esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Design and Modeling  
### Design  
The MBBP-REH with dynamic axial load for rotational energy harvesting is shown schematically in Figure 1. The excitation magnets are arranged to have the same or alternative parallelity along the circumference. For the parallel arrangement, the excitation magnets can be in phase (Figure 1a) or out-of-phase (Figure 1b) with the center magnet on the buckled beam. The arrangement with alternative parallelity is shown in Figure 1c. The excitation magnets are attached to the revolving host. The buckled beam is made of bronze substrate that is partly covered with two serially connected piezoelectric patches at one end. One end of the beam is clamped and fixed, while the other end carries a moving mass and a magnet, thus it can move along the axial direction under the dynamic axial load. A magnet with the opposite magnetism direction is fixed on the frame facing to the moving mass, with a small clearance between each other. The interaction force between these two magnets enables the initial buckling state of the beam. By adjusting the initial gap between two magnets, different initial buckling states can be established.  
Figure 1: Schematics of the magnetic-coupled buckled beam piezoelectric rotational energy harvester (MBBP-REH), the rotational excitation magnets are arranged to have the same ((**a**) in phase or (**b**) out-of-phase with the center magnet) or alternative (**c**) parallelity along the circumference.
# Ride Comfort Prediction on Urban Road Using Discrete
## 4 Results  
### DRI and Ride Comfort  
To study if the DRI can identify local features and characterize ride comfort, a 470 m long road section was selected as an example. The corresponding IRIs of the left and right wheel paths were 3.34 and 3.49, respectively. Considering the computational cost, we calculated the DRI on every 1.0 m long section. Figure 7 illustrates the DRI, the road profiles, and the vertical acceleration on the driver's seat. The transient WRMSA (WRMSA in 1 s) curve is also plotted in Figure 6. Note that the simulation was implemented at a speed of 80 km/h.  
Figure 5: Road profile and filtered results.  
Figure 6: DRI and IRI of the road profiles. (Blue boxes indicate the DRI distribution of each road section).  
Figure 7: DRI, road profiles, vertical accelerations, and transient WRMSA.  
Firstly, as shown in the section of 0-50 m, the corresponding DRI is much higher than the DRI in the other sections, and the corresponding acceleration also has a significant fluctuation. See the section of 70-180 m. The DRI is relatively small and corresponding to a flat road surface and a slight vibration. This demonstrates that the DRI can localize the uneven pavement profile and the violent vibration on the driver's seat. Based on Equation (9), we can calculate the WRMSA of the whole section, which is 0.937 m/s\({}^{2}\), corresponding to the comfort level of \"fairly uncomfortable\", while the transient WRMSA exceeds 0.937 m/s\({}^{2}\) at some locations. The transient WRMSA at the beginning and at around 300 m can reach 2.5 m/s\({}^{2}\) and 2.0 m/s\({}^{2}\), respectively, which both correspond to the comfort level of \"very uncomfortable\". This indicates that the discomfort resulting from uneven short sections may be neglected if we only consider the ride comfort of driving on a long road section; thus, it is necessary to propose a new prediction method for the ride comfort in consideration of the effect of short sections.
## 4 GPU Performance Issues  
Traditional approaches using the CPU have fallen behind in meeting the processing rate requirements with the development of high-speed networks. Unlike the CPU, the GPU reserves more transistors for data processing, instead of the cache and flow control. Therefore, GPUs, which have superior parallel-processing power compared to CPUs, can serve in DPI applications to provide high matching rates [6]. GPU cards consist of a series of streaming multiprocessors (SM), each containing a series of streaming processors (SP). Threads operated on SMs are grouped into blocks divided into smaller units called warps, each with 32 consecutive threads. Each SM creates, manages, programs, and executes threads in warp groups. For example, if there are 128 threads in an SM, four warps can be operated in parallel in this SM. In this respect, a warp is a structure that constitutes the timing unit of the SM. Threads running concurrently on SMs are managed based on a single-instruction multiple-thread (SIMT) architecture. Each SP operates the same command by processing them into different data during any clock cycle.  
Despite the computationally intensive and parallel computing capability, the GPU also has performance sensitivities. These sensitivities make it hard to develop well-performing systems for DPI applications with high computational costs and memory requirements. In this subsection, GPU sensitivities that limit performance in the DPI-matching process are explained in subheadings.
