esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Description of Control Schemes  
In this section, first, the mathematical model for the MPC is derived, then the MPC-SMO hybrid combination is derived, followed by the combination of the MPC-EKF and, finally, the proposed MPC-UKF hybrid combination. The PMSM is operated through 3-phase Voltage Source Inverter (VSI) that is controlled through Space Vector PWM (SVPWM). The 3-phase current and voltage of the motor are converted into the \(dq\)-reference framework for the input of any one of the following observers, such as SMO, EKF, and UKF. One of the outputs (speed) of the UKF/EKF/SMO acts as feedback for the PI controller that proportionally provides the reference current \(i_{q}\) to the MPC. The reference current is calculated based on the difference in reference speed and estimated speed. The schematic diagram of the proposed technique is given in Figure 1. The UKF or EKF can be selected by a selector switch. Therefore, both combinations, such as MPC-UKF and MPC-EKF, can be evaluated and analyzed. Similarly, the SMO is also implemented and acts as an observer to achieve a wider range of comparison. The UKF/EKF/SMO act as observers in the feedback form and estimate those states that are not directly measurable without sensors. The Park transformation is used for \(abc\) to \(dq\) coordinates conversion. The estimated speed \(\widehat{\omega}_{r}\) from the UKF/EKF/SMO is used as feedback for the PI controller. The error between estimated speed and reference speed is reduced by the PI controller that proportionally increases or decreases the value of reference current \(i_{q}\)[9]. The objective function of the MPC decides the voltage levels for the PWM. Moreover, the measured stator currents after transformation are also given as feedback currents to the MPC for comparison with the reference currents. Then, the MPC within constraints decides the input voltages amplitude. Furthermore, the MPC regulates the switching of the IGBTs in VSI efficiently and quickly [28]. All the measured currents and voltages given to UKF/EKF/SMO block are passed through zero-order holds (ZOH) to avoid zero-crossings problem. The following state-space equations are used in the Clarke and Park transformation for \(\alpha\beta\) and \(dq\) conversion, respectively [29]:  
\[\left[\begin{array}{c}i_{a}\\ i_{\beta}\end{array}\right]=\left[\begin{array}{cc}1&-\frac{1}{2}&-\frac{1}{ 2}\\ 0&\frac{\sqrt{3}}{2}&-\frac{\sqrt{3}}{2}\end{array}\right]\left[\begin{array}[] {c}i_{a}\\ i_{b}\\ i_{c}\end{array}\right] \tag{6}\]  
\[\left[\begin{array}{c}i_{d}\\ i_{q}\end{array}\right]=\left[\begin{array}{cc}\cos\theta&\sin\theta\\ -\sin\theta&\cos\theta\end{array}\right]\left[\begin{array}{c}i_{a}\\ i_{\beta}\end{array}\right] \tag{7}\]
# Sensor-Level Mosaic of Multistrip KOMPSAT-3 Level 1R Products
## 1 Introduction
The proposed algorithms deal with radiometric differences in images caused by seasonal changes [8], image registration and cloud detection with removal [9; 10], efficient processing [11], and color balancing [12; 13]. Most studies are carried out with photogrammetrically processed orthoimages. However, the cost of these photogrammetric processes should increase with the number of images in the delivered bundle. With a mosaicked image at the sensor level delivered only with radiometric correction, users should take advantage of more efficient and convenient photogrammetric data processing and management for the simplified data. However, no relevant work on the sensor-level image mosaic was carried out before a photogrammetric process. Firstly, if users are delivered with a single image with single sensor model information instead of multiple data sets, the sensor modeling processing burden should be lifted. This is because users do not have to identify the ground control points on the multiple images. In addition, the tie point extraction process over multiple images is not required for accurate co-registration between the images. Secondly, the orthorectification and mosaic process is simplified because the single image orthorectification is simpler, and mosaic methods, including the seamline generation, are not required. Therefore, we propose a sensor-level mosaic to generate a seamless image product with geometric accuracy to meet mapping requirements. The approach is different than the ground-level mosaic, as depicted in Figure 1. The ground-level mosaic is carried out with the orthorectification of each image strip to the ground, followed by the seamline extraction and mosaic. As a result, each pixel in the mosaicked image is assigned with map coordinates. In contrast, in the sensor-level mosaic, each image is projected into a reference sensor plane to be merged. The resulting image has single sensor modeling information to relate each mosaic image to the ground. The proposed method begins with setting one image to the reference. Each pixel of the other images is projected to the ground using their sensor model information and SRTM (Shuttle Radar Topography Mission) [14] and then projected into the reference using the reference sensor model information. The problem is that the sensor model information is erroneous such that a large geometric discrepancy occurs due to the satellite's inaccurate position and attitude information. Therefore, we aimed to model and remove the irregular difference along the image line using the image matching and outlier removal in the overlapped area. The paper is structured as follows. In Section 2, the methodology is described with the flowchart with RPCs as the sensor model for image projections. In Section 3, the experimental results are presented for three KOMPSAT-3 strips. The conclusion is presented in Section 4.
