esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Effect of Intensity Processing Procedures
#### 3.2.1 Intensity Inhomogeneity Correction
Moreover, the addition of N3 iter = 4 to default BET and BET 1 R (Figure 5 fj) resulted in increases in SDs from 0.0531 and 0.0467 to 0.0793 and 0.0616, respectively, rather than decreases, as was the case for the T1-MPRAGE MRIs. For BET 1 B and BET 1 B,f0.2,g0.3, both iterations significantly increased the DSC relative to the control pipeline (\(p<0.05\)). However, for BET 1 B,f0.1 and optiBET, the increases produced by both iterations were not statistically significant. Interestingly, in the BET 1 R configuration, N3 iter = 1 demonstrated a non-significant increase in DSC, while N3 iter = 4 showed a non-significant decrease; these were the highest- and lowest-performing pipelines, respectively, for BET 1 R. For BET configurations without a primary option, N3 iter = 4 consistently showed a larger negative effect than N3 iter = 1. Default BET, BET 1 f0.2, BET 1 f0.2,g0.3, BET 1 f0.8, BET 1 g(-0.3), and BET 1 g0.3 resulted in significant decreases in DSC relative to the control pipeline. For default BET and BET 1 g(-0.3), only N3 iter = 4 led to significant declines (\(p<0.01\)), while N3 iter = 1 showed non-significant decreases. Conversely, in BET 1 f0.2, BET 1 f0.2,g0.3, BET 1 f0.8, and BET 1 g0.3, both iterations resulted in significant decreases, with each configuration having \(p\)-values less than 0.01 for N3 iter = 1 and less than 0.001 for N3 iter = 4. Across these configurations, the negative impact of N3 iter = 4 on the DSC was notably larger than that of N3 iter = 1 (\(p<0.001\) for each).
# The MINLP Approach to Topology, Shape and Discrete Sizing Optimization of Trusses
## 4 Solution of the MINLP Truss Synthesis Problem
### The Proposed MINLP Strategies for Truss Synthesis
#### 4.4.2 The Sequential Two-Phase (STP) MINLP Strategy  
The Sequential Two-Phase (STP) MINLP (see Figure 4) strategy similarly to the MTP strategy consists of running a sequence of alternating topology optimization phases with continuous sizing variables and discrete sizing optimization phases (for better topologies only, \(z_{n}^{t}<z_{\text{OPT}}\)). The main difference between these two strategies is that the STP strategy starts with the initial phase (\(n=1\)), where all alternative elements are active. The initial topology is thus the maximal topology. The process starts with the shape and continuous sizing optimization in the initial maximal topology, followed by the pre-screening of discrete dimensions. The subsequent discrete sizing optimization yields the first discrete solution with the value of the objective \(z_{1}^{d}\). This solution is adopted as the currently best solution (\(z_{\text{OPT}}=z_{1}^{d}\)).  
Further optimization cycles are then carried out identically according to MTP strategy, but without the deactivations of integer cuts. The discrete sizing variables are then relaxed into continuous parameters. The process proceeds (\(n=n+1\)) with the topology and shape optimization searching for a topology that would yield a solution (value of the objective \(z_{n}^{t}\)) better than the currently best discrete solution, i.e., a solution which would satisfy the condition \(z_{n}^{t}<z_{\text{OPT}}\). Only when the condition is satisfied is  
Figure 4: The Sequential Two-Phase MINLP strategy.  
sizing optimization at the fixed current topology performed, yielding the solution \(z_{n}^{\rm d}\). Two successive phases, i.e., the topology optimization and the discrete sizing optimization, together form the Two-Phase (TP) cycle. When the discrete solution \(z_{n}^{\rm d}\) is obtained, the current TP cycle is concluded, and the next cycle begins. The process is terminated if a new topology satisfying the condition \(z_{n}^{\rm t}<z_{\rm OPT}\) is not obtained in the defined number (\(n_{\rm fin}\)) of successive MINLP iterations.
