esa-sceva/satcom-chunk-collection huggingface dataset:
# A High-Dynamic-Range Optical Remote Sensing Imaging Method for Digital TDI CMOS
## 5 Experiment and Data  
In order to verify the effectiveness of the image fusion method based on gradient pyramid decomposition and entropy weighting, the 5-stage and 13-stage images enhanced by EMAHE are used as source images, and perform the comparison experiment, as shown in Figure 13. Their entropy and average gradient are figured out as follows: (a) Entropy = 5.46, average gradient = 7.04; (b) Entropy = 5.42, average gradient = 7.55; (c) Entropy = 5.47, average gradient = 7.23; and, (d) Entropy = 5.49, average gradient = 8.45.  
Figure 12: Schematic of the proposed image fusion method  
Figure 13: Comparison of fusion effects based on different pyramid decomposition and weighting strategies. (**a**) is the produced image by algorithms of [PERSON] [14]; (**b**) is the produced image by algorithms of [PERSON] [15]; (**c**) is the produced image by algorithms of [PERSON] [16]; and, (**d**) is the produced image based on the proposed approach.  
It can be seen from the experimental results that the fused image based on gradient pyramid decomposition and entropy weighting has an obvious advantage in terms of entropy and average gradient.  
Further, in order to verify the validity and universal applicability of the proposed method, several typical scenes are selected for testing. We collected the digital TDI image sequences of the scenes. Then, we selected two of them which have the largest entropy or the largest average gradient as the source images. Enhanced and fused by the proposed method, the obtained images are shown in Figure 14. It can be seen that the proposed methods are adapted to different typical scenes, and can present good visual effect.  
Moreover, we fuse the original images and their enhanced images by HE/EMAHE with the proposed method. In addition, we fuse the EMAHE enhanced images with Laplace pyramid decomposition and entropy weight. The value of entropy and average gradient of the obtained images is shown in Table 2.  
Figure 14: Experimental images of different typical scenes. (**a**) Scene with building but without cloud; (**b**) scene with cloud but without building; (**c**) scene without building and cloud; and,(**d**) Scene with both building and cloud.  
It can be seen from the table that, for different scenes, the EMAHE enhancement and the proposed fusion algorithm provides the processed images an obvious advantage in terms of entropy and average gradient.
# A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems
## 6 Future Directions and Real-World Challenges
### Scalability Issues  
FRSs hold great promise for improving the quality and accuracy of personalized recommendations, but they can also be difficult to scale. As the number of sources and users grows, the complexity of managing data across multiple sources and ensuring the accuracy of recommendations can become increasingly difficult:  
* **Future directions**: In order to address the scalability issues associated with FRSs, researchers are developing improved methods for managing and processing data across multiple sources. This includes optimizing algorithms to reduce the number of data transfers, as well as utilizing distributed computing architectures and artificial intelligence to manage data better. In addition, researchers must continue to explore methods of improving the accuracy of federated recommendations, such as using transfer learning, ensemble learning, and other latest machine learning techniques.
* **Real-world challenges**: There are scalability concerns about data privacy and security, as well as the need for robust methods for managing user profiles and managing conflicts between multiple sources. Moreover, as federated systems become more complex and involve more stakeholders, it will be important to develop governance models and mechanisms for cooperation among the different parties involved.
# Personalized Contextual Information Delivery Using Road Sign Recognition
## 4 Experiments and Results  
In this section, we present experimental results evaluating the proposed system. Firstly, we assess the performance of the road sign recognition modules, including object detection and text extraction accuracy. Secondly, we perform a comparative analysis of our system against traditional image captioning models, demonstrating how context-aware integration significantly enhances usability and interpretability.
