esa-sceva/satcom-chunk-collection huggingface dataset:
# FPGA Implementation of a BPSK 1D-CNN Demodulator
###### Abstract  
In this paper, we propose a field programmable gate array (FPGA) implementation of a one-dimensional convolution neural network (1D-CNN) demodulator for binary phase shift keying (BPSK). The 1D-CNN demodulator includes two 1D-CNNs and a decision module. Discrete time series of BPSK signals are imported into the well-trained 1D-CNNs. The 1D-CNNs detect the phase shifts' moment and type, including phase shift from 0 to \(\pi\) and that from \(\pi\) to 0. The decision module combines results of the two 1D-CNNs and outputs the demodulated data. In order to improve the efficiency of resource utilization and operation speed of the FPGA circuit, a time-delay network for convolutional calculation and a structure for piecewise approximation for the activation function were designed. To enhance the performance of the 1D-CNN demodulator, universal and diversity training data considering five impact factors were generated specially. Experimental results under different channel conditions show that the proposed demodulator has good adaptability to frequency offset and short latency. The demodulation loss of the proposed demodulator can almost be kept within 2 dB.  
BPSK; digital demodulation; neural networks; FPGA +
Footnote †: journal: Applied Sciences
# Advances and Challenges of Corrosion and Topology Detection of Grounding Grid
## 4 Challenges and Opportunities
#### 2.2.3 Corrosion detection of down-line  
The grounding grid down conductor is a communication channel between the power equipment and the grounding network. The stray current density is large near the surface layer of the soil, and because of the direct contact of the air, it is susceptible to changes in air and soil humidity, so that the corrosion rate of the part of the grounding conductor is higher than that of the horizontal conductor of the grounding grid. Therefore, the down-line underground part is easily corroded. Moreover, diagnosing down-line corrosion is very difficult, and studies on the corrosion diagnosis of the down-line are lacking.
## 5 Discussion
### Limitations  
The design of this research includes several limitations. While the lab experiment produced a data set of over 1000 exercise executions, the data were collected from ten study participants only due to the restrictions caused by the ongoing COVID-19 pandemic. The limited number of participants might limit the _external validity_ of this research. The participants' traits further limit the external validity. While covering heights between 156 cm and 198 cm, their body mass index was in a normal range. In addition, all participants had a lighter skin tone. The experiment was conducted in a laboratory with controlled background and lighting conditions.  
Even though the study setup aimed at reducing possible influences on the study's _internal validity_ which were not part of the observation, the impact of additional factors cannot be eliminated. Possible factors include the influence of the specific performance of the exercises by the subjects or the effect of the clothing worn. Furthermore, the subjects were recruited from the social surroundings of the researchers. They might not be representative of the whole population. The internal validity is further affected by the sliding window approach to compensate for the time lag due to missing clock synchronization and processing time. While the approach is limited to a maximum window of approximately two seconds, this shift could still have improved the results above the observable results. Additionally, the data set contained a reduced amount of exercise data for the upper body joints due to the export problems of the iPad on the side position. We applied the Welch ANOVA test to identify dependencies of the MAE instead of the ANOVA test, as the variance of the individual factors was not equally distributed. However, another prerequisite for (Welch) ANOVA and Welch \(t\)-test, normally distributed data, was only partially given for the MAE, even though the ANOVA analysis is said to be quite robust against this problem. We applied a logarithmic transformation to the data before performing the ANOVA and \(t\)-tests to overcome these limitations. Moreover, the observations used in (Welch) ANOVA should be independent of each other. In our experiment setup, the recording of angle motion happened simultaneously in all subjects and exercises. The observed angle deviations of the systems are expected to be independent. However, a poorly tracked angle might cause a higher risk to affect another angle's accuracy in a real-world scenario. Thus, the assumption of independent observations is hard to verify. Moreover, ARKit is only one example of a mobile 3D motion capture framework. Other frameworks rely on different technologies and algorithms and could exhibit different results and limitations.
