esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Approach to Privacy-Preserving Multiple Independent Data Publishing
## 2 Background and Related Work
### Privacy-Preserving Data Publishing Context  
In order to preserve user privacy, an essential privacy context needs to be defined for the privacy-preserving data publishing. For determining a particular privacy setting, recently published studies [26; 27; 28; 29] classified the essential privacy terminology for the cyberspace, and these include sender, recipient, attacker, anonymity, pseudonymity, identifiability, identity confidentiality, unlinkability, undetectability, unobservability, and identity management. [PERSON] and [PERSON] [27; 28; 29] represent a privacy context that illustrates the relationship between fundamental privacy terms.  
In the privacy setting, a sender transfers his dataset to a recipient where an attacker will not be able to gain any knowledge about that dataset. This privacy setting is followed for the privacy-preserving data publishing event. In the privacy-preserving data publishing context, a data publisher publishes the data to the public, and it is open to everybody. An attacker, i.e., adversary also receives the published data, and he might use previously gained background knowledge to identify a person by linking with some publicly available data sources [6]. Hence, the requirement for anonymity is significantly present in the privacy-preserving data publishing context [26]. The anonymity of an individual is the anonymous properties of the particular individual in the dataset in which an attacker cannot recognize the record owner within a set of other records, which is called the anonymity set [29]. By applying some anonymization techniques on the published dataset, the anonymity set can be created to protect the dataset from making such a link to identify a person. Therefore, the anonymous dataset will be protected from privacy attacks, and it will ensure the identity confidentiality in the published dataset.
###### Abstract  
The necessity of the correction of errors emerging during the optical encryption process led to the extensive use of data containers such as QR codes. However, due to specifics of optical encryption, QR codes are not very well suited for the task, which results in low error correction capabilities in optical experiments mainly due to easily breakable QR code's service elements and byte data structure. In this paper, we present optical implementation of information optical encryption system utilizing new multilevel customizable digital data containers with high data density. The results of optical experiments demonstrate efficient error correction capabilities of the new data container.  
optical encryption; digital data containers; spatially incoherent illumination; spatial light modulator; diffraction optical element; divergent beams; QR code +
Footnote †: journal: Applied sciences  
and sensitive to noise pollution. To analyze and calculate multilevel customizable digital data container (MCDDC) error correction capabilities, we conducted a series of numerical and optical experiments; the results of which are presented in this paper.  
The rest of the paper is organized as follows: Section 2 discusses data containers for an optical encryption, such as QR codes and MCDDC. Section 3 provides a detailed description of the algorithm of data packing and unpacking for MCDDC. Section 4 presents the results of numerical simulation of MCDDC application in an optical encryption system. In Section 5, the use of MCDDC in an experimentally implemented optical encryption system with spatially incoherent illumination is demonstrated.
# Particle Formation Mechanism of TiCl\({}_{4}\) Hydrolysis to Prepare Nano TiO\({}_{2}\)
## 4 Results and Discussion
### Effect of Ethanol Amount on Particle Formation Behavior  
Under the conditions of 3 mL H\({}_{2}\)O, a pH of 7, a reaction temperature of 25 \({}^{\circ}\)C, and a reaction time of 120 min, conclusions can be drawn based on the results depicted in Figures 10 and 11. Figure 10 reveals that without the addition of ethanol, the nucleation time is relatively scattered and quick, reaching formation within just 50 s. However, the introduction of ethanol results in a more consolidated nucleation time, peaking when the ethanol volume is at 4 mL. At this point, the nucleation reaches its maximum, with the highest particle count of 199 mesh per cubic micron. If the ethanol volume is less than or more than 4 mL, the nucleation time prolongs and the quantity of nucleation decreases. Figure 11, presenting a D\({}_{50}\) of 0.24 nm and a PSD curve within the range of 0.2 nm with a central vertical of 0.24 nm at a volume of 4 mL of ethanol, shows a normal distribution of the PSD curve, characterized by a small particle size and concentrated distribution. At this stage, the D\({}_{50}\) content is also at its highest, at 14.51%. The low ethanol content accelerates the exothermic reaction, which in turn speeds up the evaporation rate of ethanol. This creates favorable conditions for crystal growth and grain reduction. However, if the ethanol volume is too small, it weakens the inhibition effect on the TiO\({}_{2}\) precursor, instead quickening the hydrolysis reaction. This leads to faster crystal growth and more intense agglomeration, resulting in an increase in the particle size. Hence, when the ethanol volume  
Figure 8: Variation curve of TiO\({}_{2}\) crystal nucleus number with time at different pH values.  
