esa-sceva/satcom-chunk-collection huggingface dataset:
# A Feasibility Study of the Drive-By Method for Damage Detection in Railway Bridges
## 3 Plan of the Simulations
### Train Speeds  
Speeds from 80 km/h to 140 km/h, corresponding to normal traffic conditions of the considered commuter trains, are selected for the analysis.  
In the bridge/train interaction, a main excitation source is related to the train axle spacing, determined by the number of vehicles, each vehicle length, bogie spacing, and axle spacing within a bogie. This source of excitation is characterized by a wide spectrum [14; 15], which is mainly visible through sensors placed on the track. For sufficiently long trains, the main spectral peaks occur at multiples of the vehicle passing frequency, depending on the ratio between the train velocity and the vehicle length (i.e., \(\upsilon/l_{c}\), with \(l_{c}\) being indicated in Figures 5 and 6 for the CSA and TSR trains respectively). The vehicle length \(l_{c}\) corresponds to the distance between bogies in the case of the CSA train, and to the distance between two pairs of adjacent bogies, in the case of TSR train. A second source of excitation, visible on vehicle accelerations, is the span passing frequency. It is related to the time that is needed for each axle and bogie to cross the bridge, and it is equal to the ratio between the train velocity and the bridge length (i.e., \(\upsilon/l_{span}\)). Table 3 reports, for all the considered speeds, the values of the span-passing frequencies and the excitation frequencies related to vehicle lengths. The latter are higher in the case of CSA train than the TSR train, due to the lower \(l_{c}\) value. In the considered speed range, all of these frequencies are well below the first mode of vibration of all the bridge model considered. Resonance-like phenomena are therefore not generated due to the span passing frequencies and for the high-resonance bridge #1, whereas bridge natural frequencies may still be excited by multiples of the vehicle passing frequency. This will be verified from the results of numerical simulations.
###### Abstract  
The special spatial structure and the long-term harsh working environment make the transient response of wire rope complicated, so accurate assessment and prediction of its mechanical characteristics are of great significance to ensure the safe and stable operation of related equipment. In this paper, a method is proposed for the analysis of the mechanical characteristics of a 6 \(\times\) K31 WS + FC wire rope with or without wire breakage. Firstly, an accurate parametric geometric model of the wire rope is established based on the Frenet frame method, the material properties of the wire rope are acquired by experiments, and the finite element model of the wire rope is built. Then, a mechanical model of the wire rope is proposed to verify the validity of the finite element model. Finally, the influences of the number, distribution, and location of the broken wires on the mechanical characteristics of the wire rope are thoroughly analyzed. This work proposes a comprehensive framework that can quantitatively analyze the mechanical characteristics of the complex wire rope with or without wire breakage, which provides a practical method for its reliability and maintenance evaluation and has guiding significance for the shape design and structural optimization of the wire rope.  
wire rope; wire breaking; geometric modeling; finite element analysis; mechanical model
## 2 Materials and Methods
### Instrumentation  
A motion analysis system consisting of 12 infrared cameras (100 Hz; Vicon Motion Analysis Inc., Oxford, UK) and 14 mm diameter reflective markers was used. Six of the twelve infrared cameras used were MXT010 models. These are digital cameras with high resolution and a maximum sampling rate of 250 Hz. These cameras, due to their larger size, tended to be positioned higher up (3 m above the ground) and further away from the working volume (2.5-3 m). The remaining six infrared cameras were Bonita models. The cameras were also high-resolution digital cameras and, in this case, the maximum sampling rate was 100 Hz. These cameras, due to their more compact size, were positioned on tripods at the ground level and closer to the participant (1.5 m approximated). The infrared camera models had different maximum sampling frequencies; therefore, when adjusting the equipment, the sampling frequency was set to the lower of the two, in this case, 100 Hz. All data from the twelve infrared cameras were synchronised via a central console called Giganet from the company Vicon(r). This central console also had the function of sending all the data from the recordings to the computer. In order to process these data, commercial software Nexus Vicon(r) 2.12.1 (Vicon Motion Analysis Inc., Oxford, UK) was used.  
