esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Collection of Ship Navigation Data  
The AIS is the primary method for collecting ship navigation data among the equipment. It automatically transmits and receives ship data between vessels and shore-based stations, including the date and time, ship name, call sign, position (latitude and longitude), speed over ground, course over ground, gyro heading, and rate of turn [22]. The emergence of this technology has revolutionized the collection and utilization of this information, allowing the real-time tracking and management of ship movements. Using dedicated frequencies, as shown in Table 1, the transmission interval for AIS static data were 6 min, whereas the actual transmission interval for dynamic data ranged from 2 to 12 s (3 min for anchored vessels). Data are transmitted in real-time by automatic data communication at intervals specified in IMO Resolution MSC 74(69) [23] according to the ship speed. These data were displayed on electronic displays or digital charts for immediate access and analysis. In this study, among the ship navigation data that AIS can collect, we used position data collected in latitude and longitude to measure the similarity of ship trajectories, ship speed data as a factor for removing outliers in data refinement, and the reception time of position data to create uniform intervals in ship trajectories.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
## 3 Problem Formulation  
The main task objective of this paper is to recommend recipes to users that match their personalized preferences. Given a set of users \(\mathcal{U}=\{u_{1},u_{2},\ldots,u_{M}\}\) and recipes \(\mathcal{R}=\{r_{1},r_{2},\ldots,r_{N}\}\), the interaction matrix is denoted as \(\mathbf{Y}\in\mathbb{R}^{M\times N}\), where \(\mathrm{M},\mathrm{N}\) denote the number of users and recipes, respectively. The user-recipe interaction diagram can be represented as \(\mathcal{G}_{ui}=\{(u,y_{ur},r)\mid u\in\mathcal{U},r\in R\}\), where \(y_{ur}\) denotes whether user \(u\) performs an interaction behavior (e.g., commenting or making) with recipe \(r\), as defined by:  
\[y_{ur}=\left\{\begin{array}{ll}1,&\text{if user $u$ interacted with recipe $r$}\\ 0,&\text{otherwise.}\end{array}\right. \tag{1}\]  
Note that some research shows that sentiment analysis of user review information and incorporation into the recommendation system is beneficial to improve the recommendation quality [23]. In some other studies, instead of using user interactions (binary values), multivalued ratings are used. These ratings can either be obtained explicitly from users or can be obtained by applying sentiment analysis techniques on comments or reviews on the items. However, due to the low quality of explicit feedback on the recipe dataset, we assign it different weights and treat it as interaction in the preprocessing process. We define the knowledge-aware heterogeneous graph \(\mathcal{G}=\{(h,r,t)\mid h,t\in\mathcal{E},r\in\mathcal{R}\}\) to denote a real-world recipe containing rich attributes, where the entity-relation-entity triplet \((h,r,t)\) denotes the head entity \(h\) connected to the tail entity \(t\) by the relation \(r\). \(\mathcal{E}\) and \(\mathcal{R}\) denote the set of entities and relationships. In the knowledge-aware recipe heterogeneous graph, the recipe contains rich attributes and relationships such as ingredients, tastes, cooking methods, and types. The task goal of recipe recommendation is to recommend recipes to users by the learned function \(\hat{y}=\mathcal{F}(\mathcal{G},\Theta)\), where \(\Theta\) is a model parameter. We specify the task as follows:  
Input: Users \(\mathcal{U}\), recipe \(R\), user-recipe interactions \(\mathbf{Y}\), heterogeneous graph \(\mathcal{G}=\{(h,r,t)\mid h,t\in\mathcal{E},r\in\mathcal{R}\}\).  
Output: A predictor function \(\hat{y}=\mathcal{F}(\mathcal{G},\Theta)\), which estimates the probability of user \(u\) interaction with the recipe \(r\).
## 5 Simulation Model and Results
### Optimum Data Payload Size  
Figure 4a presents the throughput efficiency as a function of data payload size for the C-HARQ, CARQ, and standard S&W ARQ protocols. It clearly shows that the error correction capabilities of C-HARQ boost the throughput efficiency of CARQ to a much higher level, thus outperforming both CARQ and S&W ARQ by a big margin. Further, considering a wide range of applications of the sensor networks resulting in variation in the number of neighbour nodes, and to estimate an overall average value of the optimum data payload size, the throughput efficiency of C-HARQ is plotted for \(nCN=2\), 5 and 8. The payload size which achieves the maximum throughput efficiency is found to be in the range of 5,000-7,000 bits for \(nCN=2\) to 8. Thus, for further analysis of C-HARQ we set the optimum payload size to 6,000 bits, which is far greater than that of CARQ (1,000 bits) and S&W ARQ (600 bits); this allows more amounts of data transfer on a single successful attempt.  