## 2 CNN Deployment in Automotive
### The Single Shot Detector (SSD)  
The Single Shot Detector employs a feed-forward convolutional network to generate a pre-established set of bounding boxes and scores for object detection. The base network is designed for high-quality image classification and forms the initial layers of this network. It is followed by one or more convolutional layers on existing feature maps to predict a predefined number of bounding boxes along with corresponding confidence scores. A non-maximum suppression step is then applied to these detections to obtain the final results. The predicted boxes are displaced concerning predetermined and fixed prior boxes, positioned across a feature map and at the midpoint of each cell [21].  
When training a detector using the SSD [21] method, the key distinction from training a typical detector employing region proposals lies in the necessity to assign ground-truth information to specific outputs within the fixed set of detector outputs. Following this assignment, the loss function and backpropagation are applied end-to-end. The training process involves selecting the set of default boxes and scales for detection, as well as the hard negative mining and data augmentation strategies. Throughout training, the network is tasked with determining which default boxes correspond to a ground-truth detection and adjusting accordingly. Each ground-truth box is matched to the default box with the highest Jaccard overlap, and default boxes are matched to any ground truth with Jaccard overlap exceeding a threshold of 0.5. This approach simplifies the learning process, enabling the network to predict high scores for multiple overlapping default boxes, as opposed to mandating the selection of only the one with maximum overlap, as seen in the MultiBox method.  
During the training process, the SSD [21] focuses on optimizing two types of loss: localization (Lloc) and confidence loss (Lconf), which are then combined into a weighted sum (where \(\alpha\) represents the weight term) to obtain the overall loss function (L) for N matched prior boxes. The localization loss is dedicated to regressing the offsets between the prior boxes and the model's predictions, considering both the position (center) and size (width, height) of the object. Simultaneously, the confidence loss is calculated using a Softmax with cross-entropy loss. For more in-depth details about these two loss functions, further information can be found in reference [21].  
\[\text{L}=\frac{1}{\text{N}}*(\text{L}_{\text{conf}}+\alpha*\text{L}_{\text{ loc}}), \tag{1}\]
## 2 Materials and Methods
### Evaluation Metrics  
The performance of the two-hybrid neural network (CNN-ANN and CNN-LSTM-ANN) models, as well as that of the ANN model, will be evaluated with different deep learning statistical indicators and metrics. These include correlation coefficient (r), mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The mean square error (MSE) is used as a loss function for the training and testing of the model. These metrics have been used in different literature [32; 47]. They serve as deterministic models to evaluate the performances of the models. The mathematical representation of these metrics can be found in Equations (14)-(19). It is noteworthy that the smaller the value of MAE and RMSE, the more accurate the model. Additionally, a model is said to be more accurate as the r value approaches 1. In addition to other metrics, the promoting percentage of MAE (\(\lambda_{\text{MAE}}\)) and RMSE (\(\lambda_{\text{RMSE}}\)) presented in literature [45] are also adopted to check the model performance. Finally, the change in MAE and RMSE (\(\lambda\)RMSE and \(\Delta\)MAE) for the training and test task is used to check the models' performances. While a small value of \(\Delta\)RMSE and \(\Delta\)MAE signifies that the developed model is good for the prediction task, a \(\Delta\)RMSE, and \(\Delta\)MAE of zero does indicate that the model is not good.  