The tests were performed on the Orbea H50 commercial bicycle, which was anchored to a training roller (Elite Novo Force (Elite S.R.L, Fontaniva (PD), Italia)). To measure the power and pedalling cadence, a potentiometer was installed on the left pedal (4 iiii FC-5800-170 (4 iiii Innovations Inc., Cochrane, AB, Canada)); this information was processed and shown by a display Garmin Edge 130 (Garmin Ltd., Olathe, KS, USA). In order to verify that the information coming from the potentiometer was correct, cadence was also monitored by acoustic signals. Similarly, the measurement equipment developed by [PERSON] et al. [26] was used to verify the correct pedalling power. This equipment had a measurement error of less than 4%, similar to that used and developed by other works [27]. For tests, the bicycle was equipped with clipless pedals and the participants used shoes with cleats. Shoes were provided to the participants. The correct placement of the cleats was verified for each participant following the method recommended by [PERSON] and [PERSON] [28], which defines optimal cleat positioning as aligning the plantar fat pads with the pedal axle. To ensure this alignment, a preliminary test was conducted prior to motion capture. The participants pedalled at low cadence and high resistance to help them identify the area of the foot applying the greatest force. If this area corresponded to the recommended position, no adjustments were made. Otherwise, the cleat's position and orientation were modified, and the test was repeated. All assessments and adjustments were performed by the same researcher for all 19 participants to minimise variability associated with cleat positioning.
## 3 Materials and Methods
### Calibration Accuracy Setup  
The calibration procedure is necessary to accurately link the digital dental model to its marker. During calibration, the user wearing the HoloLens 2 headset focuses their gaze on the physical dental model and calibration probe markers with the intention of correctly recognizing them. The correct recognition and the beginning of the tracking are indicated for the user on the headset display with a visibly seen superimposed digital probe on the physical probe and four virtual points appearing on the corners of the dental model marker. If all conditions are met, the user can start the calibration by pinpointing predefined intrinsic surface points in sequence on the physical dental model with the probe. Four points were chosen for the calibration procedure. The location where the user must place the probe tip can be seen on the auxiliary digital dental model for reference throughout the entire calibration duration. For user convenience, an auxiliary dental model is fixed on the left side of the headset display. The tracked probe tip coordinate is saved and linked to the dental model marker using the HoloLens voice recognition system by saying the word \"save\". The saved probe tip coordinate is calculated from the averaged data samples (size of 120), which are recorded in a couple of seconds after the saving command is given. Furthermore, after each saved coordinate, the reference point location in the auxiliary model is updated. Finally, after all predefined points are marked and paired with the marker, an automatic superimposition of the digital to the physical model occurs. Therefore, using the AR headset digital dental model after the calibration procedure can be seen as an overlay of its physical counterpart. After each calibration procedure, all predefined and unaggregated (120) calibration point coordinates with known location indices with the inclusion of marker points are saved in _csv_ file format. The predefined points are positioned relative to their paired calibration points without changing their interconnection distance. All data were imported into MATLAB 2020b software (MathWorks, Natick, MA, USA) for further analysis. The calibration data annotation and measurement scheme is shown in Figure 2. Measurements of the Euclidean distance (\(\Delta_{\text{D}}\)) between the average of the calibration points and the coordinates of the marker point in each position were made. Angular measurements were evaluated using the angle (\(\alpha\)) between the plane normal defined using the averages of calibration points and predefined points. Both angle (\(\alpha\)) and distance (\(\Delta_{\text{D}}\)) measurements were chosen as trueness descriptors. Furthermore, to evaluate the calibration (\(\Delta_{\text{K}}\)) and marker (\(\Delta_{\text{C}}\)) precision values, the Euclidean distances between each data point and the respective coordinate averages were measured.  
Figure 1: Designed calibration probe (**left**) and dental model marker (**middle**) with their respective measurements. Three-dimensional (3D)-printed dental model with the marker (**right**).
