esa-sceva/satcom-chunk-collection huggingface dataset:
# High-Frequency Artifacts-Resistant Image Watermarking Applicable to Image Processing Models
## 2 Preliminaries and Proposed Method
### Origins of High-Frequency Artifacts  
In this section, we investigate the origins of high-frequency artifacts in the current generative model watermarking [17; 19], building upon insights from previous research. We identify two primary sources of these artifacts: the watermark embedding network and the watermark embedding process. [PERSON] et al. [32] attributed grid-like anomalies in spatial images to the up-sampling process. Similarly, [PERSON] et al. [23] explicitly highlighted that incorporating up-sampling within the generative network introduces high-frequency artifacts in the frequency domain. These findings collectively suggest that the use of CNNs for image generation inherently results in structural high-frequency artifacts. Thus, we contend that the prevalent CNN-based watermark embedding networks in generative model watermarking inevitably introduce high-frequency artifacts when generating marked images. Moreover, [PERSON] et al. [24] observed that employing image patching triggers in backdoor attacks also leads to the emergence of high-frequency artifacts. Further insights from [PERSON] et al. [21] explained that embedding information using deep neural networks essentially adds coded high-frequency information to the carrier image. We posit that the watermark embedding process in generative model watermarking shares similarities with backdoor attacks or DNN-based information embedding. This process requires introducing perturbations to the marked image, leaving a distinguishable trace in the frequency domain due to time-frequency coherence. Without appropriate constraints, watermarking perturbations tend to exhibit high-frequency characteristics, inevitably introducing high-frequency artifacts into the marked image.  
In summary, existing model watermarking techniques employ watermark embedding networks that inherently introduce high-frequency artifacts. Additionally, the addition of watermarking perturbations naturally gives rise to high-frequency artifacts. In light of these findings, we propose an image watermarking technology designed to suppress high-frequency artifacts.
## 4 Properties of 3D Printed Pla-Metal Biocomposites
### Acoustic Properties  
Simulations show that the combined structure with a nickel-coated antenna and a silver-coated diplexer has a return loss of over 10 dB and an excellent gain efficiency of 12 to 17 dBi in the RX band of 10.7 to 12.8 GHz and the TX band of 17.42 to 18.81 GHz, respectively. The measurement findings of the feed system verify these predictions. Additionally, the rejection stage between the TX and RX ports is greater than 60 dB. This low-cost production method for making prototypes or testing ideas for Ku-band systems is shown to be effective because it has a complicated design with many shapes inside, unlike the more expensive CNC-based metal manufacturing methods [122].
# Optimization of Bit Allocation for Spatial Multiplexing in MIMO VLC System with Smartphones
###### Abstract
Therefore, the optimization of communication systems, such as the water-filling (WF) method [14], can modify channel equalization with the power or quantity of bits to be transmitted. In the MIMO VLC system, each available channel has its maximum capacity. With the limitation of whole power according to the variety of channel capacity, the available power of channels can dominate the transmission efficiency [15; 16] because the available power varies depending on various conditions, including interference and noise. Therefore, the management of signal powers for MIMO channels is one interesting aspect for improving the performance of the MIMO VLC system. Based on the properties of VLC, most systems consider the intensity modulation and direct detection (IM/DD) to realize data communication. In [17], a direct-view MIMO VLC was constructed with a nonlinear optimization in the receiver. Similar to the WF method, the channel with maximal SNR has limitations in transmission power. However, the characteristics of mobile devices are quite different when compared with the conventional MIMO VLC systems. Issues surrounding signal power, response delay, system configuration, and data digitization are still open in the design and implementation. This paper proposes several techniques to construct a more practical system with an appropriate channel model and effective bit allocation for the modulation design.
## 3 Testbed Implementation,Prototype, and Software Development
### TCP Server Socket-Based GUI  
In our C\(\sharp\) TCP server socket program, the server terminal opens the specified TCP port. It waits for the battery's (client) connection, takes multiple connections and listens to the connected battery's messages (bytes array). Every battery client that is connected to the TCP server is wrapped-up in the connected battery client object and it gets added to the battery collection. The connected battery client is instantiated for each battery that connects to the server. The messages coming from the connected battery client utilize the socket listener class that listens and delegates the message. The TCP server host instantiates the server terminal, registers for the appropriate events and calls the start listening class. As a result, multiple battery clients start sending messages by connecting to the specified port. Server terminals listen to the messages coming to a socket, allowed by the socket listener class. The socket listener determines whether the arrived message represents a new data or whether it represents a 'connection dropped' message. It calls forth the message received event and waits for the next message in case the arrived message represents a new data; it calls forth the disconnected event and exits in case the message indicates that the connection has been dropped.  
