esa-sceva/satcom-chunk-collection huggingface dataset:
# BeiDou Augmented Navigation from Low Earth Orbit Satellites
## 4 Results and Discussion
### Spp  
The standard point positioning (SPP) with pseudo-range measurements was carried to investigate the performance of incorporating LEO constellation to augment BeiDou-3 for the selected station. The simulated code measurements with 1 m random noise were processed with precise satellites' orbit and clock for observation simulation. The ionosphere delay has been corrected with ionosphere-free combination, and the wet and dry troposphere delay was corrected with Saastamoinen model and global mapping functions (GMF). In this case, only four epoch-wise parameters including coordinates and receiver clock were estimated. Two solutions with BeiDou-3 only or combined BeiDou-3 and LEO measurements were performed.  
In general, the inclusion of LEO data has limited effect on SPP. Taking CENT station for example, the positioning accuracy has been improved from 105.3 to 100.7 cm in earth direction, 99.1 to 93.2 in north direction, as well as 293.7 to 261.3 cm in Up direction, once LEO data have been used. Table 7 shows the SPP precision of these three stations in north, east and up direction.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Station**} & \multicolumn{3}{c}{**BDS**} & \multicolumn{3}{c}{**BDS + LEO**} \\ \cline{2-7}  & **North** & **East** & **Up** & **North** & **East** & **Up** \\ \hline CENT & 99.1 & 105.3 & 293.7 & 93.2 & 100.7 & 261.3 \\ POTS & 152.7 & 131.8 & 290.7 & 121.3 & 87.4 & 229.9 \\ NTUS & 91.6 & 114.9 & 217.2 & 84.5 & 101.5 & 199.2 \\ \hline \hline \end{tabular}
\end{table}
Table 7: SPP precision of both BDS and BDS + LEO (cm).  
Figure 4: Global distribution for PDOP of BeiDou-3-only (**a**) and BeiDou-3/LEO combined constellation (**b**).
# Holistic Utility Satisfaction in Cloud Data Centre Network Using Reinforcement Learning
## 4 Reinforcement-Learning-Based Cloud Resource Allocation
### Q-Learning Model
If we assume that 1000 simultaneous users want to use the CDCN resources, the state space has a large dimension of 6000, which makes the resource allocation problem have a large computational complexity. In such situations, the ordinary Q-learning algorithm may not be a good candidate, and a deep RL mechanism (such as DQN) should be adopted. **Agent:** The agent in this paper is considered to be the cloud manager/provider (CP). At first, the CP receives the state information including the requested users' demand quadruples (CPU, RAM, storage, bandwidth) and available resources from cloud data centres in each time slot \(t\) (see Figure 4), then the CP assigns some resources to each cloud user based on the proposed reinforcement-learning-based algorithm. **Action:** The action is the vector of virtualised resources that are allocated to each distinct cloud user in state \(\ell\) for each time slot \(t\) and represented by \(\mathbf{Z}_{t}^{\ell}\). The action space \(\mathcal{Z}^{\ell}(t)\) depends on the current state \(\mathbf{S}_{t}^{\ell}\) and is in fact a reverse-shifted version of state space \(\mathcal{S}(t)\) (see Figure 5 for a simple two-dimensional example). In Figure 5, a sample state space for a two-dimensional space with sample state variables \(x_{1}\) and \(x_{2}\) (\(0\leq x_{1}\leq a,0\leq x_{2}\leq b\)) and the constraint (\(x_{1}+x_{2}\leq c\)) is depicted. As can be verified in this figure, the action space is a function of the current state \(\mathbf{S}_{t}^{\ell}\). **Reward:** The reward is the weighted sum of all cloud user utility functions and the cloud provider/manager utility function as: \(\mathbf{R}_{t}^{\ell}\triangleq\Gamma\sum_{i=1}^{M(t)}\mathcal{U}^{i}(t)+(1- \Gamma)\mathcal{U}^{CP}(t)\), where \(\Gamma\) is a positive constant, \(0<\Gamma<1\).
