esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Analysis of FMT Bacterial Composition Using Next-Generation Sequencing (NGS)
#### 2.3.1 Sequencing Data Processing  
Paired-end reads were merged using FLASH (V1.2.7) [20]. Quality filtering of the raw tags was performed to obtain high-quality clean tags according to the QIIME (V1.7.0) [21] quality control pipeline. Chimera sequences were identified and removed by comparing the tags against the SILVA138 reference database [22] using the UCHIME algorithm [23], resulting in effective tags for downstream analysis.  
\begin{table}
\begin{tabular}{c c c} \hline \hline Anamnesis & - & Must not have had past gastrointestinal disorders, recent diarrhoea diseases, irritable bowel syndrome, celiac disease, Inflammatory bowel disease, colitis, and _Helicobacter pylori_ infection. \\  & - & No ingestion of antibiotics and immunosuppressive preparations in the past 3 months. \\  & - & No travel to developing countries in the past 6 months. \\ \hline  & & Complex laboratory examination \\ \hline Bacterial examination & - & For the presence of pathogens _Salmonella_, _Shigella_, and _Clostridioides difficile_ (GDH and toxins A, B). \\ \hline Virological examination & - & For _cytomegalovirus_ (CMV), _rotavirus_, _norovirus_ I and II, and _adenovirus_ 40 and 41. \\ \hline Serological examination & - & For the presence of HIV, hepatitis A, B, C and E, _Treponema pallidum_, _Escherichia coli_ (enterotoxigenic, enterovirus, enteropathogenic, enteroaggregative, and Shiga-like toxins producing strains), species _Vibrio cholerae_, _Campylobacter_ spp. \\ \hline Parasitic examination & - & For the presence of parasites such as _Strongyloides steroralis_, _Giardia intestinalis_, _Cryptosporidium_ spp., and _Entamoeba histolytica_. \\ \hline Immunological examination & - & Determination of inflammatory marker calprotectin. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Criteria disqualifying the potential FMT donor.
# A Robust \(H_{\infty}\) Application for Motor-Link Control Systems of Industrial Manipulators
## 7 Conclusions and Future Works  
In this paper, guidelines for the application of the \(H_{\infty}\) approach to a motor-link control system have been determined for an industrial manipulator. In particular, the importance of a proper design of the weighting functions has been investigated. The partial maintenance of the system dynamics in the definition of the control action has allowed the proposed controller to provide robustness, high disturbance rejection, stability, and good performances on the link side, even if applied on the motor side, without any sensor located on the link. Both simulations and experimental results confirmed the validity of the proposed technique.  
It must be noticed that the control system has been designed starting from a single operating condition, i.e., for a given vibration mode of the axis (of about 6 Hz) and the corresponding link inertia (of about 1547 Kgm\({}^{2}\)). The problem is that, in order to perform its task, a robot does not remain trapped in a single position, but it moves by causing a change in the link inertia value. For this reason, future works will investigate the possibility of developing a Linear Parameter Varying (LPV) \(H_{\infty}\) Controller, able to adapt to the various operative conditions and to guarantee an invariant time response over all the robot working area.  
Figure 23: Velocity reference (dashed blue line), motor velocity (black line), link estimated velocity (dark red line).  
Figure 24: Velocity reference (dashed blue line), motor velocity (black line), link estimated velocity (dark red line) for a single movement.  
**Author Contributions:** Investigation, M.I., M.B., S.P. and V.P.; Methodology, M.I. and M.B.; Resources, S.P. and V.P.; Supervision, M.I., S.P. and V.P.; Validation, M.B.; Writing--Original draft, M.I. and M.B.; Writing--Review & editing, S.P. and V.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Millimetre-Wave Backhaul for 5G Networks: Challenges and Solutions
## 5 Framework Design for 5G Mm-Wave Backhaul
### Scheduling  
After transmission path selection, the scheduling process is necessary to decide the transmission sequence of every hop in each requested flow. In conventional scheduling, links that can coexist are selected to transmit simultaneously. By considering new physical layer techniques in the mm-wave backhaul, one device may act as both the transmitter and the receiver of a single link or multiple links, and multiple hops in the same route may transmit together, which bring changes and challenges in scheduling.  
