esa-sceva/satcom-chunk-collection huggingface dataset:
# Omnidirectional Haptic Guidance for the Hearing Impaired to Track Sound Sources
## 3 System Configuration and Procedure
### Driving the Vibrator Using the Mean of the Standard Deviation  
We mentioned in Section 2.3 that, the farther the sound source, the more the frequency response of the interaural phase difference of the sound pressure measured in the room will vary (Figure 5). When the degree of variation along the frequency axis of the interaural phase difference is expressed as the mean value of the standard deviation, that value increases almost in proportion to the distance to the sound source. Using this relationship, we can estimate the distance from the interaural phase difference of sound pressure measured indoors. However, this difference depends on the direction of the sound source and has directivity (Figure 6). It is maximum in the direction of \(90^{\circ}\) from the user and minimum in the front-back direction. Therefore, the relationship described in Figure 5 requires that the direction of the sound source be kept constant. In general, it is difficult to satisfy such a condition. Thus, we can estimate the motion of the sound source by estimating the distance to the sound source with it always in front of our face. If we always keep the sound source in front of our face, we can tell whether the source is approaching or moving away. Therefore, if the user can always face the direction of the sound source while wearing vibrators driven by the time difference between the two ears, as described in the previous section, it is possible to provide the user with information about the distance to the sound source. In this study, information about the distance is also transmitted by changing the vibration intensity of the left and right vibrators.  
Figure 11: Relationship between time difference between two ears and X-shaped voltage output from FPGA module to left and right vibrators. Output voltage to the vibrator on the left side increased proportional to time difference between ears and decreased inversely on the right side.  
Figure 12 shows the relationship between the average value of the standard deviation and the output voltage of the vibrators. The smaller the mean value of the standard deviation (the closer to the sound source), the stronger the vibration intensity. The range of the mean value of the standard deviation that causes the output voltage to change linearly is 40 to 70\({}^{\circ}\). When the average value of the standard deviation is 40\({}^{\circ}\), the coefficient value is multiplied by 1, and when it is 70\({}^{\circ}\), the coefficient value is multiplied by 0.25. If we convert this standard deviation value into distance, it covers a range of 1.2 to 2.8 m (Figure 5). The left and right output voltages are multiplied by the same factor.
## 1 Introduction
In addition, the thermal power of the district heating system is about 40 MW [18]. Another example for an existing geothermal application is the power plant in Bruchsal (Germany). This system started to operate in 2012 and is driven by a geothermal heat source of 122 \({}^{\circ}\)C. The electrical capacity of the power plant in Bruchsal is 550 kW [19]. In addition, existing studies of low-grade heat sources only pay small attention to tri-generation systems. On the other hand, previous analyses are based on constant ambient temperature with fixed values of power, cooling and heating generation without paying attention to heating and cooling periods as well as corresponding frequency demands. In this context, tri-generation systems are mostly studied under design conditions. However, it is obvious that changing the operating parameters due to the different geometry, characteristics and performance curves of the component, mainly heat exchangers and turbine, have been affected by the system performances corresponding off-design behavior [20; 21]. Therefore, the off-design evaluation of the system is inevitable and should be considered in the system design in order to annual simulation. The present works addresses all issues mentioned above by introducing a novel configuration of the tri-generation system driven by geothermal water. The system works only by one working fluid as ammonia-water solution. Due to the variable boiling temperature of an ammonia-water solution during the heat transfer process, the irreversibilities of heat exchangers decrease as well as the efficiency of the system increases [22; 23]. In this context, the main objectives in this work are:  
* to define heating and cooling demand in winter and summer conditions (different ambient temperatures) to evaluate the system performance using operational data of an existing geothermal district heating network in the German Molasse basin [24]. In particular, the geothermal source, mean ambient temperatures and heating/cooling demands are based on this case;
* to validate the power part (Kalina cycle) and to verify the cooling part in order to confirm the validity of the simulation model;
* to determine the main operational parameters to analyze the system;* to develop and apply an off-design model of the tri-generation system;
* to apply the quasi-stationary simulation of a tri-generation system in different weather conditions to fulfill the heating and cooling demands;
* to obtain mean annual system performances;
* to optimize the system considering two criteria, maximum exergy efficiency and minimum total exergy destruction rate.
