esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Preparation of Test Data
#### 3.1.2 The Selection of Representative Classes  
The data prepared with the assignment of roof type classes to building contours were subjected to analysis. It was observed that gable roofs exhibited a notable prevalence in comparison with the other roof types. The graphical representation, which is a bar chart, is presented in Figure 4. The horizontal axis represents the numbers of the adopted areas (target fragments of the orthophotos), and the vertical axis represents the percentage share of each roof type class. Approximately half of the total number of roofs (with an average value of 51%) are gable roofs. The remaining roof types, in descending order of prevalence, are flat roofs (5%), shed roofs (10%), hip roofs (9%), pyramid roofs (10%), and other roofs (10%). These roof types collectively comprise the other half of the total classes. A normal (Gaussian) distribution graph is employed to present the percentage of classes (Figure 5). The flattened shape of the curve attributed to pitched roofs indicates a significant variation in the size of this class between areas. This class is present in each of the areas under analysis. In the majority of areas, the proportion is greater than 50%. In accordance with the three-sigma rule, in 99.73% of the areas under consideration, this class occurs in a proportion between 13% and 91%. The characteristic curves for the remaining classes are not as flat and are not consistently present in all areas. In comparison with gable roofs, the number of individual classes is significantly smaller.  
Given the above interpretations of the graphical visualizations of the classes created, two representative classes were selected for further research. The division into two classes constitutes a preliminary step in the ongoing research project. Subsequent iterations of this study will involve the expansion of the number of classes to include a greater variety of roof types. The first one is gabled roofs, while the other classes were combined into one class as other roof types. These two created classes have a similar distribution in terms of their frequency in the created learning datasets. These classes were converted to YOLO in the resulting text files.  
Figure 3: Scheme of assumptions for creating data for the learning set of YOLO (source: author’s own elaboration).
## 5 Experiments and Results
### Filtering Evaluation
Datasets  
As mentioned before, the 3D model dataset used for the image synthesis was the Bosphorus Database [42]. This dataset allowed us to test and improve the last two steps of the pipeline: the rendering of new views and the printing phase. As for the first two steps of the pipeline--the acquisition and the modeling (3D reconstruction and filtering)--we used an dataset built in-house and comprising 20 persons. For the filtering phase, we also used the Bosphorus dataset to provide the database of exemplars, as described previously.
# Development of Automatic Berthing Support Program for Autonomous Ships
## 2 Methods
### Forces Acting on the Hull During Berthing
#### 2.1.2 Current Pressure and Current Moment  
The current pressure acting on the hull during berthing is a combined effect of the current-induced pressure (\(F_{C}\)) and the pressure due to berthing speed (\(F_{U}\)), as indicated in Equation (4). The resulting current moments (\(M_{C}\), \(M_{U}\)) are calculated using Equation (5).  
\[\begin{split} F_{C}&=\frac{1}{2}\rho_{w}C_{yc}L_{ BP}dV_{c}^{2}\frac{1}{1000},\\ F_{U}&=\frac{1}{2}\rho_{w}C_{yc}L_{BP}dU^{2}\frac{1}{1000 },\\ M_{C}&=\frac{1}{2}\rho_{w}C_{mc}L_{BP}{}^{2}dV_{c} ^{2}\frac{1}{1000},\\ M_{U}&=\frac{1}{2}\rho_{w}C_{mc}L_{BP}{}^{2}dU^{2} \frac{1}{1000},\end{split} \tag{4}\]  
where \(F_{C}\) represents the current-induced pressure, \(F_{U}\) represents the pressure due to berthing speed, \(M_{C}\) represents the current-induced moment, \(M_{U}\) represents the moment due to berthing speed, \(\rho_{w}\) represents the seawater density, \(C_{yc}\) represents the lateral current force coefficient, \(C_{mc}\) represents the current moment coefficient, \(L_{BP}\) represents the water-line length, \(d\) represents the draft, \(V_{c}\) represents the current speed, and \(U\) represents the berthing speed.  
