esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Samples Characterization
#### 3.1.4 SEM-EDXS Analysis  
SEM-EDXS was performed on the longitudinal surface of the specimens, that were in contact with the mold. Figure 7 presents SEM images of 50% wt stabilized sample filled epoxy composite in comparison with 50% wt CaCO\({}_{3}\) filled epoxy composite. SEM images demonstrate a significant difference between stabilized sample and CaCO\({}_{3}\) based composite. Indeed, the composite material with stabilized sample filler exhibits a non-homogeneous dispersion of particles with different dimensions, characterized by large agglomerates. These results are confirmed by the EDX maps (see Figure 8) that show the distribution of Ca in different % wt filler. On the other hand, a homogeneous distribution is associated with a better dispersion, with particles with diameter below 60 \(\upmu\)m observed for composite with CaCO\({}_{3}\). Ca was chosen because is present in both fillers and its quantity is limited in the epoxy-resin matrix. This allows us to highlight that particle agglomeration takes place in the case of the new fillers composite. Besides, particle cluster occur above 10% wt of stabilized sample, thus leading to a larger particle size and hence, strengthening decrease. When particles aggregation occurred, due to the interactions between them within the polymer matrix, the particle size increased and led to the creation of stress concentration points, which act as crack initiation and eventually weakened the interfacial adhesion between the matrix and particle [46]. The addition of stabilized sample fillers to the composite material leads to the poor distribution of particles in the resin matrix [47].  
Figure 8: Energy dispersive X-ray spectroscopy (EDX) maps of Ca distribution of stabilized sample-composite: 10% (**a**), 50% (**b**), 70% (**c**) and CaCO\({}_{3}\)-composite 10% (**d**), 50% (**e**), 70% (**f**).  
Figure 7: Scanning electron microscope (SEM) images of stabilized sample-composite material 50% wt (**a**) and CaCO\({}_{3}\) composite material 50% wt (**b**) at magnification 2000\(\times\).  
From the elemental characterization provided by different chemical EDXS analyses (Table 3), the increase of filler amount causes a decrease in the C and Cl content, while increases Ca amount. In case of composite with stabilized sample, Al, Mg, K and S presence has been noticed, due to the initial chemical composition of the FA. It is important to highlight the limit of the instrument with light elements such as C; however, although the C amount is expressed in a semi-quantitative way, it becomes very useful for the comparative analysis between the two examined compounds.
## 3 Results
### Load-Mass Parametric Uncertainty Modelling  
Many mechatronic systems are employed in uncertain operating conditions affecting their overall dynamic response. A typical example is variable machine geometry within admissible work space or changes in the mass, inertia or position of the attached load. A fundamental issue from the control perspective is then the proper development of robust  
Figure 17: Example of FEM model geometry/stiffness modification: adjustment of the flexible shaft structure and stiffness allowed to improve model fidelity, left plot-amplitude FRF comparison prior to modifications (**top**) and after the proposed adjustments (**bottom**).  
feedback loops capable of preserving certain closed-loop performance under all assumed plant variations.  
A parameter variation scenario was formulated in order to evaluate the extrapolation capability of the developed full-scale geometric FEM model and test the proposed robust control method. The position of the attached mass was chosen as a variable parameter, which can reach any value in the interval between 180 mm and 200 mm, simulating either a change in the attached load, multiple existing variants derived from one machine design, or a second degree of freedom of the flexible arm manipulator; see Figure 18. This uncertainty affects the location of the oscillatory modes, mainly in the low-frequency range corresponding to arm bending modes, as shown in Figure 19. It is worth mentioning that an accurate physics-based model is needed for delivering this kind of extrapolation due to parameter variations. To acquire the same result from the experiments with a real plant, a wholse set of prototypes or lengthy experiments with variable machine configuration would be required. This may be relatively simple for our model benchmark scenario but may turn out to be infeasible in other practical applications. The role of high-fidelity full-scale nonlinear model is crucial in this case.  