The battery client's message that arrives in the server is a stream of bytes of an array that constitutes one data frame, as explained earlier in Section 2.5. Based on the unique battery id that is  
Figure 12: Server socket programming framework for our proposed system.  
represented by the first four bits of the data frame, we divide the received data frame into four bits each and display it in the data-grid-view for a meaningful representation. The battery's data is divided and displayed in the respective columns and the newly-connected battery's data is added continually in same manner in next rows of the data-grid-view. Figure 13 shows the TCP server socket GUI for our proposed system with three clients connected and receiving the data in real-time.
# An Adaptive INS-Aided PLL Tracking Method for GNSS Receivers in Harsh Environments
###### Abstract  
As the weak link in global navigation satellite system (GNSS) signal processing, the phase-locked loop (PLL) is easily influenced with frequent cycle slips and loss of lock as a result of higher vehicle dynamics and lower signal-to-noise ratios. With inertial navigation system (INS) aid, PLLs' tracking performance can be improved. However, for harsh environments with high dynamics and signal attenuation, the traditional INS-aided PLL with fixed loop parameters has some limitations to improve the tracking adaptability. In this paper, an adaptive INS-aided PLL capable of adjusting its noise bandwidth and coherent integration time has been proposed. Through theoretical analysis, the relation between INS-aided PLL phase tracking error and carrier to noise density ratio (\(C/N_{0}\)), vehicle dynamics, aiding information update time, noise bandwidth, and coherent integration time has been built. The relation formulae are used to choose the optimal integration time and bandwidth for a given application under the minimum tracking error criterion. Software and hardware simulation results verify the correctness of the theoretical analysis, and demonstrate that the adaptive tracking method can effectively improve the PLL tracking ability and integrated GNSS/INS navigation performance. For harsh environments, the tracking sensitivity is increased by 3 to 5 dB, velocity errors are decreased by 36% to 50% and position errors are decreased by 6% to 24% when compared with other INS-aided PLL methods.  
GNSS; INS-aided PLL; high dynamic; signal attenuation; noise bandwidth; coherent integration time; adaptive adjustment +
Footnote †: journal: Sensors
_Applied_  
_sciences_  
_Article_  
**Analysis of Weighted Fraction of Length for Interfacial Gap in Cervical Composite Restorations as a Function of the Number of B-Scans of OCT Volume Scans**  
**[PERSON] \({}^{\star}\), [PERSON], [PERSON] and [PERSON] \({}^{\copyright}\)**  
Department of Cardiology, Endodontology and Periodontology, University of Leipzig, Liebigstrasse 12, 04103 Leipzig, Germany; [EMAIL_ADDRESS] (T.M.); [EMAIL_ADDRESS] (C.R.); [PERSON]ESS] (R.H.)  
\({}^{\star}\) Correspondence:[EMAIL_ADDRESS]; Tel.: +49-341-97-212-63  
**Featured Application: A method to determine the mean fraction of interfacial gap length in cervical composite restorations with SD-OCT is presented. Twenty-one images were required for the system to determine the mean, with an error of \(\pm\)2.5%.**  
**Abstract:** In dental research, the morphometric assessment of restorations is a challenge. This also applies to the assessment of the length of interfacial adhesive defects in composite restorations as a measure of tooth-restoration bond failure. The determined mean fractions of interfacial gap length on enamel and dentin interfaces deviate from the true means (N \(\rightarrow\infty\)), depending on the number (N\({}_{\text{J}}\)) of object layers assessed. Cervical composite restorations were imaged with spectral domain optical coherence tomography (SD-OCT). The mean fractions of interfacial gap length on enamel and dentin were determined for an increasing number of OCT cross-sectional images (B-scans) per restoration and were graphically displayed as a function of the number of B-scans. As the number of B-scans increased, the calculated object means approached a range of \(\pm\)2.5%. This analysis is appropriate for displaying the relationship between the determined mean fraction of interfacial gap length at the enamel/ dentin-restoration interface and the number of B-scans.  
**Keywords:** cervical composite restorations; spectral domain optical coherence tomography; analysis of cross-sectional OCT images; mean fraction of interfacial gap length; mean calculation
## 3 Advancements in Imaging and Augmented Reality (AR)
### Limitations and Future Directions  
Despite its promise, AR adoption is not without challenges. Manual model superimposition is time-consuming and often limited to centers with dedicated technical support. Current models may also fail to account for intraoperative organ deformation, which affects the precision of virtual overlays.  
