esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Multi-Port Ship Traffic Prediction Method Based on Spatiotemporal Graph Neural Networks
## 3 Scenario and Data Sources
In our experiment, the central hubs selected were Rotterdam Port, Shanghai Port, Singapore Port, Boston Port, Antwerp Port, Hong Kong Port, and Incheon Port. This approach allows for the more efficient processing of the data while still capturing the essential connectivity among ports. Using Rotterdam Port as an illustration, we initially filter the OD dataset to include only maritime shipping data where Rotterdam Port is either the origin or destination. We then identify the destination (or origin) ports that exhibit a mutual traffic count that satisfies the threshold constraint, thereby indicating direct connections to Rotterdam. Through this procedure, we identify a total of 250 port nodes that are directly linked to Rotterdam Port. Subsequently, we shift our attention to these 251 ports and proceed to compute the direct vessel traffic count between each pair of ports. Employing a similar methodology as before, if the traffic count surpasses the predefined threshold, a unidirectional edge relationship is established between the two ports. Finally, we obtain a sub-network structure comprising multiple ports, with Rotterdam Port as the central hub. The corresponding adjacency matrix is generated as a result. Figure 6 provides a visual representation of the schematic diagram depicting the multi-port graph structure centered around Rotterdam Port. The geographical coordinates and location information of the seven central ports are displayed in Figure 7. The comprehensive data attributes of the port cluster network scene, centered around these seven ports, are recorded in Table 1. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Central Port Name** & **Port Nodes** & **Edges** & **Data Size** \\ \hline Rotterdam & 251 & 3036 & 91,615 \\ Shanghai & 241 & 10,876 & 87,965 \\ Singapore & 388 & 10,017 & 141,620 \\ Boston & 121 & 1054 & 44,165 \\ Antwerp & 217 & 2706 & 79,205 \\ Hong Kong & 182 & 7731 & 66,430 \\ Incheon & 86 & 2733 & 31,390 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Dataset attributes for seven port subnetworks.
## 3 Preliminaries
### Basic Facts Regarding the Physical Geometric Space
#### 3.2.2 Motion Spaces  
The motion of a rigid robot's manipulator or end effector can be decomposed into rotational and translational motion. The origin and coordinate basis vectors of the world will be referred to as the world frame, and any point in the world can be expressed in terms of the world frame. Thus, motion causes transformations of distances, angles, and orientations in reference to the world frame.  
**Definition 6**: _A rotation causes the transformation of the attitude of a rigid body in accordance with the special orthogonal group, \(\mathrm{SO}(3)\):_  
\[SO(3):=\{R\in\mathbb{R}^{3\times 3}|det(R)=1,RR^{T}=I\}\]  
_where 1 is the three-dimensional identity matrix and \(R\) is called a rotation matrix._  
**Definition 7**: _Whereas the group \(SO(3)\) is used to represent the rotation space, a topological space is established to describe both rotations and translations; this space is called the special Euclidean group, \(\mathrm{SE}(3)\):_  
\[SE(3):=\{\big{(}\begin{smallmatrix}R&v\\ \mathbf{0}_{i\times 3}&1\end{smallmatrix}\big{)}\in\mathbb{R}^{4}|R\in SO(3),v \in\mathbb{R}^{3}\}\]  
_The part of the matrix above denoted by \(R\) realizes rotation, and the part denoted by \(v\) realizes translation._
# M-Emu: A Platform for Multicast Emulation
## 3 Implementation
### Multicast-Emulation Framework
#### 3.2.2 Construction and Maintenance Mechanism of Multicast Tree  
As shown in Figure 2, the controller can deploy RP, DR, NRS and Emu-nodes in the network according to the logic of its function deployment module. All RPs register their NAs on NRS with the same specific identity. The data source generates the data and the multicast service ID and sends it to DR (arrow 0). After receiving the data from the source, DR sends the RP request (arrow 1) to NRS, and NRS replies with the RP NAs list (arrow 2). DR selects an RP as the root of the multicast distribution tree according to the RP-selection method and sends data to the selected RP (arrow 3). Our Multicast-Emulation Framework considers that no multicast transmission path is constructed between the source-side DR and RP, and thus the data is transmitted through unicast in this stage.  
