esa-sceva/satcom-chunk-collection huggingface dataset:
# Simultaneous Coded Plane-Wave Imaging Using an Advanced Ultrasound Forward Model
## Appendix C Estimation of the Condition Number of Kernel \(\boldsymbol{\
abla}\)  
The condition number \(K_{\boldsymbol{\
abla}}\) of kernel \(\boldsymbol{\
abla}\) can be deduced as follows. In Section 2.10, we showed that the filters \(f^{(k)}(t)\) are adapted to quasi-orthogonal waveforms \(e^{(k)}(t)\). This means that \(f^{(k)}(t)\) is also almost orthogonal and the resulting Toeplitz matrices \(\mathbf{F}^{(k)}\) verify \((\mathbf{F}^{(k)})^{T}\mathbf{F}^{(k)}\approx\mathbf{I}\), \(\forall k\in\{1..N_{\text{pw}}\}\) where \((\ )^{T}\) is the transpose operator and \(\mathbf{I}\) is the identity matrix. We can use the Gram matrix rank property to deduce the rank \(\varrho_{\boldsymbol{\
abla}}\) of the matrix \(\boldsymbol{\
abla}\):  
\[\varrho_{\boldsymbol{\
abla}}=\varrho_{(\boldsymbol{\
abla})^{T}\boldsymbol{ \
abla}}\] (A6)  
Using Equation (A3), we can deduce the analytical expression for the matrix product \((\boldsymbol{\
abla})^{T}\boldsymbol{\
abla}\):  
\[\begin{split}(\boldsymbol{\
abla})^{T}\boldsymbol{\
abla}=\sum \limits_{k=1}^{N_{\text{pw}}}\sum\limits_{i=1}^{N_{\text{el}}}(\boldsymbol{ \Delta}_{i})^{T}(\mathbf{F}^{(k)})^{T}\mathbf{F}^{(k)}\boldsymbol{\Delta}_{i} \approx\\ \sum\limits_{k=1}^{N_{\text{pw}}}\xi^{(k)}\sum\limits_{i=1}^{N_{ \text{el}}}(\boldsymbol{\Delta}_{i})^{T}\boldsymbol{\Delta}_{i}\approx\Xi\sum \limits_{k=1}^{N_{\text{pw}}}\xi^{(k)}\\ \text{with: }\begin{cases}\quad\xi^{(k)}=\|\mathbf{F}^{(k)}\|\\ \Xi=\sum\limits_{i=1}^{N_{\text{el}}}(\boldsymbol{\Delta}_{i})^{T}\boldsymbol{ \Delta}_{i}\end{cases}\end{split}\] (A7)  
As \(\varrho_{\boldsymbol{\Delta}_{i}}\leq N_{y}\), we can use the subadditivity property of the matrix rank to write:  
\[\varrho_{\boldsymbol{\Xi}}\leq\sum\limits_{i=1}^{N_{\text{el}}}\varrho_{( \boldsymbol{\Delta}_{i})^{T}\boldsymbol{\Delta}_{i}}\leq N_{\text{el}}N_{y}\] (A8)  
Combining Equations (A6)-(A8), we obtain:  
\[\varrho_{\boldsymbol{\
abla}}\leq\varrho_{\boldsymbol{\Xi}}\leq N_{\text{el} }N_{y}\] (A9)  
Given that the kernel \(\boldsymbol{\
abla}\) is of size \(N_{\text{pw}}N_{\text{el}}(N_{y}+N_{f}-1)\times N_{x}N_{z}\), the result of Equation (A9) means that \(\boldsymbol{\
abla}\) is not full rank, which means by definition that its condition number \(K_{\boldsymbol{\
abla}}\) is very high (\(\sim\)\(10^{22}\)).
