esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodological and Operational Framework for CDTs
### Understanding CDTs  
A DT in manufacturing industry is defined as a digital replica of the physical system (e.g., the entire supply chain) or integral parts of it (e.g., a factory, a production line) that: (i) is an accurate enough virtual model of the real system, (ii) can simulate both the physical state and behaviour of the system, (iii) can be uniquely associated with any single specific instance of the system and its entities and (iv) it is for that purpose connected to it, updating itself in response to known changes to the system's state condition or context [18]. A Digital Twin has a bilateral integration with the physical object by both getting information from the object and controlling the physical object. It also combines an advanced data-acquisition system, information technology and network technologies to create a virtual, digital replica of a production system with various capabilities in manufacturing industry. Consequently, it must:  
* Create and manage a virtual model of the system.
* Virtualize the behaviour of the physical entity.
* Have different capabilities allowing to simulate, predict and optimize.
* Connect virtual (model) entities with physical ones, updating itself in response to known changes to the entity's state condition or context.  
An advanced functionality of the DT is \"Cognition\" (i.e., CDT): a DT able to \"reason\" and \"act\" taking into consideration the data, the states of the object and its behaviour [19]. This means that a CDT is able to understand, early detect and predict the impact of different types of behaviour observed [20]:  
* The Predictable Desired (PD), which is the desired (normal) behaviour of the system.
* The Predictable Undesired (PU), which are problems that are expected but we cannot understand why they happen.
* The Unpredictable Desired (UP), which is the \"surprise\" and benefits/good behaviour of the system that we did not expect to happen.
* The Unpredictable Undesired (UU), which is a serious fact and relates to behaviour that we did not expect to happen, and we do not know why they happen.  
A key differentiation between DTs and CDTs is that while some cognitive functions are present in DTs, such as simulations, those focus on solving a specific task but lack a holistic view of the existing models and aspects addressed by the DT. The CDT provides such a view to relate their outcomes among different CDTs, learn how UP and UU events affect the physical counterpart, taking cognition abilities to a higher abstraction level. CDTs address how to enhance cognition over time by providing means to improve a specific model through knowledge gathered from past UP and UU events or relating different types of knowledge that are introduced to the CDT or emerge from different models or components present in a DT [21; 22].
## 3 Results and Discussion
### Results of Numerical Simulations  
Table 9 presents the numerical simulation results applied to the fatigue models.  
From the results presented in Table 9, the following hierarchy can be established for the asphalt mixtures, in terms of fatigue lifespan, starting by the longest one: structure S3 (surface with SBS 60/85 mixture), structure S4 (surface with 3% NC + 2% SBS mixture), structure S2 (surface with 3% NC mixture), and structure S1 (surface with conventional mixture).  
First, comparing the structures S1 and S2, it could be observed that the use of the 3% NC mixture, in substitution to the conventional one, would represent an increase of 1.5 times in the fatigue lifespan  
Figure 8: Curves obtained in fatigue tests.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Structure/Asphalt Surface** & **Strain (\(\mu\varepsilon\))** & **Fatigue Model** & **Number of Cycles (N)** \\ \hline S1/Surface with CAP 50/70 & 65 & \(N=1.32\times 10^{14}\)\(\mu\varepsilon^{-425}\) & \(2.60\times 10^{6}\) \\ S2/Surface with 3% NC & 75 & \(N=1.09\times 10^{16}\)\(\mu\varepsilon^{-4.92}\) & \(6.49\times 10^{6}\) \\ S3/Surface with SBS 60/85 & 80 & \(N=1.66\times 10^{19}\)\(\mu\varepsilon^{-5.84}\) & \(1.28\times 10^{8}\) \\ S4/Surface with 3% NC + 2% SBS & 61 & \(N=2.80\times 10^{17}\)\(\mu\varepsilon^{-5.41}\) & \(6.15\times 10^{7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Numerical simulations results applied to the fatigue models.  
of the surface. When the same comparison is carried out between the S1 and S4 structures, it is noted that the simultaneous addition of the polymer to the nanoclay modified mixture would enhance these gains to about 23 times. However, despite the high performance of the 3% NC + 2% SBS mixture and keeping the thicknesses of the layers, it is noted that the use of the SBS 60/85, in substitution to the mixture modified simultaneously by nanoclay and polymer, would double the fatigue lifespan of the surface. In this sense, looking for the equivalent performance of these two mixtures, it requires an increase in the thickness of the 3% NC + 2% SBS modified surface. Even so, considering a possibility of cost reduction from the substitution of the SBS 60/85 mixture by the 3% NC + 2% SBS (due to the lower polymer content), it is expected that this substitution, besides being technically feasible, could also be economically viable. This finding highlights the application potential of the 3% NC + 2% SBS mixture, which may present as a competitive alternative to the other mixtures studied.