# A Capacity Enhancement Method for Frequency-Hopping Anti-Jamming Communication Systems
###### Abstract  
In this paper, we study the enhancement of channel transmission information by expanding the transmission channel in the frequency-hopping rate dimension in a communication system. This is achieved using the frequency-hopping spread spectrum (FHSS) without increasing communication resources, such as power and bandwidth. The anti-jamming capability of the original information is maintained during this process. The spectral characteristics of the extended signal for frequency-hopping (FH) transmission are investigated, a demodulation method based on carrier reconstruction is proposed, the bit error rate performance is simulated, and the capacity enhancement and anti-jamming ability of the extended signal for FH transmission are analyzed.  
anti-interference communication; FHSS; capacity enhancement; carrier wave reconstruction Article
## 3 Data and Methodology
### Methodology
#### 3.2.1 Data Preprocessing  
Our dataset is limited, as there are not many publicly available companies in the MENA region. Rather than list-wise deletion, it is more suitable to deal with missing values by replacing them [58; 59; 60; 61]. In this work, the missing values in the dataset were replaced using the within-country mean values. Normalization was carried out using MinMaxScaler. Detailed descriptive statistics were obtained, and outliers in the dataset were detected and excluded. Feature Importance was calculated by calculating the Gini importance value for each feature. Each feature was then sorted in descending order and the top k features were selected. The top features are highly linked to the target variable. We found that keeping all attributes yielded better results than avoiding the least important ones. As our dataset was imbalanced and small, we balanced it with synthetic minority data by oversampling [62], which has been extensively and successfully used in the literature for similar datasets [63; 64; 65].
# Study of Information Dissemination in Hypernetworks with Adjustable Clustering Coefficient
## 4 Simulation Results and Analysis
### The Results of Information Communication Theory Analysis
#### 4.2.1 The Simulation Result of the Density of I-State Nodes under Steady State  
The values of the parameters were chosen as follows: \(m_{1}=1,3,5\), \(m_{2}=3\), \(m=3\), \(\gamma=0.1\), and \(p=0.6\). The curves in Figure 5 show the theoretical results obtained by using Equation (20), while the density of I-state nodes in the steady state, \(\rho\), is depicted by discrete points. Our findings show that information spreads throughout the whole hypernetwork, even if its effective propagation rate \(\lambda\) is low. Therefore, the propagation threshold of the RP strategy does not exist.  
Figure 4: The hyperdegree distributions of hypernetworks with varying clustering coefficients \(p\). The black line is the result of the theoretical analysis, and the blue dots with different shapes are the simulation results of different clustering coefficients.
## 5 On-Orbit Mode to Support Intercalibration with Radiometric Traceability
### Quasi-Synchronous Intercalibration Transfer Mode by Orbital Maneuver  
LIBRA will be operated such that its sub-satellite track is close to and overlaps with the track of the satellite to be intercalibrated (as seen in Figure 8). The intercalibration is carried out in near real-time in the nadir zone.  
Calibration biases due to differences in viewing angles and sub-satellite tracks have to meet matching constraints between LIBRA and the target satellite to be calibrated. There is no need to carry out attitude maneuvers because collocated observations can be obtained over longer time periods.  
In this mode, altitude and inclination adjustments for LIBRA are needed to conduct an intercalibration and to provide match-ups for intercalibration over a longer period. Because of the substantial fuel consumptions required to perform the orbit maneuvers, the quasi-synchronous mode will only be used when required by the target satellite. The frequency of such intercalibrations will depend on the available fuel of the satellite platform.
