esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Analytical Solution for Birdcage RF coil
### Equivalent Circuit Analysis
\right] \tag{14}\]  
Since an arbitrary transmission matrix can be converted to a two-port network, the block diagram of the equivalent circuit of a birdcage RF coil containing two-port equivalent circuit model of the \(N\)-\(I\) segments connected in cascade with the two-port equivalent circuit model of the \(N^{th}\) segment is shown in Figure 6.  
Figure 5: The block diagrams of: (**a**) The unit segment of a conventional birdcage RF coil; (**b**) Two port equivalent circuit of the unit segment. The equivalent circuit impedances \(Z_{ER}\) and \(Z_{L}\) can be determined by using the elements of the equivalent transmission matrix \(T_{e}\) with the help of following Equations (15) and (16) respectively. \[Z_{L}=\frac{1}{C_{e}} \tag{15}\]  
\[Z_{ER}=\frac{A_{e}-1}{C_{e}} \tag{16}\]  
In a birdcage RF coil port can be established by interfacing the external circuit across any leg or end-ring segment but it exhibits the similar frequency characteristics. However, the port impedance as viewed from the external circuit would be different at both positions which results in different expressions for the general analytical solution. By considering the port connected across the end-ring segment \(Z_{er}\)/2, the general analytical solution can be developed by calculating the equivalent impedance of the circuit model shown in Figure 6 as follows. \[Z_{a}=\frac{(Z_{er}+Z_{ER})}{2} \tag{17}\]  
\[Z_{b}=\frac{(Z_{er}+Z_{ER})}{4} \tag{18}\]  
\[Z_{c}=\frac{(Z_{er}+Z_{ER})}{4}+(Z_{l}+Z_{L}) \tag{19}\]  
\[Z_{eq}=\frac{(Z_{1}\cdot Z_{3})}{(Z_{1}+Z_{3})}=\frac{(Z_{er}+Z_{ER})\cdot[4(Z _{l}+Z_{L})+(Z_{er}+Z_{ER})]}{8(Z_{l}+Z_{L})+6(Z_{er}+Z_{ER})} \tag{20}\]  
The final expression of the general analytical solution for the case when the port is created in the end-ring segment is given by the Equation (21). Figure 6: Equivalent circuit model of \(N\) segments birdcage RF coil by converting each segment into a two-port network. \[Z_{ER\_p}=\frac{1}{2}\cdot\frac{Z_{\sigma}\cdot\left(Z_{ER}+2Z_{\alpha q}\right)}{Z _{\sigma}+Z_{ER}+2Z_{\alpha q}} \tag{21}\]  
In a similar way the general analytical solution for the case when the port is created in the leg segment can be derived and the final expression is given by the Equation (22). Figure 5:0  
The proposed method allows the input impedance to be obtained directly from the leg or end-ring of the birdcage RF coil. Precisely speaking, unlike the existing solutions for analyzing the relationship between the components values and the resonant frequency, the input impedance can be easily obtained at any point of the birdcage coil. Therefore, the method is not only useful to determine the resonance frequencies of the birdcage RF coil, but also to provide the port impedance which is an essential parameter to be known for interfacing any external circuitry like impedance matching circuit, detuning circuit etc. to the birdcage RF coil.
# Integrated Adaptive Cruise Control with Weight Coefficient Self-Tuning Strategy
## 4 Predictive Control Algorithm for IACC
### Multi-Objective Weights Self-Turning Strategy  
Regarding the multi-objective model predictive control algorithm, the value of the weight coefficient indicates the importance of the corresponding sub-objective. As previously mentioned, the traditional MPC control with a constant weight matrix (refer to Equation (24)) has inadequate adaptabilities against some transient traffic scenarios in particular. In addition, sub-objectives commonly conflict with each other. To increase the weight coefficient of one sub-objective will probably weaken the performance of others, which might cause the overall performance to be sub-optimal and sometimes even worsen the control quality. This indeed challenges the design of the ACC system especially, with regards to taking the complicated and volatile traffic scenarios into account.  
To this end, it is worthwhile and also necessary to attempt to dynamically adjust the weight matrix with the change of traffic scenarios, as shown in Figure 6. For instance, when the road adhesion is poor or road curvature is large, the vehicle lateral stability should be paid more attention to in view of driving safety; accordingly, the corresponding weight coefficients will be larger than ever before. When the lateral stability is well-satisfied, priority should be given to the longitudinal tracking capability.  
