esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Consumer Acceptance Rating of ST Muscle  
The mean scores for the consumer acceptance test of beef, assessing the impact of the cooking treatments studied, are presented in Figure 2. Low-temperature HT methods resulted in higher consumer ratings for all characteristics tested, except for cross-section color and tenderness, compared to the control (boiling) sample. In contrast, the \(\Delta\)T40-treated sample received the lowest cross-section color and tenderness ratings. The SV55 treatment led to the highest acceptability of juiciness and cross-section color. Considering the results of instrumental color measurements, the SV55 samples, which exhibited the most uniform color in the cross-section (lowest \(\Delta E\)) and the highest redness (\(a\)*), received the highest consumer ratings. On the other hand, the highest overall quality ratings were recorded for the SV75-treated samples, with slightly lower ratings for the \(\Delta\)T20- and R75-treated samples. These results correspond with the research of [PERSON] et al. [53], where sous vide cooking was associated with higher juiciness ratings across different muscles compared to traditional cooking methods. Additionally, tenderness ratings were higher in sous vide-treated meat regardless of aging time (2 vs. 21 days). Juiciness is a crucial quality attribute that affects consumer satisfaction with cooked beef following tenderization. The meat's moisture content, which determines its juiciness, is primarily influenced by total water content and cooking loss. [PERSON] et al. [54] showed that low-temperature, long-time sous vide cooking significantly impacts both cooking loss and the total water content of beef. We found that juiciness acceptance decreased as the applied heat treatment temperature increased. [PERSON] et al. (2018) [12] explained this by noting that most of the water in muscles is retained within myofibrillar proteins. As the temperature increases, the meat structure undergoes greater shrinkage due to the denaturation of myofibrillar proteins, leading to water being squeezed out and resulting in higher toughness.  
Figure 1: The myoglobin denaturation (\(MD\)) [%] in ST muscle depending on the HT method. letters (a, b...) show the significant differences (\(p\leq 0.05\)).
## 4 Simulation Results
Figure 3: BER versus SNR for SISO DFE and MIMO DFE in the Vehicular A channel with \(\eta_{\max}=0.6\) dB and \(N_{f}=50\). Figure 2: Single example of the SISO DFE by the three design method in Vehicular A channel with \(\eta_{\max}=0.6\) dB, SNR = 20 dB and \(N_{f}=25\). To verify the performance of the proposed scheme in different channels, Figures 5 and 6 show the simulation results in the exponential decay channel. Comparing with the OMP method, the results also show that the proposed scheme has better BER performance and smaller NMSE with the same sparsity level. Therefore, the proposed scheme provides excellent performance in different channel conditions. Figure 4: NMSE versus SNR for SISO DFE and MIMO DFE in the Vehicular A channel with \(\eta_{\max}\) = 0.6 dB and \(N_{f}\) = 50. Figure 5: BER versus SNR for SISO DFE and MIMO DFE in the exponential decay channel with \(\eta_{\max}\) = 0.6 dB and \(N_{f}\) = 50.
# Multi-Classification of Chest X-rays for COVID-19 Diagnosis Using Deep Learning Algorithms
## 5 Experimental Results
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Study** & **\# Images** & **Data Split (Train:Test:Val)** & **\# Classes** & **Model** & **ACC** \\ \hline [PERSON] et al. [23] & 4560 & (60\%\(\times\)25\%:15\%) & 2 & DenseNet201 & 98.5\% \\ [PERSON] et al. [24] & 3000 & 5 and 10-fold CV & 15 & TL + ML classifiers & 99.40\%, 99.77\% \\ [PERSON] et al. [25] & 21,165 & (72\%:10\%:18\%) & 4 & ResNet50 & 94\% \\ [PERSON] et al. [26] & 12,357 & 10-fold CV & 16 & ResNet50 + semantic segmentation & 71.90\%, \\ [PERSON] et al. [27] & 10,373 & (70\%:20\%:10\%) & 3 & DenseNet201 & 92.19\% \\ [PERSON] et al. [32] & 8461 & (60\%:20\%:20\%) & 3 & VGG16 & 99\% \\ [PERSON] et al. [14] & 2500 & (64\%:20\%:16\%) & 2 & EpistoNet & 95\% \\ [PERSON] et al. [15] & 6286 & (60\%:20\%:0) & 4 & CSEN & 95.4\% \\ Proposed model & 7395 & (80\%:10\%:10\%) & 3 & Xception + GAP\& activation layers & 99.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Previous works for COVID-19 classification. Here, MCSVM, CSEN, ML, TL, CV, and ACC stand for multi-class support vector machine, convolution support estimation network, machine learning, transfer learning, cross-validation, and accuracy, respectively. Figure 9: Confusion matrices of the pre-trained models with learning rate \(\eta=0.1\), ReLU activation function, and Adam optimizer. (**a**) VGG16, (**b**) Xception, (**c**) DenseNet121, (**d**) InceptionV3, (**e**) ResNet50, and (**f**) MobilNet.
