esa-sceva/satcom-chunk-collection huggingface dataset:
# Maritime Traffic Evaluation Using Spatial-Temporal Density Analysis Based on Big AIS Data
## 2 Materials and Methods
### AIS Data  
This study performs a maritime traffic-density analysis on AIS data collected by the General Information Center on Maritime Safety and Security (GICOMS), which is operated by the Ministry of Oceans and Fisheries of the Republic of Korea. The data were collected through AIS base stations and satellites for monitoring the ships and preventing marine accidents [39]. AIS message information is divided into dynamic and static information. Dynamic information includes the locations (latitude, longitude), dates and times, speeds, and courses of ships, while the static information includes the Maritime Mobile Service Identity (MMSI) number, ship type, International Maritime Organization (IMO) number, ship length, and ship width [21].  
The AIS data in the present analysis cover the entirety of Korea's coastal waters and their temporal range is several months (covering various seasons). The ship types in AIS data are classified into 90 identifier numbers [40]; excluding the identifier numbers reserved for future use, 20 types of ships with different use characteristics can be identified. This study targets four types of ships with constant and customary traffic patterns: cargo ships, tankers, passenger ships, and towing ships. As these four types of ships carry cargoes or people, they establish a regular and clear traffic pattern based on the major trade ports. Such traffic patterns have large-scale consequences in the event of a marine accident. For example, when the oil tanker MV Hebei Spirit collided with a barge carrying a crane that was towed by a tugboat, a large amount of crude oil spilled off the west coast of Korea, causing serious marine pollution [41]. To identify the main traffic patterns of the ships, the density calculations were based on occupancy times alone, ignoring the data of ship berthing, unething, mooring, and anchoring. The dynamic information of reporting intervals of Class A ships is specified in the ITU technical standards (see Table 1). Note that the reporting intervals of anchored or moored ships depend on speed (\(\geq\)3 knots (kts) or <3 kits) [40]. This study excludes the trajectory data of ships moving at 3 kits or less. Because the navigation patterns of small ships are irregular and widely distributed, the trajectories of ships <60 m length are excluded.  
Figure 1: Flowchart of the proposed maritime traffic density analysis using an automatic identification system (AIS) data and density map creation.  
Table 2 compares the characteristics of our data with those used by [PERSON] et al. [37] who previously analyzed maritime traffic in Korea. Both studies used the seasonal data in March, June, September, and December of 2018. [PERSON] et al. used one week of data in each of the four seasons, whereas this study uses one month of data in each of the four seasons. Both studies considered the same types and lengths of ships, but this study imposes additional conditions on ship speed as noted above.
## 2 Theoretical Background
### Elliptic Curve Groups
#### 2.3.2 Elliptic Curve Key Generation  
Consider an elliptic curve \(\mathbf{E}\) defined over a finite field of integers \(\mathbb{F}_{p}\) with \(G\)_as_ a point in \(\mathbf{E}(\mathbb{F}_{p})\) and suppose point \(G\) has a prime order \(n\). The cyclic subgroup of \(\mathbf{E}(\mathbb{F}_{p})\) generated by point \(G\)_is_ then \(G=\{\ \infty,\ G,\ 2G,\ 3G,\ \ldots\ldots\ (n-1)G\}\). The prime \(p\), the equation of the elliptic curve \(E\), and the point \(G\) and its order \(n\) are the public domain parameters.  
An integer private key \(d\), which is selected uniformly at random from the interval \([1,n-1]\), can be used to generate the corresponding public key \(Q\) (treated as a point on the curve) using the relation below as a scalar multiplication of private key \(\mathsf{d}\) with generator point \(G\) as represented by Equation (3).  
\[Q=dG \tag{3}\]  
The problem of determining \(d\) given the domain parameters and \(Q\) is the elliptic curve discrete logarithm problem.
## 1 Introduction  
Carbon and metallic nanomaterials have shown wide applications in various fields from electrocatalysis [1; 2], energy materials [3] and biosensors [4], to biomedical materials [5]. Graphene is a two-dimensional (2D) carbon material with monolayered hexagonal sp2 hybridized carbons. It has attracted increasing attentions due to its uniform 2D structure, high conductivity, unique mechanical property, and large surface area [6; 7; 8]. To extend its potential applications in materials science, nanotechnology and biomedicine, many nanoscale building blocks such as nanoparticles (NPs) [2; 9; 10; 11], quantum dots [12; 13], biomolecules [4; 14], polymers [15; 16], and others have been utilized to conjugate with graphene to form graphene-based hybrid nanomaterials, which showed enhanced performances in comparison with sole graphene materials.  
