esa-sceva/satcom-chunk-collection huggingface dataset:
# Comprehensive Analysis of Maritime Cybersecurity Landscape Based on the NIST CSF v2.0
## 3 Research Methodology
### Research Scope, Aim, and Research Questions  
Maritime cybersecurity holds paramount significance both within the maritime industry and in the global economy at large. This literature review is centered around the application of the NIST CSF v2.0 within the maritime industry, with a particular emphasis on the elements integral for effective cybersecurity. The research questions addressed in this paper, which are based on the NIST CSF, are in line with our research goal:  
1. What are the most recent advancements in the literature on maritime cybersecurity with reference to the NIST CSF functions and their related cybersecurity categories?
2. How mature are the respective cybersecurity functions in the maritime domain?
3. Are there any identified gaps in maritime cybersecurity that necessitate attention from the scientific community?  
By underscoring these aspects, we aim to assess the maturity of the NIST CSF, identify existing security gaps, and highlight potential areas for development. In addition, this review points out the scarcity of research in certain areas while advocating for further investigation and collaborative efforts within the academic community. By enhancing the understanding of the NIST CSF's implementation in maritime operations, this research seeks to strengthen the overall cybersecurity posture of the industry.
## 4 Discussion
The biggest difference for the Y coordinate equal to 0.024 m was obtained for point 10040 (VRS). However, the smallest difference for the Y coordinate equal to 0.000 m was obtained for points: 10030 (SRS) and 10070 (VRS). The biggest difference for the height (for Z coordinate), equal to \(-\)0.059 m, was obtained for point 10010 (VRS). On the other hand, the smallest difference for the Z coordinate equal to \(-\)0.005 m was obtained for point 10030 (MAC). In Figures 4 and 5, the frequency of the minimum and maximum differences after applying GNSS occupation using the individual types of corrections are displayed (SRS, MAC, or VRS). The minimal differences in coordinates were practically even for SRS, MAC, and VRS corrections. However, the maximal differences in the coordinates were stated for VRS correction, in contrast to MAC correction, which is in accordance with the literature [44]. It is also practically correct that the high-quality solution was obtained for positioning with the use of a single reference station (SRS correction). The performed studies did not show any significant difference in the results of the measurements using the GPS system or the GLONASS system. Of course, with the use of both systems (GPS and GLONASS), the number of satellites in the constellation was bigger and the accuracy of positioning of control network points was, therefore, also higher. It was especially noticeable at the points located in more difficult field conditions (near trees and buildings); these are the control points numbered: 10040, 10070, and 10080. Generally, for the control network points that were determined by 30 min sessions of static occupation, the differences of coordinates (X, Y, and Z) to the reference coordinates were no more than 0.030 m. Some of the results of the presented investigations are not very typical, as they were probably influenced by the time window of the satellite positioning and the approximately north-south orientation of the sequence of measured points. For example, the environments of points 10040 and 10070 are relatively poor, but these points have even better results than the other points (see Figure 3). In accordance with the literature [45], the urban environment is characterized by the presence of an excessive number of obstacles that produce a multipath effect in the positioning of the GNSS receiver. Additional assessments were also used in the paper, for results of GNSS positioning were applied in assessing the expected accuracy [40]. The uncertainty interval calculated for the horizontal and vertical components together corresponds quite well with those presented in this work of the differences of the coordinates. When they are totally included, these could represent the displacement distance (as a vector obtained from D\({}_{X}\), D\({}_{Y}\), and D\({}_{Z}\) in a three-dimensional Cartesian system). In the case of multi-variant surveys (terrestrial and satellite hybrid measurements) the observations were adjusted using a classical approach [46]. The available publications also present the results of a further integration of satellite positioning and terrestrial measurements [47; 48].
# Analysis of Total-Forms of Cyanotoxins Microcystins in Biological Matrices: A Methodological Review
## 2 Quantification of Total MC by the MMPB Method
### Lemieux Oxidation Step
#### 2.2.1 Cyanobacterial Matrices  
Regarding Lemieux oxidation applied to water or cyanobacteria cells, the oxidation was originally performed by [PERSON] et al. [65] for 4 h at 0.024 M KMnO4 and 0.02 M NaIO4. Later on, other concentrations were adopted: 0.05 M KMnO4/NaIO4 [66], 0.35 M  
Figure 3: The different stages of MMPB method. These steps have been the subject of optimization work described in the following sections.  
