esa-sceva/satcom-chunk-collection huggingface dataset:
# Cardiac Electrophysiology Meshfree Modeling through the Mixed Collocation Method
## 2 Materials and Methods  
### Mixed Collocation Form of the Monodomain Equation  
The propagation of an electrical impulse in the heart is modeled through the monodomain model by the reaction-diffusion equation:  
\[\begin{array}{ll}\frac{\partial V}{\partial t}=\mathbf{\
abla}\cdot(\mathbf{D}\mathbf{ \
abla}V)-\frac{1}{\mathcal{C}}l_{ion}(\mathbf{s},V,t)&\text{in }\Omega\\ \frac{\partial s}{\partial t}=\mathbf{f}(\mathbf{s},V,t)&\text{in }\Omega\\ \mathbf{n}\cdot(\mathbf{D}\mathbf{\
abla}V)=0&\text{on }\partial\Omega\end{array} \tag{1}\]  
where \(\partial V/\partial t\) is the time derivative of the transmembrane potential \(V=V(t)\) and \(\mathbf{s}\) is the vector of cellular gating variables and ionic concentrations. The function \(\mathbf{f}(\mathbf{s},V,t)\) describes the nonlinear cellular dynamics. \(l_{ion}(\mathbf{s},V,t)\) denotes the ionic current per cell membrane area and \(C\) the capacitance of the cell membrane per unit area. \(\Omega\) represents the domain of interest and \(\partial\Omega\) its boundary, while \(\mathbf{n}\) is the outward unit vector normal to \(\partial\Omega\). \(\mathbf{D}\) is the diffusion tensor given by  
\[\mathbf{D}=d_{0}[(1-\rho)I\otimes\mathbf{I}+\rho\mathbf{I}] \tag{2}\]  
where \(d_{0}\) denotes the diffusion coefficient along the cardiac fibers, \(\rho\leq 1\) is the transverse-to-longitudinal conductivity ratio, \(\mathbf{I}\) denotes the cardiac fiber direction vector, \(\mathbf{I}\) is the identity matrix, and \(\otimes\) is the tensor product operation.  
The nonlinear cellular dynamics involved in the computation of \(I_{ion}\) make the solution of the coupled model in Equation (1) cumbersome. For large-scale tissue simulations, it is common to solve a decoupled model where the nonlinear term containing \(I_{ion}\) is solved separately from the linear diffusion term. The operator-splitting method described in [30] is used here to solve two subsystems in each time step. On the one hand, the nonlinear ODE system that describes the cellular reactions and the ionic current is solved:  
\[\begin{array}{ll}\frac{\partial\mathbf{s}}{\partial t}=\mathbf{f}(\mathbf{s},V,t)&\text{ in }\Omega\\ \frac{dV}{dt}=-\frac{1}{C}I_{ion}(\mathbf{s},V)&\text{ in }\Omega\end{array} \tag{3}\]  
and, on the other hand, the linear PDE system that describes the electric signal propagation is solved:  
\[\begin{array}{ll}\frac{\partial V}{\partial t}=\mathbf{\
abla}\cdot(\mathbf{D}\mathbf{ \
abla}V)&\text{ in }\Omega\\ \mathbf{n}\cdot(\mathbf{D}\mathbf{\
abla}V)=0&\text{ on }\partial\Omega\end{array} \tag{4}\]  
Applying the second-order Strang splitting method [31], the solution is advanced from time \(t^{n}\) to time \(t^{n+1}=t^{n}+dt\) with a time step \(dt\) in three phases:  