# A New GNSS-Derived Water Vapor Tomography Method Based on Optimized Voxel for Large GNSS Network
## 3 Experiment and Validation
### Study Area and Data Preprocessing  
Nanjing, China, is the selected study area (latitude: 31.2\({}^{\circ}\)N-32.6\({}^{\circ}\)N, longitude: 118.3\({}^{\circ}\)E-119.3\({}^{\circ}\)E). As shown in Figure 5, 20 GNSS stations (NJCORS network) and 1 RS station (ID code: 58238) exist in the experimental coverage area. The 20 uniformly distributed stations (mean distance between stations is approximate 25 km) can continuously provide GNSS observations, while the RS can provide accurate WV profiles and ground temperature. These profiles can be regarded as both prior information and the truth value. The former is used as the initial value for the tomographic equation, while the latter is used as the truth value for the tomographic algorithm verification. To evaluate the performances and applicability of the tomographic approaches, we collected the GNSS observations from NJCORS in two periods under different weather conditions, 1 July to 3 July (day-of-year (DOY): 182 to 184) and 1 September to 3 September (DOY: 244 to 246). The weather conditions in the above two periods are shown in Table 1.  
The six-day GNSS observations from 20 sites are processed by the double-difference model in the GAMIT software (version 10.71) [38]. Regarding the processing strategy, we use the International GNSS Service (IGS) final precise ephemeris and precise satellite clock correction to mitigate the satellite orbit and clock biases and set a sampling rate of 30 s and cut-off elevation angle of 10\({}^{\circ}\) for GNSS observations. After the above process, we can obtain the PWV and ZWD products of each station. The SWD in all signal directions can be then retrieved using Equation (4).
# Review of the Research Progress in Soft Robots
## 4 Applications of the Soft Robots
### Medical Rehabilitation  
The soft robot has been widely used in the field of medical rehabilitation, mainly because of its simple structure, light weight, and convenience for patients to easily achieve free bending and twisting. It also has the advantages of fast response speed, soft touch, good affinity, a high degree of bionics, and can safely carry out human-computer interaction [136].  
Shown in Figure 11a, [PERSON] et al. designed a portable, assistive, soft robotic glove that could assist people with hand disabilities with daily living and at-home rehabilitation activities. The glove used a soft body brake consisting of a molded elastic cavity and fiber-reinforced material that could produce specific bending, twisting, and extending trajectories under fluid pressure [22]. Shown in Figure 11b, [PERSON] et al. built one such eXosuit: Compliant Robotic Upper-extremity eXosuit (CRUX). The CRUX was compliant, lightweight, multi-degree-of-freedom, and portable. The wearer was able to empower themselves in many unconventional environments [137]. Shown in Figure 11c, [PERSON] et al. designed a soft bionic tongue based on pneumatic actuators that could simulate the functions of swallowing and chewing. They developed a non-linear mathematical model, carried out a simulation study of the soft bionic tongue, and related experimental validation to prove the rationality of the design [138].  
Figure 10: (**a**) Apparatus for measuring the force of the soft gripper under different effective lengths [133]. (**b**) Proposed robotic system for soft gripper-enabled berry harvesting [135].
# AUV Trajectory Planning for Optimized Sensor Data Collection in Internet of Underwater Things
## 3 Results and Discussion
### Single-Point Design Comparison  
In this analysis, we investigate the impact of waypoint selection on overall mission performance. The proposed method selects optimal points from multiple candidates distributed along each CH's horizontal communication circle and is compared with a simplified strategy that uses a single fixed point located directly above each CH. Both strategies use the same forward DP and path-planning framework but differ in waypoint flexibility.  
The proposed method offers the forward DP algorithm a broader selection of candidate points, enabling it to identify routes with shorter travel times and improved Vol retention (see Figure 7). In contrast, the single-point approach constrains the AUV to less optimal trajectories, resulting in longer paths and increased Vol decay. The proposed method preserves 65.95% of the total Vol, while the fixed-point method retains only 50.38%, reflecting a 15.6% gain in Vol preservation driven by enhanced routing flexibility and reduced travel distance, which in turn leads to lower mission time and higher data preservation (refer to Table 2).
# Design of a Fixed IF Down-Conversion Double-Balanced Mixer for UHF Band Applications
## 4 Simulation and Experimental Results
### Experimental Validation of Down-Conversion Double-Balanced Mixer Prototype  
This section will discuss the measured parameters of the improved double-balanced Gilbert-cell mixer.