## 3 Current Status of the Circuit Component in the MMW Regime
### Mixer
#### 3.3.2 Mixer in SiGe Technology  
Figure 11 illustrates the conversion gain _versus_ fractional bandwidth of the published MMW SiGe mixes. In [80], with careful design of the degeneration resistors of the transconductance stage in the Gilbert cell topology, the linearity and the conversion gain can be improved. In [136], according to the multi-tanh triplet principal, the linearity of the transconductance stage in the Gilbert cell mixer is able to be mended as well. In [137], the dc power consumption is reduced by the current injection technique and the conversion gain is enhanced by the dual negative resistance compensation technique. In [138], the 245 GHz subharmonic balanced mixer has a measured conversion loss of \(-\)7 dB with a fractionalbandwidth of 5% in 130 nm SiGe technology. In [139], two cross-couple pairs with four base-emitter external capacitances are combined by the dual balun. The voltage across the base and emitter junction will increase owing to the four base-emitter external capacitances, and thus; the conversion gain is boosted. In [140], the common mode noise can be reduced owing to the natural of the anti-parallel diode subharmonic mixer. Moreover, it has a measured conversion gain of 8 dB with a fractional bandwidth of 6.6%.
# A Novel Geo-Localization Method for UAV and Satellite Images Using Cross-View Consistent Attention
## 4 Experiments
### Ablation Study
#### 4.4.2 Ablation Study of Loss Function  
The results of the ablation study of the piecewise loss function are listed in Table 6. The uses of the original triplet loss function, advanced triplet loss function, and piecewise triplet loss function are tested, respectively. In Table 6, \"Triplet loss\" refers to the original triplet loss in Equation (5). \"Advanced triplet loss\" refers to the triplet loss function with the positive restriction in Equation (6). \"Piecewise triplet loss\" refers to the triplet loss function in Equation (7). The testing result of using the advanced triplet loss function is 4.47% higher at R@1 than using the original triplet loss function. However, the convergence speed of the model based on the advanced triplet loss function is quite slow due to the more stringent convergence conditions. The proposed piecewise loss function ensures that the similarity between positive and negative samples is large enough in the early training stage according to the convergence direction of the original triplet loss function. The advanced triplet loss function is used in the later stage, aiming at increasing the convergence difficulty. The proposed loss function ensures convergence speed and accuracy improvement in the late stage of model training. The testing accuracy of using the proposed loss function is 6.97% higher at R@1 than using the original triplet loss function and 2.5% higher than using the improved triplet loss function. The results illustrate that employing the newly proposed loss function is more conducive to UAV-to-satellite geo-localization.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **R@1** & **R@5** & **R@10** & **AP** \\ \hline Triplet loss & 84.60 & 96.41 & 97.93 & 87.24 \\ Advanced triplet loss & 89.07 & 98.47 & 99.58 & 91.23 \\ Piecewise triplet loss & **91.57** & **99.29** & **99.68** & **93.31** \\ \hline \hline \end{tabular}
\end{table}
Table 6: The ablation study of loss function. (Bold numbers mean the best results).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **R@1** & **R@5** & **R@10** & **AP** \\ \hline Ours & **91.57** & **99.29** & **99.68** & **93.31** \\ Ours w/o color transfer & 84.61 & 96.18 & 97.64 & 87.17 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The ablation study of the color transfer module. (Bold numbers mean the best results).  
Figure 7: An example of the attention map of the last layer is demonstrated. The result illustrates that the transformer encoder can perform better for learning correct cross-view consistent image information after color transfer.