## 3 Results and Discussion
### Effect of the Culture Conditions on the Pollutant Elimination
#### 3.2.4 Summary of the Removal Yield of CBZ after 60 Days  
The removal yield of CBZ as a function of culture media, initial inoculum concentrations, and initial concentration of CBZ after 60 days has been summarized in Table 3. This table shows that the values ranged between 60% and 90%, which is quite high for a biological treatment, accounting for the biorefractory character of CBZ. This assesses the ability of _A. braunii_ to remove CBZ. The results achieved in this work were quite similar to those reported for the degradation of carbamazepine or clofibric acid by white-rot fungi, including _Trametes versicolor_[53]. It is also important to note that the removal efficiencies obtained with _A. braunii_ were much higher than the ones reported previously based on the activated sludge systems [54,55].  
As discussed before, Table 3 emphasizes that the lowest yields in both media corresponded to a high initial concentration of 10 mg/L of CBZ. It also shows that for any value of the initial concentration of _A. braunii_ in both media, the removal yield decreased by about 10% for a factor of four increase in the initial concentration of CBZ. In addition to this, for any value of the initial concentrations of CBZ, a factor of ten increase in the initial inoculum concentration leads to around a 7% increase in CBZ elimination. In other words, this shows that a factor of four increase of the initial concentration of CBZ had more influence than a factor of ten inoculum concentration on the elimination of CBZ. This  
Figure 4: Effect of CBZ initial concentration on the elimination of CBZ at initial _A. braunii_ content: (a) \(10^{5}\) cells/mL, and (b) \(10^{4}\) cells/mL.  
demonstrates again the toxic effect of CBZ on _A. braunii_ in the range of concentrations studied, but also that this toxicity did not significantly impair the ability of the algae to remove CBZ.
# Semi-Direct Point-Line Visual Inertial Odometry for MAVs
## 4 Experimental Results and Analysis
### Computational Performance
#### 4.2.2 Average Time for Marginalization  
Marginalization is another time-consuming aspect of the back-end, so the average time consumption was also analyzed. Taking V102 as an example, as explained in Section 3.3.2, when the back end of SDPL-VIO used the basic marginalization method, one-step marginalization was carried out, resulting in a very large amount of computation, and the average time needed for marginalization was 39 ms. When the back end used the improved  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sequence** & **SDPL-VIO** & **PL-VIO** \\ \hline MH01 & 28.54 & 84.40 \\ MH02 & 33.55 & 88.05 \\ MH03 & 27.51 & 85.73 \\ MH04 & 30.45 & 83.18 \\ MH05 & 30.10 & 83.10 \\ V101 & 29.65 & 82.52 \\ V102 & 29.48 & 83.35 \\ V103 & 32.81 & 87.61 \\ V201 & 30.15 & 81.24 \\ V202 & 31.65 & 89.76 \\ V203 & 33.56 & 90.40 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average time (ms) spent tracking an image.  
Figure 13: The ratio of the number and tracking time between keyframes and non-keyframes on the V201 dataset.  
marginalization method, the high-dimension matrix corresponding to the cost function was decomposed step by step. Therefore, the amount of computation was significantly reduced, and the average time needed for marginalization was 16 ms.
