esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results of Simulation Experiments and Discussion  
### Experiment Settings  
Figure 5 shows the layout of a smart factory represented by a grid map (13 \(\times\) 18 cells) including machining centers (dark green), corridors (white), docking zones for mobile robots (light blue), and storage zones (dark gray). The storage zones are specified as raw material areas (\(A_{1},A_{2}\)), semi-finished product areas (\(B_{1},B_{2}\)), and finished product areas (\(C_{1},C_{2}\)). The mobile robots (dark blue circles) can perform loading or unloading tasks in the light green cells. After completing all tasks assigned to it, the robot will stop in the docking area to avoid collisions with other robots that are still in working mode.  
After receiving the starting signal, a mobile robot leaves from the docking area, goes to the raw material area to load the cargo, and transports the cargo to a machining center. Then, the machining center starts to process it. After that, this robot will be assigned to other transport tasks. When the machining center finishes the assigned task, the output (e.g., processed parts) will then be picked up by one of the available robots and transported to another machining center or a shelf in the storage zone. This process will iterate until all tasks are completed.  
We examined the performance of our approach in this working scenario with different numbers of robots and tasks. Moreover, we developed a simulation platform in MATLAB R2023a. This platform can visualize the layout of the machining centers, storage zones, and docking zones of the smart factory, and mark out the starting positions and goal positions of all the transportation tasks. It can also dynamically display the moving paths of the robot team to verify the feasibility of the proposed approach (e.g., we can observe whether two robots will collide with each other when performing tasks).  
Usually, the quantity of transportation tasks that can be completed per work shift in a smart factory is limited by the processing capability of the machining centers. Therefore, we tested 10 to 1000 tasks in this experiment. The initial positions of mobile robots and the starting and goal positions of tasks are randomly assigned. The assignment of weights reflects the decision maker's preference for these objectives. In this experiment, we set \(w_{t}\) to 0.6 or 0.4 and \(w_{e}=1-w_{t}\). The energy consumed by mobile robots is not considered since it is usually proportional to the transportation time, and is relatively insignificant compared to the energy consumed by machining centers.  
Figure 5: Layout of a simulated smart factory [28].
## 4 Discussion
### Stress Biomarkers  
Because the standardized warm-up protocol included 20 min of steady-state low-intensity rowing, the decline in cortisol and alpha-amylase levels after the warm-up may be explained by the effects of the low-intensity aerobic stimulus. [PERSON] et al. [27] showed that cortisol response differentiates between exercise intensities, rising with high- and moderate-intensity exercise (corresponding to 60% VO\({}_{2}\) max and higher) and decreasing with low-intensity exercise (equivalent to 40% VO\({}_{2}\) max). Moreover, the rowing sport is characterized by rhythmic cyclic movements to maintain a specific number of strokes per minute, and it was found that rhythmic aerobic exercise can cause a decrease in alpha-amylase levels over time [28]. Since higher alpha-amylase levels indicate sympathetic nervous system activation and parasympathetic inhibition, it can be speculated that the rhythmic steady-state warm-up activity resulted in increased parasympathetic activity, reduced stress in the athletes, and decreased alpha-amylase levels [29]. Post-race hormonal response aligns with this explanation as cortisol and alpha-amylase levels were influenced by prolonged high-intensity rowing, affected by strong sympathetic activation [29]. Previous studies showed that low-intensity exercise does not influence cortisol levels in moderately trained men, while moderate-to-high-intensity efforts result in a cortisol increase [27]. In 2014, [PERSON] and [PERSON] [20] found that the intensification of carbohydrate metabolism triggered by high energy demands results in a higher rate of alpha-amylase release. Rowing a 6 km race represents a great submaximal physical effort, which is why both variables experienced a significant post-race increase. This finding seems to follow [PERSON] et al. [30], who also reported higher post-race cortisol levels in competitive rowers compared to baseline levels. Their study included 16 competitive lightweight (n = 8) and heavyweight (n = 8) rowers who performed the 2000 m on-water rowing race. Although our study involved an ergometer indoor race over 6000 m with a greater aerobic component and duration, the results of both studies suggest that cortisol levels increase after high-intensity rowing stimulus. Similar cortisol dynamics are reported in other sports with different physical demands, with an increase in cortisol levels from pre- to post-match rise in football (85%), basketball (61%), and rugby (71%) [6; 31; 32]. Since both cortisol hormone and alpha-amylase enzyme indicate sympathetic activity, they reported similar competitive environment dynamics.
