esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Control-Relevant Modelling Using Finite Element Method  
Utilisation of the analytical modelling approach by means of the Euler-Bernoulli beam theory outlined on the previous pages may be impractical for systems with complex geometry, kinematics, and variable material properties. In such cases, geometrical modelling using finite element method is often preferred in industry thanks to its universal applicability and possibility of rapid model development with the aid of available computer software. Our goal is to compare the fidelity of the models derived from both analytical and FEM approaches with respect to the physical setup dynamics. Furthermore, their suitability for control algorithm synthesis is evaluated by experiments. This section focuses on the FEM approach, discussing individual steps for the derivation of _control-relevant_ model of the discussed benchmark motion system.  
A typical workflow for control-oriented modelling involves the following stages of development:  
1. Building, linearising and reducing a model of the system that has to be controlled. This can include a model of the uncertainty.
2. Controller design with the reduced model.
3. Simulation or co-simulation with the original non-linear model.  
Each step may be executed by different engineers and may require different software tools. A disadvantage of many commercial software tools is that the reduced model obtained in Step 1 still needs a licence to run. Control engineers can then only use the reduced model if a costly licence is available. Models that can be used without a licence are clearly preferred.  
The models need to be exchanged between the tools that are used in the different steps described above. The Functional Mock-up Interface (FMI) standard aims to be a standard for model exchange and co-simulation. Many tools support FMI import and/or export of models.  
The central idea in step 1 is to make a model of a system with finite element software and find the best way to linearise, reduce and export this model to a FMI model. This workflow is schematically shown in Figure 4 along with the tools that were used: Abaqus, Python and OpenModelica. Of these tools, only Abaqus is a commercial software package. The reduced model is in standard state space form, so no Abaqus licences are required to run it.  
The advantage of this approach is that the FEM model can be a very accurate description of the system, and it will be available for validation in Step 3 as well. The model reduction step will determine quite clearly which effects to include and which to exclude in the reduced model. Furthermore, if the system is built and measured, deviation between the model and system response can be attributed to parts of the system that were incorrectly produced (or incorrectly modelled).  
This workflow is applied to the flexible arm benchmark in Section 3.2. The methods that were used (for step 1) are: finite element analysis, model order reduction, and FMU generation. These methods are discussed next.
# Event-Triggered Robust Fusion Estimation for Multi-Sensor Time-Delay Systems with Packet Drops
## 1 Introduction
The consistent boundedness of the estimation error is proved. Several simulations verify the effectiveness of the fusion estimator. The rest of this paper is briefly described below. The problem description and a brief description of the event-triggered transmission strategy are given in Section 2. A robust fusion estimator for multi-sensor systems with state delays, deterministic control inputs, random packet drops, and communication constraints is derived in Section 3. The boundedness of the fusion estimator is studied in Section 4. Several sets of simulations are analyzed in Section 5. Section 6 concludes the paper.
## 4 Discussion
### Methanol Electrocatalytic Oxidation at the Modified Electrodes
Interestingly, by addressing the electrocatalytic activity of the tertiary metal oxide CuCoNiOx@CNTs/GCE (curve e), significant features appear. The first feature is that the oxidation current increased dramatically in comparison to the mono-metal oxides (curves a, b, and c); this could be attributed to the synergism between the three metal oxides that resulted in enrichment of the surface with Ni(III), mentioned in Equation (5), which reflects the higher oxidation current in the forward scan according to the proposed mechanism in the above-mentioned equations, in addition to the fact that the presence of CuOx with NiOx in the same alloy enhanced the generation and stabilization of the \(\beta\)-NiOOH phase, the pioneer phase in the alcohol oxidation process in alkaline medium [18]. Furthermore, the higher produced oxidation current may be because of the fact that the three metals coexist in the same alloy and act as active sites for the methanol oxidation simultaneously. Moreover, the presence of CNTs as a support for the prepared ternary metal oxide, which affects the electrocatalytic activity of the catalyst by increasing the surface area and also by impeding the catalyst sintering and aggregation during the electrooxidation process of methanol, resulted in such high electrooxidation current. More specifically, in comparison to [PERSON] et al. [20], the presence of CNTs as a support resulted in a uniform distribution of the electrodeposited materials, which has a direct impact on surface area and this was clearly noticeable from the SEM images where the average particle size at 1200 s is around 200 nm in comparison to more than 1 \(\upmu\)m for G/NiCuCo [20]. This effect on the particle size and distribution through the entire surface has a direct impact on the electrocatalytic activity. The second feature is a negative shift in the onset potential by about 50 mV; this is due to the alloying process that occurred between both CoOx and NiOx caused a negative shift in the Ni(II)/Ni(III) transformation according to the literature [19]. Note that, the mechanisms mentioned before for methanol oxidation are proposed for the single state oxide and in the case of the tertiary oxide, the mechanism is the same with the three oxides acting as active sites for the oxidation process at the same time, according to Figure 7. Figure 8 shows the variation of the reaction function (\(I_{\mathrm{p}}/\
u^{1/2}\)) with \(\
u\) obtained at the CuCoNiOx@CNTs/GCE electrode for methanol electrooxidation. It is worthy to mention that with a scan rate more than 50 mV\(\cdot\)s\({}^{-1}\), \(I_{\mathrm{p}}/\
u^{1/2}\) does not change significantly with the scan rate, which is a characteristic feature of catalytic reactions [35].
