esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### PCA and HCA Results
#### 5.3.2 HCA-1 Results  
At first, in the hierarchical cluster analysis (HCA-1), only scores from PC1 and PC2 were considered. HCA-1 reveals the presence of five distinct groups of samples (Figure 8). Cluster 1 is the most abundant, consisting of 90 samples with low scores on PC1 and PC2, indicating weakly sorted medium grain size sediments (bimodal medium-to-coarse silts). Cluster 2 is less common, with 11 samples that exhibit bimodal fine-to-medium silts. Cluster 3 represents finer and more heterolithic samples, likely associated with mixed deposition processes. Cluster 4 comprises only two coarser samples (medium sand). Lastly, Cluster 5 represents finer and better-sorted sediments.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Principal Component**} & \multicolumn{1}{c}{**Eigenvalue**} & \multicolumn{1}{c}{**\% of Variance**} & \multicolumn{1}{c}{**\% of Cumulative Variance**} \\ \hline
1 & 1.79 & 44.68 & 44.68 \\
2 & 1.04 & 25.88 & 70.57 \\
3 & 0.84 & 20.94 & 91.51 \\
4 & 0.34 & 8.49 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Total variance of PCA-1.  
Figure 8: Principal Component Analysis: (**a**) score plot, (**b**) circle of correlation, and (**c**) biplot of first and second principal components with cluster groups.  
Based on the stratigraphic organization of the HCA groups, core CHA01 can be divided into four major units (Figure 5). In more detail, U1 (900-749 cm) consists of samples belonging to Clusters 1, 4, and 5, although Cluster 1 samples dominate this unit. U2 (748-488 cm) is predominantly composed of samples from Cluster 5, with a limited contribution from Cluster 1 samples. U3 (487-264 cm) exhibits high fluctuations between samples from Clusters 1, 2, and 3, with a slight predominance of Cluster 1 samples. Finally, U4 (263-0 cm) is a homogeneous unit solely composed of Cluster 1 samples.
# Privacy-Preserving Outsourced Artificial Neural Network Training for Secure Image Classification
## 3 Preliminaries
### Functional Encryption for Inner-Product (FEIP)  
Functional encryption (FE) is a new paradigm of the public key that allows one party \(P_{1}\) to learn a function of ciphertext encrypted by another party \(P_{2}\). In this paper, we employ a functional encryption for inner product (FEIP) scheme [27], which allows \(P_{1}\) to compute the inner product between vectors \(\mathbf{x}\) and \(\mathbf{y}\), where \(\mathbf{x}\) is encrypted by \(P_{2}\) and \(\mathbf{y}\) is a plaintext vector given by \(P_{1}\). A FEIP scheme consists of five algorithms. More specific details of FEIP scheme refers to [27].  
* _Setup_: The setup algorithm takes a security parameter as input and generates a mathematical group.
* _Master key generation_: The key generation algorithm creates a public key \(pk^{FEIP}\) together with a master secret key \(msk^{FEIP}\).
* _Functional key derivation_: The functional key derivation algorithm takes the master secret \(msk^{FEIP}\) and the vector \(\mathbf{y}\) as input to generate a functionally derived key \(dk_{\mathbf{y}}\).
* _Encryption_: The encryption algorithm applies to message vector \(\mathbf{x}\) and obtains a ciphertext \(ct_{\mathbf{x}}\) by using the public key \(pk^{FEIP}\).
* _Decryption_: Given the ciphertext \(ct_{\mathbf{x}}\) of a message \(\mathbf{x}\), the holder of the key \(dk_{\mathbf{y}}\) is able to compute the value of \(\langle\mathbf{x},\mathbf{y}\rangle\) using the decryption algorithm.  
Consider the ANN training scenarios. The computation of inner product of weight vector \(\mathbf{w}\) and training sample \(\mathbf{x}\) is a formidable step during the ANN training in the encrypted domain. A user wants to keep his sample \(\mathbf{x}\) private but lets a server be able to compute the inner product \(\langle\mathbf{w},\mathbf{x}\rangle\). During the _Setup_ phase, a trusted third-party authority (\(\mathcal{TPA}\)) provides the public key to the user. Then, the user encrypts \(\mathbf{x}\) and transfers the ciphertext to the server. In order to acquire \(\langle\mathbf{w},\mathbf{x}\rangle\) for the server, the \(\mathcal{TPA}\) generates a functionally derived key that depends on the weight \(\mathbf{w}\). The server decrypts the ciphertext using the functionally derived key and obtains the result \(\langle\mathbf{w},\mathbf{x}\rangle\). In this way, the server is able to perform the inner product operation while maintaining the privacy of the user.
