esa-sceva/satcom-chunk-collection huggingface dataset:
## 10 Conclusions  
White Rot Fungi (WRF) have recently emerged as a reliable candidate for the biodegradation of industrial pollutants, especially when recalcitrant and complex. Their ability to produce extracellular lignin-degrading enzymes, such as manganese peroxidase (MnP), lignin peroxidase (LiP), and increase, make WRF unique and stand out in breaking down complex organic and inorganic pollutants into less toxic byproducts, or even complete mineralization.  
Several WRF-based bioremediation technologies, such as solid-state fermentation, submerged fermentation, immobilized fungal systems, fungal-bacterial co-cultures, and enzymatic treatment with isolated WRF enzymes, have been established and explored to treat industrial pollutants. Each of these technologies has its merits and demerits, and the adoption of certain technology rests on the specific conditions/requirements of the wastewater treatment system.  
Despite the numerous advantages of utilizing WRF-based wastewater treatment systems, certain challenges and issues need to be addressed to incorporate the technology at an industrial and commercial scale. These challenges include appropriate fungal strains selection, strains improvement through genetic engineering, enzymes isolation, stability and activity, design of bioreactors and subsequent optimization, combination with other treatment technologies, economic feasibility, and understanding remediation pathways. Future extensive and rigorous research in these specified areas must be carried out to provide stakeholders with an efficient WRF-based wastewater treatment system that can degrade industrial pollutants not degraded by conventional treatment plants.  
In conclusion, WRF-based wastewater treatment systems provide a great solution to industrial water pollution, especially after the emergence of novel recalcitrant and complex pollutants that conventional technologies do not treat. In order to bring WRF-based wastewater treatment systems from the lab scale to the industrial scale, overcoming existing challenges is extremely necessary.  
Conceptualization, W.L. and S.P.; methodology, W.L.; formal analysis, W.L.; resources, W.L. and M.S.; data curation, M.I., C.C., M.S. and S.P.; writing--original draft preparation, W.L.; writing--review and editing, W.L., M.I., C.C., S.P. and M.S.; supervision, S.P. and C.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 1 Introduction
In practice, industrial data are often noisy, incomplete, or imbalanced, making it harder for ML models to perform effectively. Furthermore, many existing models struggle to generalize to new, previously unseen fault conditions, which limits their reliability in rapidly changing environments [3; 4; 6; 7]. Several emerging techniques show promise in overcoming these limitations. Transfer learning, few-shot learning, and semi-supervised learning can help lessen the reliance on large labeled datasets by utilizing knowledge from related tasks or integrating limited supervision with unsupervised learning strategies [3; 4; 6; 7]. By integrating information from diverse sources including sensor outputs, environmental data, and maintenance records fault diagnosis systems can achieve greater precision and reliability, enhancing decision-making in complex and unpredictable industrial environments [3; 4; 5; 7]. Ultimately, machine learning is transforming the detection and diagnosis of faults in industrial systems. While data quality and model generalization challenges persist, the potential of ML technologies remains vast. As these tools evolve, they are expected to enhance fault diagnosis and enable more cost-effective and efficient predictive maintenance strategies. The influence of ML across industries worldwide is set to grow as these innovations continue to develop [6, 7, 8].