As the variance reflects the deviation degree of the random variable from the mathematical expectation, it can reflect the fluctuation of the variable more accurately than the mean.  
Figure 6: Schematic diagram of IACC control algorithm with tunable weight coefficients.  
The variance of each sub-objective is defined as  
\[D_{k}(\cdot)=\frac{1}{p}\sum_{i=0}^{p-1}{(\cdot)}^{2}-[\frac{1}{p}\sum_{i=0}^{p-1 }{(\cdot)}]^{2} \tag{26}\]  
where \(D_{k}(\cdot)\) is the variance of the corresponding sub-objective, such as \(\Delta d\), \(\Delta v\), \(a_{x,des}\cdots\).  
The ratio of the sub-objective variance in the current cycle to that in last cycle is regarded as a tunable factor to adjust in correspondence with the weight coefficient. As a consequence, the weight coefficient self-tuning strategy is suggested as  
\[w_{k+1}(\cdot)=\left\{\begin{array}{ll}\lambda_{\min}\,w_{k}(\cdot)&,\,\, \lambda_{k}<\lambda_{\min}\\ \lambda_{k}(\cdot)\,w_{k}(\cdot)&,\,\,\textit{otherwise}\\ \lambda_{\max}\,w_{k}(\cdot)&,\,\,\lambda_{k}>\lambda_{\max}\end{array}\right. \tag{27}\]  
where \(\lambda_{k}(\cdot)=\frac{D_{k}(\cdot)}{D_{k-1}(\cdot)}\) is the adjustment factor of the weight coefficient of the corresponding sub-objective, \(\lambda_{\min}\) together with \(\lambda_{\max}\) are the design parameters, and \(w_{k+1}(\cdot)\) is the tuned weight coefficient for the cost function optimization in the next receding horizon.
# _CoVSCode_: A Novel Real-Time Collaborative Programming Environment for Lightweight IDE
## 5 Major Technical Issues and Solutions
### Consistency Maintenance of Replicated Source Code Copies
#### 5.3.1 Consistency Maintenance in File-Level Sessions  
In a file-level session, programmers concurrently edit the content of a shared source code file. Technically, a source code file is a string of textual characters with a single linear address space, and each character can be addressed by a positional value. There are two primitive editing operations that can be executed:  
* _insertString_[_p_, _s_]: to insert a string \(s\) at the position \(p\) of the source code file.
* _deleteString_[_p_, _l_]: to delete a string starting from the position \(p\) with the length \(l\).  
All complex editing operations will result in one or several primitive editing operations defined above. For example, replacing several lines of the source code by pasting texts may lead to a _deleteString_ operation and an _insertString_ operation. The registered listener (item \(b\) in Table 1) will automatically detect the textual changes in the source code file and produce these primitive operations for further processing.  
As discussed in Section 2.1, concurrent editing operations may lead to inconsistent document states if they are directly applied at remote sites. The operational transformation controller in the client adaptor is dedicated to file-level source code consistency maintenance, which has embedded mature OT mechanisms and techniques that were derived in prior works [21; 23; 25]. Whenever a local editing operation is detected and presented by the registered listener, it will be processed by the operational transformation controller, and then transmitted to the server. Whenever a remote editing operation arrives, it will be processed by the operational transformation controller, and then applied in the source code file.  
Figure 4: Local operation processor (LOP) and remote operation processor (ROP).
## 4 Results
### Similarity between Human and CNN Attention Maps
#### 4.2.2 Cross-Correlation  
For cross-correlations, the ANOVA revealed a main effect of human-CNN comparison, \(F\)(1,2,69,2) = 122.978, \(p\)\(<\) 0.001, \(\text{np}^{2}\) = 0.683 but no main effect of image type, \(F\)(2,57) = 1.235, \(p\) = 0.298, \(\text{np}^{2}\) = 0.042 and no interaction, \(F\)(4,114) = 439, \(p\) = 0.780, \(\text{np}^{2}\) = 0.015 (see Figure 5B, red bars). The main effect of human-CNN comparison indicated that Grad-CAM correlated higher with drawing (0.54) than with spontaneous gaze and gaze-pointing (0.28 and 0.29, respectively), both \(p\)s \(<\) 0.001, while the two types of eye movement maps did not differ in their correlation with Grad-CAM, \(p\) \(>\) 0.9. As indicated by the lack of an interaction, the same pattern was found for all three image types: higher correlations of Grad-CAM with drawing than with the two eye movement tasks, all \(p\)s \(<\) 0.001, and similar, low correlations of Grad-CAM with the eye movement tasks, all \(p\)s \(>\) 0.7.  
