 32.44% and 25.91% as the cooling water temperature rises by 12 \({}^{\circ}\)C and the flow rates of hot water, cooling water in the compression subsystem and chilled water come down by 80%, respectively. Particularly, the COP of the absorption and compression subsystems is independent of the flow rate of the circulation water in the subcooler.  
[PERSON] contributed to the conceptualization, methodology and writing. [PERSON] contributed to formal analysis. [PERSON] contributed to the validation. [PERSON] contributed to the data curation.  
This work is supported by: (1) National Natural Science Foundation of China under contract No. 51206056, (2) Natural Science Foundation of Guangdong Province under contract No. 2018A030313310, (3) Fundamental Research Funds for the Central Universities under contract No. 2017 ZD023, (4) Key Laboratory of Efficient and Clean Energy Utilization of Guangdong Higher Education Institutes under contract No. KLB10004.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Statistical Analysis
#### 3.1.1 Communication Type  
Because the minimization and predictability of failures was crucial for cost optimization, we examined the dependence of failures on selected parameters. The impact of the meter communication type is presented in Table 2.  
The meters of the \"walk by\"-type had a very low percentage of failures, but the sample was small in relation to other types of communication, so the conclusion that they were the most reliable was a bit premature. Meters without a communication module failed almost twice as often as those with communication. We can certainly conclude that the meters with remote communication, in other words, the new generation of devices, were far less likely to fail.  
Using the methods presented in [22], the distribution of the probability vector can be determined. In Figure 3, each state from 1-10 represents the age of the meter in years. It can be observed that there was a significant difference in failures between meters with and without communication, also in the long term. The linearity visible in that figure confirmed that the failure intensity was weakly dependent on the meter's operating time. Moreover, information about stationary distribution allowed for better planning of inventory.  
Therefore, in the context of smart meters and smart buildings, devices with remote reading are definitely a better choice. Not only are they more \"user friendly\" (the presence of the resident is not required during readings), but also more reliable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Communication Type** & **Meter \#** & **Failure \#** & **\% of Failures** \\ \hline Walk by & 1045 & 18 & 1.72\% \\ Funk & 10,996 & 1716 & 15.61\% \\ Bus & 12,631 & 1778 & 14.08\% \\ Without communication & 27,577 & 8273 & 30.00\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dependence of failure on the type of communication.
# Entropy-Based Ensemble of Convolutional Neural Networks for Clothes Texture Pattern Recognition
## 3 The Proposed Method
### Fusion Based on Entropy Voting  
Each base model \(j\) is trained independently, yielding a predicted label \(y_{j}\) and a vector of probabilities \(p_{j}=(p_{j}^{1},p_{j}^{2},...p_{j}^{c})\) which is fused for the final prediction of unseen patterns.  
The base models are trained independently, and the predictions from these models are fused to determine the final prediction. The simplest and most commonly used fusion method is max voting (i.e., majority voting), where the label of a test sample is given according to the number of classifiers who vote for some specific label. The main drawback of this approach is that it does not consider the uncertainties in base learners' decisions. We propose a new fusion method in this work: entropy voting. Entropy measures the uncertainty in that the higher the entropy, the higher the uncertainty. For any unknown sample \(x\), each base learner \(j\) computes the vector \(p_{j}=[p_{j}^{1,x},p_{j}^{2,x},...,p_{j}^{c,x}]^{T}\) of posterior probabilities belonging to each label. These probabilities are used to compute the entropy \(e_{j}^{x}\) for classifier \(j\) according to the following equation [28]:  
\[e_{j}^{x}=\sum_{i=1}^{|C|}p_{j}^{i,x}log(p_{j}^{i,x}),j=1,2,...,k \tag{5}\]  
where \(C\) is the number of labels (classes) and \(p_{j}^{i,x}\) is the probability that a test sample \(x\) belongs to class \(i\) predicted by classifier \(j\), and \(k\) is the number of base learners.  
The label of a classifier with the lowest entropy is chosen as the predicted label for a test sample as the decision of this classifier involves the lowest uncertainty. Formally, let \(e_{j}^{x}\) be the entropy calculated for a test sample \(x\) by the classifier \(j\), then the set \(E=\{e_{j}^{x},\forall j\in 1..k\}\)  
Figure 7: ResNet50 with NL block. \(ResG_{i}\) is the \(i\)th group of ResNet blocks.  
represents the set of entropies for all \(k\) base learners of the ensemble. The predicted label assigned to the sample \(x\) by entropy voting is calculated as follows:  
\[J=argmin_{j}\{e_{j}^{x}\} \tag{6}\]  
and the predicted label of x is  
\[\ell=\ell_{I} \tag{7}\]  
\[\ell_{I}=argmax_{C}\{p_{j}^{1,x},p_{j}^{2,x},...,p_{j}^{c,x}\} \tag{8}\]  
The predicted label \(l_{J}\) is based on the base model, which has the least uncertainty and the highest level of confidence.