## 3 Results and Discussion
### Results for Time Domain
#### 3.2.1 Discrimination of Guided Wave Modes  
The time-domain waveforms at different acquisition interfaces with a porosity of 0.1 and a finite porosity medium layer thickness of 0.05 m are presented in Figure 7. In Figure 7, the time-domain waveform of the liquid-pipe interface in the infinite porous medium consists of two unknown wave-packets, tagged wave-packet 1 and wave-packet 2, respectively. From Figure 7, a similar observation is made for the case of the external interface of a finite porous medium. There is a wave-packet here, tagged wave-packet 3. Therefore, the conclusion that wave-packet 1, wave-packet 2, and wave-packet 3 at 25 kHz belong to mode L(0,1), \(\alpha\) and L(0,1)\({}_{P}\), respectively, is deduced.  
To verify the above conclusions, further numerical calculations are performed. For the liquid-filled pipe embedded in an infinite porous medium, the group velocity of wave-packet 1 is 5454 m/s, and the group velocity of the wave-packet 2 is 1333 m/s. These two velocities agree with the group velocity of modes L(0,1), \(\alpha\) at 25 kHz, which is highlighted by square marks in Figure 8, such that the wave-packets 1 and 2 are confirmed as the modes L(0,1) and \(\alpha\), respectively. Similarly, the velocity of wave-packet 3 with the arrival time and propagation distance is 1973 m/s at the external interface of the finite porous medium. This value is close to the velocity of mode L(0,1)\({}_{P}\) at 25 kHz, as highlighted by the circular mark in Figure 8. Thus, it can be concluded that wave-packet 3 is in the mode L(0,1)\({}_{P}\) at the external interface of the finite porous medium. The inversion results show that only the \(\alpha\), L(0,1), and L(0,1)\({}_{P}\) modes are found at two acquisition locations at a frequency of 25 kHz, and they have an excellent match with the dispersion curves in Figures 2 and 3.  
Figure 7: Time-domain waveform of radial displacement with respect to time (\(\beta\) = 0.1) (**a**) at the liquid–pipe interface in infinite porous medium and (**b**) at the external interface of the finite porous medium. The thickness of the finite porous medium is \(d\) = 0.05 m. The vertical and horizontal axes depict the displacement amplitude and time.
## 3 Results and Discussion  
The numerical results illustrating the theory derived in the previous section are presented in this section. The main discussion involves the effects of the wrapping layer material, thickness, and the pore parameters on the propagation characteristics of the guided waves. The time-domain waveforms of guided waves are obtained by numerical inversion, and the influences of pore parameters on these waveforms and attenuation coefficients are simulated. Considering the structure of a liquid-filled pipe embedded in porous medium, the relevant parameters selected for numerical simulations are shown in Tables 1 and 2 [38; 39]. This study focuses on guided waves in a liquid-filled pipe of 14 mm internal and 16 mm external radii.
# Time Series Prediction of Sea Surface Temperature Based on an Adaptive Graph Learning Neural Model
## 3 Experiments
### Baselines  
In this section, we compare our model with the baselines on the two data sets. The four baselines are as follow:  
CGMP [22]: Based on a convolution gated cyclic unit (GRU) and a multi-layer perceptron, the model can not only capture neighbor influence effectively in the spatial dimension, but also process historical information effectively in the temporal dimension.  
