esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experimental Implementation
#### 2.3.1 Experimental Setup
For the sake of readability, only the subscripts x, y, and z are reported in the diagram. The y-axis and the z-axis correspond to the rotation axis and the radial direction when \(\alpha_{\rm rot}=0\), respectively. Due to possible experimental misalignment, the translation axis is assumed to be close to the x-axis but may not exactly match. The scanner in action is shown in Video S1. aperture of the array and the wavelength \(\lambda_{\rm c}\) at its center frequency, the steered angles must theoretically be spaced by at least \(\sim\)0.9\({}^{\circ}\) to have independent transmitted wave vectors [8]. We chose here an angular spacing of 2\({}^{\circ}\). Moreover, the steered angles were chosen centered around zero to match the directionality of the array elements. Pulse-echo signals were recorded at a sampling frequency of 20 MS/s, with a constant gain (no time gain compensation was applied) and for a time interval adapted to the distance range between 5 mm and 60 mm in front of the array. The transmitted pulse amplitude and the reception gains were adjusted to ensure a good amplitude digitalization of received signals without saturation. Ultrasound signals were stored in internal memories of the acquisition system. We used two high-precision motorized stages. The rotation stage (M-061.PD, Physik instrumente, Karlsruhe, Germany) was mounted on the translation stage (L-511.44 AD00, Physik instrumente, Karlsruhe, Germany). The two stages were controlled with a C-884 DC Motor Controller (Physik instrumente, Karlsruhe, Germany). Using integrated encoders, the positions of the stages were recorded with an accuracy of 0.001 degrees and 50 nm, respectively, at the time of arrival of an external trigger pulse to the controller. The positions were stored in an internal memory of the controller. To synchronize the US plane wave emission and the recording of the stage positions, a trigger generator (BNC Model 577, Berkeley Nucleonics, San Rafael, CA, USA) was used to send external triggers simultaneously to both the programmable US system and the motion controller (Figure 3). The number of pulses and the pulse repetition frequency (PRF) depend on the acquisition parameters (Table 1) and were programmed in the pulse generator prior to each acquisition. For each tomographic acquisition, the following sequence of events was used. First, the motors were positioned at the first tomographic position and the ultrasound system was initialized. Then, the ultrasound system and the motion controller were set to wait for a trigger pulse. The first trigger pulse started the motion of the stages following programmed trajectories (see Section 2.3.2). This trigger pulse and the next ones triggered the simultaneous recording of the stage positions and of the pulse-echo ultrasound signals. At the end of the scan, all the data from the ultrasound system and the motor controller were transferred to a computer.
# Power Dissipation and Wear Modeling in Wheel-Rail Contact
## 5 Conclusions  
Besides [PERSON]'s wear model, in the paper, the power dissipation approach has been used to estimate the wear phenomenon and its impact on wheel-rail elasto-plastic contact with frictional heat flow. This approach combined with the application of semi-smooth Newton and Cholesky methods to numerically solve contact problems allows us to follow the modification of the dissipated power, the wear depth distributions as well as the location of the contact surfaces. From numerical results, it follows that, in the elasto-plastic case, the calculated contact patches are longer than in the elastic case. Moreover, the contact stress is also lower than in the elastic case. The power dissipated as well as the wear rate are strongly dependent on the friction coefficient. Moreover, the obtained results confirm the robustness and efficiency of the proposed method, including the update of the contact interface location and geometry, in estimation of the wear distribution along the contact patch. On the other hand, this research indicates problems which are not fully clear and will be the subject of future research. The dependence of the wear process on hardening as well as temperature and material parameters requires additional research. The computations for 3D wheel-rail contact are planned to be executed. Future research will also include the treatment and investigation of the wear evolution process in terms of the shape optimization problem. The location and shape of the contact area may be considered as a design variable [36]. The volume of the worn material or the dissipated power may be chosen as an optimization criterion. The calculated wheel or rail parameters will ensure the minimal volume of the removed material.  
Conceptualization, A.M. and A.C.; methodology, A.M.; software, A.M.; validation, A.M. and A.C.; formal analysis, A.M. and A.C.; investigation, A.M.; resources, A.M.; data curation, A.M. and A.C.; writing--original draft preparation, A.M.; writing--review and editing, A.M.; visualization, A.M. and A.C.; supervision, A.C.; project administration, A.M.; funding acquisition, A.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions.  