# A Study on the Effects of Vertical Mass Irregularity on Seismic Behavior of BRBFs and CBFs
## 4 Seismic Performance
### Drift
\begin{table}
\begin{tabular}{c c c c|c|c c c|c c|c c c|c c c} \hline \hline \multicolumn{1}{c}{**Type of Frames**} & \multicolumn{1}{c|}{**Deffift**} & \multicolumn{1}{c|}{**Displacement (m)**} & \multicolumn{1}{c|}{**Base Shear (\& \& \multicolumn{3}{c|}{**Acceleration (m/k\({}^{2}\))**} \\ \hline \multirow{6}{*}{10-Story} & Frame & Value & Event & story & Frame & Value & Event & story & Frame & Value & Event & Frame & Value & Event & story \\  & R-CBF & 0.011 & Kocaeli & 4 & MR-CRP & 0.181 & Kocaeli & 10 & R-CRP & 26,096 & Kocaeli & R-CRP & 7,665 & Gilroy & 10 \\  & MR-CRP & 0.011 & Kocaeli & 5 & R-CRP & 0.183 & Kocaeli & 10 & MR-CRF & 26,752 & Kocaeli & MR-CRP & 7,579 & Gilroy & 10 \\  & R-RRB & 0.022 & Kocaeli & 3 & R-BRB & 0.317 & Kocaeli & 10 & 6-BRB & 28,709.1 & Kocaeli & R-BRB & 7,506 & Gilroy & 10 \\  & MIR-BRB & 0.022 & Kocaeli & 4 & MR-BRB & 0.322 & Kocaeli & 10 & MIR-BRB & 29,331.2 & Kocaeli & MIR-BRB & 7,877 & Gilroy & 10 \\ \hline \multirow{6}{*}{15-Story} & MR-CRP & 0.022 & Kocaeli & 7 & R-CRP & 0.465 & Kocaeli & 15 & R-CRP & 10,862.1 & Shin-Osaka & R-CRP & 7,764 & Gilroy & 15 \\  & R-CRP & 0.023 & Kocaeli & 7 & MR-CRP & 0.469 & Kocaeli & 15 & MR-CRP & 26,909.88 & El centre Array & MR-CRP & 8,458 & Gilroy & 15 \\  & R-RRB & 0.026 & Kocaeli & 7 & R-BRB & 0.500 & Kocaeli & 15 & R-BRB & 32,406 & Centro Imp. Co & R-BRB & 8,748 & Gilroy & 15 \\  & MIR-BRB & 0.029 & Kocaeli & 7 & MR-BRB & 0.523 & Kocaeli & 15 & MIR-BRB & 34,582.4 & Centro Imp. Co & MIR-BRB & 9,141 & Gilroy & 15 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Maximum amount of seismic evaluation parameters. It should be noted that, the permissible drift stipulated in the Iranian code of practice for seismic resistant design of buildings should be applied to the design of structures based on the equivalent static analysis, in which the first-mode effects are mostly considered. In contrast, the effects of higher modes, which are significant on high-rise buildings, were considered in the non-linear time history analysis of the structures. The present study compared the drift values of the 10-story and 15-story frames with the permissible drift of the seismic resistant design of buildings to show that the drift obtained from a non-linear dynamic analysis of the frames designed as per the equivalent static analysis might exceed the threshold in the code.
