esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Delay-Doppler Block Division Multiplexing
### Property Analysis
Therefore, whenever the received signal bandwidth is less than the main lobe of the signal, \(\varsigma_{\text{OTFS}}<1\) and \(\varsigma_{\text{DDBDM}}=R^{\prime}{}_{\text{DDBDM}}(0)/R(0)=1\). This means that there is a peak value loss of the ACF for OTFS, but not for DDBDM. Figure 4 compares the ACFs of OTFS and DDBDM when receiving half of the bandwidth, with the blue solid line representing the ACF of OTFS, using the total bandwidth as the baseline. It can be observed that the main lobe of the ACF of the half-bandwidth OTFS experiences significant attenuation and becomes flattened, which is detrimental to accurate TOA estimation. In contrast, although the main lobe of DDBDM is also widened, it does not experience attenuation, allowing users to process signals with a smaller bandwidth, and thereby reduce the signal processing burden. Third, DDBDM eliminates the limitation on the number of navigation users, while retaining the reliability advantages of data transmission. Public PRN codes with good correlation can be used as downlink broadcast signals to provide wide-area passive positioning services, removing the limitation on the number of positioning users present in the OTFS communication system. Meanwhile, DDBDM divides the orthogonal resource blocks in the DD domain along the delay direction, ensuring that the navigation signals can be continuously tracked in the time domain, which allows for higher measurement accuracy than burst pilot/preamble in communication systems. Additionally, since DDBDM still  
Figure 4: A comparison of the ACFs of the original OTFS and DDBDM when receiving half the signal bandwidth. transmits data in the DD domain, the demodulation process remains consistent with the original OTFS [18]. It is anticipated that DDBDM will keep the BER low in the LEO high-mobility channel. For INAC users, the high-speed data transmission of the communication component can be used to assist in broadcasting the necessary data for navigation, thereby reducing the receiver's TTFF. Additionally, the DDBDM can also be used to transmit the error correction parameters required for high-precision applications, so as to provide better PNT services.
# Dynamic Behavior Analysis and Stability Control of Tethered Satellite Formation Deployment
## 4 Stable Deployment Laws of Tether Release Rate and Tether Tension Control
### Tether Release Rate Control  
The tether release rate is directly controlled by a winch control mechanism in the mother satellite, and the influences of tether tension and \(\phi\) are ignored. Hence, Equations (15)-(17) can be rewritten as follows:  
\[\theta^{\prime\prime}=2(\theta^{\prime}+1)[\frac{e\sin\
u}{\kappa}-\frac{l^{ \prime}}{l}]-\frac{3}{2\kappa}\sin 2\theta \tag{26}\]  
where \(l^{\prime}/l\) is the pseudo damping term, which makes \(\theta,\ \phi\) convergent.
# Blind Recognition of Forward Error Correction Codes Based on a Depth Distribution Algorithm
## 6 Depth Distribution Algorithm Based on Parallel Convolutional Turbo Codes  
### Principles of Parallel Convolutional Turbo Coding  
The most important feature of Turbo code is the introduction of interleave and deinterleave to achieve coding randomness, and the effective combination of several short codes by code word deletion and reuse to achieve long codes, making its performance close to the Shannon limit. Turbo codes can be divided into three categories according to the different arrangement structures of code words; namely, Parallel Cascaded Convolutional Codes (PCCC), Serial Cascaded Convolutional Codes (SCCC) and Hybrid Cascaded Convolutional Codes (HCCC) [32]. Among them, the parallel cascade convolutional code is the most widely used permutation, and it is also the main research object in this paper.  
The structure of a parallel cascade convolutional code encoder mainly includes interleave, a component encoder, an eraser and a multiplexer. The function of interleave is to reset the position of each bit in the input information sequence, change the weight distribution of the code, control the distance characteristics of the code sequence, and effectively reduce the correlation between the test sequences. The eliminator and multiplexer can combine the short code to improve the transmission efficiency of the system. At the same time, the interleave is mainly used to scatter the distribution of error bits and improve the error-correcting ability of the system [33]. In parallel, a cascaded convolutional code component encodes a recursive system convolutional encoder (RSC), which is generally used to implement fractional encoding [34; 35]. Recursive system convolutional encoders are based on linear feedback shift registers (LFRS) to randomize input data. At the same time, using RSC can achieve better performance compared with traditional convolutional encoding at low SNR. According to whether the RSC of an interleave frame in PCCC returns to zero, the Turbo code can be divided into non-return to zero Turbo code and a return to zero Turbo code. Generally speaking, the non-return to zero of the encoder will have a certain negative impact on the coding performance. If the length of interleaving is small, zero processing must be carried out. However, when the interleaving length is large, the return to zero processing can be ignored, so as to reduce the complexity of the coding system. The classical structure diagram of PCCC is shown in Figure 6.  