\[r=\left(\frac{\sum_{i=1}^{N}\Bigl{(}\bigl{(}G_{i}^{m}-\bigl{\langle}G_{i}^{m} \bigr{\rangle}\bigr{)}\Bigl{(}G_{i}^{p}-\bigl{\langle}G_{i}^{p}\bigr{\rangle} \Bigr{)}\Bigr{)}}{\sqrt{\sum_{i=1}^{N}\bigl{(}G_{i}^{m}-\bigl{\langle}G_{i}^{ m}\bigr{\rangle}\bigr{)}^{2}}\sqrt{\sum_{i=1}^{N}\bigl{(}G_{i}^{p}-\bigl{\langle}G_{i}^{ p}\bigr{\rangle}\bigr{)}^{2}}}\right)^{2} \tag{14}\]  
\[\text{MAE}=\frac{\sum_{i=1}^{N}\Bigl{|}G_{i}^{m}-G_{i}^{p}\Bigr{|}}{N} \tag{15}\]  
\[\text{MAPE}=\sum_{i=1}^{N}\Biggl{|}\frac{G_{i}^{m}-G_{i}^{p}}{G_{i}^{m}}\Biggr{|} \times 100 \tag{16}\]  
\[\text{RMSE}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\Bigl{(}G_{i}^{m}-G_{i}^{p}\Bigr{)} ^{2}} \tag{17}\]  
\[\lambda_{\text{RMSE}}=\left|\frac{\text{RMSE}_{1}-\text{RMSE}_{2}}{\text{ RMSE}_{1}}\right| \tag{18}\]  
\[\lambda_{\text{MAE}}=\left|\frac{\text{MAE}_{1}-\text{MAE}_{2}}{\text{MAE}_{1}}\right| \tag{19}\]where \(G_{i}^{m}\) is the measured value and \(G_{i}^{p}\) represents the predicted value, and \(\big{<}\left<G_{i}^{m}\right>/\big{<}G_{i}^{p}\big{>}\) are the average values of \(G_{i}^{m}\) and \(G_{i}^{p}\), respectively. MAE\({}_{1}\) and RMSE\({}_{1}\) are the training performance metrics while the MAE\({}_{2}\) and RMSE\({}_{2}\) are the prediction performance metrics. \(N\) is the number of the dataset used and \(\lambda_{\text{MAE}}/\lambda_{\text{RMSE}}\) are the promoting percentage of MAE/RMSE.
## 1 Introduction
Furthermore, the boundary sets that should be mapped are to be easily recognized (e.g., well-known control area tie points) due to the regional characteristics and rarely changed. However, a large number of mappings are required so that the ID mapping for each pair of the same objects becomes difficult in cases where most power system resources are included in boundary sets because two systems handle the same power system resource from different points of view. The local ID mapping problems in such cases have not been addressed in previous works. It is important to create an accurate mapping table for fast and efficient identification of data exchanged because it can cause system malfunction due to wrong data interpretation if the mapping table for data exchange is created incorrectly. Furthermore, an autonomous local ID mapping table not affected by a database schema of each system is necessary to avoid increased costs of development and maintenance due to changes in the data for each system. To deal with this problem, this paper proposes an autonomous local ID mapping scheme for power system resources by making use of a CIM-based topology searching process. The main contribution of this paper is summarized as follows:  
* The proposed scheme can create a unique feature of a power system resource by using topological characteristics;
* An accurate mapping table of different local IDs issued by individual systems can be automatically generated for fast and efficient identification of the same power system resources;
* System malfunctions due to wrong data interpretation and increased maintenance costs can be avoided;
* Data reliability is secured by accurately identifying all the resources. This paper is organized as follows: Sections 2 and 3 describe the overview of the CIM framework for interoperability and identifier-related challenges in applying the CIM framework, respectively. Section 4 presents the proposed topology-based local ID mappingschemes. Case studies considering various conditions are presented in Section 5, and finally, the conclusion is drawn in Section 6.
## 3 Results
### Comparison of Scanpath Length According to Age, Reading Conditions, and Text Types  
A three-way ANOVA revealed a significant main effect of age (\(F\)(5, 545) = 3.286, \(p\) = 0.006) and an interaction effect between age and reading condition (\(F\)(5, 545) = 8.634, \(p\) < 0.001) in regards to scanpath length. Subsequently, a simple main effect test was performed in response to the observed interaction effect. Irrespective of the text type, a significant main effect of age emerged within the AR condition (fictional text: \(F\)(5, 545) = 4.38, \(p\) < 0.001; non-fictional text: \(F\)(5, 545) = 8.66, \(p\) < 0.001). In the AR condition, there was a trend of escalating scanpath length as age increased, whereas this age-related tendency was absent within the R condition. Among younger children, the scanpath length was observed to be shorter in the AR condition in comparison to the R condition, regardless  
Figure 2: Total fixation duration in each reading condition when reading fictional and non-fictional text. ** \(p\) < 0.01.  
