esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Qualitative Analysis of the Application of AR in Industrial Assistance and Training
### AR-Aided Design Tools  
In the field of engineering, two-dimensional (2D) illustrations have long been the primary communication tool. However, 2D representations often prove inadequate when depicting designs that are visually intensive. In contrast, AR offers new possibilities, enhancing both interactive and immersive design by offering more complete and dynamic representations.  
In the field of _interactive design_, AR has transformed the user experience by enabling users to navigate three-dimensional spaces through dynamic representations in various formats. For example, [PERSON] et al. [71] introduced INTERVALES, a virtual and augmented interactive framework for the design of industrial environments and scenarios. [PERSON] et al. [45] explored the potential of AR to assist novice operators in the design of virtual content within industrial contexts. This innovation enables users to design, evaluate and visualise aspects such as internal structures, temperature variations and electronic components. It has been demonstrated that operators utilising these AR tools demonstrate enhanced design abilities, outperforming those who rely on traditional paper-based methods.  
In the field of _immersive design_, AR has established itself as a transformative tool in industrial design, providing users with deeply immersive virtual experiences. [PERSON] et al. [84] advanced this field by achieving intense situational immersion and multimodal interaction in industrial applications, all enhanced by artificial intelligence. Similarly, [PERSON] et al. [93] presented a methodology for the creation of demonstration models that utilise AR to facilitate the comprehension of advanced technologies. This approach provides an interactive, hands-on learning environment that accurately reflects the industrial setting, thus facilitating the acquisition of knowledge and skills. Furthermore, [PERSON] and [PERSON] [41] developed an immersive AR-based learning system for electronic design utilising dynamic marker techniques. These developments demonstrate that the ability to enter a three-dimensional space and interact with virtual components through AR significantly enhances understanding, innovation and efficiency in design, far surpassing the limitations of traditional two-dimensional methods.
# siEDM: An Efficient String Index and Search Algorithm for Edit Distance with Moves
## 6 Search Algorithm
### Computing Positions  
The algorithm also computes all the positions of \(val(X_{i})\), denoted by \(P(X_{i})=\{p\in\{1,2,\ldots,|S|\}:S[p,p+|val(X_{i})|-1]=(X_{i})\}\). Starting from \(X_{i}\), the algorithm goes up to the root in the ESP-tree built from \(S\). \(p\) is initialized to \(0\) at \(X_{i}\). If \(X_{k}\) through the pass from \(X_{i}\) to the root is the parent with the right child \(X_{r(k)}\) on the pass, non-negative integer \((|val(X_{k})|-|val(X_{r(k)})|)\) is added to \(p\). Otherwise, nothing is added to \(p\). When the algorithm reaches the root, \(p\) represents a start position of \(val(X_{i})\) on \(S\), _i.e._, \(val(X_{i})=S[p,p+|val(X_{i})|-1]\). To compute the set \(P(X_{i})\), the algorithm starts from \(X_{i}\) and goes up to the root for each parent in \(Right\mathit{Parents}(X_{i})\) and \(Left\mathit{Parents}(X_{i})\), which return sets of parents for \(X_{i}\). Algorithm 2 shows the pseudo-code.  
```
1:functionComputePosition(\(X\))
2:\(P\leftarrow(Q)\)\(\triangleright\) Initialize solution set
3:Recursion(\(X\), \(1\))
4:functionRecursion(\(X\),\(p\))
5:if\(X\) is the root mode then
6:\(P\gets P\cup\{p\}\)
7:return
8:for each \(X_{p}\in Right\mathit{Parents}(X)\)do\(\triangleright\)\(X\) is the right child of \(X_{p}\)
9:Recursion(\(X\),\(p+|val(X_{p})|-|val(X)|\))
10:for each \(X_{p}\inLeft\mathit{Parents}(X)\)do\(\triangleright\)\(X\) is the left child of \(X_{p}\)
11:Recursion(\(X\),\(p\))