# A Numerical Analysis of the Nonlinear Dynamics of Shimmed Conical Gas Foil Bearings
## 3 Model Comparison  
According to the author's knowledge, no models for transient simulations of CGFBs have been published in the publicly available literature. Therefore, a direct numerical validation of the presented model is not possible. To investigate if the results of the present model are credible, a comparison with results from transient simulations for cylindrical GFBs is drawn. For this, the half-cone angle in the present model is set to a small value of \(\varphi=0.001^{\circ}\). This allows the comparison with the results from [17; 19; 44]. Table 1 shows the used parameters.  
Figure 3 shows the trajectories of the rotor after starting the transient simulation from the bearing center with and without the application of the Guembel condition. The resulting trajectories are consistent with the results from [17; 19; 44] regardless of whether the Guembel condition is used or not. These agreements verify the correct implementation of the presented model.  
\begin{table}
\begin{tabular}{l l l} \hline Ambient pressure & \(p_{\text{a}}\) & \(1.01\times 10^{5}\) Pa \\ Viscosity of air & \(\mu\) & \(1.95\times 10^{-5}\) Pa s \\ Nominal clearance & \(c_{0}\) & \(31.8\times 10^{-6}\) m \\ Half cone angle & \(\varphi\) & \(0.001^{\circ}\) \\ Maximum radius & \(r_{1}\) & \(19.05\times 10^{-3}\) m \\ Bearing length & L & \(38.1\times 10^{-3}\) m \\ Foil stiffness & \(k_{\text{b}}\) & \(4.739\times 10^{18}\) N/m\({}^{3}\) \\ Loss factor & \(\eta\) & 0.25 \\ Rotational speed & \(n\) & 10,000 rpm \\ Rotor mass & \(m_{\tau}\) & 3.061 kg \\ Rotor unbalance & \(m_{\tau}u\) & 0 kgm \\ \hline \end{tabular}
\end{table}
Table 1: Bearing parameters and fluid properties used for the model comparison to represent a cylindrical CFB (\(\varphi=0.001^{\circ}\)) to allow the comparison with results for GFJB.  
Figure 3: Trajectories after a drop from the bearing center for comparison with results from the literature without **(a)**[17] and with **(b)**[44] Guembel condition.  
The shown results are calculated with a grid size of \(N_{\theta}=64\) and \(N_{z}=16\). To show the effect of the grid size, Figure 4 shows the deviation of the dimensionless y-displacement of the equilibrium position \(\varepsilon_{\text{yeq}}\) of the case shown in Figure 3b for different grid sizes. The maximum deviation from the calculated value is \(0.14\%\) (\(N_{\theta}=64,N_{z}=28\)). This shows that a refinement beyond \(N_{\theta}=64\) and \(N_{z}=16\) does not result in a significant change of the calculated value and therefore this grid size is used for the rest of the presented simulations.
# The Usefulness of Video Learning Analytics in Small Scale E-Learning Scenarios
## 3 Methods and Materials
### Context  
The focus of our research is the use of educational video in higher education environments, but it should also apply to other levels (such as secondary education) and contexts (such as informal education).  
The Open University of Catalonia (UOC) is a fully online university created in 1994. Its educational model is learner and competence centred. The university's assessment model is based on Continuous Assessment (CA), and learners receive qualitative feedback after delivering learning activities. Teachers and tutors communicate with the learners through personal and group-class communication spaces through the Virtual Learning Environment (VLE). The VLE includes all communication spaces to promote social interaction, a digital library and teachers and tutors to support learners across courses. The university's enrolled students are mostly aged between 25 and 45 years old. Most of them already have a full-time job and familiar commitments, so there are some time constraints to overcome when enrolling in online courses. Learning activities, tools, resources and learning materials are offered taking such conditions into consideration. Teachers design activities and provide feedback to learners to reduce isolation and guide learners as much as possible across their learning path.  