Automated platforms, like iKidney, represent an important step toward scalable solutions, but further validation and regulatory approvals are required before widespread adoption. Future advancements may include real-time deformable models, fully integrated AI-guided overlays, and cloud-based model sharing for remote collaboration. Furthermore, cost and access disparities, especially in resource-limited settings, remain significant barriers to equity in the implementation of these technologies.
## 2 Related Work
### Ensemble Learning Techniques  
Ensemble classifiers have shown promise of correct diagnosis, particularly when employing many features. A related study utilized the majority vote method for classification and regression [35]. Ensemble learning is a commonly employed technique that integrates data-level and algorithmic-level approaches to address the issue of imbalanced data [36].  
\begin{table}
\begin{tabular}{c c c} \hline
**Dataset** & **No. of Features** & **Feature Selection** \\ \hline NHANES (1999–2014) [14] & 157 & Reduce multi-collinearity, Remove self-reported depression questions \\ \hline KLoSA [23], UFMG [24] & 50, 55 & Genetic algorithm \\ \hline NHANES (2005–16) [25] & 20 & Features taken from factors based on related work and the PHQ-9 depression questionnaire \\ \hline NHANES (2005–2012) [26] & 51 & Features selected based on Type 2 diabetes mellitus and those factors correlated with depression \\ \hline Thasala Hospital [22] & 12 & Max-Relevance Min-Redundancy (MRMR) \\ \hline \end{tabular}
\end{table}
Table 1: Feature selection method used in related studies.  
In this study, various models, including gradient boosting machine (GBM), KNN, regularized greedy forest, and logistic regression, were employed to forecast the occurrence of depression among elderly person individuals [37].  
Voting Classifier: One of the most straightforward ensemble learning strategies is the voting classifier [38], which combines the results of multiple base classifiers. There is some flexibility in the composition of the classifiers. The majority vote of the classifiers determines the sample's label in the hard-voting method.  
\[Y_{P}=\max\sum_{i=1}^{z}P_{ik} \tag{1}\]  
where \(y_{p}\) is a predicted class label, \(z\) is the number of base learners, and \(P_{ik}\) is the probability of the \(i\)-th label from the \(k\)-th classifier.  
Stacking: Combining a base model (a single predictive model) with a meta-model [39] would take advantage of the best features of each model while mitigating their worst flaws, hence improving the resultant model's reliability. In addition, a stacked ensemble has been frequently used for categorizing and creating predictive machine learning models to enhance generalization ability.  
Boosting: The primary objective is to enhance the speed and accuracy of competitive efforts. XGBoost employs a boosting strategy to improve accuracy by combining weak classifiers. Currently, XGBoost is used for imputing missing values and is recognized for its exceptional enhancement in predictive accuracy. With the help of XGBoost, depression cases may be predicted based on many balanced samples acquired using different resampling techniques.
# Borehole Condition and Limit Pressure Differential Analysis in Carbonate Reservoirs
## 4 Determination of Limit Test Pressure Differential
By using the wellbore wall state analysis method proposed in Section 2, the average ratio of the plastic zone, failure zone to wellbore volume, and the maximum failure angle on the wellbore wall concerning the test pressure differential were calculated, as shown in Figures 8 and 9. The results indicate that the variation trend of the plastic zone and failure zone around the wellbore with test pressure differential is nonlinear, with an accelerating increase as the pressure differential increases. However, the  
Figure 6: The percentage curves of plastic zone and failure zone to borehole volume under different test pressure differentials. wellbore wall failure angle increases linearly with the test pressure differential. Therefore, considering the three factors directly related to the instability of the open-hole testing well, the following three criteria are proposed to maintain wellbore stability during the testing process:  
* The average ratio of the failure zone volume to wellbore volume should be less than 10%;
* The average ratio of the plastic zone volume to wellbore volume should be less than 20%;
* The maximum failure angle on the wellbore wall should be less than 90\({}^{\circ}\). Figure 8: The average ratio of plastic zone, failure zone to borehole volume varies with the test pressure differential. Figure 7: The failure angle curve of wellbore under different test pressure differential. Breach of any of these three criteria signifies uncontrolled wellbore collapse, potentially leading to extensive instability and compromising the safety of the testing operation. According to the calculation results of Figures 8 and 9, when the average ratio of the volume of the damage zone around the borehole to the volume of the borehole is less than 10%, the corresponding test pressure differential is less than 25.3 MPa; when the average ratio of the plastic zone volume around the borehole to the borehole volume is less than 20%, the corresponding test pressure differential is less than 23.2 MPa. When the maximum angle of the failure zone on the borehole wall is less than 90\({}^{\circ}\), the corresponding test pressure differential is less than 23.5 MPa. Therefore, the limit test pressure differential of the vertical well section is 23.2 MPa.
