esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Construction of the Control Strategy
### Fuzzy-BPNN-PID Controller Design
#### 3.2.1 Design of Fuzzy Preprocessing Controller  
In this paper, The error of pH E and the deviation of change of pH error EC were selected as input variables for the input of the fuzzy preprocessing control module, and the fuzzed system output \(v(k)\) was taken as the output variable. The role of this module is to fuzzify and normalize the system state variables \(e\) and \(ec\). The error of pH \(e\), the deviation of change of pH error \(ec\) and the output quantity \(v(k)\) of the fuzzification processing module all adopt the triangular membership function, and the area centroid method was chosen as the clearing method of the fuzzy controller. The controller sets the fuzzy linguistic values for both the pH deviation \(e\) and the rate of change of deviation \(ec\) of water and fertilizer as {NB, NM, NS, ZO, PS, PM, PB}, where NB, NM, NS, ZO, PS, PM and PB represent negative large, negative medium, negative small, 0, positive small, positive medium and positive large, respectively. The fuzzy domain takes on the value of the eigenpoints corresponding to {\(-6\), \(-4\), \(-2\), \(0\), \(2\), \(4\), \(6\)}. The fuzzy language values of the output signal \(v(k)\) of the fuzzy controller are set as {NB, NS, ZO, PS, PB}, and the fuzzy theoretical domain takes the value points {\(-4\), \(-2\), \(0\), \(2\), \(4\)}. The fuzzy control rules for the controller output are shown in Table 1. The fuzzified output \(v(k)\) is determined according to the fuzzy theoretical domain and the affiliation function, and then the obtained values are sent to the input layer of the neural network. The subordination functions of the input and output of the fuzzy processing and the fuzzy surface diagrams between them are shown in Figures 5-7.  
In Figure 5, each triangle is part of the affiliation function. For example, the blue triangles represent input variables e and ec with a theoretical domain between \(-4\) and \(0\) and output linguistic variables of NS. The work is based on the theoretical domain of the input quantities and the area centre of gravity method to determine their output linguistic variables.  
Figure 5: Plot of the error and the affiliation function for the rate of change of the error.  
Figure 6: Fuzzy output \(v(k)\) subordinate degree function.  
Figure 7: Fuzzy control surface diagram.  
variables. In Figure 6, each triangle is used to determine the output linguistic variable for the fuzzy variable \(v(k)\). For example, the red triangle indicates that the theoretical domain of \(v(k)\) is between \(-2\) and \(2\) and the output linguistic variable is Z. Figure 7 represents a surface plot of the fuzzy algorithm design, from which it can be seen that when e and ec are small, the output is in the yellow peak portion; when e and ec are large, the output is in the blue low portion; and when the values of e and ec are in the middle of the theoretical domain, the output is in the green stable portion.
# Acquisition and Processing of UAV Fault Data Based on Time Line Modeling Method
## 2 Related Work
### Related Fault Detection or Intrusion Detection Models  
#### 2.1.1 Machine Learning-Based Models  
In 2017, [PERSON] et al. [6] proposed using the Support Vector Machine (SVM) algorithm to detect engine faults, the process of training and testing using gyroscope and accelerometer data, and the principal component analysis algorithm as a method to reduce the spatial feature dimension. The authors used a MAKO drone model to generate data to test the designed algorithm. In 2019, [PERSON] et al. [7] used Linear Discriminant Analysis (LDA) as a model and acceleration information from an Inertial Measurement Unit (IMU) as data to detect actuator failures in drones. In 2022, [PERSON] et al. [8] implemented the fault detection of drones using the K-Means algorithm and the vibration data of drones. The authors used custom hardware to collect vibration data from UAV propellers. The K-Means algorithm was used to cluster the three states of the drone.