## 2 Proposed Methods
### Adaptive Focus Window  
To improve autofocus speed and accuracy, most IBAF methods select partial regions as the focus window for detection. An ideal focus window should meet the following criteria [18]: Firstly, the evaluation curve of selected regions has satisfactory performance; secondly, the focus position of selected regions can represent the actual focus position; thirdly, the window is automatically calculated according to image contents.  
Considering the window selection in a fundus image collected by the device shown in Figure 1, if the window covers contents such as out-of-field redundant information (ORI) and stray light (SL), irrelevant regions are involved in sharpness calculation, which leads to multimodal and flat evaluation curves. Rich and accurate target fundus contents with steeper curves contribute to reliable autofocus, for which the following adaptive focus window is proposed (Figure 2).  
Figure 1: Fundus image captured by NMFP device during near-infrared preview (ORI: out-of-field redundant information; SL: stray light; FOV: field of view).  
Figure 2: Adaptive focus window procedure. (**a**) The _Mask\({}_{FOV}\)_ of the image green channel is first extracted based on imaging prior information. (**b**) Since FOV takes up a higher percentage of the histogram than SL, a grid screening mechanism is designed based on OTSU and its modification to extract _Mask\({}_{SL}\)_. (**c**) Finally, the adaptive window is obtained by fusing the above two masks and morphological operations, and the image sharpness is evaluated in this region.
## 3 Results and Discussion
### Example BAC Method  
In this subsection, we demonstrate the implementation of the BAC method in our in-house reconstruction package and compare to the Paganin method, using a propagation-based micro-CT of a coffee bean. The scan was performed using the same scanner as in the previous subsection. The scan parameter were slightly different: the tube was operated at 65 kVp and a current of 150 \(\mu\)A.The geometrical magnification was 2.53 and the voxel size of the CT scan was 7.1 \(\mu\)m. The CCD camera was used with 2 \(\times\) 2 pixel binning. The images were acquired with 4.0 s exposure time per projection. 1568 projections were taken over 360 degree sample rotation. Figure 2a shows a projection image of the coffee bean and in Figure 2b shows the result of the optimization process for the \(\alpha\) and \(\gamma\) parameters as is explained in the previous section. Micro-CT images of the coffee bean are given in Figure 3. These show that the separation in the gray-value histogram increases, i.e., both the Paganin and BAC methods increase the CNR of the reconstructed images (see Table 1 for actual values). Furthermore, the magnified regions of interest (ROIs) framed in orange show how the different retrievals might blur the visibility of small scale feature (see, e.g., red arrows). The BAC method, as it has been reported earlier [29; 30; 36], improves significantly the CNR compared to the attenuation-based image but, in general, blurs less the images than the Paganin method and therefore enables resolving very tiny features (see red arrows in Figure 3).  
Finally, we compare the attenuation-based, Paganin phase retrieval and BAC methods for the horse fly sample from the previous subsection as well. This is shown in Figure 5.
## 3 Results and Discussion
### Optimization of the Electrochemical Aptasensor  
During the development of the electrochemical aptasensor, both the concentration of GO and GO functionalization were optimized. Several conditions, including (1) measurement techniques, (2) concentration and incubation time for aptamer immobilization, (3) concentration and duration of blocking, and (4) the incubation time with the target solution, were optimized. The signal-to-noise ratio (SNR) was altered in CV because of the high background current (or noise) that masks the Faradaic current (signal) [34]. Among the voltammetric measurements, SWV enables faster analysis, lessens electrode surface fouling, and yields higher sensitivity, a lower detection limit, and greater discrimination of signal and background current [35]. Differential pulse voltammetry (DPV) offers advantages similar to those of SWV. EIS is useful for the characterization of surfaces, corrosion, batteries, and semiconductors. Faradic impedance spectrometry is used for biosensing of protein binding, such as antigen-antibody reactions [36; 37]. Occasionally, non-specific impedance changes were observed in EIS and were unable to discriminate between specific and non-specific interactions [38]. Several other factors, such as repetitive measurements,  
Figure 3: FTIR spectra of the original GO (blue), and EDC/NHS activated GO (red).  
electrode contamination, and additional voltammetric measurements, cause non-steady EIS signals [39]. According to its property of high sensitivity towards surface modification, EIS gave a false-positive reaction in our experiments. Certain concentrations of GA detected by three electrochemical techniques including. CV, DPV, and SWV were compared (Figure S2). SWV analysis was used for the detection of GA protein by our fabricated aptasensor.