# Laboratory Evaluation of Tensile Creep Behavior of Concrete at Early Ages
## 2 Theoretical Background
### Creep and Relaxation Characteristics  
For one-dimensional loading, the constitutive equations reduce to  
\[\sigma(t)=\int\limits_{0}^{t}\,E(t,t^{\prime})\frac{\partial\varepsilon(t)}{ \partial\,tt}dt \tag{6}\]or equivalently in a stress formulation,  
\[\varepsilon(t)=\int\limits_{0}^{t}\,D(t,tt)\frac{\partial\sigma(tt)}{\partial\,t }dt\prime \tag{7}\]  
where \(E(t,tt)\) and \(D(t,tt)\) are the time- and age-dependent uniaxial relaxation modulus and creep compliance, respectively. As a counterpart of the relaxation modulus, creep compliance describes the strain at time \(t\) caused by a unit constant stress applied at time \(t^{\prime}\). Viscoelastic material behavior defined by the constitutive relations given in Equations (6) and (7) can be analogized by their mechanical models shown in Figure 1. These mechanical models, respectively, define the relaxation modulus and creep compliance in a series of real exponents referred to as the Dirichlet series or the Prony series [31]. Equations (8) and (9) represent the Dirichlet series relaxation modulus and creep compliance functions for aging concrete. Mathematical difficulties from the aging effect are simplified in these representations by formulating the functions in a sum of products of single time-dependent variables [32; 33].  
\[E(t,tt)=E_{\infty}(tt)+\sum\limits_{i=1}^{m}E_{i}(tt)\cdot e^{-\frac{t-t^{ \prime}}{\rho_{i}}} \tag{8}\]  
\[D(t,tt)=D_{0}(tt)+\sum\limits_{i=1}^{m}D_{i}(tt)\cdot\left(1-e^{-\frac{t-t^{ \prime}}{\rho_{i}}}\right) \tag{9}\]  
Material parameters (\(E_{\infty}\), \(E_{i}\), \(D_{0}\), and \(D_{i}\)) in Equations (8) and (9) correspond to the internal modulus and compliance of the spring units of the mechanical models in Figure 1. The internal viscosity (\(\eta_{i}\)) of each unit of the models characterizes the time-dependent viscous behavior of the material, which are involved in the exponents \(\rho_{i}\) and \(\tau_{i}\), referred to as the relaxation time and retardation time, respectively.  
The two viscoelastic material properties, relaxation modulus and creep compliance, are uniquely related to each other as presented in Equation (10). Therefore, once one of the properties is identified, the other can be estimated by the relationship [30]. As will be  
Figure 1: Experimental mechanical models for viscoelastic materials.  
seen later, the relationship can be verified by the Laplace transform of the relaxation and creep functions.  
\[\int\limits_{0}^{t}E(t-\tau)\frac{\partial D(t)}{\partial\tau}\,d\tau=\int \limits_{0}^{t}D(t-\tau)\frac{\partial E(t)}{\partial\tau}\,d\tau=1\,\,\,\text{ for all }t \tag{10}\]  
It is noted that the relationship above does not involve the age-dependency of the material properties. This means the interrelation applies for a particular age, \(t^{\prime}\), between \(D(t)\) and \(E(t)\). If the conversion of the material properties is required for an age-dependent stress or displacement analysis, therefore, the conversion scheme must be continuously incorporated into each time step of the analysis procedure.
## 2 Literature Review
### Studies Focusing on QCSP  
Some studies have focused on the QCSP. [PERSON] and [PERSON] [PERSON] and [PERSON] (2009) formulated a MIP model for the QCSP. However, due to NP-hard they provided a greedy randomized adaptive search procedure (GRASP), which includes a B & B and heuristic to deal with this problem. [PERSON] and [PERSON] [PERSON] and [PERSON] (2009) proposed another heuristic to solve this problem. That heuristic first decomposed the QCSP into sub-problems, and then solved them. [PERSON] et al. [PERSON] et al. (2009) proposed a hybrid approach combining a queuing network model with discrete event simulation to deal with the QCSP. The throughput and completion time of the QCs of each generated solution were evaluated. The objective of that approach was to maximize the CT efficiency. Their experiments showed encouraging results. [PERSON] et al. [PERSON] et al. (2009) first formulated a MIP for the QCSP and then proposed a GA to determine QC schedules for each task of the ships. They concluded that the proposed GA was effective and efficient. [PERSON] and [PERSON] [PERSON] and [PERSON] (2009) modeled the QCSP with a MIP model with the aim of minimizing the number of QC operational cycles required for loading and unloading all the containers. Finally, they proposed a hybrid heuristic to solve this problem, taking inter-stage sequencing (batch sequencing) and intra-stage sequencing (stack sequencing in the same hatch) into account. Their experimental results showed that the proposed approach could mostly find the optimal solution. In addition, they concluded that the schedule resulting from that approach was much better than that constructed by human planners. [PERSON] et al. [PERSON] et al. (2009) proposed a rich model to solve QCSP with factors including individual crane service rates, ready times, due dates, safety requirements, and precedence relations among container groups taken into account. They also used a timed Petri net model to determine the loading and unloading QC schedules. Their experimental results showed the desired results. Given the constraint of non-crossing for QCs and sequence requirements among tasks, [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) used a GA to solve the QCSP. In another study, [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) also employed a GA to deal with the QCSP. Their experimental results showed that the proposed GA was as good as many of the existing algorithms, but it needed less computational time. However, the aforementioned studies did not consider the BAP.