The lateral current force coefficient considerably varies with the depth-to-draft ratio (h/d), requiring a greater force to move the hull laterally as the UKC decreases. The Oil Companies International Marine Forum (OCIMF) conducted model tests on tankers and presented the corresponding lateral current force coefficients, as shown in Figure 2 [25]. Additionally, [PERSON] [24] proposed formulas for calculating the lateral current force and moment coefficients for VLCCs, with adjustments of 90% (LNG carriers), 85% (container  
Figure 1: Resultant wind pressure coefficient [24].  
ships), and 75% (PCC) for other ship types. This study used the lateral current force and moment coefficients proposed by [PERSON] [24].  
\[\begin{split} C_{yc}&=\bigg{(}\frac{0.75}{\frac{1}{2} -0.9}+1\bigg{)}sin\beta,\\ C_{mc}&=\bigg{(}\frac{0.075}{\frac{1}{2}-0.9}+1 \bigg{)}sin2\beta,\end{split} \tag{6}\]  
where \(C_{yc}\) represents the lateral current force coefficient, \(C_{mc}\) represents the current moment coefficient, \(h\) represents the water depth, \(d\) represents the draft, and \(\beta\) represents the incident angle.
## 5 Materials and Methods
### Classifier Performance  
The performance of the studied models is evaluated using the area under the curve ROC, commonly referred to as AUC. This metric is widely used in binary classification problems as it measures a model's ability to discriminate between positive and negative classes, regardless of the threshold chosen for classification. Unlike accuracy, AUC considers the trade-off between sensitivity (true positive rate) and specificity (true negative rate), making it less susceptible to imbalanced datasets.  
Recall or sensitivity is defined as the proportion of physical violence data correctly identified (true positives) or how effectively a classifier identifies positive labels; and specificity is the proportion of non-physical violence data correctly labeled (true negatives) or the effectiveness of a classifier to identify negative labels. In the context of binary classification, recall (or sensitivity) and specificity are important performance metrics that provide insights into how well a classifier distinguishes between positive and negative classes. These metrics are typically derived from a confusion matrix. The formal definitions of AUC, sensitivity, and specificity can be found in Ref. [63].  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Parameters** & **DenseNet121** & **EfficientNetB0** & **MobileNetV2** \\ \hline Pooling & Average 2D & Average 2D & Average 2D \\ Dense & 512/ReLU & 512/ReLU & 512/ReLU \\ Dropout & 0.5 & 0.5 & 0.5 \\ Dense & 256/ReLU & 256/ReLU & 256/ReLU \\ BN & True & True & True \\ Dense & 2/Softmax & 2/Softmax & 2/Softmax \\ \(\eta\) & 0.0001 & 0.0001 & 0.001 \\ Batch size & 64 & 64 & 32 \\ Epochs & 29 & 27 & 11 \\ Freezing & 10 & 10 & 4 \\ \(\mu\) & 0.3934 & 0.3934 & 0.3934 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Setup summary of the pre-training models studied in this work.
# Synthesis of Nitrogen-Doped Graphene Quantum Dots from Sucrose Carbonization
## 3 Results and Discussion
### Absorption Spectra and Tauc Plots  
The UV-VIS absorption spectra of the GQDs produced in this work are shown in Figure 7. The absorption spectrum of the undoped GQDs showed an absorption peak centered at 275 nm, which refers to the \(\pi\rightarrow\pi^{\star}\) transition related to the presence of sp\({}^{2}\) hybridized carbons [4; 55]. An absorption peak around 330 nm was also observed, which is associated with the n \(\rightarrow\pi^{\star}\) transition, which occurs due to the presence of oxygen heteroatoms in the structure of the quantum dots [4; 55]. The absorption spectrum of N-GQDs was wider and redshifted in relation to the absorption spectrum of undoped GQDs. The broadening of the absorption peak can be caused by the superposition of the absorption band referring to the \(\pi\rightarrow\pi^{\star}\) transition centered at 280 nm with the n \(\rightarrow\pi^{\star}\) transition, which must occur around 300 nm and extends up to 800 nm [4; 55]. Theoretical analyses employing the Density Functional Theory (DFT) have already shown that the position of the absorption bands depends on the number of carbon atoms in the nanoparticles as well as the presence of heteroatoms and how they bind to GQDs [29]. In general, the presence of oxygen functional groups and the presence of nitrogen in the GQDs' structure give rise to absorption bands that can extend from 300 to 1000 nm [29]. The DFT results also indicate that the presence of nitrogen atoms bound to pentatonic rings (pyrrole nitrogen) can give rise to the redshift of the absorption band, as observed in this work [30; 56].  