# Accurate Collaborative Globally-Referenced Digital Mapping with Standard GNSS
## 4 Globally-Referenced Electro-Optical SLAM (GEOSLAM)
### GNSS Aiding
#### 4.2.1 Coordinate Frames  
The GNSS-aided visual SLAM system has three coordinate frames of interest: the \(K_{i}\) camera frame \(\mathcal{C}_{i}\), the local SLAM frame \(\mathcal{S}\), and the global frame \(\mathcal{G}\). The SLAM frame \(\mathcal{S}\) adopts the position and orientation of the first keyframe prior to optimization as its origin and orientation. Thus, \(\mathcal{S}\) is fixed relative to \(\mathcal{G}\), but each \(\mathcal{C}_{i}\) changes relative to \(\mathcal{G}\) as the platform moves.  
Note that the structure-from-motion states in Equation (4) are represented in the \(\mathcal{S}\) frame, whereas the \(K_{n}\)th keyframe's corresponding GNSS measurement, denoted \(\mathbf{z}_{\mathcal{A}_{n}}^{\mathcal{G}}\in\mathbb{R}^{3}\), is natively represented in the \(\mathcal{G}\) frame. The latter is transformed to the \(\mathcal{S}\) frame through an unknown but fixed rotation, \(R_{\mathcal{G}}^{\mathcal{S}}\in SO(3)\), and translation, \(t_{\mathcal{G}}^{\mathcal{S}}\in\mathbb{R}^{3}\). This transformation is estimated at initialization as explained in Section 4.2.2. After initialization, \(\mathbf{z}_{\mathcal{A}_{n}}^{\mathcal{G}}\) is rendered in \(\mathcal{S}\) as  
\[\mathbf{z}_{\mathcal{A}_{n}}^{\mathcal{S}}=R_{\mathcal{G}}^{\mathcal{S}} \mathbf{z}_{\mathcal{A}_{n}}^{\mathcal{G}}+\mathbf{t}_{\mathcal{G}}^{\mathcal{ S}}.\]  
GEOSLAM estimates the 6-DoF pose of the left camera, but the GNSS antenna phase center is not co-located with the camera center; rather, it is offset from the camera center by a fixed vector (same for all \(n\)) in \(\mathcal{C}_{n}\) denoted, \(\mathbf{t}_{\mathcal{A}_{n}}^{\mathcal{C}_{n}}\in\mathbb{R}^{3}\). Thus, the error term associated with the GNSS position estimate for \(K_{n}\) is given as  
\[\mathbf{e}_{\mathcal{A}_{n}}=\mathbf{z}_{\mathcal{A}_{n}}^{\mathcal{S}}-\left( \mathbf{x}_{\mathcal{C}_{n}}^{\mathcal{S}}+R\left(\mathbf{\theta}_{\mathcal{ C}_{n}}^{\mathcal{S}}\right)\mathbf{t}_{\mathcal{A}_{n}}^{\mathcal{C}_{n}}\right).\]  
Under the assumption of temporally-uncorrelated GNSS errors, the updated BA cost function to be minimized is  
\[C_{i}=\sum_{n\in\text{cov}(i,N)}\left[\sum_{m\in\mathcal{M}_{n}}\rho\left( \mathbf{e}_{nm}^{T}\Omega_{nm}^{-1}\mathbf{e}_{nm}\right)+\mathbf{e}_{ \mathcal{A}_{n}}^{T}\Gamma_{n}^{-1}\mathbf{e}_{\mathcal{A}_{n}}\right],\]  
where \(\Gamma_{n}=R_{\mathcal{G}}^{\mathcal{S}}\Gamma_{n}^{\prime}\left(R_{\mathcal{ G}}^{\mathcal{S}}\right)^{T}\) and \(\Gamma_{n}^{\prime}\) is the covariance matrix of the GNSS position estimate associated with \(K_{n}\), expressed in \(\mathcal{G}\).