```  
**Algorithm 2** to compute the set \(P\) of all occurrence of \(val(X)\) on \(S\) for \(X\in V(S)\).  
**Theorem 6**: _The computation time of \(P(X)\) is \(O(occ\lg|S|)\), where \(occ\) is the number of occurrences of \(X\) in \(T(S)\)._  
**Proof.** Using the index structures of \(Right\mathit{Parents}(X)\) and \(Left\mathit{Parents}(X)\), we can traverse the path from any node with label \((X)\) to the root of \(T(S)\) counting the position. The length of the path is \(O(\lg|S|)\).  
**Theorem 7**: _The search time is \(O(n|Q|\lg\lg(n+\sigma)(\lg|S|+\lg|Q|)+occ\lg|S|)\) using the data structure of size \(n(n+1)\lg|S|+(n+\sigma)\lg(n+\sigma)+2n+\sigma+o((n+\sigma)\lg(n+\sigma))\) bits._  
**Proof.** The time for computing \(T(Q)\) and \(F(Q)\) is \(t_{1}=O(|Q|\lg^{*}|S|)\). The time for finding candidates and computing \(\|F(Q)-\sum_{j=1}^{m}X_{i}\|_{1}\) is \(t_{2}=O(n|Q|\lg\lg(n+\sigma)(\lg|S|+\lg|Q|))\) by Theorem 5. The time for computing positions is \(O(occ\lg|S|)\) by Theorem 6. Thus, the total time for a query search is \(t_{1}+t_{2}+t_{3}=O(n|Q|\lg\lg(n+\sigma)(\lg|S|+\lg|Q|)+occ\lg|S|)\). The size of the data structure is derived by the results in Section 5.  
In Theorem 7, \(n\) and \(occ\) are incomparable because \(occ>n\) is possible for a highly repetitive string.
## 3 Numerical Model and Validation
### Train Track Dynamic Model  
The dynamic response of the train track dynamic model can be solved by train track coupled dynamic theory [32,33], in which a train consists of a series of vehicles, and each vehicle consists of a total of 10 degrees of freedom, as shown in Figure 6. The car body and bogies are only considered vertical displacement \(w_{c}\), \(w_{1}\), \(w_{2}\), and rotation \(\theta_{c}\), \(\theta_{11}\), \(\theta_{2}\), respectively, and the wheelsets are considered vertical displacement \(w_{w1}\), \(w_{w2}\), \(w_{w3}\), \(w_{w4}\). The train mathematical model is established based on the aforementioned content and subsequently integrated with the established track mathematical model into a system through specific contact relationships to obtain dynamic responses.  
The vertical wheel-rail contact force \(F_{v/r}\) is defined with a nonlinear Hertz contact relationship [34], in which the Hertz contact introduces the non-uniform distribution of the wheel-rail contact force, thereby accounting for wear, fatigue, and deformation in the actual contact area, resulting in more accurate simulation results. \(F_{v/r}\) can be defined as  
Figure 5: Vibration acceleration levels of soil, ground column, and 1 st floor in \(1/3\) octave band frequency.  
Figure 6: Train track dynamic model.  
\[F_{v/r}=\left\{\begin{bmatrix}\frac{1}{G}(q_{v}-q_{r}-h)\end{bmatrix}^{\frac{3}{2}}&q _{v}-q_{r}-h\geq 0\\ 0&q_{v}-q_{r}-h<0\end{aligned}\right. \tag{1}\]  
where \(q_{v}\) and \(q_{r}\) represent the vertical displacement of the contact point between the wheel and track, respectively. The variable \(h\) represents the vertical irregularity amplitude of the rail, while \(G\) represents the wheel-rail contact constant.  
