esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Contextual and Game-Based Training for
## 2 Information Security Awareness Training  
ISAT has been discussed in the scientific literature for several decades, and the importance of providing ISAT as a means of improving user behavior is widely acknowledged [33; 34; 35]. ISAT intends to increase user knowledge and awareness through training. There are many and diverse, options for ISAT, and recent publications [35; 36; 37] categorize ISAT methods differently. In general terms, ISAT methods can be described as seen in Table 1. Table 1 is based on the classifications by [36; 37].  
While ISAT has been long discussed in scientific literature and used in practice, several publications suggest that many ISAT methods fail to adequately support users towards secure behavior [26; 27]. This notion is emphasized by the continuous reports of incidents where human behavior is a key component [38; 39]. Three core reasons for why ISAT does not always provide its intended effect can be found in recent research:  
* Knowledge acquired during training deteriorates over time [21].
* It is challenging to get users to participate in training delivered on-demand [28].
* Users are provided with knowledge, but not motivated to act in accordance to that knowledge [20].  
The ISAT methods included in this research are game-based training and Context-Based Micro-Training (CBMT). Gamified training means that game concepts are applied to ISAT, with the intent to better motivate users to actively participate [28]. It is considered in this research since it is argued to better motivate and engage users when compared to other ISAT alternatives. There are several examples of gamified ISAT. The landscape includes multi-player competitive games, story-based single-player games, board games, role-playing games, quizzes, and more [28; 40].  
CBMT is an example of contextual training. ISAT using the CBMT method is delivered to users in short sequences and in situations where the training is of direct relevance. Phishing training is, for instance, delivered to users that are in a situation with an elevated risk of being exposed to phishing. It is argued to counter the knowledge retention and user  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Description** \\ \hline Classroom training & Typically provided on-site as a lecture attended as a specific point in time. \\ \hline Broadcast online training & Typically, brief training delivered as broadcast to large user groups using e-mail or social networks. \\ \hline E-learning & ISAT typically delivered using an online platform that is accessible to users on-demand. \\ \hline Simulated or contextual training & Training delivered to users during a real or simulated event. \\ \hline Gamified training & Gamified training is described as using gamification to develop ISAT material. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of ISAT methods.  
participation problems by automatically appearing in those relevant situations [32]. It is also argued to motivate users towards secure behavior by providing them with training that directly relates to the users' current situation.
## 3 Dynamic Multi-Hop Routing (DMR) Technique
### Phases of the DMR Protocol
#### 3.2.3 The Table Preparation Phase  
For each network node--whether a CH, IN, or N--the previous phase provides a clear view of the surrounding nodes. Based on the announcement messages received, the nodes begin developing their routing tables. Consequently, the Ns located at the second level of the clusters dispatch JOIN-REQ messages to the Ns at the first level. The N receiving this request identifies a time slot in its TDMA schedule; if a free slot is available, it sends an acceptance message to the requester. Otherwise, the request is ignored. The requester updates its routing table (RT) based on the Ns that have sent their approvals. It is worth noting that a cost function is used to arrange the table's entries. Moreover, the CHs and INs positioned at the second and upper levels attempt to construct their RTs by transmitting JOIN-REQ messages to the CHs and INs at the lower levels. The CHs and INs that send acceptance messages become candidates for the requester, which then arranges them in its routing table.
# Monitoring Safron Crops with UAVs
## 3 Field Study Design Methodology
### Evaluation  
The two evaluation methods that were utilized for the above algorithms are as follows:  
* **10-Fold Cross-Validation:** Cross-validation [28] is a method of estimating the performance of a trained model. It is a process that enhances the accuracy of the results, helping to draw generalized, safe conclusions about the behavior of the categorizer in a data set. During the categorization process, the data set is divided into a training set (training set) and an evaluation set (test set). The general idea is that the data set is divided into equal parts and follows an iterative process in which, each time, one part is used for evaluation and the rest for the training of the whole in a circular order.  
* **Confusion matrix:** One of the basic concepts of classification efficiency is the confusion matrix table, which depicts the model's predictions versus ground-truth labels. Each confusion matrix row represents the instances in a predicted class, and each column represents the instances of a real class. A confusion matrix table is a key tool for evaluating the methods used in this study. The confusion matrix [29] is an overall picture of the categorization results. It is a table that compares the predicted categories in which the data were listed to those that they actually belong to and is used in the control phase of the model. It is c x c of dimensions, where c is the number of categories. After the matrix completion, we used the three evaluation metrics described below: \(\circ\)**Precision:** The ratio between all the data that are assigned to the correct category, to the total number of data that are assigned to a category (correctly or incorrectly). \(\textbf{Precision}=\text{True Positive/Predicted Positive}\) \(\circ\)**Recall:** The ratio of all the data that are estimated correctly to the data that are actually correct. \(\textbf{Recall}=\text{True Positive/Actual Positive}\) \(\circ\)**Accuracy:** The ratio of all the data that are estimated correctly that belong and do not belong to a category, to the total number of data. \(\textbf{Accuracy}=\text{(True Positive + True Negative)/All}\)
## 4 Data and Examples
### Analysis of Multivariate Traffic Parameter Characteristics  
Using the data from December 2 nd as an example, based on the length of the road section and the speed limit rules, the raw data set was cleaned to remove redundant and abnormal data, and a case data set suitable for analysis was obtained. The multivariate traffic parameter characteristics were presented within a typical road section, as shown in Figure 3, and the time-varying characteristics of headway, vehicle speed, and travel time were obtained.