Figure 7: UV–VIS absorption spectra of GQDs and N-GQDs.  
Tauc plots were obtained from the absorption spectra of the GQDs and are shown in Figure 8. The direct energy gaps associated with the n \(\rightarrow\pi^{\bullet}\) transitions were 3.29 and 3.09 eV for the GQDs and the N-GQDs, respectively. Furthermore, it was evident that the transition at the edge of the conduction band was not abrupt, which is an indication that energy states originated by the presence of heteroatoms may extend to the bandgap region. The functional groups in the graphene lattice can act as electron donors, playing a role in the increase of the fermi energy of the GQDs [26]. It is noteworthy that N-GQDs presented a lower bandgap value associated with n-\(\pi^{\bullet}\) transitions. The lower bandgap value in the N-GQDs was associated with the higher density of energy states caused by the doping process, as already demonstrated in theoretical work [29].
# An Intelligent Real-Time Driver Activity Recognition System Using Spatio-Temporal Features
## 2 Experiment Setup and Data Collection
### RGBDT Module Sensor  
In this work, an RGBDT sensor module was applied which comprises RGB, depth, and thermal infrared sensors. Most existing driver behavior recognition approaches have mainly used RGB sensors or visible RGB images due to their wide availability. However, the lighting conditions represent one of the most challenging problems when dealing with RGB images. The RGB sensor applied in this study was capable of capturing 1280 (H) \(\times\) 720 (V) pixels with a 30 fps frame rate. In addition, the thermal IR sensor captures the driver activity with 1-200 Hz (Full) and 4-800 Hz (Quarter) frame rates which can also view up to 120 \(\times\) 90\({}^{\circ}\) within a temperature range of 20 to 85 \({}^{\circ}\)C. The 3D depth sensor can capture 640 \(\times\) 480 pixels with a frame rate of 10 fps at a range of 0.2 m up to 2.5 m. However, in this work, only RGB and IR sensors were considered.
# A Medical Image Encryption Scheme for Secure Fingerprint-Based Authenticated Transmission
## 3 Materials and Methods
### Running Efficiency Analysis  
Medical image transmission is a real time task, and thus the running time of the proposed system is an important requirement. Table 5 shows the encryption and decryption times (in seconds) of the proposed scheme for each medical image of a test. The encryption and decryption times depend on the size of the medical image because, when the image is larger, there is more data to embed and extract from the reference image. The medical image size is 512 \(\times\) 512, and the reference images size is 1024 \(\times\) 1024. To test running time, the experiment is performed on a computer with an AMD A10-9620P @ 2.50 GHz and 8 GB of RAM.  
Table 5 shows that the proposed scheme achieves high encryption and decryption efficiency with images of high size. Therefore, the proposed scheme can be used to protect medical image transmission in real-world applications.
## 6 Conclusions  
An approach to automatically cluster callers' behaviour and detect callers' outliers using only information from CDR is proposed. The main novelty of the framework proposed is that it works in real-time, and the clusters obtained can be updated considering all the previous data recursively. Thus, the results change in time with any new call or caller considered and this evolution of the data stream is being analysed automatically as it takes place.  
To evaluate our proposal, a CDR of 9834 calls (400 phone users) is used. This dataset has already been used in many different research works because it is very suitable for evaluating different tasks. It is important to remark that the pre-processing of the initial CDR is essential. In this research work, one of the specifications is that the extracted callers and calling patterns are related with a pre-defined time interval. The reason for this is that the obtained patterns need to be clearly understood. Thus, two different methods were applied: one of them is related with the calls done per hour and the other is related with a typical time division: morning, afternoon, evening, and night.  