# Robust Backstepping Control of Wing Rock Using Disturbance Observer
## 5 Simulation Study
### Wing Rock Control  
In this subsection, the control objective is to track a desired attitude command while suppressing the wing-rock motion. The simulations are given by considering a reference attitude angle trajectory as  
\[\beta_{c} = 0^{\circ}\] \[\mu_{c} = 20\sin\left(0.4\pi t\right)^{\circ} \tag{57}\]  
The controller is executed at 5 s after the wing-rock motion appears, and the simulation results are presented in Figures 5-14.  
Figures 5 and 6 show that the wing rock motion appears, and the aircraft starts to oscillate at the beginning. Then, after the controller (41) is executed in the fifth second, the states \(\mu\), \(\beta\) converge to the desired attitude command (57) quickly, which means that the controller can achieve satisfactory tracking performance while suppressing the wing rock motion. Figures 7-10 show that the ESO can estimate the uncertainty quite accurately. In Figure 7, the compounded disturbance \(\mathbf{\Psi}_{s1}\) diverged quickly in fifth seconds, and the proposed disturbance observer can still estimate the disturbance well. The estimation errors are plotted in Figures 11 and 12, which show that the proposed disturbance observer not only has good steady-state performance, but also has good transient performance. Meanwhile, the corresponding control input histories are given in Figures 13 and 14, respectively.  
From the simulation results, we know that the developed ESO based robust control law is valid for the nonlinear MIMO lateral model of an aircraft with system uncertainty, unsteady aerodynamic disturbance, and input saturation.  
Figure 5: Roll angle tracking.  
Figure 6: Sideslip angle tracking.  
Figure 8: Estimation of \(\Psi_{s2}\) in attitude tracking.  
Figure 7: Estimation of \(\Psi_{s1}\) in attitude tracking.  
Figure 9: Estimation of \(\Psi_{f1}\) in attitude tracking.  
Figure 11: Estimation error of \(\Psi_{s}\).  
Figure 12: Estimation error of \(\Psi_{f}\).  
Figure 10: Estimation of \(\Psi_{f2}\) in attitude tracking.  
One of the focuses of this paper is the robust control of wing rock motion suffering multiple disturbances. To enhance the system robustness, an improved ESO is proposed combined with the RBFNN. Next, the simulation will be carried out to compare the performance of the proposed ESO with existing traditional ESOs.
## 3 Test Results
### Variation Patterns of Various Factors on SRNR Performance
#### 3.3.2 Variation Patterns of Various Factors on Noise Reduction Performance  
As shown in Figure 9, as the water-binder ratio increases, the noise value first decreases and then increases. This phenomenon is mainly due to the fact that when the water-binder ratio is 0.43, the corresponding exposed aggregate depth is the largest, and there are the most voids between the tire and the EAC surface. Relevant studies have shown that the voids between the tire and the pavement can effectively reduce the pumping noise generated when the tire contacts the pavement surface [36; 37]. Therefore, when the water-binder ratio is 0.43, the noise value of the EAC surface is the lowest.  
As the sand ratio increases, the noise value gradually rises, with the maximum value increasing by 1.33 dB compared to the minimum value. When the sand ratio increases from 28% to 31%, the exposed aggregate depth decreases by 0.26 mm, and the voids between the tire and the surface decrease, leading to an increase in the noise value. When the sand ratio increases from 31% to 34%, the exposed aggregate depth changes little, decreasing by only 0.01 mm; however, the exposed aggregate area ratio increases by 2.38%. At this point, the increase in the noise value is mainly due to the increase in the exposed aggregate area ratio, which results in the accumulation of more coarse aggregates on the EAC surface. This accumulation reduces the voids between the coarse aggregates, enhancing the air pumping effect between the tire and the pavement, as well as the vibration noise of the tire itself.  