From the point of view of energy, the three protocols are first tested with the number of neighbour nodes set to 5; their energy efficiency is plotted in Figure 4b. Additionally, the energy efficiency of C-HARQ for \(nCN=2\) and 8 is also presented. The plot reveals that C-HARQ outperforms the simple  
Figure 4: (**a**) Throughput efficiency _vs._ data payload length; (**b**) Energy efficiency _vs._ data payload length.  
CARQ protocol over the entire range of payload length. When compared to S&W ARQ, C-HARQ delivers more energy efficiency especially for larger data payloads (\(\geq\) 600 bits), but for smaller payloads (\(\leq\)500 bits) S&W ARQ is found to be more energy conserving than the cooperative protocols, the reason being the additional energy consumption of the _CNodes_ due to their inherent property of listening to the original data transmission even when not required, which is the case with smaller payloads where the packet error rate (PER) is low. In case of conventional S&W ARQ, it has been assumed that the _CNodes_ know about the on-going communication and hence remain in idle mode for the entire duration, adding only the idle mode energy expenditure to the total consumption.  
Further, C-HARQ with \(nCN=2\) proves to have better energy efficiency than \(nCN=5\) and 8, for payload of smaller size but for larger payload size \(nCN=5\) and 8 have the better efficiency. This is again because of the additional energy consumption of the _CNodes_ in overhearing the original transmission when their cooperation is not required. Whereas, for larger payload size the PER is high and _CNodes_ are required to play their role in improving the performance, thus overshadowing their overhearing energy expenditure. From the aspect of energy efficiency, it is difficult to assign a common optimum payload size for C-HARQ for all types of network configurations, as the optimum size is found to vary in the range of 600 bits (\(nCN=2\)) to 4,000 bits (\(nCN=8\)). Thus we use the optimum payload setting obtained from throughput efficiency analysis.
## 2 Theoretical Background: The Lever Rule and the Short-Range Order Configuration Model
### The Original SROC Model
Then, for an intermediate composition, for example, the borate glass with \({\rm x}=0.3\), where \(X_{\underline{\rm N}}=0.57\) and \(X_{\underline{\rm M}}=0.43\) from Figure 1, it is assumed that the SRO structure is a linear combination of the SROCs of glasses with \({\rm x}=0\) and \({\rm x}=0.5\) with coefficients \(X_{\underline{\rm N}}=0.57\) and \(X_{\underline{\rm M}}=0.43\), respectively. In the original SROC model, where only the fundamental nodes \(\underline{\rm N}\), \(\underline{\rm M}\), \(\underline{\rm P}\) and \(\underline{\rm Q}\) are considered, the composition interval [0,0.75) of \({\rm x}\) is divided into three distinct subintervals. Specifically, we have region I with \({\rm x}\in\) [0,0.5), region II with \({\rm x}\in\) [0.5,0.67), and region III with \({\rm x}\in\) [0.67,0.75). By applying condition II, in region I contribute only the SROCs \(\underline{\rm N}\) and \(\underline{\rm M}\), in region II contribute SROCs \(\underline{\rm M}\) and \(\underline{\rm P}\), and in region III contribute SROCs \(\underline{\rm P}\) and \(\underline{\rm Q}\). With these constraints, the molar fraction of each particular borate entity \(B_{j}\), \(\widetilde{B}_{j}\) can be evaluated at any value of \(x\) with the following equations:  
\[\widetilde{B}_{j}(x)=f_{\underline{\rm N},j}X_{\underline{\rm N}}(x)+f_{ \underline{\rm M},j}X_{\underline{\rm M}}(x),\text{for }0.0\leq x<0.5 \tag{7a}\] \[\widetilde{B}_{j}(x)=f_{\underline{\rm M},j}X_{\underline{\rm M}}(x)+f_{ \underline{\rm P},j}X_{\underline{\rm P}}(x),\text{for }0.5\leq x<0.67\] (7b) \[\widetilde{B}_{j}(x)=f_{\underline{\rm P},j}X_{\underline{\rm P}}(x)+f_{ \underline{\rm Q},j}X_{\underline{\rm Q}}(x),\text{for }0.67\leq x<0.75 \tag{7c}\]  
In the framework of the original SROC model, the only input needed is the specific borate speciation for the meta-, pyro-, and orthoborate glass compositions. Alternatively, if the SRO structures of meta-, pyro-, and orthoborate glass compositions are known from existing experimental or theoretical investigations, then the array of \(f_{\underline{\rm M},j}\) values for each borate unit \(j\) within each SROC \(q\), where \(q\in\{\underline{\rm N},\ \underline{\rm M},\ \underline{\rm P},\ \underline{\rm Q}\}\), is known. Then, the set of Equation (7) permits a direct evaluation of the SRO structure of any glass composition. Further details are reported in ref. [44].