The equations of motion for the train track dynamic model can be denoted as  
\[[\mathbf{M}]\big{\{}\ddot{\mathbf{q}}\big{\}}+[\mathbf{C}]\big{\{}\dot{ \mathbf{q}}\big{\}}+[\mathbf{K}]\big{\{}\mathbf{q}\big{\}}=\{\mathbf{F}\} \tag{2}\]  
where \(\mathbf{M}\), \(\mathbf{C}\), and \(\mathbf{K}\) denote global mass, damping, and stiffness matrices of the assembled train track dynamic model, respectively, and specific parameters of the train and track structure can be referred to in [PERSON] et al. (2014); \(\mathbf{q}\), \(\ddot{\mathbf{q}}\), \(\ddot{\mathbf{q}}\) denote the displacement, velocity, and acceleration vectors of the whole model, respectively. The input to the load vector \(\mathbf{F}\) is provided by the wheel-rail contact force.  
To solve the equation of motion for the train track dynamic model, the Wilson-\(\theta\) method is used. The calculation mainly considers the effect of vertical track profile irregularity. The American track spectrum of sixth grade and the Sato irregularity spectrum ([PERSON] et al., 2014; [PERSON] et al., 2014) are adopted. The wheel-rail contact force is shown in Figure 7.
# Named Entity Recognition for Sensitive Data Discovery in Portuguese
## 4 Experiments and Results
### Neural Network Model Evaluation  
The final experiment was the implementation of a Bi-LSTM. This model, unlike the others, was trained and tested only with the SIGARRA News Corpus, this is due to the insufficient number of samples in the other corpus to train the model. The corpus was previously divided into three sets: Training, development, and testing. The embed function that creates word-level embeddings was used to generate an embedding representation for each word of the text. The parameters used for training the model were: 80 epochs, 0.68 dropouts, 275 LSTM state size, and three convolutional widths. After training the model and generating the final outputs through the softmax layer, in IOB tagging format, it was possible to perform the evaluation of the model presented in the last row of Table 2.  
We can see that the results obtained by this model are higher than those obtained by the statistical models. This model obtained an f1-score of 78.25%, about 13% higher compared to the best statistical model implemented, for the same corpus. In an attempt to compare the state-of-the-art models to the current one tested, we did not find an approach for the same corpus, but we were able to understand that the same Bi-LSTM model applied to the English language has results 12% higher on average [16], [17]. A similar approach with an LSMT-CRF model [16], for a corpus in Portuguese, presents f1-score results of 76.03%, lower than the 78.27% we achieved with this model. By analyzing similar models and improving results with the statistical models tested, we were able to understand that with a larger corpus the results with this type of model greatly improve [17].  
Finally, it is possible to see, for each corpus tested, the comparison results by entity class for each approach. The results achieved with the SIGARRA News Corpus were significantly better in all experiments than the results achieved with the HAREM Golden Collection. This was due to the fact that the SIGARRA corpus is larger than the HAREM, which improves the training process. In addition, the SIGARRA News Corpus contains many documents with the same structure, making it easier to learn.  
In Table 3, we can see the average of the results for each entity class in each model. The best results achieved for each class are represented in bold on the table, and we can see that the best results for the entities TEMPO and VALOR were achieved with the Lexicon-based models and, for the remaining entities, the best results were achieved by the Bi-LSTM model.
