esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Watermarking Approaches
### Watermarking Methods
#### 3.2.5 Watermark Embedding Through Initial Noise Modification  
Another approach to watermarking focuses on embedding information into the initial noise used during the diffusion process. These methods utilize the properties of the initial noise to ensure that the embedded watermark is imperceptible while being resilient against removal attempts.  
[PERSON] et al. [23] introduce Tree-Ring Watermarking, a method that embeds watermarks into the Fourier space of the noise used during image generation. It does not require additional training and is highly robust against transformations such as rotations, crops, and noise. The watermark is invisible to human observers and can only be detected by inverting the diffusion process to retrieve the initial noise. [PERSON] et al. [29] build on Tree-Ring Watermarking by modifying the initial noise distribution in Fourier space. METR allows embedding multiple unique binary messages as concentric circles in the latent noise, providing flexibility in encoding large amounts of data while maintaining invisibility and resilience to attacks. [PERSON] et al. [30] leverage pretrained diffusion probabilistic models to embed watermarks using the Discrete Wavelet transform. By embedding watermark data in high-frequency sub-band coefficients, WaterDiff ensures robustness and imperceptibility. The reversible nature of diffusion models allows for watermark extraction through a reverse process, achieving a balance between watermark capacity, imperceptibility, and resistance to attacks like JPEG compression.  
[PERSON] et al. [22] embed watermarks into the latent space without degrading image quality. Mapping watermarks to Gaussian latent representations maintains the integrity of generated images while ensuring provable detection. The method is resistant to compression attacks, ensuring robust watermarking without performance loss. [PERSON] et al. [42] introduce ZoDiac, a watermarking framework that embeds invisible watermarks into the latent space of images using pre-trained Stable Diffusion models. ZoDiac is highly resistant to removal, even by advanced attacks, and requires no model retraining. The watermark is injected into the Fourier space of the latent vector, with detection performed using a DDIM inversion, ensuring robustness against multiple removal attempts. [PERSON] et al. [21] embed invisible watermarks into latent variables during the sampling phase of image generation, ensuring resilience against various image processing attacks without compromising quality or requiring retraining. The watermark can be flexibly modified and traced without affecting model performance. Embedding watermarks into the initial noise used in diffusion models ensures that the watermark remains imperceptible while being deeply integrated into the generative process. This method provides a secure and resilient watermarking solution that is effective against various image processing transformations [23].