Finally, it is very important to remark that in this research, the calling behaviour of a user during a day has been clustered. However, in future work, it can also be analysed how a user behaves during several days to check, for example, if their calling behaviour changes in time or not. In addition, in future work, the proposed method will be validated on real mobile phone data.  
This work has been supported by the Spanish Ministry of Science and Innovation (MICINN) under projects: TRA2015-63708-R and TRA2016-78886-C3-1-R.  
[PERSON] and [PERSON] worked in the evolving algorithm proposed and performed the experiments. [PERSON] and [PERSON] designed the experiments and analysed the data. [PERSON] wrote the paper.  
The authors declare no conflict of interest.
## 7 Research Directions
### Application Data Security  
IoT devices operate on lossy networks and use IPv6 addresses and CoAP to communicate among themselves. However, these devices communicate to IoT service providers located in the Cloud using IPv4 addresses and HTTP. As shown in Figure 25, a Gateway device located between IoT devices and IoT Cloud services enables communications between IoT devices and the Cloud service by translating one protocol to another, such as IPv4 to IPv6, or HTTP to CoAP, and vice versa. However, the Gateway device can learn the contents of application payloads during the protocol translation.  
Security at the application layer can prevent the protocol translators, such as Gateway, from learning application payloads. The application data security also simplifies the security requirements for the underlying layers. It eliminates the overheads for providing security for every packet at the underlying layers, such as Transport and Network layers, because only applications data needs to be secured. Figure 25 presents a process to ensure the security and privacy of application data. Unlike the Transport Layer Security that encrypts both headers and payloads at the session presentation layer, only payloads are encrypted at the application layer. Therefore, application data are not exposed to the Gateway when it handles and processes messages exchanged between IoT devices and clients.  
S/MIME [176] and SRTP [177] standards can be utilized for application data security. However, further research is required to apply S/MIME and SRTP to the CoAP-enabled IoT devices and networks because these security standards are not primarily designed for resource-limited environments. The standards may need to be modified to make them suitable for IoT systems.  
Figure 24: An overview of the Data Transparency Service.  
Figure 25: Application data security during protocol translation.
# A Subjective Study on User Perception Aspects in Virtual Reality
## 2 Overview and Related Work
### Related Work
#### 2.2.3 Cybersickness  
While watching 360 video, users may feel nauseous or dizzy, which is called \"cybersickness\" [28; 29]. Such a symptom usually appears when a user perceives differences between content motions and his/her movements [30]. In [31], it is found that an abrupt speed change in vibrational and translational camera motions can lead to cybersickness. To alleviate cybersickness in first person 360 video, a solution using image processing and a wearable omnidirectional camera is proposed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Factor**} & \multicolumn{3}{c}{**QoE Aspects**} \\ \cline{2-4}  & _Perceptual Quality_ & _Presence_ & _Cybersickness_ \\ \hline _Gender_ & [16] & & [17] \\ _Age_ & & & \\ _Usage history_ & & & \\ \hline \multirow{3}{*}{_Encoding parameters_} & QP & [25] & \\  & Resolution & [16; 17; 23; 24; 25] & [17] \\ \cline{1-1}  & Frameate & [24] & \\ \cline{1-1}  & Bitrate & [23; 24; 26] & \\ \hline _Rendering device_ & [17] & [17] \\ _Delay_ & [16] & & [16] \\ _Content motion_ & [17; 26] & & [16; 17] \\ _Self-avatar_ & & [14] & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptions of factors and QoE aspects considered in related work.  
Previous studies have also shown significant impacts of gender [17], resolution [17], and content motion [16; 17] on the cybersickness. Meanwhile, no significant effect on the cybersickness is observed for the two factors of rendering device [17] and delay (i.e., Motion-to-High-Resolution Latency) [16].