# Magnetic Nanoparticles: Synthesis, Characterization, and Their Use in Biomedical Field
## 2 Synthesis Methods
### Physical Methods
#### 2.1.2 Mechanical Milling  
The ball milling technique is a popular physical process that involves the mechanical grinding of coarse-textured particles into fine nanoparticles, using a top-down approach. This technique, initially pioneered in the 1970s, is straightforward and practical. It entails utilizing a cylindrical jar with a hollow center (containing steel balls) that crushes the solid substance into a powder of nano/micrometric dimensions. Although ball milling is successful, it is limited by the possibility of product contamination and the generation of particles with a broad range of sizes [4].
## 5 Case Study
### Experimental Deployment  
A single sensor was mounted on a tripod at 1.8 meters above the ground facing a roadway in a quiet urban parking lot. This location was chosen to allow for sustained data capture in more controlled conditions than a busy urban intersection. The application pipeline was executed prior to multiple runs of vehicle passes, including: (1) motorised vehicle (car, truck or motorcycle) passes with and without horn use, (2) bicycle and pedestrian passes and (3) various combinations of simultaneous passes. A total of \(\approx\)1 h of continuous data collection was performed, which would equate to \(\approx\)53 GB/h of dual 5 MP video with MJPEG compression and 12 channel audio if all data were to be captured and uploaded. These amounts of data would overwhelm the majority of cellular data plans, requiring at least 15 MB/s of bandwidth to transmit these data from edge sensor to server, regardless of the inevitable saturation of monthly data plan limits.  
Figure 7 shows one frame captured from the left video camera for illustration purposes. At this instance, a cyclist was passing by the sensor with two cars driving in the opposite direction while honking their horns. The object detection block has returned confident detections including their bounding boxes around the car, person and bicycle. The event detection block raised an event flag, as there was the combination of a cyclist and motorised vehicle within the frame, which resulted in the successful upload of 10 s of video and multichannel audio of the event via the cellular modem. At the figure bottom, time vs. SPL is plotted with the green region showing the portion of time that the object detector block is able to confidently identify the motorised vehicle passby, mainly due to the limited field of view of the camera setup. Of note is the much wider red region showing the extent over which the SPL computation block is able to detect the elevated sound level of the car horn, highlighting the advantages of this multimodal approach.  
Figure 6: Multimodal sensor built for the real-world case study. It includes two 5 MP cameras, a 12 channel microphone array (4 \(\times\) 3) and a cellular modem. The sensor is waterproof for outdoor operation and powered by the NVIDIA Jetson Nano, which is thermally coupled to the aluminum enclosure for passive/silent cooling.
## 4 Discussion  
### Differences between Assistance Modes  
As previously mentioned, the assistance level is estimated through the force assistance value. Results suggest that the fixed assistance mode has a significantly higher assistance level than the others (Figure 10).  
The score is a parameter commonly used to estimate the exercise performance [25,26]. It allows us to evaluate whether or not patients can perform the exercise correctly. In this study, a high level of performance is obtained in all modes. These results indicate that the assistance provided to the patients was adequate to perform the exercise correctly.  
In fixed assistance mode, the robot assists equally towards all targets. However, this is not the case of the assistance modes where the therapist is involved. The therapist can decide the right way and time to assist the patient to reach each target. This means that he can decide not to provide assistance and let the patient put more effort into those in which he can reach by himself. In this way, the assistance profile is not the same in all targets as it happens in the fixed assistance mode, which is why the assist forces are better adapted to the patients.  
Regarding the result of the IMI (Figure 11), patients' perceived exertion is greater when assistance is provided by the therapist. However, patients indicate that they enjoy all three modes equally. Furthermore, they are satisfied with their performance in all modes. Therefore, there is no evidence to suggest that the patients' perceived exertion can demotivate them in any of the modes.  
Based on these results, we can conclude that in the fixed assistance mode, assistance level was greater than necessary, or in other words, the assistance forces provided by the therapist are better adapted to the needs of the patient.  
Figure 12: Mean scores of the System Usability Scale (SUS) given by the patients and the clinician staff.  