## 2 Battery and Hydrogen Energy Storage System
### Hydrogen Energy Storage Systems (HESSs)
Compared to battery electric vehicles (BEVs), hydrogen fuel cell electric vehicles (FCEVs) have longer ranges and require less time for refueling. In the industrial sector, hydrogen is utilized in refining processes and as a feedstock for the production of compounds like ammonia. In order to improve grid stability, HESSs also help with grid balancing by storing excess renewable energy during off-peak hours and releasing it during peak demand [58; 59; 60; 61; 62]. The advantages of HESSs (Table 6) include improving energy security, lowering reliance on imported fossil fuels, and permitting the integration of renewable energy \begin{table}
\begin{tabular}{c c c} \hline \hline
**No** & **Production Method** & **Details** \\ \hline \multirow{3}{*}{1} & \multirow{3}{*}{Electrolysis} & Water molecules (H\({}_{2}\)O) are split into hydrogen (H\({}_{2}\)) and oxygen (O\({}_{2}\)) using electricity, which is often generated from renewable energy sources like solar and wind power. \\ \hline \multirow{3}{*}{2} & Steam Methane & Produces carbon dioxide (CO\({}_{2}\)) as a byproduct when hydrogen is extracted from natural gas; this process is less environmentally friendly than electrolysis. \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \hline \multirow{3}{*}{3} & \multirow{3}{*}{Other Methods} & Includes the less popular and effective thermochemical and biomass gasification methods for producing hydrogen on a big scale. \\ \cline{1-1}  & & \\ \end{tabular}
\end{table} 
Table 4: Hydrogen Production Methods. \begin{table}
\begin{tabular}{c c c} \hline \hline
**No** & **Methods** & **Details** \\ \hline \multirow{3}{*}{1} & Compression & Stores hydrogen gas at high pressures, requiring robust infrastructure but offering flexibility in deployment. \\ \cline{1-1}  & & At extremely low temperatures (\(-253\)\({}^{\circ}\)C), it transforms hydrogen into liquid, allowing for larger storage densities but necessitating energy-intensive cooling. \\ \cline{1-1}  & & It absorbs hydrogen into materials like metal hydrides, providing safe storage and potential for high energy density. \\ \hline \multirow{3}{*}{2} & \multirow{3}{*}{Liquefaction} & It absorbs hydrogen into materials like metal hydrides, providing safe storage and potential for high energy density. \\ \cline{1-1}  & & \\ \end{tabular}
\end{table} 
Table 5: Storage Method. by providing long-term storage solutions. Because of its high energy density per unit mass, hydrogen can be used in spaces that are constrained. Furthermore, by lowering greenhouse gas emissions, promoting the shift to a low-carbon economy, and enhancing urban air quality, HESSs support environmental sustainability. HESSs are positioned to be a key player in worldwide efforts toward sustainable energy systems as infrastructure and technology grow [63; 64; 65; 66; 67].
# AI-Enabled Electrocardiogram Analysis for Disease Diagnosis
## 1 Introduction
### Survey Goal  
With the ever-evolving sensor technology, signal processing capability, and machine learning/deep learning algorithms, it is essential to incorporate these advancements into the latest survey paper. This survey paper aims to provide a comprehensive overview of the research conducted in electrocardiogram (ECG) analysis using machine learning and deep learning techniques for clinical diagnosis. To begin, section two presents a framework for searching relevant and recent materials for this survey. The third section discusses the ECG signal analysis techniques, which include signal processing methods like noise reduction, filtering, and feature extraction. The fourth section elaborates on using machine learning with ECG for clinical analysis, including classification, clustering, and regression techniques. This paper delves deeper into the clinical analysis of heart diseases using ECG signals. The first part of this analysis is based on heart disease discussions, including coronary artery disease, arrhythmia, and heart failure. The second part of the clinical analysis focuses on discussing other diseases, followed by an analysis of the application of deep learning using ECG for diagnosis. DL algorithms have great potential in ECG analysis, especially in arrhythmia detection and classification. Section five discusses the challenges and future recommendations.  
The literature survey aimed to address three primary research questions related to using electrocardiogram (ECG) with artificial intelligence (AI) for diagnostic purposes.  