Figure 4: (**a**) Routing results of the shortest path algorithm and the capacity optimization method in the mm-wave backhaul network; and (**b**) the scheduling scheme in four scenarios based on the optimization routing results in (**a**) in a mm-wave backhaul network.
## 3 RL-Based Resource Allocation Algorithm
### Link Configuration Decision  
Reinforcement learning can maximize long-term returns in sequential decision-making problems, and enable agents to interact with complex environments and seek optimal spectrum allocation strategies through continuous trial and error. In [28], a kind of deep Q network is proposed. With the help of the end to end reinforcement learning method, an excellent policy can be directly learned from the high-dimensional output, enabling agents to solve a series of challenging tasks.  
Figure 1: Schematic diagram of neural network structure.  
This paper treats [PERSON] as an Agent in RL. The agent seeks a spectrum selection strategy with maximum cumulative reward by trial and error through continuous interaction with a complex unknown environment. RL can be modeled as a Markov decision process (MDP). As shown in Figure 2, the specific process can be divided into three steps:  
1. At the time step \(t\), select the action \(A_{t}\) to be performed according to the current state \(S_{t}\).
2. Select the state \(S_{t+1}\) after the transfer based on the current state \(S_{t}\) and action \(A_{t}\).
3. According to the action \(A_{t}\) taken in the current state \(S_{t}\) gives the corresponding reward \(R_{t+1}\).
## 11 Conclusions  
1. Microstructure's Impact on Magnetic Properties:  
The intricate relationship between the microstructures and magnetic properties in electrical steels has been a focal point of this review. Advances in microscopy techniques have enabled a deeper understanding of how grain boundaries, crystallographic orientations, and defects influence magnetic behaviour. Recognizing the profound impact of microstructure allows researchers and engineers to tailor electrical steel compositions and processing methods for optimal performance in specific applications.  
2. Efficiency Enhancement through Loss Analysis:  
Losses within electrical steels significantly affect the efficiency of electrical systems. Through sophisticated loss analysis methods, encompassing both hysteresis and eddy current losses, we have gained insights into the energy dissipation mechanisms within these materials. The pursuit of enhanced energy efficiency requires a continuous focus on minimizing losses via refined material design and processing techniques.  
3. Alloying Elements' Role in Magnetic Customization:  
The magnetic properties of electrical steels are intimately linked with their alloying elements and chemical compositions. Our exploration into these relationships, including the influence of silicon, aluminium, and specialized texturing, underscores the potential for tailoring magnetic characteristics. Precision in alloy composition presents avenues for fine-tuning magnetic permeability, coercivity, and saturation flux density, aligning materials more closely with application demands.  
4. Coatings: Dual-function Enhancements:  
The evolving landscape of electrical steels recognizes coatings as dual-function components. Not only do coatings shield against environmental degradation, but they also exert an influence on magnetic properties. Recent developments in coating technologies,encompassing insulating layers and thin films, have marked a paradigm shift in improving electrical steel performance. This synthesis of protection and controlled magnetic influence opens doors to broader application possibilities.  
In summation, this comprehensive review underscores the interwoven nature of microstructure, loss analysis, magnetic properties, alloying elements, and coatings in shaping the realm of electrical steels. Delving into each facet uncovers the intricacies that collectively define material performance. As technology and energy systems continue to advance, the insights garnered from this study will fuel the ongoing progress of tailored electrical steel designs across diverse applications.