The authors declare no conflicts of interest.  
Figure 11: The change in the contact zone length for different material models.
# Frequency Range Optimization for Continuous Wave Terahertz Imaging
## 2 THz-FDS System for Generating and Detecting THz Waves
If frequency depended absorption is introduced to Equation (4), the detected intensity could be estimated as:  
\[I_{T}(\
u)=I_{0}e^{-\alpha(\
u)d} \tag{6}\]  
where \(d\) is the thickness of the observed medium, and \(I_{0}\) is the initial THz beam intensity. From classical electromagnetic theory, dispersion in the medium is introduced. The dispersion or change in the propagation speed will result in phase difference \(\Delta\phi_{D}\), estimated as:  
\[\
u=c\cdot n\cdot\frac{\Delta\lambda}{\lambda_{1}\lambda_{2}} \tag{1}\]0  
where \(n_{1}\ll n_{2}\). By combining Equations (6) and (7), the intensity \(I_{T}\) can be estimated as:  
\[\
u=c\cdot n\cdot\frac{\Delta\lambda}{\lambda_{1}\lambda_{2}} \tag{1}\]1  
A photocurrent is induced in detector PCA when the THz field hits the antenna structure. Induced current in detector PCA can then be detected using an LIA and, based on Equation (8), it will have a sinusoidal shape. The amplitude and phase spectrum can be estimated from the detected photocurrent. The amplitude spectrum can be estimated by identifying the peak envelope, which is carried out by detecting signal maxima and using spline interpolation. The phase spectrum can be estimated by determining zero crossings and linear interpolation. \[\
u=c\cdot n\cdot\frac{\Delta\lambda}{\lambda_{1}\lambda_{2}} \tag{1}\]2 Peak envelope and unwrapped phase estimated from the detected phase fringes. The peak envelope represents the amplitude spectrum, and the unwrapped phase represents the phase spectrum. \[\
u=c\cdot n\cdot\frac{\Delta\lambda}{\lambda_{1}\lambda_{2}} \tag{1}\]3 Collimation of the THz beam in transmission mode. There are two main problems with the presented system: The relation between the emitted wavelength and temperature can only be determined empirically [24], and wavelength-tuning using temperature is a slow process and unsuitable for fast wavelength shifts [23]. The empirically determined relation between the emitted wavelength and the DFB laser diode's temperature can never be completely accurate. There are also no cheap and readily available methods for measuring optical frequency in the THz band. Because of those two shortcomings, the set THz frequency and actual THz carrier frequency will always be different, which can impact the measurements' repeatability. The second major drawback, slow wavelength sweeps, results in time-consuming measurements and makes the technology unsuitable for fast quality control applications in industry. The third major drawback is the environment itself. Changes in temperature and a change in absolute humidity will impact the measurements drastically. In ideal conditions, measurements should be performed in a vacuum or near vacuum, or air should be supplemented with other gases. However, this reduces the robustness of the processing methods and increases the complexity of the system.
# Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools
## 4 Challenges and Future Directions
### Distributed Solutions and Platforms  
As seen in Section 3, big data tools are often operated in a distributed manner, or designed with the real-time parallel processing capability for streaming data, which is missing in the current store and then processes paradigms for traffic estimation and prediction studies.  
Some efforts for building and applying distributed solutions and platforms have been made in recent years, however, mainly in the industry, e.g., Didi Brain ([[https://www.didi.global.com/science/brain](https://www.didi.global.com/science/brain)]([https://www.didi.global.com/science/brain](https://www.didi.global.com/science/brain))) for travel-demand prediction and JD Urban Spatio-Temporal Data Engine (JUST) ([[http://just.urban-computing.com/](http://just.urban-computing.com/)]([http://just.urban-computing.com/](http://just.urban-computing.com/))) for traffic trajectory and order management. In addition to further adopting the existing tools, other new technologies may also be applied for traffic estimation, traffic prediction, or other relevant problems. For example, blockchain is used as a promising solution for traffic big data exchange trust and privacy protection [112] and federated learning is used for privacy-preserving traffic flow prediction [113].