KMnO\({}_{4}\)/0.4 M NaIO\({}_{4}\)[64], or 0.5 M KMnO\({}_{4}\)/0.015 M NaIO\({}_{4}\)[63; 66], with oxidation times of 1 h [64; 66] or 30 min [63]. Two studies were interested in the impact of varying the oxidant concentration on the yield of Lemieux oxidation. [PERSON] et al. [67] varied the KMnO\({}_{4}\) concentration from 6.33 to 63.29 mM in saturated NaIO\({}_{4}\) solution (200 mM) and observed that the yield of MMPB increased by increasing the KMnO\({}_{4}\) concentration, with the best yields obtained for the initial KMnO\({}_{4}\) concentrations of 50 mM. These observations are supported by [PERSON] et al. [66], who tested KMnO\({}_{4}\) and NaIO\({}_{4}\) concentrations between 10 and 100 mM, with an optimum concentration of 50 mM. Both studies reported that a concentration of oxidants above 50 mM could cause the degradation of the compounds and significant signal variability. Regarding the pH, both studies have concluded, after testing different pH values from 2 to 10.5, that MMPB yields are better with a low alkaline pH value (pH-9) [66; 67]. Concerning temperature, [PERSON] et al. [67] concluded that the temperature did not influence the reaction, which can be held at room temperature after comparing temperatures between 0 and 35 \({}^{\circ}\)C at pH 9 and with oxidant concentrations of KMnO\({}_{4}\) (50 mM) and NaIO\({}_{4}\) (200 mM). Finally, the reaction time has also been optimized. Reaction times between 0.5 and 5 h were compared for oxidant concentrations between 10 and 100 mM at room temperature and alkaline pH. While after 3 h, a degradation of the Lemieux oxidation product was observed, between 1 and 3 h no significant difference was observed, and the shortest reaction time (1 h) was chosen [66]. The optimization of these parameters resulted in average yields of 86.7% (50 mM KMnO\({}_{4}\) in saturated 200 mM NaIO\({}_{4}\) solution at ambient temperature, and at pH-9, with a time reaction between 1 and 4 h) [67] and 103% (50 mM KMnO\({}_{4}\), NaIO\({}_{4}\) at ambient temperature, at pH-9, with a time reaction between 1 and 3 h) [66]. The optimized methods have shown higher yields than previous studies, with average yields of 65% (0.35 M KMnO\({}_{4}\) in saturated 0.4 M NaIO\({}_{4}\) solution at ambient temperature at pH-9 with a time reaction of 1 h) [64] but close to those obtained by [PERSON] et al. [65] (84-98% yield with 24 mM KMnO\({}_{4}\) and 20 mM NaIO\({}_{4}\) at room temperature for 4 h).
# A Military Object Detection Model of UAV Reconnaissance Image and Feature Visualization
###### Abstract  
Military object detection from Unmanned Aerial Vehicle (UAV) reconnaissance images faces challenges, including lack of image data, images with poor quality, and small objects. In this work, we simulate UAV low-altitude reconnaissance and construct the UAV reconnaissance image tank database UAVT-3. Then, we improve YOLOv5 and propose UAVT-YOLOv5 for object detection of UAV images. First, data augmentation of blurred images is introduced to improve the accuracy of fog and motion-blurred images. Secondly, a large-scale feature map together with multi-scale feedback is added to improve the recognition ability of small objects. Thirdly, we optimize the loss function by increasing the loss penalty of small objects and classes with fewer samples. Finally, the anchor boxes are optimized by clustering the ground truth object box of UAVT-3. The feature visualization technique Class Action Mapping (CAM) is introduced to explore the mechanisms of the proposed model. The experimental results of the improved model evaluated on UAVT-3 show that the mAP reaches 99.2%, an increase of 2.1% compared with YOLOv5, the detection speed is 40 frames per second, and data augmentation of blurred images yields an mAP increase of 20.4% and 26.6% for fog and motion blur images detection. The class action maps show the discriminant region of the tanks is the turret for UAVT-YOLOv5.  
object detection; feature visualization; unmanned aerial vehicle image; YOLOv5; data augmentation
## 3 Results and Discussion
### Wind Erosion Test  
Wind erosion resistance tests were performed on MICP-treated and MICP combined with HPMC-treated specimens at wind speeds of 4 m/s and 12 m/s, respectively. The mass loss of the specimens before and after the wind erosion resistance test was measured, and the mass loss rate of the specimens was calculated for different amounts of MICP solution and different times of treatments, and the results are shown in Figures 11 and 12. The mass-loss rate of the specimens treated with HPMC for MICP (Figure 11) was lower than that of the specimens treated with MICP without HPMC (Figure 12) at both wind speeds.  
