esa-sceva/satcom-chunk-collection huggingface dataset:
# Steady State Response of Linear Time Invariant Systems Modeledby Multibond Graphs
## Appendix B Proof of Lemma 2  
\(\mathbf{F}\) is a constant and diagonal matrix, which is invertible. From the third line of (22) with (23) and (24):  
\[\underline{D}_{\underline{in}}^{d}(t)=\left(\mathbf{I}-\mathbf{J}_{22} \mathbf{L}^{d}\right)^{-1}\left(\mathbf{J}_{21}^{11}\underline{\underline{ \underline{\underline{\sigma}}}}(t)+\mathbf{J}_{21}^{12}\underline{\underline {\underline{\sigma}}}(t)+\mathbf{J}_{21}^{13}\mathbf{X}_{GY}^{d}\underline{I }_{GY}^{d}(t)+\mathbf{J}_{23}\underline{u}(t)\right) \tag{103}\]  
From the second line of (22) with (23) and (24):  
\[\underline{I}_{GY}^{d}(t)=\left(\mathbf{I}-\mathbf{J}_{11}^{33} \mathbf{X}_{GY}^{d}\right)^{-1}\left(\mathbf{J}_{11}^{31}\underline{\underline {\underline{\sigma}}}(t)+\mathbf{J}_{11}^{32}\underline{\underline{\sigma}} (t)+\mathbf{J}_{12}^{31}\mathbf{L}^{d}\underline{D}_{\underline{in}}^{d}(t)+ \mathbf{J}_{13}^{31}\underline{u}(t)\right) \tag{104}\]  
\(\mathbf{L}^{d}\) is a constant and diagonal matrix, and \(\mathbf{X}_{GY}^{d}\) is skew-symmetric. Furthermore, \(\mathbf{J}_{22}\) and \(\mathbf{J}_{11}^{33}\) are skew-symmetric matrices, and \(\left(\mathbf{I}-\mathbf{J}_{22}\mathbf{L}^{d}\right)\) and \(\left(\mathbf{I}-\mathbf{J}_{11}^{33}\mathbf{X}_{GY}^{d}\right)\) can be invertible by solving (103) and (104):  
\[\underline{D}_{\underline{in}}^{d}(t) = \left(\mathbf{I}-\mathbf{J}_{22}\mathbf{L}^{d}-\mathbf{J}_{21}^{ 13}\mathbf{N}_{X}\mathbf{J}_{12}^{31}\mathbf{L}^{d}\right)^{-1}\left[\left( \mathbf{J}_{21}^{11}+\mathbf{J}_{21}^{13}\mathbf{N}_{X}\mathbf{J}_{11}^{31} \right)\underline{\underline{\sigma}}(t)+\right. \tag{105}\] \[\left.\left(\mathbf{J}_{21}^{12}+\mathbf{J}_{21}^{13}\mathbf{N}_ {x}\mathbf{J}_{11}^{32}\right)\underline{\underline{\sigma}}_{d}^{\bullet}(t)+ \left(\mathbf{J}_{23}+\mathbf{J}_{21}^{13}\mathbf{N}_{x}\mathbf{J}_{13}^{31} \right)\underline{u}(t)\right]\]  
\[\underline{I}_{GY}^{d}(t) = \left(\mathbf{I}-\mathbf{J}_{11}^{33}\mathbf{X}_{GY}^{d}-\mathbf{ J}_{12}^{13}\mathbf{N}_{L}\mathbf{J}_{21}^{13}\mathbf{X}_{GY}^{d}\right)^{-1}\left[ \left(\mathbf{J}_{11}^{31}+\mathbf{J}_{21}^{31}\mathbf{N}_{L}\mathbf{J}_{21}^{1 1}\right)\underline{\underline{\sigma}}(t)+\right. \tag{106}\] \[\left.\left(\mathbf{J}_{11}^{32}+\mathbf{J}_{21}^{31}\mathbf{N}_ {L}\mathbf{J}_{21}^{12}\right)\underline{\underline{\sigma}}_{d}^{\bullet}(t)+ \left(\mathbf{J}_{13}^{31}+\mathbf{J}_{12}^{31}\mathbf{N}_{L}\mathbf{J}_{23} \right)\underline{u}(t)\right]\]  
The matrices \(\mathbf{J}_{21}^{13}\) and \(\mathbf{J}_{12}^{31}\) are related by \(\mathbf{J}_{21}^{13}=-\left(\mathbf{J}_{12}^{31}\right)^{T}\), then \(\left(\mathbf{I}-\mathbf{J}_{22}\mathbf{L}^{d}-\mathbf{J}_{21}^{13}\mathbf{N}_ {x}\mathbf{J}_{12}^{31}\mathbf{L}^{d}\right)\) and \(\left(\mathbf{I}-\mathbf{J}_{11}^{33}\mathbf{X}_{GY}^{d}-\mathbf{J}_{12}^{31} \mathbf{N}_{L}\mathbf{J}_{21}^{13}\mathbf{X}_{GY}^{d}\right)\) can be invertible matrices.
