esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Study
### Results Analysis and Discussion
#### 4.4.3 Analysis of the Comparative Study (2) for MaF and WFG Problems with 2, 3 and 7 Objectives  
For the second comparative study (2), thirteen multiple-objective evolutionary approaches (MSOPS-II, MOEA/D, HypE, PICEA-g, SPEA/SDE, GrEA, NSGA-III, KnEA, RVEA, two_Arch2, \(\theta\)-DEA, MOEA/DD, AnD) are first compared to the new proposed DB-CSA system based on a set of multiple-objective optimization problems, as denoted by the MaF and WFG test suites, with 2, 3 and 7 objectives, including different numbers of decision variables, as detailed in Table 5. The results are reported in Table S6 of the Supplementary File, showing the IGD results of the 14 compared Many-Objective Evolutionary Algorithms regarding their ability to solve nine MoPPs (WFG1-WFG9), characterized by the dynamic shape of the POF, which changes from convex to concave. The DB-CSA algorithm was first ranked to solve seven WFG test suites from nine (7/9), including WFG1, WFG3, WFG4, WFG5, WFG6, WFG8 and WFG9 and failed only for solving WFG2 compared to HypE and \(\theta\)-DEA, which have almost the same mean values as the IGD metric for WFG7 when the number of objectives is equal to 2. By increasing the number of objectives to 3 and 7, the WFG becomes more complex and the issue of a lack of convergence and diversity presents a challenging task. Based on the reported IGD values of the tri-objective WFG functions in Table S6 of the Supplementary File, we can conclude the efficiency of the newly proposed DB-CSA approach to deal with the increasing number of objectives. Table S6, showed the best values for MoOPS, with seven objectives. In addition, Table S7 of the Supplementary File shows the mean and the standard deviation values over the IGD metric to solve the MaF test suite (MaF1-MaF7) with 2, 3 and 7 objective functions. Figure 12 presents the approximated POF for the MaF test suite. The new DB-CSA is presented a good method for solving the MaF test suite compared to the thirteen state-of-the-art MaOEAs. Table 10 shows the importance of DB-CSA over the [PERSON] signed rank test, while all the computed \(p\)-values are less than 0.05, assuming the statistically significant difference in DB-CSA compared to the thirteen MaOEAs, including MSOPS-II, MOEA/D, HypE, PICEA-g, SPEA/SDE, GrEA, NSGA-III, KnEA, RVEA, two_Arch2, \(\theta\)-DEA, MOEA/DD, AnD, to solve the MaF test suite with the 2, 3 and 7 compared objectives. The dynamic treatment of both convergence and diversity concepts is very useful when solving a set of complex MaOPs with a high number of objectives. 4.4 Analysis of the Comparative Study (2) for DTLZ and WFG Problems with 3, 5, 8, 10 and 15 Objectives  
In the second part of the comparative study (2), seven MaOEAs (PMEA-MA, PMEA*-MA, SPEA2/SDE, NSGA-II/SDR, MaOEA/IGD, VaEA, SPEA) were compared to the new
## 2 Materials and Methods
### NDVI Measurements
#### 2.4.3 MODIS NDVI Measurements  
The NASA Terra and Aqua satellites, which have orbited Earth since 1999 and 2002, respectively, each carry a MODIS (Moderate Resolution Imaging Spectroradiometer) sensor. These satellites pass daily over most of Earth's surface and provide a nominal 250 m spatial resolution dataset in 36 spectral bands. NDVI values derived from MODIS NDVI products (MOD13Q1 from Terra and MYD13Q1 from Aqua) were downloaded from the Oak Ridge National Laboratory Distributed Active Archive Center website ([[http://daacmodis.ornl.gov/cgi-bin/MODIS/GLBVIZ_1_Glb/modis_subset_order_global_col5.pl](http://daacmodis.ornl.gov/cgi-bin/MODIS/GLBVIZ_1_Glb/modis_subset_order_global_col5.pl)]([http://daacmodis.ornl.gov/cgi-bin/MODIS/GLBVIZ_1_Glb/modis_subset_order_global_col5.pl](http://daacmodis.ornl.gov/cgi-bin/MODIS/GLBVIZ_1_Glb/modis_subset_order_global_col5.pl))) The data were derived from a single MODIS pixel (nominally 250 m \(\times\) 250 m) overlapping each eddy covariance tower locations for each site (E3 and E5). MODIS bands 1 (red, 620-670 nm) and 2 (infrared, 841-876 nm) were used for MODIS NDVI calculation. For each MODIS sensor (Aqua and Terra), the 16-day aggregation period was based on the best observations during the composite period, and the actual collection date for each optimal observation was used to provide more accurate time series. The date-corrected time series was then interpolated to produce a daily MODIS value for comparison with daily flux values (Figure 3).