# Bearing Fault Diagnosis Approach under Data Quality Issues
## 4 Results and Discussion
### Effect of Data Anomalies on the Fractal Characteristics of Vibration Signals  
In the real-world environment, vibration measurements are susceptible to data quality issues during the data acquisition stage. So, we devoted this section to study the effect of these issues on estimating the fractal characteristics of vibration measurements. Here, we focused on the impact of induced noise and data loss on the estimated scaling exponents of the vibration signals under different health conditions.
## 3 Results
### Evaluation of Detection Accuracy Using Additional Metrics
#### 3.2.1 IoU Above Threshold Rates  
The IoU-based analysis evaluated the proportion of bounding boxes achieving a spatial overlap above a threshold of 0.5. The results, summarized in Table 2, indicate consistent detection performance for all artery types. The mean IoU rates were 77.2% for the RCA, 79.3% for the LCA-LAD, and 71.1% for the LCA-CX. Notably, the LCA-LAD demonstrated the highest mean IoU rate among the three arteries, suggesting better spatial alignment for this class. The fold-wise results showed some variability, with the RCA achieving its highest rate of 82.0% in fold3 and the LCA-LAD reaching a peak of 84.2% in fold4. For the LCA-CX, the rates were relatively lower, with a peak of 76.7% in fold1.
## 4 Experimental Results and Analysis
### Tests in Different Coastal Scenes  
In the second experiment, a set of single-look quad-polarization SAR images were used to evaluate the proposed method, including the coasts of Zhanjiang, Fujian, Tianjin, Lingshui, and Boao in China and Berkeley in America. The Pauli pseudocolor images of the data are shown in Figure 8a1-f1. We can see that there is a port in each image, except in the Boao data. The area of water interfered with by the port is large in Zhanjiang, Berkeley, and Tianjin. In Zhanjiang, Fujian, and Lingshui, there is a wide intertidal flat, which makes the water difficult to be separated from the land. The area of the port is large in Tianjin but is small in Fujian. There are many small harbors in Zhanjiang and Tianjin. The target ports are difficult to identify from harbors if the geometric method is used. The target port in Fujian is difficult to detect because the interference water is narrow. The ROI extraction results are shown in Figure 8a2-f2. We can see that almost all interference water regions are correctly detected from the results. The extracted ROI in each image correctly covers the target port, except in Boao. The ROI recognition results are shown in Figure 8a3-f3, where the detected port is marked in a red box and the ground truth is marked in a green box. Compared with Figure 8a2-f2, we can see that the covering boxes are more accurate due to the process of land region location. The false alarm detected in Boao is correctly removed by the process of ROI censoring in Figure 8f3. The zooming recognition results near the target ports are shown in Figure 8a4-f4. We can see that all the detected ports are close to the ground truth. The IoU indexes are listed in Table 2. The largest value is 77.8% in the Berkeley data, while the smallest value is 44.18% in the Lingshui data. The indexes of Zhanjiang and Berkeley are both larger than 75%. All the indexes are larger than 50%, except the smallest value.
## 3 Results and Discussion
### Effect of Electromagnetic Parameters
At magnetic field intensity of 0.1 T, 0.2 T, 0.3 T, and 0.4 T, the inverter maximum temperature decreases from 171.03 \({}^{\circ}\)C to 58.11 \({}^{\circ}\)C, 100.67 \({}^{\circ}\)C to 44.08 \({}^{\circ}\)C, 76.39 \({}^{\circ}\)C to 37.67 \({}^{\circ}\)C, and 64.89 \({}^{\circ}\)C to 32 \({}^{\circ}\)C, respectively, when the voltage increases from 0.1 V to 0.5 V.  