## 3 Materials and Methods
### An Overview of YOLOv8  
YOLO is a well-known object detection and classification model, representing the state of the art for real-time and low-resource usage applications. The YOLOv8 model has been developed as an extension of YOLOv5 and is characterized by a modified backbone network, an anchor-free detection head, and a new loss function. The input segment carries out mosaic data augmentation, adaptive anchor calculation, and adaptive grayscale padding on the input image. The backbone makes the extraction of hierarchical features at different scales while the neck aggregates and refines the features extracted by the backbone by focusing on enhancing the spatial and semantic information across different scales [45]. YOLOv8 outperforms its previous versions in terms of mean average precision and performance consistency [46; 47; 48]. In addition to being faster and more accurate, YOLOv8 also requires fewer parameters to achieve its performance.  
A performance comparison of YOLOv8 with newer releases up to YOLOv11, and other architectures like Faster R-CNN, is instead introduced in [49; 50], while YOLOv8 is a single-stage object detection model, Faster R-CNN is a two-stage object detection model composed of a Region Proposal Network (RPN) for generating proposals and then doing object detection and classification via bounding box regression. The experimental results confirm YOLOv8's rapid inference times that make it suitable for real-time applications. It is twice as fast as YOLOv9, while achieving comparable accuracy. Its performance is also comparable with YOLOv10 and YOLOv11. The Faster R-CNN model implemented via Detectron2 instead demonstrated comparatively lower performance in both speed and accuracy compared to the whole family of YOLO algorithms.
# Design and Performance Analysis of Navigation Signal Based on OFDM
## 3 ACF and PSD Derivation Verification of OFDM Navigation Signal Modulation System  
The ACF and PSD of the OFDM navigation signal are two very important parameters in the performance analysis and evaluation of the navigation signal [27]. Deriving the ACF of the OFDM signal and the analytical expression of power spectral density can simplify the amount of simulation analysis operations, improve the analysis efficiency of the navigation signals, and facilitate the analysis and design of various characteristics concerning the navigation signals. In this paper, the ACF and PSD analysis expression of the OFDM navigation signal are derived in detail, and the ACF, PSD, and analytical expression operation results are compared in the form of a simulation to verify the correctness of the analytical expression derivation of the two parameters.  
Figure 1: OFDM frame structure diagram.
## 4 Biological Applications
### Therapeutic Applications
#### Drug Delivery
synthesized PEGylated FePt-Fe\({}_{3}\)O\({}_{4}\) nanoassemblies for the pH-sensitive release of DOX in a cell-mimicking environment [109]. The authors found higher release of DOX in acidic  
Figure 12: Schematic illustrations of (**a**) the synthesis of DOX/Au@Pt-cRGD, and (**b**) ROS scavenging by catalyzing the platinum shell while attenuating DOX-induced organ oxidative injury. Reproduced with permission from ref. [95]. Copyright 2017 American Chemical Society. environments (pH 4.3) than at physiological pH (pH 7.4) due to the protonation of DOX under acidic media. In addition, due to the magnetic nature of the nanoparticles, the DOX release was enhanced under an alternating magnetic field, which was used to increase the Brownian motion of the nanoparticles. Another example of a pH-responsive bimetallic nanogel was reported by [PERSON] et al. (2019). The nanogel was composed of core-shell Ni@Ag bimetallic nanoparticles coated with poly(ethylene glycol-co-meth-acrylic acid) [p(EG-MAA)]. Due to the nature of the composite, the material could be magnetically manipulated, and drug release was regulated via the pH-sensitive properties of the polymeric material.