FC-LSTM [5]: The model is composed of an LSTM layer and a full connection layer. It can apply recursive neural network to SST prediction.  
SVR [13]: Support vector regression is widely used in time series prediction.  
CFCC-LSTM [11]: The model is composed of a fully connected LSTM layer and a convolution layer, which can combine temporal and spatial information well to predict SST time series data.  
GED [15]: GED is a model with a GRU encoder-decoder with SST code and a dynamic influence link (DIL).
# Injuries and Pain Associated with Goalkeeping in Football--Review of the Literature
## 1 Introduction  
Football is a popular sport with the highest risks for injury [1, 2] and has the most significant number of injured players yearly. However, injury and pain assessments in football goalkeepers (GKs) are scarce relative to their essential role. Indeed, a football GK is vital, and the game mechanics differ entirely from outfield positions. Undoubtedly, GKs' playing and training conditions can put them at a high risk of injury as well. Thus, a comprehensive understanding of sustained injuries and pain reports in football GKs would be valuable for medics, physiotherapists and coaches.  
Since the 1990s, the GK's position has evolved dynamically. Although shot stopping will always be an essential skill, newer demands imposed on modern GKs, such as ascertain of the defensive line, making clearances and active foot play, bring additionalinjury risks. GKs fulfill unique and crucial defensive and offensive roles. As a result, GKs often possess different abilities and attributes than outfield players. For example, GKs demonstrate better high jump performance but perform worse on speed and VO2 max tests than outfield players. Thus, it would be reasonable to expect that the rate and type of injuries may also differ.  
Such differences are in part due to the selection process. In professional football, criteria for GKs and field players are different and include biological age, maturity, body height, predicted height, handgrip strength and jump performance [3]. This may explain why GKs tend to be statistically higher, heavier and have more % of body fat than any other player or age group [4].  
Nowadays, most publications concerning sustained injury and pain in GKs consist of case studies, a small sample size, or provide data using different methodology and definitions which impede direct comparison. Thus, insufficient and varied studies preempt a systematic review.
## 2 Materials and Methods
### Fruit Extract Analysis  
The antioxidant activity by the DPPH method [19] and total phenolic content (TPC) by [PERSON]'s method [20] and total flavonoid content (TFC) by aluminum chloride meyjod [21] were examined in the fruit extract. Briefly, 0.1 mM DPPH in methanol was mixed with extract samples (0-10%) in ratio of 100:1 (\(v/v\)), respectively. After 30 min of incubation in dark at ambient temperature the absorbance readings were taken at 515 nm against methanol. Results were expressed as umol Trolox equivalent (TE) per ml of fruit extract. TPC was measured as followed, 20 \(\upmu\)L of fruit extract sample (2% in distilled water) was mixed with the 100 \(\upmu\)L [PERSON]'s reagent. After 3 min of incubation, 300 \(\upmu\)L sodium carbonate (20%) was added and the sample was filled up to 2 mL with distilled water. The mixture was incubated in dark for 2 h. The absorbance was read at 765 nm and the results were expressed as mg gallic acid equivalent (GAE) per ml of fruit extract. TFC was measured at 410 nm. Fruit extract sample (2%) was mixed with aluminum chloride (2% in methanol) at 1:10 ratio and after 15 min of incubation at dark the absorbance was read against methanol. Results were expressed as mg quercetin equivalent (QE) per ml of fruit extract.