As the coarse aggregate volume ratio increases, the noise value first decreases and then increases. When the coarse aggregate volume ratio increases from 40% to 42%, the exposed aggregate area ratio only increases by 0.59%, while the exposed aggregate depth increases by 0.22 mm, resulting in a decrease in the noise value. When the coarse aggregate volume ratio increases from 42% to 44%, the exposed aggregate area ratio increases by 5.93%, while the exposed aggregate depth decreases by 0.15 mm. The increase in coarse aggregates on the EAC surface, coupled with the decrease in exposed aggregate depth, leads to the blockage of air escape channels between the tire and the surface, thereby increasing the noise value.  
As the proportion of aggregates > 9.5 mm increases, the noise value first decreases and then increases. When the proportion of aggregates > 9.5 mm increases from 30% to 50%, the exposed aggregate area ratio decreases by 3.74%, while the exposed aggregate depth only decreases by 0.01 mm, resulting in a decrease in the noise value. When the proportion of aggregates > 9.5 mm increases from 50% to 70%, the exposed aggregate area ratio decreases by 5.79%, and the exposed aggregate depth decreases by 0.30 mm. At this point, the noise value increases by 2.96 dB compared to when the proportion of aggregates > 9.5 mm is 50%. This indicates that, compared to the exposed aggregate area ratio, the exposed aggregate depth has a greater impact on the noise value.
# Semantic Distributed Data for Vehicular Networks Using the Inter-Planetary File System
## 4 Differences between a Static P2P Network and a Vehicular P2P Network
#### Resource Allocation in Vehicular P2P Networks  
Compared to traditional wired networks, resources in vehicular networks are very scarce. Providing satisfactory quality of service (QoS) becomes a challenging task due to the high mobility of the vehicles, and the limited spectrum. Therefore, it is essential to allocate and utilize the available wireless network resources in an appropriate manner. As comprehensively described in [41], multiple allocation methods are available or in development. With respect to the content-deliveryservice provided by the IPFS network, these would be the relevant requirements and constraints to consider when allocating network resources:  
* Vehicle-to-RSU communication. Satisfactory vehicle-to-RSU communication is the central pillar of the IPFS content-delivery network. RSU-to-RSU communication has higher bandwidth requirements, but it can be done outside the vehicular network. Vehicle-to-vehicle communication is not required for this service. The communication is solely unicast. Broadcast is not required.
* Latency. Low latency is important but not critical. The service does not have real-time requirements, and it should be considered less important than other services, such as emergency, safety, or platooning messages.
* Bandwidth. The content-delivery network is intended for compressed semantic data. As measured in Section 7, a typical transaction between vehicle and RSU involves just a few kilobytes of data.
* Packet loss and congestion. IPFS uses either TCP or QUIC connections. Both protocols provide retransmissions and advanced methods to avoid congestion. QUIC has been specially designed to cope with the limitations of mobile networks.
* Sudden network disconnections. Finally, IPFS is protected against abrupt disconnections. In those situations, the service would be temporarily suspended until the connection to the network is restored.  
In summary, the content-delivery service provided by IPFS is extremely flexible, and it should be able to adapt to the allocated resources without a significant impact in the service. With unrestrained resources, the service is quick and responsive. With limited resources, the time required to retrieve content increases, but the process is never interrupted or compromised. Section 7 of this paper provides latency and bandwidth figures that could be used by network operators to select the appropriate resource allocation for this service.
## 4 Methods and Data Analysis  
In this section we provide an overview on the principles used to retrieve some useful parameters, such as zenith tropospheric delay (ZTD), zenith wet delay (ZWD) and precipitable water vapor (PWV), from the analysis of GPS data.
## 5 Experiment and Result  
### Experimental Setup  
LJSpeech [16] is a single speaker English dataset containing about 24 h worth of speech and 13,100 utterances. We randomly left out 50 utterances for validation and testing. The speech was resampled to 16 KHz for simplicity. Before training TTS, we computed the phoneme alignment of the training data with an HMM-GMM ASR model trained on Librispeech [17] and then extracted the duration of each phoneme from the alignment for training.  