After receiving data from the source-side DR, the RP adds an entry to the MFT for the multicast service. The RP needs to register the mapping between the multicast service ID and its NA to NRS (arrow 4). After receiving data requests from the receivers (arrows 5-6), the receiver-side DR requests the NAs of the nodes in the multicast distribution tree from NRS based on the multicast service ID. After receiving the NA list replied to by NRS, DR selects an appropriate GP according to the GP-selection algorithm. DR sends a joining request (arrows 8, 9, 16 and 17) to the selected GP. After receiving the joining request, GP sends the Multicast Data Reply to the receiver-side DR (arrows 10, 11, 18 and 20).  
Each node on the forwarding path determines whether to add an entry for the multicast service. If the node creates an entry in MFT for the multicast service, the node also needs to add its NA to the entry of the previous hop node. Furthermore, the node needs to register the mapping between its NA and the multicast service ID to NRS (arrow 12 and arrow 19). If the node does not create an entry in MFT for the multicast service, the node only needs to forward the multicast data to the receiver side DR.  
Figure 2: Multicast-Emulation Framework.  
Multicast Data Reply contains the DR NA and the next Branching Point (BP) NA. Next BP refers to the node that is reachable for GP through the same outgoing interface as the receiver-side DR. If a node found that the unicast paths of the receiver-side DR and the next BP diverge, the node becomes a new branch node for the multicast tree. This mechanism is essentially the same as NBM [34].
## 2 Methodology
### Ensemble Approach to Anomaly Detection Using Spherical Detectors
#### 2.5.1 Motivation  
The algorithms for generating sets of spherical detectors are stochastic procedures, meaning each execution yields a different set of detectors for the same task and data. A single detection system may have regions of the feature space that are not covered, while in other areas excessively small detectors may be unnecessarily placed where anomalies do not occur. Introducing majority voting allows the ensemble to average out these effects and mitigate such negative impacts.
## 2 Materials and Methods  
### Description of Datasets  
In this paper, we consider MNIST-10 [30] and SARS-CoV-2-RBV1 [36] as the input datasets for Stage 2 and the EEG dataset [37] as the input dataset for Stage 1 in the NNetEn algorithm. For simplicity, we call MNIST-10, SARS-CoV-2-RBV1 and the considered EEG dataset as Dataset 1, Dataset 2 and Dataset 3 respectively.  
* Dataset 1: The MNIST dataset contains handwritten numbers from '0' to '9' with 60,000 training images and 10,000 testing images. Each image has a size of 28 \(\times\) 28 = 784 pixels and is presented in grayscale coloring. This dataset is well balanced since the number of vectors of different classes is approximately the same. The distribution of elements in each class is given in Table 1.
* Dataset 2: The SARS-CoV-2-RBV1 dataset contains information on 2648 COVID-19 positive outpatients and 2648 COVID-19 negative outpatients (control group), for 5296 patients, as described by [PERSON], [PERSON] and [PERSON] in [36]. A data vector containing 51 routine blood parameters is included in this dataset. The dataset can be classified into two classes using binary methods such as COVID positive or normal control (NC). We used the entire dataset for both training and testing. As a result, the training and test sets coincided, and the resulting accuracy is equivalent to the accuracy on the training data.  
Here, Dataset 1 and Dataset 2 are balanced so that simple metrics such as classification accuracy can be successfully applied to them.  
In the study of NNetEn, we also measured the dependence of the signal separation efficiency on the database usage fraction \(\mu\). For example, \(\mu\) = 0.01 means using 600 samples for training and 100 samples for testing for Dataset 1, and 53 samples for Dataset 2. Varying this parameter allows you to vary the entropy calculation time and its functionality.  