Both covalent and noncovalent methods could be used for the functionalization of graphene for further synthesis of graphene-based hybrid nanomaterials [17; 18; 19; 20]. As the irreversible nature of these covalent bonds formed in the covalent modification process would potentially hinder the electronic properties of graphene, the noncovalent modifications, by using both \(\pi\)-\(\pi\) and electrostatic interactions, offer promising ways to achieve the plane-specific functionalization of the graphene surface [21; 22; 23]. Previously, a few sequence-designed peptides such as EPLQLKM, YWYAF, and GAMHLPWHMGTL with highly specific graphene-binding ability have been discovered through both experimental and theoretical studies [24; 25; 26; 27]. In addition, the formed graphene-peptide nanohybrids have shown high potentials to fabricate graphene-NPs [23; 28] and graphene-hydroxyapatite [22; 29] nanohybrids for various biomedical applications.  
In this work, we demonstrated that a phenylalanine (F)-rich peptide molecule with the amino acid sequence of (FEFEKFK)\({}_{2}\) shows high binding affinity with graphene oxide (GO) nanosheetsby the noncovalent \(\pi\)-\(\pi\) interaction, which promotes the formation of a dense network-like peptide structure on both sides of GO nanosheets. The conjugated peptide molecules not only provide the nucleation and growth templates for the bioinspired synthesis of silver nanoparticles (AgNPs), but also serve as the bridges for connecting GO with the formed AgNPs to create GO-peptide- AgNPs nanohybrids. The synthesized GO-peptide-AgNPs nanohybrids were directly used to modify a glass carbon electrode (GCE) to fabricate a nonenzymatic electrochemical hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) sensor, which reveals high sensing performance with a detection limit of 0.13 uM and a wide linear range from 0.02 to 18 mM.
# Tolerance Modeling and Analysis Considering Form Defects for Spaceborne Array Antenna
## 2 Tolerance Analysis Considering Form Errors  
The novel tolerance analysis method was developed based on Non-Uniform Rational B-Splines (NURBS) surface and the variance separation method. Compared with the traditional process, the new single loop process is much more efficient and accurate. For the convenience of description, the basic principle of the variance separation method is briefly explained first [19; 20; 21].
# Photonic Technology for Precision Metrology
## 1 Introduction  
Metrology is a science that covers the issues of measurement related to both experimental and theoretical determinations. It includes the definition and practice realization of measurement units, and reference standards. The most important purpose is to provide a common understanding of measurement principles, not only for research, but for every area of life. The development of technology and applied sciences is very closely related to the development of new and improvement of existing measurement methods. As a result, less measurement uncertainty as well as greater accuracy and precision can be ensured. It manifests itself in increasingly sophisticated scientific discoveries and improvements in everyday devices. What is more, the pursuit of better and better measurements somehow forced the redefinition of International System of Units (SI), which took place in 2019 [1].  
Photonics plays an important role in metrology as a technology for the precise measurements of physical and chemical factors ensuring a high level of accuracy, precision and resolution. As the science of photon, it has a decisive influence on the recent scientific and technology achievements. It includes aspects of photon generation, photon-matter interaction and detection. Although it finds many applications in the whole optical range of wavelengths, most solutions operate in the visible and infrared ranges. Since the laser invention, the source of the highly coherent optical radiation, optical measurements have become a perfect tool for highly precise and accurate measurements. Such measurements have additional advantages of being non-contact methods and having fast rates suitable for in-process metrology. However, their extreme precision is ultimately limited by, e.g., the noise of both lasers and photodetectors. Therefore, breakthroughs in the field of optical sources, detectors and optics play a crucial role. They provide the state-of-the-art of photonic technology for precision metrology, and to identify directions for its future development. As a result, the cutting-edge optoelectronic systems are developed and improved in many fields of science and technology (e.g., industry, environment, healthcare, telecommunication, security and space). The most significant areas of photonics applications are:  
* Spectroscopy and interferometry;
* Imaging, reflectometry, radiometry photometry and polarimetry;
* In situ, remote and stand-off sensing;* PICs (photonic integrated circuits), QPICs (photonic quantum sensors) and hybrid sensing;
* Smart sensors and sensors for robots and unmanned systems;
* High-resolution and frequency metrology, and optical clocks.  
The importance of photon-based technology, selected principles of metrology and measuring instrumentation in spectroscopy in solving difficult engineering problems in the detection of trace matter are presented in the further and tutorial sections of the article.
## 3 Results and Discussion
### Application of the Proposed Model to the G-Band Images Dataset  
The G-band image dataset (GID) is composed of 24 studies of chromosomes in metaphase. The dataset and its manual classification by an expert were provided by the laboratory of cytogenetics of the CHT. Using automatic and semi-automatic segmentation tools developed by our team, the chromosomes were individually segmented and a dataset of 1097 G-band chromosome images was composed. If the karyotypes were taken from healthy people, the total number of chromosomes of the dataset should be 1104, but some of the provided studies presented some anomaly in the number of chromosomes, for example, some studies lacked chromosome 4 or 18. It is worth noting that the number of elements of the G-band dataset is only 20% of that of the Q-band dataset. Figure 13 shows an example of the chromosomes in G bands.  