# Multilevel Privacy Assurance Evaluation of Healthcare Metadata
## 3 Multilevel Privacy Assurance Evaluation: Results and Evaluation
### Multilevel Privacy Assurance Curve  
The below-given curve is a hypothetical multilevel privacy assurance path partially inspired by the aforementioned normative and empirical evaluations. The Figure 10 shows the multilevel privacy assurance path across two axes. The x-axis shows the level of individual control over one's PII in the Dutch healthcare landscape. The y-axis shows the level of inter- and intra-organizational control over data subjects' PII. The figure suggests that the individual control over Personally Identifiable Information (PII) is maximum before sharing sensitive personal information with a healthcare provider at the onset of data collection. Once the informed consent is taken from the healthcare provider, the control over personal information starts reducing. This control becomes weaker when, at first, the information is shared amongst horizontally located (intraorganizational) and then with vertically distributed interorganizational caregivers. The least individual control is when the patient's PII is used for third-party metadata processing for further data evaluation. Each quadrant suggests a stage of patients' value care where stage 1 (S1) is when she or he gives informed consent at the onset of data collection. Stage 2 (S2) is when healthcare metadata are shared across horizontally distributed caregivers, and patients' treatments are started. Stage 3 (S3) is when patients' PII is shared across vertically distributed caregivers, and the patient is treated, and the case is closed. The time for the latter situation can vary as per each patient's condition. Stage 4 (S4) is when a third-party data processor shares the stored, interoperable healthcare metadata for the sake of further data evaluations. At this stage, the individual's control over PII is the least and that of the organization (i.e., healthcare provider). Because then the Trusted Third Party (TTP) becomes potent and can exploit the available information to his or her end. The dotted area at the peak is when DPO can launch a Materialized Privacy Claim against healthcare providers to compensate for privacy breaches; see Figure 11.
## 4 Analysis of Research Variables
### DL PRB Usage Rate  
The Physical Resource Block (PRB) is the smallest unit of resource that can be allocated to a user in an LTE network. If the frequency domain is observed, the resource blocks consist of 12 subcarriers of 15 kHz each or 24 subcarriers of 7.5 kHz each. This means that the bandwidth of the PRB is 180 KHz. In most cases, the number of subcarriers used per block of resources is 12 [36].  
In the time domain, one physical block of resources occupies one slot (seven symbols) in duration of 0.5 ms, which represents 1/20 of the structure of the radio frame used for DL and UL data transmission in the LTE [37]. The smaller resource unit and the smallest discrete part of the radio frame is a resource element (RE), which has the dimensions 1 symbol \(\times\) 1 applicant, which means that one PRB has 84 resource elements. The described structure of one physical block of resources with the resource elements is shown in Figure 3.  
Figure 2: A view at the topology of the coverage of the M9J segment with LTE network technology cells.  
The independent variable or a _counter_ labeled _DL PRB Usage Rate_ represents the average utilization of DL physical blocks of resources (expressed as a percentage). It is calculated as the ratio of the average number of used physical blocks of the physical DL shared channel (_Physical Downlink Shared Channel_--PDSCH [38]) and the total available number of the DL physical resource blocks multiplied by 100. PDSCH is a physical DL common transport channel which, in addition to transmitting the user data, has the function of transmitting the data important for control, and specific information of higher layers and DL system information.