\begin{table}
\begin{tabular}{c l c c c c c c} \hline \hline  & & & \multicolumn{2}{c}{**Dice Score**} & \multicolumn{3}{c}{**Cross-Correlation**} \\ \hline  & & **Objects** & **Indoor** & **Landscapes** & **Objects** & **Indoor** & **Landscapes** \\  & & & **Scenes** & & & **Scenes** & **Landscapes** \\ \hline \multirow{3}{*}{Human–CNN} & SponG\_Grad-CAM & 0.40 (0.17) & 0.20 (0.16) & 0.13 (0.10) & 0.31 (0.06) & 0.24 (0.13) & 0.30 (0.07) \\  & GPoint\_Grad-CAM & 0.37 (0.17) & 0.28 (0.17) & 0.13 (0.17) & 0.33 (0.08) & 0.26 (0.14) & 0.28 (0.09) \\  & Draw\_Grad-CAM & 0.53 (0.19) & 0.31 (0.27) & 0.06 (0.11) & 0.55 (0.21) & 0.52 (0.21) & 0.55 (0.19) \\ \hline \multirow{3}{*}{Human–human} & SponG\_GPoint & 0.66 (0.08) & 0.54 (0.11) & 0.44 (0.09) & 0.64 (0.09) & 0.80 (0.16) & 0.74 (0.20) \\  & SponG\_Draw & 0.57 (0.09) & 0.36 (0.17) & 0.17 (0.14) & 0.28 (0.08) & 0.50 (0.22) & 0.40 (0.24) \\ \cline{1-1}  & GPoint\_Draw & 0.60 (0.07) & 0.42 (0.16) & 0.30 (0.16) & 0.38 (0.06) & 0.53 (0.15) & 0.46 (0.15) \\ \hline \hline \end{tabular} SponG = spontaneous gaze, GPoint = gaze-pointing, Draw = drawing.  
\end{table}
Table 1: Means and standard deviations (in parentheses) for all human–CNN comparisons and human–human comparisons, depending on task and image type.  
Figure 5: Comparisons between attention maps depending on image type, for both human–CNN comparisons (red bars) and human–human comparisons (grey bars). (**A**) Dice score and (**B**) cross-correlation. SponG = spontaneous gaze, GPoint = gaze-pointing, Draw = drawing. Error bars represent standard errors of the mean.
## 6 Results and Discussion
### Saturation Index (SI) Evolution of Key Minerals  
The saturation index (SI) trends, presented in Figure 5, provide critical insights into mineral stability and precipitation dynamics throughout the simulation period. Key observations include the following:  
1. Calcite, Aragonite, and Vaterite exhibit near-equilibrium SI values, indicating a continuous dissolution-precipitation cycle that contributes to crack healing and permeability reduction.
2. Quartz, Chalcedony, and Pozzolan are initially undersaturated, suggesting early-stage silica dissolution, which later stabilizes as these phases reprecipitate into cementitious silicates, reinforcing the matrix.
3. Montmorillonite and Barite remain undersaturated, reflecting limited reactivity under the simulated conditions; however, their presence contributes to cement density stability and resistance against chemical degradation.
4. Portlandite and ECSH2 display a sustained presence, confirming their role in long-term structural reinforcement and enhancing cement integrity.  
Pyrite and Vivianite remain significantly undersaturated, indicating minimal precipitation in the absence of reducing conditions, which is expected under standard wellbore environments.  
The overall SI trends confirm that the self-healing cement formulation achieves long-term chemical stability, effectively mitigating excessive dissolution and maintaining structural robustness over extended operational periods. These saturation index trends align closely with the observed porosity evolution and support the mechanistic understanding of the self-healing behavior. Differences of up to two orders of magnitude in the SI values across mineral phases are attributed to variations in mineral solubility, kinetic reactivity, and stability under CO\({}_{2}\)-rich conditions. Phases such as Aragonite, Calcite, and ECSH2 tend toward equilibrium, while highly soluble or less reactive phases such as Barite and Montmorillonite remain significantly undersaturated. These trends are consistent with expected mineralogical behavior in reactive wellbore environments. Specifically, the formation of C-S-H, Calcite, and Portlandite--phases with SI values at or above equilibrium--explains both early crack sealing and long-term stability. While Barite remains undersaturated, its role in initial cement density and sulfate buffering remains important. Pozzolan and Chalcedony dissolution supply reactive silica that fuels C-S-H precipitation. The interaction among these mineral phases confirms that the self-healing cement is not only chemically robust but also functionally reliable under sustained CO\({}_{2}\)-rich conditions.  