# Low Power QC-LDPC Decoder Based on Token Ring Architecture
## LDPC Codes and Their Decoding--Preliminaries
### LDPC Decoding Algorithm
Considering the data processing parallelism in an implementation, the decoder architecture can be serial, partially-parallel or parallel. Partially-parallel decoders are the best choice for most applications as being a compromise between decoding throughput and hardware resources overhead. The parity check matrix of QC-LDPC code fits perfectly into the concept of partially-parallel decoders by structuring the \(\mathbf{H}\) matrix into \(\mathbf{P}\) submatrices that have only single \"1\" value in each column and each row. This allows the individual decoding steps to be parallelized and messages efficiently propagated with the use of memories, with single memory word grouping messages corresponding to the single submatrix of \(\mathbf{H}\). Figure 3 presents the concept of partially-parallel decoder architecture. We have noticed that this architecture of the QC-LDPC decoder is consistent with the idea of a Token Ring network, one of the methods of creating a Local Area Network (LAN) developed by IBM [40]. In the concept of \"token passing\", a token is awarded in a given moment to this network element that is about to start processing and transmitting data. We have applied this concept in our partially-parallel QC-LDPC decoder implementation. Here the token is passed between the control and bit nodes computing units, which require obtaining the result of the previous iteration, according to the presented MS decoding algorithm, to perform the next iteration of computation. The proposed token passing scheme for the partially-parallel QC-LDPC decoder architecture is shown in Figure 3.
## 6 Real-Time Monitoring and Fault Diagnosis Technology  
By monitoring the operating status of the scraper in real time, possible faults can be predicted, so that proper solutions can be prepared to reduce the rate of failure, ensuring efficient, safe and reliable operation of the scraper.
## 5 Conclusions  
Space borne GNSS-R altimetry from the ISS is feasible with a RMS error smaller than 30 cm\({}_{\text{rms}}\) within the whole swath (\(\theta_{i}\leq 35^{\circ}\)) using Galileo signals (with GPS as well, but errors are beyond the  
Figure 17: RMS height error sensitivity to antenna directivity (P) at nominal values +3 dB, u = [dB].  
GEROS-ISS requirements), when the space vehicles transmit at least their nominal power, and when dual-frequency observations are used in the ionospheric-free combination to cancel ionospheric effects (background TEC and refractive scintillations). Diffractive scintillations cannot be compensated for, but since they occur mostly after sunset and before midnight, either the measurements are discarded (or not even acquired), or a Sun-synchronous orbit with 6 a.m. or 6 p.m. LTAN (local time of the ascending node) can be selected.  
Multiple scattering from the ISS structure has revealed that the strength of the received signals is affected (important for scatterometry), that the waveform peak is shifted by \(\sim\)0.06 C/A chips = 18 m (important for altimetry), that there is a region where signals are blocked by the ISS itself, then, 4) that there are other regions in the space where two or up to three \"rays\" may reach the antenna arrays. In any case, multipath can be compensated for by computing the variance of the intensity waveforms, instead of the incoherent averaging.  
Finally, the Sensitivity of the GNSS-R altimetry performance to instrumental errors has been computed, showing the criticality of the antenna pattern directivity, and the importance of keeping low noise figures in the receiver's front-ends.
## 2 Study Area and Data
### Satellite Data and Preprocessing  
The Envisat ASAR satellite was equipped with a C-band SAR (5.405 GHz) with multipolarization, 7 observation angles and 5 operating modes. In the paper, ASAR data were used, whose operating mode was alternation polarization with two types of polarizations (VV and VH) and high space resolution (\(12.5\times 12.5\) m per pixel). The ASAR data were provided on 11 July 2008 and 24 May 2008.  
The satellite data were preprocessed with European Space Agency SNAP software. Radiometric calibration was applied, which can transform the digital number (DN) of each pixel of the original single-look complex product into the backscattering coefficient in decibels. An LEE filter with a \(7\times 7\) moving window was applied to weaken the effect of speckle noise. Then, range Doppler terrain correction was performed based on ACE30 DEM.