Figure 12: Achitecture of the ANN used in the 3 wide classes model.  
The segmented GID was then used to train the ANN architecture presented in Figure 12. Using binary and multi-class classifiers, in phase 1, the accuracies were 95.26%, 90.56% and 93.6%, for the groups 1, 2, and 3, respectively. In phase 2, the accuracies were 91.12%, 77.08% and 84.4%, for the same groups. Finally, the overall accuracy was 80.99% for the whole set.
###### Abstract  
Accurate short-term traffic flow prediction is a prerequisite for achieving an intelligent transportation system to proactively alleviate traffic congestion. Considering the complex and variable traffic environment, so that the traffic flow contains a large number of non-linear characteristics, which makes it difficult to improve the prediction accuracy, a combined prediction model that reduces the unsteadiness of traffic flow and fully extracts the traffic flow features is proposed. Firstly, decompose the traffic flow data into multiple components by the seasonal and trend decomposition using loess (STL); these components contain different features, and the optimized variational modal decomposition (VMD) is used for the second decomposition of the component with large fluctuation frequencies, and then the components are reconstructed according to the fuzzy entropy and Lempel-Ziv complexity index and the Pearson correlation coefficient is used to filter the traffic flow features. Then light gradient boosting machine (LightGBM), long short-term memory with attention mechanism (LA), and kernel extreme learning machine with genetic algorithm optimization (GA-KELM) are built for prediction. Finally, we use reinforcement learning to integrate the advantages of each model, and the weights of each model are determined to obtain the best prediction results. The case study shows that the model established in this paper is better than other models in predicting urban road traffic flow, with an average absolute error of 2.622 and a root mean square error of 3.479, both of which are lower than the prediction errors of other models, indicating that the model can fully extract the features in complex traffic flow.  
short-term traffic flow prediction; two-level decomposition; feature selection; reinforcement learning; combinatorial models +
Footnote †: journal: Applied  
doi: 10.3390/app12167978
# On the Impact of Mobility on Battery-Less RF Energy Harvesting System Performance
## 2 Energy Trade-Offs for Mobile Systems
### Task Size  
Last but not least, the task size is an important factor in the design of the energy harvesting system. Even the most basic applications require the ability to combine light tasks, such as sensing or computation, with large tasks, such as communicating with other nodes or the base station. These tasks are often separated in time with light tasks dominating most of the time and larger tasks executed occasionally to upload sensed data or reprogram the node firmware. The light tasks may benefit from a small storage to quickly start up the node and execute a task. On the other hand, energy-intensive tasks require a larger storage, which again comes into conflict with the ability to boot quickly and charging efficiency.  
To summarise, the design of a battery-less energy harvesting systems is driven by a number of trade-offs and sometimes contradicting requirements. The energy storage size is the key design parameter, which has implications on the charging problem, keeping the charge and maximum task size. In this paper, we argue that these challenges can be met by a hybrid storage solution, which combines several capacitors of various sizes.
# Deep Learning-Based Detection Algorithm for the Multi-User MIMO-NOMA System
## 3 Proposed Method
### FDNN Model  
Detecting symbols sent by users in a communication system essentially involves a judgment process akin to a multi-categorization problem. So, we employ one-hot coding to represent different classifications and leverage feedback deep neural networks for non-linear classification. Using one-hot coding helps reduce computational complexity when calculating the cross-entropy of the vectors, which facilitates the training and testing of the network. The FDNN receiver follows a detection sequence from strong to weak user signals. After each detection level, the reconstructed signal is fed back to the input terminal and subtracted from the superimposed signal. Subsequently, the detection process for the next user ensues. The FDNN receiver model is depicted in Figure 2.  
The received signal \(\mathbf{y}\) and the channel parameter vector \(\mathbf{\Theta}\) serve as the inputs of the FDNN network. They undergo multiple hidden layers, each comprising a fully connected layer followed by a ReLu layer. In the last hidden layer, the input from the preceding layer undergoes a fully connected layer and a softmax layer. The output then passes through a reconstruction layer, and this reconstructed signal is subtracted from \(\mathbf{y}\). The rest of it will become the new received signal for the subsequent user's FDNN receiver.