Figure 10: Mass loss under rainfall conditions when HPMC was not added.  
Figure 9: Mass loss under rainfall conditions when HPMC was added.  
When the wind speed was 12 m/s, the maximum mass loss rate of the specimens treated with HPMC for MICP was 0.828%, while the maximum mass loss rate of the specimens treated with MICP was 0.1487%. [PERSON] et al. [31] found that the mass loss of the MICP-treated specimens was 1.02% at a wind speed of approximately 12 m/s when the urea concentration was 1 M. The addition of HPMC, a viscous substance, increased the viscosity of the MICP solution, which allowed more calcium carbonate to be produced in the surface layer of the specimens and improved the resistance of the surface layer to wind erosion. It can also be seen from Figures 10 and 11 that the mass loss rate of the specimen surface layer gradually decreased with an increase in the number of MICP treatment, a trend consistent with the results of [PERSON] et al. [32].  
Figure 11: Mass loss rate when adding HPMC.  
Figure 12: Mass loss rate when HPMC was not added.
# STGAT: Spatial-Temporal Graph Attention Neural Network for Stock Prediction
## 3 System Framework
### Seasonal-Trend Decomposition Based on Loess
#### 3.1.2 Cycling Steps  
STL consists of two main procedures: an inner loop and an outer loop. The inner loop calculates and smooths the trend and seasonal components, while the outer loop regulate robustness in the next internal cycle. In taking the \(k\)th iteration of the inner loop as an example, the inner loop is computed as follows:  
1. Detrending: Obtain the detrended time series \(D\): \[D=Y_{v}-T_{v}^{k},\] (3) where \(Y_{v}\) represents the original value, and \(T_{v}^{k}\) is the trend component obtained in the \(k\)-th iteration.
2. Cycle Subseries Smoothing: Divide \(D\) from Step 1 into cycle subseries and regress them using LOESS. The result is denoted as \(C_{v}^{k+1}\).
3. Low-Pass Filtering: Apply a low-pass filter consisting of three steps: * A moving average of length \(n\); * A moving average of length 3; * A Loess regression with \(d=1\) and \(q=n\). The result is denoted as \(L_{v}^{k+1}\).
4. Obtaining Seasonal Components: Compute the seasonal series \(S_{v}^{k+1}\) by subtracting low-pass components: \[S_{v}^{k+1}=C_{v}^{k+1}-L_{v}^{k+1}\quad\text{for $v=1$ to $n$}.\] (4)
5. Deseasonalizing: Obtain the deseasonalized series by subtracting the seasonal component \(S\) from the original series \(Y\).
6. Trend Smoothing: Apply [PERSON] regression to the result of Step 5 to obtain the trend series \(T_{v}^{k+1}\).  
The inner loop terminates if it converges, and the random term is obtained. Subsequently, the outer loop updates the robustness weights through a well-defined weighting function; this adjustment mechanism effectively mitigates the influence of outliers, thereby ensuring the robustness of the fitting process before initiating the next internal cycle:  
1. Execute the inner loop.
2. Calculate residual component \(R_{v}\): \[R_{v}=Y_{v}-T_{v}-S_{v}\] (5)
3. Calculate robustness weight \(\rho_{v}\): \[\rho_{v}=B\bigg{(}\frac{|R_{v}|}{h}\bigg{)},\quad\text{where $h=6\times\text{median}(|R_{v}|)$}.\] (6)The bisquare weight function \(B(u)\) is defined as  
\[B(u)=\begin{cases}(1-u^{2})^{2}&\text{for}\;0\leq u<1,\\ 0&\text{for}\;u\geq 1.\end{cases} \tag{7}\]
# Towards Integrated Digital Twins for Industrial Products: Case Study on an Overhead Crane
## 3 Results
### Usage of APIs in the Use Cases  
All of the described use cases (Sections 3.7-3.14) are connected to some other building block of the DT as all of them use real data from the crane. The data exchange methods were chosen individually for each use case to fulfill their specified needs. The details of API usage in the cases are shown in Table 1 and the selection processes of the APIs are described in the following paragraphs. Case 1: Bearing lifetime estimation was developed as an opener case for the project and was planned as a collaborative effort among all project members. The planning phase included an iterative process of finding usable data sources for the crane and finding a topic that serves the goals of the project. The goals were to leverage operational data of the crane from multiple sources and to support machine design activities. Initial ideas included combining information from multiple systems, such as enterprise resource planning and maintenance, but this turned out too ambitious due to practical reasons: access to the systems was limited or they did not have suitable APIs. The case finally leveraged two systems: MindSphere and Teamcenter. MindSphere was set up to receive usage data from the crane OPC UA interface so it was a natural choice for Case 1. The MindSphere database provided a REST API which was used in a separate web application for two purposes: refining the data and presenting the data. Refining the data required both reading and writing through the REST API and presenting. The REST API authentication (acquiring credentials and learning to use the credentials) required setup, but this proved manageable during the project. Teamcenter was selected as the target system for the digital twin interface because it was already used by machine designers, had a lot of design data, and was supposed to store product lifecycle information. Teamcenter had a SOAP (Simple Object Access Protocol) API, but it was not used because none of the project personnel had used it before and the time required to learn to use it was considered too high. It was also unclear what the API would actually offer. However, embedding a web browser element to Teamcenter was easy, and this was the method to integrate the MindSphere-based usage data visualization to Teamcenter.