## 2 Reference Current Calculation
### CPC Power Theory and Reference Current Calculation
\begin{array}{l}\mathbf{Y}_{A}=G_{A}+jB_{A}=\frac{I_{LA}}{I _{A}}\\ \mathbf{Y}_{B}=G_{B}+jB_{B}=\frac{I_{LB}}{I_{B}}\\ \mathbf{Y}_{C}=G_{C}+jB_{C}=\frac{I_{LC}}{I_{C}}\end{array} \right. \tag{5}\]  
Figure 2: Reactive compensation circuit. In a three-phase circuit, the admittance corresponding to active current and inductive reactive current is three-phase equilibrium admittance. In terms of active power, the corresponding equilibrium conductance \(G_{e}\) is  
\[G_{e}=\frac{1}{3}(G_{A}+G_{B}+G_{C}) \tag{6}\]  
In terms of inductive reactive power, the corresponding equilibrium susceptance \(B_{e}\) is equal to  
\[B_{e}=\frac{1}{3}(B_{A}+B_{B}+B_{C}) \tag{7}\]  
In three-phase unbalanced loads, \(Y_{n}\) and \(Y_{z}\) are equivalent negative- and zero-sequence unbalanced admittances:  
\[ \begin{bmatrix}Y_{n}\\ Y_{z}\end{bmatrix} =\frac{1}{3}\bigg{[} \begin{array}{c}Y_{A}+\alpha Y_{B}+ \alpha^{*}Y_{C}\\ Y_{A}+\alpha^{*}Y_{B}+\alpha Y_{C}\end{array} \bigg{]} \tag{8}\]  
where \(\alpha=\mathrm{e}^{2\pi/3}\) and \(\alpha^{*}=\mathrm{e}^{2\pi/3}\). With these vectors,  
\[\left\{ \begin{array}{l}i_{a}=\sqrt{2}\mathrm{Re}\big{\{}G_{e}Ue^{j\pi t} \big{\}}\\ i_{r}=\sqrt{2}\mathrm{Re}\big{\{}jB_{e}Ue^{j\pi t}\big{\}}\\ i_{n}=\sqrt{2}\mathrm{Re}\big{\{}Y_{n}\big{[}\begin{array}{ccc}1&\alpha& \alpha^{*}\end{array}\end{array} \big{]}^{T}U_{A}e^{j\pi t}\big{\}}\\ i_{z}=\sqrt{2}\mathrm{Re}\big{\{}Y_{z}\big{[} \begin{array}{ccc}1&1&1\end{array}  \end{array}\right]^{T}U_{A}e^{j\pi t}\Big{\}} \tag{9}\]  
The three-phase RMS values of these current components are  
\[\left\{ \begin{array}{l}\|i_{a}\|=\sqrt{3}\sqrt{G_{e}^{2}U_{A}^{2}}\\ \|i_{r}\|=\sqrt{3}\sqrt{B_{e}^{2}U_{A}^{2}}\\ \|i_{n}\|=\sqrt{3}\sqrt{\chi_{n}^{2}U_{A}^{2}}\\ \|i_{z}\|=\sqrt{3}\sqrt{\chi_{z}^{2}U_{A}^{2}}\end{array} \right. \tag{10}\]  
where \(U_{A}\) is the effective value of the \(A\) phase voltage. These components are mutually orthogonal so that the three-phase RMS values of the components of all current satisfy the following relation:  
\[\|i_{L1}\|^{2}=\|i_{a}\|^{2}+\|i_{r}\|^{2}+\|i_{n}\|^{2}+\|i_{z}\|^{2} \tag{11}\]  
The reference current \(i_{cc}^{*}\) of APF is calculated according to the orthogonal current component decomposed using CPC theory:  
\[i_{cc}^{*}=w_{r}i_{r}+w_{n}i_{n}+w_{z}i_{z}+w_{g}i_{g} \tag{12}\]  
where \(w_{r}\), \(w_{n}\), \(w_{z}\), and \(w_{g}\) are the weight coefficients of inductive reactive current \(i_{r}\), zero-sequence reactive current \(i_{z}\), negative-sequence reactive current \(i_{n}\), and generative current \(i_{g}\), respectively. To address the different power quality problems in a power grid only by modifying the value of the weight coefficient corresponding to the current component, different reference currents of APF were obtained to achieve different compensation objectives. In some special environments, the capacity of APF is limited, and the compensation amount is compensated preferentially; that is, the compensation target of APF selectivity can be achieved.