## 2 Materials and Methods
### Materials and Design
The design contains a total of 64 bi-symbol pairs, involving 16 simple and disordered bi-symbol pairs, 16 simple and ordered bi-symbol pairs, 16 complex and disordered bi-symbol pairs, and 16 complex and ordered bi-symbol pairs. To avoid the fact that all subjects had the same experimental material, the stimuli for each subject were randomly selected from each of the four groups (i.e., 16 of the 20 graphical symbols from each group were selected to randomly generate 16 bi-symbol pairs). Each list comprised only one of the four bi-symbol pair types and contained exactly 16 bi-symbol pairs associated with a unique English word. Subjects were forced to memorize an entire combination containing one bi-symbol pair (not just the left or right symbol of a bi-symbol pair) and one associated English word, that is, a bi-symbol pair. Given that each symbol on a list was repeated exactly twice in two different bi-symbol pairs, each symbol in a bi-symbol pair should contribute equally to the learning of the association. We chose a total of sixty-four English words from the MRC Psycholinguistic Database ([[http://websites.psychology.uwa.edu.au/school/MRCDatabase/uwa_mrc.htm](http://websites.psychology.uwa.edu.au/school/MRCDatabase/uwa_mrc.htm)]([http://websites.psychology.uwa.edu.au/school/MRCDatabase/uwa_mrc.htm](http://websites.psychology.uwa.edu.au/school/MRCDatabase/uwa_mrc.htm)), accessed on 25 July 2024) with familiarity scores of 600 or higher. The word lengths ranging from three to six letters. The resolution of the screen was 1280 \(\times\) 800 pixels, and each character/radical measured 148 \(\times\) 148 pixels. The distance for viewing was approximately 55 cm. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Complexity**} & \multicolumn{2}{c}{**Familiarity**} & \multicolumn{2}{c}{**Order**} & \multicolumn{2}{c}{**Concreteness**} \\ \cline{2-9}  & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** \\ \hline Simple and Disordered & 2.02 & 0.43 & 1.21 & 0.32 & 2.21 & 0.56 & 1.23 & 0.37 \\ \hline Simple and Ordered & 1.98 & 0.38 & 1.12 & 0.40 & 1.79 & 0.36 & 1.14 & 0.31 \\ \hline Complex and Disordered & 4.06 & 0.54 & 1.18 & 0.32 & 3.98 & 0.46 & 1.18 & 0.32 \\ \hline Complex and Ordered & 3.94 & 0.33 & 1.24 & 0.37 & 3.74 & 0.37 & 1.09 & 0.41 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mean scores and standard deviations of attributes for the four groups of graphical symbols. Figure 3: Examples of simple and disordered, simple and ordered, complex and ordered, and complex and disordered graphical symbols.
# RoSe-Mix: Robust and Secure Deep Neural Network Watermarking in Black-Box Settings via Image Mixup
## 4 Experimental Results
### Robustness
JPEG compression at a quality factor of 55 **leads to a performance drop of 2.58%, 6.21%, and 3.65% in TA for CIFAR-10, transfer learning, and GTSRB, respectively**. Additionally, GTSRB experiences the largest \(Rec_{T}\) and \(Rec_{Ts}\) reductions (7.2% and 8.6%), indicating that lower-resolution distortions can interfere with the embedded watermark. However, even in this extreme scenario, the watermark recovery rate remains sufficiently high (>\(\tau\)), ensuring that ownership verification is still feasible. The robustness of RoSe-Mix can be attributed to **two primary factors**:  
1. **Mixup-based embedding**: Unlike traditional backdoor-based watermarking schemes, RoSe-Mix does not rely on a fixed set of trigger inputs. Instead, Mixup continuously generates new trigger samples, preventing an adversary from isolating and removing the watermark through fine-tuning or pruning. This also explains why \(Rec_{Ts}\) remains significantly higher than TA under aggressive pruning--the watermark is encoded as a distributed manifold rather than discrete patterns. 2. **Cryptographic hashing mechanism**: The use of **one-way hash functions** ensures that, even if an attacker attempts to regenerate trigger samples, they cannot feasibly recover the correct key-label mappings without access to the private key. This is evident from the minimal USR values in Table 2, showing that unauthorized attempts to extract the watermark result in high failure rates. **Pruning evaluations** (Figure 6) further validate RoSe-Mix's robustness. While MNIST and FashionMNIST experience **30% and 90% TA reductions at a pruning rate of 0.4**, their respective watermark recovery rates **remain higher than 80% for both**. In contrast, CIFAR-10 suffers greater TA drops (to 55% and 40%) at pruning rates of 0.1, yet \(Rec_{Ts}\) remains higher than or comparable to TA. This suggests that while pruning reduces the model's expressiveness, it does not completely remove the watermark. The **interplay between Mixup-generated triggers and deep feature representations ensures that the watermark remains embedded even when substantial portions of the network are removed. Figure 6: Comparison of Pruning method for MNIST, FashionMNIST, and CIFAR10. Finally, any drop in \(Rec_{Tr}\) and \(Rec_{Ts}\) closely follows a drop in the accuracy of the main task of the model, ensuring that an attacker cannot remove the watermark without significantly deteriorating the usability of the model. This property makes RoSe-Mix highly resistant to watermark removal attacks, since the attacker must sacrifice the core performance of the model to erase its ownership markers.