## 3 Results
### Accuracy of Control-Free Nap-of-the-Object Photogrammetry Images
#### 3.2.1 DSM Positioning Accuracy
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Date**} & \multirow{2}{*}{**CFI or IC**} & \multirow{2}{*}{**Norm**} & \multicolumn{8}{c}{**Ground Checkpoint Name**} & \multirow{2}{*}{**MAE**} & \multirow{2}{*}{**RMSE**} \\  & & & **1** & & **2** & & **3** & & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline \multirow{8}{*}{22 June} & \multirow{8}{*}{CFI} & \(\Delta\)X & 0.01 & 0.02 & 0 & 0.01 & \(-\)0.02 & 0.01 & 0.01 & 0.01 & 0 & 0.01 & 0.01 \\  & & \(\Delta\)Y & 0 & 0 & \(-\)0.01 & 0.01 & 0.02 & 0.01 & \(-\)0.01 & 0 & 0 & 0.01 & 0.01 \\  & & \(\Delta\)Y & 0.01 & 0.02 & 0.01 & 0.01 & 0.03 & 0.01 & 0.02 & 0.01 & 0 & 0.01 & 0.02 \\  & & \(\Delta\)Z & 0.01 & 0.01 & \(-\)0.05 & \(-\)0.03 & \(-\)0.03 & \(-\)0.03 & \(-\)0.03 & \(-\)0.03 & \(-\)0.03 & \(-\)0.04 & 0.03 & 0.03 \\ \cline{2-13}  & & \(\Delta\)X & 0 & 0.01 & 0 & 0.01 & \(-\)0.02 & 0.01 & 0.01 & 0.01 & \(-\)0.01 & 0.01 & 0.01 \\  & & \(\Delta\)Y & 0 & 0 & \(-\)0.01 & 0.01 & 0.02 & 0.01 & \(-\)0.01 & 0 & 0 & 0.01 & 0.01 \\  & & \(\Delta\)Y & 0 & 0.01 & 0.01 & 0.01 & 0.03 & 0.01 & 0.01 & 0.01 & 0 & 0.01 & 0.01 \\  & & \(\Delta\)Z & 0.02 & 0.01 & \(-\)0.03 & \(-\)0.01 & \(-\)0.01 & \(-\)0.02 & \(-\)0.01 & \(-\)0.02 & 0.02 & 0.02 \\ \hline \multirow{8}{*}{15 July} & \multirow{8}{*}{CFI} & \(\Delta\)X & 0.01 & 0.02 & 0.01 & 0.01 & \(-\)0.02 & 0.01 & 0.02 & 0.02 & 0 & 0.01 & 0.01 \\  & & \(\Delta\)Y & \(-\)0.01 & \(-\)0.01 & \(-\)0.01 & 0 & 0.01 & 0 & 0.02 & 0 & 0 & 0.01 & 0.01 \\  & & \(\Delta\)Y & 0.01 mainly due to the larger error values of ground checkpoints No. 2 and No. 3. The main factor affecting the accuracy is that checkpoints No. 2 and No. 3 were located at the bottom and top of the study area, respectively, where the 3D point error was significant. The UAV control-free nap-of-the-object photogrammetry technology demonstrates high positioning accuracy in terms of DSMs, with the error reaching the subcentimeter level. The 2D error kernel density distribution was plotted based on the planimetric and elevation errors of the DSMs generated by control-free nap-of-the-object photogrammetry images, as shown in Figure 13. The elevation error for 22 June is mainly concentrated in the range of \(-0.02\) to \(-0.04\) m, and the planar direction error is mainly concentrated around 0.1 m. The elevation error for 15 July is mainly concentrated around 0.02 m, and the planar direction error is mainly concentrated between 0.01 m and 0.02 m.