Figure 18: Assumed parametric uncertainty: variable-load mass position emulating varying machine dynamics due to production and assembly tolerances, design variations, or second degree of freedom motions.
## 5 Conclusions  
In this study, a deep convolutional neural network (CNN) model for classifying different forms of digital modulation is presented. In order to assess the model's durability at different SNR rates, spanning from 0 dB to 25 dB, we first used a transfer learning technique. These results allowed us to draw the conclusion that the model can categorize various modulation types appropriately. The effectiveness of the model was assessed using all six SNR rates. The experiment's findings demonstrate that the modulation types can be determined using the CNN model. The model's classifications are accurate to a level greater than 99%. On the other hand, we contrasted GoogLeNet's performance on the same task with that of other popular CNN models. The suggested strategy surpassed all others in terms of accuracy and efficiency, as well as training time, when the different CNN models were compared. Using the hybrid methodology we suggested, we have had tremendous success categorizing digital modulation signals. We may use this application in conjunction with the newly suggested communication hardware to recognize modulation signals in the future. This capacity is necessary for both the real-time demodulation and the automated detection of this kind of modulated signal. To improve the performance of the proposed method in future work, feature-selection methods, such as (multivariate ridge regression). MRR and (Neighbourhood Component Analysis) NCA, could be used to select the optimal subset of features. These methods can increase the accuracy and speed of detection by removing the redundancies between the feature vectors.  
Data curation, A.M.A.; Formal analysis, A.M.A., H.M.A.G. and A.A.; Funding acquisition and Visualization, F.A.; Methodology, W.K.A.-A.; Resources, A.H.A.; Supervision, S.K.; Visualization, I.M.A.; Writing--original draft, A.M.A., A.S.A. and A.A.; Writing--review & editing, S.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 2 Test Rig
### Work Principle of the Test Rig  
The main test sample is the friction pair of the roller and inner ring. The components of the test rig are shown in Figure 5. In order to ensure the safety of the experiment, a protective cover with an observation window was designed and machined. Furthermore, the support of the test foundation platform is composed of a linear guide rail and a dovetail platform, which can precisely adjust the roller in the x and y directions. In addition, it is convenient to dismantle and install the new bearing after the experiment. The roller and the inner ring are controlled by the two motorized spindles, respectively.  
As for the alignment of the test rig and attainment of a better linear contact between the roller and inner ring, the following procedures were taken before the experiment: The first step is to install the test rig and measure the levelness of the supporting platform through the gradienter. The second step is to align the test rig. After each part is assembled, the dial indicator is fixed on motorized spindle 1, and the tip of the dial is aligned with the inner ring. Then, the dovetail platform moves along the x direction and the dial indicator hardly turns, so the roller and inner ring have better parallelism and alignment. The third step is to observe the load fluctuation. After the load is applied, we rotate the inner ring slowly to drive the roller to rotate. In the test system, the fluctuation of the load during the rotation is observed, and the pressure sensor shows a fluctuation error of about 5%, which indicates that the contact load fluctuation is small. If the fluctuations are large, we adjust the position of the motorized spindle of the roller to reduce the fluctuation.  
Before operating the test system to see if it works, we turned on the water chiller to cool the motorized spindle; meanwhile, we also turned on the lubrication system and loading system. According to the inner ring speed, we regulated the frequency of the frequency converter to control spindle 2. Once the test rig starts, the roller rotates, following up the inner ring, and the data acquisition system adopts the upper computer to process the data, which are collected by the displacement sensor and pressure sensor. Moreover, the data are converted into speed and load information and storedin the computer. The temperature in the key contact area is recorded by the infrared thermal imager, and the data can be stored in the computer. Thus, based on the data information such as speed, load, temperature, and slip rate, the skidding damage mechanism of the rolling bearing can be obtained from the perspective of multi-information integration.  
In order to measure the real slip of the cylindrical roller bearing, the skid damage test rig simulates the dynamic contact between the bearing ring and roller by starting the inner ring motorized spindle to run the roller, and the inner ring speed, radial load, the amount of lubricating oil can be adjusted. To sum up, the mechanism of skidding damage of the roller bearing under different working conditions is fully studied.