The VLE consists of a number of classrooms that students and teachers access. The classrooms contain different spaces. Some of those spaces are dedicated to communication, both unidirectional from teacher to students, and multidirectional, among students and teachers. In an activity-centred approach, the main space in the classroom is dedicated to the different learning activities (including assessment activities) to be carried out during the course, and the spaces for each activity contain the learning resources suggested to students in order to carry out those activities. Those learning resources can include educational resources, such as the educational videos that we analyse in the present work. As with all other learning resources, they can be either mandatory watching, central to the activity, or additional content.
# Bond Graph-Based Approach to Modeling Variable-Speed Gearboxes with Multi-Type Clutches
## 3 BG Models of PGTs
### Double-Pinion PGT  
The double-pinion PGT (D-PGT) has two planetary gear transmissions between the ring and the sun. The kinematic relationship between the D-PGT components is shown in Figure 4. P1 and P2 are planetary gears meshed with the sun gear and the ring gear, respectively. The other symbols are the same as for the S-PGT.  
The following relationship can be obtained by assuming equal linear velocities at the gear meshing point:  
\[\omega_{p1}r_{p1}=(\omega_{c}-\omega_{s})r_{s} \tag{6}\]  
\[\omega_{p2}r_{p2}=(\omega_{r}-\omega_{c})r_{c} \tag{7}\]  
\[\omega_{p1}r_{p1}=\omega_{p2}r_{p2} \tag{8}\]  
the characteristic equation is:  
\[\omega_{s}-(1-\beta)\omega_{c}-\beta\omega_{r}=0 \tag{9}\]  
The BG model for the elastic contact in the D-PGT is shown in Figure 5. Node 1 was added to the BG of the S-PGT, to represent an additional planetary gear. Here, \(\theta_{sp}\) is the relative angular displacement between the planet and the sun, while \(\theta_{rp}\) is the relative angular displacement between the planet and ring. The parameter \(\theta_{p}\) is the relative angular displacement between the two planets.  
Figure 4: Kinematic diagram of the D-PGT.  
Figure 5: BG model of the D-PGT.  
Similarly, the speed and torque equations for the D-PGT can be obtained as follows:  
\[\dot{\theta}_{sp}=\omega_{c}-\omega_{r}+\text{TF}_{3}\omega_{p1} \tag{10}\]  
\[\dot{\theta}_{rp}=\omega_{c}-\omega_{r}-\omega_{p2}\text{TF}_{1}^{-1} \tag{11}\]  
\[\dot{\theta}_{p}=\omega_{p2}-\omega_{p1} \tag{12}\]  
the torque balance equation for the D-PGT is  
\[\tau_{s}=\theta_{sp}C_{sp}^{-1}-I_{s}\dot{\omega}_{s}-R_{s}\omega_{s}\]  
\[\tau_{r}=\theta_{rp}C_{rp}^{-1}-I_{r}\dot{\omega}_{r}-R_{r}\omega_{r}\]  
\[\tau_{c}=I_{c}\dot{\omega}_{c}+R_{c}\omega_{c}+\tau_{sp}+\tau_{rp}\]  
\[\tau_{p}\text{TF}_{2}^{-1}=\tau_{sp}\text{TF}_{3}+I_{p1}\dot{\omega}_{p1}+R_{ p1}\omega_{p1}\]  
\[\tau_{p}=\tau_{rp}\text{TF}_{1}^{-1}-I_{p2}\dot{\omega}_{p2}-R_{p2}\omega_{p2}\]  
The power transmission paths for the D-PGT and S-PGT are similar. The difference is that using additional planetary gears changes the direction of the torque acting on the ring gear; for the D-PGT, the torque on the ring gear is opposite in direction to the torque on the sun gear.  
Causality is not accounted for by the above two common PGT BG-based models, because a complete transmission system usually adopts a multi-level PGT combination, needs a power source to input power, and requires clutches to cooperate, for realizing the gear-shifting operation. There is no fixed mode for the matching method of a multi-stage PGT with a power source and a clutch. Therefore, the sun gear, the gear ring, and the planet carrier in the PGT are arranged with interfaces that can be connected to the outside, serving as an effort/flow source in BG-based models.