## 8 VLC-IoT: Challenges, Solutions, and Future Directions
### Challenges and Solutions
Suggested possible solutions The problem of extended link distance in outdoor VLC-IoT applications can be resolved by the following suggested solutions: (1) the use of relays to extend the transmission range of VLC-IoT applications, (2) deploying LEDs-to-camera VLC for extended range of transmission, e.g., RGB LEDs (with a data rate of >10 Gb/s over 1.5 to 2 m) and RGB/white LEDs (with data rates of 125 Mbits/s over 5 to 6 m), (3) development of hybrid OFDM and CDMA resource allocation algorithms that are capable of reducing ISI and ICI for extended transmission range [12]. 5. Line-of-sight (LOS) signal loss and shadowing effects The requirement for the transmitters and receivers to be aligned for short-distance communications with small field of views (FOVs) is a major source of concern in VLC-IoT especially in mobile VLC-IoT devices, e.g., in robots with moving parts. This is because a clear and uninterrupted path between transmitter and receiver is always required to maintain effective communications [46]. Similarly, when humans are deployed together with robots, e.g., in smart factories the quality of the VLC link can be degraded by moving human bodies due to shadowing effects. 6. Suggested possible solutions We suggest the following solutions to mitigate the problem of LOS in VLC-IoT including: (1) using antennas with high spatial diversity to ensure that at least one uninterrupted signal connection is always guaranteed at any point in time, (2) using multiple MIMO antennas to reduce the risk of LOS signal loss due to interrupted signals, and (3) using transmitters with ultra-wide FOVs to eliminate loss of signals. 7. Lack of recognized channel models for VLC-IoT communications Though several aspects of VLC have witnessed considerable advancements due to increased research contributions in VLC, the same, however, cannot be said about channel models for VLC. The lack of a recognized channel model for VLC is still an open research challenge. Presently, channel modeling for visible light links especially for outdoor non-line-of-sight environments is still mainly based on preliminary empirical measurements, e.g., LEDs are treated as Lambertian emitters when calculating the channel gains in VLC. Other factors that may affect visible light links include alignment between the transmitter and receiver, scattering, free space loss, absorption, and scintillation noise due to atmospheric turbulence [16; 38]. 8. Suggested possible solutions We propose the following solution for solving the lack of recognized channel models for VLC links: (1) the characteristics and theoretical details of channel models for indoor and outdoor VLC should be identified and investigated, (2) the validity of existing theoretical channel models for VLC-aided networks should be tested in different transmission medium so as to establish a recognized channel model for VLC links.
## 2 Related Works  
With the significant growth of ML or DL, they can serve many purposes that benefit people, especially in Natural Language Processing (NLP). As a part of NLP, speech recognition is making a machine recognize a spoken word or sentence regardless of the different people, tone, variation, or pitch. As a product of speech recognition, STT is a process that transcribes spoken words into written text. Although STT is synonymous with speech recognition, the definition of speech recognition describes a broader process of speech understanding [5]. The architecture of STT, as stated in [6], is depicted in Figure 1.  
The STT became well-known to the public around 1990 after the speech recognition algorithm's performance based on the Hidden Markov Model (HMM) was deemed sufficient to provide a plausible transcript, although it is often inaccurate. STT is ubiquitous  
Figure 1: The Architecture of Speech-To-Text. and embedded into many applications, from smartphones to cars and household items. Furthermore, STT, especially the well-known open-source STT, could provide a cheap, reliable, easy-to-access, and quick way to automatically transcribe speech data in many languages regardless of gender, fluency, intonation, pitch, and voice [7]. As stated in [8], there are several algorithm approaches to performing an STT:  
* Template-based (matching unknown speech with known information);
* Knowledge-based (variations in speech are saved into the system, and the interference system deduces a complex rule);
* Neural network-based (uses a neural network to detect speech based on the training data automatically);
* Dynamic time warping-based (matching sequences of the same speech with varying time or speed);
* Statistical-based (uses automatic learning procedure on large training data samples). Many related previous works have discussed the use of STT to measure, evaluate, or score spoken sentences [9; 10; 11; 12; 13; 14; 15], as shown in Table 1.  
Although the previous works in Table 1 have shown a significant contribution, there is still a research gap that this paper intends to fill. The majority of the mentioned related works focused on English as the target language, except for [12], which targeted Korean and [13], which targeted the Jawi character (Malay writing script based on the Arabic script). A Speech Recognition model developed for a specific language cannot be directly used for another language because it needs much tweaking or a complete model rebuild.