1. Equation (4) is solved for \(t^{n}<t\leq t^{n}+dt/2\), using the solution at \(t^{n}\) as initial condition.
2. Equation (3) is solved for \(t^{n}<t\leq t^{n}+dt\), using the solution from phase 1 as initial condition.
3. Equation (4) is solved for \(t^{n}+dt/2<t\leq t^{n}+dt\), using the solution from phase 2 as initial condition.
# Alginate Modification and Lectin-Conjugation Approach to Synthesize the Mucoadhesive Matrix
## 4 Alginate Modification and Coupling Strategies
### Hydroxyl Groups
#### 4.2.3 Sulfation  
Sulfation is a modification method that involves the formation of a C-O-S bond; it is mainly utilized to synthesize heparin-like molecules (Figure 6). Because alginate is the only polysaccharide containing carboxylic acid functionalities in each of its uronic acid units, it is suitable for generating heparin-like molecules by sulfation reaction. Several studies have reported the development of heparin-like polysaccharides by sulfation, mainly using chlorosulfonic acid as the sulfation agent. Recently, one study discussed the microspheres of the sulfated alginate derivative to promote a drug-controlled release behavior in human induced pluripotent stem cell (hiPSC)-derived endothelial cells [85]. The most common method used to synthesize the microspheres of sulfated alginate utilizes the lower density of sulfate and heparin groups. This modification is aimed at the bioactivity enhancement of immobilized vascular endothelial growth factor (VEGF). [PERSON] et al. used a different approach using a tetrabutylammonium (TBA) alginate salt to increase their solubility in pyridine. However, this method required more studies related to the reproducibility of sulfation degree [86].  
Another strategy employed a carbodiimide-H\({}_{2}\)SO\({}_{4}\) intermediate or TBA salt in DMF [87; 88]. Both are described methods in strong acid conditions that may not always favor the specific application. A deteriorating effect on the gelation ability is also found in sulfated alginate, resulting in a lower stiffness gel and increased swelling rate compared to unmodified alginate. However, heparin-like alginate hydrogels for encapsulation are still of great interest in their applications [89].
## 3 Analytical Non-Stationary S2A Channel Model
### Channel Impulse Response
\[\sigma\left(x,y\right)=\pi k^{2}\left|\mathbf{\Gamma}^{2}\right|\frac{d_{\text{ RD},l}^{2}}{h_{\text{R}}^{4}}P\left(x,y\right) \tag{21}\]  
\[P\left(x,y\right)=\frac{1}{\sqrt{2\pi}\sigma_{x}\sigma_{y}\sqrt{1-b_{xy}^{2}}} \cdot\exp\left(-\frac{1}{2\left(1-b_{xy}\right)^{2}}\right) \tag{22}\]  
where \(\sigma_{x}\) and \(\sigma_{y}\) are the root mean square of the slop of \(x\) and \(y\), respectively, \(b_{xy}\) is the correlation coefficient of \(x\) and \(y\), and \(k\) is calculated as in Equation (23). \[k=\sqrt{x^{2}+y^{2}} \tag{23}\]  
Thus the term \(g_{\text{DIF},l}\left(t\right)\) in Equation (18), containing the phase and frequency shifts in the impulse response of the scattering components, can be denoted as Equation (24),  
\[g_{\text{DIF},l}\left(t\right)=\exp\left(\text{j}\varphi_{l}+\text{j}k_{0}d_{ \text{DIF},l}\left(t\right)+\text{j}\frac{2\pi}{\lambda}\int_{0}^{t}\left( \mathbf{n}_{\text{TD},l}^{\text{T}}\left(t\right)\cdot\mathbf{v_{T}}\left(t \right)+\mathbf{n}_{\text{RD},l}^{\text{T}}\left(t\right)\cdot\mathbf{v_{R}} \left(t\right)\right)\text{d}t\right) \tag{24}\]  
where \(\mathbf{n}_{\text{TD},l}=\left(n_{\text{TD},l\_x},n_{\text{TD},l\_y},n_{ \text{TD},l,z}\right)^{\text{T}}\) and \(\mathbf{n}_{\text{RD},l}=\left(n_{\text{RD},l\_x},n_{\text{RD},l\_y},n_{ \text{RD},l,z}\right)^{\text{T}}\) are the unit directional vectors towards the \(l\)th diffuse scatterer D observed from T and R, separately. \(\varphi_{l}\sim U\left[-\pi,\pi\right]\) is an additional random phase for the \(l\)th scattering subpath.
# A Model of Information Visualization Interpretation
## 4 Discussion
### Remarks on the Scale Parameter  
The term \(\tau\) has proven to be an important parameter. We know from Corollary 5 that the mean value of the random variable \(D_{12}\) is increasing in \(\tau\). This means that the mean is decreasing as \(\tau\) approaches zero positively. Furthermore, Corollary 6 shows the mean asymptotically approximates \(h\) as \(\tau\to 0^{+}\). From this, it follows that the expected visual measurement error is very small when \(\tau\) is small.  