# Reinforcement Learning of Bipedal Walking Using a Simple Reference Motion
## 3 Proposed Method
### Reward
#### 3.4.2 Velocity Reward \(r^{\text{v}}\)  
To encourage the robot's speed to approach the target speed, we define the velocity reward, as follows, based on [10]:  
\[r^{\text{v}} = \begin{cases}\exp(-l)&\text{if sign}(v^{\text{I}}_{x})>0\;\;\text{ and}\;\;r^{\text{o}}>0.8,\\ 0&\text{otherwise,}\end{cases}\] \[l = K_{x}\Bigl{[}(v^{\text{cmd}}_{x}-v^{\text{b}}_{x})^{2}+(v^{ \text{cmd}}_{x}-v^{\text{I}}_{x})^{2}\Bigr{]} \tag{9}\] \[+K_{y}\Bigl{[}(v^{\text{cmd}}_{y}-v^{\text{b}}_{y})^{2}+(v^{ \text{cmd}}_{y}-v^{\text{I}}_{y})^{2}\Bigr{]}\] \[+K_{\omega}(\omega^{\text{cmd}}_{z}-\omega_{z})^{2},\]  
where \(v^{\text{b}}_{x}\) and \(v^{\text{b}}_{y}\) are the linear velocities of the robot's torso along the \(x\) and \(y\) axes in the world coordinate system, respectively. \(v^{\text{I}}_{x}\) and \(v^{\text{I}}_{y}\) are the linear velocities of the torso along the \(x\) and \(y\) axes in the local coordinate system of the torso, respectively. \(\omega_{z}\) is the angular velocity around the \(z\) axis in the world coordinate system. \(v^{\text{cmd}}_{x}\), \(v^{\text{cmd}}_{y}\), and \(\omega^{\text{cmd}}_{z}\) are the corresponding target values.  
We note that in Equation (9), we have two types of torso velocities, i.e., \(v^{\text{b}}_{x}\) and \(v^{\text{b}}_{y}\), which are represented in the world coordinate system, as well as \(v^{\text{I}}_{x}\) and \(v^{\text{I}}_{y}\), which are represented in the local coordinate system of the torso. In Equation (9), the terms involving \(v^{\text{b}}_{x}\) and \(v^{\text{b}}_{y}\) encourage the robot to move in the target direction in the world coordinate system, and the terms involving \(v^{\text{I}}_{x}\) and \(v^{\text{I}}_{y}\) encourage the robot to move forward in the local coordinate system. Without the former, the robot is not penalized for moving in a different direction. Without the latter, the robot is not penalized for walking in a strange manner, such as a sideways walk [10].  
In all the experiments in this study, we set \(v^{\text{cmd}}_{x}=0.15\) [m/s], \(v^{\text{cmd}}_{y}=0\) [m/s], and \(\omega^{\text{cmd}}_{z}=0\) [rad/s]. The value of 0.15 m/s for \(v^{\text{cmd}}_{x}\) is faster than the maximum speed that the robot can physically achieve but was used to encourage the robot to walk as quickly as possible [30]. \(K_{x},K_{y}\), and \(K_{\omega}\) are weight parameters. In all the experiments in this study, we set \(K_{x}=15\), \(K_{y}=50\), and \(K_{\omega}=25\).
# Dynamic Segmentation for Physical Activity Recognition Using a Single Wearable Sensor
## 4 Proposed Method
### Selection of Peaks  
Peaks, which represent the local maximum values in the Y-axis acceleration signal, were first discerned. To avoid including false-positive peaks, as illustrated in Figure 4a, a threshold value was used. To be detected, a peak must be equal to or greater than Threshold 1. This can be calculated by separately averaging the peaks in the learning dataset of each of the seven physical activities and, in turn, choosing the minimum value among them as follows:  
\[\textbf{Threshold 1}=\text{min}\ (\text{avg}_{\text{max}}\ (\text{PA}_{1}), \text{avg}_{\text{max}}\ (\text{PA}_{2}),\ \ldots\,\text{avg}_{\text{max}}\ (\text{PA}_{\text{n}})) \tag{1}\]  
where avg denotes the average, max is the local maximum in the processed axis (i.e., Y-axis), PA denotes physical activity, and n refers to the number of physical activities to be classified. The identified peaks after applying the threshold are shown in Figure 4b.  
Moreover, to avoid detecting more than one peak within the data points that represent a single activity, as shown in Figure 4a, a minimum distance between peaks was assigned. This value can be obtained by calculating the average duration needed to perform the shortest activity as follows:  
\[\textbf{Distance = min}\ (\text{avg}_{\text{duration}}\ (\text{PA}_{1}), \text{avg}_{\text{duration}}\ (\text{PA}_{2}),\ \ldots\,\text{avg}_{\text{duration}}\ (\text{PA}_{\text{n}})) \tag{2}\]  
Figure 4: Detected peaks in acceleration signal: (**a**) Initial selection of peaks; (**b**) After applying threshold and minimum distance.