###### Abstract  
The focus of present research endeavor was to design a robust fractional-order proportional-integral-derivative (FOPID) controller with specified phase margin (PM) and gain cross over frequency (\(\omega_{gc}\)) through the reduced-order model for continuous interval systems. Currently, this investigation is two-fold: In the first part, a modified Routh approximation technique along with the matching Markov parameters (MPs) and time moments (TMs) are utilized to derive a stable reduced-order continuous interval plant (ROCIP) for a stable high-order continuous interval plant (HOCIP). Whereas in the second part, the FOPID controller is designed for ROCIP by considering PM and \(\omega_{gc}\) as the performance criteria. The FOPID controller parameters are tuned based on the frequency domain specifications using an advanced sine-cosine algorithm (SCA). SCA algorithm is used due to being simple in implementation and effective in performance. The proposed SCA-based FOPID controller is found to be robust and efficient. Thus, the designed FOPID controller is applied to HOCIP. The proposed controller design technique is elaborated by considering a single-input-single-output (SISO) test case. Validity and efficacy of the proposed technique is established based on the simulation results obtained. In addition, the designed FOPID controller retains the desired PM and \(\omega_{gc}\) when implemented on HOCIP. Further, the results proved the eminence of the proposed technique by showing that the designed controller is working effectively for ROCIP and HOCIP.  
f _Article_  
Fractional calculus; fractional-order PID controllers; sine-cosine algorithm (SCA); continuous interval systems; modeling; Routh approximation
## 4 Discussion
### Psychomotor Performance  
We examined the computer-based cognitive function test including PVT and DMS task to evaluate psychomotor performance during recovery. The supplementation of WGE following acute eccentric exercise did not provide a benefit on psychomotor performance in male adults. Consistent results have been identified in the previous study that 14 days of Korean ginseng (GINST15) supplementation prior to resistance exercise were not associated with the improvement of cognitive performance (i.e., quick board reaction time test) in healthy male and female adults [39]. A single dose of Panax ginseng (G115) for eight days also did not improve the working memory process in healthy subjects [40]. In contrast to our findings, [PERSON] et al. reported that a single dose of Panax Ginseng (200 mg, G115) improves cognitive performance, such as serial seven task in healthy adults, but high dosages (400 mg, G115) and placebo conditions did not improve the cognitive performance [41]. Other Ginseng studies also demonstrated that cognitive functions, such as speed of the attention task [42], and secondary memory performance of cognitive research battery [43], improved following a single dose of Panax Ginseng (200 mg, 400 mg, respectively). Some possible explanations, such as the modulation of blood glucose and nitric oxide (NO) production have been proposed as potential mechanisms that may contribute to the improvement of cognitive performance [41]. It was believed that one of the active ingredients, ginseng, contributes to the enhancement of NO synthesis [44], and it modulates the glucose metabolism and insulin secretion in patients with type II diabetes mellitus [45]. Although some possible mechanisms, as well as benefits on cognitive function, have been demonstrated in the previous studies, our study confirms that five days of ginseng supplementation following acute eccentric exercise does not provide benefits on psychomotor performance recovery in male adults. In the present study, the DMS task correction and total time were significantly reduced across the time both in WGE and placebo conditions. We assume that decreased DMS correction partially related to'speed accuracy trade-off'. Therefore, it is difficult to confirm that a decrease in cognitive function, such as DMS correction, was due to acute eccentric exercise.