It is reasonable to explore how the mean behaves as \(\tau\to 0^{+}\). We found  
\[\frac{du}{d\tau}\sim 2h\tau(1-\rho). \tag{90}\]  
This suggests that \(\mu(\tau)\) is approximately quadratic in \(\tau\) as it becomes small. We have already seen this in Equation (87), which was derived in a different manner. We provide more on this in the next section.  
Figure 6: A visualization of the various cases surrounding values of the correlation.  
The need for small \(\tau=\sigma/h\) is pervasive across the literature. In their book [23], [PERSON] and [PERSON] address the topic several times in a variety of settings, but mostly concerning the quality of statistical estimators. [PERSON] observes [31; 32], for instance, that if \(\sigma\ll h\), simple empirical estimators are as good as more complex maximum likelihood estimators. It should be noted that they often use a slightly different \(\tau\): namely, \(\sigma\) divided by the triangle's longest side, which is \(d_{13}\). This affects the parametric analysis in interesting ways but will be saved for a future line of work.  
Since we have been interest in the correlation, we notice the role \(\rho\) plays. Strongly positive correlation will diminish the effects of scale on the mean, whereas strong negative correlation increases the effects of \(\tau\). Recall the interpretation that positive correlation results in less change in \(D_{12}\) and more rigid displacement. It seems reasonable that there would be less change in \(\mu\) as \(\tau\) changes, as indicated in asymptotic Equation (90). However, negative correlation was reasoned to be associated with a potentially greater length change. Therefore, \(\mu(\tau)\) having a steeper slope near zero indicates greater change is happening.  
The term \(\tau\) is the parameter that ties the viewer and the visualization together. And \(\sigma\) is a property of the fixations and their spread about the ends of the bar. The term \(h\) is a property of visualization. The ratios of their values determine the quality of the visual assessment of the bar. In studies of figure shapes (which can be subdivided into sets of triangles if needed), \(\tau\) is estimated [33; 34]. This questions whether \(h\) and \(\sigma\) can be individual, separate parameters, or is their ratio intrinsic to the viewer? That is, will a viewer increase \(\sigma\) as \(h\) increases to keep some unstated value of \(\tau\) constant? This seems like an interesting question for future experimental research.
# InMemQK: A Product Quantization Based MatMul Module for Compute-in-Memory Attention Macro
## 4 Simulation Results
### Algorithm Simulation Results  
We first verified the effectiveness of the proposed HFR algorithm. Specifically, we performed product quantization (PQ) on the key and value matrices in the BERT model and progressively replaced the MatMul calculations (\(Q\cdot K\) and \(S\cdot V\)) in 10 attention layers from back to front. Initially, we implemented PQ using the L2 distance metric as the default approach (denoted as \"L2\" in Table 1). As shown in Table 1 (L2), when L2 distance is used as the default metric for PQ, performance on most datasets in the GLUE benchmark drops significantly, especially on the COLA dataset, where the Matthews correlation coefficient drops to 0.07. Next, we applied the proposed HFR algorithm to optimize the centroids of PQ, which were initialized using the L2 metric (denoted as \"HFR\" in Table 1). This optimization significantly improved accuracy, recovering 68-93% of the original performance. Finally, we applied RL-FT on top of the HFR optimization to further reduce quantization error and mitigate the impact of device non-idealities, improving overall model performance further (denoted as \"RL-FT\" in Table 1). The same progressive methodology (L2, HFR, RL-FT) was also applied to ALBERT and RoBERTa, achieving consistent improvements in accuracy.  
It is worth noting that we introduced 10% Gaussian noise in the experiment to simulate manufacturing variation in NVM devices. As shown in Table 1 (HFR*), even with the introduction of device variation, the BERT model's performance did not degrade significantly. This might be because we utilized centroids stored in a codebook, where slight input signal jitters did not notably affect computational accuracy.  
To align with the designed time-domain MAC circuit, we quantized the codebook centroids to 6 bits, considering the limited precision requirements of NVM. To mitigate accuracy loss, we used RL-FT and PyTorch's quantization-aware training (QAT). Table 1 (RL-FT) shows model performance after considering device variation and quantization using these methods.
