esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Phases of the Proposed Approach  
Before we go further into the design, it is essential to define the fuzzy logic system. Fuzzy logic is a predicting system to make accurate decisions based on fuzzy rules. Figure 2 shows how a fuzzy system works. The fuzzy logic system consists of two main phases: fuzzification and defuzzification.  
Fuzzification converts crisp values to fuzzy set values; in this stage, it is essential to draw the membership function and fuzzy sets and use the established fuzzy rules to generate fuzzy set outputs [22]. Many types of membership functions convert crisp data to fuzzy sets; however, in this design, the triangular membership function is selected as it provides accurate results and suits the design.  
Figure 1: Proposal design.  
The triangular function is given below:  
\[\mu_{A}(X)=\begin{cases}0&x\leq a\\ \frac{x-a}{n-a}&a<x\leq m\\ \frac{b-x}{b-m}&m<x<b\\ 0&x\geq b\end{cases} \tag{1}\]  
where \(X\) is the new input that requires decision making; and \(a\), \(m\), and \(b\) are the points to determine the interval of each triangle in the membership function, with \(\mu_{A}(X)\) as the output of Equation (1).  
Defuzzification converts the output values of the fuzzy set into crisp values by using some linguistic rules of if-then and logical operators [23]; in this design, we will use the AND operator. There are many equations to calculate defuzzification, and the centroid of area (CoA) is used in this design. The defuzzification equation is as follows:  
\[x^{*}=\frac{\sum_{i=1}^{n}x_{i}\times\mu(x_{i})}{\sum_{i=1}^{n}\mu(x_{i})} \tag{2}\]  
Our proposed approach consists of two phases. Phase 1 assists in data categorization. Phase 2 assists in allocating resources depending on the categorized data in phase 1 and Mist node capacity.
## 2 Materials and Methods
### Integration of the Coordinate Systems of the RGB-D Camera and the Robot Arm  
The coordinates of the rock identified using the RGB-D camera represented the pixel positions of the rock in the RGB image, and the robot arm had a coordinate system for each of its six joints. Thus, it was necessary to establish a correspondence between the coordinate systems of the RGB-D camera and the robot arm to move the gripper of the arm to the location of the detected rock. Figure 9(a) depicts the five correction points on the hopper selected to match the coordinate system of the RGB-D camera and the robot arm. The relationship between the x- and y-coordinates of the robot arm and the camera are depicted graphically in Figure 9(b). That is, for the five points in Figure 9(a), the coordinates on the camera and the coordinates of the robot arm were acquired, respectively, and are shown as graphs in Figure 9(b). To obtain the coordinates of the camera, the x and y pixel positions for coordinate points were obtained from the LabVIEW software. In addition, to obtain the coordinates of the robot, the states of the six motors of the robot arm were converted into x and y coordinates at each coordinate point.  
The coordinates of the camera and robot arm corresponding to all five positions used during coordinate correction are listed in Table 2. Of the 5 points required for correction, points 1, 4, and 5 have identical x-coordinates, and points 1, 2, and 3 have identical y-coordinates. In Figure 9(b), a linear regression line is generated to capture the correlation between the coordinates of the camera and the robot arm. From the results in Table 2, the linear regression line was constructed using linear formulae such as Equations (3) and (4), and represents the formula that can be used to calculate the x- and y-coordinates of the robot arm with respect to the x- and y-coordinates of the camera. The Z coordinates were calculated through the depth value of the detected mineral location. Since most of the minerals are jammed around the crusher inlet and the crusher inlet is almost flat, we calculated constant z-coordinates, and all of them were calculated with the same z-coordinate. In future field experiments, if a large-scale hopper and a high-accuracy RGB-D camera are used, the z-coordinate can be calculated in the same way as the x and y-coordinates.  