* Dataset 3: This dataset contains records of EEG signals recorded at the AHEPA General Hospital of Thessaloniki's 2 nd Department of Neurology [46]. This dataset consists of electroencephalograms of 88 patients divided into three groups: controls (29 people), Alzheimer's disease patients (36 people), and dementia patients (23 people). In order to record EEG, the authors of the dataset used a Nihon Kohden EEG 2100 device with 19 electrodes (channels) located on the head according to the 10-20 scheme: Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, O2. Each channel's signal was digitized at a sampling rate of 500 Hz. The duration of EEG recordings ranged from 5 min to 21.3 min. A resting EEG was recorded with the eyes closed.
## 4 Materials and Methods
### Statistical Analysis  
Considering the limited time series of GPP, this study only analyzed the spatial variations rather than the temporal variations for Vgreenup. The indicators of Dgreenup, Agreenup, and Vgreenup were derived from each site-year. Then, the multi-year indicators were averaged at each site to analyze the spatial pattern of Dgreenup, Agreenup, and Vgreenup. One-way analysis of variance (ANOVA) and least significant difference (LSD) multiple comparisons test were performed to determine significant differences in Vgreenup among PFTs. The significant level was set at \(\alpha\) = 0.05. Simple linear regression was adopted to analyze the relationship between Vgreenup and geographical factors (latitude, longitude, and altitude). Correlations between spatial anomalies of Vgreenup and its components (Dgreenup and Agreenup) were analyzed to identify whether the spatial variations in Vgreenup were affected by Dgreenup and Agreenup. Partial correlation analysis was also conducted to evaluate the isolated effects of spring climatic factors (TA, PRE, SW, VPD, WS, and SM) on Dgreenup, Agreenup, and Vgreenup. The statistical significances of the regression and correlation coefficients were examined using the t-test. The \(p\)-values less than 0.05 were considered significant. The direction and magnitude regarding driving factors on spatial variations of Vgreenup were compared between Europe and North America. Besides, the results in this study were also compared with the relevant findings in previous studies.
## 2. Saturated-Unsaturated seepage Model
According to the test results, a formula is fitted to describe the value of the hydraulic gradient, as shown in Equation (9). \[k_{s}=k_{i}\quad\left[(u_{a}-u_{w})\leq(u_{a}-u_{w})_{b}\right. \tag{7}\]  
\[\Delta u_{i}=u_{t}-u_{t-1} \tag{8}\]  
\[i_{i}=\frac{h_{fi}}{l_{i}}=\frac{\Delta u_{i}}{\gamma_{w}l_{i}} \tag{9}\]  
where \(h_{fi}\) is the head loss at a point in saturated soil, in kPa; \(\Delta u_{i}\) is the permeability pressure difference at a point, in kPa; \(u_{t}\) and \(u_{t-1}\) is the permeability pressure at time \(t\) and \(t-1\), in kPa; \(l_{i}\) is the seepage path, in m; \(i_{i}\) is the hydraulic gradient; and \(\gamma_{w}\) is the unit weight, in kN/m\({}^{3}\). In order to facilitate the engineering application, this paper simplifies the water and soil characteristic curve of unsaturated soil and generalizes the relationship between the suction and permeability coefficients of unsaturated soil into a linear relationship, as shown in Equation (10). \[k_{w} =k_{i}-c\left[\Psi-\left(u_{a}-u_{w}\right)_{b}\right]\quad\left[ \left(u_{a}-u_{w}\right)>\left(u_{a}-u_{w}\right)_{b}\right. \tag{10}\]  
where \(c\) is the fitting coefficient of the equation. The head loss in unsaturated soil can be calculated according to Equation (11):  
\[h^{\prime}_{fi}=\frac{\Delta u_{i}}{\gamma_{w}}-\Psi \tag{11}\]  
where \(h_{fi}\) is the head loss at a point in unsaturated soil, in kPa. \[\alpha=\eta_{0}+d(e-e_{0}) \tag{3}\]0 Relation of saturated-unsaturated soil volumetric water content, permeability coefficient and matric suction. **(a)** Soil-water characteristic curve; **(b)** permeability coefficient and matric suction curve. Equation (12) is obtained using the simultaneous solutions of Equations (10) and (11). \[\alpha=\eta_{0}+d(e-e_{0}) \tag{3}\]1  
Equations (10)-(12) above take into account the matrix suction on unsaturated soil, which means that the seepage stability of unsaturated soil is affected by the variation of pore water pressure and the permeability coefficient.