# Greening the NetFPGA Reference Router
## 3 The Architecture of the Reference Router and the Frequency-Scaled Router  
### Reference Router  
The NetFPGA reference pipeline is designed in a modular style [22]. In the pipeline, each stage is a separate module, which enables developers to design and implement their own project without starting from scratch. New functions like energy-efficient mechanisms can be integrated by adding custom modules or by modifications to the existing modules.  
As shown in Figure 1, the NetFPGA reference pipeline consists of multiple modules, including eight receive queues, eight transmit queues and the user data path [23]. Both receive queues and transmit queues are divided into two groups: four media access control (MAC) interfaces and four central processing unit (CPU) direct memory access (DMA) interfaces. The receive queues receive packets from I/O ports, such as the Ethernet ports and the Peripheral Component Interconnect (PCI) over DMA, while the transmit queues send packets out of the I/O ports instead of receiving.  
The pipeline in the user data path is 64 bits wide, and all of the internal module interfaces use the standard request grant first-in-first-out (FIFO) protocol [23]. In the user data path, the input arbiter module decides which receive queue to service next, pulls the packet from  
Figure 1: The architecture of the reference router.  
hands it to the output port lookup module. The output port lookup module decides out of which port a packet comes. After that decision is made, the packet is then handed to the output queues module, which stores the packet in the corresponding output queue and sends the packet out of the output queue when the corresponding transmit queue is ready to accept the packet for transmission.
## 2 Related Work
### Maximum Likelihood Estimation (MLE)  
The MLE is known as asymptotical and impartial for Gumbel distribution. Nonetheless, it is not always optimal in small samples [11]. The MLE technique is to identify the most likely values of the mode and the scale parameter (\(\alpha\) and \(\beta\)) for a given sample by maximizing the likelihood function.  
\[\begin{split} L\;(\;\alpha,\;\beta)&=\prod_{i=1}^{n }f\;(x,\;\alpha,\;\beta)\\ \beta>\;0,-\infty<x\;and\;\alpha<\infty\end{split} \tag{3}\]where \(L\) is the log-likelihood. Taking the first derivatives of (1) or (2), as shown in (4), yields  
\[\frac{\partial Log(L\ (\ \alpha,\ \beta))}{\partial\ (\ \alpha,\ \beta)}=0 \tag{4}\]  
With regard to the parameters of any PDF, the output are two equations for \(\alpha\) and \(\beta\). The maximum likelihood estimators for the Gumbel distribution are given in [28; 29]. The MLE of the minimum case is shown in (5) and (6). For the maximum case, values of \(x_{i}\) become negative.  
\[\hat{\beta}=-\overline{x}+\frac{\sum_{i=1}^{n}x_{i}\ e^{\frac{\overline{x}_{i }}{\beta}}}{\sum_{i=1}^{n}e^{\frac{\overline{x}_{i}}{\beta}}} \tag{5}\]  
\[\hat{\alpha}=-\hat{\beta}\ Log\ \left[\ \frac{1}{n}\sum_{i=1}^{n}e^{\frac{ \overline{x}_{i}}{\beta}}\ \right] \tag{6}\]  
where \(n\) and \(\overline{x}\) are the length and the mean of the given samples, respectively.  
Figure 1: Samples of Gumbel probability density function for minimum and the maximum cases.
## 3 FE Analysis
### Element Types  
The accuracy of finite element analysis is crucial for simulating the behavior of beam components and supports. Therefore, appropriate element types were carefully selected in this study. The eight-node linear brick with reduced integration element (C3D8R) [17] was chosen to model concrete effectively. The two-coded truss element (T3D2) [17] was used to model longitudinal and transverse reinforcements, which interacted with concrete through an embedded constraint, thus reducing computational costs. Additionally, discrete rigid shell elements [17] were used to model rigid bodies, such as steel pins and supports, due to their ability to accurately simulate contact between the RC beam and load pins and supports. A detailed simulation of the RC beam is illustrated in Figure 2.  