# New Approaches in Dynamic Metrics for Lighting Control Systems: A Critical Review
## 3 Methods  
Having thoroughly reviewed the principal static and dynamic daylighting metrics, this section outlines the methodology employed to offer a comparative analysis of them. The aim is to critically examine the correlation, utility, and potential symbiosis among these diverse metrics when applied under a consistent study situation. By doing so,  
\begin{table}
\begin{tabular}{c c c} \hline
**Aspect** & **mEDI** & **CSA** \\ \hline Origin & CIE S 026:2018 & [PERSON] et al. [61; 62] \\ \hline Basis & \begin{tabular}{c} Spectrally weighted \\ equivalent to D65 daylight \\ \end{tabular} & \begin{tabular}{c} Non-linear physiological \\ model of melatonin \\ suppression \\ \end{tabular} \\ \hline Input Parameters & \begin{tabular}{c} Spectral power distribution \\ (SPD) \\ \end{tabular} & \begin{tabular}{c} SPD, intensity, exposure \\ duration, circadian timing \\ \end{tabular} \\ \hline Temporal Dimension & \begin{tabular}{c} Static (but adaptable to \\ dynamic simulations) \\ \end{tabular} & \begin{tabular}{c} Explicitly time-based \\ and dynamic \\ \end{tabular} \\ \hline Physiological Accuracy & \begin{tabular}{c} Indirect (based on ipRGC \\ sensitivity) \\ \end{tabular} & \begin{tabular}{c} Direct modeling of \\ biological response (but \\ with the model still \\ in development) \\ \end{tabular} \\ \hline Simplicity of Use & High—requires only SPD & \begin{tabular}{c} Medium—requires \\ multiple biological inputs \\ \end{tabular} \\ \hline Standardization and & \begin{tabular}{c} High—used in WELL, \\ DIN/TS 67600:2022-08, \\ ISO standards \\ \end{tabular} & \begin{tabular}{c} Lower—cited, but not part \\ of international standards \\ \end{tabular} \\ \hline Main Limitation & \
\begin{tabular}{c} No modeling of exposure \\ duration or adaptation \\ \end{tabular} &
\begin{tabular}{c} Model complexity and \\ fewer tools available \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative summary: mEDI vs. CSA.  
this research seeks to not only understand their interrelationships and sensitivities to specific design parameters but also to uncover the inherent limitations in the information they provide, thereby contributing to a more nuanced understanding of their overall practical applicability in architectural design and building performance assessment. The methodology involved the development of a virtual room model, serving as the basis for calculating each of the previously discussed metrics. These calculations were performed on a 50-point analysis grid, with the study systematically varying parameters such as the room's geographical location, orientation, window-to-wall ratio, and typical occupied hours. The specifics of the method employed will be detailed in the subsequent sections.
## 4 Numerical Examples and Validation of the Software  
In this section, multiple numerical experiments are treated to validate the robustness and the functionality of the numerical scheme. In all three subsections, the numerical examples that are given through the parameters in the Tables 1-3 are discussed. Furthermore, for all numerical examples, the starting values for the augmented Lagrangian algorithm were chosen as \(\alpha^{(0)}=\frac{5}{6}\), \(\mu^{(0)}(x)=0\) for all \(x\in\Omega\), \(\tau=0.9\), and \(\lambda^{\max}(x)=0.02\) for all \(x\in\Omega\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Species** & \(D\) **in** \(\frac{\mathbf{m}^{2}}{\mathbf{s}}\) & \(k_{1}\) **in** \(\frac{1}{s}\) & \(k_{2}\) **in** \(\frac{\mathbf{m}^{3}}{\mathbf{mol}\,\mathbf{s}}\) & \(\gamma s\) \\ \hline \(A\) & 1/2 & - & - & 0 \\ \(B\) & 1/3 & - & - & 0 \\ \(C\) & 1/4 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The first example of physical parameters of the diffusion–reaction model for the complexation of \(K\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Species** & \(D\) **in** \(\frac{\mathbf{m}^{2}}{\mathbf{s}}\) & \(k_{1}\) **in** \(\frac{1}{s}\) & \(k_{2}\) **in** \(\frac{\mathbf{m}^{3}}{\mathbf{mol}\,\mathbf{s}}\) & \(\gamma s\) \\ \hline \(A\) & 1/2 & - & - & 0 \\ \(B\) & 1/3 & - & - & 0 \\ \(C\) & 1/4 & 0 & 1 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Second example of physical parameters of the diffusion–reaction model for the complexation of \(K\).