\[X_{Robot} =0.0013\times\ X_{Camera}\ -0.1182 \tag{3}\] \[Y_{Robot} =0.0018\times Y_{Camera}\ +0.4813 \tag{4}\]
## 2 About the Chosen Digital Signature Algorithms
### Falcon
#### 2.2.2 Advantages and Disadvantages According to the Authors  
According to [24], the advantages include:  
* The most bandwidth-efficient algorithm, as shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Dilithium—NIST Security Level 3** & **Falcon—NIST Security Level 1** \\ \hline Key Generation Speed & 6 M/10 KB & 171 M/16 KB \\  & 8 M/70 KB & \\ Signature Speed & 26 M/11 KB & 40 M/40 KB \\  & 6 M/21 KB + 48 KB Flash & 21 M/25 KB + 57 KB Flash \\ Verification Speed & 2.7 M/11 KB & 0.5 M/4 KB \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of Dilithium [21] and Falcon [9] by execution speed on Cortex M4 (execution speed (cycles)/memory).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Falcon-512** & **Falcon-1024** \\ \hline NIST Security Level & 1 & 3 \\ Public Key Length (bytes) & 897 & 1793 \\ Signature Length (bytes) & 666 & 1280 \\ Key Generation Time (ms) & 8.64 & 27.45 \\ Key Generation (RAM) & 14,336 & 28,672 \\ Signatures per Second & 5948.1 & 2913 \\ Verifications per Second & 27,993.0 & 13,650.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters for two versions of Falcon [23].  
* The modular design; for instance, NTRU lattices could be replaced with other lattice types.
* Extremely fast verification.
* A broad and deep body of research supports lattice security.
* Better-examined security against side-channel attacks.  
Disadvantages:  
* Complex key and signature generation processes.
* Key and signature generation relies on floating-point arithmetic.
* For Falcon, and generally for all other schemes, the risk of side-channel attacks still needs further research.
## 3 The Proposed Protocol
### Mutual authentication between UAVs
#### 3.3.5 Key Refreshing and Dynamic Joining of Drones  
Due to the dynamic nature of UAVs, keys need to be refreshed within a fixed time frame or due to malicious events (e.g., a damaged drone dropping a packet) and UAV malfunction. In these cases, the key must be updated to secure the UAV communication. The key will be updated in the following steps:  
1. The GCS generates a new session key and authentication key by refreshing the key. \[\begin{array}{l}SK_{GU}=MAC(MK_{GU},SC(G,U)\oplus RK_{GU}{}^{\prime}||1)\\ AK_{GU}=MAC(MK_{GU},SC(G,U)\oplus RK_{GU}{}^{\prime}||2)\end{array}\]  
Figure 4: Selection of the key node.  
Figure 5: Refresh of the key node.  
2. The GCS send encrypted messages to the UAV. \[\begin{array}{l}E(SK_{GU},Counter_{GU})\\ MAC(AK_{GU},E(SK_{GU},Counter_{GU}))\end{array}\]
3. The UAV receives the encrypted message and generates its own new session key and authentication key, and then decrypts the encrypted message. If the decryption is successful, the key refresh is successful, otherwise, it fails, this means that the drone may be controlled or badly damaged. \[\begin{array}{l}SK_{UG}{}^{\prime}=MAC(MK_{UG},SC(U,G)\oplus RK_{UG}{}^{ \prime}||1)\\ AK_{UG}{}^{\prime}=MAC(MK_{UG},SC(U,G)\oplus RK_{UG}{}^{\prime}||2)\end{array}\]
4. Immediately afterwards, the UAV generates a new refresh key and then the UAV sends an encrypted message to the GCS to keep the counters in synchronization. 5. The GCS also generates a new refresh key, while both the UAV and the ground control station delete the old key.
## 3 Broadband Stator Model for Data Storage
### High-Frequency Modeling of PMSM
#### 3.2.3 HF Approach Based on PMSM Phase Design  
The approach based on the machine phase design developed in [13] takes into account capacitive coupling phenomena between the coils and with the machine ground, which are neglected when building a traditional EEC model. The consideration of these couplings increases the accuracy of the model. Furthermore, the formulation in state equations makes the numerical resolution of the problem fast and straightforward. Finally, because the model is based on a portion of the winding, its symmetry properties provide a simple and effective tool for representing the behavior of a single coil or of the entire stator winding by placing state models in series or in parallel.  
Figure 11: Comparison of the experimental frequency response and the proposed transfer function model of CM impedance—taken from [35].  
[PERSON] explains in [13] the problem formulation for three healthy turns of a winding. The drawing of the studied structure helps to consider the different phenomena encountered (capacitive phase-to-ground couplings, couplings between two adjacent turns, and inductive couplings between adjacent half-turns). This structure is described in Figure 12.  