## 3 Communication and Channel Modeling
### Efficiency with Multiple-Access Technologies and Cognitive Radio Communication
#### 3.3.1 Non-Orthogonal Multiple Access  
Non-Orthogonal Multiple Access (NOMA) enables simultaneous communication with multiple users using the same physical resources (i.e., time, frequency, and coding) by leveraging superposition coding at the transmitter and successive interference cancelation (SIC) at the receiver [75]. The error probability in RISeUAV-based NOMA systems has been investigated in [76; 77], accounting for practical constraints such as hardware impairments (HWI) at the transceivers, inter-cell interference (ICI), and imperfect channel state information (CSI).
# Event-Sampled Adaptive Neural Course Keeping Control for USVs Using Intermittent Course Data
## 2 Problem Formulation and Preliminaries
### Problem Formulation
During maneuvering, the USV is inevitably affected by the external disturbance, and the energy of external disturbance is finite. Otherwise, the safety of USVs cannot be guaranteed, so there is no need to discuss the control issue. It is a common requirement for the smoothness of \(y_{r}\), and such a requirement is also involved in references [14; 19; 20]. Therefore, Assumptions 1-3 are reasonable._  
For the navigation problem of USVs, the cyber-physical system must be a core communication pathway, which is used to realize the data transmission between the vessel and the shore. However, the fact remains that the communication capacity of the network is finite due to the constraint of bandwidth. To address this, it is critical to retrench the network resources. In this context, only the signal of the vessel course is transmitted, and this work in the control design only involves the intermittent course data. To implement such a task,an ESM is constructed in the S-C channel in order to obtain the intermittent course data. The ESM is designed as follows:  
\[ \begin{cases}\ddot{\psi}(t)=\psi(t_{\kappa}),\forall t\in[t_{\kappa},t_{\kappa+1} ),k\in\mathrm{N}\\ t_{\kappa+1}=\inf\{t>t_{\kappa}||e_{\psi}\mid\geq\beta\}\end{cases}  \tag{3}\]  
where \(t_{\kappa}\) is the sample time constant, \(e_{\psi}=\psi-\ddot{\psi}\) is the sample error of vessel course, \(\beta\) is a design constant, and N is the set of natural numbers. The ESM proposed in (3) is designed based on the intermittent course data, and has the characteristics of cooperation between sensor and ETM. Compared with continuous sampling, the ESM can quickly obtain intermittent heading data, effectively reduce the course data transmitted by the USVs to the shore, and save network communication resources. In addition, the amount of sensor-controller channel heading data transmission decreases, thus reducing the computational workload of the controller. Control objectiveFor the vessel CKC system of the USVs described by (1), under the Assumptions 1-3, we will establish an event-triggered control solution when only the intermittent course data are available, such that the vessel course \(\psi\) can track \(\psi_{r}\), and all signals in the CLCS of the vessel course are guaranteed to be bounded.
## 3 Remote-Sensing System Framework and Problem Defined
### The SWIR Camera and Frame Grabber  
Traditional RGB cameras, such as the Zenmuse X4S, cannot see features behind fog or smoke, making them more environmentally limited than SWIR. SWIR cameras can see through fog or smoke [26] and capture clear images from longer distances. Additionally, the SWIR spectrum shows several features that are not obtained in the visible spectrum. Thus, we used an SWIR camera equipped with a 0.4\(\sim\)0.7-\(\upmu\)m sensor spectral and 1.1\(\sim\)2.3-\(\upmu\)m lens filter to capture SWIR images at 1.1-\(\upmu\)m and 1.7-\(\upmu\)m bands. These images bring useful complementary information to situations where visible imaging cameras are ineffective [27].  
Due to the SWIR camera (Raptor OWL 640) applying the camera link standard for data communication we could not use traditional methods, such as Ethernet and USB to capture the images. The camera link standard is a serial communication protocol for camera interface applications. It was designed for industrial video products and image capturing using a frame grabber (iPORT CL-U3 external frame grabbers) connected to the SWIR camera with a camera link cable and another USB port link to an onboard computer. In this study, we applied the USB 2.0 cable between the frame grabber and the onboard computer, since USB 3.0 devices generate radio interference at 2.4 GHz, causing damage to  
Figure 3: The predefined flying path for a drone remote-sensing task.  