## 3 Results and Discussion
### Solid-State Studies of \(\gamma\)-CD-Phloroglucinol Inclusion Compound
#### 3.3.2 Powder X-ray Diffraction  
Powder X-ray diffraction (PXRD) is a useful tool in identifying the formation of inclusion complexes. It also contributes to investigating the presence of any eventual impurities that may appear, such as crystallites of the host or the guest. PXRD data is presented in Figure 4.  
Figure 3: Fourier-transform infrared spectra of phloroglucinol (PG), \(\gamma\)-CD, their 1:1 physical mixture (1:1 mix) and the freeze-dried solid products starting from mixed solutions with a \(\gamma\)-CD:PG ratio of 1:1 (\(\gamma\)-CD-PG).  
Phloroglucinol presents a diffractogram with several well-resolved peaks that are indicative of its high crystallinity. The most intense reflections occur at 12.7, 13.0, 17.2, 18.3, 22.0, 22.5, 25.7, 27.2 and 31.7 degrees of 20.  
The diffractogram of the product of \(\gamma\)-CD-PG, as obtained after the freeze-drying process, revealed a mainly amorphous phase (which was expected as a result of the preparation method) that was lacking reflections that would allow for an adequate structural analysis. To restore the hydration waters and some of the crystallinity of this sample, it was placed at ambient temperature in a water-saturated atmosphere for approximately 16 h. The PXRD pattern of the rehydrated \(\gamma\)-CD-PG showed a set of diffraction peaks corresponding to the formation of a new phase with no traces of crystallites of \(\gamma\)-CD heptahydrate. The main reflections presented maxima at 6.2, 7.5, 8.5, 9.6, 10.5, 11.4, 12.3, 13.7, 14.9, 15.8, 16.6, 18.8, 20.2, 20.7, 21.2, 21.8, 22.4 and 23.6 degrees of 20. The overall diffraction envelope for this sample had a fair degree of similarity with the one calculated for \(\gamma\)-CD-12-crown-4-ether [40], herein used as a representative model for the only known isostructural series of \(\gamma\)-CD inclusion complexes [41]. This similarity suggests that \(\gamma\)-CD-PG belongs to this  
Figure 4: Experimental powder X-ray diffractograms of phloroglucinol (PG), \(\gamma\)-CD and the inclusion compound \(\gamma\)-CD-PG. The diffractogram on top of the image is the calculated trace of the inclusion complex \(\gamma\)-CD-12-crown-4-ether [40] starting from its atomic coordinates (data available at the CCDC under the refcode DOCYD) and using the software Mercury 3.5.1 (Copyright CCDC 2001–2014). The inset depicts the spatial arrangement of cyclodextrin molecules in the crystals of compound \(\gamma\)-CD-12-crown-4-ether, as viewed from the top (crystallographic \(c\) axis) and from the side (\(a\) axis).  
isostructural series; thus, we assume that it has \(\gamma\)-CD molecules stacked in infinite channels as reported for all the inclusion compounds of this isostructural series (inset in Figure 4).
## 5 Simulation of Coded Laser Pilots-Based HILPB WPT System  
### Simulation Environment  
The simulation was performed by placing three PRXs (R1 to R3) that require different MDP and MPQ and four PTXs (T1 to T4) with different MTP, as shown in Figure 13. A three-dimensional Cartesian coordinate system indicates the power receiver and power transmitter's positions, while \(x\), \(y\), and \(z\) represent the horizontal, vertical, and height axis, respectively, with the bottom left as the origin.  
The PTX device is mounted on a traffic light and communicates with an EV via V2I. It transmits pilots and power to a desired WPC using a laser beam capable of controlling the power. The PTX transmits the DS-OCDMA-encoded pilots as a laser beam and transmits the HILPB to the selected WPC. It receives a message to power transmission through V2I and operates accordingly. The four PTXs used in the simulation can transmit different MPT, and their characteristics are shown in Table 4.  
Figure 12: Timeline for WPT protocol.  
Figure 13: Power transmission simulation environment.  