# SemABC: Semantic-Guided Adaptive Bias Calibration for Generative Zero-Shot Point Cloud Segmentation
## 3 Methods
### Three-Stage Training Strategy  
Next, a three-stage training strategy is introduced to progressively train the modules of our dual-branch network. The first stage trains the encoder \(E\) and segmentor \(S_{sent}\) with the point clouds \(P_{s}\) of seen classes \(c\) from the training set. We calculate the cross-entropy loss between the prediction and ground truth labels \(Y_{s}\):\[L_{s}=-\sum_{c}Y_{s}\log(S_{seen}(E(P_{s}))), \tag{11}\]  
The second stage freezes the encoder \(E\) and trains the generator \(G\) on the data of seen classes from the training set. The goal is to ensure that the synthesized features from \(G\) closely resemble the real features extracted by the frozen encoder \(E\). The loss function for this stage is  
\[L_{G}=\sum_{c}(L_{align}+L_{con}+L_{MMD}+\alpha L_{est}), \tag{12}\]  
where \(L_{align}\) refers to the semantic-visual alignment loss, which computes the cosine distance between the semantic and visual features of the seen classes. \(L_{con}\) represents the contrastive loss between the features of real and synthetic data for the seen classes. \(L_{MMD}\) is the maximum mean deviation (MMD) loss to reduce the distributional mismatch between the synthesized features \(\vec{F}_{s}\) and real features \(F_{s}\) of seen classes. \(L_{est}\) denotes the semantic-visual consistency loss, which ensures that the distance distributions of seen and unseen classes are consistent in synthesized visual and semantic space by minimizing the cosine distance. \(\alpha\) is a hyperparameter for loss balance.  
The third stage freezes the encoder \(E\) and generator \(G\) to train the visual-semantic fusion branch and the final segmentor \(S\). It leverages \(E\) to acquire real features of seen classes \(c\) and the \(G\) to produce synthesized features of unseen classes \(c^{\prime}\). The loss for this stage is as follows:  
\[L_{all}=\lambda\times L_{seg}+(1-\lambda)\times L_{aux}, \tag{13}\]  
where \(\lambda\) is the hyperparameter to balance the two losses.  
The segmentation loss \(L_{seg}\) is defined as:  
\[L_{seg}=-\sum_{c}Y_{s}\log(S(F_{s}))-\sum_{c^{\prime}}Y_{u}\log(S(\vec{F}_{u} )), \tag{14}\]  
where \(Y_{u}\) denotes ground truth labels of unseen classes.
###### Abstract  
This study investigates the formation of by-product species during flame spray synthesis (SFS) of superparamagnetic maghemite (\(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)) nanoparticles. Four samples are synthesized by utilizing two standardized burner types (SpraySyn1 and SpraySyn2) and varying the iron (III) nonahydrate (INN) concentration (0.1 M and 0.2 M) in the precursor feed while using ethanol and 2-ethylhexanoic acid as solvent. Conducting complementary powder analysis revealed a predominant presence of carboxylates and carbonates as by-product species (\(\sim\)14-18 wt.%), while no strong indications for elemental carbon and precursor/solvent residues can be found. Carbonates/carboxylates are located on particle surfaces, and the particles' surface loadings by these species are independent of the precursor concentration but depend on burner type, with SpraySyn2 exhibiting lower values, indicating a more complete combustion for this burner. Through time-resolved thermophoretic sampling, we further demonstrate that carbon forms temporally in the visible flame center when using SpraySyn1. Since carbon solely forms momentarily within large flame pulses and decomposes further downstream, its temporal formation is of minor relevance for the final particle purity. However, its local co-existence aside from \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\) in the flame has potential to bias in situ diagnostics.  