## Appendix B  
Proof 1 of Theorem 2.: The following facts prove the theorem:  
1. The dynamics of the agent according to Equations (10)-(11) are equivalent to the simultaneous perturbation stochastic algorithm and the sequences of agent movements during \((x(0),\ x(1),x(2)\ldots\ldots,(t\rightarrow\infty)\) and converge to w.p.1, which is under the conditions of (10) to (11). 2. The relation holds for even time \(t\in\{0,2,4,\ldots\ldots\}\) and odd time \(t\in\{1,3,5,\ldots\ldots\}\) as per Equations (10) and (11), which will also affect the next agent state as stated in Equation (10). \[B(t)=P(x(t))\] (11) \[B(t+1)=P(x(t))+(c(t)+\Delta\_i\ (t))\] (12)
3. For \(t\in\{0,2,4,\ldots\ldots\}\), the relation between BET represented by Equation (11) is held. \[x(t+2)=x(t)-\left[a(t)\frac{B(t+1)-B(t)}{c(t)}*\Delta_{i}^{-1}(t)\right]-k \sum_{j\in N_{i}}a_{ij}|x_{i}-x_{j}|\] (13) Substitution of Equations (10) and (11) into Equation (13) will obtain Equation (14), which can be simplified to Equation (15). \[ \begin{array}{c}x(t+2)=x(t)-a(t)\frac{(P(x(t)+c(t)\Delta_{i}(t))-P(x(t))}{c (t)}\Delta_{i}^{-1}(t)\\ -k\ a_{ij}\sum\limits_{j\in N_{i}}(x_{i}-x_{j})\end{array} \] (14) \[x(t+2)=x(t)-a(t)\ d(x(t),\ \Delta_{i}(t),\ c(t))-k\ a_{ij}\sum \limits_{j\in N_{i}}(x_{i}-x_{j})\] (15)  
By applying [PERSON]'s theorem to Equation (12), the expected value of \(d(x(t),\ \Delta_{i}(t),\ c(t))\) is near to the gradient of \(P(x(t))\), as shown in Equation (13). \[E(d(x(t),\ \Delta_{i}(t),\ c(t)|x(t))=\
abla\ P(x(t)+O(c(t)(c(t)\to 0) \tag{16}\]  
Thus, the agent state of \(x(t+2)\) is represented by Equation (14), in which the gradient descent method has been integrated with the sum of the agent communication error between the connected agents. With these, \(x(t)\) will converge to a local minimum point of \(P\). \[E((x(t+2)|x(t))\cong x(t)-a(t)\;P(x(t))-k\;a_{ij}\sum_{j\in N_{i}}(x_{i}-x_{j})\] (10)
4. The collective dynamics movements of agent BET at \(|x(1)-x(0)|\), \(|x(3)-x(2)|\), \(|x(5)-x(4)|\),........ and \(|x(n+1)-x(n)|\) show that the agents will converge to \(x_{d}\) or 0 w.p.1. The dynamics are determined with the aim of asymptotically achieving consensus locally and globally, as highlighted in Theorems 1 and 2. 5. If (ET1), (ET2), (A3) and (A4) hold, the dynamics in Equation (1) will converge to a minimum point of function \(P\), represented by the quadratic function (Equation (8)) that measures the degree of achievement when the agents move from the initial state \(x_{0}\) to the target point \(x_{d}\) w.p.1. The settings of broadcast (A3) and (A4) and event-triggered (ET1) and (ET2) are imposed for tuning \(L_{i}\) and \(G\) as defined in Equations (7) and (11). \[\bullet\quad\text{(ET1)}\;0<k<1/N\text{, then }\lim_{t\to\infty}x_{i}(t)-x_{j}(t)=0\] \[\bullet\quad\text{(ET2)}\;f(e_{i}(t),x_{i}(t))\leq\sigma_{i}(z_{i}(t))\text{, then }e_{i}(t)=0\text{, else }e_{i}(t)=x_{i(t)}-x_{i(t)}\] (11)
6. If the conditions of SPSA convergence satisfy (A1)-(A7) and the conditions of ET satisfy (ET1) and (ET2), the system will converge when all agents reach the desired target \(x_{d}\).