## 3 Proposed System
### PMIPv6/ AAND--Various Multi-Homing Topologies
#### 3.4.3 Topology 3--PMIPv6/AAND  
The MN is attached to MAG1 in this topology, and the CN node is attached to MAG3. The MN passes from MAG1 through MAG2 to MAG3. But between CN and MN, the LR path is created, and it is created only for the MAG concerned.  
There is therefore a need for the re-establishment of a link between CN and MN with M-MAG, since there is another new MAG, i.e., MAG2 is inserted into the domain. The SAND field also provides the LL-ID information of the mobility with the MN-ID in Topology 3. Figure 9 displays the PMIPv6/AAND Multihoming Topology 3.
# Thrust Augmentation of Micro-Resistojets by Steady Micro-Jet Blowing into Planar Micro-Nozzle
## 2 Micro-Nozzle and Active Flow Control Configuration Using Secondary Injection
### Secondary Injection Configuration  
Concerning the configuration of the secondary injection system, two micro-jets, from here on denoted as \"jet 1\" and \"jet 2\", have been placed along the sidewalls of the divergent region, with jet centerlines located at an axial distance from the throat section, equal to xjet,1 = 1.73 \(\times\) 10\({}^{-3}\) m and xjet,2 = 2.475 \(\times\) 10\({}^{-3}\) m, as shown in Figure 2. As summarized in Table 2, the micro-jets have been injected from micro-channels owning exit cross section of rectangular shape, and dimensions of 40 \(\upmu\)m \(\times\) 120 \(\upmu\)m and 50 \(\upmu\)m \(\times\) 120 \(\upmu\)m, respectively for jet 1 and jet 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Micro-Nozzle Parameters** & **Dimensions** \\ \hline \(A_{init}\) & 1070 \(\upmu\)m \(\times\) 120 \(\upmu\)m \\ \(A_{exit}\) & 1760 \(\upmu\)m \(\times\) 120 \(\upmu\)m \\ \(A_{t}\) & 150 \(\upmu\)m \(\times\) 120 \(\upmu\)m \\ \(R_{t}\) & 75 \(\upmu\)m \\ \(\alpha_{\text{conv}}\) & 45\({}^{\circ}\) \\ \(\alpha_{\text{div}}\) & 15\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Micro-nozzle geometry and dimensions.  
Figure 1: Planar characteristics of the symmetrical half geometry of the micro-nozzle [33].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Secondary Injection Parameters** & **Dimensions** \\ \hline \(x_{jet,1}\)1 & 1.730 mm \\ \(A_{jet,1}\) & 40 \(\mu\)m \(\times\) 120 \(\mu\)m \\ \(x_{jet,2}\)1 & 2.475 mm \\ \(A_{jet,2}\) & 50 \(\mu\)m \(\times\) 120 \(\mu\)m \\ \hline \hline \end{tabular} jet centerline location with respect to the throat section.  
\end{table}
Table 2: Secondary injection configuration.  
Figure 2: Computational domain with indication of the secondary jet location.  
that Equation (2) provides a measure of the relative intensity of the jet with respect to the core flow.  