Keywords:flame spray synthesis; powder characterization; nanoparticles; thermophoretic sampling; superparamagnetic maghemite +
Footnote †: journal: Applied sciences
# Research on Misalignment Fault Isolation of Wind Turbines Based on the Mixed-Domain Features
## 2 Establishment of Mixed-Domain Feature Library
### Feature Extraction of Vibration Signal in the Time-Frequency Domain
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Type** & **RMS** & **Square Root** & **Variance** & **SD** & **Kurtosis** & **Waveform** & **Peak** & **Pulse** & **Margin** & **Kurtosis** \\  & (\(\times\)10) & **Amplitude** & (\(\times\)**10\({}^{6}\)) & (\(\times\)**10)** & **Kurtosis** & **Index** & **Index** & **Index** & **Index** & **Index** \\ \hline \multirow{8}{*}{0} & 10,107 & 71,069 & 10,212 & 10,105 & 2.49 & 1.21 & 3.42 & 4.15 & 4.86 & 2.50 \\  & 10,112 & 70,651 & 10,223 & 10,111 & 2.53 & 1.22 & 3.02 & 3.68 & 4.32 & 2.54 \\  & 10,117 & 69,920 & 10,229 & 10,114 & 2.55 & 1.22 & 3.28 & 4.01 & 4.74 & 2.56 \\  & 10,135 & 70,627 & 10,261 & 10,130 & 2.52 & 1.22 & 3.44 & 4.19 & 4.93 & 2.54 \\  & 10,136 & 69,844 & 10,267 & 10,132 & 2.59 & 1.23 & 3.03 & 3.72 & 4.40 & 2.60 \\ \hline \multirow{8}{*}{1} & 68,743 & 203,770 & 47,184 & 68,690 & 14.19 & 1.89 & 6.26 & 11.81 & 21.11 & 14.40 \\  & 70,048 & 213,120 & 49,046 & 70,033 & 15.37 & 1.85 & 5.44 & 10.07 & 17.89 & 15.51 \\  & 70,194 & 208,350 & 49,209 & 70,149 & 14.69 & 1.90 & 5.21 & 9.90 & 17.57 & 14.94 \\  & 70,720 & 217,070 & 49,931 & 70,662 & 16.04 & 1.91 & 5.11 & 9.75 & 16.66 & 16.30 \\  & 71,651 & 227,770 & 51,201 & 71,555 & 12.08 & 1.84 & 5.57 & 10.24 & 17.53 & 12.23 \\ \hline \multirow{8}{*}{2} & 176,820 & 689,720 & 30,935 & 175,880 & 7.41 & 1.64 & 4.42 & 7.24 & 11.34 & 7.58 \\  & 177,180 & 702,010 & 31,389 & 177,170 & 6.72 & 1.63 & 4.87 & 7.92 & 12.28 & 6.72 \\ \cline{1-1}  & 177,930 & 680,850 & 31,660 & 177,930 & 7.52 & 1.67 & 4.78 & 8.00 & 12.50 & 7.52 \\ \cline{1-1}  & 178,990 & 679,150 & 31,589 & 177,730 & 7.71 & 1.69 & 3.84 & 6.48 & 10.13 & 8.28 \\ \cline{1-1}  & 179,010 & 706,130 & 32,048 & 179,020 & 6.77 & 1.64 & 5.04 & 8.26 & 12.77 & 6.77 \\ \hline \multirow{8}{*}{3} & 601,360 & 1,681,900 & 36,139 & 601,160 & 14.83 & 1.95 & 5.82 & 11.35 & 20.81 & 15.09 \\ \cline{1-1}  & 625,910 & 1,801,800 & 39,127 & 625,520 & 13.28 & 1.91 & 6.70 & 12.76 & 23.26 & 13.58 \\ \cline{1-1}  & 656,970 & 2,154,200 & 43,123 & 656,680 & 11.11 & 1.79 & 7.30 & 13.09 & 22.27 & 11.22 \\ \cline{1-1}  & 664,860 & 2,069,300 & 44,194 & 664,780 & 11.38 & 1.82 & 6.80 & 12.37 & 21.85 & 11.51 \\ \cline{1-1}  & 671,750 & 2,101,200 & 45,063 & 671,290 & 12.30 & 1.83 & 6.82 & 12.50 & 21.81 & 12.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The partial data of the time-domain indexes.