To simplify the formulation, an equivalent circuit of the three machine turns is defined. Then, the state variables of the model are the voltages across the equivalent capacitors and the currents flowing in each of the equivalent inductances. For a three-turn coil, as in [13], there are 13 such variables. The state vector aggregates these different variables, allowing us to formulate the problem as (4). Finally, after linear transformations, we obtain a state model (5) capable of describing the behavior of 3 inductively and capacitively coupled PMSM turns.  
\[\dot{X}=G.\dot{X}+H.X+K.U \tag{4}\]  
\[\left\{\begin{array}{l}\dot{X}=A.X+B.U\\ Y=C.X+D.U\end{array}\right. \tag{5}\]  
where \(X\) is the state vector, \(Y\) is the output vector, and \(U\) is the input vector.  
Therefore, thanks to this state-space formulation of the behavior of the coupled turns, the precise behavior of both the low-frequency and high-frequency behavior of the complete MSAP can be evaluated with fast numerical resolution. The intermediate step is the estimation of the various model parameters. This estimation is done in [13] using an output error optimization algorithm. The advantage of this formulation, which is based on the machine design, is that it takes into account the winding topology and capacitive couplings, which are generally neglected. As explained earlier, these couplings are representative of the quality of the insulation between the conductors and consequently characteristic of the winding aging and the development of interturn short circuits.
## 4 Query Processing Algorithm
### Parameters and Pruning Criteria  
Three parameters are used to design the pruning criteria. They are \(d(s,o)\), \(D(s,o)\), and \(d(s,s^{\prime})\), which are described as follows.  
* Distance \(d(s,o)\): it refers to the minimal distance between the sensor \(s\) and the approximate region of object \(o\). As shown in Figure 5, \(d(s_{5},o_{1})\) is the minimal distance of the sensor \(s_{5}\) to the approximate region of \(o_{1}\). Note that the minimal distance \(d(s_{5},o_{3})\) is equal to 0 as \(s_{5}\) is enclosed in the approximate region of \(o_{3}\).
* Distance \(D(s,o)\): it refers to the maximal distance between the sensor \(s\) and the approximate region of object \(o\). Consider again the example in Figure 5. \(D(s_{5},o_{5})\) is the maximal distance between the sensor \(s_{5}\) and the approximate region \(o_{5}\).
* Distance \(d(s,s^{\prime})\): it refers to the minimal distance from the sensor \(s\) to the grid cell monitored by the sensor \(s^{\prime}\). For example, \(d(s_{5},s_{3})\) is the minimal distance between \(s_{5}\) and the grid cell monitored by \(s_{3}\).  
Figure 4: Update mechanism.  
Having defined the three parameters, we develop the following pruning criteria to efficiently prune the objects that are impossible to be the KNN results.  
* Pruning criterion 1: if the maximal distance \(D(s,o)\) of sensor \(s\) to object \(o\) is less than the minimal distance \(d(s,o\,^{\prime})\) of sensor \(s\) to object \(o\,^{\prime}\), then \(o\,^{\prime}\) cannot be the NN and thus is pruned.
* Pruning criterion 2: if the maximal distance \(D(s,o)\) of sensor \(s\) to object \(o\) is less than the minimal distance \(d(s,s\,^{\prime})\) of sensor \(s\) to the grid cell monitored by another sensor \(s\,^{\prime}\), then the objects monitored by sensor \(s\,^{\prime}\) can be pruned as their distances to sensor \(s\) must be greater than that of object \(o\).
# Robust Speed Control of a Multi-Mass System: Analytical Tuning and Sensitivity Analysis
## 3 Development of the Control Algorithm
### Frequency Analysis
#### 3.2.2 Measurement Noise Amplification  
The noise amplification of the closed-loop-controlled system is evaluated by the noise transfer function from noise \(n\), shown in Figures 5 and 6, to the electromagnetic motor moment \(M_{M}\)[10]. The noise transfer functions of \(PI\), \(LQ\), and \(LQI\) are determined through Equations (26)-(28), respectively. Figure 12 shows the noise rejection for each  
Figure 11: Nyquist plot in logarithmic scale for LQI-controlled system.  