In the simulation, V2I-enabled EVs are stopped at intersections, waiting for the light to turn green. EVs that continuously measure the RCPI, convert the HILPB received via the PV array into electricity, and communicate via V2I is used as the PRX. The PRX can broadcast power transmission-request messages to PTXs via V2I and decode and process the laser pilots encoded with DS-OCDMA from PTXs received through the PV array. The three PRXs used in the simulation have different MPQ but are equipped with a PV array with the same surface area and characteristics, as outlined in Table 5.
## 1 Introduction
proposed an improved algorithm called adaptive gradient estimation SPGD for beam clean-up of a solid-state laser to obtain high output beam quality and increase the convergence speed and algorithm stability [28]. Summarizing, although the above studies obtained promising results, most of them were aimed at specific optical problems, lack applications in the CFSOC system, and cannot be applied to achieve efficient ME and decrease the BER directly. The feasibility of these algorithms in the CFSOC system requires further verification and implementation. In order to solve the above problems, this study first analyzes the ME and BER of the CFSOC system according to coherent communication theory, and establishes the relationship between system performance indicators and the fitness of the optimization algorithm in SLAO. Inspired by Nesterov-accelerated adaptive moment estimation (Nadam) in deep learning [29], a novel algorithm named NadamSPGD is proposed. The proposed NadamSPGD combines Nesterov's accelerated gradient (NAG) momentum and the adaptive gain coefficients with SPGD to improve the correction speed and robustness of SLAO without noticeably increasing the complexity of the algorithm. The wavefront correction effect of the proposed algorithm is analyzed through numerical simulations and laboratory experiments. The results demonstrate that NadamSPGD can significantly increase the convergence speed and robustness in SLAO, and the negative influence of atmospheric turbulence on ME and BER can be efficiently suppressed, which is of great significance to the CFSOC system. The structure of this paper is as follows. In Section 2, system models and working principles of CFSOC and SLAO are described and analyzed. The basic principles of the SPGD algorithm and the Nadam optimizer are introduced, and a novel hybrid algorithm NadamSFGD making full use of their characteristics is proposed in Section 3. In Section 4, the phase aberration model based on the Zernike polynomial is established, and related simulations and comparisons are presented to demonstrate the improved performance of NadamSPGD in the CFSOC system. We also performed a 97-element closed-loop SLAOexperiment to verify the feasibility of the proposed algorithm. Finally, Section 5 draws the conclusions.
## 2 Modeling of Vehicles Interactions Due to Simultaneity
### Interactions between Vehicles and Convoys Simultaneously Traveling on Two or More Lanes  
As already remarked, if simultaneity on one lane is considered through the afore mentioned modified traffic flows, only interactions between vehicles and convoys simultaneously traveling two or more lanes need to be taken into account. The problem can be reduced again to an Erlang's problem, modeling the multilane bridge as a multichannel system without the waiting queue. When all channels are busy, or in other words the number of requests exceeds the number of channels \(r\), the surplus, discharged by the system, is lost and cannot enter a second time the system (see Figure 7). Obviously, the availability rate \(\alpha~{}=~{}v/L\) considered here can differ from that evaluated in the previous sections. The \(r\) channels of the system reproduce the \(r\) bridge lanes, so that the probability \(P_{r,i}\) that vehicles are simultaneously on \(i\) lanes, \(2~{}\leq~{}i~{}\leq~{}r\), corresponds to the probability that \(i\) channels of the system are busy. A classical procedure, very similar to that depicted in Section 2.1, leads trivially to the solution of a classical Erlang type problem. Recalling that the total modified flow, \(N^{*}_{s}\), on the \(s\)-th lane of the bridge is expressed by Equation (13), the total modified flow on the \(r\) bridge lanes \(N^{*}\) results  
\[N^{*}~{}=~{}\sum_{s=1}^{r}N^{*}_{s}. \tag{21}\]  
Figure 7: Outline of the model with vehicles traveling simultaneously \(i\) lanes. Let \(u\) be the arrival rate of \(N\,^{*}\), i.e., the average number of occurrences of elements (vehicles and convoys) of the total modified flow in the time unit. The \(r\,+\,1\) possible states of the system, \(\Omega_{i}\), are:  
\(\bullet\)\(\Omega_{0}\) the system is free: No vehicle on the bridge;  
\(\bullet\)\(\Omega_{1}\) one channel is busy: One vehicle is traveling the bridge;  