Figure 9: Distribution curves of TiO\({}_{2}\) particles at different pH values.  
is less than 4 mL, the D\({}_{50}\) is larger, and the particle size distribution is broader than when the ethanol volume is 4 mL. Excessive ethanol can also form complexes with titanium ions in the TiO\({}_{2}\) Sol, thereby affecting the growth of TiO\({}_{2}\) crystals [28]. Consequently, when the ethanol content exceeds 4 mL, the particle size increases and the particle distribution broadens. As the ethanol content escalates, the mass fraction also diminishes.
### Literature Review
In [39], a model for a CHP unit with improved accuracy is offered, and the CHP unit sizing problem integrating efficient operation under contingencies is modeled via a mixed integer linear program. A bi-level optimization model is presented in [40] for expansion planning of multi-energy system considering carbon emission constraints under a decentralized framework. To better capture the uncertainty in the planning stage, a scenario-based stochastic programming model is proposed in [41] to design a wind-powered energy hub; reliability indices including loss-of-load expectation and expected energy not supplied are considered. The capacity planning of an energy hub with wind power, storage, and demand response is cast as a stochastic programming problem in [42]; uncertainty of wind generation is represented by 10 scenarios. Because the size of the optimization problem grows rapidly with an increase in the number of scenarios, a Benders decomposition method is proposed in [43] to reduce the computation burden. The stochastic optimization model is decomposed into a planning master problem and a collection of operation subproblems associated with scenarios; the impact of planning strategy on operation is reflected by the dual variables of subproblems. To relax the requirement on the exact probability distribution of uncertainty factors, the two-stage robust optimization method is applied in [44]; the uncertainty is restricted in an ellipsoid set; the first-stage determines the planning variables, including the capacities of energy hub components, and the second stage represents the operation under the given planning strategy; the life-cycle cost in the worst-case scenario is minimized. A similar optimization paradigm is also adopted in [45] to co-optimize the planning of energy hubs in the integrated electricity and natural gas system with high penetration of volatile renewable generation; the uncertainty set is a polyhedral one, and in the problem solving, the lower-level problem is replaced by a Karush-Kuhn-Tucker (KKT) optimality condition. A distributionally robust optimization method for capacity planning of energy hub is proposed in [46]; the probability distributions of wind generation and demand are assumed to be close to respective empirical distributions in the sense of KL(Kullback-Leibler)-divergence, and the deterministic counterpart is derived based on conditional-value-at-risk and gives rise to a linear program. Cogeneration system and energy hub have been a hot research topic for many years. Extensive research work can be found. We recommend comprehensive survey articles [47; 48; 49; 50] for readers who wish to develop a holistic overview on this topic.
## 3. Numerical Examples
### Earthquake Ground Motion Records  
In conjunction with the frame models described in the previous section, 73 ground motions selected from the PEER Strong Motion Database are used to investigate the simplified method. A detailed list of the earthquakes can be found in a previous study [22].
# LieTom-RTDETR: A Lightweight Real-Time Tomato Detection System for Plant Factories
## 4 Experimental Results and Analysis
### Comparative Analysis of Tomato Detection Performance  
#### 4.3.1 Tomato Recognition  
The enhanced tomato detection capabilities of the LiteTom-RTDETR model were validated through systematic comparative experiments. Four detection models--Faster-RCNN, YOLOv8m, original RTDETR, and LiteTom-RTDETR--were rigorously evaluated on the tomato image dataset described in Section 3.1 to establish performance benchmarks. The \(P\), \(R\), _F1_, and _mAP50_ metrics for these four models were systematically evaluated, with the results presented in Table 9.  
Clearly, the LiteTom-RTDETR model exhibited superior performance metrics for tomato recognition, attaining peak values of 89.6% for \(P\), 85.1% for _F1_, and 88.2% for _mAP50_. Compared with the Faster-RCNN, YOLOv8m, and original RTDETR models, LiteTom-RTDETR exhibited significant improvements in \(P\) of 28.4%, 1.7%, and 1.9%, respectively, in _F1_ of 16.9%, 0.3%, and 0.2%, respectively, and in _mAP50_ of 13.4%, 0.1%, and 0.6%, respectively. These results demonstrate the advantages of LiteTom-RTDETR in agricultural target detection, particularly for tomato recognition tasks requiring high precision and multiscale adaptability.