# Optimisation of Energy Transfer in Reluctance Coil Guns: Application to Soccer Ball Launchers
## Principles of Coil Guns
### Electromagnetic Theory and Simulation Software  
The model of an electromagnetic actuator has to take into account many non-linearities such as:  
1. The saturation of magnetic materials under high currents [15] as shown in Figure 10.
2. The impact of the plunger position leading to change locally the relative magnetic permittivity by a factor 5000 or more.  
Considering this, it is difficult to find a theoretical solution to calculate the strength of the force applied to the rod. For taking non-linearities into account, the model used is a finite elements one obtained using an open-source simulation tool called _FEMM 4.2_. It has been developed by [PERSON]. This tool calculates force and inductance values under different conditions [16]. More precisely, magnetic field \(\overrightarrow{B}\) and potential vector \(\overrightarrow{A}\) are calculated everywhere using a successive approximation finite element solver on an axisymmetric model with a spherical boundary as shown in Figure 11.  
The mesh used for this computation is determined using an heuristic approach having the following characteristics: a maximum allowable mesh size is then computed as 1% of the length of the diagonal of the bounding box of any region, leading to generate a default mesh with about 4200 elements in an empty square region as shown in Figure 12. Fine meshing is also forced in all corners and a five-degree default discretization is used for arc segments.  
Figure 10: Example of magnetic field saturation [15].  
Evaluating force and inductance value which are integral values on the mesh is also done by _FEMM 4.2_ on pre-defined specific parts of the system such as the iron plunger or the inductance of the coil. Computation needs approximately 5 s on a standard _Intel Core I7_ processor.  
In order to compute force and inductance for all combinations of currents and plunger position, a _LUA_ script is used in _FEMM 4.2_. Simulations have been done for 30 different positions of the plunger and 6 different currents for each coil: 0 A, 40 A, 80 A, 120 A, 160 A and 200 A. Simulation times are as follows:  
* One coil: 180 combinations--15 min
* Two coils: 1080 combinations--90 min
* Three coils: 6480 combinations--9 h
* Four coils: 38,880 combinations--54 h  
Considering the high computational cost of the electromagnetic simulations, our study has been limited to four coils, but we will show later that it is not necessary to go further.
## 2 Materials and Methods  
With the rapid increase in real-time human health monitoring and the seamless interaction between humans and machines, many research efforts have been made in recent years on wearable sensors. Figure 1 exemplifies different types of sensors to interact with human beings. Some of these sensors perform real-time measurements of body temperature, heart rate, pulse oxygenation, respiratory rate, blood pressure and glucose, electrocardiogram, electromyogram, and electroencephalogram signals, and so on.  
Figure 1: Types of body sensors for human wireless monitoring.  
In this work, we implemented a network with different sensors, such as level, pressure/temperature/humidity (PTH), motion, noise, gyroscope, light, and air, to monitor the home's main spaces. We presented a descriptive experimental investigation of an average house in Guadalajara, Mexico. We invited forty test subjects (20 women and 20 men) aged between 55 and 75 years old to participate in our research. We deployed the network at each subject's home for one week and removed it for the second week. In both weeks, we measured four vital signs of the subjects: heart and respiratory rates, body temperature, and sleep rhythm.  
The implemented sensors encompass the work of an intelligent system by analyzing the different stimuli at home, transforming them into a digital impulse captured by a more robust device called a collector node. We carried out the experiments in 40 residences in the city of Guadalajara, Mexico, for one week. The measured body variables are pertinent for the subjects' daily life at home. The internal and external house temperature helps regulate the indoor climate and predict the way of dressing. Presence monitoring in the various sectors of the house helps quantify the passage of people in each one of them. Light-intensity detection controls the lights turning on and off, according to the time and the day's conditions. Moisture is one of the leading causes of deterioration in home living, and on many occasions, it is invisible to the eye. Noise sensors are capable of picking up a considerable amount of sounds, not just voices.