## 2 Theoretical Problem
\begin{array}{l}I_{0}\frac{\partial^{2}w}{\partial t^{2}}+\frac{\partial}{ \partial x}\Big{[}I_{1}\frac{\partial^{2}u}{\partial x}-I_{2}\frac{\partial^ {3}w}{\partial x\partial t^{2}}\Big{]}+\frac{\partial^{2}}{\partial x^{2}} \Big{[}D_{11}\frac{\partial^{2}w}{\partial x^{2}}-B_{11}\left(\frac{\partial u }{\partial x}+\frac{1}{2}\left(\frac{\partial w}{\partial x}\right)^{2} \right)\Big{]}\\ -\frac{\partial}{\partial x}\Big{[}A_{11}\left(\frac{\partial u}{\partial x} +\frac{1}{2}\left(\frac{\partial w}{\partial x}\right)^{2}\right)-B_{11} \frac{\partial^{2}w}{\partial x^{2}}\Big{]}\frac{\partial w}{\partial x}\\ -\frac{\partial^{2}w}{\partial x^{2}}\Big{[}A_{11}\left(\frac{\partial u}{ \partial x}+\frac{1}{2}\left(\frac{\partial w}{\partial x}\right)^{2}\right) -B_{11}\frac{\partial^{2}w}{\partial x^{2}}\Big{]}=\Delta P\end{array}  \tag{31}\]  
By ignoring the axial inertia, and the rotational inertia, Equations (30) and (31) take the following form [52]:  
\[\frac{\partial}{\partial x}\Bigg{[}A_{11}\Bigg{(}\frac{\partial u}{\partial x }+\frac{1}{2}\Bigg{(}\frac{\partial w}{\partial x}\Bigg{)}^{2}\Bigg{)}-B_{11 }\frac{\partial^{2}w}{\partial x^{2}}\Bigg{]}=0 \tag{32}\]  
\[\begin{array}{l}I_{0}\frac{\partial^{2}w}{\partial t^{2}}+\frac{\partial^{2 }}{\partial x^{2}}\Bigg{[}  
The integration of Equation (35) leads to  
\[u=\int_{0}^{x}-\frac{1}{2}\bigg{(}\frac{\partial w}{\partial x}\bigg{)}^{2}dx+ \frac{B_{11}}{A_{11}}\frac{\partial w}{\partial x}-\frac{N_{0}x}{A_{11}}+N_{1}(t) \tag{36}\]  
For the cantilever beam, the following boundary condition can be considered [53]  
\[u=0,w=0,\frac{\partial w}{\partial x}=0\qquad\qquad\text{at}\qquad x=0 \tag{37}\]  
\[\frac{\partial u}{\partial x}=0,\frac{\partial^{2}w}{\partial x^{2}}=0,\frac{ \partial^{3}w}{\partial x^{3}}=0\quad\text{at}\qquad x=L \tag{38}\]  
Using Equations (35)-(38) yields  
\[\frac{N_{0}}{A_{11}}=-\frac{1}{2}\bigg{(}\frac{\partial w(L,t)}{\partial x} \bigg{)}^{2}+\frac{B_{11}}{A_{11}}\bigg{(}\frac{\partial^{2}w(L,t)}{\partial x ^{2}}\bigg{)} \tag{39}\]  
\[N_{1}(t)=-\frac{B_{11}}{A_{11}}\frac{\partial w(0,t)}{\partial x} \tag{40}\]  
By substitution of Equations (32) and (35) into Equation (33), we obtain the following nonlinear differential equation of motion:  
\[I_{0}\frac{\partial^{2}w}{\partial t^{2}}+\bigg{(}D_{11}-\frac{{B_{11}}^{2}}{ A_{11}}\bigg{)}\frac{\partial^{4}w}{\partial x^{4}}+N_{0}\frac{\partial^{2}w}{ \partial x^{2}}=\Delta P \tag{41}\]  
where \(N_{0}(t)\) is defined in Equation (39).
## 5 Performance Analysis
### Baseline Implementation  
For comparative analysis, we implemented FedAvg as our baseline following [PERSON] et al.'s seminal work [22]. The FedAvg implementation performs standard federated averaging without the security enhancements of EdgeGuard. In each communication round, the server selects a fraction of available clients (\(C=0.8\)) and broadcasts the current global model. Each selected client trains the model on their local data for \(E\) epochs and returns the model updates. The global model is then updated using the following:  
\[w_{g}^{t+1}=\sum_{k=1}^{K}\frac{n_{k}}{n}w_{k}^{t} \tag{42}\]  
where \(n_{k}\) represents the size of the local dataset at client \(k\), \(n\) denotes the total dataset size, and \(w_{k}^{t}\) represents the local model parameters at round \(t\). This vanilla implementation differs from EdgeGuard in several key aspects:  
* No quality-based weighting of client updates.
* No reliability assessment of participating devices.
* No blockchain-based security mechanisms.