## 2 Review of Design Provisions and Concepts
### Canadian Masnorry Standards  
In Canada, the standard method for structural design is the limit states design, which applies to masonry, as well as other construction materials. According to the CSA S304.1-14 [25] standard, limit states are defined as conditions where a building structure no  
Figure 1: Moments from calculation of eccentricities according to Eurocode 6.  
longer fulfils its intended function. This standard serves as a guideline for masonry design and specifies the prescribed values for the compressive strength of unreinforced masonry via tabulated data that correspond to specified unit strengths. The Canadian standard introduces a constraint whereby the maximum resistance of URM walls against axial load is limited to 80%. This implies that the axial load-bearing capacity should not surpass a specified value, denoted as P, and is represented as Equation (4).  
\[\mathrm{P}=0.8\ \big{\{}\Phi_{m}\ \big{(}0.85f^{\prime}_{m}\big{)}\big{\}}\, \mathrm{A_{e}} \tag{4}\]  
where \(\Phi_{m}\) represents the material resistance factor for masonry and \(f_{m}\) denotes the specified compressive strength of the masonry. The effective cross-sectional area, \(\mathrm{A_{e}}\), represents both the mortar bedded area and the area of voids filled with grout. The Canadian standard also provides designated values for the compressive strength of grouted masonry. The standard dictates that the minimum compressive strength of grout, prepared according to specified guidelines and cast in non-absorbent cylinder moulds, should fall within the range of 10 MPa to 12 MPa. In cases where test data are unavailable, an assumed in situ grout strength of 20 MPa can be adopted.  
Recognizing the practicality that axial loads are seldom applied accurately along the centroidal axis of a masonry wall and absolute vertical alignment is rarely achieved, clause 7.7.3 of the Canadian standard stipulates that the eccentricity of axial load application must not be less than 10% of the wall's thickness at both ends. The Canadian standard also specifies the following slenderness limits:  
For \(\mathrm{kh/t}<(10-3.5\mathrm{e_{1}/e_{2}})\) neglect slenderness effect;  
For \(\mathrm{kh/t}>30\mathrm{revise}(\mathrm{not\ applicable})\);  
For \(30>\mathrm{kh/t}>(10-3.5\mathrm{e_{1}/e_{2}})\) account for slenderness effect;  
where \(\mathrm{kh}\) is the effective height of the wall, and the values \(\mathrm{e_{1}}\) and \(\mathrm{e_{2}}\) correspond to the smaller and larger virtual eccentricities at the ends of the wall, respectively.
## 4 Selected Use Cases and Opportunities
### Video Streaming  
In this scenario, the UAV captures video footage and transmits it to a relay node using THz communication. THz frequencies are ideal for high-bandwidth applications like video streaming due to their ability to support high data rates, enabling the transmission of high-resolution video with minimal latency. The relay node, positioned to maintain a stable connection with the UAV, receives the video data and then forwards it to the end user inside the building using VLC, as illustrated in Figure 6.  
Using VLC for the final leg of transmission inside the building is advantageous for several reasons. VLC can leverage existing lighting infrastructure, such as LED lights, to transmit data, reducing the need for additional hardware. Furthermore, VLC provides a secure communication channel as the light waves are confined within the room, minimizing the risk of eavesdropping from outside the building. This dual-mode communication approach, combining THz for long-range, high-bandwidth transmission and VLC for secure, indoor delivery, ensures efficient and reliable video streaming from the UAV to the end user.  
A similar use case will be analyzed in detail in the next few sections.  
Figure 5: This diagram illustrates the use of THz and VLC in a military application. The system employs a fixed-wing UAV to facilitate dual-hop communication between a command center and the ground troops. THz communication is utilized for outdoor communication, while indoor communication utilizes VLC.