###### Abstract
As VR technology evolves, it has found widespread application in cognitive rehabilitation [8]. In cognitive rehabilitation, VR technology is primarily applied in several ways. Cognitive training uses virtual environments to enhance patients' attention, memory, and problem-solving abilities through various tasks and exercises. Environmental reconstruction recreates daily life scenarios, such as supermarkets, parks, and traffic scenes, to help patients restore their daily living skills. Situational rehabilitation provides controlled and safe environments for patients with specific cognitive disorders, such as acrophobia and social anxiety, to gradually adapt and overcome these situations. Attention training employs visual and auditory stimuli in virtual environments to improve patients' attention. Virtual travel offers immersive experiences for patients who are immobile or unable to go outside, helping to alleviate depressive symptoms and enhance overall quality of life. Furthermore, research has demonstrated that VR technology can improve cortical function and promote neural regeneration and functional rehabilitation through cognitive and motor training. Hence, VR technology not only presents new opportunities for cognitive rehabilitation but also holds potential as a crucial tool for enhancing patients' quality of life and rehabilitation outcomes [9; 10]. This paper systematically reviews cognitive rehabilitation technologies based on virtual reality and proposes the Multi-Dimensional Virtual Reality Cognitive Rehabilitation Theory Model (MD-VRCRTM). The model categorizes cognitive rehabilitation technologies into six types: individual immersive systems, individual semi-immersive systems, individual non-immersive systems, multiplayer immersive systems, multiplayer immersive systems, and multiplayer non-immersive systems. This classification approach facilitates a better understanding of the strengths and limitations of various technologies in the cognitive rehabilitation process, providing a theoretical basis for selecting appropriate technologies for practical applications. Furthermore, this paper analyzes the current state of VR technology applications in the field of cognitive rehabilitation and discusses their potential for use in the cognitive rehabilitation of patients with neurological disorders. The aim of this paper is to provide a valuable reference for researchers and practitioners involved in cognitive rehabilitation, promoting the application and development of virtual reality technology in the cognitive rehabilitation of patients with neurological disorders. A comprehensive understanding of the characteristics and application effects of various VRtechnologies can help optimize cognitive rehabilitation programs, enhance rehabilitation outcomes, and improve the quality of life for patients. The content of this article is divided into three parts: firstly, a systematic search and selection of the relevant literature; secondly, the construction of a theoretical model to guide practice; and finally, an in-depth analysis of the therapeutic effects of different types of virtual reality technologies.
## 7 Discussion
### Limitations  
While the proposed framework demonstrates favorable predictive performance, several limitations merit consideration. First, the dataset employed was relatively small and institution-specific, which may constrain the external validity of the model. Although internal validation was performed via a stratified train-test split and considered cross-validation strategies, independent validation using larger, heterogeneous, and multi-center cohorts is essential to confirm generalizability.  
Second, the evaluation metrics employed, namely AIC and overall accuracy, provide an initial assessment of model performance but do not fully capture clinical discriminability. Future work should incorporate complementary performance metrics such as sensitivity, specificity, area under the receiver operating characteristic curve (AUC-ROC), F1 score, and precision-recall analysis to more comprehensively evaluate clinical utility.  
Lastly, the model assumes linear relationships between covariates and the outcome, which may oversimplify the underlying biological and clinical complexity of COPD. However, the current GLM framework cannot fully capture nonlinear relationships, temporal progression, and latent confounders. Parametric studies will be crucial in future research to systematically evaluate the sensitivity and robustness of model parameters, identify key thresholds, and enhance personalized risk stratification. These analyses will enable the development of more flexible predictive models that better align with the complex clinical course of COPD.
# Hybrid Engineered Water-Polymer Flooding in Carbonates: A Review of Mechanisms and Case Studies
## 4 Hybrid Engineered Water-Polymer Flooding (EWFF)
### Lab-Scale Studies  
In order to establish the EOR potential of hybrid EWPF in carbonates, a number of lab-based studies were carried out over the past ten years [133; 183; 184; 185; 133; 183]. [PERSON] et al. [119] presented a detailed experimental workflow for an LSPF field design at the lab scale. The experiment involved an aged reservoir core and confirmed that using LSW in PF can reduce the project cost by lowering the polymer concentration, also providing additional oil recovery resulting from the wettability modification and the improved viscoelastic behavior of the polymer. Figure 16 shows the recovery and pressure drop profiles for sequential injection of three polymer solutions of different salinities and concentrations. LSPF resulted in \(\sim\)52% additional oil recovery compared to HSW.  
Oil displacement experiments conducted by [PERSON] and [PERSON] [139] at reservoir conditions (99 \({}^{\circ}\)C) for Arabian carbonate reservoir aged cores showed \(\sim\)6 to 10% higher oil recovery for the hybrid LSW and sulfonated polyacrylamide polymer process compared to the recovery using the standalone processes (Figure 17). The higher negative zeta potentials for LSP solutions indicated the presence of more repulsive force in the system, which helped to detach the adsorbed oil and increased the recovery factor. Based on their results, oil recovery can be directly related to zeta potential, whereby the higher the negative zeta potential value, the higher the oil recovery from that system. In addition, the higher recovery values in coreflood-1 were because the pore system in the core sample had good connectivity.  