## 3 Results
### Temperature-Programmed Reduction (TPR) and Desorption (TPD) Characterization
Instead, in the case of Ni-nR the consumption was significantly higher (0.97 mmol-g\({}^{-1}\)), confirming a strong interaction of NiO with the CeO\({}_{2}\) surface with nanorod morphology, favouring the Ce\({}^{4+}\)\(\rightarrow\) Ce\({}^{3+}\) reduction. Indeed, the percentage of Ce\({}^{3+}\) of the Ni-nR catalyst was about 23%, much higher than that of the nR support, which was about 13%, while, for the Ni-nC catalyst and the nC support, the percentage of Ce\({}^{3+}\) was essentially the same, about 5%. On the other hand, the reduction of ceria bulk was not significantly affected by the introduction of Ni, and it occurred in the same temperature range (\(\gamma\) region). The temperature-programmed desorption trend of chemisorbed hydrogen (TPD) with respect to the supports and the Ni catalysts are shown in Figure 3. From this analysis, the dispersion D (%) and the particle size d (nm) of the active Ni sites was determined, and reported in Table 3. The TPD profile of the nC support showed a very weak peak at  
Figure 3: **(A)** TPR and **(B)** TPD profiles of: **(a)** nC, **(b)** Ni-nC, **(c)** nR, **(d)** Ni-nR. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{4}{c}{**Hydrogen Consumption/mmol g\({}^{-1}\)**} & \multirow{2}{*}{_D (\%)_} & \multirow{2}{*}{_d (nm)_} \\ \cline{2-2} \cline{4-6}  & \(\alpha\) & \(\beta\) & \(\gamma\) & **Total** & \\ \hline nC & & 0.16 & 1.05 & 1.20 & & \\ Ni-nC & 0.12 & 0.32 & 1.03 & 1.47 & 14 & 7 \\ nR & & 0.38 & 0.79 & 1.17 & & \\ Ni-nR & 0.44 & 0.53 & 0.67 & 1.64 & 20 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Amount of hydrogen consumed during TPR measurements. Ni dispersion, \(D\) (%), and the active Ni sites dimension, \(d\) (nm), determined from TPD analyzes..  
about 400 \({}^{\circ}\)C, while the nR one revealed a more intense peak that started at temperatures higher than 400 \({}^{\circ}\)C without ending until 500 \({}^{\circ}\)C. These labile peaks were due to the release of chemisorbed H\({}_{2}\) from the CeO\({}_{2}\) surface. The TPD of the Ni-nC catalyst showed three peaks at about 160, 220, and 330 \({}^{\circ}\)C, which were due to the desorption of H\({}_{2}\) from the Ni\({}^{0}\) surface with different H-binding strength. From the peak area, the \(D\) (%) was 14%, and the dimension of the catalytically active Ni sites was \(d\) (nm) = 7. The Ni-nR catalyst exhibited a different TPD profile, with a single large peak, which was much more intense with a maximum at about 165 \({}^{\circ}\)C, indicating a more homogeneous H-binding strength over the Ni active surface; the \(D\) (%) was 20%, and the size of the active sites was \(d\) (nm) = 5.