On the other hand, the assistance level provided by the therapist remotely is generally greater than locally. The only difference between the three conditions in this study is the way the patient is assisted. Therefore, the only difference between the therapist assisting the patient locally or remotely is the interaction with the user. According to the feedback we have obtained from the clinical staff, remotely, communication with the patient is more difficult, especially with certain pathologies. That is why it is more difficult to assess when to assist the patient. The way of working with patients differs from what clinical staff is used to. The therapists' decisions, and therefore the results, would improve with training and knowledge of the system.  
Even so, the results are better than those obtained with the fixed assistance mode; therefore, although communication with the patient is more complicated, the assistance proves to be better adjusted to the patients.
## 5 Conclusions  
This work is focused on the development of a photonic radar system, utilizing mode division multiplexing, for applications in intelligent transportation systems. A homodyne configuration was employed for the detection of two individual targets, while the use of a 2 \(\times\) 2 MIMO technique and IIR filter enhanced the system's ability to operate effectively under turbulent weather conditions. The proposed system underwent evaluation via numerical simulations, showcasing effective detection of both targets. Target 1 was detected at a range frequency of 200 MHz, while Target 2 was identified at 146.30 MHz. These detections were achieved utilizing a bandwidth of 4 GHz. The impact of the IIR filter was also analyzed, resulting in power improvements of 200% for target 1 and 276% for target 2. The system's detection capabilities were successfully validated through testing under high attenuation conditions of 75 dB/km. Additionally, the system was assessed for its ability to determine target speed, direction, and range resolution. This evaluation resulted in a range resolution of 7 cm, utilizing a bandwidth of 4 GHz. The outcomes from these numerical simulations were in consonance with the results calculated mathematically, offering additional validation for the efficacy of the proposed system.
# Heart Failure and Diabetes Mellitus: Biomarkers in Risk Stratification and Prognostication
## 3 Biomarkers in Diabetics with Known HF
### Biomarkers of Inflammation and Fibrosis
#### 3.2.2 High-Sensitivity C-Reactive Protein  
Belonging to the superfamily of pentraxins, C-reactive protein (CRP) is a protein of acute systemic inflammation [95]. CRP as a biomarker is useful in identifying CVD risk when it is measured with highly sensitive assays, even in substantially low concentrations (now known as high-sensitivity CRP (hs-CRP) [96].  
Serum levels of hs-CRP have been found to be independently predictive for the development of T2 DM and CV disease in the general population [97; 98]. T2 DM was associated with elevated levels of hs-CRP and other indicators of diabetes-related CV risk, such as homeostatic model assessment, the levels of intact proinsulin and insulin, BMI, and beta-cell dysfunction, but not with fasting glucose [99; 100]. However, [PERSON] et al. (2018) [101], in a population-based study, showed that hs-CRP predicted CV complications including HF and diabetic kidney disease in patients with overt T2 DM [101]. Moreover, they found that hs-CRP significantly improved the discriminative capacity of conventional CV risk scores when added to it [101]. In patients with established atherosclerotic CVD, elevated levels of hs-CRP were associated with poor clinical outcomes, especially among non-T2 DM patients [102; 103]. Additionally, [PERSON] et al. (2020) [104] presented evidence for a positive association between the levels of hs-CRP and the severity of major depression. A reduction in hs-CRP levels during the therapy of dyslipidemia or CAD has been shown to be closely related to an improvement in long-term prognosis [105]. Finally, in HFpEF, high hs-CRP levels were associated with greater comorbidity burden, including T2 DM and abdominal obesity, even though almost 40% of HFpEF patients had normal values of hs-CRP [106]. In contrast, the levels of hs-CRP in HFrEF patients were higher when compared to those with HFpEF, and it predicted myocardial dysfunction and HFrEF after acute myocardial infarction [107; 108]. Surprisingly, a decrease in hs-CRP levels in acute HF was associated with a significant reduction in the 3-year risk of death in HFpEF patients, but not in HFrHF [109]. In conclusion, although the association of hs-CRP with clinical events has been found to be an independent predictor of CV morbidity and mortality [110], there is sufficient uncertainty in the predictive ability of hs-CRP in patients with different phenotypes of HF and consequently a high heterogeneity in CV risk factors present.