Laboratory experiments were conducted on a reservoir composite core to assess the feasibility of using the polymer SAV10 combined with LSW (2935 ppm TDS) for a high-salinity and high-temperature carbonate oil reservoir in Kuwait (113 \({}^{\circ}\)C and 239,000 ppm) with a relatively low permeability of \(<\)10 md [15]. Secondary LSWF reduced the S\({}_{\text{or}}\) to 23%, followed by a further 10% reduction by LSPF, whereas the pressure drop data suggested no significant plugging by the polymer (Figure 18). The results showed that LSPF can be applied to harsh reservoir conditions and low permeability reservoirs if the process is designed carefully.  
Figure 16: Oil recovery and pressure trend values for LSPF in a reservoir core [119].
# Inductance Estimation Based on Wavelet-GMDH for Sensorless Control of PMSM
###### Abstract
In this paper, we conducted a study on sensorless PMSM drive inductance estimation based on wavelet denoising based machine learning to minimize the rotor position and speed errors and compensate for external periodic torque fluctuations. Firstly, we analyzed the impact of rotor position errors and speed estimation caused by magnetic saturation phenomena induced by external load conditions in extended electromotive force-based sensorless control. To minimize the uncertainty caused by magnetic saturation and improve the real-time control performance, we proposed a method to estimate the inductance of permanent magnet synchronous motors using a wavelet-GMDH algorithm, which combines time-frequency analysis and data-driven methods, and performed compensatory control for magnetic saturation. For reducing estimation errors and improving control precision, we performed optimization learning for the inductance for various load torque patterns. Through the analysis of wavelet transform, the noise classification and reduction of specific signals are conducted without distortion or delay, effectively improving the quality of input signals for GMDH learning. Wavelet noise reduction was used to filter high-frequency outliers in the current data, facilitating the smooth training of the learning algorithm for inductance estimation. Furthermore, to appropriately respond to situations such as unknown disturbances or increasing errors in estimates, we implemented the real-time estimation and monitoring of the position error magnitude. The main contribution of this paper is to estimate the inductance in real time by applying machine learning called GMDH, beyond the simple current signal analysis of motors through wavelet transformations. Moreover, by integrating GMDH with denoising wavelet transform into a sensorless control system, improved control performance can be achieved even under conditions of high noise and severe load. The structure of this paper is as follows. In Section 2, the sensorless control theory of PMSM and the issues related to magnetic saturation are analyzed. Section 3 presents a mathematical model for wavelet-GMDH based inductance estimation and the proposed method. In Section 4, an algorithm proposed for wavelet-GMDH based compensation control is implemented and modeled using MATLAB R2023a. Finally, in Section 5, we validate the proposed wavelet-GMDH based magnetic saturation compensation method and confirm the results of the control performance through software simulations. To replicate the load conditions and parameter variations equivalent to the actual system, experiments are conducted to measure speed ripples, inductance variations, and the torque component of the current, and the resulting values are incorporated into the simulation as load conditions.
# Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap
## 5 Future Roadmap: The Promise of AI
### Neural Networks in Chaos: Chaotic Neural Networks and Their Applications  
The use of chaotic neural networks is wide in application. For example, to solve the problem of degrading performance in Code Division Multiple Access (CDMA) systems due to system capacity and multiple access interferences, [PERSON] et al. [207, 208] used a CNN to detect multiple users, a task that is NP-complete. Experimental results demonstrated superior performance to the existing Hopfield-Neural-Network (HNN)-based detector. CDMA is a communication technique where multiple transmitters can send signals simultaneously over a single channel.  
CNNs can even be applied on a hardware level. For example, [PERSON] et al. [209] used a novel Chaotic Hopfield Neural Network (CHNN) to optimize the SF6 circuit breaker. Work by [PERSON] et al. [210] used CNNs to solve a hysteresis bottleneck for the usage of the Magnetic Shape Memory Alloy (MSMA) smart material with promising properties for use in micro-positioning applications.
# Challenges and Solution Directions of Microservice Architectures: A Systematic Literature Review
## 5 Identified Challenges and Solution Directions
### RQ1. What Are the Identified Challenges in the MSA Domain?
#### 5.1.4 Performance Prediction, Measurement, and Optimization  
Performance is a key quality factor for the systems employing MSA because it is addressed at different levels for the stages of system design, implementation and operation. Usually, it is beneficial to estimate the performance of a software system before it is implemented because it may be very difficult or costly to change the system afterward. After the implementation, performance measurement and optimization become important in order to satisfy the quality of MSA-based system requirements.