## 2 System Model and Problem Formulation
Define energy efficiency as the ratio of throughput to energy consumption, then the user clustering and power allocation problem for the energy efficiency maximization in the downlink NOMA system with \(n\) users and \(c\) clusters can be formulated as  
\[\max_{\delta,P}\frac{\sum_{j=1}^{c}\sum_{i=1}^{n}\delta_{ij}\omega_{j}b\log_{2} \biggl{(}1+\frac{H_{i}P_{i}}{\omega_{j}+\sum_{k=1}^{i-1}\delta_{ij}H_{i}P_{k}} \biggr{)}}{\sum_{i=1}^{n}P_{i}} \tag{6}\]  
under the following constraints:  
* maximum transmission power constraint of the BS \[\sum_{j=1}^{c}\sum_{i=1}^{n}\delta_{ij}P_{i}\leq P_{B},\] (7)
* maximum transmission power constraint of each cluster \[\sum_{i=1}^{n}\delta_{ij}P_{i}\leq\omega_{j}p_{b},\] (8) \[P_{1}+P_{2}+\cdots+P_{n}\leq P_{B}. \tag{1}\]0
* necessary power constraint for efficient SIC of each cluster \[P_{1}+P_{2}+\cdots+P_{n}\leq P_{B}. \tag{1}\]1 (9) \[P_{1}+P_{2}+\cdots+P_{n}\leq P_{B}. \tag{1}\]2
* minimum data transmission rate requirement of each user \[P_{1}+P_{2}+\cdots+P_{n}\leq P_{B}. \tag{1}\]3 (10) \[P_{1}+P_{2}+\cdots+P_{n}\leq P_{B}. \tag{1}\]4
* user clustering and resource allocation constraints \[\left\{ \begin{aligned} & 2\leq\sum_{i=1}^{n}\delta_{ij}\leq n, \forall j\in\{1,2,\cdots,c\},\\ &\sum_{j=1}^{c}\delta_{ij}=1,\forall i\in\{1,2,\cdots,n\},\\ &\sum_{j=1}^{c}\delta_{ij}\omega_{j}\leq m,\forall i\in\{1,2, \cdots,n\},\end{aligned} \right.\] (11)
* domain constraints \[\left\{ \begin{aligned} &\delta_{ij}\in\{0,1\},\forall i\in\{1,2, \cdots,n\},j\in\{1,2,\cdots,c\},\\ & 0<P_{i}<P_{B},\forall i\in\{1,2,\cdots,n\}.\end{aligned} \right.\] (12)
## 6 Results and Discussion
### Instance Validation  
In the instance validation part, considering the actual environment of the shipyard, we set a warehouse layout as high-rise shelves with 4 aisles, 8 sides, 10 floors, 20 columns, and 2 synchronous picking stations, as well as 10 available AGVs. We solved the instance problem of optimizing 12 pallet-picking tasks with 120 orders under the above warehouse layout conditions by MADPSO.  
Tables 3 and 4 and Figure 14 show the final solution of the instance problem, where Table 3 shows the AGV task sequence, Table 4 shows the stacker scheduling scheme, and Figure 14 shows the task Gantt chart of the picking station. In addition, Figure 15a shows the evolution curve of the number of individuals in \(F_{1}\) layer with the number of iterations. It can be seen from the figure that after 100 iterations, the scale of the Pareto solution set has stabilized to more than the expected value of 80. It can be preliminarily considered that the algorithm has converged at this time. Figure 15b shows the evolution curve of the reference solution cost of the optimal ideal reference point selected by the TOPSIS operator in each generation with the number of iterations. It can be found from the curve that the algorithm converges in about 100 iterations, which verifies that MADPSO can converge within finite iterations when solving SACOP problems from another perspective. Figure 15c shows the Pareto front distribution. MADPSO uses the selection operator of the classic NSGA-III to ensure the diversity of individual distribution in the final Pareto solution set.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**NO.**} & \multicolumn{3}{c}{**Stacker 1**} & \multicolumn{3}{c}{**Stacker 2**} & \multicolumn{3}{c}{**Stacker 3**} & \multicolumn{3}{c}{**Stacker 4**} \\ \cline{2-13}  & **ORN\({}^{\text{1}}\)** & **TST
# A Sparse Multiclass Motor Imagery EEG Classification Using 1D-ConvResNet
## 3 Results
### Performance of the Proposed Method with Different Epoch Lengths  
Each trial of the MI task in the dataset lasts 4 s, and any subject can perform the MI task many times during one trial. According to [10], one MI lasts for 0.5 s. Therefore, we used an initial length of the epoch as 0.5 s. However, it is essential to decrease the epoch length to reduce the computational time for a real-time BCI system. Therefore, we extracted different epoch lengths to investigate which epoch length yields a better classification accuracy and lesser computational time in training and testing. Table 1 presents the training and testing computational time and accuracy of different epoch lengths for the proposed method. It is evident that the training time increased with decreasing length of the epochs, and testing time was significantly reduced. However, classification accuracy was highly compromised for 0.2 s followed by 0.3 s. We can consider 0.4 s epoch length for optimal accuracy and computational time, but 0.5 s epochs have greater accuracy than other epoch lengths.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