# On Multidimensional Congestion Games +
## 6 Bounds for Bidimensional Unweighted Games
### Price of Stability  
In order to bound the price of stability, we can use the same primal formulations exploited for the determination of the price of anarchy with the additional constraint \(\Phi(\mathbf{\sigma})\leq\Phi(\mathbf{\sigma}^{*})\), which, by Equation (1), becomes  
\[\sum_{r\in R}\left(\alpha_{r}\left(\sum_{j=0}^{d}\left(k_{r,j}^{2}+k_{r,j}-l_{r,j}^{2}-l_{r,j}\right)+2k_{r,0}\sum_{j=1}^{d}k_{r,j}-2l_{r,0}\sum_{j=1}^{d}l_{r,j}\right)+2\beta_{r}\sum_{j=0}^{d}(k_{r,j}-l_{r,j})\right)\leq 0.\]  
**Theorem 5**.: _For each bidimensional affine congestion game \((\mathcal{G},\mathcal{C})\), \(\mathit{PoS}(\mathcal{G},\mathcal{C})\leq 1+\frac{2}{\sqrt{7}}\) under the social cost function \(\mathsf{Pres}\) and \(\mathit{PoS}(\mathcal{G},\mathcal{C})\leq 2.92\) under the social cost function \(\mathsf{Perc}\)._  
Proof.: For the social cost function \(\mathsf{Pres}\), set \(\gamma=1+\frac{2}{\sqrt{7}}\), \(x=y=\frac{1}{\sqrt{7}}\) and \(z=\frac{1}{2}+\frac{1}{2\sqrt{7}}\). The second dual constraint is always satisfied, as \(\min\{x,y\}\geq 1\) and \(\max\{x,y\}+2z\leq\gamma\). Thus, we shall focus again on the first constraint \(g_{1}(\mathbf{k},\mathbf{I})\geq 0\). For any \(r\in R\), \(g_{1}(\mathbf{k},\mathbf{I})\) becomes  
\[k^{2}(3-\sqrt{7})-k(2l-1-\sqrt{7})+2k_{1}k_{2}(\sqrt{7}-3)+2(k_{1}l_{2}+k_{2}l _{1})+(l^{2}-l)(3+\sqrt{7})-2l_{1}l_{2}(3+\sqrt{7})\geq 0.\]  
The claim follows by applying Lemma A9 reported in the Appendix A.  
For the social cost function \(\mathsf{Perc}\), set \(\gamma=2.92\), \(x=0.68\), \(y=1.3\) and \(z=0.81\). Again, the second dual constraint is always satisfied, as \(\min\{x,y\}\geq 1\) and \(\max\{x,y\}+2z\leq\gamma\). Thus, we shall focus again on the first constraint \(g_{1}(\mathbf{k},\mathbf{I})\geq 0\). For any \(r\in R\), \(g_{1}(\mathbf{k},\mathbf{I})\) become \(49k^{2}+k(62k_{1}+62k_{2}-68l-62l_{1}-62l_{2}+81)+130k_{1}l_{2}+130k_{2}l_{1}- 422k_{1}k_{2}+211l^{2}-149l+2(81l_{1}l_{2}-31l_{1}-31l_{2})\geq 0.\) The claim follows by applying Lemma A13 reported in the Appendix A.  
For these cases, unfortunately, we are not able to devise matching lower bounds. The following result is obtained by suitably extending the lower bounding instance given in Reference [17] for the price of stability of congestion games (the proof is deferred to the Appendix).  
**Theorem 6**.: _For any \(\epsilon>0\), there exist two bidimensional linear congestion games \((\mathcal{G},\mathcal{C})\) and \((\mathcal{G}^{\prime},\mathcal{C}^{\prime})\) such that \(\mathit{PoS}(\mathcal{G},\mathcal{C})\geq\frac{1+\sqrt{5}}{2}-\epsilon\) under the social cost function \(\mathsf{Pres}\) and \(\mathit{PoS}(\mathcal{G}^{\prime},\mathcal{C}^{\prime})\geq\frac{5+\sqrt{17}} {4}-\epsilon\) under the social cost function \(\mathsf{Perc}\)._
# A Novel Cloud Approach for Connected Vehicles
## 5 Evaluation
### Simulation Results
#### 5.1.3 Data Freshness Simulation Results
Despite this decrease in GeoVCDN, it outperforms all other approaches even with high data frequency updates thanks to the benefits of V2V communications.