In the case of two jets, \(c_{\mu,tot}\) will be the sum of \(c_{\mu,jet\,I}\) and \(c_{\mu,jet2}\).
# Review and Evaluation of MAC Protocols for Satellite IoT Systems Using Nanoatellites
## 4 Background on MAC Protocols
### Evaluation Metrics
#### 4.1.5 Complexity of Implementation  
In the context of CubeSats and low-cost satellite solutions, the complexity of implementation turns out to be a relevant factor. For this reason, aspects such as the need of high processing availability, the presence of very specialized hardware, and large amounts of required storage, should be considered as directly impacting the complexity of a given MAC protocol.  
Usually, on-board computers (OBC) employed on CubeSats are microcontrollers such as the Microchip PIC24 or the Texas Instruments MSP430, which are very limited in terms of computational resources. Newer OBCs using the ARM Cortex family or ATMEL devices are already available in the market for nanosatellites, but they still are in the category of modest processors.
## 2 Materials and Methods
### Simulated Setup  
In this study, the imaging system represented in Figure 1 was simulated. This setup was used in our previous study [5] for the identification of functional areas based on RGB imaging. A continuous wave white light source illuminated the patient exposed cortex. The light source was a halogen bulb (OSRAM Classic 116W 230V) whose spectra was measured with a USB 2000 spectrometer [31]. The intrinsic reflection spectra were acquired by a hyperspectral camera (XIMEA MQ022 HG-IM-SM5X5-NIR) and standard RGB camera (BASLER acA2000-165 uc). Both RGB and hyperspectral cameras acquire spectrally and spatially resolved images, but these two systems differ in their spectral resolution. The RGB camera acquires three broad and overlapping spectra over 300 nm to 700 nm using a Bayer filter [32] mounted on a CMOS sensor. The hyperspectral camera acquires 25 narrow and contiguous spectral bands in the red in the near-infrared range. A \(5\times 5\) mosaic filter (25 Fabry-Perot filters) mounted on a CMOS sensor was used for the acquisition of the 25 spectral bands. Each Fabry-Perot filter has two transmission peaks. The first or the second peak can be selected by mounting an interference filter on the camera lens. In our study, a long pass filter mounted on the hyperspectral camera lens selected the wavelength included between 675 nm and 975 nm.  
Figure 1: Schematic of the simulated imaging system.  
The intensity \(R\) measured by the spectral channel \(k\) of the camera (\(k\in[1;3]\) for RGB imaging and \(k\in[1;25]\) for hyperspectral imaging) is expressed by the following equation:  
\[R_{k}=\int_{\lambda_{min}}^{\lambda_{max}}D_{k}(\lambda).\Phi(\lambda).d\lambda. \tag{1}\]  
The integral runs from \(\lambda_{min}=400\) nm to \(\lambda_{max}=700\) nm for RGB imaging and from \(\lambda_{min}=675\) nm to \(\lambda_{max}=975\) nm for hyperspectral imaging. These wavelengths delimit the ranges of the spectral sensitivity profiles provided by the camera manufacturers. \(D_{k}\) is the spectral sensitivity of spectral channel \(k\) of the camera, provided by the camera manufacturer. \(\Phi\) is the diffuse reflection spectra of the illuminated tissue.
# Secure K-Means Clustering Scheme for Confidential Data Based on Paillier Cryptosystem
## 7 Security Analysis
### Correctness  
We first state the correctness of the protocol:  
**Lemma 1** (Correctness).: _If all parties follow the protocol honestly, the final output received by the client is the same as the output produced by running the plaintext K-means algorithm on the input dataset._  
Proof.: The protocol correctly performs the following operations over encrypted data:  
* Encrypted distance computation using Paillier homomorphic properties.
* Encrypted cluster assignment via secure minimum selection.
* Encrypted cluster center updates via secure aggregation and division.  
Since all operations preserve the correctness of their plaintext counterparts, the final clustering result is correct.