# Anomalous Beam Transport through Gabor (Plasma) Lens Prototype
## 5 Characterisation of Lens Performance
Crosses represent variation in high voltage (and therefore electric field) with no current through the magnetic coils. between the beam axis and the symmetry axis of the coil. The two sets of data, for 15 kV and 20 kV, were taken on consecutive days which indicated that the driving mechanism associated with the observed plasma dynamics was a characteristic of the geometry and operation of the lens. The variation in \(x\) and \(y\) diameter of the six beam spots from Setup 2 is given in Figure 16: with a constant applied voltage of 15 kV, the variation in spot diameter with magnetic field is shown. A non-linear increase in spot size with change in magnetic fields was observed, with the rate of increase in diameter getting larger at high magnetic fields. This indicated that the increase in plasma density with magnetic field was faster than linear and thus that the plasma trapping efficiency varied with the magnetic field strength. Since the points remained within the lines shown, there was an indication of a trend for the change in diameter for a given spot. The ratio variation being solely dependent on initial position of the spot indicates that this effect is caused by the density distribution of the plasma in the transverse plane. Figure 17 shows the change in \(xy\) ratio of the six beam spots from Setup 2 with variation in magnetic field. The focusing force in the \(x\) direction compared to the focusing force in the \(y\) direction changed differently for each beam spot. The difference between the two focusing forces was more significant for those beam spots at greater distances from the beam axis. This indicated that the centre of the lens has a low plasma density, while further from the axis of the lens, the plasma density increased with radius. In addition, there was some perturbation that caused the motion of the plasma to evolve with time. Figures 14-17 may be understood in terms of a position dependent plasma density, with the bulk of the plasma shifted radially from the lens centre and rotating around the central axis. Figure 15: The \(x\) and \(y\) position of the centroids of the beam spots imaged with the Setup 1 (blue) and Setup 2 (red) with increasing magnetic field strength.
## 5 Experiments and Results
### Performance Overhead Evaluation
#### 5.2.1 SPU Performance Overhead  
The performance overhead of the CPU-SPU collaborative SoC is shown in Table 2. In the selected benchmarks, OpenECC has the largest numbers of instructions and BBs, and its \(INF_{ref}\) table for integrity monitoring occupies the maximum on-chip storage space of 26.30 KB in the monitor memory accordingly. We also found that the average hit rates of I-Cache and M-Cache configured with 8 KB both exceeded 98%, and even the hit rate of the M-Cache reached 99%. The primary reason is that the BBs \(INF_{ref}\) blocks were stored into monitor memory by the order of BBs execution.  
Their high-hit rates contributed by both temporal locality principle and spatial locality principle can effectively maintain the SPU with a low performance overhead. The benchmark of quicksort had the highest M-Cache hit rate (99.83%); its small number of program BBs determined both a lower number on BB overlaps and a higher proportion in caching \(INF_{ref}\) blocks from the monitor memory. The higher proportion in caching \(INF_{ref}\) blocks from the M-Cache can avoid frequent access to monitor memory; therefore, it had the lowest performance overhead (0.43%).  
The indicator CPI tended to increase with the number of benchmark instructions, which indicates that the probability of M-Cache missing increases. When the overlapped BBs occupied a larger proportion in the program partition, that caused \(INF_{ref}\) block discontinuity in the monitor memory.  
For example, running the benchmarks of OpenECC and basicmath requires searching the \(INF_{ref}\) table cached in M-Cache. A small minority of discontinuous \(INF_{ref}\) blocks are not cached in the current M-Cache at the time of executing the jump instructions, and the monitor memory is accessed in response; thus, their CPI values are higher than the other benchmarks. After we complete the calculations of the experimental data from these ten benchmarks, the average performance overhead induced by SPU was 1.92%, ranging from 0.43% (quicksort) to 3.36% (OpenECC).