# Clinical Applications of Low-Intensity Pulsed Ultrasound and Its Underlying Mechanisms in Dentistry
## 4 LIPUS Contributes to the Stability of Implants via Facilitating Osseointegration
The result suggested that LIPUS can promote MHCs osteogenic differentiation and LIPUS has shown potential to be applied in bone healing [114]. Fortunately, previous studies explored the mechanism of LIPUS regulating MHCs expression. In MHCs, LIPUS was able to stimulate the expression of BMP, which has an effect on inducing bone formation. Thus, BMP may participate in the stimulating effect of LIPUS on MHCs [115]. The proposed mechanism may provide new ideas and new targets for the treatment of mandibular fractures. As a matter of fact, the effect of LIPUS on mandibular fractures was demonstrated in RCTs in humans. A total of 68 samples were included in two RCTs. Mandibular fracture healing was found to be greater in the experimental group than in the control group, as assessed by ultrasound. In addition, the pain index was lower in the study group than that in the controls, as evaluated by the patients themselves [116; 117]. Therefore, LIPUS played a double role in a mandibular fracture, and it can not only accelerate bone healing, but also reduce pain. In fact, LIPUS can also play a role in temporomandibular joint disorders and temporomandibular joint injuries. At present, great progress has been made in the role of LIPUS in the TMJ. LIPUS not only has a protective effect on articular cartilage, but also contributes to bone healing and maintenance of bone mass. In addition, in TMJ-related diseases, LIPUS has been proven to be effective in the human body to alleviate symptoms, supporting its broad application prospects.
## 3 Materials  
The technologies, hardware, and software components selected for this work were chosen with the specific goal of ensuring viability, replicability, and resilience in rural environments. The Smart Rural IoT Lab relies exclusively on low-cost commercial devicesand open-source tools, to demonstrate that IoT and cybersecurity research can be conducted without the need for high-end infrastructure.  
All devices and tools were selected for their low power consumption, compatibility with open-source ecosystems, and support for various communication protocols. By integrating modular and widely compatible hardware, such as ESP32 microcontrollers, sensors, and Dragino gateways, with containerized software platforms such as Node-RED, InfluxDB, and Grafana, the laboratory provides a solid foundation for experimentation in IoT, cybersecurity, and AI in real-world rural conditions.  
This section describes the components of the laboratory, including the sensors, gateways, computing devices, and software stack implemented to manage the data acquisition, communication, storage, and visualization.  
To ensure the system reflects the challenges of rural environments, the laboratory was designed with devices that have limited computational power and energy consumption, and it uses robust protocols under constrained network conditions. The system prioritizes edge computing and local data storage. Although the laboratory is currently deployed indoors, its modular and portable design allows for future deployment in real rural settings.
# MEMS-Based Vibration Acquisition for Modal Parameter Identification of Substation Frame
## 2 Field Test of Vibration Data Acquisition with Portable Devices
### Field Test  
To collect vibration responses using MEMS sensors, the first step is to select an appropriate acquisition device. In this test, an iPhone 8 smartphone was chosen as the vibration acquisition device. This device is equipped with precise MEMS sensors capable of continuously capturing structural vibration responses within the frequency range of 0-100 Hz. It should be noted that the primary natural frequencies of civil engineering structures are often concentrated in the low-frequency range (below 10 Hz), which means that using a smartphone as a vibration acquisition device can fully meet engineering requirements. Traditional vibration testing methods typically involve either artificial excitation or ambient excitation. Ambient excitation, which utilizes environmental factors such as microseisms and wind loads as excitation sources, requires no special excitation equipment and provides broadband excitation conditions for the test. Given these advantages, ambient excitation was selected as the excitation condition for this experiment.  