# Relationship between Fault Level and System Strength in Future Renewable-Rich Power Grids
## 3 Fault Level Estimation Using Measurement-Based Thevenin Impedance
### Thevenin Impedance Measurement Using Capacitor Bank Switching  
Capacitor banks are usually utilized for steady-state voltage support at both transmission and distribution levels (often close to the large load center). By switching them on, capacitor banks can change the voltage level by injecting a reactive current at the bus where they are connected. Although this switching might be accompanied immediately by a transient inrush current, the steady-state voltage would be enhanced remarkably [32]. Both the transient current and resulting steady-state voltage step change are influenced by the system's strength, which is indicated by the Thevenin impedance (i.e., the fault level) at the connection point. For the purpose of this paper, which accounts only for steady-state conditions, the transient inrush current is not considered and the whole transient state is ignored. Note that less voltage changes result from capacitor banks switching at stronger buses (i.e., low equivalent impedance). Hence, similar to the load change method described earlier, capacitor banks switching events can be employed to estimate the Thevenin impedance at the node of connection. As depicted in Figure 3, the node at which the capacitor bank is connected can be represented by its Thevenin equivalent. Hence, by acquiring two sets of measurements of the voltage and current before and after the capacitor switching event, we can simply calculate the Thevenin impedance as expressed in Equation (4):  
\[Z_{th}\ =\ \left|\frac{\overline{V_{L,1}}-\overline{V_{L,2}}}{\overline{I_{L,1}}- \overline{I_{L,2}}}\right|\ =\ \left|\frac{\Delta V_{L}}{I_{C}}\right| \tag{6}\]  
where \(I_{C}\) is the rated value of the capacitor current.  
Note that the perturbation caused by the injected reactive current \(I_{C}\) should not result in a voltage beyond the steady-state voltage limits defined by the grid operator (e.g., \(\pm 5\%\) of the nominal voltage). It is worth highlighting the applicability of this method for the estimation of the Thevenin impedance, not only at load buses but also at other unloaded buses at both transmission and distribution levels. By doing so, the obtained Thevenin impedance at the node of interest can then be employed to calculate the fault level MVA at the node of interest as previously expressed in Equation (2).
# An Algebraic Approach to Identifiability
## 3 Observability and Identifiability
### Global Observability  
For the observability test, the parameters are assumed to be known. Therefore, the extended state space is restricted to \(\mathcal{Z}=\mathcal{M}_{p}^{2}\times\Theta\
i z=(x,\bar{x},p)\). We identify equal extended states by  
\[\mathcal{J}=\{z=(x,\bar{x};p)\in\mathcal{Z}\mid x=\bar{x}\},\]  
i.e., by equal ordinary state. Thus, the corresponding ideal to the Zariski closure thereof reads  
\[\mathfrak{J}=\mathfrak{J}+\langle x_{1}-\bar{x}_{1},\ldots,x_{n}-\bar{x}_{n} \rangle\subset\mathbb{R}[x,\bar{x};p].\]  
The ideal generated by the extended output maps is closed under the Lie derivatives with respect to the vector fields  
\[F_{k}(x,\bar{x};p)=f_{k}(x;p)\tfrac{\partial}{\partial x}+f_{k}(\bar{x},p) \tfrac{\partial}{\partial\bar{x}}.\]  
This differs only in the part in direction \(\tfrac{\partial}{\partial x}\), which is evaluated at the same parameter \(p\). Using the same notation, the system is globally observable if and only if \(\hat{\mathfrak{J}}=\mathfrak{J}\). This holds for all admissible parameters.  
In addition, the parameter subset for the given global observability can be computed by solving the quantifier elimination problem  
\[\forall x,\bar{x}:(x,\bar{x};p)\in\mathcal{Z}\wedge\Big{(}(x,\bar{x};p)\in \mathbf{var}^{\mathbb{R}}(\mathfrak{J})\implies x=\bar{x}\Big{)}.\]
# Service-Driven Dynamic Beam Hopping with Resource Allocation for LEO Satellites
## 4 Simulation Results and Analysis
### Computational Complexity Analysis  
The algorithm in this paper will be compared in performance with four other different dynamic beam hopping (DBH) schemes, namely Random Beam Hopping (R-BH), Periodic Beam Hopping (P-BH), Genetic Algorithm Beam Hopping (GA-BH), Delay Fairness Beam Hopping (DF-BH), and Deep Q-Network (DQN). A complexity analysis of each algorithm is presented as follows.  
* R-BH: The R-BH algorithm randomly selects \(K\) beam positions from \(M\) beam positions for irradiation, and the beam power is randomly selected from \(N\) elements of the transmitting power set. Therefore, the complexity of the R-BH algorithm is expressed as \(O(K+N)\).