The TTS models in this work are based on FastSpeech2 [5] with GMM-based prosody modeling [13]. The number of the Gaussian components in the GMMs was set to 20, and the \(\beta\) in Equation (5) was set to 0.02. An Adam optimizer [18] was used for TTS training in conjunction with a Noam learning rate scheduler [15]. We used a 320-dimensional mel-spectrogram as the acoustic feature with a 12.5 ms frame shift and 50 ms frame length. MelGAN [19] was used as the vocoder for waveform reconstruction.
## 4 Results
### Model Analysis
#### 4.2.2 Classification Rules  
On the basis of the data, eight classification rules were obtained according to the energy consumption levels, as shown in Table 7. In the above analysis, the classification rules were summarized according to the feature weights. In this study, the energy-saving feature trends were evaluated. The lowest vehicle speed, motor speed, and the highest total voltage were used as the deduction criteria.  
According to the energy-saving feature trends, we deduced the driving indicators for different power consumption levels as follows:  
**Low energy consumption (deduction from Rules 1, 2, and 3)** The motor speed was below 425.5 rpm, or the total voltage exceeded 560.15 V;  
**Medium energy consumption (deduction from Rules 4, 5, and 6)** The motor speed ranged from 425.5 rpm to 779.5 rpm, or the total voltage ranged from 560.15 V to 549.25 V;  
**High energy consumption (deduction from Rules 7 and 8)** The motor speed exceeded 779.5 rpm, or the total voltage fell below 549.25 V.  
The purpose of this study was to deduce applicable eco-driving indicators. From using the driving indicators for different energy consumption levels, we inferred the following:  
Figure 9: Feature weights of the optimal random forest model.
# CoAR: Congestion-Aware Routing Protocol for Low Power and Lossy Networks for IoT Applications
## 3 The RPL and Problem Description
### Objective Function  
RPL constructs the topology based on the OF. The OF defines how a node chooses an optimized path within an RPL instance based on routing metrics and constraints. It also defines the specific criteria of optimization, for instance, minimizing latency, hop count, etc. In route construction, the OF can combine different node and link characteristic routing metrics. Along with, it helps to determine the candidate parent set and preferred parent, so that the data traffic is transmitted through the preferred parent. Currently, there are two standard OFs used that are objective function zero (OF0) [27] and the minimum rank with hysteresis objective function (MRHOF) [28]. OF0 uses hop count as the routing metric, whereas MRHOF uses expected transmission count (ETX) as the routing metric for path calculation. The metric that OF uses is determined by the metrics in the DIO metric container. The RPL instance uses the OF to compute the rank for the node. The rank value is an abstract distance to the DODAG root within the DODAG version. In order to avoid the loop, the rank must monotonically increase from the root towards the child. The way of the rank is calculated depending on the OF.
# Fast Normalization for Bilinear Pooling via Eigenvalue Regularization
## 3 Our Approach
### Discussion  
We insist on explicit square root normalization at the first stage. The reason is that our method RegCov needs the accurate targets \(\bar{D_{e}}\) and \(\bar{M_{e}}\) from Equation (9). Across different datasets and network architectures, the targets \(\bar{D_{e}}\) and \(\bar{M_{e}}\) can vary greatly. Inaccurate target values make bilinear representation dissimilar to the normalized one and eventually negatively influence classification performance.  
In addition, the computational complexity of [PERSON]'s iteration is \(O(D^{3})\), which is much bigger than the \(O(D)\) of the state-of-the-art approach DropCov in the training phase. This limitation slows down the training speed for each epoch. However, the following Section 4.3 will show that [PERSON]'s iteration needs less of a training epoch. Eventually, training time spent on our network is acceptable. Moreover, by replacing [PERSON]'s iteration with a more efficient matrix square root, called MPA [51], the training speed of our approach can be further accelerated. In Section 4.7, the feasibility of MPA will be studied.  