The behavior of the Nusselt number for ferrofluid cooling under various conditions of magnetic field intensity and voltage is depicted in Figure 12. The lower values of the inverter maximum temperature indicate that a higher amount of heat is dissipated from the traction inverter to ferrofluid, which means the heat transfer coefficient and Nusselt number are superior. The higher heat transfer rate from the traction inverter to ferrofluid improves the heat transfer coefficient, which results in a higher Nusselt number. The Nusselt number increases with  
conventional pump. This will help to reduce the power consumption with desired cooling performance for the thermal management of the traction inverter to achieve extended driving range and efficiency of electric vehicles. Apart from electromagnetic parameters, the ferrofluid distribution in MHD pump-based cooling systems is significantly affected by magnetic field patterns. In our previous work, the heat transfer characteristics of ferrofluid for cooling of power electronics in EVs were investigated considering no magnetic field pattern and I-, L-, and T-shaped magnetic field patterns. For no magnetic field pattern, the heat gets accumulated near the heat source and heat dissipation follows the paths of I, L, and T based on corresponding magnetic field patterns. The heat flow rate and direction to dissipate the heat from power electronics to the cooling source using ferrofluid could be optimized based on criteria of lowest thermal resistance [25]. The findings from the previous study about the ferrofluid behavior under the influence of various magnetic field patterns could be referenced to design MHD pump-based cooling systems for thermal management of EV traction inverters with lower power consumption (based on lower thermal and flow resistances from heat source to heat sink). Figure 11: Inverter maximum temperature for water and ferrofluid cooling under various conditions of electromagnetic parameters.
## 3 Results and Discussion
### Correlations between TPC, TFC, DPPH, and ABTS  
The correlations between TPC, TFC, DPPH, and ABTS interacted with each other according to the proposed results. As shown in Figure 1, higher correlations are observed with R\({}^{2}\) values that are close to 1. According to the extraction conditions, the highest correlation (R\({}^{2}\) = 0.9138) was found between the two radical scavenging effects (DPPH and ABTS) (Figure 1a). On the other hand, the correlation (R\({}^{2}\) = 0.0102) was difficult to find between the TPC and TFC (Figure 1b). As shown in Figure 1c-f, DPPH and ABTS radical scavenging abilities showed a high correlation, with 0.8766 and 0.8299 R\({}^{2}\) values, respectively, whereas there was no correlation with TPC (R\({}^{2}\) < 0.05).
# A Study on the Essential and Parkinson's Arm Tremor Classification +
## 5 Results
### Received Data  
A total of 43 samples were collected, 12 were from patients diagnosed with PT, 3 with ET, while the other 28 were from volunteers with no diagnosed tremor (NT). The data extracted from the measuring device include the signals of acceleration in \(x,y,z\) axes as a function of time \(t\) and are utilized to calculate the frequency spectrum of amplitude of the resultant acceleration vector of the measured signal for the extraction of the dominant tremor frequency. Indicatively, Figure 4a-c present the data acquired via a (a) Parkinson's-diagnosed patient, (b) an ET-diagnosed patient and (c) a volunteer without tremor evidence. The acceleration measurements refer to the extended arm position and for the index \(l_{2}\). It is noted that for the amplitude spectrum plots in Figure 4 a high-pass filter was applied with a cutoff frequency of 0.5 Hz to avoid the DC artifact at 0 Hz.  
By observing Figure 4a, it is easy to distinguish the occurring PT particularly after the 16 th second, when the volunteer started to tire. In this case, the maximum tremor frequency is extracted from the frequency spectrum as 5.91 Hz. From Figure 4b, the main extracted ET frequency for the specific measurement is observed close to 4.93 Hz. An observed difference from the Parkinson's patient was that the volunteer's index was trembling throughout the duration of the measurement. At the same time, the PT case was observed to have a lower maximum amplitude of the frequency spectrum, which was measured close to 0.25 Hz, while the amplitude in the ET case was measured at 0.5 Hz.  
From Figure 4c, it is easily distinct how the volunteer keeps his arm extended without shaking significantly. However, as shown by the spectral diagrams, some small amplitude frequency peaks appear, the causes of which are attributed to the measurement noise from the accelerometer. Finally and as expected, the NT volunteer's tremor frequency shows a significantly smaller amplitude in comparison to the tremor-diagnosed volunteers.  
It is important to point out that there were Parkinson's-diagnosed volunteers who showed no tremor during the measurements, which was verified in the subsequent processing of the acquired data. This is expected, as tremor may be one of the basic characteristics of the disease, but it is not a necessary and unique trait of the disease, it may not have a lasting nature and may not occur during experimentation.