## 4 Discussion
### Reliability of Climbing-Specific Tests  
Our study further contributes to climbing-specific research by reporting absolute reliability indices (ICC, SEM, MDC95), metrics that are rarely documented in previous studies [22]. Recent research reinforces the importance of testing posture, demonstrating that seated positions with 90\({}^{\circ}\) elbow flexion offer the highest reliability (ICC > 0.95) and strongest correlation with climbing performance [13]. Their findings support the use of standardized seated protocols for assessing climbing-specific finger strength, as applied in the current study. The three-finger drag and half-crimp tests demonstrated excellent reliability (ICC = 0.969 and 0.962, respectively; CV < 6%), consistent with prior work [11; 34], which reported ICC values of 0.96 and 0.88, respectively. The half-crimp grip exhibits superior test-retest reliability for isometric finger strength, making it a robust measure for climbing-specific adaptations [14]. Its strong reliability, along with absolute indices like SEM and MDC95, provides valuable benchmarks for monitoring training progress and evaluation of strength performance improvements. However, methodological variability (e.g., grip type, posture, depth of the hold) complicates cross-study comparisons. For instance, lower ICC values (0.88) and an SEM of 5.52 kg were reported for full-crimp grips, while hand- and grip-specific differences were observed in overhead testing, with ICC values ranging from 0.605 for the three-drag grip on the left hand to 0.963 for the half-crimp on the right hand [11; 14]. Our value for the MDC95, approximately 50 N, indicates that subthreshold strength gains may reflect measurement error, highlighting the need for standardized protocols to ensure consistency and reliability in future research. This is consistent with earlier results, the higher MDC95 for handgrip dynamometry (~80 N) underscores the necessity of climbing-specific assessments.
## 2 System Composition and Dynamic Model  
As shown in Figure 1, the large panel is a component sample of the space station cabin. Due to the good stiffness of the panel and tooling, they are considered a fixed boundary in this paper. When the AGV is well-positioned, the supporting legs will extend and support the AGV to improve the stability of the system. At this time, both the supporting legs of the AGV and the cantilever workpiece are in contact with the fixed boundaries, resulting in an approximate closed-loop structure of the ARGW system composed of AGV, clamping robot KR 60 HA, gripper, and cantilever workpiece.  
The elements in the ARGW system can be divided into rigid body elements and hinge elements. Moreover, rigid body elements include single input end and single output end elements, multiple input ends and single output end elements. Synchronously, hinge elements are unified as spatial elastic hinge elements with three-direction translational stiffness and three-direction rotational stiffness.  
The dynamics model of the system is described in Figure 2, and its topology figure is shown in Figure 3. The four supporting legs, AGV, seven links of the robot, gripper, and workpiece are considered rigid bodies 2, 5, 8, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, and 31, respectively. Additionally, the hinge elements are numbered as 1, 3, 4, 6, 7, 9, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, and 32 in sequence. The ground and the panel are fixed boundaries,
## 6 Limitations of UAVs
### Real-Time Data Processing  
The ability to process and analyze UAV data in real-time is critical for applications like disaster response and surveillance. However, achieving real-time processing with high accuracy is challenging due to the aforementioned computational demands. Models such as those evaluated in the DarkTrack2021 and UAVDark135 datasets show promise but often require optimization to balance speed and accuracy effectively. Real-time processing also necessitates robust algorithms capable of handling dynamic environments and changing conditions without significant delays.
## 3 Materials and Methods
### Data Acquisition and Processing Protocol  
The FBG sensor cables were routed through grooves and connected to the demodulation device. Under operational traffic conditions, the sensor arrays embedded within the pavement structure recorded mechanical responses of the asphalt layer whenever vehicles traversed the seven monitored cross-sections, as illustrated in Figure 5. Given the high vehicle speeds on the SCE, the FBG interrogator was configured with a 1 kHz sampling frequency to ensure real-time data accuracy, utilizing a built-in network interface card for seamless data transmission and export.  
The experimental road section experienced high traffic volume, and the monitoring system operated at a high sampling frequency, necessitating the preprocessing of the collected massive datasets to extract valid signals. Due to the inherent randomness in vehicle axle configurations, load magnitudes, and travel paths, significant variations in wavelength shifts were observed by the sensors. Consequently, this study focused on the statistical patterns of modulus outcomes. By analyzing the mechanical response data  
Figure 4: Process of FBG sensor installation.  
