esa-sceva/satcom-chunk-collection huggingface dataset:
# Fair Benchmark for Unsupervised Node Representation Learning
## 3 Unsupervised Node Representation Learning Algorithms and Datasets
### Unsupervised Node Representation Learning Methods Used in FURL
#### 3.1.1. Node Representation Learning for Pure Networks
* **node2 vec**[31]: It performs balanced random walks to smoothly interpolate between breadth first sampling and depth first sampling. * **NetMF**[38]: It proves that the existing negative sampling based on NRL algorithms could be considered as the variations of matrix factorization. Based on this theoretical analysis, it proposes an advanced matrix factorization for graphs. * **ProNE**[39]: It first leverages sparse matrix factorization to learn the initial node representations. Then, it uses the high-order Cheeger's inequality to modulate the spectral space of the graph and plot the initial representations on the adjusted graph. In this way, it can integrate the local smoothing information and the global clustering information. * **NetSMF**[41]: It makes NetMF scalable by applying sparsification. * **HOPE**[42]: It proposes to preserve asymmetric transitivity by approximating high-order proximity. * **SDNE**[43]: It is a semi-supervised deep model with multiple layers of non-linear functions, aiming to preserve both the global and local network structures. * **LINE**[10]: It can scale up to networks with millions of vertices and billions of edges. It has carefully designed objective functions that preserve both the first-order and second-order proximities, which are complementary to each other. An efficient and effective edge-sampling method is proposed for model inference, which solved the limitation of stochastic gradient descent on weighted edges without compromising efficiency.
# Implementation of a Lossless Moving Target Defense Mechanism
###### Abstract  
The contemporary world, dominated by information technologt (IT), necessitates sophisticated protection mechanisms against attacks that pose significant threats to individuals, companies, and governments alike. The unpredictability of human behavior, coupled with the scattered development of applications and devices, complicates supply chain maintenance, making it impossible to develop a system entirely immune to cyberattacks. Effective execution of many attack types hinges on prior network reconnaissance. Thus, hindering effective reconnaissance serves as a countermeasure to attacks. This paper introduces a solution within the moving target defense (MTD) strategies, focusing on the mutation of Internet protocol (IP) addresses in both edge and core network switches. The idea of complicating reconnaissance by continually changing IP addresses has been suggested in numerous studies. Nonetheless, previously proposed solutions have adversely impacted the quality of service (QoS) levels. Implementing these mechanisms could interrupt Transmission Control Protocol (TCP) connections and result in data losses. The IP address mutation algorithms presented in this study were designed to be fully transparent to transport layer protocols, thereby preserving the QoS for users without degradation. In this study, we leveraged the benefits of software-defined networking (SDN) and the Programming-Protocol-Ondependent Packet Processors (P4) language, which specifies packet processing methodologies in the data plane. Employing both SDN and P4 enables a dynamic customization of network device functionalities to meet network users' specific requirements, a feat unachievable with conventional computer networks. This approach not only enhances the adaptability of network configurations but also significantly increases the efficiency and effectiveness of network management and operation.  
moving target defense; software-defined networks; Programming-Protocol-Independent Packet Processors (P4) language; IP address mutation; cybersecurity Article
## 3 Results
### Linking Bibliometric Findings to the Evolution of MaaS  
The bibliometric findings suggest that MaaS research has followed a structured evolution:  
1. Conceptual Foundation (2012-2016): Initial papers focused on defining MaaS and its connection with cloud manufacturing, servitization, and Industry 4.0 concepts.
2. Technological Advancements (2017-2020): Research started investigating blockchain integration, digital marketplaces, and decentralized manufacturing platforms, leading to high-impact studies.
3. Application and Optimization (2021-Present): Recent studies prioritize real-world case studies, machine learning applications, and supply chain optimization strategies to support MaaS implementation.  
This development aligns with publication trends, revealing a shift from theoretical studies to industry-driven applications. The post-2020 surge in research reflects MaaS's technological maturity and growing business adoption. The keyword and co-citation analyses further highlight this evolution, emphasizing its expanding interdisciplinary nature. Future research should refine trust mechanisms, pricing models, and large-scale adoption strategies to accelerate MaaS integration across industries.