of the text type, which is deemed to contribute to the interaction effect between age and reading condition (See Figure 3). The Bonferroni post hoc test was conducted in order to examine the differences in scanpath length across age and reading conditions. Within the AR condition when reading fictional text, there were significant differences in scanpath length between grade 1 and grade 3 (\(p\) = 0.017), grade 1 and grade 4 (\(p\) = 0.021), and grade 1 and grade 5 (\(p\) = 0.023). In the context of non-fictional text under the AR condition, the scanpath length in grade 1 showed a significant difference compared to grade 3 (\(p\) = 0.013), grade 4 (\(p\) < 0.001), grade 5 (\(p\) < 0.001), and grade 6 (\(p\) < 0.001). Similarly, the scanpath length in grade 2 also exhibited a significant difference compared to grade 4 (\(p\) = 0.027), grade 5 (\(p\) = 0.036), and grade 6 (\(p\) = 0.044). When reading fictional text, significant differences in scanpath length between the R and AR conditions were observed in grade 1 (\(p\) = 0.003), grade 2 (\(p\) = 0.031), and grade 4 (\(p\) = 0.025). Regarding non-fictional text, however, it was only within grade 1 that the significant difference in scanpath length between the two conditions appeared (\(p\) < 0.001).  
Neither reading condition (\(F(1,\,545)\) = 0.409, \(p\) = 0.523) nor text type (\(F(1,\,545)\) = 1.23, \(p\) = 0.268) had any significant main effects on scanpath length. The two-way interaction effects between age and text type (\(F(5,\,545)\) = 0.78, \(p\) = 0.562) and reading condition and text type (\(F(1,\,545)\) = 0.01, \(p\) = 0.919), as well as a three-way interaction effect among age, reading condition, and text type, were not statistically significant.
## 3 Sensorless FOC with Luenberger-Type Back-EMF Observer
### Tracking Observer  
The tracking observer is an essential part of sensorless FOC with a back-EMF observer. Since the output of the observer has an estimation position error \(\theta_{err}\), it is necessary to employ a structure that can force such error to zero (\(\theta_{err}=0\)). The PLL can satisfy this requirement and thus provide zero displacements between the \(dq\) and estimated \(\gamma\delta\) reference frame. The output of PLL is the estimated speed \(\hat{\omega}_{e}\) and the estimated position \(\hat{\theta}_{e}\). The PLL allows estimating the rotor speed without an undesirable position derivative. The estimated speed is used as a feedback value for the speed loop and to calculate cross-coupling terms, while the estimated position is employed as an angle to transform the voltages and currents from \(dq\) to the estimated \(\gamma\delta\) reference frame. The block diagram of the back-EMF and tracking observer with the PLL mechanism is shown in Figure 12.  
Figure 11: Luenberger-type back-EMF observer in the estimated rotating reference frame \(\gamma\delta\).  
Figure 12: Back-EMF and tracking observer with PLL mechanism.  
However, the tracking observer contains the PI controller, whose parameters have to be correctly calculated. They can be calculated from the linearized version of the observer shown in Figure 13.  
Similarly, the parameters can be calculated with the pole-placement method, yielding the following proportional and integral gains of the PLL controller [57]:  
\[\begin{split} K_{P_{lu}}=2\xi_{to}\omega_{0_{so}}\\ K_{I_{to}}=\omega_{0_{so}}^{2}\end{split} \tag{32}\]
## 2 Materials and Methods
### Cyanobacterial Biomass and Extraction  
Freeze-dried biomass from _Phormidium papyraceum_ Gomont ex Gomont 1892, strain PACC 8600 (Oscillatoriales, Cyanobacteria) was provided by the Plovdiv Algal Culture Collection (PACC), Paisii Hilendarski University of Plovdiv, Bulgaria. Briefly, the cyanobacterial strain was cultured under sterile conditions (75 cm\({}^{2}\) culture flasks, TPP, Trasadingen, Switzerland) in alkaline Z-nutrient medium [20], with a photoperiod of 12 h/12 h light and dark, respectively, and at a light intensity of 10 umol photons s\({}^{-1}\) m\({}^{-2}\), provided by 40 W cool-white fluorescent tubes. The biomass of _P. papyraceum_ culture was collected by centrifugation, frozen, and freeze-dried.  