\begin{tabular}{c} **Epoch** \\ **(Length of Trial in Seconds)** \\ \end{tabular} & **Accuracy (\%)** & **Training Time (s)** & **Testing Time (s)** \\ \hline
0.1 & 76.08 \(\pm\) 0.45 & 300 & 0.25 \\ \hline
0.2 & 62.59 \(\pm\) 0.25 & 259.06 & 0.33 \\ \hline
0.3 & 72.8 \(\pm\) 0.24 & 140.8 & 0.39 \\ \hline
0.4 & 90 \(\pm\) 0.16 & 110.63 & 0.45 \\ \hline
0.5 & 96.6 \(\pm\) 0.18 & 98 & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance of the proposed method with different epoch lengths.  
Figure 8: Subject-independent classification using transfer learning. We trained the model using training subjects’ data. Later, we classified test subjects’ data using the trained weights.  
Figure 9: \(Cont.\)
## 2 System Model
### Parr  
Let us calculate the average power of OFDM symbols and derive a mathematical relationship between the PAPR and OFDM number of subcarriers in this subsection. Firstly, the average of OFDM symbols is calculated. The PAPR can be defined as the ratio of max (transmitted signal)/average transmitted power. It can be written as  
\[PAPR=\frac{\max[x_{n}]}{E[|x_{n}|^{2}]} \tag{6}\]  
Here \(E[|x_{n}|^{2}]\) is the average signal power.  
The average signal power is evaluated further as \(=E[x(n).x^{*}(n)]\)  
\[=\frac{1}{N^{2}}.E[\sum\limits_{i=\frac{N}{2}}^{\  
Thus, the average power of transmission is \(\frac{a^{2}}{N}\). To analyze the peak power when QAM symbols have an amplitude of \(\pm a\), we consider all the information symbols of QAM as \([X(0).X(1)\ldots X(N-1)]\), which has an amplitude of \(+a\)  
\[\begin{array}{l}x(0)=\frac{1}{N}\sum\limits_{i=\frac{N}{2}}^{\frac{N}{2}-1}X( i)\\ =\frac{1}{N}.N.a=a\end{array} \tag{10}\]  
Hence, we see that the peak power is \(a^{2}\). The ratio of peak to average power (PAPR) = \(\frac{a^{2}}{\frac{a^{2}}{N}}=N\). \(N\) is the number of subcarriers, which can be 32, 64, 128, 256, or more than 512. In conclusion, the PAPR in an OFDM system is high when the number of subcarriers increases by order because the symbols of data in the subcarriers are added up, which produces the high peak valued signals. The signal which passes through a PA or transmitting transducer can be split into two different components. The first one is a distorted part, and the second is with no distortion. By doing this, we have an opportunity to estimate the distorted part of the signal at the receiver side. If the model of the PA is known, then the distortion term can be estimated efficiently. Therefore, the machine learning algorithm can help us to assess the PA model at the receiving side. The performance and computational capabilities of this algorithm are very high, and it can estimate accurately. Hence, this allows us to design a signal processing algorithm that will estimate the distortion term and give us improved performance.
# A High-Resolution SAR Focusing Experiment Based on GF-3 Staring Data
###### Abstract  
Spotlight synthetic aperture radar (SAR) is a proven technique, which can provide high-resolution images as compared to those produced by traditional stripmap SAR. This paper addresses a high-resolution SAR focusing experiment based on Gaofen-3 satellite (GF-3) staring data with about 55 cm azimuth resolution and 240 MHz range bandwidth. In staring spotlight (ST) mode, the antenna always illuminates the same scene on the ground, which can extend the synthetic aperture. Based on a two-step processing algorithm, some special aspects such as curved-orbit model error correction, stop-and-go correction, and antenna pattern demodulation must be considered in image focusing. We provide detailed descriptions of all these aspects and put forward corresponding solutions. Using these suggested methods directly in an imaging module without any modification for other data processing software can make the most of the existing ground data processor. Finally, actual data acquired in GF-3 ST mode is used to validate these methodologies, and a well-focused, high-resolution image is obtained as a result of this focusing experiment.  