## 4 Stability Analysis of the Powertrain
### Influence of the Machine Inductances on the Stability of the Powertrain  
The objective is now to consider the stability analysis with respect to certain key system parameters starting here with the machine inductances. In that case, the control dynamic is kept to a bandwidth of \(1\) Hz.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Gas Turbine** & **Unstable Speed** & **Unstable Speed** & **Unstable Speed** \\
**Dynamic (Hz)** & **Range (rpm) from** & **Range (rpm) from** & **Range (rpm) from** \\  & **Root Locus** & **Time Simulation** & **Experiments** \\ \hline
0.9 & 260 \(\rightarrow\) 660 (\(\Delta\) = 400) & 280 \(\rightarrow\) 610 & 270 \(\rightarrow\) 620 \\
1 & 260 \(\rightarrow\) 620 (\(\Delta\) = 360) & 290 \(\rightarrow\) 580 &  
The initial values of the inductances of both machines connected through the AC bus are set to 6.5 mH for the generator and to 2.7 mH for the motor making a series inductance of 9.2 mH. The issue is here to increase this series inductance with added values between 1 and 6 mH.  
The results of these parameter variations are summarized in Table 3.  
For all values of inductances, the results shown in Table 3 are consistent, although it should be noted that the reduction in the unstable speed range is less marked for the experimental results compared to the other two study methods. This slight difference can be explained: on the experiment, the physical addition of inductances may show slight unbalance between phases due to the use of single-phase inductances with potential differences, that may alter the observed phenomena. Contrarily, the added values of inductances are perfectly accurate for theoretical studies.  
Nevertheless, whatever the means of study, it can be observed that increasing the series inductances of the AC bus coupling leads to:  
* Moving the unstable (oscillation) domain towards lower speeds;
* A decrease in the speed range (\(\Delta\)) inside which the AC powertrain is unstable or oscillates (on the left column, the unstable speed interval (\(\Delta\)) is reduced by more than 30%.  
In the same way, the influence of the load torque applied to the propeller on the system stability may complete that analysis. The results are not detailed in the paper, but the main trend shows that increasing the load level leads to an increase in the stability level of the powertrain.
## 3 Results and Discussion
### Characterization of Starch/MCC Films
#### 3.2.3 Morphology  
The FE-SEM micrographs provided in Figure 5 illustrate the surface and cross-section characteristics of cassava starch films both with and without the inclusion of MCC. In Figure 5a, the micrograph depicts a smooth surface of the starch film, indicating a uniform composition without any apparent phase separation. This suggests that starch film alone exhibits homogeneous properties. Figure 5b-d depict starch films with varying contents of MCC (1%MCC, 3%MCC, and 5%MCC); there is visible evidence of MCC distribution on the surface of the films. This indicates that MCC is effectively incorporated into the starch matrix and is evenly dispersed across the surface. Moreover, the cross-section images reveal good adhesion between MCC and the starch matrix, with no discernible phase disengagement because of the strong interfacial bonding between MCC and the starch matrix, as confirmed by FTIR results.  
Figure 4: FTIR spectra of MCC, starch film, and starch/MCC films.  
Figure 5: FE-SEM micrographs of surface and cross-section of films (**a**) Starch, (**b**) Starch/1%MCC, (**c**) Starch/3%MCC, and (**d**) Starch/5%MCC.
## 5 Datasets and Model Performance Evaluation Metrics
### Accuracy, Recall, Precision and F-Measure  
Accuracy, recall, precision and F-measure are indispensable parts of evaluations of target detection models, vehicle division models and so on. First, we defined the binary prediction categories as follows:  
1. True Positive (TP): the positive result is predicted to be positive;
2. True Negative (TN): the negative result is predicted to be negative;
3. False Positive (FP): the negative result is predicted to be positive;
4. False Negative (FN): the negative result is predicted to be negative.  
The corresponding operation formula is given as follows:  
\[ACC=\frac{TP+TN}{total} \tag{1}\]  
\[REC=\frac{TP}{TP+FN} \tag{2}\]  
\[PEC=\frac{TP}{TP+FP} \tag{3}\]  
\[F=\frac{\left(\alpha^{2}+1\right)PEC\cdot REC}{\alpha^{2}(PEC+REC)} \tag{4}\]  
where \(\alpha\) is the adjustable parameter.  
Accuracy is the most common performance evaluation metric; it can describe the quality of the prediction model. Recall and precision usually need to be considered comprehensively, so a comprehensive evaluation metric F-measure is proposed which is the weighted sum average of the recall and precision, and its value is proportional to the performance of the model. In this paper, the performance and characteristics of the representative methods based on deep learning over CVIS are summarized in Table 2.