Prior to the test, it was necessary to determine the arrangement of the measurement points. In this test, a total of four measurement points was arranged, with two sensors placed at each point--one aligned with the parallel truss direction and the other aligned with the vertical truss direction. The layout of the measurement points is illustrated in Figure 3, with red dots and numbers denote the measurement points' location and numbers, respectively. As shown, two accelerometers were placed on the central column to capture overall vibration responses, while the other two points were positioned on either side of the column to collect torsional responses.  
Upon the researchers' arrival at the test site, the vibration acquisition system was assembled, as shown in Figure 4. In addition to setting up the MEMS acquisition system, a complete 941b vibration pickup system was installed at corresponding locations to serve as a comparison with the MEMS system. Anemometers were also installed at the test site to monitor wind speed variations during the experiment. After the system setup was complete, the sampling frequency was set to 100 Hz, and the acceleration response of the structure under ambient excitation was continuously recorded for 1800 s.  
Figure 3: Layout of the measurement points.
## 3 Correction of Mathematical Model
### Correction Process
#### 3.2.2 Empirical Formula Correction  
According to the mathematical model correction method described in Section 3.1, with the temperature rise experiment as a reference, the equivalent coefficient is calculated iteratively to correct the calculation formula of heat generation and the convection heat transfer coefficient of a 6311 ball bearing. The application of radial load and speed under each working condition of the temperature rise experiment are shown in Table 5. During the iteration process, the change in the equivalent coefficient in heat generation under different working conditions is recorded, as shown in Figure 6. The polynomial data fitting method is used to fit the equivalent coefficient at different speeds, and the fitted equivalent coefficient formula of heat generation is obtained as follows:  
\[n_{\text{G}}=-1.65\times 10^{-12}n^{3}+2.65\times 10^{-8}n^{2}-1.49\times 10^{- 4}n+0.3695 \tag{8}\]  
Figure 5: Schematic diagram of transient evaluation indexes.  
where \(n\) is the bearing speed. The equivalent coefficient of convective heat transfer under different working conditions is a constant value, and its expression is  
\[n_{g}=0.363 \tag{9}\]  
The corrected heat generation can be described as  
\[\begin{split}& G_{new}=n_{G}\times\pi Mn/30\times 10^{-3}\\ &=\left(-1.65\times 10^{-12}n^{3}+2.65\times 10^{-8}n^{2}-1.49 \times 10^{-4}n+0.3695\right)\times\pi Mn/30\times 10^{-3}\end{split} \tag{10}\]  
and the corrected lubricant convection heat transfer coefficient can be described as  
\[h_{g-new}=n_{g}\times 0.0332k_{g}P_{g}\frac{1}{3}\left(\frac{\pi n_{c}}{60v_{g} }\right)^{\frac{1}{2}}=0.363\times 0.0332k_{g}P_{g}\frac{1}{3}\left(\frac{\pi n _{c}}{60v_{g}}\right)^{\frac{1}{2}} \tag{11}\]  
The comparison curve between the corrected temperature rise simulation value and the experimental value is shown in Figure 7, which demonstrates that the experimental value is well consistent with the simulation value, and the steady-state temperature field analyzed by the corrected mathematical model can reflect the actual bearing temperature distribution. The durability experimental data used to verify the accuracy of the empirical formula after correction with the verification results is shown in Figure 8. The simulation temperature of the inner surface of the inner ring is largely consistent with experimental temperature, while the simulation value of the outer surface of the outer ring is slightly higher than the experimental value. Considering the complexity of the experimental conditions, the corrected mathematical model is accurate for transient temperature analysis.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Time/min** & **90** & **180** & **270** & **360** & **450** & **540** \\ \hline Speed/rpm & 1500 & 2400 & 4200 & 4900 & 5600 & 6200 \\ Radial load/N & 2650 & 2650 & 2650 & 2650 & 2650 & 2650 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Bearing temperature rise experiment condition.  
Figure 6: Equivalent coefficient of heat generation under different working conditions.
