esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### GNAT-HTH (GaCTA)  
A TA pair composed of a toxin harboring the GNAT domain (GCN5-related N-acetyltransferases, originally derived from GCN5 (general control non-repressible 5), a histone acetyltransferase [37]), and an antitoxin harboring the HTH (Helix-Turn-Helix) domain or its variants, RHH (Ribbon-Helix-Helix) [38] or wHTH (winged Helix-Turn-Helix) belongs to type II TA families [25; 39; 40; 41; 42].  
Each of the four _Geobacillus_ strains harbors one GNAT-wHTH pair. We suggest calling this TA family \"GaCTA\" (_Geobacillus_acetyltransferase toxin-antitoxin) in accordance with the previously reported RHH-GNAT TA families, KacAT of _Klebsiella pneumoniae_[43] and TacAT of _Salmonella enterica_ serovar Typhimurium [44], while also considering the reverse order of the TA-coding genes, as discussed below.
# Effect of Data Augmentation on Deep-Learning-Based Segmentation of Long-Axis Cine-MRI
## 4 Discussion  
This section presents the results of the experiments, with a specific focus on the effect of data augmentation. Before delving into the effects of data augmentation, the results of K-fold cross-validation are presented to demonstrate the consistency of the models and the training procedure.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**Average ED Volume (mL)**} & \multicolumn{2}{c}{**Average ES Volume (mL)**} & \multicolumn{2}{c}{**LVEF (\%)**} \\ \cline{2-7}  & **DA (gt)** & **No DA (gt)** & **DA (gt)** & **No DA (gt)** & **DA (gt)** & **No DA (gt)** \\ \hline ENet 2 cav test & **180 \(\pm\) 67** (181 \(\times\) 70) & **177 \(\pm\) 69** (181 \(\times\) 70) & **85 \(\pm\) 71** (91 \(\pm\) 72) & **88 \(\pm\) 71** (91 \(\pm\) 72) & **56.65 \(\pm\) 19.70** (53.84 \(\pm\) 19.97) & **54.60 \(\pm\) 20.15** (53.84 \(\pm\) 19.97) \\ ENet 2 cav val & **177 \(\pm\) 70** (179 \(\pm\) 71) & **177 \(\pm\) 68** (179 \(\pm\) 71) & **86 \(\pm\) 64** (88 \(\pm\) 66) & **87 \(\pm\) 63** (88 \(\pm\) 66) & **55.31 \(\pm\) 18.06** (55.12 \(\pm\) 17.33) & **54.53 \(\pm\) 18.14** (55.12 \(\pm\) 17.33) \\ \hline ENet 4 cav test & **152 \(\pm\) 59** (157 \(\pm\) 59) & **155 \(\pm\) 72** (157 \(\pm\) 59) & **79 \(\pm\) 53** (33 \(\pm\) 57) & **82 \(\pm\) 58** (83 \(\pm\) 57) & **50.32 \(\pm\) 17.62** (50.21 \(\pm\) 17.57) & **49.60 \(\pm\) 17.66** (50.21 \(\pm\) 17.57) \\ ENet 4 cav val & **146 \(\pm\) 58** (151 \(\pm\) 61) & **142 \(\pm\) 57** (151 \(\pm\) 61) & **74 \(\pm\) 51** (80 \(\pm\) 52) & **73 \(\pm\) 49** (80 \(\pm\) 52) & **53.43 \(\pm\) 15.51** (51.42 \(\pm\) 14.74) & **51.82 \(\pm\) 14.99** (51.42 \(\pm\) 14.74) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average left ventricular clinical metrics obtained with augmented and nonaugmented configurations. The ground-truth values from manual annotations are shown in parentheses.  
Figure 5: Bland\(-\)Altman plots obtained over 2- and 4-chamber test sets with augmented and nonaugmented models. A positive bias is observed with 2-chamber models in agreement with results reported in Table 2.
