esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Magnetic Negative Stiffness in QZS Isolators  
### Introduction to Magnetic Negative Stiffness  
Magnetic negative stiffness is a phenomenon arising from magnetic fields and is frequently harnessed in control systems to attain adaptable force or displacement regulation. The system consists of a mobile magnet and a stationary magnet, with the stiffness characteristics modifiable by adjusting the intensity or orientation of the magnetic field. The magnetic negative stiffness system functions based on the interaction force between magnets. When two magnets are close together, they generate an attractive force; conversely, when they are distant, a repulsive force arises. This interaction force dictates the stiffness characteristic of the system, which is modifiable by varying the intensity or direction of the magnetic field. This facilitates control over the output force or displacement. In a magnetically negative stiffness system, the mobile magnet usually encompasses a magnetized movable component, whereas the fixed magnet is affixed to the remaining system components. The negative stiffness characteristics of the system can be modified by adjusting the distance, angle, or magnetic field strength between the mobile and fixed magnets.  
Primarily, these systems afford an extensive adjustable range and exceptionally precise control, enabling exceedingly delicate force or displacement regulation through manipulation of the magnetic field's strength or orientation. They exhibit rapid response times, facilitating high-frequency vibration control. Additionally, they boast low energy consumption, frictionlessness, and a prolonged lifespan. Magnetic negative stiffness systems find wide application across numerous fields. In robotics [63,64], they can facilitate adaptable force control to accommodate diverse environmental conditions and task specifications. Inaerospace applications [65], they can mitigate aircraft vibrations and enhance flight stability. In automotive engineering [66], they can serve in vibration damping or suspension systems, thereby enhancing the driving comfort. These systems also find extensive applications in medical equipment, precision positioning control, and various other domains. By varying the strength or direction of the magnetic field, one can attain flexible and precise control to cater to the requirements of diverse fields.
# A Fast, Decentralized, Self-Aligned Carrier Method for Multicellular Converters +
## 4 Simulation Results
### Evaluation of the Proposed DSA-PSC Method  
Figure 12 shows the simulation results of the proposed DSA-PSC principle for the multiphase converter with four carriers (see Figure 10a). It can be seen that the self-alignment process of the carriers with the inter-carrier phase-shift \(\Delta\theta_{\text{base}}\) = 90\({}^{\circ}\) is perfectly realized, and the steady-state can be reached very quickly (after eight time steps). Figure 13 shows the PWM control signal generation for the high-side switches of each cell.  
Figure 14 shows that the proposed control system works properly with fast transient responses on converter output voltages, load currents, and leg currents. In this figure, the system starts with four cells connected in series. At the time 0.4 s, cell 3 is removed, therefore, the current of leg 3 reduces to zero, and the converter output voltage fluctuates around 10% in about 1 ms before reaching steady-state. At time 0.7 s, cell 3 is restored, and leg 3 current quickly increases to the value 0.25I\({}_{total}\) after a transient time of 1 ms.  
The advantage of the DSA-PSC method in terms of fast dynamic reconfigurations is also guaranteed when the number of cells is dynamically either deactivated or activated. To validate this point, Figure 15 shows the simulation of reconfigurations for a four-cell converter. At the beginning, the system starts with four interleaving cells involving four carriers with inter-carrier phase-shift \(\Delta\theta_{\text{base}}\) = 90\({}^{\circ}\). Then, at time 0.5 ms (at time step 0.5 \(\times\) 10\({}^{4}\)), the cell 3 is removed (using a internal by-pass function of the local controller), and the three remaining cells quickly adjust their own inter-carrier phase-shifts to reach \(\Delta\theta_{\text{base}}\) = 120\({}^{\circ}\). Next, at time 1 ms (at time step 1 \(\times\) 10\({}^{4}\)), the cell 2 is removed, and the system is still well interleaved by self adjusting the phase-shifts to \(\Delta\theta_{\text{base}}\) = 180\({}^{\circ}\). Then, the cell 2 is reinserted at time 1.5 ms (at time step 1.5 \(\times\) 10\({}^{4}\)), resulting in a new inter-carrier phase-shift \(\Delta\theta_{\text{base}}\) = 120\({}^{\circ}\). Finally, the cell 3 is re-activated at time 2 ms (at time step 2 \(\times\) 10\({}^{4}\)), and the system returns quickly to the original configuration with a correct interleaving of the carriers.  