## 3 Results and Discussion
### Evaluation of the Dynamic Response of the Compliant Elements
Figure 10 shows AS of the low frequency test bench in detail. As previously mentioned, in the low frequency range the influence of mass is negligible. The correction by \(HI_{pp}(f)\) on \(arg(AS)\) is small; however, \(HI_{pp}(f)\) has a decisive influence on the phase angle \(arg(AS)\). The uncorrected phase \(arg(AS_{meas. })\) changes from negative values to positive values with increasing frequency. The dynamic calibrated phase \(arg(AS_{testobj. })\) stays nearly constant over frequency at around 0.1 rad. The calibrated measurement results are more realistic, since the non-calibrated ones cannot be described mechanically with a positive damping coefficient. A negative phase angle of AS means that the force is behind the displacement signal in time domain. This correlation can not be represented by the mechanical equation of motion (Equation (1)) with a sinusoidal displacement (Equation (2)) having a positive damping coefficient \(c\). The real part of AS is described by the stiffness and mass. The imaginary part is only described by the damping and is therefore the only part to change the phase angle from 0 and correspondingly \(n\cdot\pi\). It is clear that the negative phase shift is due to a delay in the measuring system, and not given by a negative damping coefficient. Figure 11 shows the calibrated frequency response functions AM, MI, AS and its phase for two compliant elements: one with double rubber buffer in each stack (Figure 4a) and the other one with a single rubber buffer in each stack (Figure 4b). Halving the stacks of the rubber buffer doubles the stiffness from compliant element A to B. This can be clearly seen in the low frequency range of \(AS_{meas. }\) and increases as well the natural frequency. Both compliant elements show a stiffness dominated behavior. The stiffness of element B with 540 N/mm is not twice as large as that of element A with 300 N/mm. This is most likely due to the nonlinear behavior of the rubber buffers themselves, since the single stacks are compressed twice as much as the double stacks at the same amplitude. The phase difference of both compliant elements are almost equal in front of the first natural frequency. The calibrated measurement of compliant element A has its natural frequency at approximately 190 Hz (Figure 11 blue dots) and compliant element B at 240 Hz (Figure 11 black dots). For element A it is shown that the non-calibrated measurement provides a natural frequency of about 80 Hz (Figure 9) and the non-calibrated measurement of the compliant element B determines a natural frequency of 110 Hz. The relative difference between the non-calibrated to the calibrated measurement for the given elements is larger than the difference between the two elements themselves. This again shows the high sensitivity of the test results by mass cancellation and _measurement systems FRF_\(HI_{pp}\).
## 3 Experimental Method and Principle
### Processing of Video Signals
#### 3.2.1 Image Pre-Processing  
The static core images obtained from the video contained various kinds of system noises, which would not only bring down image quality but also be harmful for later quantitative analysis. Therefore, the first step of image processing was to attenuate noises and enhance signal to noise ratio by using a filtering algorithm while keeping the details and margin information of the images. Filtering algorithms commonly used include Gaussian filtering, median filtering and mean filtering. In comparison, the median filter will not make the image boundary fuzzy while simultaneously smoothing noise effectively; thus, the outline features of the image can be kept well. Hence, the median filter was adopted in this study to attenuate noises in the original gray images of the shale sample.  
As observed from Figure 6a, since the picture is extracted from the video file generated by the camera, the picture is relatively blurred. After filtering, the cracks in the image appear clearer (Figure 6b). At the same time, the gray images after median filtering had a natural transition between the fractures and matrix, smooth boundaries and important feature information in the images were well preserved, laying a solid foundation for the next image segmentation of fractures and background matrix.
# A Methodology Based on Deep Learning for Contact Detection in Radar Images
## 3 Methodology
### Phase 2: Generation of the Object Detection Model
#### 3.2.1 Automatic Cropping  
In this process, several artificial vision techniques are used to perform: edge extraction, considering the intensity values and the dimensions of the objects of interest. The edge extraction process is based on the work of [39]. First, land portions such as islands and coastal zones are removed using a template. Subsequently, thresholding techniques are applied within the image, considering the pixels corresponding to a specific range of intensity values. The intensity values considered correspond to the reflectivity or Radar Cross-Section of the objects of interest. Likewise, the width considered for edge extraction is a maximum of one pixel, which is convenient for obtaining the contour of the objects of interest. Then, the discrimination of the objects is performed, considering the dimensions of the desired objects (ships). Object discrimination results in the cutouts of the possible objects of interest together with their coordinates. The flow chart of the automatic cropping process is shown in Figure 10.  