# Three-Dimensional High-Energy Electron Radiography Method for Static Mesoscale Samples Diagnostics
## 3 The Start-to-End Simulation Studies of TDHEER Method  
### The Imaging Lens Design  
The imaging lens is designed with a magnification of 1 using eight quadrupoles. The designed maximum field gradient of the quadrupoles is 12 T/m, the inner diameter is 20 mm, and the length is 10 cm. The imaging lens structure is optimized using COSY INFINITY9.1 [12] by tuning the quadrupoles field to achieve a magnification of 1 (R11 = R33 = -1) and point to point imaging (R12 = R34 = 0). The designed imaging lens parameters are shown in Table 1. All quadrupoles have the same structural design for manufacturing convenience. The electron beam trajectory is shown in Figure 2 with optimized quadrupoles field and magnification factor R11 = R33 = -1, also showing a good point-to-point imaging performance both in the \(x\) Figure 2a and \(y\) Figure 2b plane. The chromatic lengths in the \(x\)-plane and \(y\)-plane are 3.05 and 3.24 m, respectively. With this imaging lens, the T116 and T336 are zero; therefore, the matched beam should be parallel, shown in Figure 1. More details of the beam matching requirement for HEER are referred to in [5].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Quads**} & **Flux Density at** & **Transport Matrix** & **Optimized Transport** & **Drift Distance** \\  & **Pole Tip B (T)** & **Element** & **Matrix Element Values** & & **(m)** \\ \hline Q1 & 0.036593 & R11 = R33 & \(-\)1.0 & L1 & 0.4 \\ \hline Q2 & \(-\)0.03382 & R12 = R34 [m/rad] & 0 & L2 & 0.1 \\ \hline Q3 & \(-\)0.03382 & T116, T126 [m/rad] & 0, 3.05279 & L3 & 0.8 \\ \hline Q4 & 0.036593 & T336, T346 [m/rad] & 0, 3.24576 & L4 & 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The optimized parameters for imaging lens structure.  
Figure 2: Electron beam trajectories from the object plane to image plane through the imaging lens in the (**a**) \(x\) plane and (**b**) \(y\) plane.
# Guaranteeing QoS for NOMA-Enabled URLLC Based on \(\kappa\)-\(\mu\) Shadowed Fading Model
## 1 Introduction
### Main Contributions  
For 6G URLLC requirements, we combine the potential key technology of future 6G, CF mMIMO [48], to build the uplink wireless communication system, and extend the \(\kappa-\mu\) shadowed fading model to the CF mMIMO system, for lossless characterization of its fading channel in the complex communication scenario of 6G [23; 24]. The key contributions of the paper can be summarized as follows.  
* We derive approximate closed-form expressions for statistical characteristics (the PDF, CDF and MGF) of the sum of independent and nonidentically distributed (i.n.i.d.) \(\kappa-\mu\) shadowed random variables (RVs). The analysis is nontrivial, as we optimize the theoretical analysis performance of the \(\kappa-\mu\) shadowed fading model and improve its mathematical tractability.
* Based on the \(\kappa-\mu\) shadowed fading model, we derive approximate closed-form expressions for the PDF, CDF and MGF of the postprocessing signal-to-noise ratio (PPSNR)after the zero-forcing detector in the proposed CF mMIMO system, and extend the \(\kappa-\mu\) shadowed fading model to 6G NOMA wireless communication systems.
* By utilizing FBL information theory, SNC and the Mellin transform on the service process, we exploit the UB-QDVP in the proposed CF mMIMO system under the \(\kappa-\mu\) shadowed fading model. Furthermore, based on extensive simulations, we analyze the system performance with the delay and UB-QDVP indicators and validate the necessity of analyses performed under the \(\kappa-\mu\) shadowed fading model; the CF mMIMO system outperforms the OMA system and PD-NOMA system. (For the OMA and PD-NOMA systems, there are \(L\) antennas equipped in their base station and \(K\) users. For illustration convenience, in the PD-NOMA system, we divide \(K\) users into \(\mathcal{N}\) pairs based on the channel gains of the users (\(K=2\mathcal{N}\)). Specifically, we divide \(K\) users into a group of \(\mathcal{N}\) \"strong users\" and a group of \(\mathcal{N}\) \"weak users\" according to their channel gains and sort the users in the descending order of their channel gains within each of the groups. The \"strong user\" and \"weak user\" with the same label are grouped in the same pair.)