## 4 Data Smoothing Algorithm
### Detection Accuracy Results and Analysis of Inertial Track Geometry State Detector
#### 5.3.3 Accuracy Consistency Analysis of Track Geometric Parameter Detection  
The line test was repeated and accurately measured three times using an Amberg GRP1000 type track geometry state detector. The displacement sensor for measuring the gauge and the tilt sensor for measuring the level were built into the detector. The total station was placed statically near the center of the measured track, and 6-8 control points were surveyed. The total station coordinates were obtained based on the resection algorithm. In the total station survey, the prism was placed on the track detector to obtain its three-dimensional coordinates. After obtaining all of the measurement data in this mode for each sleeper, the three-dimensional coordinates of the two rails and the track centerline could be derived based on the mechanical parameters calibrated in advance by the track detector. The average of the three test results obtained by the GRP1000 was used as a reference for this test.  
The absolute deviation comparison of the track direction, high and low, and horizontal (ultra-high) parameters of the three repeated tests' data is shown in Figure 8. Figure 8a shows the track direction, high and low, and horizontal (ultra-high) parameters obtained by the inertial detector for the three repeated detections of the test line and the detection results of the GPR1000 detector. Figure 8b shows the difference between the track direction, high and low, and horizontal (ultra-high) parameters obtained by the three repeated tests and the test results of the GPR1000 detector. As can be seen from Figure 8, compared with the results of the GPR1000 detector, the error in the track direction, high and low, and horizontal (ultra-high) parameters detected by the inertial detector based on the inertial/GNSS/odometer combined navigation was better than 0.2 mm, which improved the accuracy of the inertial track geometry state detector system.
# Efficient Task Scheduling Using Constraints Programming for Enhanced Planning and Reliability
## 3 Methodology
### Application of Constraints Programming
#### 3.2.3 Mathematical Model  
Variables  
* \(S_{\bar{y}i}\): Start time of task \(J_{i}\) in workplace \(W_{j}\) (\(i=1,\,2,\ldots,\,\text{N};j=1,\,2,\ldots,\,\text{M}\)).
* \(C_{\bar{y}i}\): Completion time of task \(J_{i}\) in workplace \(W_{j}\).
* \(C_{max}\): Maximum completion time among all tasks. Domains
* \(S_{\bar{y}i}\in\){0, 1, 2, \(\ldots,\,T\)}, where \(T\) is the maximum possible time. Constraints
* Constraint-1: Each task can only be executed in specific workplaces. \(a_{\bar{y}i}\) is a binary variable indicating if task \(J_{i}\) can be executed in workplace \(W_{j}\) (1 if possible, 0 if not possible): \[S_{\bar{y}i}\;\;a_{\bar{y}i}=S_{i}\;\forall i,j\]
* Constraint-2: The completion time of each task is the sum of its start time and its duration: \[C_{\bar{y}i}=S_{\bar{y}i}+d_{i}\;\forall i,j\]
* Constraint-3: Tasks in the same workplace must not overlap. For tasks \(J_{i}\) and \(J_{k}\) in workplace \(W_{j}\), one of the following must hold: \[S_{\bar{y}i}+d_{i}\leq S_{\bar{k}j}\;\text{or}\;S_{\bar{k}j}+d_{k}\leq S_{\bar{ y}i}\;\forall i\
eq k,\;\forall j\]
* Constraint-4: The overall completion time \(C_{max}\) is the maximum of the completion times of all tasks: \[C_{max}\geq C_{\bar{y}i},j\] Objective Function
* The goal is to minimize the overall completion time: Minimize \(C_{\text{max}}\)Example  
Suppose there are 3 tasks and 2 workplaces:  
* Task \(J_{1}\): duration \(d_{1}=3\), possible workplace \(W_{1}\)
* Task \(J_{2}\): duration \(d_{2}=2\), possible workplaces \(W_{1}\), \(W_{2}\)
* Task \(J_{3}\): duration \(d_{3}=4\), possible workplace \(W_{2}\)  
Constraints:  
* \(a_{11}=1\), \(a_{12}=0\)
* \(a_{21}=1\), \(a_{22}=1\)
* \(a_{31}=0\), \(a_{32}=1\)  
Based on these constraints, the domain, and objective function, a constraint programming solver can find the optimal task schedule.  