# A Dynamic Multiple-Query RRT Planning Algorithm for Manipulator Obstacle Avoidance
## 3 Background: BG-RRT Algorithm and Motion Planning
### Motion Planning  
Trajectory planning is an important part of manipulator motion planning and the basis of trajectory-tracking control, as shown in Figure 3. Trajectory planning uses path points to generate a smooth trajectory execution, e.g., minimum snap optimization [21], B-spline trajectory optimization [22], kinodynamic constraints [23; 24], etc. Considering the non-uniformity of the waypoint, a non-uniform B-spline method is adopted to fit the discrete waypoints to generate the executable trajectory for manipulator. The specific process is described in reference [8]. As shown in Figure 4, the trajectory planning is realized on the path points, which are plotted with green, after the rarefying by the Douglas-Peucker algorithm. The red path represents the connection of waypoints planned by the BG-RRT algorithm. The green waypoints are the rarefied paths by the Douglas-Peucker algorithm lined with blue. The cyan continuous trajectory is an executable trajectory generated by the non-uniform B-spline method.
## 1 Introduction
Moreover, multi-objective optimization has been used in many other previous works and proved to be an effective method. [PERSON] et al. [25] put forward a systematic comparison of different S-CO\({}_{2}\) Brayton cycle layouts based on multi-objective optimizations. Their results suggested that the inter-cooling cycle layout and the partial-cooling cycle layout provided the most excellent performances. [PERSON] et al. [26] also performed multi-objective optimizations of the supercritical CO\({}_{2}\) Brayton cycles integrated with a solar receiver. However, these studies did not consider economic comparisons between the power cycles. In fact, economic cost is also considered as one of the important indicators of comparisons between different cycles [27]. Based on the above literature review, we introduce the preliminary economic analysis of the comparison of the S-CO\({}_{2}\) cycles with PTC. The primary contributions of this work are as follows:  
1. Three S-CO\({}_{2}\) power cycles coupled with PTC (namely, simple regeneration, intercooling and re-compression) are established and their parameters are analyzed for preliminary comparison. 2. A multi-objective optimization method using genetic algorithm (GA) is presented to compare the performance, including thermodynamic performance and preliminary economic cost of cycles. The advantages and disadvantages of the three cycle kinds are thoroughly compared. 3. The sensitivity analysis of a typical optimization result of a simple regeneration cycle is carried out; the influence of different parameters on system performance is obtained. This work can provide a solid foundation for the fundamental design and construction of S-CO\({}_{2}\) Brayton cycles. We hope that this research will ignite interest in the application of S-CO\({}_{2}\) cycles coupled with PTC and the performance comparison of cycles considering thermodynamics and economics.
## 2 Materials and Methods
When entropies are expressed as a function of the probability of occurrences of positional fluctuations \(\Delta R\), \(TE_{i\to j}(\tau)\) becomes,  
\[TE_{i\to j}= -\langle log_{2}\ p\big{(}\Delta R_{j}(t),\Delta R_{j}(t-\tau) \big{)}\rangle+\langle log_{2}\ p\big{(}\Delta R_{j}(t-\tau)\big{)}\rangle \tag{7}\] \[+\langle log_{2}\ p\big{(}\Delta R_{j}(t),\ \Delta R_{j}(t-\tau),\ \Delta R _{i}(t-\tau)\big{)}\rangle-\ \langle log_{2}\ p\big{(}\Delta R_{j}(t-\tau),\ \Delta R _{i}(t-\tau)\big{)}\rangle\]  
A similar expression for transfer entropy was used for rotameric states, where \(\Delta R\) was replaced by \(\Theta\), which includes the information of the rotameric state of all rotatable \(sp^{3}\)-\(sp^{3}\) bonds in each residue \((\varphi,\ \psi,\chi_{i},\ i=1,2,3,4)\). However, for a residue pair such as _Lys-Arg_, where both residues include four side-chain rotatable bonds, each having 3 alternative rotameric states, the joint probability \(p\big{(}\Theta_{j}(t),\ \Theta_{j}(t-\tau),\ \Theta_{i}(t-\tau)\big{)}\) is comprised of \(3^{18}\) (\(=3^{6}\times 3^{6}\times 3^{6}\)) different rotameric states. For a protein system with 312 residues, the calculation of the transfer entropy becomes computationally intractable as it exceeds the maximum size an array can hold. Thus, only the first side-chain rotameric state was considered together with two backbone torsion angles, i.e., \((\varphi,\ \psi,\chi_{1})\), which yielded \(3^{9}\ \big{(}=3^{3}\times 3^{3}\times 3^{3}\big{)}\) different states per residue. Finally, the net transfer entropy was determined by taking the difference between TE from \(i\) to \(j\) and that from \(j\) to \(i\) as,  
\[NetTE_{i\to j}=TE_{i\to j}-TE_{j\to i} \tag{8}\]  
The source codes for both mutual information and transfer entropy calculations were written by the authors using C programming language and can be provided upon request.