# Characterization of Ancient Marquetry Using Different Non-Destructive Testing Techniques
## 3 X-ray Micro-Tomography  
X-ray micro-tomography (micro-CT) is a non-destructive technique to image the three-dimensional microstructure of an object using X-ray photons which are attenuated depending on their material composition.  
An industrial CT system is basically built of three components as it can be seen in Figure 3: X-ray source, rotating sample holder, and X-ray detector. The widely spread microfocus tube uses an electron beam focused on a target material emitting breaking and characteristic radiation. As more than 99% of the electron energy is deposited in the target material, the target is commonly made of materials with a high melting point such as tungsten and can also be equipped with a cooling system. The emitted photons pass  
Figure 2: Air-UT setup. (**a**) Front view, (**b**) lateral view, and (**c**) back view.  
through the object placed on a rotational stage and hit the detector. The contrast mechanism in X-ray micro-CT is based on the attenuation of X-ray radiation, which depends on material composition, object thickness, and X-ray energy. Materials with a high atomic number and density lead to high X-ray attenuation. The remaining X-ray intensity follows the Lambert-Beer's law:  
\[I=I_{0}\,e^{-\mu d} \tag{1}\]  
For X-ray detection, direct or indirect converting systems are often used. In a direct converting detector, the incoming X-ray generates electron-hole pairs in a semi-conductor material like Si or CdTe. The measured charge in one pixel correlates with the energy of a photon. In an indirect converting detector the photon is initially converted to visible light in a scintillator, which generates an electrical current in a photodiode.  
To obtain a radiographic image, the object is magnified geometrically and projected on the detector. Thereby, the magnification is defined as the quotient of focus-detector distance (FDD) to focus-object distance (FOD). The combination of a small electron beam and a thin target leads to a small X-ray focal spot of a few microns or less resulting in low geometrical blurring on the detector and high spatial resolution. To visualize a three-dimensional volume, the object is scanned from projection angles over 360\({}^{\circ}\), which are used as input for the volume reconstruction. The reconstruction algorithm is mostly based on filtered back-projection or iterative techniques like the algebraic reconstruction techniques [16; 17].  
CT inspection was performed at an in-house developed micro-CT system at Fraunhofer IZFP, using a tube voltage of 220 kV and a tube power of 100 W. For the inspection of the sample investigated in this study, 1600 projections over 360\({}^{\circ}\) were acquired with an exposure time of 2 s per projection and reconstructed afterwards.
# An Experimental Framework for 5G Wireless System Integration into Industry 4.0 Applications
## 4 Framework Applicability Example: 5G Autonomous Mobile Robots
### 5G Mobile Edge-Cloud Planner for Autonomous Mobile Robots
In the navigation test, a mission was configured to make the AMR navigate over an obstructed route between two selected waypoints within the lab, ensuring that the robot needed to re-plan its path upon detection of the obstacle. The route was covered 40 times in order to ensure that the overall mission times were long enough to observe any potential differences between the reference operation with cabled planner and the 5G-emulated one. For the docking accuracy test, the robot was set to execute a docking maneuver into its charging station from a starting point located at 1 m distance. This test was repeated 15 times in order to obtain an insight into the average experienced accuracy over 5G as compared to the REF. The docking accuracy was evaluated with the assistance of an external optical positioning system with mm-accuracy [44]. The results from the navigation performance tests are presented in Figure 12a, and they indicate that a very small increase in navigation time of 1.5% is expected in the worst case with the 5G MEC planner as compared to its reference operation with the on-board planner. For the  
Figure 11: (a) Architecture of the 5G-integrated MEC AMR planner solution including the AMR, WMAGW, 5G network infrastructure, and AMR fleet manager controller and planner deployed in edge-cloud configuration. (b) Picture of the 5G-integrated MEC AMR planner solution deployed for 5G emulation at the lab. docking accuracy test, the results are shown in Figure 12b, and they illustrate that the use of the 5G MEC planner would result in a slight loss of accuracy of maximum 1-3 mm, which would be negligible for reliable operation of the AMRs. Upon completion of STEP 3, where it was demonstrated by emulation that the AMR would continue to operate in an accurate and reliable manner when having its planner operating in 5G edge-cloud configuration, it would be time to move to the 5G-integration and deployment in STEP 4. However, no further details will be reported for this specific case as the work is still ongoing.