GPS devices [28]. Consequently, shields can be used to isolate USB 3.0 radiation interference signals or apply the USB 2.0 cable to the entire system.
# PDDQN-HHVBF Routing Protocol Based on Empirical Priority DDQN to Improve HHVBF
## 5 Performance Analysis and Simulation
### Network Performance Metrics  
This part will introduce four network performance indicators for comparison in the following simulation experiments: these four network performance indicators are packet delivery ratio PDR, Energy Tax, End-to-End Delay, and network lifetime.  
(1) PDR: this indicator refers to the success rate that the AUV transmits the final Sink node hop-by-hop after collecting the data packet in the M-UWSNs. The calculation method is as Formula (18):  
\[PDR=\frac{R_{\text{packets}}}{s_{\text{packets}}} \tag{18}\]  
(2) Energy Tax: This indicator represents the average energy consumption of the system after each node transmits data packets to the Sink node, that is, the average energy consumption of the system when the Sink node successfully receives data packets. The specific calculation method is as shown in Formula (19):  
\[\text{Energy Tax}=\frac{E_{\text{consumed}}}{\text{Num}_{\text{nodes}}*R_{ \text{packets}}} \tag{19}\]  
(3) End-to-End Delay: The end-to-end delay is the average time consumed by a packet sent from the AUV until it is received by the Sink node.  
(4) Lifetime: Lifetime means that one node in the M-UWSNs runs out of energy, and the M-UWSNs cannot continue to operate.
## 2 Antenna Design and Optimization
### Parametric Analysis
#### 2.2.2 Axial Ratio
Specifically, when (arc_r) is set to 3 mm, the AR is finely tuned across the entire established frequency range, demonstrating effective CP characteristics. Further examination revealed that decreasing the diameter to values below 3 mm resulted in a sharp increase in the AR, often exceeding the critical 3 dB threshold, thereby compromising the antenna's polarization performance. Conversely, increasing the diameter beyond 3 mm led to a broader bandwidth but at the cost of AR stability, indicating a trade-off that must be carefully managed during the design process. These findings underscore the significance of the diameter of the added arcs in the overall antenna design. By systematically adjusting (arc_r) in conjunction with other parameters, the antenna can achieve a balanced configuration that supports robust dual-band operation while ensuring effective CP. This careful tuning process ultimately enhances the antenna's applicability for reliable communication in UAV environments. The surface current distribution of the final proposed antenna at various frequencies for both top and bottom sides is shown in Figures 11 and 12. In Figure 11, it can be observed at 2.4 GHz that a considerable amount of surface current is distributed in the horizontal rectangular cut on the top side at 2.4 GHz, whereas on the bottom side it is on the upper side of the ground plane. In Figure 12, the surface current is distributed near the opposite truncated corners and edges of the centered circular cut on the top side at 5  
In Figure 13, a 3D radiation pattern at 2.4 GHz and 5 GHz is presented for the designed antenna, illustrating maximum gains of 5.01 dBi and 5.27 dBi, respectively. These patterns reveal that the proposed antenna exhibits a commendable azimuthal coverage, indicating its ability to provide uniform radiation characteristics across the operational spectrum. This high gain, combined with favorable radiation patterns, suggests that the antenna is well suited for applications in UAVs where reliable communication is crucial. The high gain observed at both frequencies indicates that the antenna can effectively transmit and receive signals over extended distances, making it particularly advantageous for UAV applications involved in remote sensing, surveillance, and data transmission. Additionally, the ability to maintain good radiation characteristics in varying orientations enhances the antenna's robustness in real-world environments.
###### Abstract  
There is an increasingly urgent need for humans to interactively control robotic systems to perform increasingly precise remote operations, concomitant with the rapid development of space exploration, deep-sea discovery, nuclear rehabilitation and management, and robotic-assisted medical devices. The potential high value of medical telerobotic applications was also evident during the recent coronavirus pandemic and will grow in future. Robotic teleoperation satisfies the demands of the scenarios in which human access carries measurable risk, but human intelligence is required. An effective teleoperation system not only enables intuitive human-robot interaction (HRI) but ensures the robot can also be operated in a way that allows the operator to experience the \"feel\" of the robot working on the remote side, gaining a \"sense of presence\". Extended reality (XR) technology integrates real-world information with computer-generated graphics and has the potential to enhance the effectiveness and performance of HRI by providing depth perception and enabling judgment and decision making while operating the robot in a dynamic environment. This review examines novel approaches to the development and evaluation of an XR-enhanced telerobotic platform for intuitive remote teleoperation applications in dangerous and difficult working conditions. It presents a strong review of XR-enhanced telerobotics for remote robotic applications; a particular focus of the review includes the use of integrated 2D/3D mixed reality with haptic interfaces to perform intuitive remote operations to remove humans from dangerous conditions. This review also covers primary studies proposing Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) solutions where humans can better control or interact with real robotic platforms using these devices and systems to extend the user's reality and provide a more intuitive interface. The objective of this article is to present recent, relevant, common, and accessible frameworks implemented in research articles published on XR-enhanced telerobotics for industrial applications. Finally, we present and classify the application context of the reviewed articles in two groups: mixed reality-enhanced robotic telemanipulation and mixed reality-enhanced robotic tele-welding. The review thus addresses all elements in the state of the art for these systems and ends with recommended research areas and targets. The application range of these systems and the resulting recommendations is readily extensible to other application areas, such as remote robotic surgery in telemedicine, where surgeons are scarce and need is high, and other potentially high-risk/high-need scenarios.  