## 3 Forecasting Approach
### The Proposed Hybrid Methodology
#### 3.1.2 Step II: Sarima  
A SARIMA model is fitted to each cluster generated by the SOM in Step I, obtaining different predicted and residual values of these clusters. The main reason to select SARIMA as a forecasting model is its ability to capture linear patterns. In order to assess the forecasting performance, and due to the deterministic nature of linear models, a hold-out validation technique was applied during the process. The data (of each cluster) was divided into two groups: the training set containing two-thirds of the dataset, and the test set comprising the rest of the samples. The parameters of the model were adjusted using the training set and the test set was used to validate the model. Different values of the model parameters were tested using a trial-and-error procedure. Only the \(ph=1\) prediction horizon was tested. The values of the parameters tested within each cluster are, for the non-seasonal part: \(p=0\), \(1\), \(2\), \(3\), \(4\); \(d=0\), \(1\), \(2\) and \(q=1\), \(2\), \(3\), \(4\); and for the seasonal part: \(s=2\), \(5\), \(7\); \(P=0\), \(1\), \(2\); \(D=0\), \(1\), \(2\) and \(Q=0\), \(1\), \(2\), \(3\).
## 3 Results and Discussions
### Accuracy of LCZ Mapping  
LCZs for Harare were mapped with very high accuracy, as indicated by an Overall Accuracy of 92.4% and Kappa index value of 0.92 (Table 1). Producer accuracy ranged between 63.47 and 100%, while User Accuracy ranged between 76.45% (in the Compact midrise LCZ) and 100% (in the Water LCZ). User accuracy was at least 76% for all classes, with a lowest value of 76.45% (in the compact lowrise LCZ) and highest of 100% (in the water LCZ). Dense forest, lightweight lowrise, low plants, open lowrise, and water LCZ were mapped with very high accuracies, as indicated by both Producer and User accuracies greater than 90%.
# Impacts of MT-HVDC Systems on Enhancing the Power Transmission Capability
## 1 Introduction
Methods on enhancing the inter-area power transfer capability with security region determination is lacking in the literature. The influence of MT-HVDC on AC grids is discussed in [32] but without DC OPF. Even the international council on large electric systems (in French: Conseil International des Grands Reseaux Electriques, CIGRE) joint working group B4, C1 and C4 did not discuss the inter-area power transfer capability and security margins in its report [33]. Despite the fact that a lot of research has already been done to develop appropriate DC grid models and control schemes but the impacts of MT-HVDC system on dynamic security and stability of power systems (PS) has not been discussed fully yet. For this concern, an important feature is the transfer capacity in inter-area mode that shows how much power can be transferred without violating the system stability limits. To the authors best knowledge, the impacts of MT-HVDC systems on enhancing the power transmission capability (PTC) has not been addressed in literature yet. Transfer capability is an important factor that influences the planning and operation of electrical power systems [34]. Topological changes caused by unexpected events and scheduled maintenance can extremely modify the reliable transfer of electric power [34]. The intermittent behavior of renewable energy resources may vary the power flow and impose negative or positive impact on system operation. Especially during low wind conditions, a space in dedicated DC transmission line may be observed that can be used for increased power transmission capacity. This work suggests the use of MT-HVDC grids to enhance power transfer capability of transmission lines. The DC grid is exploited as an extra transmission route, specifically during low wind conditions. This idea relates with the theme of capacity factor. In [35], it is observed that Danish WFs have an average capacity factor of 41%, which means that the associated DC system is free for 59% of the time and available for some alternate use. The main contributions of this paper are:  
1. Small signal stability during bulk power transfer over long transmission path, when damping criterion is violated by any small change. 2. Transfer capability is assessed by analyzing power transfer through a specific corridor and security region determination. The DC network is used as an additional inter-area transmission path especially under low wind scenarios. 3. Computation of maximum allowable power transfer capability through security regions (Nomogram) using OPF based control architecture. 4. Proposed strategy is evaluated via simulations developed in Matlab/Simulink for [PERSON]'s two area AC system integrated with CIGRE B4 5-terminal VSC-HVDC system and security region nomograms. 5. Established that power transfer capability and security of AC system can be enhanced by coupling it with an HVDC grid.