Figure 10: Nyquist plot in logarithmic scale for LQ-controlled system.  
controller command moment (i.e., demanded moment by the controller from the electrical loading machine).  
\[N_{PI}(s)=-G_{t}(s)\,\left[k_{p}+\frac{k_{i}}{s}\right] \tag{26}\]  
\[N_{LQ}(s)=-G_{t}(s)\,\left[\boldsymbol{K_{LQ}}(s\boldsymbol{I}-\boldsymbol{A} )^{-1}\boldsymbol{L_{LQ}}\right] \tag{27}\]  
\[N_{LQI}(s)=-G_{t}(s)\,\left[\boldsymbol{K_{LQI}}(s\boldsymbol{I}-\boldsymbol{A })^{-1}\boldsymbol{L_{LQI}}+\frac{K_{\xi}}{s}\right] \tag{28}\]  
Apparently, the noise rejection quality increases with the frequency for each control loop. Moreover, the \(LQI\) has equivalent characteristics to \(LQ\), even in the noise rejection quality. It can also be seen that the \(PI\) controller provides the worst noise rejection evaluation. The spiral Nyquist form, which results from an internal delay, reflects that high phases offset high frequencies [39]. This means that the phase of the \(LQ\)-controlled system is the least sensitive to time delay. After evaluating using frequency response analysis, the earlier conclusions will be approved with the time-domain simulation.
## 3 The Adaptive Droop Control of the ILC
### The Control Principle of ILC  
Combined with the above analysis, the ILC in islanding mode adopts a dual closed-loop control strategy with power outer loop and current inner loop, as shown in Figure 10.  
The adaptive weight coefficient is brought into Equation (11), and the startup threshold condition is considered to obtain Equation (14) to calculate the ILC active power reference value. The current inner loop control is performed after the reference power is brought into the calculation to obtain the current reference value using the active power formula. The results obtained from the current inner loop control are transformed to the three-phase stationary coordinates and modulated by SPWM to generate pulse signals to control the ILC.  
Figure 9: Bidirectional droop control curves. (**a**) Bidirectional droop control curve without threshold; (**b**) bidirectional droop control curve with threshold.
# Passive Radar-Based Parameter Estimation of Low Earth Orbit Debris Targets
## 6 Effects of Noise and Interference
### Thermal Noise  
In the operating environment, noise sources can include cosmic/galactic noise and solar noise [44]. However, since the L1 C/A center frequency is \(>\)1 GHz, there are negligible effects from galactic noise. In addition, solar noise is only significant when the receiver is pointed towards the sun. Therefore, in this case, the only noise to note is that derived from the internal components of the receiver system. i.e., thermal noise. While many techniques exist to reduce noise, such as averaging, windowing, and matched filtering [44], we will demonstrate the self-mixing performance gains by coherently summing the received inputs from array elements.  
It was shown in Section 5.2.2 that the phase difference of the direct signal between the ULA elements is effectively zero. Therefore, if the signals are coherently summed, the amplitude of the direct signal is increased by the number of elements being summed. This, in turn, increases the amplitude of the filtered signal as there is dependence on both the reflected and direct signal amplitudes, as seen in Equation (19). Figure 9 displays the FFT performance in a severely noisy environment when receiver inputs from elements are added.  
In Figure 9, a single target reflection is received at the first receiver (\(R_{x1}\)) with a Doppler value of \(\omega_{D,1}=1.5\) Hz. The next two elements also receive some of the signal energy from the same target at slightly different Doppler values (due to bistatic geometry), which we have arbitrarily equated to 2.5 and 3.5 Hz. At the first receiver, the SNR present is \(-12.7\) dB. The target Doppler is not discernable. However, after coherently summing signals from the subsequent elements, we achieve an SNR increase relative to the factor increase of \(A_{dir}\), and the magnitude of the 1.5 Hz component is much greater than the noise level.
# Rapid Characterization Method for SMC Materials for a Preliminary Selection
## 4 Experimental Results
### Coercivity: Inorganic Layer  
Regarding the inorganic layer, Figure 14 shows the coercivity values in green and heat-treated SMCs. After heat treatment in steam, the obtained coercivity (417 A/m) was similar to the coercivity of the green specimen (424 A/m). Conversely, the best result was reached in the vacuum environment: 327 A/m.  
In Figure 15, the effect of the temperature was investigated. In the steam environment, the coercivity slightly decreases from 400 \({}^{\circ}\)C to 500 \({}^{\circ}\)C, remaining almost constant with respect to the green state. Instead, at 600 \({}^{\circ}\)C, the coercivity in the steam environment is strongly reduced by 32% with respect to the value at 500 \({}^{\circ}\)C. On the other hand, for all the other environments, the coercivity strongly decreases at regular steps (\(-\)30% for vacuum and \(-\)15% air) with increasing temperature. The best value has been obtained for vacuum treatment at 600 \({}^{\circ}\)C (194.5 A/m).  