Figure 5: Saturation index evolution of key minerals over time, showing the stability and precipitation-dissolution behavior of self-healing cement components under CO\({}_{2}\)-rich conditions, with Aragonite, Calcite, and ECSH2 maintaining near-equilibrium states, ensuring long-term cement integrity.
## 3 Methods
### Segmenter
#### 3.1.3 Channel Spatial Attention Enhancement Module
The computational pipeline operates as follows: Given the channel-refined feature map \(M_{c}(F)\), the mechanism first applies both max pooling and average pooling operations along the channel dimension to generate two spatial descriptors (\(F_{1}\) and \(F_{2}\)) of size H \(\times\) W \(\times\) 1. These descriptors are subsequently concatenated along the channel axis to form a composite feature representation of dimension H \(\times\) W \(\times\) 2. This concatenated feature map is then processed through a convolutional layer with a \(7\times 7\) kernel and stride of 1, which effectively captures broad spatial contextual information. Finally, a sigmoid activation function is applied to generate the spatial attention weights \(M_{s}(F)\), where each element represents the relative importance of the corresponding spatial location. The complete mathematical formulation is provided in Equation (9), demonstrating the transformation from channel-refined features to spatially weighted outputs through this carefully designed sequence of operations. \[M_{s}(F)=\sigma\Big{\{}Conv\Big{[}concat\Big{(}pool_{avg}(F_{1}),pool_{max}( F_{2})\Big{)}\Big{]}\Big{\}} \tag{9}\]  
In the equation, \(\sigma\) denotes the sigmoid activation function, \(Conv\) represents the convolution operation, average pooling is denoted by the symbol \(pool_{avg}\), and max pooling is indicated by \(pool_{max}\). The complete computational procedure is illustrated in the spatial attention module of Figure 5.  
The CBAM module's operational pipeline follows a carefully designed sequential attention mechanism, as formally characterized below. The input feature map \(F\) first undergoes channel-wise refinement through multiplication with the channel attention vector M\({}_{c}(F)\), producing the intermediately enhanced feature representation \(F_{1}\). This channel-attentive feature map then enters the spatial attention phase, where it is modulated by the spatial attention weights \(M_{s}(F)\) to yield \(F_{2}\). The final output \(F^{\prime}\) is obtained through a resid  
Figure 4: Channel attention module diagram. ual summation that combines the original features with the doubly attentive modifications, as mathematically formulated in Equations (10)-(12). \[F_{1}=pool_{avg}(F) \tag{4}\]0  
\[F_{1}=pool_{avg}(F) \tag{4}\]1  
\[F_{1}=pool_{avg}(F) \tag{4}\]2  
In the formulation, \(M_{c}(F)\) denotes the channel attention map, \(M_{s}(F)\) represents the spatial attention map, and the operator \(\odot\) indicates element-wise multiplication (Hadamard product). The complete computational procedure is illustrated in \[F_{1}=pool_{avg}(F) \tag{4}\]3
## 4 Implementation and Discussion
### Performance Analysis
#### 4.2.3 Comply with Privacy Policy  
Proposed system stores PII and PPII in local databases. Additionally, the hash of locally stored data (PII and PPII) and NPII are stored in the block (blockchain). Local databases of P, D, I\({}_{\text{c}}\), and C\({}_{\text{P}}\) can delete PII and PPII at any time. As leftover hashes stored in DB\({}_{\text{loc}}\) do not contain any sensitive data and are of no use, proposed PoF based scheme complies GDPR (right to forget and right to erase) [27].