* No differential privacy protections.  
Both EdgeGuard and the FedAvg baseline were implemented using the same optimization framework to ensure a fair comparison. The optimization configuration incorporates SGD with momentum as the base optimizer and includes additional enhancements such as cosine annealing for learning rate scheduling and gradient clipping to improve training stability. Table 3 provides a comprehensive list of all parameters used in our experiments, including the detailed optimization configuration that was previously omitted.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Device** & **CPU Cores** & **MIPS** & **RAM (GB)** & **Storage (GB)** & **Power (W)** \\ \hline E1 & 2 & 2660 & 4 & 32 & 5 \\ E2 & 4 & 3067 & 8 & 64 & 8 \\ E3 & 8 & 3467 & 16 & 128 & 12 \\ \hline \end{tabular}
\end{table}
Table 2: Edge device configurations.
# Optimizing Design of Smart Workplace through Multi-Objective Programming
## 2 Smart Environment and Technologies  
In this section, we present the result of the conducted survey of the state-of-the-art and common smart technologies and features. However, before, it is important first to define what is meant by a smart environment, and what its basic constituents are. A typical smart environment would consist of the following basic building blocks or elements [22; 23]:  
**Environment objects**: Appliances, furniture, equipment, devices, and any other resources that provide essential necessities or needs for the occupants or users. Smart environment objects are those equipped with sensors, actuators, and communication capabilities.  
**Sensors**: monitors activities, behavior status of users or resources, and send messages to a control system.  
**Actuators**: receive control signals to perform real-time physical action.  
**Interfaces**: enable user's communication with the system.  
**Control systems**: apply a set of programmed rules, using the information sent by sensors and through instructions sent to actuators to achieve high-level goals of the environment.  
**Networks**: allows communication between the control system, sensors, actuators, smart environment objects. This includes wireless networks: LAN, WiFi, Bluetooth, etc.  
**Middleware**: The integration and interoperability of heterogeneous devices are achieved by an intermediary software layer called middleware.  
Actually, currently there exist so many options and brands for various sensing technologies, like motion, position, physiological and biomedical, and various physical measurement sensors (e.g., positioning sensors: RFID [24], motion sensors [22; 24], pressure sensor [23], physiological sensors: Electrocardiography (ECG) [24; 25] and EMG (Electromyography) [24; 26], etc.). Additionally, other sophisticated smart systems include Smartwatch [27], Eye tracker [28], Computer brain interface (CBI) [29]). However, even though the rapid and continual emergence of SETs and availability of so many alternatives for each technology type, actually this makes the problem of assessing and justifying investment in these technologies more and more difficult. Currently, there are no structured, objective, and systematic means for differentiating among these wide spectra of available SETs, nor exist an approach that considers the costs of the technologies and the available budget for the project of developing smart hospitals.  
The table in Appendix A, shows a set of relevant smart enabling technologies (SETs), studied based on the current literature. The table lists what they measure, their potential application in hospitals or healthcare facilities, and their estimated cost ranges. In this article, we will not have an interest in our investigation on a specific trademark, brand, or developing firm of smart environment technologies, but rather we will focus on the capability and functionality.  
Next section, we present the proposed methodology for optimizing the design of the smart workplace.
## 3 Designing Virtualization of Wireless Sensor Network
### Challenges behind Designing VSNs
#### Managing Limited Resources  
Resource allocation in a sensor network virtualization environment refers to static or dynamic allocation of virtual sensor nodes and links on physical nodes and paths, respectively. This is sometimes known as virtual sensor network embedding. Embedding of virtual sensor networks, with constraints on nodes and links, can be reduced to the NP-hard problem even when all virtual network requests are known in advance. The embedding problem has been discussed in many papers for the virtual networking environment, but the embedding approach of traditional networks is not compatible with WSNs for the lack of its storage, computing power and limited battery power. That's why comprehensive research for efficient embedding of virtual sensor network requests to the physical WSN is needed. Although it may seem simple, it is really an important design goal to be considered for the virtualization of resource constrained sensor node.