The cyanobacterial biomass (500 mg) was mixed with 3 mL MeOH and vortexed for 1 min, followed by an ultrasonic bath extraction (Branson 5510R-DTH, Wilmington, NC, USA) for 20 min under periodical vortexing. Then, 6 mL of chloroform were added to the suspension and shaken for 20 min at 15 rpm. Three millilitres of Milli-Q water were added and vortexed for 1 min. The extract was centrifuged at 4000 rpm for 20 min, and the methanol/chloroform fraction was collected and filtered by Millex-FG, 0.20 um, hydrophobic PTFE filter (Merck KGaA, Darmstadt, Germany). In this study, we were interested more in the nonpolar compounds (methanol/chloroform fraction). Experimental design to assess the immunomodulatory properties, antibacterial activity, and chemical composition of the cyanobacterial extract is shown in Figure 1.  
Figure 1: Schematic overview of the experimental design for assessment of the _P. papyraceum_ extract.  
For LC-MS analysis, 2 mL of the filtered, nonpolar fraction was transferred to a standard autosampler vial, capped, and placed in the 4 \({}^{\circ}\)C Peltier cooled autosampler tray.  
The resting amount of the filtered nonpolar fraction was used for testing of immunomodulatory properties and antibacterial activity. To remove the organic solvents, the filtered fraction was evaporated under a vacuum at 37 \({}^{\circ}\)C using a Savant SpeedVac Concentrator (SAVANT Instruments Inc., Farmingdale, NY, USA). Then, the dry residue was dissolved in 50% DMSO aqueous solution (\(w/v\)) to a final concentration of 5 mg/mL (5 mg of the dry residue was dissolved in 1 mL of DMSO/water, 1:1) and stored at 4 \({}^{\circ}\)C until further use. Working solutions were prepared by additional dilution of the stock (5 mg/mL) in Dulbecco's Phosphate-Buffered Saline (DPBS, Gibco(r), Life Technologies(tm), Paisley, Scotland, UK).
# SIV: Raise the Correlation of Second-Order Correlation Power Analysis to 1.00
## 2 Related Works
### Second-Order CPA  
If every mask is independent, the masked cryptography is secure against the CPA. However, in general implementation, some intermediate value shares the same mask because of the spatial and time complexity. In this case, the implementation might be vulnerable to the CPA.  
The SOCPA is an analytical method that reveals the secret key by combining the power consumption of the two intermediate values \(x,y\) that share the same mask \(m\). The analyst can disclose the key based on the fact that HW \((x\oplus y)\) is linearly related to _pre_ (HW \((x\oplus m)\), HW \((y\oplus m)\)) for some preprocessing function _pre_. Therefore, the SOCPA utilizes the  
Because \(x\oplus y\) is a guessable intermediate value, and two points of power consumption \(P_{x\oplus m}\), \(P_{y\oplus m}\) are linearly related to \(\text{HW}\left(x\oplus m\right),\text{ HW}\left(y\oplus m\right)\), respectively, Equation (1) can be modified to Equation (2). Therefore, the analyst can perform the CPA by combining two intermediate values and two points of power consumption.  