# Comprehensive LCA of Biobased Sustainable Aviation Fuels and JET A-1 Multiblend
## 2 Materials and Methods
### SAF to Airport Supply Chains  
For the environmental assessment presented in this article, two different supply chains for multiblend JET A-1 consisting of either two or three SAF with conventional JET A-1 were considered (hereafter referred to as case A and case B). The elements of these supply chains followed the planned and realized set up within the DEMO-SPK project. It is expected that with broader market implementation of SAFs in the future, the design of these supply chains will also be optimized. The following basic conditions applied to both the considered cases:  
* Only ASTM-certified, biobased SAF was taken into consideration.
* JET A-1 was taken into account as the fossil component.
* A separate tank farm was used as a place for blending SAF and JET A-1. Due to safety regulations, blending could not be performed directly on the airport premises and was therefore done at an external tank farm. Composition and blending procedures had to comply with existing regulations.
* The place of multiblend use was defined as an airport whose tank farm is supplied exclusively via tank wagons using existing airport infrastructure.  
In addition to these conditions, further case-specific assumptions are explained below together for the two cases.  
Multiblend JET A-1, Case A: This case reflected the supply chain demonstrated within the DEMO-SPK project. The sustainable aviation fuels ATJ and HEFA were produced in the USA and hence had to be shipped to the port of Hamburg in ISO tank containers and then trucked to the blending site (partially via a container terminal) [6]. The individual types of kerosene were transported in different ways as they were manufactured at different locations. The fossil JET A-1 was produced in Germany and hence simply transported by rail tank car to the blending site. After mixing according to ASTM standard (stepwise JET A-1 with ATJ 6.03 vol.% and HEFA 32.23 vol.% [10]), the multiblend JET A-1 was transported to the airport.  
Multiblend JET A-1, Case B: The supply chains of HEFA, ATJ, JET A-1, and the produced multiblend JET A-1 were congruent to case A, the supply chain demonstrated within the DEMO-SPK project. In order to evaluate a multiblend JETA-1 with three SAF, one more theoretical supply chain was introduced. In case B, only SIP was added to the other components for multiblend production. The SIP was filled into tank containers at the refinery site in Brazil and transported to Hamburg. Similar to ATJ, it was transported via Hamburg as an import hub to the blending site (JET A-1 with ATJ 3.32 vol.%, HEFA 16.61 vol.%, and SIP 4.98 vol.%).
# Communication-Efficient Vertical Federated Learning
###### Abstract  
Federated learning (FL) is a privacy-preserving distributed learning approach that allows multiple parties to jointly build machine learning models without disclosing sensitive data. Although FL has solved the problem of collaboration without compromising privacy, it has a significant communication overhead due to the repetitive updating of models during training. Several studies have proposed communication-efficient FL approaches to address this issue, but adequate solutions are still lacking in cases where parties must deal with different data features, also referred to as vertical federated learning (VFL). In this paper, we propose a communication-efficient approach for VFL that compresses the local data of clients, and then aggregates the compressed data from all clients to build an ML model. Since local data are shared in compressed form, the privacy of these data is preserved. Experiments on publicly available benchmark datasets using our proposed method show that the final model obtained by aggregation of compressed data from clients outperforms the performance of the local models of the clients.  
federated machine learning; heterogeneous federated learning; communication efficient; data privacy +
Footnote †: journal: 2022, 15, 273. [[https://doi.org/10.3390/a15080273](https://doi.org/10.3390/a15080273)]([https://doi.org/10.3390/a15080273](https://doi.org/10.3390/a15080273))
# Remote Sensing Road Extraction by Road Segmentation Network
## 2 Materials and Methods
### The Proposed Method
#### 2.2.1 Network Architecture  
An overview of the network architecture is shown in Figure 1. The network adopts a UNet-like architecture to extract the road map from a high-resolution remote sensing image. The U-Net architecture concatenates multiple features from high layers to low layers, which is widely used in remote sensing images [14]. The proposed method extracts features by convolution layers and further produces a dense pixel-wise output by deconvolution layers. However, multiple features may contain redundant noise information from non-road pixels. To enhance the context information in adjacent layers, an attention mechanism exploits the hierarchical features to generate attention maps \(w_{1},~{}w_{2},~{}w_{3}\). These weight maps are involved in skip connections and adjacent layers to enhance the context information. Then, the low-level features are weighted and fused into the corresponding layers in the deconvolution layers to emphasize the context features of the road. After multiple deconvolutions and sigmoid layers, the features are restored to the size of input image. To enhance the strip information of a road, a network with strip pooling is designed to refine segmentation results.  