Figure 6: PCCC encoder structure.
## 2 Numerical Simulation  
### Governing Equation  
Regardless of the complexity of the fluid flow in the pump, the basic laws of physics must be obeyed, of which the laws of conservation of mass, momentum, and energy are the three most basic conservation laws in fluid flow problems.  
(1) The continuity equation  
The continuity equation, also called the mass conservation equation, has the following specific expression:  
\[\frac{\partial\rho}{\partial t}+\frac{\partial(\rho u_{i})}{\partial t}=0 \tag{1}\]  
where \(u_{i}\) is the fluid velocity component and \(\rho\) is the fluid density (kg/m\({}^{3}\)).  
For steady flow, the above equation can be replaced by:  
\[\frac{\partial(\rho u_{i})}{\partial t}=0 \tag{2}\]  
For incompressible fluids, the equation can be changed to:  
\[\frac{\partial u_{i}}{\partial x_{i}}=0 \tag{3}\]where \(x_{i}\) is the coordinate component and \(i=1,2,3\).  
(2) Momentum equation  
The momentum equation, namely the N-S equation, for incompressible viscous fluids has a specific expression as follows:  
\[\frac{\partial u_{i}}{\partial t}+u_{j}\frac{\partial u_{i}}{\partial x_{j}}=F_{ i}-\frac{1}{\rho}\frac{\partial P}{\partial x_{i}}+v\frac{\partial^{2}u_{i}}{ \partial x_{i}\partial x_{j}} \tag{4}\]  
where \(u_{j}\) is the fluid velocity component, \(x_{j}\) is the coordinate component, \(j=1,2,3\), \(F_{i}\) is the mass force (N), \(P\) is the pressure (Pa), \(\rho\) is the fluid density (kg/m\({}^{3}\)), and \(v\) is the kinematic viscosity coefficient.  
(3) Energy conservation equation  
The law of conservation of energy states that the total amount of energy always remains the same, regardless of how it changes during the process of transfer and conversion. For incompressible fluids, the specific expression is as follows:  
\[\frac{D}{Dt}(\frac{1}{2}u_{i}u_{i})=u_{i}F_{xi}+\frac{1}{\rho}\frac{\partial( m_{ji}u_{i})}{\partial x_{j}}-\frac{1}{\rho}\frac{\partial(pu_{i})}{ \partial x_{j}}+\frac{p}{\rho}\frac{\partial u_{i}}{\partial x_{j}}-\frac{m_ {ij}}{\rho}\frac{\partial u_{i}}{\partial x_{j}} \tag{5}\]  
where \(m_{ij}\) is the viscous stress tensor.  
For a fluid microcluster per unit volume, the specific expression for the increment of internal energy per unit time is:  
\[\frac{D\epsilon}{Dt}=q-\frac{1}{\rho}\frac{\partial q_{i}}{\partial x_{i}}+ \frac{\Phi}{\rho}-\frac{p}{v}\frac{\partial u_{i}}{\partial x_{i}} \tag{6}\]  
where \(e\) is the internal energy per unit mass, \(q\) is the heat gained per unit of volume, \(q_{i}\) is the thermal energy per unit of time volume, and \(\Phi\) is the dissipation function, which can be expressed as \(\Phi=m_{ji}\frac{\partial u_{i}}{\partial x_{j}}\).  
From this, the energy equation for an incompressible viscous fluid is given by:  
\[\frac{D}{Dt}(e+\frac{1}{2}u_{i}u_{i})=Q+u_{i}F_{xi}-\frac{1}{\rho}\frac{ \partial q_{i}}{\partial x_{i}}+\frac{1}{\rho}\frac{\partial(m_{ji}u_{i})}{ \partial x_{j}}-\frac{1}{\rho}\frac{\partial(pu_{i})}{\partial x_{j}} \tag{7}\]
## 4 Results
### Experiment Two: Test the Performance of the Proposed Model in Terms of Fault Tolerance  
Objective: The second set of experiments was conducted to confirm the efficiency of the suggested DR-tree-based model in terms of replication time and message cost for grid computing networks; the aim is to decrease replication time and message cost and this will cause an increase in the probability of a job being completed. In general, after building the logical grid computing model and executing the load balancing stage, the grid network size influences the time taken for jobs to be completed. So, if the grid size of the network is minimized, the probability of the job being completed is maximized.  