# Iterative QR-Based SFSIC Detection and Decoder Algorithm for a Reed-Muller Space-Time Turbo System
## 3 The Novel Iterative QR-Based SFSIC Detection and Decoder Algorithm
### Iterative QR-Based SFSIC Detector (Inner Decoder)
#### 3.1.3 Calculating the Extrinsic LLR and A Posterior LLR of Encoded Bits  
Furthermore, if we rewrite Equation (5) as [13; 16; 19]:  
\[\begin{split}\hat{x}_{i_{n}}&=(\mathbf{w}_{i_{n}})^ {H}\mathbf{y}_{i_{n}}\\ &=(\mathbf{w}_{i_{n}})^{H}\mathbf{r}_{i_{n}}x_{i_{n}}+(\mathbf{ w}_{i_{n}})^{H}\Bigg{(}\Big{[}\mathbf{r}_{i_{1}i_{n-1}}\ \mathbf{r}_{i_{n+1}i_{N_{t}}}\Big{]}\left[\begin{array}{c}\mathbf{x}_{i_{n-1 }}-\overline{\mathbf{x}}_{i_{n-1}}^{f}\\ \mathbf{x}_{i_{n+1}}-\overline{\mathbf{x}}_{i_{n+1}}^{f}\end{array}\right]+ \mathbf{n}\Bigg{)}\\ &=\mu_{i_{n}}x_{i_{n}}+\gamma_{i_{n}}\end{split} \tag{14}\]  
where \(\gamma_{i_{n}}\) is the residual interference plus noise that can be approximately regarded as a Gaussian random variable with mean zero and variance [19], expressed as:  
\[\begin{split}(\sigma_{i_{n}})^{2}&=E\Big{[}\left|\gamma _{i_{n}}\right|^{2}\Big{]}\\ &=(\mathbf{w}_{i_{n}})^{H}(R\mathbf{\Sigma}_{i_{n}}R^{H}+\sigma^{2} \mathbf{I}_{N_{t}}-\mathbf{r}_{i_{n}}\mathbf{r}_{i_{n}}^{H})\mathbf{w}_{i_{n}} \\ &=(\mathbf{w}_{i_{n}})^{H}\mathbf{r}_{i_{n}}-\Big{|}(\mathbf{w}_{i_ {n}})^{H}\mathbf{r}_{i_{n}}\Big{|}^{2}\end{split} \tag{15}\]  
The extrinsic LLR of \(d_{i_{n},m}\) is denoted by:  
\[L_{i_{n},m}^{\epsilon 1}=\ln\frac{P(\hat{x}_{i_{n}}|d_{i_{n},m}=1)}{P(\hat{x}_{i_ {n}}|d_{i_{n},m}=0)}=\ln\frac{\sum_{\theta\in\Theta_{m}^{1}}\exp\left(-\frac{ \left|\hat{x}_{i_{n}}-\mu_{i_{n}}\theta\right|^{2}}{\left(\sigma_{i_{n}}\right) ^{2}}\right)}{\sum_{\theta\in\Theta_{m}^{0}}\exp\left(-\frac{\left|\hat{x}_{i_{n }}-\mu_{i_{n}}\theta\right|^{2}}{\left(\sigma_{i_{n}}\right)^{2}}\right)} \tag{16}\]  
where \(\Theta_{m}^{1}\) and \(\Theta_{m}^{0}\) represents the set of constellations of the \(m\)th bit that could be \(1\) and \(0\).  
Combining the extrinsic LLR with a priori LLRs, the a posteriori LLR is obtained according to Equation (9), which is then exploited to calculate the reliability metric of symbols in the residual segment \(\vartheta_{l}\). Hereafter, we continue to iteratively detect symbols in \(\vartheta_{l}\) until \(\vartheta_{l}\) becomes void. Finally, followed by de-interleaving, the extrinsic information is fed into the outer decoder as a priori information.