## 3 Results
### Identification and Enrichment of Differentially Expressed Proteins  
To explore the dynamic changes on the proteomic level during the leaf development, we considered proteins whose fold change >1.2 or <0.83 with a \(q\)-value <0.05 as differentially expressed proteins (DEPs). Based on this standard, 3186 DEPs were identified, of which 1245 proteins were up-regulated and 1941 proteins were down-regulated in 65d (Figure 2a, Table S2).  
Figure 2: Analysis of differentially expressed proteins at different development stages. (**a**) Volcano plot of DEPs between 15 d and 65 d. Red: up-regulated proteins in the 65 d (\(q\)-value < 0.05, FC > 1.2). Blue: down-regulated proteins in the 65 d (\(q\)-value < 0.05, FC < 0.83). (**b**) GO enrichment analysis of DEPs. (**c**) KEGG pathway enrichment analysis of DEPs. (**d**) Expression profiles of DEPs in the metabolic pathway. Red: up-regulated proteins in the 65 d. Purple: down-regulated proteins in the 65 d.  
The GO enrichment analysis showed that most DEPs were enriched in 12 categories, including protein-chromophore linkage, photosynthesis, light-harvesting, photosynthesis, 3\({}^{\prime}\)-UTR-mediated mRNA destabilization, translation, photosynthetic-electron transport in photosystem II, cell redox homeostasis, glycolytic process, protein folding, ATP synthesis coupled proton transport, glycerol ether metabolic process, and chloroplast organization (Figure 2b). To KEGG enrichment analysis, a large proportion of DEPs was markedly enriched in the metabolic pathways (829 DEPs), followed by biosynthesis of secondary metabolites (Figure 2c). Further analysis of the metabolic pathways showed that 431 proteins were up-regulated and 398 proteins were down-regulated in 65 d compared with 15 d (Figure 2d). Taken together, these results suggested that many crucial biological processes such as metabolism, biosynthesis of secondary metabolites, and photosynthesis were significant changes at different development stages.
# KeyNet: An Asymmetric Key-Style Framework for Watermarking Deep Learning Models
## 6 Conclusions and Future Work  
We have presented _KeyNet_, a novel watermarking framework to protect the IP of DL models. We use the final output distribution of deep learning models to include a robust WM that does not fall in the same decision boundaries of original task classes. To make the most of this advantage, we design the watermarking task in an innovative way that makes it possible (i) to embed a large amount of WM information, (ii) to establish a strong link between the owner and her marked model, (iii) to thwart the attacker from overwriting the WM information without losing accuracy in the original task, and (iv) to uniquely fingerprint several copies of a pretrained model for a large number of users in the system.  
The results we obtained empirically prove that _KeyNet_ is effective and can be generalized to various data sets and DL model architectures. Besides, it is robust against a variety of attacks, it offers a very strong authentication linking the owners and their WMs, and it can be easily used to fingerprint different copies of a DL model for different users.  
As a future work, we plan to extend the framework to cover computer vision tasks that take images as input and output images as well. We also intend to study ways to estimate the watermark capacity of a deep neural network depending on its topology, the complexity of the learning task, and the watermark to be embedded.  
Conceptualization: N.M.J. and J.D.-F.; methodology, N.M.J.; validation: N.M.J., D.S., and A.B.-J.; formal analysis, N.M.J. and [PERSON]; investigation, [PERSON] and [PERSON]; writing--original draft preparation: [PERSON]; writing--review and editing, [PERSON]-F. and D.S.; funding acquisition, J.D.-F. and D.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the European Commission (projects H2020-871042 \"SoBigData++\" and H2020-101006879 \"MobiDataLab\"), the Government of Catalonia (ICREA Academia Prizes to [PERSON] and [PERSON], FI grant to [PERSON] and grant 2017 SGR 705), and the Spanish Government (projects RTI2018-095094-B-C21 \"Consent\" and TIN2016-80250-R \"SecMCloud\").  