\[\begin{split}&\text{Corr}\left(\text{HW}\left(x\oplus y\right),\text { }pre\left(P_{x\oplus m},\text{ }P_{y\oplus m}\right)\right)\\ &=\text{Corr}\left(\sum_{i=0}^{l_{l}-1}\left(x_{i}\oplus y_{i} \right),\text{ }pre\left(P_{x\oplus m},\text{ }P_{y\oplus m}\right)\right).\end{split} \tag{2}\]  
Note that the Hamming weight is only modifiable to power consumption when \(l_{l}\) is equal to the length of the register \(l_{R}\). If \(l_{l}<l_{R}\), the remaining \(l_{R}-l_{l}\) bits act as noise. For example, if data \(d=\left(d_{7}d_{6}\cdots d_{0}\right)_{2}\) is stored in an 8-bit register and an analyst uses only a 1-bit intermediate value \(d_{7}\), then the remaining 7 bits \(\left(d_{6}d_{5}\cdots d_{0}\right)_{2}\) behave similar to noise:  
\[P_{d}=\epsilon\times d_{7}+\left(\sum_{i=0}^{6}d_{i}+P_{noise}\right). \tag{3}\]  
The correlation of Equation (2) may be much lower compared to the correlation of Equation (1) because of the effect of noise. Consequently, even though the correlation of Equation (1) is 1.00 when bit length is 1, generally, the 1-bit intermediate is not an optimal choice. Table 3 shows the theoretical correlation coefficients of Equation (2) when the bit length of the register is 8. In Table 3, the correlation of the 1-bit intermediate value is only up to 0.13, which is much less than that of the 8-bit intermediate value. The theoretical correlation of the table is calculated as Equation (4). Unlike Equation (1), the upper limit of the sigma notation of the preprocessing function input is fixed at 7.  
\[\text{Corr}\left(\sum_{i=0}^{l_{l}-1}\left(u_{i}\oplus v_{i}\right),\text{ }pre \left(\sum_{i=0}^{7}\left(u_{i}\oplus m_{i}\right),\text{ }\sum_{i=0}^{7}\left(v_{i}\oplus m_{i}\right)\right) \right). \tag{4}\]
# Multimodal Magnetic-Plasmonic Nanoparticles for Biomedical Applications
## 2 Synthetic Methods
### Solvolothermal Methods  
The solvothermal method is now routinely used to prepare magnetic iron oxide nanoparticles [47]. In the first studies on this synthetic approach, cupferron transition metal ion complexes were used as precursors to numerous metal oxide nanoparticles [48]. Nowadays, the solvent thermal method can also be extended to the synthesis of magnetite-gold nanocomposites, as is now commonly carried out. [PERSON] et al. utilised a solvothermal approach to synthesise Fe\({}_{3}\)O\({}_{4}\) nanoparticles in a single step--using FeCl\({}_{3}\) salts dissolved in gelatin in ethylene glycol with sodium acetate reacted at 200 \({}^{\circ}\)C for 6 h [49]. Gold seeds were prepared using chloroauric acid and tetrakis-hydroxymethyliphophonium chloride (THPC), and seeded onto the gelatin coated Fe\({}_{3}\)O\({}_{4}\) nanoparticles through stirring. A gold shell was obtained through seeding of the gold nanoparticles using K\({}_{2}\)CO\({}_{3}\). PEGylation was utilised to make the resultant nanoclusters more bio-compatible. Gelatin was used as a mediator as it can be degraded by a subclass of matrix metalloproteinases, and superparamagnetic Fe\({}_{3}\)O\({}_{4}\) nanoparticles have been shown to be biodegradable in vivo. A similar approach was used by [PERSON] et al.--except in this case an APTES functionalised silica coating is used in place of gelatin, and THPC is also used to reduce a layer of gold onto the Fe\({}_{3}\)O\({}_{4}\) nanoparticles [50]. An interesting method of obtaining concave magneto-plasmonic core-shell supraparticles is used by [PERSON] et al. [51]. The Fe\({}_{3}\)O\({}_{4}\) cores were prepared by the solvothermal method using PEI as a capping ligand. Electrostatics are then exploited to seed the gold nanoparticles onto the surface of the nanoparticles--this is done by sonication of the Au seeds followed by shaking for two hours. The thickness of the gold shell can be carefully controlled through layer by layer deposition of gold seeds. In this case, graphene quantum dots were anchored to the surface in order to produce an enhanced Raman signal.