Figure 5: Loading scenarios of actual vehicle passages.  
from numerous vehicle passes, the mean elastic modulus and standard deviation were calculated for each section, enabling a scientifically quantitative evaluation of the grouting effectiveness across different materials.
## 4 Experiment Simulation and Discussion
### Experiment Cases Construction  
Because of the lack of benchmarking examples for downlink scheduling problem aiming at request waiting time, on the base of benchmark for downlink scheduling proposed by [30], we propose a test case to verify the correctness of SATLD. The origin benchmark shown in Table 3, there are 16 benchmark instances for each size. However, this benchmark is not constructed for downlink scheduling considering request waiting time. The reason for not adapting to this problem is that it mainly considers the conventional communication between the satellite and the ground station, and it needs to communicate with the ground station once in each circle. In addition to regular requests, downlink requests also need to be satisfied as soon as possible. The randomness of task requests has not been considered, which means that the arrival time of each request is different. The arrival time of each request may not be within the access range of all the ground stations, or it can access to a certain ground station when the request arrived. Based on this, we made a modification on the basis of the original benchmark. Each satellite considers only one downlink request, and the arrival time of each downlink request in the entire planning cycle is random.
# Bi-Directional Point Flow Estimation with Multi-Scale Attention for Deformable Lung CT Registration
## 3 The Proposed Approach
### Progressive Flow Refinement
#### 3.4.3 Post-Processing for Dense Deformation Estimation  
Although our model estimates the displacements only at sparse vessel-derived points, a dense deformation field across the entire lung volume is required. To achieve this, we employ Thin Plate Spline (TPS) interpolation, a classical method in landmark-based registration. Specifically, given the predicted displacements at a sparse set of source points, this interpolates the transformation to the entire 3D space, allowing the displacement vectors for all of the voxels in the volume to be computed. This design is motivated by the anatomical coupling between the vascular structures and lung parenchyma deformation, ensuring that clinically relevant motion patterns are faithfully captured across the lung.
## 5 Simulation Results and Discussion
### Case 1 (2 \(\times\) 2) VL Column System  
The benchmark VL column example provided by [PERSON] (1986) is a TITO process with greater input and output interaction [54]. The VL column transfer function structure is provided below.  
\[M(s)=\begin{bmatrix}-\frac{2\,{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{ \rm{  
The ultimate control assumption, which forms the basis of the EOTF/ETF models, can only be verified by including a decoupler in the open-loop model. Decouplers can be created using Equation (3) as  
\[D(s)=\begin{bmatrix}1&0.5909\\ \frac{(5.9907s+0.6512)e^{-1.45\text{s}}}{9.5+1}&e^{-0.7s}\end{bmatrix} \tag{42}\]  
The decoupler \(D\)(s) includes an additional time delay,  
\[G(s)D(s)=\begin{bmatrix}g_{11}(s)&g_{12}(s)\\ g_{21}(s)&g_{22}(s)\end{bmatrix}\begin{bmatrix}1&d_{12}(s)e^{0.7s}\\ d_{21}(s)&e^{-0.7s}\end{bmatrix}=\begin{bmatrix}s_{11}^{*}(s)&0\\ 0&g_{22}^{*}(s)\end{bmatrix} \tag{43}\]  
The decoupled processes are  
\[g_{11}^{*}(s)=\frac{-1.3535e^{-0.9559s}}{6.691s+1} \tag{44}\]  
\[g_{22}^{*}(s)=\frac{2.6455e^{-1.034\text{s}}}{8.794s+1} \tag{45}\]  
Additionally, the described optimization issue in Equation (36) is where the decentralized FOPI controller is derived.  
\[C(s)=\begin{bmatrix}-2.5858-\frac{0.3865}{s}&0\\ 0&1.6066+\frac{0.1941}{s}\end{bmatrix} \tag{46}\]  
The MFPOA method was implemented in MATLAB. The parameters utilized in this paper's MFPOA-based simulations are shown in Table 1.  
Figure 7 displays the closed-loop response for the VL column. Both references apply the unit step changes. The setpoint of loop 2 is altered from 0.2 to 1 at t = 100, as shown in Figure 8. A traditional PID controller and a proposed controller are contrasted. It is evident from the figure that the proposed FOPID controller outperforms the PID controller in terms of performance. The proposed FOPID controller's quicker regulatory reaction significantly minimized the interaction effect.  