\(\bullet\)\(\dots\)\(\dots\)\(\dots\)\(\dots\)  
\(\Omega_{i}\)\(i\) channels are busy: \(i\) vehicles are on \(i\) different lanes;  
\(\bullet\)\(\dots\)\(\dots\)\(\dots\)\(\dots\)  
\(\bullet\)\(\Omega_{r}\) all \(r\) channels of the system are busy: \(r\) vehicles are on the \(r\) available lanes. Recalling what was remarked in Section 2.1, the probability \(P_{r,i}(t)\) that \(i\) lanes are occupied at time \(t\) comes from the solutions of the system of ordinary differential equations  
\[\left\{\begin{array}{rl}\frac{dP_{r,0}}{dt}\,=\,-\mu\;P_{r,0}(t)\;+\;\alpha \;P_{r,1}(t)\\ \frac  
Obviously, the number of interacting vehicles on each lane is  
\[\underline{N}_{(\text{lane}J_{1},J_{2},\ldots,J_{i})^{*}}\ =\ \frac{\underline{N}_{( \text{$\left\lfloor{\text{$1$}}\right\rfloor$}_{2},\ldots,\text{$\left\lfloor{ \text{$1$}}\right\rfloor$})^{*}}}{i}\ \ \ \ \ \ 1\ \leq\ i\ \leq\ r. \tag{36}\]  
In case \(i\ =\ r\), Equation (35) coincides with Equation (29). Moreover, Equations (29) and (35) can be easily modified, as in Section 2.1, to specially consider vehicles belonging to given categories. Moreover, in the present case, the most relevant real situation refers to two lane traffics.
# Model Checking Resiliency and Sustainability of In-Vehicle Network for Real-Time Authenticity
## 6 Implementation and Experiments
### Message Authentication Time  
When an ECU transmits a CAN message, it always generates a message authentication tag (i.e., a MAC value). The CAN message then is verified by the authenticator and a  
Figure 14: Model checking results for MAuth-CAN’s resiliency to BoA.  
Figure 13: An ECU controller model of the attacker in TA.  
report message (an AFR message) is generated if there is a verification failure. The normal ECUs verify an AFR message to see if it is transmitted by the authenticator only when they receive it. We tested each operation one hundred times, and the average the computation time and the corresponding standard deviation for individual cryptographic operations are presented in Table 4.
# A Security Assessment Mechanism for Software-Defined Networking-Based Mobile Networks
###### Abstract  
Software-Defined Networking-based Mobile Networks (SDN-MNs) are considered the future of 5G mobile network architecture. With the evolving cyber-attack threat, security assessments need to be performed in the network management. Due to the distinctive features of SDN-MNs, such as their dynamic nature and complexity, traditional network security assessment methodologies cannot be applied directly to SDN-MNs, and a novel security assessment methodology is needed. In this paper, an effective security assessment mechanism based on attack graphs and an Analytic Hierarchy Process (AHP) is proposed for SDN-MNs. Firstly, this paper discusses the security assessment problem of SDN-MNs and proposes a methodology using attack graphs and AHP. Secondly, to address the diversity and complexity of SDN-MNs, a novel attack graph definition and attack graph generation algorithm are proposed. In order to quantify security levels, the Node Minimal Effort (NME) is defined to quantify attack cost and derive system security levels based on NME. Thirdly, to calculate the NME of an attack graph that takes the dynamic factors of SDN-MN into consideration, we use AHP integrated with the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) as the methodology. Finally, we offer a case study to validate the proposed methodology. The case study and evaluation show the advantages of the proposed security assessment mechanism.  
5G; security assessment; software-defined networking based mobile networks; attack graph; analytic hierarchy process +
Footnote †: journal: Sensors
# New On-Line MFL Testing Method and Apparatus for Winding Mine Hoist Wire Rope
## 3 Analysis of the Novel Probe
### Open-Loop Permanent Magnetizer
The lifting distance is the distance between the magnetic sensing element and the surface of the wire rope. In general, the amplitude of the detection signal decreases rapidly with the increasing lifting distance, so the lifting distance is usually chosen to range from 2 to 10 mm [19; 29; 30]; however, the lifting height should be as high as possible in order to prevent friction between the device and the wire rope, thus extending the probe's service life. A height of 5 mm was used in this study. In the simulation, the values of ID, OD, H, and L were varied by a single control variable, and the magnetic induction intensity (axial and radial components) was extracted along an axial direction at a height of 5 mm from the defect center of the steel bar surface in order to establish the influence law of size on the defect leakage magnetic field so that an open-loop wire rope leakage magnet detector could be designed to improve the defect detection performance and reduce the design size and weight.