Figure 11 shows cuts of objects obtained through automatic cropping. These objects of interest are normalized to facilitate the next stage of classification and labeling by the expert.  
With the cuts of objects of interest, the text file log.txt is generated. This file contains each of the cutouts of the objects of interest with x and y coordinates. The file contains the following fields:  
* Filename: It is the identifier of the radar return.
* Xmin: left horizontal coordinate, between 0 and 2048.
* Xmax: right horizontal coordinate, between 0 and 2048.
* Ymin: upper vertical coordinate, between 0 and 1024.
* Ymax: lower vertical coordinate, between 0 and 1024.  
Figure 10: Automatic cropping flow chart.
# Grid Nodes Selection Strategies for Power Quality Monitoring
## 4 Problems and Future Prospects
### Case Study: PQ Monitoring System Development in Lithuania
It has been estimated that 15-30 portable PQ monitors are required in total at the initial stage of PQ system development (2-4 monitors per department). These monitors will be used for both customers' complaints and nodes research upon DSO decision. The recommendations were partially influenced by the survey presented in [36], where the total number of respondents is 114: 64% DSOs, 30% TSOs, and 6% of companies which operate both systems; 5 responses came from Africa, 30--Americas, 8--Asia, 61--Europe, and 10--Oceania. In general, DSOs have more PQ monitors--both portable and fixed--than TSOs (Figure 13). Some additional information about the most common responses which are noteworthy: 83% of respondents highlighted the importance of monitor compliance with standards, 70%--selected customers' complaints as the monitoring aim (86% DSOs). Figure 13: Number of responses regarding PQ analyzers in the possession of grid operators in 2013. The total number of respondents is 114. The median value of portable monitors is 9 units, the median value of fixed PQ monitors—45 [36]. In addition, high sampling analyzers are recommended for atmospheric transients' monitoring: the estimated number for the initial stage--7 units. The priority is given to the municipalities with higher lightning probability. The available historical weather data are given in [37] (see Figure 14): the territory of Lithuania is divided into two zones regarding annual thunderstorm duration--up to 40 h and more than 40 h.
###### Abstract  
The signal power in wireless communication systems is influenced by various factors, including the environment. These factors include path differences, operational frequency, and environmental conditions. Consequently, designing a communication system that generates a stronger signal is highly challenging. To address this, large-scale path-loss models are employed to estimate the path loss and signal power across different frequencies, distances, and environments. In this paper, we focused on the urban micro, urban macro, and rural macro environments to estimate path loss and signal power at millimeter wave frequencies. We compared the path loss and received power among different path-loss models developed by standard organizations. Simulation results indicate that the fifth-generation channel model provides enhanced path loss and signal power in urban micro environments, while the third-generation partnership project model performs well in urban macro and rural macro environments when compared to other path-loss models.  
5G networks; millimeter wave; path-loss models; rural environment; urban environment +
Footnote †: journal: _articles
# Distributed Interference-Aware Power Optimization for Multi-Task Over-the-Air Federated Learning
## 2 System Model  
As illustrated in Figure 1, we consider a parallel multi-task Air-FL system composed of \(M\) cells. Each cell includes a dedicated AP, indexed by \(m\in\mathcal{M}\triangleq\{1,\ldots,M\}\), and serves \(K\) single-antenna edge devices, indexed by \(k\in\mathcal{K}_{m}\triangleq\{1,\ldots,K\}\). Define the global device set as \(\mathcal{K}\triangleq\{\mathcal{K}_{1},\ldots,\mathcal{K}_{M}\}\). Each cell independently collaborates to train a distinct machine learning model, which highlights the heterogeneity across tasks.