For example, tasks could be assigned, and their start and completion times determined as follows:  
* \(S_{11}=0\), \(C_{11}=3\)
* \(S_{m21}=3\), \(C_{21}=5\)
* \(S_{32}=0\), \(C_{32}=4\)  
In this case, the overall completion time \(C_{max}=5\). This modeling approach enables the efficient allocation and scheduling of tasks.
## 4 Simulation Results  
### Simulation Environment and Discussion  
In the simulation experiment for the MIMO communication system, there are two transmitting antennas, two receiving antennas, and two eavesdropping antennas, i.e., \(N_{A}=N_{B}=N_{E}=2\). \(\mathbf{H}\) and \(\mathbf{G}\) are \(2\times 2\) Rayleigh fading channels. The distributions of \(\mathbf{H}\) and \(\mathbf{G}\) both have a mean value of 0 and a variance of 0.5. The information-bearing signals are random complex vectors. The transmission power is 10 (i.e., \(P=10\)). The Gaussian noise in the channel changes with the SNR. The SNR of \(\mathbf{H}\) increases from 0 to 30, while the SNR of \(\mathbf{G}\) is 5 dB. All Parameters are shown in Table 6.  
For the SISO communication system, \(N_{A}=N_{R}=N_{E}=1\) by definition. The information-bearing signals are a random complex number. The SNR of H increases from 0 to 30, while the SNR of G is 5 dB and \(P=10\).
## 5 Analysis and Discussion
### Computational Aspects  
The proposed algorithm is better in terms of computation cost and memory consumption. The proposed algorithm uses ECC. ECC arithmetic is based on an operation that is performed in a finite field forming an Abelian group [39]. It uses point addition and point multiplication that is different from normal multiplication; it is faster than typical multiplication. ECC keys are very short, providing similar security compared to typical techniques such as RSA or DH, as shown in Table 1; they are extracted from [40; 41; 42]. It shows that usage of FHEEP will result in less computational overhead and handling of keys as the required bits are x number of times less than those of RSA and DH. It also shows that memory consumption and traffic over the network will be reduced due to fewer bits being set.
# A Polarization-Based Method for Maritime Image Dehazing
## 4 Results and Discussion  
This paper compared the proposed approach with established image dehazing methods such as DCP [4], Retinex [30], and BCCR [31]. The results of the comparative analysis are presented in Figure 8, showcasing the outcomes of the different methods. The experiments were conducted on an Intel(R) Core(TM) i5 CPU (Intel, Santa Clara, CA, USA) operating at 3.2 GHz with 8 GB of RAM, and the implementation was carried out using the Python (Python 3.12. 0a1) programming language.  
Figure 8: Comparison of results from different methods. (**a**,**b**) show images of island buildings with fog; (**c**) shows an image of ships in sea fog.  
Figure 7: The workflow of the proposed method.
## Processing Setup and Description
### Uncertainty Modeling
#### 3.5.2 LEO Random Uncertainty Estimates  
For the estimation of random uncertainty we consider the error propagation in Bernese based on formal errors of position and velocity as introduced in [68; 69]. However, the estimation from the variance-covariance information of the parameter estimation of the POD depends mainly on the observation constellation and hence is considered an optimistic estimate. We therefore conservatively apply a coverage factor of 2 for an increased confidence interval for the estimate. In line with advice in JCGM [98], suggesting coverage factors typically in the range 2 to 3, the role of such a factor is to ensure that non-quantified components of the overall uncertainty are accounted for within the expanded uncertainty range. In our case the factor of 2 serves this purpose. The radial, along-track, and cross-track uncertainty components of position and velocity (Figure 2) are also combined in a 3D-RMS sense.