# Model-Free Adaptive Control Based on Fractional Input-Output Data Model
## 5 Experimental System and Results  
To validate the performance of the proposed controller, the direct-current motor torque control system is constructed, as shown in Figure 1. A high-gain Namiki 22 CL-3501 PG motor is driven by push-pull transistors through the input voltage \(u(k)\) [V], which is generated by the analog output of CONTEC AIO-160802 LPE. The output \(y(k+1)\) [kg\(\cdot\)cm] is measured by the torque-converter that is equipped with the analog-input.  
The system is considered as an unknown discrete-time nonlinear system, and only input-output data are considered to be available. Thereafter, the membership functions of \(y(k)\in\pm 10\) [kg\(\cdot\)cm] and \(|u(k-1)|\leq 8\) [V] are designed according to the operating ranges depicted in Figure 2.  
Figure 3 shows the tracking performance with the plots of the output \(y(k)\) and the desired trajectory \(r(k)\). Furthermore, the plot of \(\hat{g}(k)\) is provided in the same figure to demonstrate the reliability of the equivalent model. The control voltage \(u(k)\) is illustrated in Figure 4. The time-varying behavior of \(|\Delta_{N_{\text{zz}}}^{\text{n}}u(k)|^{2}\) is shown in Figure 5, which shows condition (18) using Lemma 1.  
Figure 1: Experimental system.  
Figure 3: Tracking and model performance.  
The experimental study is repeated by introducing the unknown disturbance \(d_{s}(k)\). Figure 6 shows the plots of \(y(k)\) along with \(r(k)\) and \(\hat{y}(k)\) to represent the closed-loop performance, and the model proficiency, regarding \(d_{s}(k)\). The amplitude of \(d_{s}(k)\) is more than 20% of the nominal output amplitude, but the disturbance is rejected by the proposed controller. The control voltage \(u(k)\) is displayed in Figure 7 that leads a fast response. Finally, the plot of \(|\Delta_{s}^{N_{u}}u(k)|^{2}\), for the case with a disturbance, is given in Figure 8.  
Figure 4: Control effort \(u(k)\).  
Figure 6: Tracking and model performance: with disturbance \(d_{s}(k)\).  
Figure 7: Control effort \(u(k)\): with disturbance \(d_{s}(k)\).
## 5 Conclusions  
1. Through temperature-controlled consolidation tests, it has been proven that temperature has an impact on MSW primary compression consolidation. As the temperature increased, MSW primary compression consolidation increased.
2. This article determined that the modification primary compression index (MPCI) of high-organic-matter-content waste (H-MSW), moderate-organic-matter-content waste (M-MSW), and low-organic-content-waste (L-MSW) was 0.26166\(-\)0.29623, 0.21109\(-\)0.23894, and 0.15443\(-\)0.16948 using a consolidation test considering the temperature effect; the MPCI increased with the increase in temperature, organic matter content, and dry unit weight and the decrease in moisture content on a wet basis.
3. A modified primary compression index calculation model was created considering temperature, organic matter content, moisture content on a wet basis, and the dry unit weight of MSW according to the results of the consolidation test and fitting model using data from the literature.