# Demonstration of Wastewater Recycling in a Slaughterhouse
## 3 Results and Discussion
### Energy Consumption of the System  
In addition to the technical performance, the energy consumption should also be considered when evaluating the system. Table 8 shows the average energy consumption of the individual process steps in comparison with energy consumption data from other studies. In order to record the energy consumption of each treatment process, the daily operating times for the individual consumers were recorded and offset against their power consumption.  
A comparison with other systems shows that the specific energy consumption is high. The flotation is at the upper end of the typical energy consumption for this technology, although it is still the technology with the lowest specific energy consumption of the entire process.  
The MBR is also at the upper end of the specific energy consumption in comparison. This is due to the fact that it is a pilot plant that was not designed to minimize energy consumption. The plant did not have the necessary monitoring and control equipment to minimize energy consumption. Neither the pumps nor the blower could be regulated in their output. The volume flows were only regulated by closing and opening valves, which led to a considerable loss of energy. Also, only a comparatively small amount of wastewater of about 55 m\({}^{3}\).d\({}^{-1}\) is treated here; the comparison of many plants has shown that the specific energy consumption decreases with increasing wastewater volume [48]. A reduction in specific energy consumption therefore appears realistic with optimized design and at larger sites.  
The relatively high specific energy consumption observed with the RO system can be attributed to the fact that the high-pressure pump was not controlled via a frequency converter. Instead, the feed to the modules was regulated using a valve, which needed to be 70% closed during normal operation to maintain a system pressure of 13 bar. As a result, the system required 4.1 kWh to produce 1 m\({}^{3}\) of irrigation water and 5.2 kWh for 1 m\({}^{3}\) of process water.
# Evolutionary Optimization for Robust Epipolar-Geometry Estimation and Outlier Detection
## 5 Experimental Results and Discussion
### Performance of Inlier Classification with Adaptive Thresholding
Because of the uncertainty of the fundamental matrix and the noise value of the points participating in the estimation of the fundamental matrix, the threshold value should be larger than the ideal one to detect all of the inliers correctly. Although the covariance-based method tried to consider this effect, it underestimated the potential inliers. The main reason was that the uncertainty of the fundamental matrix was estimated only from the minimal sample set. As shown in Figure 7, our algorithm calculated the threshold adaptively. Frequently, the estimated threshold value was slightly higher than the ideal median-based threshold. This was reasonable since the calculated fundamental matrix, although determined from an outlier-free sample set, was not necessarily perfect. However, for the other two methods, the threshold values were approximately around the true median-based threshold, which could be suitable threshold values only if the estimated fundamental matrices were as accurate as the true one.
## 3 Results  
### Performance of the NIER Operational Model (WRF-CMAQ)  
Before comparing the capability of AOD estimation between different methods, we summarized the performance of the NIER operational air quality system from the temporal distributions of daily PM\({}_{2.5}\). Figure 3 shows a time series of observed and simulated daily PM\({}_{2.5}\) concentrations in the Seoul (25 stations marked in Figure 1b) and Busan (19 stations) metropolitan areas from March 2016 to February 2017. The model was spun up for 12 h every day and daily 12 h (f12) and 35 h (f35) forecasts from 15:00 to 14:00 UTC the next day were compared with observations. The model results were interpolated to the location of the observation site using the Cressman method. Although the concentration of PM\({}_{2.5}\) is higher in D01 compared with D02 in most periods, the operational forecast shows a reliable air pollutant simulation overall. Simulated values typically tend to be underestimated in summer and overestimated in winter; however, the model bias does not vary significantly over time. Therefore, we used all forecasts, except for the spin-up time, to investigate AOD estimation. A detailed analysis of the predictability of operational WRF-CMAQ using statistical scores is beyond the scope of this study; instead, we evaluated different algorithms of AOD estimation using the NIER operational model.
# A Topology Optimization Method for Reducing Communication Overhead in the Kalman Consensus Filter
## 3 Network Topology Optimization Method  
In this section, a network topology optimization method is presented to reduce the communication overhead in the KCF algorithm.