## 1 Introduction
The NCCL performs automatic hardware topology detection, which it then uses in graph search algorithms to identify the communication paths that offer the highest bandwidth and lowest latencies for communication between intra- and inter-node GPUs [24]. Currently, this technology has not been used in FDTD algorithms. Therefore, this paper studies an inter-node multi-GPU FDTD algorithm parallel scheme based on the NCCL technology on the CUDA platform using OpenMPI 4.0.1 for process and computation task allocation. Each GPU is mounted onto a single CPU process, and the GPUs within a node are connected using NVLink. InfiniBand network cards are used to communicate between nodes. The NCCL is used to control GPU communication. Neither CPU forwarding nor a PCIe bus during data exchange is required in the scheme, which significantly improves the parallel efficiency of FDTD algorithms and promotes the broad application of FDTD methods in practical electromagnetic problems. The structure of this paper is as follows: Section 2 briefly gives the FDTD algorithm. Section 3 provides the 3D FDTD parallel algorithm on the CUDA platform, including the FDTD calculation domain decomposition scheme and data transmission scheme based on the NCCL. Section 4 presents the simulation results and analysis. Finally, Section 5 concludes the paper.
## 2 Object Detection
### Regression-Based Object Detectors (One-Stage Detectors)
#### 2.4.2 Single Shot Multi-Box Detector (SSD)  
[PERSON] et al. [41] implemented the single shot multi-box detector (SSD), as shown in Figure 11. SSD is designed purely for real-time object detection in a deep learning era. Instead of taking two shots as in the RCNN series, one for generating region proposals and another for detecting the object of each proposal, it uses only a single shot to detect multiple objects within an image. To improve the detection accuracy of SSD, particularly in detecting small objects, it introduced \"multi-reference and multi-resolution detection\" techniques. To improve Fast RCNN's real-time speed detection accuracy, SSD eliminated region proposal network (RPN). SSD300 achieves, on the VOC-2012 dataset, mAP = 74.3% at 59 FPS, while SSD500 achieves, on the VOC-2007 dataset, mAP = 76.9% at 22 FPS, which outperforms Faster RCNN (mAP = 73.2% at 7 FPS) and YOLOv1 (mAP = 63.4% at 45 FPS). The drawbacks of SSD: at the cost of speed, accuracy increases with the number of default boundary boxes. SSD detector has more classification errors when compared to RCNN but low localization error while dealing with similar categories.
## 4 The Proposed DSB Workflow Scheduling Algorithm
### Computational Complexity  
Consider \(n\) as the total number of workflow tasks, \(m\) as the number of BoTs, \(k\) as the total number of available VMs, \(d\) as the number of dependency constraints and \(e\) as the number of directed edges. The time complexity of this scheduling algorithm requires the computation of some basic operations. Calculating priority, dependency constraints and sub-deadline of all tasks have complexity \(O(n.k)\). Sorting groups based on their priority \(O(m)\). Computing start time, completion time and solving the MIP for all VMs \(O(k)\). Mapping tasks on VMs \(O(n.k)\). The total time is \(O(n.k+n(m+k+nk))\), therefore complexity of the proposed algorithm is \(O\big{(}n^{2}.k)\).
## 2 Experimental Data  
The meteorological and radiometric weather station that recorded the experimental data for this study is located on the roof of the Higher Polytechnic School building (EPS) of Burgos University (42\({}^{\circ}\)21\({}^{\prime}\)04\({}^{\prime}\) N, 3\({}^{\circ}\)41\({}^{\prime}\)20\({}^{\prime}\) W, 856 m above mean sea level). Figure 2 shows the location of the meteorological station on the flat roof of the EPS building, where the climatic parameters are measured: ambient temperature, relative humidity, atmospheric pressure, wind speed and direction, and rainfall. A complete description of the experimental facility and its location can be found in previous papers [33; 42].  
Global horizontal irradiation, \(R_{s}\) data were measured by a pyranometer (model SR11, Hulseflux, Delft, The Netherlands). An ML-020P photon meter was used to measure \(Q_{p}\). The sky luminance and irradiance distribution were determined by a commercial MS-321 LR sky scanner. Both instruments were manufactured by EKO Instruments Europe B.V., Den Haag, The Netherlands). The technical specifications of the various measurement instruments are shown in Tables 1-3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **MS-321 LR Sky Scanner** \\ \hline Dimensions (W \(\times\) D \(\times\) H) & 430 mm \(\times\) 380 mm \(\times\) 440 mm \\ Mass & 12.5 kg \\ FOV & 11\({}^{\circ}\) \\ Luminance & 0 to 50 kcd/m\({}^{2}\) \\ Radiance & 0 to 300 W/m\({}^{2}\) \\ A/D Convertor & 16 bits \\ Calibration Error & 2\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sky scanner technical specifications.  