# Research on Bandwidth Improvement of Fine Tracking Control System in Space Laser Communication
## 4 Discussion  
Using the existing experimental platform, we successfully enhanced the error suppression bandwidth of the fine tracking system and verified the influence of the hysteresis characteristics of PZT FSM on the error suppression bandwidth based on the experimental  
Figure 19: Error suppression bandwidth of three control methods.  
Figure 18: Comparison of tracking error of three control methods. (**a**) 1 Hz, (**b**) 20 Hz, (**c**) 30 Hz, (**d**) 50 Hz.  
results. Compared to the traditional closed-loop control method, this method improves the nonlinearity of PZT FSM without reducing its response speed. Additionally, the experimental results are similar to the simulation results. It is worth noting that the hysteresis nonlinearity of PZT FSM will affect the performance of the ne tracking system. Therefore, we can focus on improving the accuracy of the hysteresis model in future research in order to further enhance the performance of the ne tracking control system. At the same time, the feedback controller can also be further improved.
# Disentangled Feature Learning for Noise-Invariant Speech Enhancement
## 4 Experiments and Results
### Disentangled Feature Representations  
We further explored the effect of disentangled feature learning by visualizing the speech latent feature (\(\mathbf{z}_{s}\)) using t-distributed stochastic neighbor embedding (t-SNE) [55]. The t-SNE is a popular data visualization method which projects high dimensional data into a subspace with a smaller dimension. The projection serves as a useful tool to visually inspect feature representations learned by the model. We extracted speech latent features from a subset of the test samples through trained models and projected the 512-dimensional \(\mathbf{z}_{s}\) into the two-dimensional space using t-SNE. Figure 7 visualizes the speech latent feature representations obtained in the matched noise conditions. Figure 7d, in particular, shows that by using two disentanglers for adversarial learning, the distribution of \(\mathbf{z}_{s}\) became almost indistinguishable. This implies that the noise attributes were highly likely to be disentangled in \(\mathbf{z}_{s}\). In contrast, without disentangled feature learning, as shown in Figure 7a,b, we were able to separate each type of noise cluster easily in the latent feature space. This indicates that the noise attributes remain intact in \(\mathbf{z}_{s}\). Figure 7c shows that the \(nDT\) model disentangled the noise components more clearly as compared to the \(sT\) and \(snT\) models, yet not as much as the \(snDT\) model. Finally, Figure 8 shows the speech latent feature representations in the mismatched noise conditions. Even though the noise types were not included in the training data, the proposed model disentangled noise components more clearly in the latent feature space compared to the conventional DNN-based models.  
Figure 6: Variances of perceptual evaluation of speech quality (PESQ) scores for the 14 different noise types in various SNR environments.  
Figure 7: _Cont_.
## 2 Related Work
### K-Fold Cross-Validation and Bootstrap  
K-fold cross-validation and the bootstrap method are widely used techniques for evaluating model performance and estimating generalization error. K-fold cross-validation involves partitioning the dataset into K folds, training the model K times, with each fold serving as a validation set once while the remaining folds are used for training. This approach helps reduce model performance variability and provides a more reliable performance estimation.  
On the other hand, the bootstrap method utilizes resampling with replacement, generating multiple new samples from the original dataset and repeatedly evaluating the model on these samples to estimate the distribution of performance metrics. This technique is particularly useful for small datasets or when sampling bias may affect the reliability of model evaluation.  
[PERSON] et al.'s method [27] compares K-fold cross-validation and the bootstrap method, discussing their advantages and limitations in model evaluation and selection. According to their findings, K-fold cross-validation generally exhibits lower variance and provides stable performance estimates, whereas the bootstrap method is advantageous for performance estimation, particularly when dealing with small datasets.
# Time-Delay Vibration Reduction Control of 3-DOF Vehicle Model with Vehicle Seat
## 2 Vehicle Model Establishment
### Vehicle Model Assumptions  
When the value of suspension mass distribution coefficient \(\varepsilon=\rho_{\mathrm{y}}^{2}/ab\) is close to 1, the vertical vibrations of the front and rear suspension mass are almost independent. The vehicle suspension system can be simplified to a 1/4 vehicle vibration system. The distances from the center of mass to the front and rear axes are a and b. The square of the turning radius of the body around the y-axis is \(\rho_{\mathrm{y}}^{2}\).  