# Using Graph Partitioning for Scalable Distributed Quantum Molecular Dynamics
## 2 Evaluating Matrix Polynomials on Partitions
Since by construction, the graph \(H\) is totally contained in the graph of its density matrix (which is the object we want to compute and thus unknown), it is sensible to approximate its connectivity from the density matrix of the previous time step of the QMD simulation. The halos can also be taken from \(H\) in practice. We propose the following algorithm for computing \(P(A)\) from \(H=G(D)\):  
1. Divide \(V(G)\) into \(q\) disjoint sets \(\{U_{1},\ldots,U_{q}\}\) and define a CH-partition \(\Pi=\{\Pi_{1},\ldots,\Pi_{q}\}\), where \(\Pi_{i}\) has core \(U_{i}\) and halo \(N(U_{i},H)\setminus U_{i}\);
2. Construct submatrices \(A_{U_{i}}\) for all \(i=1,\ldots,q\);
3. Compute \(P(A_{U_{i}})\) for all \(i\) independently using dense matrix algebra;
4. Given a vertex \(i\), let \(k\) be the index such that the set \(U_{k}\) contains \(i\). Let \(j\) be the row in \(A_{U_{k}}\) that corresponds to the \(i\)-th row in \(A\). Then, define \(P(A)\) as a matrix whose \(i\)-th row equals the \(j\)-th row of \(P(A_{U_{k}})\). This algorithm computes \(P(A)\) as demonstrated in Lemma 1. The computational bottleneck of the algorithm for \(P(A)\) is caused by the dense matrix-matrix multiplication required to compute \(P(A_{U_{i}})\) for all \(i\) in step (3). \[A= \begin{pmatrix}-1.2&1.89&0&0&0\\ 1.89&0.92&0.08&0&0\\ 0&0.08&0.85&0.11&0\\ 0&0&0.11&0.78&1.21\\ 0&0&0&1.21&-1.31\end{pmatrix} \qquad P(A)= \begin{pmatrix}5.01&-0.53&0.15&0.&0.\\ -0.53&4.43&0.14&\mathbf{0.009}&0.\\ 0.15&0.14&0.74&0.18&0.13\\ 0.&\mathbf{0.09}&0.18&2.08&-0.64\\ 0.&0.&0.13&-0.64&3.18\end{pmatrix}  \tag{1}\]  
To be precise, according to (2), computing \(P(A_{U_{i}})\) takes \(s(c_{i}+h_{i})^{3}\) operations, where \(c_{i}\) and \(h_{i}\) are the size of the core and the halo of \(\Pi_{i}\) and \(s\) is the number of superpositioned operators (see (2)). In this calculation, the computational effort for thresholding some matrix elements is excluded, since this effort is quadratic in the worst case and linear in \(c_{i}+h_{i}\) in average cases. We observe that a CH-partitioning which minimizes the effort to compute \(P(A)\) also minimizes \(\sum_{i=1}^{q}(c_{i}+h_{i})^{3}\) due to the fact that \(s\) is independent of \(\Pi\). This observation motivates our _CH-partitioning problem_, defined as follows: For an undirected graph \(G\) and an integer \(q\geq 2\), split \(G\) into \(q\) blocks \(\Pi_{1},\ldots,\Pi_{q}\) such that  
\[\sum_{i=1}^{q}(c_{i}+h_{i})^{3} \tag{2}\]  
is minimized, where \(\Pi_{i}\) has a core \(U_{i}\) of size \(c_{i}\) and a halo \(N(U_{i},G)\setminus U_{i}\) of size \(h_{i}\). As an example, the optimal CH-partitioning for a star graph of \(n\) vertices has a single non-empty block containing all vertices: this block is composed of the central vertex, whose halo contains all other vertices. In contrast, a standard (edge cut) partitioning will have \(n\) parts, thus demonstrating that a CH-partitioning minimizing (2) can be quite different from a standard balanced partitioning.