## 2 WFT-Net Model
### The Firing Rule of WFT-Nets
Definition 4 (Reachability): Let \(N=(P\), \(T\), \(F\), \(\mathcal{D}\), \(\mathcal{R}\), \(O_{\mathcal{D}}\), \(O_{\mathcal{R}}\), \(G\)) be a WFT-net, \(c\),\(c^{\prime}\in C_{c_{0}}\), and \(C\),\(C^{\prime}\)\(\subset\)\(C_{c_{0}}\). (1) There is a must-step from \(c\) to \(C\) if there is only one transition \(t\) such that \(c[t)C\), denoted by \(c\)\(\rightarrow_{must}C\). (2) There is a may-step from \(c\) to \(c^{\prime}\) if \(c\)\(\rightarrow_{must}\)\(C\)\(\wedge\)\(c^{\prime}\in C\), denoted by \(c\)\(\rightarrow_{may}\)\(c^{\prime}\). (3) There is a may-path of length \(n(>0)\) from \(c\) to \(c^{\prime}\), if there exists a configuration sequence  
Note that \(c{\rightarrow}_{may}^{*}c^{\prime}\) means that \(c^{\prime}\) is may-reachable from \(c\), \(c{\rightarrow}_{must}^{*}C\) means that \(C\) is must-reachable from \(c\), and \(\forall c^{\prime}\in C{:}c{\rightarrow}_{may}^{*}c^{\prime}\) means that there is a must-hyper-path from \(c\) to \(C\). Let \(C_{c}\) denote the set of all may-reachable configurations from \(c\). To represent all possible executions of a WFT-net, the reachability graph with all must-hyper-paths can be defined as below. Let \(C_{c}\) be the set of all configurations that are may-reachable from \(c\). The reachability graph representing all possible executions of a WFT-net should contain all must-hyper-paths, and its formal definition is shown as follows. **Definition 5** (Reachability graph, RG). : _Let \(N=(P,\,T,\,F,\,\mathcal{D},\,\mathcal{R},\,O_{\mathcal{D}},O_{\mathcal{R}},\,G)\) be a WFT-net and \(c_{0}\) be the initial configuration. \(RG(N)=(C_{c_{0}},\,E,\,L)\) is the reachability graph of \(N\), where_  
1. \(C_{c_{0}}\) _is the configuration set, which is must-reachable from_ \(c_{0}\)_, i.e., the node set of_ \(RG(N)\)_;_
2. \(E=\{(c_{i},c_{j})|c_{i},c_{j}\in C_{c_{0}},\,\exists t\in T:c_{i}[t]c_{j}\}\) _is the edge set of_ \(RG(N)\)_; and_
3. \(L{:}E{\rightarrow}T\times 2^{\mathcal{D}}\times 2^{\mathcal{D}}\times 2^{ \mathcal{D}}\times 2^{\mathcal{D}}\times 2^{\mathcal{D}}\times 2^{\mathcal{D}} \times 2^{\mathcal{D}}\times 2^{\mathcal{D}}\times(2^{\mathcal{R}}{:}2^{ \mathcal{R}})\times(2^{\mathcal{R}}{:}2^{\mathcal{R}})\times(2^{\mathcal{R}}{:}2 ^{\mathcal{R}})\times(2^{\mathcal{R}}{:}2^{\mathcal{R}})\times(2^{\mathcal{R}} {:}2^{\mathcal{R}})\times(2^{\mathcal{R}}{:}2^{\mathcal{R}})\times(2^{\mathcal{ R}}{:}2^{\mathcal{R}})\times(2^{\mathcal{R}}{:}2^{\mathcal{R}})\) _such that_ \(L(c_{i}c_{j}){=}\langle t;\,\mathcal{D}_{t}^{rd},\,\mathcal{D}_{t}^{wt},\, \mathcal{D}_{t}^{dt},\,\mathcal{D}_{t}^{sel},\,\mathcal{D}_{t}^{ins},\, \mathcal{D}_{t}^{upd},\,\mathcal{D}_{t}^{del};\,\mathcal{R}_{t}^{sel}{:}R_{t}^{ ins},\,\mathcal{R}_{t}^{upd}{:}R_{t}^{upd},\,\mathcal{R}_{t}^{del}{:}R_{t}^{id}\rangle\) _if_ \((c_{i}c_{j})\in E\) _and_ \(c_{i}[t]c_{j}\)_._