1. What is the preprocessing required for ECG signal before using it with AI techniques?
2. What are the ML/DL techniques applied in ECG-based heart diseases and other health issues?
3. Are there any limitations and challenges in using ECG signals for ML/DL-based disease classification?
# Research on a Real-Time Driver Fatigue Detection Algorithm Based on Facial Video Sequences
## 3 Experimental Data and Results
### Environment and Data Set  
The experimental platform consists of an Intel Core i7-10750 (main frequency: 2.60 GHz) with x86 architecture, NVIDIA GeForce GTX1650, 16 GB DDR4, opencv3.4.11 image library, Dlib 19.17.0 tool kit, and the deep learning computing framework is Tensorflow2.5.  
In this paper, we mainly use two types of data sets for experiments. The first type is FDDB, which is a face area data set used to study unconstrained face detection. This data set contains 5171 faces taken from the faces in the wild data set, as shown in Figure 7. The second type is our own video dataset. We collect video datasets of drivers driving vehicles, including awake drivers and sleepy drivers, as shown in Figure 8. Our video data were collected from 10 drivers (20 to 45 years old, 5 female drivers) and 30 driving tests. All of the participants were required to have a driving license, drive more than 2000 km per year, and have no reported related sicknesses. We asked to avoid drinking alcohol or caffeine-content drinks before the tests. Participants had a familiarization drive before the main tests. Each driving test was 30 min in duration. The driven track is a closed path simulating a two lane motorway that has some smooth turns and participants were required to drive about 40 km during the tests.  
In order to verify the correctness of the above driver fatigue detection algorithm, we conducted a short-term test and a long-term test verification. The purpose of the short-term test verification is mainly to identify typical signs of drowsy driving. The long-term test verification is mainly to verify whether the proposed algorithm can accurately determine whether the driver is dozing during long-term driving, and classify it according to the degree of dozing, such as mild fatigue, moderate fatigue, and severe fatigue.
## 3 Positioning Based on DVB-T
### Range Measurement
#### 3.2.1 ML Delay Estimate  
It is well known that the ML estimation of the delay of a noisy signal, \(s_{r}(t)=s(t-\tau)+w(t)\) (where \(w(t)\) is a realization of an AWGN process), is obtained by correlating \(s_{r}(t)\) with a signal, \(s(t)\), generated at the receiver side and by estimating \(\tau\) as the argument of the peak of the correlation function [18]. In our case, this rule has to be applied to the signal at the output of the receiver chain shown in Figure 1.  
In the ideal case of a noise-free channel, each received SPS, \(p\), of the \(k\)-th OFDM symbol generates, after FFT demodulation, a value:  
\[d_{p}^{k}=c_{p}^{k}\alpha e^{-j2\pi\frac{p_{n}}{N_{FFT}}} \tag{6}\]  
where \(\alpha\) is the attenuation introduced by the channel, and it is a real number; and \(n\) is the unknown delay to be estimated, normalized with respect to \(T_{samp}\). In the presence of noise, the received value will be:  
\[d_{p}^{k}=d_{p}^{k}+w_{k} \tag{7}\]  
where \(w_{k}\) is the noise contribution.  
To perform the correlation, a local replica of \(d_{p}^{k}\), with a variable delay, \(\tilde{n}\):  
\[P_{p}^{k}=c_{p}^{k}e^{-j2\pi\frac{p_{n}}{N_{FFT}}} \tag{8}\]  
has to be generated and correlated with \(d_{p}^{\,k}\). This correlation can be performed in the frequency domain, as described in [2,6], as:  
\[R_{M}(m)=\frac{1}{N_{p}}\sum_{p\in p_{s}(k)}d_{p}^{\,k}P_{p}^{k*} \tag{9}\]  
where \(N_{p}\) is the number of SPSs in the OFDM symbol, \((\cdot)^{*}\) denotes a complex conjugate, \(m=n-\tilde{n}\) is the delay offset between the received signal and the local replica and \(p_{s}(k)\) is the index set of the SPSs of the \(k\)-th OFDM symbol.  
Figure 5: Scattered pilot location of a quadruplet resulting from combining four consecutive symbols.