The setting of the optimized PID and FOPID are found through minimization of the cost function for the ETF model (\(g_{11}^{*}(s),\ g_{22}^{*}(s)\)) using the MFPOA. The optimal controller variables are presented in Table 2. The comparisons of the performance indexes are also presented in the same table. The minimum values of \(M_{P}\), \(T_{r}\), and \(T_{s}\) are recorded by the proposed FOPID controller based on the MFPOA. Thus, it can be observed that the proposed FOPID controller based on the MFPOA may reduce error and achieve the design objectives.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Population size (n) & 50 \\ Probability of switching (p) & 0.8 \\ Number of iterations & 100 \\ Number of variables & 5 \\ Maximum limits [\(K_{P}\), \(K_{I}\), \(K_{D}\), \(\lambda\), \(\mu\)] & 40, 20, 20, 1, 1 \\ Minimum limits [\(K_{P}\), \(K_{I}\), \(K_{D}\), \(\lambda\), \(\mu\)] & 0, 0, 0, 0, 0 \\ \(\beta\) & 2.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter settings for the simulation.
# Vitamin D and Rehabilitation after Stroke: Status of Art
## 2 Vitamin D Metabolism
Circulation: Cholecaliferol and ergocalciferol are prohormones transported into the blood by vitamin-D-binding protein (VDBP), but to become active they must be hydroxylated. Liver: Vitamin D2 and D3 are hydroxylated by the cytochrome P450 mixed-function oxidases encoded by _CYP2R1_ (see green box), generating the 25 hydroxy vitamin D [25(OH)D], or calcidiol. Kidney: 25(OH)D is further hydroxylated by 1 \(\alpha\)-hydroxylase encoding by _CYP27B1_ (see green box) and generating 1,25 dihydroxy vitamin D (1,25(OH)\({}_{2}\) D or calcitriol), which is the biologically active molecule. Catabolic pathway: _CYP24A1_ gene coding for a 24-hydroxylase enzyme (see green box), which can inactivate both 25(OH)D and 1,25(OH)D via hydroxylation. Target organs: 1,25(OH)\({}_{2}\)D is bound by VDBP and it reaches the target organs where the Vitamin D receptor (VDR) mediates its absorption. VDR, encoded by the _VDR_ gene (see green box), is crucial in the vitamin D endocrine and autocrine systems. The concentration of 25(OH)D more than 30 ng/mL (to obtain values in nmol/L, multiply by values of 2.496) is considered adequate according to the guidelines of Endocrine Society, while for IOM the cut-off is set to 20 ng/mL [24]. Different methods are employed to test serum 25(OH)D, such as the LC-MSMS (liquid chromatography tandem mass spectrometry), the golden standard, a unique technique able to distinguish between 25(OH)D2 and 25(OH)D3. Chemiluminescence and radioimmunoassay are largely employed together with the Elisa kit. The dietary reference intake expressed in International Units is from 400 to 1000 IU/day [23]. Ergocalciferol (Vitamin D2) assumed by diet is structurally different from cholecalciferol (Vitamin D3), with a lower affinity for VDBP; this is the reason that supplementation does not result in high serum concentrations of vitamin D in circulation [24].
# Fog Computing for Smart Cities' Big Data Management and Analytics: A Review
## 7 Toward a Fog-Based Real-Time Big Data Pipeline  
### Building a Smart City Data Pipeline  
Smart city analyses typically implement a data pipeline by connecting one task's output to another one's input. Large-scale real-time data analysis would require the deployment of a fog-based data pipeline. Currently, Hadoop [36; 37] is the cloud-based big data analytics platform widely used by the big data community. With Hadoop, computation-intensive and data-intensive analytics dispatch tasks to multiple nodes for execution. A real-time big data pipeline for smart cities should have essential characteristics to meet city stakeholders' diverse demands. Therefore, a fog-based big data pipeline system for smart cities must have the following features:  
* Scalable messaging system: The pipeline system should have a robust and scalable distributed messaging system such as Apache Kafka [21; 22] to cope with the vast volume and high velocity of IoT data streams originating from the city systems.
* Data storage: The system should have enough storage capacity to allow performing data analytics using a robust big data platform like Apache Hadoop.
* Machine learning libraries: The system should support various machine learning libraries such as Tensorflow [24], Keras [75], Apache Spark MLib [76; 77] to perform predictive analytics.
* Backend store: The analytics output should be stored in some flexible database. A NoSQL database would be preferred.
* Dashboard and visualization tools: The system should have supporting reporting and visualization tools.  
Figure 6: A fog-cloud-based architecture for smart grids.  
Building a fog-based data pipeline for a smart city is a process with five phases: data ingestion, data pre-processing, data processing and analytics, visualization and reporting, and decision making (see Figure 7).