4. The effectiveness of the model was verified by comparing the results of the bioreactor test considering the temperature effect and the calculation results of the model.
5. The calculation results of the model indicate that errors in the results will occur if a single modified primary compression index (MPCI) is used to calculate the settlement of a landfill according to the technical specifications. As the size increased, the non-uniformity became more apparent. The calculated values in the middle of the sample were greater than the measured values, while the calculated values at the top and bottom were smaller than the measured values. Therefore, the variation in the MSW  
Figure 13: Calculation and results of test 1 and test 2.  
modified primary compression index (MPCI) with depth should be considered, which will help guide the accurate processing of landfills and design storage capacity.  
Conceptualization, Y.Z.; methodology, Y.Z.; validation, Y.Z.; resources, X.W.; writing--original draft preparation, Y.Z.; writing--review and editing, X.W. and Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China: 41372268.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
# Discrete-Time Asymptotic Tracking Control System for a Satellite with a Solar Panel
## 3 Plant State Space Models and Their Properties
### Properties of the Discrete-Time State Space Model
#### 3.3.3 Transfer Function of \(\Sigma_{\mathbb{G}}^{d}\)  
We already know from (31) that for \(z\in\mathbb{C}\setminus\{-1\}\), we have the following:  
\[\frac{1}{z+1}(zI-A^{d})(\mu I-A)=\mu\frac{z-1}{z+1}I-A\,. \tag{46}\]  
It follows that \(z\
ot\in\sigma(A_{d})\) if and only if \(\mu\frac{z-1}{z+1}\
ot\in\sigma(A)\) and, so, we can transform (46) to the following form:  
\[(zI-A^{d})^{-1}=\frac{1}{z+1}(\mu\frac{z-1}{z+1}I-A)^{-1}(\mu I-A)\,. \tag{47}\]  
Using (28) and (47), we have the following:  
\[\begin{split}& C^{d}(zI-A^{d})^{-1}B^{d}+D^{d}\\ &=\frac{2\mu}{z+1}C(\mu I-A)^{-1}(\mu\frac{z-1}{z+1}I-A)(\mu I-A)( \mu I-A)^{-1}B+C(\mu I-A)^{-1}B\,,\end{split} \tag{48}\]and the right-hand side of (48) can be transformed as follows:  
\[\begin{split}& C(\mu I-A)^{-1}[\frac{2\mu}{z+1}(\mu\frac{z-1}{z+1}I-A) ^{-1}+(\mu\frac{z-1}{z+1}I-A)(\mu\frac{z-1}{z+1}I-A)^{-1}]B\\ &=C(\mu I-A)^{-1}[2\mu\frac{z+1}{z+1}I+\mu\frac{z-1}{z+1}I-A]( \mu\frac{z-1}{z+1}I-A)^{-1}B\\ &=C(\mu I-A)^{-1}[\mu\frac{z+1}{z+1}I-A](\mu\frac{z-1}{z+1}I-A)^ {-1}B\\ &=C(\mu\frac{z-1}{z+1}I-A)^{-1}B\,.\end{split} \tag{49}\]  
Thus, we have proved that, for \(\mu>0\) and \(z\in\mathbb{C}\setminus\{-1\}\), the following equality holds:  
\[G^{d}(z)=C^{d}(\mu)(zI-A^{d}(\mu))^{-1}B^{d}(\mu)+D^{d}(\mu)=C(sI-A)^{-1}B=G(s )\,, \tag{50}\]  
where \(G^{d}(z)\) and \(G(s)\) are the transfer functions of \(\Sigma_{G}^{d}\) and \(\Sigma_{G}\), respectively, and \(z\) and \(s\) are related to each other by the following equality:  
\[s=\mu\frac{z-1}{z+1}\,; \tag{51}\]  
or, equivalently, for \(s\
eq\mu\):  
\[z=\frac{\mu+s}{\mu-s}\,. \tag{52}\]
# User QoS-Based Optimized Handover Algorithm for Wireless Networks
## 2 Related Work
QoS [15] refers to the way that a network can use various basic technologies to provide better network service capabilities for specific network users, and is also a technology used to solve problems such as network delay and congestion. QoS is a control mechanism that provides different priority levels for different users or different data traffic, or according to the requirements of the application, to ensure that the performance of data traffic can reach a certain level. The measurement indicators of QoS include [16]: bandwidth, packet loss rate, error, delay, jitter (the delay difference may seriously affect the quality of streaming video and audio), and so on. QoS can also be prioritized for application data traffic to ensure that applications with high QoS