## 2 Validation and Ancillary Dataset
### AERONET Sun-Photometer  
The AERONET, a network of globally distributed ground-based sun-photometers, has been widely used to measure global AOPs. The AERONET sun-photometer measurement of direct solar  
Figure 1: Frequency distribution of the scattering angle of GEMS and OMI over the GEMS coverage range. The frequency is calculated using the actual sun- and satellite-geometry of OMI measurements and numerical calculations of the expected GEMS ground pixel per day for each month during 2006. Patterns indicate the frequency at each observation time of the GEMS. Tick values on the \(x\)-axis are the minimum value of each bin.  
radiation provides cloud-free AOD from 340 to 1020 nm with a high retrieval accuracy of about 0.01 in the visible and near infrared and about 0.02 in the UV range [16; 32; 33]. Measurements under the almucantar scenario can be inverted to produce AOPs such as the size distribution, single scattering albedo at 440, 675, 870 and 1020 nm, phase functions and the complex index of refraction [34; 35; 36]. Because total column aerosol properties provided by AERONET sun-photometer measurements are comparable to passive satellite measurements, the data have also been used extensively to validate satellite-based aerosol products.  
In this study, all quality-assumed AERONET inversion data (version 2.0 level 2 all-point products) from 1992 to 2016 were adopted to obtain optimized AOPs, the volume size distribution and refractive indices. These values were categorized according to three aerosol types following an aerosol type classification method [30] and were then applied to look-up table (LUT) calculations. In addition, level 2.0 AOD and SSA datasets at 26 AERONET sites in Asia (20\({}^{\circ}\)N-50\({}^{\circ}\)N, 100\({}^{\circ}\)E-145\({}^{\circ}\)E) that include measurements during 2005-2007 were selected to validate retrieval results. The site name, location and number of AOD data points are listed in Table 2. The initial and final days and number of data points in Table 2 summarize the AOD data used for the validation. SSA data at the same AERONET sites as listed in Table 2 were used to validate retrieved SSA.
# ICN-Based Enhanced Content Delivery for CDN
## 6 Discussion and Conclusions  
ICN in-networking caching has been widely adopted to address issues in other fields because of its advantages in terms of less content retrieval latency, low network traffic and so on. This study focuses on the design and evaluation of utilizing the ubiquitous caching of ICN as a service to enhance the performance of CDN. To this end, first, we introduce the overview of our proposed design of a CDN enhanced with ICN, called IECDN, and elaborate on the mechanisms for enabling the collaboration between CDN and ICN to achieve efficient content delivery. Second, we formulate an optimization model derived from the MCF problem, which quantifies the optimization goal for content delivery. Through a series of numerical analyses, the results demonstrate that our design not only improves content delivery efficiency but also reduces the network load and is more resilient to network traffic surge. Based on these findings, we venture the conclusion that the  
Figure 10: Average manifest size under different chunk sizes.  
ICN-based enhanced CDN distribution can economically satisfy the exponential growth of internet content without placing an unacceptable burden on link loads.  
However, the ubiquitous caching and dynamic replica selection of ICN also pose a great challenge to the security design within ICN. In our framework, an associated manifest is generated when the content is published in ICN. Therefore, the manifest can be leveraged as a handle to ensure content-level security. In our forthcoming work, we intend to further explore the solutions to security issues by considering the distinctive features of our framework and using the existing mature security mechanism support. Moreover, we will explore more advantages of ICN in the domains where it can possibly provide better solutions.  