Figure 12: Comparison of resistivity between SMCs coated with nano-errites (0.2 wt% and 800 MPa): for different heat treatments temperatures and environments.
# A Dual-Adaptive Approach Based on Discrete Cosine Transform for Removal of ECG Baseline Wander
###### Abstract  
Removal of baseline wander (BW) is an important preprocessing step before manually or automatically interpreting electrocardiogram (ECG) records. It is a challenging issue to fully remove BW while preserving original clinical information because BW is usually mingled with low-frequency ECG components. A dual-adaptive approach based on discrete cosine transform (DCT) is presented in this study. Firstly, the cardiac fundamental frequency (CFF) of ECGs is accurately calculated through DCT domain analysis. Secondly, DCT coefficients of ECGs, whose frequencies are below CFF, are used to construct an amplitude vector in which the optimal cut-point between BW and ECGs is distinctly reflected. Finally, a new filtering technique based on DCT is exploited to suppress BW with its cutoff frequency adjusted to the optimal cut-point. The proposed method is applied to both real ECG records and simulated ECGs with its results compared to those of three previous methods published in the literature. The experimental results show that substantial improvements in performance can be achieved when adopting this dual-adaptive approach.  
ECG preprocessing; baseline wander; discrete cosine transform +
Footnote †: journal: Applied  
Academic Editor: [PERSON]
## 3 Immune-Checkpoint Inhibitors--Mechanisms of Action  
Immune-checkpoint molecules, key regulators of immune responses, are expressed not only by immune cells (T cells and APCs) but also in tumor cells. Physiologically, several molecules that act as inhibitory immune-checkpoint receptors (that naturally control and contain T cell activity) and co-stimulatory immune-checkpoint proteins (that enhance T cell expansion) are expressed on the cell surface [26]. Among these molecules, therapeutic targeting of PD-1/PD-L1 and CTLA4 constitutes the backbone of immunotherapy for solid cancers. Anti-PD-1 and anti-PD-L1 antibodies block PD-1/PD-L1 interaction, thus restoring the T lymphocyte effector function (Figure 1C) [30]. CTLA4 inhibition acts on the interaction between T cells and APCs during the priming phase, favoring the binding between B7-1/B7-2 co-stimulatory ligands and CD28 in lymphocytes, leading to activation of naive CD4\({}^{+}\) and CD8\({}^{+}\) T cells (Figure 1C) [31]. Anti-CTLA4 therapy increases the number of active CD4\({}^{+}\) and CD8\({}^{+}\) T lymphocytes and decreased peripheral T cell clonality [32].  
Even though molecular classification studies suggest that about 35% of HCCs have an immune-reactive TME [33; 34] and PD-L1 expression is associated with a worst outcome [35], the activity of anti-PD-1/PD-L1 antibodies in HCC is modest compared with other malignancies. Indeed, objective radiologic responses are observed in only 15-20% of patients [36]. Although the causes of unresponsiveness to ICIs remain largely unclear, the existence of several molecules potentially involved in immune escape other than PD-1/PD-L1 and CTLA4 is a possible explanation. According to experimental and clinical data, anti-PD-1 therapy seems more active than anti-PD-L1. Recently, a network meta-analysis combining the data from 19 trials across multiple solid tumor types demonstrated higher efficacy of anti-PD-1 therapies [37]. The unfavorable pharmacokinetic properties of anti-PD-L1 antibodies [38] and the additional blockade of the PD-1/PD-L2 interaction by anti-PD-1 antibodies [39] could partly explain these findings.
# Secure Watermarking Schemes and Their Approaches in the IoT Technology: An Overview
## 2 The Concept of Watermarking
### The Watermarking Performance Metrics
#### 2.3.6 Correlation Coefficient  
The Correlation Coefficient (\(CC\)) is commonly used in signal processing and image processing to find similarity between two signals (or objects) [56]. The correlation coefficient equation is represented in (10) as follows:  
\[CC=\frac{\sum_{i}\sum_{j}W_{ij}W_{ij}^{\prime}}{h\times w} \tag{10}\]  
where \(W_{ij}\) and \(W_{ij}^{\prime}\) are the values in (\(ij\)) of the embedded and extracted watermark as well as set to \(1\) if the if the watermark bit is \(1\) otherwise set to \(-1\); and \(h\), \(w\) are the height and width of the watermark, respectively.