requirements can receive enough QoS. In [17], a scoring criterion for the handover decision, Q-score, is proposed. The Q-score is obtained by multiplying the set network service-quality-related parameters (such as RSSI, bandwidth, etc.) by the individual weights. After that, the nearby AP with the highest Q-score will be selected as the UE target handover AP. However, in the handover algorithm proposed, there is a handover condition: if the application currently being used by the UE has a demand for real-time data transmission, the connection will immediately switch to the target handover AP. However, from another point of view, if the above conditions continue to be met, they will continue to hand over. As a result, the PDR of the overall network service will drop significantly. Therefore, according to the goal that we want to achieve, of reducing the number of handovers, the immediacy of the application will not be included in the judgment condition of the handover decision to reduce the number of disconnections. However, if the user has high QoS requirements, the system will try to achieve the QoS required by the user as much as possible, regardless of the cost caused by frequent handovers. In [18], an SDN-based RSSI handover mechanism and an SDN-based AP load-balancing handover algorithm are proposed. The former, as the station is associated with the AP providing the strongest signal, may result in reduced QoSwhen the AP is overloaded. The latter balances the load when the AP is in an overlapping area, thereby ensuring good QoS for all stations. From the experimental results, it can be seen that the AP load-balancing handover algorithm performed better in terms of transmission, bandwidth, jitter, and packet loss. In [19], a QoS-aware flexible mobility management scheme for software-defined networking (SDN)-based mobile networks is proposed. It classifies flows into four classes based on the QoS requirements of the service in terms of latency and loss tolerance. According to the priority of flow classification, a QoS-based differential handover process is provided for each class. Performance analysis shows that the proposed scheme can realize the flexible utilization of network resources and can effectively meet the QoS requirements of each class.
###### Abstract  
The power converter is a significant device in a wind power system. The wind turbine will be shut down and off grid immediately with the occurrence of the insulated gate bipolar transistor (IGBT) module open-circuit fault of the power converter, which will seriously impact the stability of grid and even threaten personal safety. However, in the existing diagnosis strategies for the power converter there are few single and double IGBT module open-circuit fault diagnosis methods producing negative results, including erroneous judgment, omissive judgment and low accuracy. In this paper, a novel method to diagnose the single and double IGBT modules open-circuit faults of the permanent magnet synchronous generator (PMSG) wind turbine grid-side converter (GSC) is proposed: Primarily, by collecting the three-phase current varying with a wind speed of 22 states, including a normal state and 21 failure states of PMSG wind turbine GSC as the original signal data. Afterward, the original signal data are decomposed by using variational mode decomposition (VMD) to obtain the mode coefficient series, which are analyzed by the proposed method base on fault trend feature for extracting the trend feature vectors. Finally, the trend feature vectors are utilized as the input of the deep belief network (DBN) for decision-making and obtaining the classification results. The simulation and experimental results show that the proposed method can diagnose the single and double IGBT modules open-circuit faults of GSC, and the accuracy is higher than the benchmark models.  