Conceptualization, L.G. and X.Z.; methodology, L.G. and X.Z.; software, L.G.; writing--original draft preparation, L.G.; writing--review and editing, L.G. and X.Z.; supervision, X.Z.; project administration, X.Z.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript. This work was funded by the Goal-Oriented Project Independently Deployed by Institute of Acoustics, Chinese Academy of Sciences: Distributed Supercomputing Based on SEANET Network (project no. MBDX202114). Data are contained within the article.  
We would like to express our deepest gratitude to [PERSON] and [PERSON] for their meaningful support for this work. The authors declare no conflict of interest.
## 2 Kinematic Modeling
### Kinematics Model
The Jacobi matrix for solving Equation (6) is expressed as follows:  
\[ \begin{cases}\frac{\partial f_{1}}{\partial x}\frac{\partial f_{1}}{\partial y }\frac{\partial f_{1}}{\partial y}\frac{\partial f_{2}}{\partial z}\frac{ \partial f_{1}}{\partial x}\frac{\partial f_{1}}{\partial y}\frac{\partial f_ {1}}{\partial y}\frac{\partial f_{1}}{\partial y}\\ \frac{\partial f_{2}}{\partial x}\frac{\partial f_{2}}{\partial y}\frac{ \partial f_{2}}{\partial z}\frac{\partial f_{2}}{\partial x}\frac{\partial f _{2}}{\partial x}\frac{\partial f_{2}}{\partial y}\frac{\partial f_{2}}{ \partial y}\frac{\partial f_{2}}{\partial y}\\ \frac{\partial f_{3}}{\partial x}\frac{\partial f_{3}}{\partial y}\frac{ \partial f_{3}}{\partial z}\frac{\partial f_{3}}{\partial z}\frac{\partial f _{3}}{\partial x}\frac{\partial f_{3}}{\partial y}\frac{\partial f_{3}}{ \partial y}\frac{\partial f_{3}}{\partial y}\\ \frac{\partial f_{4}}{\partial x}\frac{\partial f_{4}}{\partial y}\frac{ \partial f_{4}}{\partial z}\frac{\partial f_{4}}{\partial x}\frac{\partial f _{4}}{\partial x}\frac{\partial f_{4}}{\partial x}\frac{\partial f_{4}}{ \partial y}\frac{\partial f_{4}}{\partial y}\\ \frac{\partial f_{3}}{\partial x}\frac{\partial f_{5}}{\partial y}\frac{ \partial f_{5}}{\partial y}\frac{\partial f_{5}}{\partial z}\frac{\partial f _{5}}{\partial x}\frac{\partial f_{5}}{\partial y}\frac{\partial f_{5}}{ \partial y}\frac{\partial f_{6}}{\partial z}\frac{\partial f_{6}}{\partial x} \frac{\partial f_{6}}{\partial z}\frac{\partial f_{6}}{\partial z}\end{cases}  \tag{7}\]  
The iterative modification process can be expressed as follows:  
\[ \begin{cases}\frac{\partial f\mathbf{p}^{(k)}}{\mathbf{p}^{(k+1)}}=\mathbf{p}^{(k)}+\delta \mathbf{p}^{(k)}\end{cases} \qquad 0<\lambda^{(k)}<1 \tag{8}\]  
where \(\mathbf{p}^{(k)}\) is the \(k\)th step iterative value, \(\mathbf{f}\) (\(\mathbf{f}=[f_{1}\ f_{2}\ f_{3}\ f_{4}\ f_{5}\ f_{6}]^{\text{T}}\)) is the vector function describing the difference between the calculated stroke lengths with Equation (4) and the measured stroke lengths with displacement sensors, and \(\lambda^{(k)}\) is the monotonic descent operator to ensure the convergence of iteration, which can be automatically selected and satisfies Equations (9) and (10). \[\left\|\mathbf{f}\Big{(}\mathbf{p}^{(k-1)}\Big{)}\right\|<\left\|\mathbf{f}\Big{(}\mathbf{p}^{( k)}\Big{)}\right\| \tag{9}\]  
\[\mathbf{p}\in\text{D} \tag{10}\]where D is the workspace without singularity of the parallel robot. The iterative termination condition of the global Newton-Raphson algorithm is expressed as follows:  
\[\left|max\left\{f\!\left(\mathbf{p}^{(k)}\right)-f\!\left(\mathbf{p}^{(k-1)}\right) \right\}\right|<\varepsilon\quad k\leq N \tag{11}\]  
where \(\varepsilon\) is defined as the permissible error (required precision level of the final solution) of the proximity of the calculated values of the vector function \(f\) between adjacent iterations, and it is an infinite decimal. \(N\) is defined as the maximal number of iterations in a control cycle.