# Ultra-Low-Power FinFETs-Based TPCA-PUF Circuit for Secure IoT Devices
## 2 Adiabatic FinFET-Based PUF
### Fundamental of FinFET  
With the scaling down of the transistor conductive channel, the short-channel effects (SCEs) become intolerable, and many multi-gate devices have been proposed to overcome the SCEs. Due to the relatively simple manufacturing process and the good compatibility with bulk CMOS, FinFET is considered to be a double gate (front gate and back gate) that can replace the high \(k\) of the planar MOSFET. The FinFET device has the advantages of higher on-state current, lower off-state current (lower leakage current), and faster switching speed [42]. The unique characteristic of the FinFET is that the conducting channel consists of thin silicon, known as \"fin\", which forms the body of the device. Moreover, FinFET is also known as a non-planner double-gate MOSFET (DG-MOSFET), in which both the front gate (FG) and back gate (BG) are tied together. A typical FinFET structure is shown in Figure 3, in which Figure 3a,b depict the three-dimensional structure and cross-sectional  
Figure 2: Comparison of the supply currents for the equivalent RC models: (**a**) CMOS logic with step voltage, (**b**) adiabatic logic with ramped step voltage, and (**c**) the peak supply current of the adiabatic logic is significantly lower than that of the conventional CMOS logic under the same parameters and conditions.  
top view of the FinFET, respectively. Figure 3c demonstrates the schematic symbol with shorted-gate (SG) mode and independent-gate (IG) mode of the n-type and p-type FinFETs. Furthermore, according to front gate and back gate being tied up or not, FinFET circuits can be divided into three different operating modes, namely, SG, IG, and the low-power gate (LP) modes [43].
# A Hybrid Deep Learning Approach for Improved Detection and Prediction of Brain Stroke
## 5 Dataset Used

Table 2: Description of the dataset used for the proposed approaches. * Work Type (Risk Factor): Occupational stress, job security worries, and sedentary lifestyles all increase stroke risk. Jobs that require little physical exercise are frequently related to higher BMI and hypertension, whereas physically demanding employment may have a protective effect against stroke [35]. Integrating biomarkers and risk variables into prediction models allows healthcare providers to improve stroke risk assessment and early intervention tactics. Figure 2 represents the class distribution of brain stroke cases in the dataset prior to and during data augmentation. Before augmentation (a), the dataset shows a considerable class imbalance, with 95.1% of occurrences categorized as \"No Stroke\" (blue) and just 4.9% as \"Stroke\" (red), showing the difficulties of predictive modeling in such imbalanced settings, where algorithms frequently prefer the majority class [35]. Following data augmentation (b), the class distribution is balanced, with 50% representation for both \"Stroke\" (red) and \"No Stroke\" (blue). This balanced distribution tackles the issue of class imbalance by allowing the model to learn more effectively from the minority class and enhancing its predictive performance for stroke patients, which is a vital step toward fair and accurate forecasts [36]. Table 3 lists the many machine learning and deep learning models that have been investigated for stroke prediction utilizing various datasets and methodologies. To improve prediction accuracy, researchers have experimented with ensemble approaches, logistic regression (LR), decision trees (DT), random forests (RF), and neural networks. To solve issues like data imbalance and feature optimization, methods including principle component analysis (PCA), feature selection, and the Synthetic Minority Over-sampling Technique (SMOTE) have been used. Although these methods increase model interpretability and accuracy, there are still significant issues with their limits, including limited datasets, unbalanced data, and the fact that certain strategies are only applicable to particular kinds of data. Figure 2: The visualizing count of classes (stroke and non-stroke) (a) before data augmentation and (b) after data augmentation.
## 4 Results and Discussion
### Compressible Channel Flow
The peaks of the streamwise (Figure 19a) and spanwise (Figure 19c) velocity variances are obvious larger by DB than DNS, the Reynolds shear stress (Figure 19d) is lower by DB, while the wall-normal velocity variances (Figure 19b) of DB agree well with DNS. This is related to the mesh resolution. Note that in Figure 19b the Reynolds stress is better resolved, on whose direction the mesh is relatively finer. To conclude, this testcase indicates the ability of the DB method working with high order FR-DG method to simulate compressible wall-bounded turbulence robustly, and its potential to provide reasonable prediction in this complex flow configuration. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Computation** & **DOF** & \(\mathbf{\Delta x}^{+}\) & \(\mathbf{\Delta z}^{+}\) & \(\mathbf{\Delta y}^{+}_{avg}\) & \(\mathbf{y}^{+}_{1}\) \\ \hline LES with DB & \(120\times 96\times 60\) & 26 & 26 & 10 & 0.88 \\ method & \(120\times 96\times 60\) & 26 & 26 & 10 & 0.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Spatial resolution for channel flow computation. Figure 18: Mean velocity profile in supersonic channel flow at \(M_{b}=1.5\) and \(Re_{b}=\)15,334 based on height of the channel.