robotics; teleoperation; extended reality; human-robot interaction; virtual reality; mixed reality; augmented reality +
Footnote †: journal: Applied Sciences
## 3 The Bike-Share Case
### Data  
Forecasting user demand for rentals and returns is the first step in allocating bike inventories to stations and anticipating balancing needs. We base our forecasts on Citi Bike's monthly trip histories published online ([[https://ride.citibikenyc.com/system-data](https://ride.citibikenyc.com/system-data)]([https://ride.citibikenyc.com/system-data](https://ride.citibikenyc.com/system-data)) (accessed on 10 October 2024)).  
The Bike Angels program incentivises users to balance inventory by offering re-deemable ride points. Incentives for users to rebalance inventory are inherently local, as they redirect returns from overflowing stations to those in need, adjusting flows between nearby stations without significantly altering overall demand. Given the asymmetries in stockout patterns between nearby stations, modelling a carefully selected cluster of proximate stations is justified to test causal hypotheses.  
We selected stations based on the following criteria: (a) availability of incentive data to correlate with demand, allowing analysis of its historical effectiveness using a causal inference model, which estimates the impact of incentives on user demand and adjusts demand accordingly, (b) at least one station in the cluster should rank high in demand or ride counts to ensure the analysis focuses on a high-impact cluster and (c) stations should be within walking distance, allowing users to switch between stations easily, thus balancing inventory through guided incentives to \"give\" at one station and \"take\" at another to improve overall balance. For a time, station incentive data was accessible via an API ([[https://layer.bicyclesharing.net/map/v1/nyc/stations](https://layer.bicyclesharing.net/map/v1/nyc/stations)]([https://layer.bicyclesharing.net/map/v1/nyc/stations](https://layer.bicyclesharing.net/map/v1/nyc/stations)) (accessed on 10 October 2024)), but this ceased after 25 August 2023. Since these data are critical for analysis (criterion a), we focused on August 2023, the most recent month with available data. To meet criteria b and c, ride count at each station was used. E 17 St and Broadway, ranked fifth out of 400 stations, was chosen as the nodal station, with the additional requirement to have several nearby stations. We then selected stations within a 350-metre radius to form our demand cluster.
# I/Q Imbalance and Imperfect SIC on Two-Way Relay NOMA Systems
## 1 Introduction
### Contributions  
In this paper, we focus on a TWR C-NOMA system with one relay and two users. Two users wish to exchange information via the DF relay in two phases: the uplink multiple access process and the downlink broadcast process. It is worth mentioning that a more practical application scenario is considered in this paper. In other words, we analyse and discuss the performance of a TWR C-NOMA system with IQI and ipSIC conditions over the Rician fading channels. The main contributions of this work are summarized as follows:  
* Based on the above works, we investigate the effects of IQI and ipSIC on the performance of a TWR C-NOMA network over the Rician fading channels. It is worth noting that this is a valuable problem for practical system design and analysis. As far as the authors know, although a system with IQI or ipSIC has been studied in some papers, the TWR C-NOMA system model with the condition of IQI and ipSIC under Rician fading channels has not been previously studied.
* We derive analytical expressions for outage probabilities of both the far and near users. The results show that IQI and ipSIC have deleterious effects on the outage performance and residual IS. In order to gain better insights into the system performance, we compare the outage performance of NOMA and OMA for both the far and near users, and the results show that the outage performance of NOMA is better than that of OMA. By comparing Rician and Rayleigh fading channel conditions, it is found that the throughput of our considered system with Rician or Rayleigh fading channels is almost the same in ideal conditions, and IQI and ipSIC have worse effects on the system throughput with Rayleigh fading channels than on Rician.
* We carry out the asymptotic analysis in the high SNR region. Furthermore, based on asymptotic outage probability, the diversity order is derived to analyse the diversity gain of the system. It is demonstrated that residual IS can result in error floors for the outage probability and zero diversity orders.