Figure 14: Output voltage, load current, and leg currents waveforms.  
Figure 13: PWM signals on the gates of the high-side switches (B\({}_{1}\)... B\({}_{4}\)) of the converter cells.
# Precise Channel Estimation Approach for a mmWave MIMO System
## 7 Simulation Results and Discussion
### Results and Discussions  
OMP determines the sparsity of the channel or dictionary matrix; VAMP also uses this concept with a few differences. On the other hand, TSSR individually exploits the low rank property of the channel matrix along with the sparsity. At first, an SVT operator was implemented to recapture the channel matrix H. The SVT threshold operator \(\rm\Gamma=\beta\|H_{q}\|\), while \(\rm\beta=\frac{3M}{N_{R}N_{T}}\) was fixed to exploit the low rank property. Channel sparsity depends upon the number of paths in the mmWave channel, i.e., the sparsity of the channel is fixed with the number of propagation paths. For the case of the Ex-ADMM algorithm, the weighting factor of the channel matrix is \(\rm\Gamma_{H}=\beta\|H_{q}\|\), where \(\rm\beta\) is set to be 0.005, and the weighting factor with respect to the sparse matrix D is obtained by \(\rm\Gamma_{D}=\frac{0.1}{\left(1-10\log\left(\rm\sigma_{q}^{2}\right)\right)}\). Under the aforementioned constraints, an analysis was conducted, and results are explained as follows.
## 4 Methodology
### Participants
Finally, in Construct 4 (feedback practices and overall satisfaction with the feedback process), both groups recognized the importance of giving peer feedback (E4: faculty = 1.81, students = 1.75). Misalignment occurred as faculty expressed less agreement for asking for feedback (E1: faculty = 2.00, students = 1.56) and self-assessment (E2: faculty = 1.37, students = 1.55). Faculty rated higher students' use of feedback to improve a task (E3: faculty = 1.44, students = 1.81) than students. Additionally, students reported greater satisfaction with the overall feedback process (E5: faculty = 1.88, students = 1.64) than faculty. Kolmogorov\(-\)Smirnov test on the research dataset reported a low \(p\)-value (typically \(\leq\)0.05), indicating that the data corresponding to learners and faculty responses was not normally distributed. Result of a [PERSON]\(-\)Whitney U test provided a measure of whether the feedback perceptions of the two groups were different in terms of their overall distributions, not just their means, revealing significant differences that are not apparent when only considering the mean values but are important in the attempt to improve the learning experience for all learners [59]. As shown in Table 5, statistically significant differences were evident, with all items having a \(p\)-value \(\leq\) 0.05 in feed-up, feed-back and feed-forward practices (B2, B3, B6, B9, C2, C3, C8, C9, D3, D5, D7, D8, D9, E2, and E3). In Construct 1, significant differences were found in the following items: B2 (deadlines to organize study time), with faculty having a lower mean rank (54.47) than students (78.55), \(p\) = 0.022; B3 (reminders of deadlines), where faculty had a significantly higher mean rank (104.50) than students (72.62), \(p\) = 0.003; B6 (questions and ideas to support writing tasks), with a lower faculty mean rank (56.44) than students (78.32), \(p\) = 0.039; and B9 (instructions to avoid plagiarism), where faculty mean rank was higher (96.00) compared to students (73.63), \(p\) = 0.034. For Construct 2, significant differences were observed for the following items: C2 (correct and incorrect essay structure), with faculty having a lower mean rank (51.63) than students (78.89), \(p\) = 0.008; C3 (correct and incorrect use of citation), where faculty had a higher mean rank (99.09) than students (73.26), \(p\) = 0.016; C8 (progress made with the
## 3 Results and Discussion
### OPA as Integrator  
In the readout circuit, integrators are commonly used for pulse shaping or charge-to-time conversion. Our proposed OPA is configured as an integrator as shown in Figure 8a, and the corresponding simulation result is shown in Figure 8b. It can be seen from Figure 8b that the square wave input signal is converted into an approximated ramp signal at the output, verifying the operation of the integrator circuit. A perfect ramp signal cannot be obtained due to the exponential charging and discharging characteristic of capacitor C1.