Another advantage of RegCov is its efficiency at the second stage. It simplifies the alignment of eigenvalues into the regularization of two scalar values: \(D_{e}\) and \(M_{e}\). As detailed in Section 4.5, RegCov can result in approximated square-rooted eigenvalues. Additionally, RegCov avoids explicit calculation of each eigenvalue via computationally expensive SVD and makes the training faster.  
RegCov first trains a teacher network whose bilinear pooling contains square root normalization. Then, its normalization is rejected and the resulting network is a student network that is finally trained with prior knowledge from the teacher. The above methodology makes RegCov similar to knowledge distillation (KD) [58], where the knowledge distilled from a teacher model improves a lightweight student model. Most of the KD methods use either the teacher's output logits [58; 59; 60] or features from intermediate layers as distilled knowledge [61; 62; 63; 64; 65]. Differently, RegCov only needs two scalar values from bilinear representation, making itself simple yet effective.
## 4 Performance Evaluation
### Effect on Elephant Flow  
#### 4.3.1 Experimental Setup  
In this subsection, we compare the performance of our hybrid proactive-reactive flow rule management with a case involving complete reactivity in the network shown in Figure 2. The performance is measured based on the response time of mice flows (i.e., ping response time). In this simulation, we randomly set up mice flows from hosts on S7 to hosts on S8 (on paths S7-S3-S8 and S7-S4-S8) and an elephant flow from H5 to H13 on the same paths from the 5 th to the 10 th second.
# HERMES CubeSat Payload Thermal Balance Test and Comparison with Finite Volume Thermal Model
## 5 OpenFOAM FVTM Comparison
### Geometry and Materials  
The geometry used for the FVTM is identical to the one provided by INAF except for three aspects. First, the connectors are simplified as the thermal resistance at the pins is not known. It would be pointless to use many cells to recreate the exact geometry if in the end the region is still full of uncertainties. Second, the geometries of the heaters and dummies are reproduced to have perfect correspondence with the test components as shown in Figure 18. Third, given the high number of parts in the simulations the crystal boxes have been clustered in order to reduce their number (Figure 19). They are not expected to have big temperature gradients nor they are critical from a thermal point of view.  
The total number of geometrical elements considered is 126. The corresponding number of cells, resulting from a grid sensitivity analysis, is 1.2 million cells and the mesh is pictured in Figure 20. All the interfaces between the regions that are in contact with each other in the geometry of the payload are extracted and considered in the simulation [7]. The number of geometrical interfaces is 458 for a total number of 1.4 million faces. The complexity of the geometry and the number of elements are a good test for the novel application of the finite volume method (FVM) in space thermal analysis.  
Figure 18: Actual geometry of the DM (**a**,**b**). Geometry used in the simulation with connectors geometry simplified (**c**,**d**).  
The materials used for the model are reported in Table 6. Given the low thickness of the PCB boards and the predominance of contact area and thermal contact conductance in the thermal heat exchange in vacuum conditions, the boards are assumed isotropic. The connection between the FEE lateral boards and the top board is made of copper strips as reported in the aforementioned table. For the structure, the rails have a black anodization  
Figure 19: Actual geometry of the DM (**a**). Geometry used in the simulation with connectors geometry simplified (**b**).  
Figure 20: Geometrical high detail accuracy mesh.  
with a different emissivity value with respect to the polished aluminium used for the base plate and the other structural components. The emissivity value for the black anodization is given by the manufacturer and it is reported in the table. The emissivity of the interior side of the radiation shield is taken as the emissivity of a second surface mirror with Kapton thickness of 127 \(\upmu\)m ([[http://www.sheldahl.com/sites/default/files/Documents/ShieldingMaterials/RedBook.pdf](http://www.sheldahl.com/sites/default/files/Documents/ShieldingMaterials/RedBook.pdf)]([http://www.sheldahl.com/sites/default/files/Documents/ShieldingMaterials/RedBook.pdf](http://www.sheldahl.com/sites/default/files/Documents/ShieldingMaterials/RedBook.pdf)) (accessed on 2 November 2022)) (5 mil).