## 4 Results and Analysis
### The Impact of Mechanical Shock  
The mechanical shock test evaluates the structural integrity and the satisfactory performance of board and components that would experience steady or constant acceleration forces. The board and components were submitted to functional tri-axis shocks from a half sine wave for 6 ms at 10 G, for six shocks, and three pulses per direction. The results are shown in Figure 14a,b. Again, maximum displacement and stress was exerted around the large central components in front of and around the heatsink, as one may expect. However, due to the short duration of the shock, all of the components survived the test and have a predicted life well in excess of the test pass/fail metric, as shown in Figure 15.  
Figure 12: (**a**) Random vibration RMS displacement. (**b**) Random vibration strain RMS displacement.  
Figure 14: _Cont_.  
Figure 13: Life prediction of PCB under vibration fatigue.
## 3 Results and Discussion
### Analysis of Volatile Compounds
#### 3.7.2 Major Volatile Compounds Analysis  
To further examine the difference in the contribution of volatile compounds to the overall flavour profile of normal Xuanwei ham and spoiled Xuanwei ham, the OAV of each compound was calculated using the content of each volatile compound and the odour threshold value (OTV) obtained from previous literature [49]. It is generally accepted that volatile compounds play a greater role in the overall flavour when OAV \(\geq 1\)[91]. A total of 26 major volatile compounds (Table 4), including 6 aldehydes, 4 alcohols, 1 ketone, 9 esters, 3 acids, and 3 other compounds, were identified as significantly impacting the overall flavour profile of Xuanwei ham based on OAV. Specifically, 3-methyl butanal and hexanal were the primary contributors to flavour in normal ham. According to [PERSON] et al. [92], 3-methyl butanal imparted a malty aroma, providing a unique flavour to buckwheat honey. It is worth noting that 3-(methylthio)propionaldehyde contributed to the flavour of normal ham. Previous research by [PERSON] et al. [93] showed that 3-(methylthio)propionaldehyde exhibited a strong odour reminiscent of soup, meat, onions, and potatoes. In this study, trimethylamine, methanethiol, hexanal, valeric acid, and hexanoic acid were found to have a significant impact on the flavour composition of spoiled ham. Among them, trimethylamineamine, methanethiol, and valeric acid were exclusive to spoiled ham. Trimethylamine contributed to a putrid fish flavour and fishy smell [90], methanethiol yielded an objectionable flavour [94], and valeric acid imparted a malodourous odour to spoiled ham [95]. The presence of these volatile compounds, which produce unpleasant odours, maybe the key factor responsible for the critical flavour differences between normal Xuanwei ham and spoiled Xuanwei ham.
### Research Gap and Proposed Solution  
Despite advancements in sliding mode control (SMC) for step-type position responses, a significant gap remains in addressing the robustness of SMC under varying rotational inertia conditions. Specifically, current research lacks comprehensive methodologies to ensure the consistent and reliable performance of SMC algorithms when applied to systems with dynamically changing rotational inertia. This paper aims to fill this gap by proposinga variable-parameter sliding mode control (VSMC) method that integrates inertia and load torque observations using a least squares observer. This approach allows for the adaptive adjustment of control parameters, enhancing the robustness of position control in systems where rotational inertia fluctuates.  