**Keywords:** power converter; fault diagnosis; intelligent algorithm; variational mode decomposition; deep belief network
# Practical 3-D Beam Pattern Based Channel Modeling for Multi-Polarized Massive MIMO Systems +
## 2 3-D Antenna Configuration
### 3-D Beam Pattern  
To make it easier for the reader to understand the use of the 3-D beam pattern technique in practice, we are going to investigate the use of 3-D antenna beam pattern, where different TX signals are fed to all the antenna elements with corresponding weights. However, we are interested in the channel between the TX antenna elements and RX antenna elements. The combination patterns for the transmit antenna array in dB are as follow [18]:  
\[A_{E}^{BS}(\phi,\theta,\theta^{tilt})=G_{E,max}^{BS}-\min\left(-\left[A_{H}^{ BS}(\phi)+A_{V}^{BS}(\theta,\theta^{tilt})\right],20\right), \tag{16}\]  
where  
\[A_{V}^{BS}(\theta,\theta^{tilt})=-\min\left[12\left(\frac{\theta-\theta^{tilt }}{\theta_{3 dB}}\right)^{2},20\right]dB, \tag{17}\]  
\[A_{H}^{BS}(\phi)=-\min\left[12\left(\frac{\phi}{\phi_{3 dB}}\right)^{2},20 \right]dB. \tag{18}\]  
Therefore, the horizontal and vertical patterns at the TX can be approximated as  
\[g_{V}^{BS}(\theta,\theta^{tilt})=-12\left(\frac{\theta-\theta^{tilt}}{ \theta_{3 dB}}\right)^{2}dB, \tag{19}\]  
\[g_{H}^{BS}(\phi)=-12\left(\frac{\phi}{\phi_{3 dB}}\right)^{2}dB. \tag{20}\]  
where, \(G_{E,max}^{BS}\) is the gain of the radiation element, which are assumed to be 7 dBi at the TX for each antenna elements. \(A_{H}^{BS}(\phi)\) and \(A_{V}^{BS}(\theta,\theta^{tilt})\) are horizontal and vertical patterns at the TX. The individual antenna radiation pattern at the UE \(gr_{V}^{UE}(\theta,\theta^{tilt})\) and \(gr_{H}^{UE}(\varphi)\) is taken to be 0 dB.  
Given the antenna configuration in Section 2.1.2 and the effective radio channel between TX and RX in Equation (11) for different antenna polarizations, can be hence be written as Equations (11.1)-(11.3).
# Multimodal Network for Object Detection Using Channel Adjustment and Multi-Scale Attention
## 4 Experiment
### Computational Complexity Analysis  
To assess the computational efficiency of MNCM, we compare it with several representative transformer-based or attention-based multimodal object detection methods. The evaluation is conducted on the MSRS dataset using a fixed input resolution of 640 \(\times\) 640 pixels, which reflects a typical real-world deployment setting. All models are tested on the same hardware (Tesla A30 GPU) under consistent inference conditions. We report the number of parameters (in millions), floating-point operations (FLOPs in GFLOPs), average inference time per image (milliseconds), and frames per second (FPS). The results are shown in Table 7.  
As shown in Table 7, MNCM achieves the lowest number of parameters (31.4 M) among all compared methods, reflecting its lightweight architectural design. While its FLOPs (70.3 G) are slightly higher than those of SwinF (69.4 G), MNCM maintains a competitive inference time of 33.6 ms and achieves 27.9 FPS, which is on par with or slightly better than transformer-based baselines such as SwinF (26.5 FPS) and CDDFuse (25.9 FPS).  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Configuration** & **Person mAP** & **Car mAP** \\ \hline Full model & 0.881 & 0.894 \\ w/o \(\mathcal{L}_{\text{box}}\) & 0.819 (\(-7.0\%\)) & 0.837 (\(-6.4\%\)) \\ w/o \(\mathcal{L}_{\text{obj}}\) & 0.843 (\(-4.3\%\)) & 0.869 (\(-2.8\%\)) \\ w/o \(\mathcal{L}_{\text{cls}}\) & 0.847 (\(-3.9\%\)) & 0.858 (\(-3.6\%\)) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation study on loss function components (MSRS validation set).  
These results indicate that MNCM provides a favorable balance between model complexity and runtime efficiency. The improvements can be attributed to the integration of the dynamic channel adjustment (DCA) and multi-scale activated attention mechanism (MAAM), which jointly enhance feature representation across modalities while maintaining efficient computation suitable for real-time multimodal object detection.