Figure 1: The architecture of the proposed network. A self-attention mechanism is exploited to enhance the inter-layer features. The final road maps are obtained through a refinement network.
## 4 Trust Model
### Indirect/Recommended Trust
#### 4.3.1 Consensus Combination  
Let, \(\mathbf{w}_{y}^{x}\) and \(\mathbf{w}_{y}^{z}\), be x's and z's trust in y respectively. Then \(\mathbf{w}_{y}^{x}=[b_{y}^{x},\ d_{y}^{x},\ n_{y}^{x},\ e_{y}^{x}]\) and \(\mathbf{w}_{y}^{z}=[b_{y}^{z},\ d_{y}^{z},\ n_{y}^{z},\ e_{y}^{z}]\). Then consensus operation of x and z about trustworthiness of y is given by \(\mathbf{w}_{y}^{x,z}=\mathbf{w}_{y}^{x}\mathbf{\Theta}\mathbf{w}_{y}^{y}=[b_{ y}^{y,z},\ d_{y}^{x,z},\ n_{y}^{x,z},\ e_{y}^{x,z}]\). Four elements of \(\mathbf{w}_{y}^{x,z}\) are as follows:  
\[\begin{split}& b_{y}^{x,z}=\frac{b_{y}^{x}e_{y}^{z}+b_{y}^{z}e_{y}^{x}}{ e_{y}^{x}+e_{y}^{z}-e_{y}^{x}e_{y}^{x}},\\ & d_{y}^{x,z}=\frac{d_{y}^{x}e_{y}^{z}+d_{y}^{z}e_{y}^{x}}{e_{y}^{ x}+e_{y}^{z}-e_{y}^{x}e_{y}^{z}},\\ & n_{y}^{x,z}=\frac{n_{y}^{x}e_{y}^{z}+n_{y}^{z}e_{y}^{x}}{e_{y}^{ x}+e_{y}^{z}-e_{y}^{x}e_{y}^{z}},\\ & e_{y}^{x,z}=\frac{e_{y}^{x}e_{y}^{z}}{e_{y}^{x}+e_{y}^{z}-e_{y}^ {x}e_{y}^{z}}.\end{split} \tag{4}\]  
The consensus operation \(\Theta\) decreases uncertainty as it comes from both vehicles subjective experience about the particular vehicle.
## 3 Incipient Fault Detection Method Based on LOPV and KL Divergence
### Optimum PV for Incipient Fault Detection
Since the objective function \(\min_{w}-J(w)\) in Equation (16) is a nonlinear function about \(w\), the solution obtained by the iterative method is not necessarily a global optimum solution, but probably a local optimum solution [31; 32]. However, considering that the optimization problem shown in Equation (16) needs to be solved when each set of online data arrives, finding the global optimum solution will undoubtedly increase the computational cost. Considering the timeliness, the iteration can be stopped when the optimization problem in Equation (16) converges to a local optimum solution that is better than the existing fixed PVs. It is necessary to ensure that the local optimum solution searched for by Equation (16) is better than all the fixed PVs of \(v_{1},v_{2},\cdots,v_{m}\) obtained by PCA. To achieve this, when the on-line data \(Y\) arrive, the on-line data \(Y\) are projected on each fixed PV \(v_{1},v_{2},\cdots,v_{m}\) and the mean value drift vector \(\triangle\mu\), respectively. The KL divergence relative deviations are calculated according to Equations (13)-(15) in each PV and the vector of mean value drift. Then the vector \(v_{j}\) that makes the largest KL divergence relative deviation is selected as the initial iteration vector \(w_{0}\) of the optimization problem in Equation (16). \[\left\{ \begin{array}{c}w_{0}=v_{j}\\ s.t.J\big{(}v_{j}\big{)}=\max\Big{(}J(v_{1}),J(v_{2}),\cdots,J(v_{m}),J\big{(}v _{m+1}=\frac{\Delta h}{\|\Delta h\|}\Big{)}\Big{)},j\in[1,m+1]\end{array} \right. \tag{18}\]