# Intelligent Transaction Scheduling to Enhance Concurrency in High-Contention Workloads
## 7 Experimental Results
### TPC-E Benchmark Evaluation  
To evaluate how well DCoS adapts to a more complex contention scenario, we conduct experiments using the TPC-E benchmark [36]. This benchmark simulates transaction workloads with varying data access patterns. We control contention by changing the frequency of updates to the SECURITY table, using a Zipfian distribution for these updates. We adjust the skew parameter \(\theta\) from 0 to 4. Higher values of \(\theta\) indicate more significant data access skew and increased contention.  
As shown in Figure 11, throughput decreases for all methods as \(\theta\) increases, reflecting the impact of contention due to skewed data access. DCoS maintains the highest throughput across all skew levels. At \(\theta=0\), DCoS achieves 1.2 M tons/s, slightly behind Silo  
Figure 10: Scalability testing (one warehouse).  
Figure 9: TPC-C performance (from high contention to low contention).  
(1.3 M txns/s), since DCoS adopts a dependency analysis, but at \(\theta=4\), DCoS still maintains 0.6 M txns/s, whereas Silo drops to 0.35 M txns/s. Silo performs best under low-skew scenarios but degrades significantly as contention increases. NoWait and IC3 consistently underperform, struggling to handle skewed workloads efficiently. These findings highlight that DCoS is more resilient to data skew than traditional CC methods, as it dynamically adapts to runtime contention.  
To analyze the combined effect of parallelism and contention, we vary the number of worker threads while maintaining a Zipfian skew of \(\theta=3\). The results, shown in Figure 12, indicate that DCoS significantly outperforms Silo and NoWait beyond 16 threads. At 32 threads, DCoS achieves a throughput that is 1.63 times higher than Silo, 1.48 times higher than NoWait, and 1.94 times higher than IC3.  
In summary, the findings from TPC-C and TPC-E demonstrate that DCoS is particularly effective at managing data skew, sustaining high throughput even in scenarios of extreme contention. These results highlight the significance of runtime-adaptive scheduling strategies employed in DCoS for optimizing transaction throughput in OLTP systems.  
Figure 11: Throughput comparison under different Zipf skew values (\(\theta\)).  
Figure 12: Scalability analysis \(\theta=3\).
## 4 A Case Study
### Test Cases with a Single Dynamic Obstacle
#### 4.1.1 Head-On  
This case includes the OS and one TS, creating a two-ship encounter situation of head-on as viewed from the OS's perspective. The OS had an initial position at \((50,\ 300)\) m, a velocity of 2.0 m/s, and an initial course of \(0^{\circ}\). The TS's corresponding initial position, velocity, and course were set to \((550,\ 300)\) m, 2.0 m/s, and 180\({}^{\circ}\), respectively.  
According to COLREGs rule 14 [29], in the case of a head-on encounter, each ship should alter its course to starboard to pass on the port side of the other. Therefore, both the OS and the TS are give-way vessels in this case. Throughout the path planning progress, it was assumed that the speed and course of TS remain the same (treated the TS as a stand-on vessel) when predicting the trajectory of TS. A configuration where \(r_{1}\), \(r_{2}\), \(r_{3}\), and \(r_{4}\) were set to 4.0, 1.5, 1.0, and 1.0, respectively, for the planning space representation, was adopted in this case. Parameter \(r_{2}\) for starboard is larger than parameter \(r_{4}\) for port, promoting a situation where the OS and TS pass on the port side of the other according to COLREGs rule 14 [29]. The corresponding safety distance thresholds \(d_{1}\), \(d_{2}\), \(d_{3}\), and \(d_{4}\) were 200 m, 75 m, 50 m, and 50 m, respectively.  