## 5 Structure Equations with Frequency-Dependent Constitutive Equation  
### Structure Equations in the Frequency Domain  
The equation of the structure occupying domain \(\Omega_{S}\) is written as  
\[-\omega^{2}\,\rho_{\mathrm{s}}\,u_{i}-\sigma_{ij,j}(\mathbf{u})=g_{i}\quad\text {in}\quad\Omega_{S}\,, \tag{11}\]  
in which \(\rho_{\mathrm{s}}(\mathbf{x})\) is the mass density of the structure. For a linear viscoelastic model, the symmetric stress tensor is written as  
\[\sigma_{ij}(\mathbf{u})=\left(a_{ijkh}(\omega)+i\omega\,b_{ijkh}(\omega) \right)\varepsilon_{kh}(\mathbf{u})\,, \tag{12}\]  
in which the symmetric strain tensor \(\varepsilon_{kh}(\mathbf{u})\) is such that  
\[\varepsilon_{kh}(\mathbf{u})=\frac{1}{2}(u_{k,h}(\mathbf{x},\omega)+u_{h,k}( \mathbf{x},\omega))\,, \tag{13}\]  
and where the tensors \(a_{ijkh}(\omega)\) and \(b_{ijkh}(\omega)\) depend on \(\omega\) and are detailed in Appendix A for the LF and MF ranges.  
On the fluid-structure external interface \(\Gamma_{E}\), the boundary condition is such that  
\[\sigma_{ij}(\mathbf{u})n_{j}^{S}=G_{i}-p_{E}|_{\Gamma_{E}}\,n_{i}^{S}\quad \text{on}\quad\Gamma_{E}\,. \tag{14}\]  
Using Equation (4) yields  
\[\sigma_{ij}(\mathbf{u})\,n_{j}^{S}=G_{i}-p_{\text{given}}|_{\Gamma_{E}}\,n_{ i}^{S}-i\omega\,\mathbf{Z}_{\Gamma_{E}}(\omega)\{\mathbf{u}\cdot\mathbf{n}^{S} \}\,n_{i}^{S}\quad\text{on}\quad\Gamma_{E}\,. \tag{15}\]  
Since \(\mathbf{n}^{S}=-\mathbf{n}\), the boundary condition on \(\Gamma\cup\Gamma_{Z}\) is written as  
\[\sigma_{ij}(\mathbf{u})n_{j}^{S}=G_{i}+p\,n_{i}\quad\text{on}\quad\Gamma\cup \Gamma_{Z}\,, \tag{16}\]  
in which \(p\) is the internal acoustic pressure field defined in Section 4.
# Potential and Limitations of the New European Ground Motion Service in Landslides at a Local Scale
## 1 Introduction
The main studies involving the EGMS have been designed to: (1) describe certain deformation examples detected in Europe at a continental and regional scale and their validation [34; 35]; (2) demonstrate the EGMS products, processing chain, and results based on point density, data quality, and ground motion validation [36; 37]; (3) develop new tools and software to exploit the extensive EGMS products quickly and appropriately [33]; (4) compare the EGMS results with other European A-DInSAR services such as the German Ground Motion Service [38]. The most recent applications of the EGMS have been the analysis of active ground motion in mining areas [39; 40], infrastructure monitoring [41], and landslide activity [42]. These works are based on the validation of the EGMS results and the monitoring accuracy using geological-geomorphological, hydrogeological, and geotechnical data, PS and DS datasets by using SARscape processing, and X-Band COSMO-SkyMed dataset. All these studies show the new EGMS is a powerful and novel tool. However, there is still an important lack of critical analysis to demonstrate the potential and limitations of the EGMS in the detection and monitoring of mass movements at the local scale. The aim of this work is to demonstrate the potential and limitations of the EGMS to detect and monitor mass movements at a local scale, especially when data from in situ instrumental devices are not available. Therefore, the purpose of this research is to apply geomorphological and climate approaches to validate the EGMS in a landslide at a local scale. Particularly, it will contrast geomorphological evidence, building damage inventory and rainfall data with EGMS results from January 2015 to December 2021 around the La Miera landslide (Asturias, NW Spain).
## 2 D2D Communication System Model
### Mobility Model  
This section describes the pattern of mobility of users in a network which includes change in location of mobile users over time and the velocity and acceleration at which mobile users change their positions to. In this research, Random Waypoint mobility model [28; 29] is used to emulate movement patterns of people in the network. In this mobility model, a node moves from a waypoint in a straight line towards the next waypoint with a certain velocity having minimum velocity of \(v_{min}\) and maximum velocity of \(v_{max}\). After arriving at the waypoint, the node waits for a constant pause time before selecting next waypoint. The waypoint is selected using Uniform distribution, which resembles the movement of people carrying mobile devices [29; 30].  
This model is suitable for nodes having limited mobility [31]. For example: a scenario where large number of people come together in a small confined area for specific time duration. This type of scenario are generally present in educational institutions, organizations, industries, or special events like concerts, conferences or exhibitions.