# A Systematic Review of Cybersecurity Risks in Higher Education
## 9 Analysis and Discussion of Cyber Risks and Countermeasures in HE
### Abuse and Misuse of University Infrastructure and Resources  
4% of the incidents in Table 15 and copyright violations is ranked the number two incident cause in Chapman [8]. Universities possess a broad range of computing resources,hosting opportunities, subscriptions, and bandwidth. The review results show that these assets can be abused by threat agents ranging from non-malicious to worst-case scenario. Several sources mention _The Silent librarian campaign_ where cybercriminals exploited compromised University accounts to steal thousands of research articles. Both criminals and insiders look to exploit University computing resources for cryptojacking for financial gain (mining for cryptocurrency using resources they do not own) [58]. Another severe risk from weak security controls occurs when the University infrastructure is abused by criminals as a stepping stone to attack third parties. Typically, they either hide the attacker's true identity or masquerade the attack as legitimate traffic between the University and the target. The resources can also be abused for cyberstalking [5].  
More benign types of abuse are copyright violations caused by illegal hosting and downloading on University networks. Our results show that these violations are commonplace in HE.  
Lastly, our results have documented large amounts of DDoS attacks occurring on the University networks. Again, this can be a relatively benign activity, for example, when students attack each other for fun, but can also be very serious when vulnerable network resources are leveraged by criminals to attack third parties.  
_Countermeasures_: A centralized IT governance could potentially mitigate abuse of infrastructure and resources at HE. The papers from [PERSON] et al. [31; 32] investigates the correlation between IT centralization, outsourcing and cybersecurity breaches in U.S. The authors found that both centralized IT and outsourcing are associated with fewer breaches. This was due to the establishment of uniform control and organization-wide security policies, better strategic alignment, and well-defined accountability. In addition, centralized IT governance facilitates universal compliance with security protocols, resulted in better security information sharing, raised awareness of security issues, and enhanced coordination between business units. The white paper from FireEye [5] does also highlight several recommendations to counter abuse. The paper recommends implementing two-factor authentication, segmenting networks, implementing incident response plans, recording data traffic, and increasing communication flow.
# 1-D Convolutional Neural Network-Based Models for Cooperative Spectrum Sensing
## 3 Overview of Spectrum Sensing and Adopted Deep Learning Algorithms  
Deep learning algorithms have demonstrated significant performance in the corresponding tasks compared to conventional methods, mainly CNN and RNN algorithms, which are complementary in their modeling capabilities. For the purpose of realizing SS based on a hierarchical DL network, in the subsequent subsections, we briefly introduce the basic concepts of spectrum sensing, the multilayer perceptron, convolutional neural networks and bidirectional long short-term memory.
# UAV-Based Privacy-Preserved Trustworthy Seamless Service Agility for NextG Cellular Networks
## 6 Security and Performance Analysis
### Performance Results  
#### 6.3.1 Authentication Delay  
We evaluate our designed authenticated privacy-friendly inter-operator agility model performance compared to standard connection procedures under normal conditions. Table 1 shows the time taken by the normal _rrc_ message connection procedure between the UE and the home network operator. Table 2 shows the time performance of our proposed modified inter-operator _rrc_ messages connection procedure, including the authentication time between all involved parties.  
In Table 2, the total inter-operator connection time is divided into four parts as follows,  
* **Delegation Phase**: The on-land delegation phase preparation time between the home network and the assigned UAV.
* **UE-UAV**: The connection time of the UE requests the accommodation service from the supporting UAV, including messages 3 through 8 in Figure 3.
* **UAV-Guest eNB**: The connection time of the UAV requests accommodation from the nearest Guest-eNB participating in the inter-operator agility services represented in Figure 3 in messages 9 through 11.
* Finally, the last subsection of the table represents the **Guest Network Porting** time, including the ID exchange of the Guest-eNB and the UE via the supporting UAV and the guest network RRC connection setup. The guest network service porting messages exchanged are represented in Figure 3 in messages 12 through 15.  
From Table 2, the connection setup total time is under 1 ms which is suitable for cellular communication authentication and connection setup. The UAV-Guest eNB connection time shown in Table 2 can be further optimized if the UAV has two parallel USRPs. In that case, the UAV will use one USRP for connecting with the guest tower so that the connection establishment will occur only once at the beginning of the connection, and then the UAV will ask for a token for each new home network UE requesting the service. In this work, we utilized a simplified system with USRPs and srsLTE package to demonstrate the proposed model efficiency; the total connection setup time will be further optimized in real-time cellular infrastructure.