## 4 Improved Implicit GPC Time-Delay Compensation Algorithm
### The Identification of the Time-Varying Parameters  
After the random time delay has been predicted through the LS-SVM algorithm, the networked control system can be written as a regression model.  
\[y(k)=\phi^{T}0(k)+v(k) \tag{14}\]  
In the above equation:\[\phi^{T}(k)=[y(k-1),y(k-2),\cdots,y(k-n);\] \[u(k-\overset{\wedge}{d}(k)),u(k-\overset{\wedge}{d}(k)-1),\cdots,u( k-\overset{\wedge}{d}(k)-m);\] \[v(k-1),v(k-2),\cdots,v(k-l)]; \tag{15}\] \[\theta^{T}(k)=[a_{1}(k),a_{2}(k),\cdots,a_{n}(k);b_{1}(k),b_{2}(k ),\cdots,\] \[b_{n}(k);c_{1}(k),c_{2}(k),\cdots,c_{l}(k)]\]  
Then, the next equations can be obtained:  
\[\overset{\wedge}{\theta}(k)=\overset{\wedge}{\theta}(k-1)+\frac{P(k-1) \phi(k-1)}{r+\phi^{T}(k-1)P(k-1)\phi(k-1)}[y(k)-\phi^{T}(k-1)\overset{\wedge}{ \theta}(k-1)]; \tag{16}\]  
\[P(k)=P(k-1)-\frac{P(k-1)\phi(k-1)\phi^{T}(k-1)P(k-1)}{r+\phi^{T}(k-1)P(k-1) \phi(k-1)} \tag{17}\]  
In the above equations, \(r\) is the forgetting factor, \(0\leq r\leq 1\); the initial value, \(P(0)=\delta^{2}I\), \(\theta(0)=0\), \(\delta^{2}\) is a sufficiently large constant and \(I\) is the unit matrix. Through the above method, the time-varying parameters \(a_{i}(k)\), \(b_{i}(k)\), \(c_{i}(k)\) of the system can be identified.
## 4 Results
### GPP Mapping and Validation  
The GPP map was generated using the predictor values determined by the three models. That is, the higher the value of probability for an area, the more likely it is to contain groundwater. The probabilities calculated by the three models was re-expressed in the form of the groundwater productivity potential index (Figure 4).  
The next step was to validate the GPP maps created using the BRT, RF, and LR models. The prediction rate of the validations was determined by comparing the GPP maps created using the RF, LR, and BRT models with the remaining 50% of groundwater wells data not used in the training set. A GPP rank with more than 10% value could explain the presence of 30% of the groundwater wells and rank with more than 60% value could explain 90% of the groundwater wells identified by the three models.  
AUC was used to comparatively analyze the results of the three models in order to quantitatively compare the results of each model. Upon validation of the GPP maps (Figure 5), the LR, RF, and BRT models produced AUC values of 0.8504, 0.8021, and 0.8166 with T values (i.e., the prediction accuracy was 85.04%, 80.21% and 81.66%), respectively, and 0.8222, 0.7857, and 0.8153 with SPC values (i.e., the prediction accuracy was 82.22%, 78.57% and 81.53%), respectively. All models indicated the presence of 90% of the potential groundwater wells in 60% of the study area.  
This study applied the RF, BRT (data mining), and LR (statistics) models to estimate GPP. The RF and BRT data mining models showed good accuracy while spatially predicting GPP. Their accuracy amounted to approximately 80% and more. The LR statistical model showed the highest verification accuracy, reaching beyond 85%.  
Figure 4: The results of GPP maps generated using LR, BRT and RF models. T/SPC values of (**a,b**) LR model, (**c,d**) BRT model and (**e,f**) RF model.
# Mathematical Foundations and Implementation of CONIKS Key Transparency
## 1 Introduction  
In the digital age, where privacy concerns are paramount, end-to-end encryption (E2 EE) has become indispensable for protecting communication. The security of E2 EE systems fundamentally depends on the cryptographic primitives and protocols employed, which must be rigorously designed to avoid vulnerabilities [1]. As key transparency systems have evolved, their integration with E2 EE frameworks has offered innovative approaches to secure key management while safeguarding user privacy. This research paper focuses on CONIKS, a key transparency system that ensures the authenticity of public keys in E2 EE communication, providing a secure and transparent method for key management.  