Body, engine, frame, front, and rear axes are rigid. Body and frame are rigidly connected.  
The car runs in a straight line at a constant speed \(v\). The tire keeps in contact with the ground without jumping.  
The automobile structure is symmetrical to the vertical plane. The road roughness of the left and right rut is the same. This means that the vehicle has no roll vibration, lateral displacement, or yaw motion. It only takes into account vertical vibrations.  
Vehicle suspension stiffness \(k_{2}\), tire stiffness \(k_{3}\) and seat stiffness \(k_{1}\) are linear functions of displacement. Suspension damping and seat damping are linear functions of relative velocity. Tire damping is ignored.  
The contact mass of the car body \(m_{\mathrm{s}}\) is 0; i.e., the mass of the front and rear parts of the car body are independent of each other.  
The person \(m_{p}\) is located directly above the axle and is fixed.  
The simplified vehicle model is shown in Figure 1 based on the above assumptions.  
In Figure 1, \(m_{p}\) is the mass of a human body; \(m_{s}\) is the mass of the body part, including all car parts of the body spring suspension; \(m_{u}\) is the unsprung mass, that is, the tire mass; \(k_{1}\) is the seat stiffness; \(k_{2}\) is the body suspension stiffness; \(k_{3}\) is tire stiffness; \(c_{1}\) is the seat damping; \(c_{2}\) is the body suspension damping; \(x_{\mathrm{p}}\) is the seat vertical displacement; \(x_{\mathrm{s}}\) is the body vertical displacement; \(x_{\mathrm{u}}\) is the wheel axle vertical displacement; \(x_{\mathrm{r}}\) is the excitation unevenness displacement.
## 6 Studied System  
To verify its superiority, the proposed optimal multi-stage UFLS is tested on a practical power system with real data. The test system under consideration, in this paper, is the SESCO grid transmission system in East Malaysia. The single-line diagram of the system under investigation is shown in Figure 4. The major generating units are in Bintulu. This system is responsible for the supply of power to Kuching load administrative center. The Kuching system has a generation that is below its demand therefore it gets extra power from the external grid. The Kuching system is linked to the external grid via a 275 kV double circuit transmission lines between Mambong and Engkilili. The transmission lines are about 170 km in length. A fault that results in the tripping of Mambong-Engkilili transmission lines would result in an islanded Kuching power system. In the event of a disturbance, a critical demand-generation imbalance would result in the Kuching system. In such cases UFLS techniques must be quickly implemented to ensure the stable operation of the units in Kuching. Generating units in Kuching are fired using coal and diesel and the system can be represented using a third order SFR model. The parameters for the islanded Kuching power system are given in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Symbol** & **Unit** & **Value** \\ \hline Center of inertia of the system & \(H_{COI}\) & seconds & 3 \\ Damping coefficient of the system & \(D\) & p.u. & 0 \\ \hline Power fraction of turbine & \(F_{1}\) & p.u. & 0.3 \\ Time constant of governor-turbine system & \(T_{1}\) & seconds & 5 \\ Generation share & \(k_{m1}\) & p.u. & 0.05 \\ Governor’s gain in unit 1 & \(R_{1}\) & p.u./Hz & 0.04 \\ \hline Power fraction of turbine & \(F_{2}\) & p.u. & 0.1 \\ Time constant of governor-turbine system & \(T_{2}\) & seconds & 30 \\ Generation share & \(k_{m2}\) & p.u. & 0.11 \\ Governor’s gain in unit 2 & \(R_{2}\) & p.u./Hz & 0.04 \\ \hline \hline \end{tabular}
\end{table}
Table 1: SFR model’s parameters of the system under investigation.