## 1 Introduction
We focus on the results obtained from a joint collaborative effort by archaeologists and remote sensing experts to test the use of high-resolution COSMO-SkyMed data in different contexts and environmental conditions. Significant test cases have been selected in Europe, Africa and Asia to extract information about landscape archaeology (e.g., palaeochannels, roads, _etc._) as well as smaller features, such as walls and building structures. These research activities have been conducted in the framework of the Chinese-Italian bilateral project \"Smart management of cultural heritage sites in Italy and China: Earth Observation and pilot projects\" [28].
## 3 Multiple UAVs Control Design  
### L1 Adaptive Control  
The fundamental advantage of using the L1 adaptive controller is its capability of adjusting quickly and robustly, as well as the decoupling of robustness and adaptability. It also provides a guaranteed time-delay margin and eliminates hardware performance constraints. The proper design of the bandwidth-limited filter is a challenging issue when using the L1 adaptive controller [28]. The state-space form required for applying an L1 controller on the dynamics' rotational in Equation (2) is described as follows:  
\[\dot{x} =A_{m}x+b(f(t,x(t))+\omega u_{ad}),\ \ x(0)=x_{0}\] \[y =c^{\top}x(t) \tag{9}\]where  
\[x\triangleq\ v_{2},\qquad\text{a known Hurwitz matrix }\triangleq\ A_{m}\in\mathbb{R}^{n\times n}\] \[\omega\triangleq I_{\text{M}}^{-1},\quad b=1,\quad u_{ad} \triangleq\ \mathcal{L}_{1}\text{ Adaptive control }\triangleq\tau\] \[I_{\text{M}}^{-1}(-(\
u_{2}\times I_{\text{M}}\
u_{2})-I_{ \text{R}}(\
u_{2}\times z_{e})\Omega-k_{r}\
u_{2})\triangleq f(t,x(t))\] \[c^{\top}=\ I_{3\times 3} \tag{10}\]  
Equation (9) can be rewritten as follows:  
\[\dot{x} =A_{m}x+b(\theta\|x\|_{\infty}+\sigma+\omega u_{ad}),\ \ x(0)=x_{0}\] \[y =c^{\top}x(t) \tag{11}\]  
Equation (11) has a predictor state of  
\[\dot{\mathbf{x}} =A_{m}\hat{x}+b(\hat{\theta}\|x\|_{\infty}+\hat{\sigma}+\hat{ \omega}u_{ad}),\ \ \hat{x}(0)=x_{0}\] \[\hat{y} =c^{\top}\hat{x} \tag{12}\]  
where \(\hat{x}\in\mathbb{R}^{n}\) represents the estimated state, \(\hat{y}\in\mathbb{R}^{n}\) represents the estimated output, and \(\hat{\theta}\) and \(\hat{\sigma}\) represent the estimated parameters. \(\bar{\theta}=\hat{\theta}-\theta\), \(\hat{x}=\hat{x}-x\), and \(\hat{\sigma}=\hat{\sigma}-\sigma\) are the error definitions. The following is the dynamic error:  
\[\dot{\hat{x}}=A_{m}\tilde{x}+b(\hat{\theta}\|x\|_{\infty}+\tilde{\sigma}),\ \ \tilde{x}(0)=0 \tag{13}\]  
The adaption law is derived as follows:  
\[\dot{\sigma} =\dot{\hat{\sigma}}=\Gamma\text{Proj}(\tilde{\sigma},-bP\tilde{x})\] \[\dot{\hat{\theta}} =\dot{\hat{\theta}}=\Gamma\text{Proj}(\tilde{\theta},-\|x\|_{ \infty}bP\tilde{x}) \tag{14}\]  
where the adaption law's rate is expressed by \(\Gamma>0\). As seen in [29], Proj indicates the operator's projection. \(Q=Q^{\top}>0\) and \(P=P^{\top}>0\) satisfy the expression  
\[-Q=PA_{m}+A_{m}^{\top}P \tag{15}\]  
Finally, the adaptive control law can be expressed as follows:  
\[u_{ad}(s)=-\frac{k}{s}(\hat{\mu}(s)-k_{g}r(s)+\omega u_{ad}(s)) \tag{16}\]  
where \(\hat{\mu}(t)\triangleq\hat{\theta}(t)\|x\|_{\infty}+\hat{\sigma}(t)\) expresses the inverse Laplace transform of \(\hat{\mu}(s)\), \(r(t)\) represents the inverse Laplace transform of the reference \(r(s)\), and the feed-forward gain is expressed by \(k_{g}\triangleq-\frac{1}{c^{\top}A_{m}^{-1}b}\). L1 adaptive control details are found in [26; 27; 28].