Figure 6 shows the path planning results of the proposed approach from the OS's perspective for this case, and Figure 6 shows the predicted collision avoidance trajectories of the head-on scenario. According to the trajectories, the shortest relative distance between the OS and the TS is 50.17 m. As shown in Figure 7, the relative distance is indicated with a filled circle red line. It can be inferred that safety is guaranteed when the OS navigates along the planned path, even if the TS takes no action.
# Parallelization of the Honeybee Search Algorithm for Object Tracking
###### Abstract  
Object tracking refers to the relocation of specific objects in consecutive frames of a video sequence. Presently, this visual task is still considered an open research issue, and the computer science community attempted solutions from the standpoint of methodologies, algorithms, criteria, benchmarks, and so on. This article introduces a GPU-parallelized swarm algorithm, called the Honeybee Search Algorithm (HSA), which is a hybrid algorithm combining swarm intelligence and evolutionary algorithm principles, and was previously designed for three-dimensional reconstruction. This heuristic inspired by the search for food of honeybees, and here adapted to the problem of object tracking using GPU parallel computing, is extended from the original proposal of HSA towards video processing. In this work, the normalized cross-correlation (NCC) criteria is used as the fitness function. Experiments using 314 video sequences of the ALOV benchmark provides evidence about the quality regarding tracking accuracy and processing time. Also, according to these experiments, the proposed methodology is robust to high levels of Gaussian noise added to the image frames, and this confirms that the accuracy of the original NCC is preserved with the advantage of acceleration, offering the possibility of accelerating latest trackers using this methodology.  
Keywords:object tracking; honeybee search algorithm; swarm intelligence; parallel computing; graphics processing unit +
Footnote †: journal: Applied Sciences
# Implicit Algorithm of the SBSP-R Model for Predicting the Non-Unique Critical State of Soils
## 6 Summary and Conclusions  
To overcome the limitation of the explicit algorithm in predicting the critical state of soils, the implicit algorithm of the bounding surface plasticity model with the spacing ratio (SBSP-R model) is formulated and verified. The main conclusions are shown as follows:  
The implicit algorithm of the SBSP-R model is formulated through three steps: elastic prediction, state judgment, and plastic correction which is the most important step. The plastic correction is conducted using the Newton-Simpson scheme with controlling equations derived from consistency condition, plastic flow, hardening parameter, structural bounding surface, plastic modulus, and mapping rule. The implicit algorithm of the SBSP-R model is right and stable in predicting the non-unique critical state of soils. In the implicit algorithm, the controlling equation of the plastic hardening modulus is essential for plastic correction in overconsolidated conditions, since the calculated stress paths will not pass the critical state line if it is not suitable. Especially, the plastic hardening modulus \(H_{l}\) related to the current stress cannot be a homogeneous linear function with an independent variable \(H_{b}\) that is the plastic hardening modulus related to image stress.  
The implicit algorithm of the SBSP-R model is further verified by comparing simulated results with experimental data on soils with different spacing ratios. On one hand, the implicit algorithm of the SBSP-R model is not obviously influenced by strain increment in predicting non-unique critical states, especially when the spacing ratio is large. On the other hand, the implicit algorithm can reproduce the non-unique critical state and stress-strain of various soils, as well as show higher computational accuracy and efficiency. These results indicate that the formulated implicit algorithm of the SBSP-R model is an alternative approach to the previous explicit algorithm.  
Methodology, X.K.; Supervision, H.L.; Validation, Q.D. and Y.H.; Writing--original draft, X.K.; Writing--review and editing, Q.D. All authors have read and agreed to the published version of the manuscript.  
The authors wish to acknowledge the financial support provided by National Natural Science Foundation of China (42102317; 42041006), and scientific research project of Education Department of Shaanxi Provincial Government (22K0445).  
Figure 12: Comparisons among experimental results and constitutive model with different \(r\) (the hydrostatic pressure at critical state \(p_{\text{cr}}\), the derivative stress at critical state \(q_{\text{cr}}\), and the pore pressure at critical state \(u_{\text{cr}}\)).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.