## 6 Numerical Results  
In this section, simulation results of transmission rate, outage probability, and EE are presented to verify the effectiveness of the designed transmission strategy with Matlab 2022a. With the Matlab, the SWIPT-enabled CRN model and the signal transmission process and communication behavior can be highly restored [18; 20]. And all the results are given with Monte Carlo simulation with 500 times loops in this paper. Specifically, the transmission performances of the following six transmission strategies, i.e., the designed energy-efficient adaptive bidirectional transmission strategy (namely, EEAB-SWIPT in the simulation), the designed energy-efficient adaptive bidirectional without SWIPT transmission strategy (namely, EEAB in the simulation), the designed adaptive bidirectional without SWIPT and OPA transmission strategy (namely, AB in the simulation), the two-way AF relay transmission strategy proposed in [30] (namely, TWBAF in the simulation), the best relay selection transmission strategy in [12] (namely, BRS in the simulation), and the non-collaborative transmission strategy (namely, NC in the simulation) are compared.  
For the simulation, all channels are modeled as following a complex Gaussian distribution, and all node noises are assumed to be AWGN with a variance of \(\sigma^{2}=1\). At the same time, all transmit signals have unit energy, i.e., \(\mathbb{E}\Big{\{}\big{|}x_{p}\big{|}^{2}\Big{\}}=\mathbb{E}\Big{\{}\big{|}x_{ 1}\big{|}^{2}\Big{\}}=\mathbb{E}\Big{\{}\big{|}x_{2}\big{|}^{2}\Big{\}}=1\). And the maximum transmit power is set to \(P_{T}^{\max}=60\) W. What is more, in this paper, as in [25], the distances from PTU to the SU\({}_{1}\) and SU\({}_{2}\) are 5 m, while the distance between SU\({}_{1}\) and SU\({}_{2}\) is 10 m. At the same time, the multi-relay nodes are randomly distributed in the secondary network. The other simulation parameters are provided in Table 2.
## 3 EEG Mechanisms and the Case of North Rhine-Westphalia  
### Renewable Energy Resources Act (EEG) in Germany  
The Renewable Energy Sources Act (_Erneuberbare-Energien-Gesetz_, _EEG_) was first enacted in 2000, and updated several times (_EEG2004_, _EEG2009_, _EEG2012_, _EEG2014_, _EEG2017_). This law aimed at promoting and steering the deployment of renewable energy technologies (RET), by guaranteeing, for ca. 20 years, fixed tariffs for electricity from RET, as well as its priority feed-in into the power grid. Such so-called feed-in tariffs (FiTs) vary according to the technology (e.g., photovoltaic, wind), the type (e.g., rooftop-, ground-mounted) and peak capacity of the installation. The law prescribes that such subsidies are financed through a fixed electricity surcharge (i.e., _EEG-Umlage_) paid by final consumers per each kWh of electricity used, which amounted to 3.53 ct/kWh in 2011. Energy-intensive industries can obtain a reduced surcharge, and such an exception is considered in our model.
# VNF-Enabled 5G Network Orchestration Framework for Slice Creation, Isolation and Management
## 3 Network Model and Mathematical Foundations
### Network Node Attributes
#### 3.4.2 Node Topology Element (NTE) and Node Bandwidth Element (NBE)  
Both NTE and NBE are determined by the physical node's number of nearby connections. NTE denotes the total number of neighbouring connections to a node, whereas NBE denotes the total bandwidth of nearby links. A node with a higher NTE and NBE is prioritized for provisioning in the network. The value of each element is updated as per the life time of each NSR. Listed below are the equations for updating the factors at time \(t\).  
\[NTE_{i}^{t}=\sum_{j=1}^{n}a_{ij,available},\quad i\
eq j \tag{8}\]  
\[NBE_{i}^{t}=\sum_{j=1}^{n}BW_{ij}^{PI},\quad i\
eq j \tag{9}\]  
where \(n\) is the number of nodes in the network, and if the \(i^{th}\) node is connected to the \(j^{th}\) node and both are unserved, then \(a_{ij,available}\) will be \(1\); otherwise, it will be \(0\).