## 2 Key Technologies
### Cryptography  
In a blockchain system, data integrity is an essential requirement since blockchain is originally proposed and developed in an untrusted environment and any data tampering must be detected. Cryptography is the key in blockchain's effort to ensure the integrity of the distributed ledger. Various cryptography techniques such as hash, public key cryptography and zero-knowledge proofs of knowledge are widely used in blockchain.  
The data integrity is protected by the hash (Merkle) tree and hash pointers together. In blockchain systems, only the current global states are maintained, and the history of past states can only be achieved by walking through the block transaction history. The current states are protected by the Merkle tree. In a Merkle tree, each leaf contains the system states; each non-leaf node contains the hash of its child nodes. Thus, the root contains the root hash of the overall ledger and any state change will result in a new root hash. Hence, Merkel trees allow efficient and secure verification of the distributed ledger. On the other hand, the block transaction history is protected by hash pointers. Since the distributed ledger is an append-only data storage, a block is immutable once it is committed to the blockchain. To ensure a block cannot be altered once it is appended to the ledger, a block with an index of \(n+1\) contains the hash of the predecessor block, i.e., block \(n\). Thus, any change in block \(n\) will result in the invalidation of all following blocks. By combining Merkel tree and hash pointers together, the data integrity is ensured.
# Experimental Evaluation and Numerical Simulation of the Thermal Performance of a Green Roof
## 3 Results and Discussion
### Building Energy Simulations  
The equivalent thermophysical properties defined in the previous section were used as inputs in the building energy software, in order to simulate the influence of the green roof on the annual energy needs. As mentioned in the methodology section, a simple detached building was created by means of TRNSYS software.  
Comparing the effects related to the installation of the green roof respect to the original one, Figure 10 shows the difference between the annual heating and cooling energy needs. It is possible to observe a percentage difference equal to \(-21.14\%\) for heating and \(-34.70\%\) for cooling.  
In addition, the energy simulations were performed, taking into account different climatic conditions. Following the climatic classification reported in [28], Table 2 lists the heating and cooling energy needs obtained using four different weather data: Rome, Manaus, Abu Dhabi and Moscow were considered as references for mild temperate, tropical, dry and snowy conditions, respectively. Comparing the green and the original roofs, the values reported in Table 2 always obtain negative percentage variations, highlighting that the green roof could be applied under different climatic conditions, showing positive effects during winter and summer.  
Starting from the obtained results, it is possible to affirm that the green roof has a good insulating effect, reducing the energy needs of the building in cold seasons and keeping it cooler under warm climatic conditions (due to its inertial behavior). It is worth mentioning that a green roof can also protect the roof's materials from temperature fluctuations, ensuring the transpiration of the layers.