* P-BH: The P-BH algorithm carries out a time division service for different beam positions through a periodic beam hopping pattern. So, the complexity of the P-BH algorithm is expressed as \(O(1)\).
* GA-BH [26]: The GA-BH algorithm needs to calculate user satisfaction and delay separately. The computational complexity of the former is \(O(K)\), and the computational complexity of the latter is \(O(KMT_{th})\). Therefore, the complexity of the GA-BH algorithm is expressed as \(O(G\times P\times KMT_{th})\), where \(G\) represents the maximum number of iterations and \(P\) represents the population size.
* DF-BH [27]: The DF-BH algorithm serves the top \(K\) beam positions with the largest delay in each time slot. So, the complexity of the DF-BH algorithm is expressed as \(O(2M+M\log_{2}M+MT_{th})\).
* DQN: The DQN algorithm learns optimal strategies through a neural network, and its computational complexity depends on the complexity of the deep neural network. For a neural network structure with \(L\) layers, the dimension of the weight matrix is \(D_{l-1}\times D_{l}\). In a single forward-propagation calculation, the computational amount of a fully connected layer is \(O(D_{l-1}\times D_{l})\), and the computational complexity of the forward propagation of the entire neural network is \(O(\sum_{i=1}^{L}D_{l-1}\times D_{l})\).  
The computational complexity of the MARL-VDN algorithm in this paper depends on the complexity of the RNN variant network. Therefore, the complexity of the algorithm in this paper is \(O(\sum_{i=1}^{L}W_{l-1}\times h_{l}\times W_{l})\), where \(L\) is the total number of network layers, \(W_{l-1}\) is the input dimension of the \(l\)-th layer network, \(h_{l}\) is the calculation cost of the activation function, and \(W_{l}\) is the output dimension of the \(l\)-th layer network. According to the parameters given in Table 2, we calculate that the algorithm's complexity in this paper is 33,280 \(h_{l}\).
# A Robust RANSAC-Based Planet Radius Estimation for Onboard Visual Based Navigation
## 3 Methods
the gradient has high values both on the true target border and on the terminator, thus points form a sickle-shaped curve that RANSAC can arbitrarily fit with a model for the outer circle or a model for the inner circle. By selecting points with a higher \(\rho\) value, we force RANSAC to estimate the model for the outer circle. In the fifth and last step, we apply a RANSAC procedure for radius estimation. The algorithm returns the best fitting circle model (as center and radius) and the set of inliers points. Compared to a Least Squares estimator, RANSAC is robust to outliers, thus we can tolerate erroneous points originated from previous steps. The RANSAC algorithm is initialized with two parameters: the maximum number of iterations and the minimum tolerance value used to assign a point to the inliers group. The maximum number of iterations can be obtained using the theoretical result from the original RANSAC paper [12]. \[k=\frac{log(1-p)}{log(1-w^{n})} \tag{2}\]  
where \(p\) is the probability of selecting a valid model in the presence of outliers and \(w\) is the probability of inliers in the dataset. In our study, we selected the number of iterations to be 100 and tolerance value to 1 and obtained less than 10 pixels error for our dataset even in the presence of noise, image distortion, and frame corruption. Furthermore, to improve the radius estimation we can perform Least Squares on the set of inliers returned by RANSAC. According to the results we obtained in our experiment, we believe that performing RANSAC is sufficient to achieve estimations with error \(<10\) pixels. The proposed algorithm is described in the block diagram in Figure 3.  
Figure 3: Procedure flowchart. The input image is processed by an edge detector filter and a simple background threshold with threshold parameter \(\tau_{1}\). The binary output image obtained from the background  
segmentation is used to obtain the enclosing bounding box and estimate rough target centroid \(c_{0}\). Also, the thresholded image is processed to obtain a binary edge mask using Equation (1). The edge image is thus masked and thresholded with tau2 in order to obtain limb point coordinates. Such coordinates are polar transformed relative to estimated centroid \(c_{0}\). The sickle effect is handled by preserving only the coordinates with higher \(\rho\) value, whereas sector binning is used to remove images with points clustered in small arcs. Finally, Random Sample Consensus (RANSAC) algorithm performs radius and centroid fitting. In Figure 4 we show the output produced by each step of the pipeline.