# Convolutional Neural Network Architecture for Recovering Watermark Synchronization
## 5 Experimental Results
### Image Quality  
Figure 8 shows the original image and the template/watermark-embedded image. As can be seen, the degradation of quality due to the proposed watermark and template insertion is hardly noticeable. We also measured the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) [38] for more objective image quality measurements. As can be seen from Table 1, the 'proposed template + proposed watermark' shows a similar image quality to that obtained using [PERSON]'s method.  
The watermarking method using DT-CWT shows a lower visual quality than other methods because the DT-CWT watermarking method greatly modifies the image to have robustness to DIBR. 'DT-CWT + proposed template' has a slightly better visual quality than the DT-CWT only method because it uses half the image area as a template, which has relatively lower energy than the DIBR watermark.  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **PSNR** & **SSIM** \\ \hline Proposed template + watermark & 43.6668 & 0.9859 \\ [PERSON]’s method & 43.2523 & 0.9850 \\ DT-CWT (DIBR watermark) & 40.3532 & 0.9788 \\ DT-CWT + proposed template & 41.1286 & 0.9804 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average PSNR and SSIM.
## 3 Results and Discussion
### Unconfined Compressive Strength
This trend of decrement also can be seen similar to the untreated soil specimen at 90 days curing period. The decrement of the secant modulus for the treated soil (lime and alkaline) can be correlated to the strength of the soil obtained from the UCS test (Figure 3b,c) where the increment in the soil strength between 28 days curing period and 90 days curing period is not too significant. The deformability index is defined as the maximum axial strain occurs at the peak of UCS for treated soil towards the axial strain at the peak of UCS for untreated soil specimen. This parameter is used to determine the deformation behaviour that occurs on soils [44; 46]. It can be clearly seen in Table 6, where the deformability index decreases as an increase of the curing periods for the treated with lime from 0.34 to 0.14. The decrement can be seen due to the less pozzolanic reaction that at the first stage of 7 days curing period which contribute to forming the specimens towards the more brittle condition. This trend is similar with findings by [44] which were that increasing the curing period and degree of saturation decreased the deformability of the soil specimens, which shows the specimen to \begin{table}
\begin{tabular}{c c c c c} \hline
**Mixture** & **Curing Period (Days)** & **Failure Strain (\%)** & **Secant Modulus (E\({}_{\text{s}}\)) MPa** & **Deformability Index (I\({}_{\text{D}}\))** \\ \hline \multirow{3}{*}{Clay} & 7 & 11.96 & 0.013 & - \\  & 28 & 10.98 & 0.018 & - \\  & 90 & 23.00 & 0.0091 & - \\  & 7 & 4.05 & 0.21 & 0.34 \\ \multirow{3}{*}{CLF} & 28 & 1.75 & 0.68 & 0.16 \\  & 90 & 3.25 & 0.48 & 0.14 \\ \multirow{3}{*}{CFAF} & 7 & 1.75 & 1.83 & 0.15 \\  & 28 & 1.90 & 4.16 & 0.17 \\ \cline{1-1}  & 90 & 4.19 & 2.16 & 0.18 \\ \hline \end{tabular}
\end{table} 
Table 6: Result of failure strain, secant modulus and deformability index for untreated and treated soil. be more brittle. Other than that, as the curing period increased towards 90 days curing period, the lesser the deformability index obtained which shows the bonding between the stabilized soil and fibre inclusion helps in strengthen the soil thus reducing the deformation behaviour. For the deformability index of treated soil with alkaline activation, the results show a not significant increment over of the curing periods with 0.15 to 0.18. This may be due to the higher compressed loading towards the soil that contributes to increasing the strain thus increasing the deformability index. Hence, lime and alkaline activation stabilizer with fibre inclusion have consequently caused an effect to compressibility behaviour of soil such as for the UCS test, failure strain, secant modulus and deformability index. This finding also similar to findings by [44; 47].