Figure 1: Geometry and steel bars of the RC beams: (**a**) dimension of the beams S1-2 and S1-4; (**b**) section A-A of beam S1-2; (**c**) section A-A of beam S1-4 (unit: mm).  
Figure 2: Detailed simulation of RC beams.
# Privacy-Preserving Data Analytics in Internet of Medical Things
## 2 Background
### I-Diversity  
To deal with the above-mentioned drawbacks, this technique emphasises the variety of sensitive attributes (such as ethnicity or medical conditions) within each group of people who share the same quasi-identifiers (non-sensitive attributes) [9]. K-anonymity guarantees that, using quasi-identifiers, every record can be be identified from at least \(k-1\) other records. It does not take into consideration how sensitive characteristics are distributed throughout these groupings. An attacker can still make inferences about an individual's sensitive information if there is no variability in the values of the sensitive characteristics within a group. The goal of this method is to prevent attackers from linking specific sensitive information to individuals based on their shared characteristics in the dataset. Similar to K-anonymity, individuals are grouped together based on identical quasi-identifiers. For example, all individuals in a group might be of the same age range and gender and living in the same ZIP code. Within each group formed by identical quasi-identifiers, I-diversity requires that the sensitive attributes be diverse. There should be at least \(\ell\) different conditions of sensitive attributes. This means that no single sensitive attribute should be overly common within the group. Still, even with I-diversity, datasets can be vulnerable to certain types of privacy attacks, like skewness and similarity attacks.
# Rotating Machinery Fault Detection Using Support Vector Machine via Feature Ranking
## 1 Introduction
utilized the Convolutional Neural Network (CNN), where Short-Time Fourier Transform (STFT) was used during the pre-processing stage to generate a time-frequency representation of images. An Image Classification Transformer (ICT) was developed to classify the faults in bearings [8]. [PERSON] and [PERSON] have also converted the sounds to images using Mel Spectrograms and have extracted features to train a CNN, specifically VGG19, for fault detection with 80.25% accuracy [9]. [PERSON] et al. have used deep statistical feature learning methods from the vibration sensors. Specifically, real-value Gaussian-Bernoulli restricted Boltzmann machines were developed for fault diagnosis for rotating machinery [10]. [PERSON] has used techniques such as rolling element faults via high order cumulant analysis, stochastic resonance, wavelet decomposition and Local Mean Decomposition (LMD) morphological filtering to detect faults [11]. Other research articles also rely on two major type of techniques which are signal processing based techniques [12; 13; 14; 15; 16; 17] or machine learning based techniques [18; 19; 20; 21; 22; 23; 24] to detect faulty conditions in rotating machines. This case study explores various machine learning models, especially with feature ranking, using data from a tachometer, accelerometers, and a microphone to predict potential failures, including misalignment, imbalance, and bearing inner and outer faults. Previous research has focused primarily on improving the performance of fault diagnostic systems, but our study targets to build a low-cost system for training and validation. Therefore, the proposed approach targets reduced computation while endeavoring to meet the classification accuracies of the previous research results. The workflow of the study is shown in Figure 1. The methodology starts with data collection and preparation, followed by data preprocessing and feature extraction to develop a robust dataset for model creation. The workflow then branches into two paths: one evaluates a base Decision Tree model, and another explores different MATLAB models. Both pathways converge at a unified model evaluation stage, where the performance of each model is thoroughly assessed. Following evaluation, the workflow incorporates a model refinement phase, focusing on feature selection to enhance model accuracy and efficiency. The final step involves deploying the refined model for real-world application, showcasing its practicality and effectiveness. This system is intended for use alongside critical rotating machinery to detect early failure, helping maintenance staff schedule routine checks more effectively and avoid unnecessary shutdowns.