Special thanks go to [PERSON], for discussions on an earlier version of this work, and to [PERSON], for implementing some of the experiments. The authors are with the UNESCO Chair in Data Privacy, but the views in this paper are their own and are not necessarily shared by UNESCO.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} DL & Deep learning \\ FMNIST5 & The samples that belong to the classes [0-4] in Fashion-MNIST data set. \\ IP & Intellectual property \\ MDPI & Multidisciplinary Digital Publishing Institute \\ ML & Machine learning \\ MLaaS & Machine learning as a service \\ MSE & Mean square error \\ MTL & Multi-task learning \\ SHA & Secure Hash Standard algorithm \\ SN & Serial number \\ WM & Watermark \\ \end{tabular}
## 4 Validation
### Discussion  
The proposed framework, integrating the Extended Analytical Hierarchical Process (AHP) with an advanced grouping model, was rigorously tested through a series of case studies specifically designed to evaluate its effectiveness in IoT application development. These case studies were carefully selected based on their diversity in scope and complexity, as well as the variety of IoT technologies they employed, ensuring a comprehensive assessment of the framework across different scenarios. Each case study involved a detailed analysis of the requirements prioritization process for an IoT application, from initial collection through to final prioritization. The studies were conducted in simulated environments  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \multicolumn{4}{c}{(**B**)} \\ \hline
**REQ\#** & **REQ21** & **REQ22** & **REQ23** & **REQ24** & **SUM** \\ \hline REQ21 & 0.53 & 0.77 & 0.41 & 0.25 & 1.96 \\ \hline REQ22 & 0.11 & 0.15 & 0.41 & 0.42 & 1.09 \\ \hline REQ23 & 0.18 & 0.05 & 0.14 & 0.25 & 0.62 \\ \hline REQ24 & 0.18 & 0.03 & 0.05 & 0.08 & 0.34 \\ \hline \multicolumn{4}{c}{(**C**)} \\ \hline
**REQ\#** & **REQ21** & **REQ22** & **REQ23** & **REQ24** & **P** \\ \hline REQ21 & 0.53 & 0.77 & 0.41 & 0.25 & 0.49 \\ \hline REQ22 & 0.11 & 0.15 & 0.41 & 0.42 & 0.27 \\ \hline REQ23 & 0.18 & 0.05 & 0.14 & 0.25 & 0.15 \\ \hline REQ24 & 0.18 & 0.03 & 0.05 & 0.08 & 0.08 \\ \hline SUM & & & & & 1.00 \\ \hline \end{tabular}
\end{table}
Table 7: _Cont._  
\begin{table}
\begin{tabular}{c c} \hline
**Existing Techniques** & **Proposed Framework** \\ \hline Subjective Grouping & Objective Grouping \\ \hline No Pair-Wise Comparison & Pair-Wise Comparison \\ \hline No Ratio Scale & Ratio Scale \\ \hline Hierarchy Level Management & Hierarchy Level \\ \hline Subjective Assignment & Numerical Assignment \\ \hline No Ordinal Scale & Ordinal Scale \\ \hline Three Groups & Four Groups \\ \hline Lack of stakeholder Management & Stakeholder Management \\ \hline Not Dealing with the Same Priority & Same-Priority Management \\ \hline \end{tabular}
\end{table}
Table 8: Comparative analysis.  
that replicated real-world IoT systems, encompassing varying degrees of requirement complexity and interdependencies. The primary criteria for evaluation included the efficiency of the prioritization process, the accuracy of requirement classification, and the overall impact on project outcomes.