## 4 Computer Simulation Results
### Evaluation Metrics
#### 4.4.2 Performance Comparison of Proposed Approach on Simulated and Recorded RIRs
Due to the random nature of the cardinality, the MVDR beamformer needs to be dynamic in nature in order to be able to accurately steer the beam towards the target source while directing nulls towards the interfering sources at different time frames. In this regard, the proposed approach seems to outperform other state-of-the-art approaches of speech enhancement using DL techniques under reverberant conditions when all the sources are in motion. While the MS-GLMB-assisted source tracking helps in target source tracking across the time frames, the presence of dense convolutions with residual blocks in the neural architecture helps to cancel the noise better than conventional convolutional modules in the U-Net architecture. Moreover, the presence of GCRNN in the bottleneck of the overall U-Net helps to exploit the implicit information, such as spatial and temporal-spectral information, in a more flexible manner as compared with conventional methodologies. Speech enhancement methods based on conventional beamforming are sensitive to the motion of acoustic sources, which explains why the performance of the conventional GSC-based beamformer degrades in dynamic conditions. Estimation of the time-varying statistics, using labelled RFS-based algorithms and DL architectures, within the conventional beamforming pipeline (such as GSC-MVDR) lead to improved speech intelligibility and speech quality than the approaches without labelled RFS and DL methodology. This can be attributed to the fact that the labelled RFS-based algorithms provide a powerful framework to model the motion of the sources, which in turn leads to better construction of the dataset by considering the effects of time-varying interference and multipath impairments such as reverberation with higher accuracy. From Tables 7 and 8, it is evident that the performance of the integrated MS-GLMB filtering-assisted neural beamforming is superior to other neural speech enhancement methods under reverberant conditions when the sources are in motion. Unlike the results in Tables 7 and 8, which were based on simulated RIRs, in this subsection, we outline the results of the application of the proposed approach to recorded RIRs. The Aachen impulse response (AIR) [57] database is used for selecting the recorded RIRs, in this case two RIRs, recorded from office and corridor environments and with RT60 = 0.52 s and 1.25 s respectively. Moreover, noise samples from the DEMAND database were added to these RIRs to create a noisy, reverberant dataset at a low acoustic SNR of 0 dB. As observed from Table 9, the proposed neural approach also outperforms other methods of speech enhancement in terms of objective quality metrics under recorded RIRs. This indicates that the proposed method is also able to generalize well to recorded RIRs, under noisy conditions.
# A Predictive Analytics Infrastructure to Support a Trustworthy Early Warning System
## 4 The Predictive Analytics Infrastructure and the Early Warning System
### The Predictive Analytics Infrastructure
#### 4.1.2 Four-Tier Architecture  
Multitier architecture is a well-known model used to develop client/server applications. The most widespread architecture is the three-tier one with the presentation, application, and data management tiers [70]. Our system proposed a four-tier architecture where the application tier is divided into a data-dependent (DD) and a data-independent (DI) one. The architecture is depicted in Figure 1.  
Figure 1: Four-tier architecture.  
This architecture allows for splitting processing data services from loading and serving data ones. This idea adds simplicity to the infrastructure and prevents dependency on institutional data representation. The DD tier is responsible for the ETL process to load data from the institution, for the messaging service (i.e., send recommendations to learners), and for presenting the data to the different stakeholders through the client tier. DI tier oversees processing the data, training models related to predicting learners' behaviors, and performing predictions on the basis of those models.  
DI tier can hold multiple models and can support data from multiple data sources. This is possible because the DD tier has been designed to create a view from the available sources coming from the university Student Informational System (SIS). This view is stored and secured in the Persistence data tier. Then, the DI tier can create and handle different models from the available data in the view. As aforesaid, this approach allows high flexibility since any change in the university SIS data model has only to be informed in the view's specification on the DD tier without affecting the DI tier models.
# A Review of Photothermal Detection Techniques for Gas Sensing Applications
## 3 Fiber-Based Photothermal Gas Sensors  
Although the preliminary experiments have proven that PT spectroscopy can with ease compete with traditional absorption gas detection techniques, the implementation of such sensors was significantly more complex compared to TDLAS or WMS approaches, even considering the fact that achieving results comparable to PTS requires implementing multipass gas absorption cells [23]. The limitations were mainly connected with the number of bulk optics required to set-up the sensor. The overall complexity limited out-of-lab applications and thus PT gas sensors were not extensively researched for years. This changed with the implementation of inexpensive, reliable and widely available fiber-based components.