# Trajectory Planning of Shape-Following Laser Cleaning Robot for the Aircraft Radar Radome Coating
## 2 Materials and Methods
### Laser Cleaning Robot Trajectory Planning
#### 2.3.2 Calculation Model for Laser Cleaning Actuator Point Coordinates
When \(\lambda\) reaches its minimum value, the corresponding vector \(\mathbf{u}_{j}\) is the normal vector of the fitted plane \(p_{j}\), which can be approximated as the normal vector of point \(\mathbf{u}_{j}\), denoted as \(u_{s_{j}}\). The direction of the normal vector can be determined using the viewpoint method. Let us consider a point q within the object under inspection. If the relationship between \(u_{s_{j}}\) and q satisfies Equation (17), then the direction of the normal vector remains unchanged. However, if the relationship does not hold, the direction of the normal vector is reversed to ensure uniformity in the direction of the normal vectors. \[\mathbf{u}_{j}\mathbf{Q}>0 \tag{17}\]  
where \(\mathbf{Q}\) is the normal vector of point q.
# 400 GbE Technology Demonstration Using CFP8 Pluggable Modules
###### Abstract  
In this article, we first review the current status of 400 GBASE client-side optics standards and multi-source agreements (MSAs). We then compare different form factors for 400 GE modules, including CFP8, OSFP and QSFP-DD. The essential techniques to implement 400 GE, such as pulse amplitude modulation (PAM4), forward error correction (FEC) and a continuous time-domain linear equalizer (CTLE), are discussed. A 400 GE physical interface card (PIC) in Juniper's PTX5000 platform has been developed, conforming to the latest IEEE802.3 bs standard. To validate the PIC's performance, a commercial optical network tester (ONT) and the PIC are optically interconnected through two CFP8-LR8 modules. The CFP8-LR8 module utilizes eight optical wavelengths through coarse wavelength division multiplexing (CWDM). Each wavelength carries 50 Gb/s PAM4 signal. The signal transmits through 10 km of single mode fiber (SMF). The ONT generates framed 400 GE signal and sends it to the PIC through the first CFP8 module. The PIC recovers the signal, performs an internal loopback, and sends 400 GE signal back to the ONT through the second CFP8 module. The optical spectrum, eye diagram, receiver sensitivity, long time soaking results, and internal digital diagnosis monitoring (DDM) result are fully characterized. The pre-FEC bit error rate (BER) is well below the KP4 FEC threshold of 2.2 \(\times\) 10\({}^{-4}\). After KP4 FEC, error-free performance over 30 km of SMF is achieved. In this way, we demonstrate both the interoperation between the PIC and the ONT, as well as the interoperation between the two CFP8 modules. This demonstration represents the successful implementation of the 400 GE interface in the core IP/MPLS router.  
optical communication; fiber optics; client-side optics; 400G Ethernet; CFP8-LR8 transceiver +
Footnote †: journal: Applied Physics Letters
## 4 Results  
### Estimation of Vegetation Phenology Parameters from CI and NDVI Time Series  
Figures 4 and 5 show the accuracy evaluation results, comparing the SOS and EOS estimated from the CI and NDVI time series for typical pixels with those from the MODIS LSP product. The accuracy evaluation metrics are the root mean square error (RMSE) and bias. For the CI time series, Figure 4a,b indicate that, except for PWe and CL1, the RMSE of the estimated SOS using the optimal threshold is less than 40, with best thresholds concentrated at approximately 40% and 80%, respectively. For the EOS, except for Wsa, PWe, CL1, and the CVM, the RMSE associated with the optimal thresholds is less than 35, and the optimal thresholds are concentrated between 80% and 90%. Figure 4c,d demonstrate that the estimated SOS using the optimal threshold shows a balance between overestimation and underestimation, where CL2-1 and CL2-2 represent the first and second vegetation cycles of biannual cropland, respectively, as indicated in the legend of Figure 4. For the EOS, underestimation is more common, indicating that the estimated EOS tends to appear earlier. For the NDVI time series, Figure 5a,b show that the optimal thresholds for estimating the SOS are mostly between 40% and 70%, whereas those for the EOS are mainly between 10% and 40%. In addition, Figure 5c,d indicate that the estimated SOS using the optimal threshold shows a balance between overestimation and underestimation. The bias of the EOS is relatively large, mostly reflecting overestimation. In general, the accuracy of estimating phenological parameters via the NDVI time series tends to have a small degree of uncertainty relative to that of the CI time series when the improved dynamic threshold method is used. This is probably due to CI being influenced by multiple factors during the inversion process, especially the high standards needed for accurately measuring hotspots of typical features of the BRDF shapes in the principal plane, which leads to reduced stability. However, the errors remain within acceptable limits, indicating that the CI exhibits a distinctly and approximately inverse trend of phenological variations compared with the NDVI. Additionally, the optimal thresholds for estimating the SOS and EOS across different IGBP classes based on CI and NDVI time series varied significantly. This finding indicates that the optimal thresholds for estimating phenological parameters are not only related to the phenological stages but also differ depending on the vegetation indices and land cover types.