Figure 2: Location of the experimental equipment on the roof of the Higher Polytechnic School building at the University of Burgos, Spain.  
Broadband solar irradiance, \(R_{s}\), and photosynthetic photon flux density data, \(Q_{p}\), were recorded every 10 min (recorded scans of 30 s on average). Experimental data were analyzed and then filtered using conventional quality criteria [43]. The sky scanner was adjusted on a monthly basis for taking measurements from sunrise to the sunset. It completed a full scan in four minutes and started a new scan every 10 min. The first and last measurements of the day (\(\alpha_{\rm s}\leq 7.5^{\circ}\)) were discarded, as well as measurements higher than 50 kcd\(\cdot\)m\({}^{-2}\) and lower than 0.1 kcd\(\cdot\)m\({}^{-2}\), following the specifications of the equipment. If a data set (\(R_{s}\), \(Q_{p}\), or sky scanner measurement) failed to pass the quality criteria, then all the simultaneous data sets were rejected.  
The experimental campaign ran between 1 April 2019 to 31 January 2020, during which time 20,631 data sets were collected, 18% of which were rejected after failing the quality criteria test. Therefore, the total data set comprised 16,937 ten-minute samples of \(R_{s}\), \(Q_{p}\), and the CIE Standard sky classification data sets.
## 3 The Influence of the Structure and Physicochemical Properties on Biological Activity  
While ciclopirox and its olamine salt form exhibit similar pharmacological effects, the therapeutic equivalence of 1% CPO (usually in cream and lotion formulations) to 0.77% CPX (in gels) is due to ethanolamine improving solubility without directly enhancing antifungal activity [7].  
The pyridinone ring exhibits an exceptional set of physicochemical properties, including an advantageous metabolic stability, good water solubility, and balanced lipophilicity, which enable it to mimic non-peptide structures and efficiently interact with biological targets. Both the free hydroxyl group and the nitrogen in the pyridinone ring can act as hydrogen bond donors or acceptors, allowing ciclopirox to form multiple hydrogen bonds with biological targets such as enzymes or receptors. This capability is crucial for its antifungal and antibacterial activities, as it enhances binding affinity and specificity [11; 15].  
Although CPO analogues are not readily available, a number of such substances with various substituents have been prepared in order to develop structure-activity relationships (SAR) by two types of synthesis as follows: via \(\alpha\)-pyrrones and through 2-methoxypyridines [3]. Research on the SAR of 1-hydroxypyridin-2-one derivatives demonstrates that the core structure is crucial for their biological activity. Ciclopirox contains methyl and cyclohexyl groups at the para and ortho positions, respectively, substituting hydrogen atoms (Figure 1a). The sixth position is designated to a lipophilic substituent, permitting the replacement of the hexyl group with another ring structure, such as phenol. This change leads to a reduction in lipophilicity while maintaining the compound's potency. A substantial reduction in efficacy occurs when the hexyl ring is shifted from the sixth position to the fifth or fourth position. The hexyl ring can be lengthened by adding additional substituents at the sixth position. A substituted benzyl group at the sixth position is favored over phenyl or other groups at the third and fifth positions. Strong electron-withdrawing groups are less suitable as substituents for the 6-benzyl group, whereas -OMe or -OH groups improve activity. To achieve submicromolar potency, a very small group, such as -Me or -OH, is necessary at the fourth position [1; 3; 16]. Table 1 summarizes the key SAR findings, focusing on modifications to the hydroxypyridone core and their impact on efficacy.  
The low molecular weight and lipophilic nature of ciclopirox are key factors that influence its skin penetration. Recent studies show that although ciclopirox partially penetrates the deeper layers of the skin, the majority of the compound remains in the stratum corneum, the outermost layer. This limited penetration may not only be attributed to its lipophilicity but also to other factors, such as the chemical properties of the stratum corneum, which restrict further diffusion [1].