Observation: Figure 6 shows the relationship between grid size and the arrival rates for different random distributions, such as random (Rand-Dist), exponential (Exp-Dist), normal (Norm-Dist), uniform (Unif-Dist), and Poisson (Poiss-Dist). The results reveal that for different arrival rates, the Poisson distribution yields a minimum grid size as compared with other distributions. It has an improvement ratio of about 4.6%. Furthermore, Figure 7 shows a 2-D figure of merits that depicts the relationship between mean load balance estimation error and mean fault tolerance estimation error. It can be inferred that the Poisson distribution gives the best stability system compared to all different distributions and the job response time algorithm. As shown in Figure 7, given the best result for the Poisson distribution, the load balance is still the same, and this confirms the stability condition of the  
Figure 5: Load balance improvement ratio.  
DR-tree model. The next step tries to enhance load balance, and the fault tolerance of the suggested model depends on different entropy values.  
Figure 8 shows the grid size at different arrival rates for the Poisson distribution with different entropy threshold values; the improvement ratio for fault tolerance in terms of grid size is 33%  
Figure 6: Fault tolerance estimation (grid size with different arrival rates).  
Figure 7: Path selectors with 2-D figure of merit.  
at an entropy value of 80%. Figure 9 shows the system stability in terms of load balance and fault tolerance estimation errors after enhancement. The results reveal that when decreasing the entropy threshold value \(\varepsilon\) to less than 80%, the stability of the system decreases. The suggested model yields an improvement ratio of 98% for load balance and 33% for fault tolerance as compared with initial conditions.  
Figure 8: Fault tolerance estimation with different entropy for Poisson distribution.  
Figure 9: Path selectors with 2-D figure of merit with different entropy for Poisson distribution.  
Discussion: As the proposed model depends on the entropy-based DR-tree index structure through which the number of nodes that have properties depends on reducing the error estimation for each of load balance and fault tolerance, these selected nodes represent the logical structure and reduce the network size and reducing the network size leads, accordingly, to reducing the mean job response time and replication time.
## 4 Discussion and Conclusions
**Funding:** This research was funded by the Spanish Science, Innovation, and Universities Ministry under the program \"Ayudas para la formacion de investigadores en empresas (Doctorados Industriales)\" REF 2019-010458. **Institutional Review Board Statement:** Not applicable. \begin{table}
\begin{tabular}{l l l l} \hline \hline AC & Alternating Current & PCB & Printed Circuit Board \\ ADC & Analog-to-Digital Converter & PD & Photodiode \\ AlGaAs & Aluminum Gallium Arsenide & PER & Packet Error Rate \\ APD & Avalanche Photodiode & PHY & Physical \\ ARM & Advanced RlS Machine & PIN & Positive-Intrinsic-Negative \\ BW & Bandwidth & QSPI & Quad Serial Peripheral Interface \\ DAC & Digital-to-Analog Converter & QPSK & Quadrature Phase Shift Keying \\ DC & Direct Current & RF & Radio-Frequency \\ DDR3 & Double Data Rate version 3 & Rx & Receiver \\ FPGA & Field-Programmable Gate Array & RISC & Reduced Instruction Set Computer \\ FSO & Free Space Optical & SD & Secure Digital \\ GBW & Gain-Bandwidth & SDRAM & Synchronous Dynamic Random-Access Memory \\ IC & Integrated Circuit & SIMO & Single-Input Multiple-output \\ IFFT & Inverse Fast Fourier Transform & SMA & Subminiature version A \\ ILA & Integrated Logic Analyzer & SNR & Signal-to-Noise Ratio \\ IoT & Internet-of-Things & SoC & System On a Chip \\ IR & Infrared & TTL & Transistor–Transistor Logic \\ JTAG & Joint Test Action Group & TV & Television \\ LAN & Local Area Network & Tx & Transmitter \\ LED & Light-Emitting Diode & UART & Universal Asynchronous Receiver–Transmitter \\ LiFi & Light Fidelity & UHD & Ultra-High Definition \\ LoRa & Long-Range & USB & Universal Serial Bus \\ MAC & Medium Access Control & VLC & Visible-Light Communication \\ OFDM & Orthogonal Frequency-Division Multiplexing & WAN & Wide Area Network \\ OOK & On–Off Keying & WiFi & Wireless Fidelity \\ OWC & Optical Wireless Communication & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Terms abbreviation list. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** For further data information, please contact to correspondence author. **Acknowledgments:** The authors want to specially thank to Valia Merino Vallina and Consultora de Telecomunicaciones Optiva Media SLU for their support to carry out this work. **Conflicts of Interest:** The authors declare no conflicts of interest.