Based on the above analysis, a variable-parameter sliding mode controller (VSMC) based on a recursive least squares (RLS) observer is designed to enhance the robustness of PMSM servo systems, ensuring that these systems maintain an optimal step position response performance even when parameters such as rotational inertia change. VSMC uses the rotational inertia and load torque values observed by the RLS observer to adjust its sliding mode surface parameters, achieving variable parameter control. Simulations and experiments indicate that this method effectively improves the step position response performance of PMSM servo systems and is insensitive to changes in rotational inertia, demonstrating a strong robustness.
# State Evaluation of Electrical Equipment in Substations Based on Data Mining
## 2 Methodology
### Data Analysis

Table 2: _Cont._The discretization process of the Gaussian mixture model begins with collecting substation electrical equipment state data. Let the dataset be \(X=\{x_{1},x_{2},\ldots,x_{n}\}\), where each \(x_{i}\) denotes a sample. \[X=\left( \begin{array}{ccccc}x_{11}&x_{12}&\ldots&x_{1(m-1)}&x_{1m}\\ x_{21}&x_{22}&&x_{2(m-1)}&x_{2m}\\ &\vdots&\ddots&\vdots&\\ x_{(n-1)1}&x_{(n-1)2}&\ldots&x_{(n-1)(m-1)}&x_{(n-1)m}\\ x_{n1}&x_{n2}&&x_{n(m-1)}&x_{nm}\end{array} \right) \tag{1}\]  
The 'fitdist' function is used to fit the data and select an appropriate probability distribution model. The Gaussian mixture models (GMM) parameters are initialized via K-Means clustering establishing initial mean \(\mu_{k}\), covariance matrix \(\Sigma_{k}\), and mixing coefficient \(\pi_{k}\). \[\left\{ \begin{array}{c}\mu_{k}=\frac{1}{N_{k}}\Sigma x_{i}\\ \Sigma_{k}=\frac{1}{N_{k}}\Sigma(x_{i}-\mu_{k})(x_{i}-\mu_{k})^{T}\\ \pi_{k}=\frac{1}{K}\end{array} \right. \tag{2}\]  
In the E-step of the Expectation Maximization (EM) algorithm, the sample's probability density function is computed,  
\[N(x_{i}|\mu_{k},\Sigma_{k})=\frac{\exp\left[-(x_{i}-\mu_{k})(x_{i}-\mu_{k})^{T }\Sigma^{-1}/2\right]}{\sqrt{(2\pi)^{k}\text{det}(\Sigma)}} \tag{3}\]  
And the posterior probability of each sample belonging to each Gaussian component is calculated,  
\[\gamma_{ik}=\frac{\pi_{k}N(x_{i}|\mu_{k},\Sigma_{k})}{\sum_{i=1}^{k}\pi_{i}N(x _{i}|\mu_{i},\Sigma_{k})} \tag{4}\]  
In the M-step, GMM parameters are updated. \[\left\{ \begin{array}{c}\mu_{k}=\frac{\sum_{i=1}^{k}\gamma_{ik}x_{i}}{\sum_{ k=1}^{k}\pi_{k}}\\ \Sigma_{k}=\frac{\sum_{i=1}^{k}(\gamma_{ik}-\mu_{k})(x_{i}-\mu_{k})^{T}}{\sum _{i=1}^{k}\pi_{k}}\\ \pi_{k}=\frac{\sum_{i=1}^{k}\gamma_{ik}}{n}\end{array} \right. \tag{5}\]  
Figure 2 illustrates the data preprocessing workflow. This process integrates minor data variations and independent data unrelated to other features. The main goals are to maintain inter-data feature correspondences and achieve approximate sample size parity across features. Figure 2: Flowchart of data preprocessing.