Key transparency addresses a critical challenge in cryptographic systems: ensuring that public keys used in encrypted communication are both authentic and untampered, without requiring users to trust third-party authorities blindly. CONIKS builds on earlier solutions like Certificate Transparency [2] and Keybase by utilizing cryptographic tools such as hash functions, digital signatures, and Merkle trees. These tools are essential for maintaining the system's integrity and transparency. Hash functions ensure data integrity, digital signatures guarantee authenticity and non-repudiation, and Merkle trees, a pivotal data structure, allow for the efficient verification of key inclusion [3].  
In this paper, we present a thorough analysis of the mathematical foundations of CONIKS, including the cryptographic primitives that it employs and the security models that ensure its resilience to attacks. The paper also provides a comparative analysis of CONIKS against other key transparency systems, highlighting its advantages in scalability,security, and user privacy. We extend the study with formal mathematical proofs that demonstrate the security of the system against a variety of attack vectors, thereby offering a robust evaluation grounded in well-established cryptographic principles.  
Furthermore, this paper delves into the architecture and implementation of the CONIKS system. We outline its key components and detail the processes of user registration and key management. Practical aspects, including the development environment, tools, and error-handling mechanisms, are discussed to give readers a comprehensive understanding of CONIKS's operational functionality.  
In conclusion, this paper contributes to the growing body of research on cryptographic systems by offering a detailed examination of the mathematical and practical foundations of CONIKS. The insights gained have significant implications for the future development of secure, scalable, and transparent end-to-end encryption systems, with potential applications extending to various digital platforms that require robust key management solutions.
###### Abstract  
Solar-induced chlorophyll fluorescence (SIF) is a powerful tool for the estimation of gross primary productivity (GPP), but the relationship between SIF and GPP under drought stress remains incompletely understood. Elucidating the response of the relationship between SIF and GPP to drought stress is essential in order to enhance the precision of GPP estimation in forests. In this study, we obtained SIF in the red (SIF\({}_{687}\)) and far-red (SIF\({}_{760}\)) bands and GPP data from tower flux observations in a Chinese cork oak plantation to explore the response of the diurnal GPP-SIF relationship to drought stress. The plant water stress index (PWSI) was used to quantify drought stress. The results show that drought reduced SIF and GPP, but GPP was more sensitive to drought stress than SIF. The diurnal non-linear relationship of GPP-SIF (R\({}^{2}\)) decreased with the increase in drought stress, but a significant non-linear correlation remained for GPP-SIF (R\({}^{2}\)_GPP-SIF\({}_{760}\)_ = 0.30, R\({}^{2}\)_GPP-SIF\({}_{687}\)_ = 0.23) under severe drought stress (PWSIbin: 0.8-0.9). Physiological coupling strengthened the GPP-SIF relationship under drought, while canopy structure effects were negligible. Random forest and path analyses revealed that VPD was the key factor reducing the GPP-SIF correlation during drought. Incorporating VPD into the GPP-SIF relationship improved the GPP estimation accuracy by over 48% under severe drought stress. The red SIF allowed for more accurate GPP estimations than the far-red SIF under drought conditions. Our results offer important perspectives on the GPP-SIF relationship under drought conditions, potentially helping to improve GPP model predictions in the face of climate change.  
solar-induced chlorophyll fluorescence; gross primary productivity; drought stress; plantation
# Copper- and Iron-Based Nanoflowers in Cancer Theranostics
## 2 Synthesis Methods for Copper- and Iron-Based Nanoflowers
### One-Pot Biomineralization  
Biomineralization, the process by which biological systems produce inorganic materials, has become an attractive method for sustainably creating functional nanomaterials. Relying on proteins or other biomolecules, this process occurs at ambient temperatures and pressures, offering a scalable, cost-effective, and environmentally friendly approach to nanoparticle synthesis [53].  
Biomineralization is a natural process used by various organisms to produce functional inorganic crystals with complex hierarchical micro- and nanostructures. Biomolecules, particularly proteins, act as templates that guide the assembly of inorganic nanomaterials, forming structures like bones, teeth, shells, and fish scales. Collagen, the most abundant protein in animals, has a unique triple-helix structure made of repetitive amino acid sequences. Its rod-like structure and self-assembly properties make it a strong building block, making collagen the primary biotemplate for synthesizing inorganic mesocrystals in the human body [97].  