## 6 Evaluation
### Learning Performance and Computational Cost  
#### 6.1.1 Experimental Environment  
The following experiments are all performed on Google Colab Pro [27]. We use Python 3.10.12 and PyTorch 2.3.0 [28] to build a CNN model with si layers. We used the MNIST [29] and Fashion-MNIST [30] datasets for our experiments. Throughout the experiments, we simulate the complete federated learning process, including local training, server aggregation, and model updates. In the client's local training, we set the training for 5 epochs, with a batch size of 32 and learning rate of 0.1. Each complete experiment operation comprises five rounds. In the experiment to evaluate effectiveness, we aimed to compare FedAvg [2], Fedshare [8], and our proposed method. FedAvg [2] is a classic federated learning approach, and Fedshare [8] uses \(n\)-out-of-\(n\) secret sharing in federated learning for privacy. We chose these two methods for comparison, to demonstrate that our method maintains accuracy while protecting the privacy of federated learning.
## 3 Results and Discussion
### Characteristic microorganisms in the PSD and AnMBR
_Methanobacteriaceae_ is recognized as a family comprising hydrogenotrophic methanogenic archaeon in anaerobic digestion and is prominent in methanogenic communities [43]. The hydrogenotrophic _Methanobacteriales_ have been shown to correlate with biogas production in 29 full-scale digester studies, confirming their role in the maintenance of digester function [44]. _Methanobacteria_ use the methanogenesis pathway, in which hydrogen reacts with carbon dioxide to produce methane [45]. The maintenance of the temperature (PSD at 37 \({}^{\circ}\)C and AnMBR at 20 \({}^{\circ}\)C) should be the primary reason for the significantly different abundance of _Methanobacteriaceae_ in these two bioreactors and should also explain why biogas production was higher in the PSD. Compared with the PSD, the relative abundance of _Syntrophaceae_ was significantly higher in the AnMBR. _Bacteria_ affiliated with _Syntrophaceae_ play important roles in acetate oxidation during anaerobic methane production and are seen as stabilizers that maintain AD stability, especially when the system undergoes environmental fluctuations [46]. Furthermore, the significance of _Syntrophaceae_ in the methanogenic degradation of waste has been acknowledged [47; 48]. The remarkably different abundances of _Syntrophaceae_ and _Methanobacteriaceae_ may reflect the functional tendencies in the AnMBR and PSD, where acetate oxidation and methane generation are the dominant microbial metabolisms, respectively, in these two types of bioreactors during wastewater treatment. The relative abundances of _Desulfomonile_, _Smithella_, and _Syntrophus_ (all within the family _Syntrophaceae_) were significantly different between samples of PSD and AnMBR (\(p<0.01\)). However, the above three genera were less abundant (\(<\)1%) in these two kinds of bioreactors. In the family _Methanobacteraceae_, _Methanobacterium_ was the only identified genus with an averagely relative abundance larger than 1% in PSD and AnMBR, and also, the identified genus exhibited significantly different abundance in these two kinds of bioreactors.
# FEM Analysis and Sensor-Based Measurement Scheme of Current Distribution for Grounding Electrode
###### Abstract  
The current distribution of the grounding electrode in a high-voltage direct current (HVDC) transmission system affects the state of power equipment in its vicinity, which depends on the soil resistivity and shape of the grounding electrode. In this paper, current distribution in the vicinity of an \(\pm\)800 kV grounding electrode is investigated by simulation and experiments. Firstly, the model to calculate the current distribution with two typical frozen soils is set up, and simulation models and experimental platforms are established; meanwhile, the finite element method (FEM) is used to calculate the current and potential dispersion of linear, cross-shaped, and ring-shaped grounding electrodes in the simulation models. After obtaining the lab current data from the simulation, an innovative method based on a \"drainage wire\" with a Hall sensor is proposed to measure the current in an experimental setup. The results show that current and potential distribution characteristics are related to the shape of the grounding electrode and soil resistivity. Meanwhile, the current measurement scheme can measure the current in soil with a lower error. This article concludes that these two typical models can reduce the complexity of frozen soil analysis, and the measurement scheme can accurately monitor the current to reduce the damage to the surrounding power equipment.  
HVDC transmission; current dispersion; grounding electrode; current measurement +
Footnote †: journal: Applied Sciences  
_Article_  
FEM Analysis and Sensor-Based Measurement Scheme of Current Distribution for Grounding Electrode