## 3 Related Work
A secure and trusted approach to V2V and V2I communication in ITS was proposed in [37], using edge infrastructures, introducing cloudlets for authorization, verification, and ensuring message integrity and anonymity, thus addressing concerns of location privacy and secure communication, with performance metrics discussed and compared to the existing ITS technologies. Formalized dynamic groups and Attribute-Based Access Control (ABAC) model (CV-ABAC-G) for smart cars ecosystem were introduced in [38; 39], addressing security and privacy concerns by considering individual user preferences and introducing dynamic groups in vehicular IoT, with a proof of concept implementation in Amazon Web Services (AWS) cloud platform demonstrating real-world use cases and performance metrics. The related works above are based on a proposal for an authorization framework published in [40] from the University of Texas in San Antonio, aiming to enhance security and privacy in the Internet of Vehicles ecosystem, highlighting vulnerabilities in vehicular IoT and advocating for Extended Access Control Oriented (E-ACO) architecture to address dynamic interactions among entities, with a focus on vehicular clouds and various access control models, alongside discussing use cases and future research directions.
# Experimental Evaluation of Display Field Communication Based on Machine Learning and Modern Design
## 3 The Proposed DCT-Based DFC System
### Data Embedding
#### 3.1.1 Power Allocation Scheme for Data Embedding  
Until now, we have described how to use DCT and addition allocator to embed data to the frequency-domain image. Although the DCT and addition allocator have the advantages of making the algorithm easier to implement and reducing the computational complexity in the data-embedding process, it has a disadvantage too. When data are embedded with an addition allocator, it is difficult to decode the data at the receiver when the pixel values and the data values are not balanced properly. Therefore, we propose a power allocation scheme for data embedding that scales data values in proportion to the average power of the frequency domain coefficients.  
Considering the frequency-domain image \(\mathbf{I}_{F}\), we propose to set the value of the data, i.e., \(X_{\text{amp}}\), as  
\[X_{\text{amp}}=\Big{(}\sqrt{P_{\text{avg}}}\Big{)}\alpha, \tag{7}\]  
where \(P_{\text{avg}}\) is the average power of a data-embedded region of \(\mathbf{I}_{F}\) and \(\alpha\)\((0<\alpha<1)\) represents the proportionality constant. Note that the PSNR value of the image should be chosen to minimize the bit error rate (BER) while the PSNR should not fall below 30 dB.  
Figure 2: Three different data embedding regions in a frequency-domain image. The white regions represent the positions of the frequency sub-bands containing data. Low, middle, and high sub-bands indicate that the lower, middle, and higher frequency coefficients are used for data embedding, respectively.  
Therefore, considering the above power allocation constraint, \(\mathbf{d}_{q}\) in Equation (3) is replaced by the _modified_ data vector \(\mathbf{s}_{q}\) as follows:  
\[\mathbf{s}_{q}=X_{\text{amp}}.\mathbf{d}_{q}, \tag{8}\]  
where \(\mathbf{s}_{q}\) is the \(q\)th column of the data matrix. Finally, the \(q\)th column vector in Equation (3) can be re-formulated as  
\[\mathbf{X}_{q}=\left[\underbrace{\mathbf{0}}_{1\times s}\quad\quad\underbrace{(\mathbf{s} _{q})^{T}}_{L}\quad\quad\underbrace{\mathbf{0}}_{1\times(P-s-L)}\right]^{T}. \tag{9}\]
# Secured Real-Time Machine Communication Protocol
###### Abstract  
In this paper, we introduce the Secured Real-Time Machine Communication Protocol (SRMCP), a novel industrial communication protocol designed to address the increasing demand for security and performance in Industry 4.0 environments. SRMCP integrates post-quantum cryptographic techniques, including the Kyber Key Encapsulation Mechanism (Kyber-KEM) and AES-GCM encryption, to ensure robust protection against both current and future cryptographic threats. We also present an innovative \"Port Hopping\" mechanism inspired by frequency hopping, enhancing security by distributing communication across multiple channels. Comparative performance analysis was conducted with widely-used protocols such as ModBus and the OPC UA, focusing on key metrics such as connection, reading, and writing times across local and remote networks. Results demonstrate that SRMCP outperforms ModBus in reading and writing operations while offering enhanced security, although it has a higher connection time due to its dual-layer encryption. The OPC UA, while secure, lags significantly in performance, making it less suitable for real-time applications. The findings suggest that SRMCP is a viable solution for secure and efficient machine communication in modern industrial settings, particularly where quantum-safe security is a concern.  
Keywords:industrial communication; post-quantum cryptography; Industry 4.0 _Article_