Figure 8 illustrates the stepwise fabrication of milk allergen, \(\beta\)-lactoglobulin biosensor on polyimide (PI) film.  
Figure 8: Stepwise fabrication of milk allergen, \(\beta\)-lactoglobulin biosensor on polyimide (PI) film involves the following steps: (**a**) acid–base treatment to introduce carboxylic functional groups, (**b**) deposition of CDI-Cu hybrid nanoflowers (NF), (**c**) modification with neutravidin, (**d**) immobilization of oligoaptamer, and (**e**) detection of \(\beta\)-lactoglobulin [98].  
The innovative biomimetic mineralization strategy known as the one-pot method enables the direct and efficient synthesis of smart biomaterials with multiple responsive capabilities in an environmentally friendly manner. This one-pot approach is simpler to operate, does not necessitate oxygen isolation, and features milder and faster reactions, resulting in reduced environmental and operator harm. This aligns with the principles of green chemistry in the preparation of multifunctional magnetic nanoparticles [52].
## 1 Introduction
It was found that historical product portfolios can be extracted to support new-product portfolio configuration, with positive feedback from the manufacturer's and customers' perspectives. The major contributions of this study can be summarized in two areas. First, from the technical aspect, the case-adaptation mechanism in the CBR cycle is revamped by the transfer-learning approach. To be specific, the attribute weights in the case-similarity evaluation function can be determined through analyzing customer reviews relevant to new products. Thus, the most similar case can be identified to support the formulation of the new-product portfolio. Second, due to the incorporation of customer reviews in the case-adaptation process, NPPM is shaped in a customer-driven manner to consider customer thoughts and perceptions, and the new-product portfolio can, therefore, be designed to satisfy not only the manufacturers' needs, but also customer requirements. Consequently, the likelihood of new-product success in the currently ever-changing business environment can be increased. The remainder of this paper is organized as follows. In Section 2, a literature review related to (i) new-product portfolio management and (ii) intelligent portfolio management approaches is presented, which identified the gaps in exploiting transfer learning in the CBR mechanism. In Section 3, the research methodology, namely the proposed architecture of the INPPCS, is presented, where the transfer-learning-based CBR mechanism is developed. In Section 4, the proposed system is implemented in a case study of a company to verify its feasibility and performance. In Section 5, the findings and managerial insights into NPPM are discussed. Finally, the conclusions and potential directions for future studies are presented in Section 6.
## 4 A Feedforward-Cascade Compound Control Scheme Based on IPFC
### Control Scheme  
The heat exchanger studied in this paper has four input variables: refrigerant flow, refrigerant inlet temperature, hot fluid flow, and hot fluid inlet temperature; the refrigerant flow is a control variable, and the thermal fluid outlet temperature is a controlled variable; the other three variables are not controllable and can only exist as a disturbance. According to the characteristics of feedforward control, feedforward control is the preferred control scheme when there are large-scale and high-frequency disturbances in the system that can be measured but cannot be used as control variables. Therefore, this paper proposes a feedforward cascade compound control scheme based on IPFC. The feedforward control is used to compensate the three interference channels of hot fluid flow, hot fluid inlet temperature, and refrigerant inlet temperature, and the other small disturbances, such as pressure fluctuations, are corrected by feedback control.  
In this paper, three feedforward channels are designed to compensate for and control the three disturbances, respectively. The thermal fluid flow is in interference channel 1, and the corresponding feedforward compensation is feedforward controller 1. The inlet temperature of hot fluid is interference channel 2, and the corresponding feedforward compensation is feedforward controller 2. The refrigerant inlet temperature is interference channel 3, and the corresponding feedforward compensation is feedforward controller 3. Meanwhile, the characteristics of feedback and cascade control systems are used to control other small disturbances.  
The schematic diagram of the control scheme is shown in Figure 7.  
Figure 7: Block diagram of feedforward—cascade compound control system based on IPFC.
# A Practical Implementation of Post-Quantum Cryptography for Secure Wireless Communication +
## 4 Practical Implementation of PQC-Based PKE Protocols  
Since there is no function for data encryption and decryption in current PQC algorithm implementations, we enhance the PQC algorithm implementation by adding encryption and decryption functions. This enables data encryption and decryption in both PQC standalone and PQC-AES hybrid protocols. Both protocols utilize the PQC key encapsulation mechanism (KEM) for secure key exchange between the client and server.