## 3 Application to Total Hip Arthroplasty with UHMWPE Liner
### Determination of the Foundation Modulus  
From Equations (6) and (9), it is immediately obvious that in the proposed model, the maximum pressure \(p_{0}\) and the maximum displacement (indentation depth) \(\delta\), are located at \(\varphi=0^{\circ}\) and directly proportional to each other. The foundation modulus represents the proportionality factor  
\[K=\frac{p_{0}}{\delta}. \tag{16}\]  
For further evaluations, the _dimensionless_ foundation modulus defined by  
\[\widetilde{K}=\frac{R_{2}}{E}\cdot K=\frac{R_{2}\,p_{0}}{E\,\delta}. \tag{17}\]  
is introduced, where \(R_{2}\) is the inner cup radius and \(E\) is the elastic modulus of the UHMWPE liner. In order to derive a universal expression for the foundation modulus that yields acceptable results for all common geometries, the maximum contact pressures \(p_{0}\) of the FEM solution are considered at a given indentation depth \(\delta\). Owing to the large difference in elastic moduli (see Table 2), it is assumed that only the liner is deformed.  
Figure 2 shows the dimensionless foundation stiffness per unit area as predicted by FEM for different Poisson's ratios plotted against normal force \(F_{N}\), radial clearance \(\Delta R\), and ratio of inner cup radius to foundation thickness \(R_{2}/L\). As shown in the left plot, the assumption of a constant foundation stiffness is only appropriate for normal forces larger than 1000 N. Slight deviations occur only for \(\
u=0.45\), which consequently become noticeable in the later results as well. During gait, up to 3000 N are commonly reached [27] such that the most important loading range can be covered by the spring model. To consider the effect of clearance and cup geometry, the foundation modulus in the range of 1000-3000 N was averaged and presented in the other plots. For different clearances in the range of interest (see Table 1), it is shown that the foundation modulus can be assumed to be approximately constant. Specifying parameters for the foundation modulus are the Poisson's ratio of the liner and the ratio of inner cup radius to liner thickness. For high Poisson's ratios, the increasing incompressibility yields larger foundation stiffnesses in very confined situations, i.e., very small clearances or small liner thicknesses.  
In order to find a universal expression of the foundation modulus in the discussed ranges, a polynomial fit of the FEM results in terms of cup geometry was obtained for each considered Poisson's ratio:  
\[\widetilde{K}=C_{1}(\
u)+C_{2}(\
u)\frac{R_{2}}{L}+C_{3}(\
u)\bigg{(}\frac{R_{2 }}{L}\bigg{)}^{2}, \tag{18}\]  
where the constants \(C_{i}(\
u)\) are listed in Table 3. In Figure 2, the fit is plotted in dotted lines. The derived stiffness function is in very good agreement with the FEM results for all [PERSON]'s ratios.
# All-Integer Quantization for Low-Complexity Min-Sum Successive Cancellation Polar Decoder
## 2 Background and System Model
### System Model  
A simple polar-coded communication system is illustrated in the block diagram in Figure 2. First, binary information of length \(K\) is generated following a uniform distribution. This binary information is then encoded into a binary polar code of length \(N\) using a polar encoder, as defined in (1). After encoding, each bit of the codeword is mapped to a modulated symbol using binary phase shift keying (BPSK), given by \(s_{i}=1-2(c_{i})\), where \(i\in\{1,2,\ldots,N\}\). The transmitted BPSK symbols are corrupted by additive white Gaussian noise (AWGN).  
To obtain LLRs, the received symbols are processed using the probability density function (PDF) of a normal distribution, considering BPSK modulation. The polar decoder then computes \(N\) LLRs using Equation (2) and retrieves \(K\)-bit information using (6). Performance evaluation is conducted by measuring the bit error rate (BER), which is determined as the ratio of bit differences between the transmitted and received information to the total number of transmitted bits. Additionally, FER is calculated as the proportion of received \(K\)-bit blocks that contain at least one erroneous bit to the total number of transmitted frames.
## 2 Wireless Sensors for Industrial Manufacturing
### Communication Requirements of the Smart Sensors for Milling Processes Use Case  
Using wireless smart sensors for monitoring milling processes enables a variety of use cases, such as collision detection, tool wear estimation, and tool breakage detection. Due to the criticality of the events that require to be detected, the requirements towards the data transmission from the sensor system are high. In Table 1, the industrial communication requirements for a high-precision milling process are given. Similar values can be found in the literature, varying depending on the specific use case [21; 22; 23]. These reference values will form the basis for the evaluation of the prototype.