## 1 Introduction  
Inversion of the Marchenko equation has proven to be an effective tool for the retrieval of Green's functions in an unknown acoustic medium from single-sided reflection data [1; 2]. For an introduction to this subject, the numerical implementation of the Marchenko equation, field data applications and recent developments, see [3; 4; 5; 6; 7; 8; 9; 10; 11], respectively. An equivalent (Marchenko) equation has also been derived for wave propagation in elastic media [12; 13; 14]. Inversion of this equation requires a priori knowledge of all forward-scattered (non-converted and converted) waveforms [15]. Moreover, a unique solution can only be obtained if the medium obeys stringent monotonicity conditions [16], which are often not met in realistic scenarios. Once a solution to the Marchenko equation is found, it can be used for various purposes, such as wavefield retrieval inside an unknown medium [17], the imaging of elastic medium properties [18] or the suppression of multiple undesired reflections in reflection data [19].  
In this paper, we show that the conditions for elastodynamic Green's function retrieval are significantly better when an elastic volume can be accessed from two sides, as is the case in particular laboratory experiments [20], non-destructive testing [21; 22], brain imaging [23; 24], transcranial ultrasound focusing [25; 26], transcranial photoacoustics [27; 28] and when using auxiliary downhole receivers in seismic data acquisition [29; 30]. Although the underlying representations of our work could be extended to account for lateral variations [19], the presence of a free surface [31; 32] and intrinsic attenuation [33; 34], we restrict ourselves to a layered lossless medium for simplicity.  
In Section 2, we derive a system of forward equations that relate the (multi-component) focusing function at a specified focal depth \(z_{l}\) to observed reflection and transmission data, which are to be acquired at depths of \(z_{ll}<z_{l}\) and \(z_{l}>z_{l}\). In Section 3, we show how the system can be inverted for the focusing function and two unknown amplitude scaling factors, \(\alpha\) and \(\beta\), which are related to transmission losses of (non-converted) \(P\)- and \(S\)-waves, respectively, between depth levels \(z_{l}\) and \(z_{l}\). Apart from the recorded (reflection and transmission) data, our scheme requires two direct arrivals, which are represented by pulses of unit amplitude, delayed with the (non-converted) \(P\)- and \(S\)-wave travel times from \(z_{l}\) to \(z_{l}\). In this way, we can apply exact (data-driven) Marchenko redatuming of two-sided data in a layered elastic medium, which is the main contribution of this paper. The retrieved focusing functions can be transformed into [PERSON]'s functions as if there were virtual \(P\)- or \(S\)-wave sources at \(z_{l}\), which could eventually be used for imaging and inversion of the elastic medium's properties. We close the paper with a discussion in Section 4.
# Adaptive Learning in AI Agents for the Metaverse:
## 5 Theoretical Application Analysis
### AltspaceVR
#### 5.2.2 Analysis of AI Adaptive Learning  
In AltspaceVR, applying the Adaptive Learning Model for AI Agents (ALMAA) showcases a significant enhancement in how virtual environments can be managed and experienced. This analysis explores how adaptive learning directly influences social interactions, ensuring that AI agents respond appropriately to diverse user inputs and proactively facilitate a supportive and engaging community.  
Dynamic Interaction Facilitation  
AI agents in AltspaceVR employ adaptive learning to tailor interactions based on real-time user feedback and engagement patterns. These agents analyze verbal and non-verbal cues to modulate their responses, ensuring they are contextually relevant and socially appropriate. For instance, AI agents can adjust the discussion flow based on the participants' engagement levels during a virtual conference or workshop, introducing polls or Q&A sessions to invigorate interaction when needed.  