# Analytical Solutions for the Propagation of UltraShort and UltraSharp Pulses in Dispersive Media
## 3 Fundamental Dispersion Theorems
### Pulse Chirping  
Let \(A(t,0)\) be an initial pulse whose dispersive dynamics is known, i.e., at the end of the dispersive medium, its profile is \(A(t,z)\); then, if the initial pulse is chirped, i.e.,:  
\[A^{\prime}(t,0)=\exp\left(-iqt^{2}\right)A(t,0) \tag{9}\]for any real \(q\), the pulse's profile at the end of the medium (after a distance z) is (see Appendix C)  
\[A^{\prime}(t,z)=(1+2\beta_{2}zq)^{-1/2}\exp\biggl{(}-i\frac{t^{2}}{2\beta_{2}z+ 1/q}\biggr{)}A\biggl{(}\frac{t}{1+2\beta_{2}zq},\frac{z}{1+2\beta_{2}zq}\biggr{)} \tag{10}\]  
Again, this theorem can be generalized to the case where the initial profile is multiplied by a generic Gaussian. That is, if the initial pulse is  
\[A^{\prime}(t,0)=\exp\Bigl{(}-qt^{2}\Bigr{)}A(t,0) \tag{11}\]  
where \(q\) can be any complex number, then as long as \(\Re q>0\), the solution at the end of the medium is  
\[A^{\prime}(t,z)=(1-i2\beta_{2}zq)^{-1/2}\exp\biggl{(}-i\frac{t^{2}}{2\beta_{2} z+i/q}\biggr{)}A\biggl{(}\frac{t}{1-i2\beta_{2}zq},\frac{z}{1-i2\beta_{2}zq} \biggr{)} \tag{12}\]
# Early Diagnosis of Dementia from Clinical Data by Machine Learning Techniques
## 2 Related Works
[4], machine learning and neural network methods were used for classifying dementia states to improve accuracy over current dementia screening tools, MMSE and the Functional Activities Questionnaire. The findings showed that the accuracy can be optimized by combining both the tests along with machine learning and neural network. [PERSON] and [PERSON] [3] previously used electroencephalography (EEG) data to classify patients with normal cognition and Alzheimer's or MCI by learning the EEG pattern of Alzheimer's patients using the SVM algorithm. As a result, EEG Epochs showed a high accuracy (79.9%) and the SVM result was about 87%. [PERSON] and [PERSON] [14] compared the CDR (Clinical Dementia Rating) score and the method of screening dementia using Naive Bayes, Decision Tree, Neural Network, and SVM. The results of the evaluation of the severity of dementia showed that Naive Bayes was the most accurate and SVM had the lowest accuracy. [PERSON] and [PERSON] [15] proposed a hierarchical double layer structure for the early diagnosis of dementia. This is a model that predicts early diagnosis of dementia using a Bayesian network in the top-layer after diagnostic prediction with FCM and PNN algorithm in the base-layer when a cognitive test such as MMSE and CERAD is performed. In this model, the accuracy of FCM and PNN was 74% and 69%, respectively, but MCI and dementia were not well classified when comparing normal, MCI, and dementia. [PERSON] and [PERSON] [16] performed CDR prediction using machine learning method and electronic medical records. For Naive Bayes, the accuracy was the highest, while for the other algorithms, it was lower than Bayesian, but it was about 70% accurate. The diagnosis of dementia consists in large part of assessing different cognitive abilities. As such, physicians frequently interpret test results in conflicting ways: this represents a major impediment to attaining high accuracy with machine learning algorithms in the absence of a specified model. In contrast to the aforementioned studies, we advance a two-tiered hierarchical approach for evaluating and making distinctions between normal, MCI, and early dementia. This approach is derived from the dementia support center's diagnostic method (a combination of cognitive screening, neuropsychological evaluation, and early diagnosis). In this research we aim to use neuropsychological and demographic information in order to predict normal, MCI, and dementia within our proposed model by applying seven frequently used machine learning models: Naive Bayes, Bayes Network, Begging, Logistic Regression, Random Forest, SVM, and MLP. This method offers diagnostics which are at once intuitional and also far-reaching.