###### Abstract  
We developed a hemodynamic mathematical model of human circulation coupled to a virtual hemodialyzer. The model was used to explore mechanisms underlying our clinical observations involving hemodialysis. _Methods_: The model consists of whole body human circulation, baroreflex feedback control, and a hemodialyzer. Four model populations encompassing baseline, dialysed, therapeutic hypothermia treated, and simultaneous dialysed with hypothermia were generated. In all populations atrial fibrillation and renal failure as co-morbidities, and exercise as a treatment were simulated. Clinically relevant measures were used to quantify the effects of each in silico experiment. Sensitivity analysis was used to uncover the most relevant parameters. _Results_: Relative to baseline, the modelled dialysis increased the population mean diastolic blood pressure by 5%, large vessel wall shear stress by 6%, and heart rate by 20%. Therapeutic hypothermia increased systolic blood pressure by 3%, reduced large vessel shear stress by 15%, and did not affect heart rate. Therapeutic hypothermia reduced wall shear stress by 15% in the aorta and 6% in the kidneys, suggesting a potential anti-inflammatory benefit. Therapeutic hypothermia reduced cardiac output under atrial fibrillation by 12% and under renal failure by 20%. Therapeutic hypothermia and exercise did not affect dialyser function, but increased water removal by approximately 40%. _Conclusions_: This study illuminates some mechanisms of the action of therapeutic hypothermia. It also suggests clinical measurables that may be used as surrogates to diagnose underlying diseases such as atrial fibrillation.  
lumped parameter model; human circulation; dialysis; ordinary differential equations; sensitivity analysis +
Footnote †: journal: _sciences_  
_Applied_  
_sciences_
# A Multiscale Approach to the Numerical Simulation of the Solid Oxide Fuel Cell
## 5 Results of the Numerical Computations and Discussion  
### Confirmation of the Model  
The numerical calculations with the same voltage as in the experimental analysis (see Figure 2.2) and with the microstructure the same as the stack (see Figure 3) were performed. For the examination of the temperature impact (see Figure 10), the cells which work under 650 \({}^{\circ}\)C and 800 \({}^{\circ}\)C (the furnace or wall temperature) were chosen. The fuel composition is 60 % of hydrogen and 40 % of nitrogen. For the examination of the fuel composition impact, the cells which work under the constant temperature of 750 \({}^{\circ}\)C (the wall temperature) with the fuel containing 40 % of hydrogen and 60 % of nitrogen as well as 70 % of hydrogen and 30 % of nitrogen were chosen. The pressure was set at 101,325 Pa and the inlet temperature was the same as the wall temperature. The inlet volume flow rates were the same as in the experiment, i.e., 0.28 L min\({}^{-1}\) at the anode channel inlet and 3.2 L min\({}^{-1}\) at the cathode channel inlet (please note that this is the value of the flow rates from Figure 2.2 divided by the number of the cells). The cell active area is 48 cm\({}^{2}\). Since in the experiment the dry fuel was used, the amount of water in the fuel is calculated using the Nernst equation from the experimental open circuit voltage (OCV) to simulate the same voltage at zero current conditions. The results are presented in Figures 10 and 11. As can be noticed from Figures 10 and 11, the numerical results are in fair agreement with the experimental data. In Figure 10, it can be observed that, for the cell which works in the lower temperature (650 \({}^{\circ}\)C), the numerical results almost completely overlap with the experimental data (see Figure 10a), whereas the cell which works in 800 \({}^{\circ}\)C tends to overestimate the performance in the high current density region (Figure 10b). Since for the fuel impact tests the temperature is set at 750 \({}^{\circ}\)C, the cells also overestimate the performance in the high current density region (see Figure 11). Since for safety reasons the current is limited for hydrogen-depleted fuel (the danger of anode oxidation), the effect is especially visible for hydrogen rich fuel (see Figure 11b).  
The discrepancy between the numerical and experimental results might be induced by several reasons. The equations for the exchange currents (see Equations (5)) are derived for a cell working under atmospheric pressure, whereas the experiment was conducted under pressurized conditions. The layered structure of the cathode and the presence of the GDC in the cathode are neglected. Nevertheless, the model can correctly predict the trends and qualitative relations.  
Figure 9: The grid system used for the computations.