## 3 Results and Discussion
### Strength of Alloys with Size-Distributed and Spatially Distributed Precipitates
The threshold volume fraction of the beginning of softening increases with the increase in typical precipitate size: a weak softening is observed for the volume fraction of 1.5% in the case of 20 nm precipitates (Figure 11a), while it starts only at 4.9% in the case of 80 nm precipitates (Figure 11d). There are two reasons for such behavior: (1) decrease in concentration and increase in the number of weak paths for large precipitates and (2) the small pliability of cutting of large precipitates because the cutting touches only precipitates smaller than 60 nm in diameter in accordance with Equation (6). The results of our dislocation dynamics calculations are summarized in the diagram in Figure 13 and supported by experimental data on the yield stress in Al-Cu alloy dependent on typical diameter and volume fraction of \(\theta^{\prime}\) phase precipitates [37]. The maximal shear stress before the beginning of softening is presented as dependent on typical precipitate diameter of the log-normal distribution and the volume fraction of precipitates. Our results are in satisfactory agreement with the experimental data, taking into account rather large errors in determining both the typical diameter of inclusions and their volume fraction. The range of parameters of alloys prone to strain softening due to the destruction of precipitates  
Figure 11: Results of 2D dislocation dynamics calculation for aluminum with \(\theta^{\prime}\) precipitates for different typical sizes ((**a**)—20 nm, (**b**)—40 nm, (**c**)—60 nm, (**d**)—80 nm) and volume fractions: stress-strain curves calculated in approximation of constant size of precipitates (“uncuttable”) and accounting for their size evolution (“cuttable”). is indicated. It follows from our calculation that precipitates with a small typical diameter provide higher maximal strength, but suffer from the strain softening and plastic flow localization. This conclusion is supported by the existing experimental results. According to our calculations, the boundary of the domain suffering from softening almost coincides with the maximal stress level of 600 MPa, which means that the alloys with higher strength are subjected to softening. It should be also mentioned that high plastic deformation inside the localization bands leads to formation of a lot of vacancies--see Section 3.3--which can facilitate the tensile fracture. Figure 12: Localization of plastic deformation in system in cases of (**a**) “uncuttable” and (**b**) “cuttable” precipitates. Figure 13: Dependence of maximal shear strength on volume fraction and typical diameter of \(\theta^{\prime}\) precipitates in Al–Cu alloy and region of strain softening due to precipitate cutting: Results of 2D dislocation dynamics accounting for precipitate evolution. Diagram is supplemented with experimental data on yield stress in Al–Cu alloy [37], given points corresponding to the experimental data on typical diameter and volume fraction of \(\theta^{\prime}\) precipitates; additionally, maximal absolute errors in experimental data are indicated.
## 4 Comparative Study
### Data Preprocessing  
The data were converted to Doppler-time maps in JPEG format using a Python script, which was developed based on the provided MATLAB file. The function transformed the sampled values of the raw radar signal into a spectrogram (see Figure 11). In the first step, the data were used to calculate the range profile over time using an FFT. Then, a fourth-order Butterworth filter was applied, and the spectrogram was calculated by applying a second Fourier transform to overlapping time-specific filtering windows, i.e., the Hann window. Subsequently, the spectrograms were imported into the Python-based application and transformed into images of \(224\times 224\times 3\) px in size after scaling. Trimming the edges and adapting the color levels was important to remove weak interfering artifacts and highlight characteristic patterns caused by frequency leakage or non-optimized windowing. In the next step, they were converted to grayscale images to reduce dimensionality since in this case, the color channels did not contain any additional information. A compression using truncated SVD was applied to reduce the data size while retaining the main information.  
\begin{table}
\begin{tabular}{c l l l l l} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Date**} & \multicolumn{1}{l}{**Number of**} & \multicolumn{1}{l}{**Number of**} & \multicolumn{1}{l}{**Number of**} & \multicolumn{1}{l}{**Number of**} \\  & & **Files** & **Activities** & **Subjects** & **Repetitions** \\ \hline
1 & December 2017 & 360 & 6 & 20 & 3 \\
2 & March 2017 & 48 & 6 & 4 & 2 \\
3 & June 2017 & 162 & 6 & 9 & 3 \\
4 & July 2018 & 288 & 6 & 16 & 3 \\
5 & February 2019 & 306 & 6 & 17 & 3 \\
6 & February 2019 & 301 & 5 & 20 & 3 \\
7 & March 2019 & 289 & 5 & 20 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Contents of the open dataset _Radar Signatures of Human Activities_[35].  
Using the ZCA method, the images were whitened. Dimensionality reduction was discarded to avoid significant loss of information. As the color values ranged from 0 to 255, normalization was then applied, which scaled the values from 0 to 1 in order to improve performance.