# A Novel Wearable Foot and Ankle Monitoring System for the Assessment of Gait Biomechanics
## 2 Materials and Methods
### Plantar Pressures Sessment  
In this work, we have performed plantar pressure assessment with six Aidong IMS C20B thin-film resistive sensors operating as force sensor resistors (FSR), deployed as illustrated in Figure 9a: one sensor on the hallux (FSR\({}_{0}\)), two sensors on the metatarsal arch (FSR\({}_{1}\) and FSR\({}_{2}\)), two sensors on the lateral arch (FSR\({}_{3}\) and FSR\({}_{4}\)), and one sensor on the heel area (FSR\({}_{6}\)). Our aim for this sensor deployment was to follow the COP progression line of normal gait. The practical realization of the resistive pressure sensor array is then depicted in Figure 9b.  
Figure 8: Logical diagram of the wearable foot biomechanics monitoring system software.  
Figure 9: Proposed pressure sensor deployment along the COP progression line for plantar pressure assessment: (**a**) diagram, (**b**) practical realization.  
An \(FSR\) is deployed in a resistive divider connection with a grounded 1 M\(\Omega\) resistance \(R\). The \(FSR\) resistance is then derived from the voltage divider law as  
\[FSR=\left(\frac{V_{CC}}{V_{R}}-1\right)\cdot R, \tag{1}\]  
where \(V_{CC}\) is the supply voltage and \(V_{R}\) is the 1 M\(\Omega\) resistance voltage drop.  
The resistance voltage drops were read with the ATmega328P \(\mu\)C with a\(f_{s\_FSR}\) = 100 Hz sampling frequency and, after filtering with a second-order \(f_{\_FSR}\) = 35 Hz cutoff frequency Butterworth lowpass filter for noise removal [1; 26], the FSR resistance values were determined according to (1).
## 2 Dielectric Properties and Channel Modeling
### Dielectric Properties of Skin and Rainwater
Parameters \(\varepsilon_{w1}\)\(=0.0671\varepsilon_{w0}\) and \(\varepsilon_{w2}\)\(=3.52+7.52\cdot T_{in}\) are the first and second high-frequency constants (\(f\)\(\rightarrow\)\(\infty\)), respectively. Parameters \(\gamma_{1}\)\(=20.20+146.4\cdot T_{in}+316\cdot T_{in}\)\({}^{2}\) and \(\gamma_{2}\)\(=39.8\gamma_{1}\) denote the primary and secondary relaxation frequencies. \(T_{w}\) is the water temperature in \({}^{\circ}\)C, and \(f\) is the operating frequency in GHz. Figure 1: (**a**) Penetration depth in dry skin, wet skin, and water layers; transmissivity through the air-dry skin and water-wet skin interfaces with normal incidence. (**b**) Basic geometry of transmission through a water layer and into the skin. (**c**) Transmissivity through a water layer covered on the skin surface under different incident angles. (**d**) Transmissivity through a water layer (with a thinness of 0.2 mm) on the skin under different water layer thicknesses with an incident angle of 30\({}^{\circ}\). As shown in Figure 1a, the penetration depth in skin is always higher than that in water because of the smaller dielectric constant of skin [23]. The penetration depth is so sensitive to the incident frequency that higher frequencies cannot arrive at the skin surface if the skin has a thick water layer covering it due to falling rain droplets. A calculation of the transmissivity through the air-dry skin and water-wet skin interfaces is conducted and shown in Figure 1a by using [PERSON]'s law with a horizontal monopololarization (hh) configuration. A higher transmission through the water-skin interface is observed, due to the smaller dielectric difference between air (free space) and skin on both sides as the water content of skin tissue can be up to 70% [35]. In the following sub-section, we conduct further calculations by assuming a layer of in vivo skin tissue instead of an in vitro one [36] and neglect the variation of its water content due to its self-adjusting capability.