# Testing Concrete for the Construction of Winemaking Tanks
## 2 Materials and Methods
### Raw Materials  
The type of cement chosen for the application is Portland cement CEM I 42.5 R (according to EN 197-1) [12], manufactured by TITAN S.A., Athens, Greece, as it does not contain any other additives besides clinker and gypsum, a basic requirement for absolute control of each additional additive that will be selected to be included in the mix. Also, this cement is characterized by high final strength, but also high early strength (R index), which is considered necessary as the tanks will not be cast on site at their point of use.  
Part of the cement quantity was decided to be replaced with natural pozzolan from Milos island, Greece, with the aim of improving, on the one hand, the environmental profile of the compositions and, on the other hand, to contribute to the consumption of portlandite. Consequently, these features will improve ultimately the early age properties of the tanks [13]. The following table (Table 1) gives the chemical composition of the ordinary Portland cement (OPC) and pozzolan (in two different fineness gradations, <20 \(\upmu\)m and <45 \(\upmu\)m) used for the test compositions. The determination of metal oxides was conducted with X-Ray Fluorescence (XRF), using a S8 Tigger instrument of Bruker, Karlsruhe, Germany. The loss of ignition was determined from room temperature up to 1000 \({}^{\circ}\)C in the oven.  
The XRF results (Table 1) show that pozzolanic materials conform with ASTM 618-03 ([PERSON] et al., 2014), according to which SiO\({}_{2}\) + Al\({}_{2}\)O\({}_{3}\) + Fe\({}_{2}\)O\({}_{3}\) content should be above 70% \(w/w\).  
In the context of this research, the use of river siliceous aggregates was chosen for three main reasons:  
* Siliceous aggregates, based on experience, exhibit lower water absorption, an important parameter for self-compacting concrete, so that the amount of free water in the mixture is not affected by the aggregates. It is typically reported that the average water absorption of limestone sand is 1.5% ([PERSON] et al., 2014), while as mentioned below the absorption of the silica sand used in the project was measured at 1.24%.
* Due to their geometry (rounded edges), river aggregates contribute more positively to the fluidity of concrete than crushed quarry aggregates, which are characterized as more angular.
* Their use reduces the calcium content in the concrete, the reduction of which is considered positive for the wine fermentation environment.
# Pinion Failure Analysis of a Helical Reduction Gearbox in a Kraft Process
## 3 Investigation on Gear Design and Alignment
### Investigation on Deformation and Stress Using the FEA Approach  
In view of the location of failure of pinion teeth, it was planned to conduct the investigation from the misalignment perspective. Thus, the deformation and stress in meshed gear pairs were investigated using finite element analysis. The geometric models of pinion and gear pairs were prepared in SolidWorks software. Tetrahedron element was used to mesh the model in ANSYS Workbench. A total of 100,710 elements and 173,987 nodes were used to mesh the model pair after performing the mesh independence test. The transmitted loads by pinion and gear were calculated based on the input data. Tangential, radial, and axial loads were applied to compute the deformation and stresses in the static analysis at 3.75 MW. In the analysis, tip-loading (instead of highest point single tooth contact (HPSTC)) conditions were assumed, considering the large magnitude of the face width (\(>30\) m) of the pinion. Except for the rotation around the shaft axis (i.e., x-axis), the remaining degrees of freedom (\(U_{x},U_{y},U_{z},ROT_{Y}\), and \(ROT_{Z}\)) were removed at the bearing locations.  
In the design of this helical gearbox, it can be seen (refer to Figure 1) that the provision for sustaining the axial thrust coming onto the pinion shaft is made through the interface of the thrust disc and gear. The thrust load is transmitted from the pinion to gear and, finally, it is sustained by the thrust washer bearing, mounted on the gear shaft towards the left side. Figure 4a,b illustrate the provision of the axial load transmission. Looking at the load on the thrust disc, it was thought to check the deformation of pinion teeth near the thrust disc at the load arising from 3.75 MW. The axial thrust applied in the analysis (tangential and radial loads are not shown) is illustrated in Figure 4c.