Cultural and Linguistic Adaptation  
One of the standout applications of ALMA in AltspaceVR is its ability to bridge cultural and linguistic divides. AI agents utilize adaptive learning to understand and respect cultural nuances, modifying their communication styles to suit different cultural backgrounds. This is critical in a global platform like AltspaceVR, where users from various linguistic and cultural environments interact. Moreover, these AI agents can provide real-time language translation services, greatly enhancing user communication and understanding.  
Community Management and Safety  
Adaptive learning extends to community management, where AI agents monitor and moderate interactions to maintain a safe and respectful environment. By learning from past incidents and user reports, these agents can predict potential conflicts or inappropriate behavior and intervene preemptively, thus maintaining community standards and user safety.  
In summary, the analysis of AI adaptive learning in AltspaceVR illustrates a profound impact on enhancing user experience and community engagement in virtual reality spaces. Through continuous learning and adaptation, AI agents effectively contribute to developing more cohesive, interactive, and inclusive virtual communities, paving the way for future innovations in social VR platforms.
## 1 Introduction
Payment messages are now carrying \"enhanced data\" elements concerned with the contextual information of a transaction, e.g., item description, item quantity, unit cost, VAT invoice number, VAT amount, Legal Entity Identifier, and more. This poses an important question concerning how these data should be exploited for their information value within a proposed Smart Money system for greater societal and economic benefit? We present Smart Money, a central bank digital currency architecture, as an essential component for an advanced digital economy. This work is motivated by the Bank of England's desire to exploit CBDCs as a potential and feasible supplement for cash, which has implications for the payment system and government authorities [31]. Furthermore, this paper considers that CBDCs have an additional role, namely to assist with other government departments' (OGDs) policies such as automating tax payments at the point-of-sale. For the first time, the design and implementation of a feasible DLT-based approach which integrates smart contracts with the existing and future payment infrastructure and causes minimal intervention and disruption to the financial system is proposed in this paper. The design specification proposed herein facilitates trusted information management and sharing among OGDs in order to mobilise data and introduce smart policy making. Of particular interest is the issue of VAT split payments, where the VAT is deducted from a payment to be transferred directly to His Majesty's Revenue and Customs (HMRC), and the supplier receives the net amount of the transaction. Within the Making Tax Smart prototype presented herein, split payments are processed via smart contracts based on the information recorded on the invoice. This approach leverages the informational value offered by payments' enhanced data feature, presented in the payment messages of the NPA [29], providing further benefits for OGDs; specifically, CBDC facilitates the gathering of economically relevant data in near real-time to support compliance and oversight of public policy planning and development. Finally, MTS introduces'smart warrants', a mechanism that enables permissioned access control over the transaction data.
## 4 Precoding Schemes
### Precoding Schemes for VLC
#### 4.2.1 Linear ZF Precoding  
First, it is worth recalling that linear ZF precoding completely cancels the interference coming from transmission to all other users, i.e., assuming a received signal according to (21), ZF precoding involves \(\mathbf{h}^{[k]T}\mathbf{w}^{[j]}=0,\forall k\
eq j\). Therefore, the resulting system corresponds to \(K\) parallel non-interfering channels, each with a specific channel gain. In [160], both linear ZF precoding and DPC are considered for VLC assuming the Shannon capacity equation. Interestingly, it is shown that ZF linear precoding obtains a performance close to the optimal one given by DPC. However, this work does not consider the correlation among the channel responses of the users. Assuming users equipped with multiple PDs characterized by distinct FoV each, the implementation of block diagonalization (BD) in a VLC system is proposed in [161]. For BD, each user cancels the interference coming from transmission to other users in blocks with a size equal to the number of PDs allocated to each user. After that, the interference among the PDs of the same user can be cancelled considering a postcoding matrix. This approach is also considered for a two-user scenario in [162].  
Assuming MRC at the multiple PDs of each user, the precoding vectors are calculated subject to obtaining a minimum rate for both users. Determining the precoding vectors for ZF typically requires inversion of the channel matrix. In [34], the performance of ZF precoding is analyzed considering the upper and lower bound of the capacity for VLC and the constraints given by the optimal channel, i.e., a real and non-negative transmitted signal. After that, a methodology for calculating the precoding vectors through convex optimization instead of inverting the channel matrix is derived. Furthermore, this work also considers both criteria maximizing the sum-rate and max-min fairness subject to ZF precoding.