# Geophysical Monitoring of Leachate Injection in Pretreated Waste Landfill
## 2 Materials and Methods
### Leachate Recirculation Monitoring  
The landfill was equipped with a leachate recirculation system consisting of 8 sub-irrigation rings of 20 m diameter located at the top of 8 biogas extraction wells and below the final waste capping. Moreover, to get a homogeneous leachate distribution, horizontal 10 m length pipes were placed in the zones not reached by the influence areas of the rings, at 0.5 m depth. In Figure 1, only two of the eight addressed boreholes are shown; particularly, we indicate the position of the two boreholes used for the leachate recirculation monitoring herein discussed.  
For the ERT monitoring, seven boreholes were drilled in the landfill. Each of them was equipped with graphite electrodes (24 in each borehole, 0.6 m spaced, at a depth between 1.5 and 15 m below the landfill capping) to perform down-hole and cross-hole ERT monitoring of the leachate infiltration displacement during the recirculation. A series of 48 surface electrodes was also installed on the landfill surface (2 m spaced electrodes).  
During leachate recirculation tests, daily geo-electrical monitoring was conducted by 2D surface ERT surveys (Wenner-Schlumberger and dipole-dipole arrays) along the 48-electrode profile and cross-hole ERT (dipole-dipole array) between the boreholes. Data were inverted with R2 code using the difference inversion algorithm for the time-lapse data.
# Microbial and Enzymatic Biodegradation of Plastic Waste for a Circular Economy
## 2 Microbial and Enzyme-Based Degradation of Plastic
### Enzymatic Degradation of Polymers
#### 2.3.4. PETase-Catalysed Depolymerisation of Plastics  
Hydrolases are a class of enzymes that catalyse the hydrolysis of bonds, often through the addition of water [130]. They include esterases, lipases, and cutinases, and each one is specialised in breaking down different ester bonds [69]. The difference between these enzymes is primarily a function of their substrate chain length and the type of bond length cleaved (as explained in Section 2.3). A PET hydrolase, termed PETase, which was originally isolated from _Ideonella sakaiensis_ exhibited optimal degradation activity at 40 \({}^{\circ}\)C. PETase has the catalytic triad S160-D206-H237 and an \(\alpha\) and \(\beta\) hydrolase fold structure, with the core domain comprising nine \(\beta\)-sheets encircled by seven \(\alpha\)-helices. In contrast to other hydrolases (i.e., lipases and cutinases), PETase possesses three additional residues (N244, S245, and N246) in the \(\beta\)8-\(\alpha\)6 region, extending the substrate binding pocket's cleft and expanding it to accommodate the PET substrate [113; 131]. PETase enzymes are named for their ability to hydrolyse PET, thereby differentiating PETase from other hydrolases, which typically have no, or less, structural adaptations necessary to effectively degrade PET. Figure 5A depicts the 3D structure and catalytic triad residues of PETase, while Figure 5B shows the structure and catalytic triad of a cutinase. This cutinase showed efficient degradation against PCL and PBS, but could not degrade PET.  
Figure 6 shows the enzymatic degradation pathways of PET in _Ideonella sakaiensis_. PETase initiates the reaction by hydrolysing the ester bonds (green highlight) in PET, breaking it down into BHET and MHET monomers [132]. Further hydrolysis is carried out by MHETase converting MHET into terephthalatic acid (TPA) and ethylene glycol. Esterases and lipases are also involved in this process; these enzymes can further hydrolyse the ester bonds in both the primary polymer and the intermediates, enhancing the overall degradation process [97]. [PERSON] et al. reported _Ideonella sakaiensis_ breaking down 75% of PET in 6 weeks using both PETase and MHETase [133]. The PET degradation pathway suggests that multiple enzymes can act synergistically to break down polymers more efficiently than a single enzyme [134]. An enzyme cocktail would be a promising approach to improving effective polymer degradation and could be a suitable option for continuous industry-scale plastic recycling processes.  
Figure 5: Structure of PETase and _Amyclatopsis mediterranei_ cutinase, (**A**) Structure and catalytic triad representation of PETase from _Ideonella sakaiensis_ (PBD ID code: 6 EQE) [128]. (**B**) Modelled structure and catalytic triad representation of the cutinase from _Amyclatopsis mediterranei_[46].