SAR; GF-3; staring spotlight; two-step algorithm; curved orbit; stop-and-go; antenna pattern; high-resolution +
Footnote †: journal: Physics Letters A
## 3 Results  
### Dependence of Rheological Parameters from Non-Volatile Media  
Preliminary tests (see Supplementary Materials) highlighted how non-volatile media can potentially affect the rheological behaviour of TBS sampled in diverse reactors. Two TBS samples were extracted from two diverse full-scale plants (with similar conditions of feeding and operational temperature) and both the consistency coefficient, and the flow behaviour index were calculated to characterize the sludge from a rheological point of view. Results show that TBS with lower TS and NVS content exhibited a higher consistency coefficient, thus suggesting a possible relation between NVS and rheological properties.  
Therefore, the influences of non-volatile media concentration and type were considered and the relation between these factors and TBS rheology was deeply studied. Figure 1 shows the results of the tests carried out by varying the concentration of diverse non-volatile media in TBS.  
The consistency coefficient (\(k_{i}\)) of the fluid conditioned with calcium carbonate was nearly constant despite a strong increase in the concentration of solids inside the tested sludge sample (Figure 1a). The variation of parameter \(k\) with respect to that of the unconditioned sample (\(k_{0}\)) was expressed by the non-dimensional ratio \(k_{i}\,k_{0}{}^{-1}\) that reached \(1.1\). However, this increase was limited when compared with an increase in NVS concentration greater than 110% (from 90 kg m\({}^{-3}\) to 190 kg m\({}^{-3}\)). Moreover, \(n_{i}\,n_{0}{}^{-1}\) also remained almost the same. It reduced from a value of 1 (unconditioned situation) to 0.98 at 190 kg m\({}^{-3}\) concentration of NVS (Figure 1b), showing an almost negligible influence of this type of non-volatile media on the rheology of TBS within the investigated concentration range.  
In the case where the sample was conditioned with sand, the sludge consistency coefficient reduced to 0.5 the value of the unconditioned sludge (Figure 1a). Apart from two values of the flow behaviour index \(n\), obtained with NVS concentration in the range 120-140 kg m\({}^{-3}\), deserving further investigation, the distance from the Newtonian behaviour was relatively small (i.e., \(n_{i}\,\,n_{0}{}^{-1}\approx 1.1\)) if compared with an increase in NVS concentration greater than 110% (Figure 1b).  
Sodium bentonite produced the greatest influence on TBS rheology among the three types of NVS (see Supplementary Materials). Increasing NVS from the initial 90 kg m\({}^{-3}\) up to 190 kg m\({}^{-3}\), the ratio \(k_{i}\,k_{0}{}^{-1}\) reached the value of 19.29 (Figure 1a). Concerning the flow behaviour index (\(n\)), adding sodium bentonite made the conditioned sludge behave like a pseudoplastic fluid as the ratio \(n_{i}\,\,n_{0}{}^{-1}\) was always lower than 1 for all NVS concentrations, reaching the minimum value of about 0.67 at the highest NVS concentration (Figure 1b).
## 3 Results and Discussion
### Discussion  
The above numerical results, including beam steering, focusing and generation of OAM vortex beams, could successfully demonstrate the validation of the proposed method. It is because the used FDTD method is a full-wave simulation algorithm, which is widely accepted in the related research fields of electromagnetic theory from visible to the microwave region [36]. From an experimental point of view, \(7\times 7\) patch elements, phase shifters, bias voltage control circuits operating at high frequency and vector network analyzer are required. By adjusting the bias voltage of the diode printed on the microstrip plate phase shifter, the states of the switch can be controlled. As a result, the phase shift can be achieved by controlling current to pass through microstrip lines with different lengths. The signal from the vector network analyzer is connected to the patch unit through the phase shifter. By adjusting the phase through the method mentioned above, beam scanning, focusing and generation of OAM wave can be obtained.