# Analysing Land Cover Change in the Valencian Community through Landsat Imagery: From 1984 to 2022
## 2 Materials and Methods
### Input Data  
For over four decades, the satellite constellation Landsat has provided valuable information on the state and dynamics of our planet, providing images with high spatial (15 m, 30 m and 100 m, depending on the bands) and temporal resolutions (8 days, with 16 observations per satellite and two satellites per mission). Other missions, such as Sentinel-2, either lack a comparable long time series or, like MODIS, do not boast a high spatial resolution. Furthermore, since the images became freely accessible in 2008, this satellite has emerged as one of the most widely utilised [34; 36; 37], with applications ranging from planning and monitoring to surveillance [38; 39; 40; 35]. Given its extensive time coverage, this satellite constellation aligns seamlessly with our study objectives, enabling the monitoring of land cover changes over the longest available timeframe.  
In this study, we employed the following datasets via GEE platform: 'LANDSAT/LT05/C02/T1_L2', 'LANDSAT/LC08/C02/T1_L2', and 'LANDSAT/LC09/C02/T1_L2', corresponding to the atmospherically corrected level-2A products of Landsat 5, 8, and 9. Its known issues with image acquisition disturbances motivated the deliberate exclusion of Landsat 7.  
To reduce the probability of encountering cloudy pixels in the images and to mitigate the impact of pronounced seasonal variations, only summer images, acquired from June 1 st to September 30 th, were analysed. Landsat 5 images were used for the years 1984, 1985, 1990, 1995, 2000, 2005, and 2010; while Landsat 8 images were used for the years 2015 and 2020; and Landsat 9 was used for 2022 and 2023 (Table 1).  
We have chosen a series of five-year intervals to observe land cover changes within a relatively short time span, ensuring the analysis remains manageable and effective. Additionally, an analysis for the year 1984 was conducted, marking the beginning of the Landsat series, to allow for direct comparison with the most recent years, 2022 and 2023, the latter being used for validation purposes.  
Figure 1: Satellite view of the Valencian Community. In the upper-left: a detailed view of Spain.
## 3 Dependability
### Security  
Security is one of the main issues in communication networks, as both nodes and the whole network are attacked by malicious users [22; 33]. The distributed and data-driven nature of future 6G communication networks and its use cases mean more data, and of course, a wider attack surface. The applications of AI or ML in communications networks are increasing at a higher pace due to apparent reasons [34]; however, AI and ML also bring their own security challenges in communications networks, as elaborated in [35; 36]. The most important part is to identify the required level of security for a certain use case and adopt the principles of the security-by-design approach. These concepts are quite important due to the diverse nature of 6G MCAs. Furthermore, the rise in the number of capable attackers targeting communication networks call for stringent security requirements. In [37], the authors explore the application of blockchain technology alongside ML in order to protect vehicular networks from cyber attacks. Similarly, in [38], the authors used a smart contract architecture in heterogeneous vehicular networks for collaboratively performing tasks between moving vehicles and parked vehicles. The smart transactions consider the characteristics of both the network and the attack models for improving security. Furthermore, physical-layer security techniques can be used to provide security with drastic changes to the network architecture [23; 39].
# Comparative Analysis of Marine Alternative Fuels for Offshore Supply Vessels
## 2 Methodology
### Technical Impact (Step 3)  
The fuel storage capacity is greatly affected by the fuel consumption and bunkering cycle. In addition, based on the characteristics of the fuel itself, the energy density of the fuel is a key feature that determines the amount of fuel to be stored. Therefore, the technical impact score was justified based on Table 6.  
The potential fuel types and propulsion systems available by 2026 are listed in Table 7, and their technical maturity levels were assessed based on the data/information (DNV, MARITIME FORECAST TO 2050. 2022). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fuel** & **Energy Density (MJ/L)** &
\begin{tabular}{c} **Tank Size Ratio to** \\ **HfO/MGO Tank** \\ **(Times)** \\ \end{tabular} & **Weight Unit (kg/m3)** & **Tank Type** \\ \hline Ammonia & 12.7 & 3–4 & 683 & Type C \\ \hline Hydrogen (LH\({}_{2}\)) & 8.5 & 7 & 71 & Type C \\ \hline Hydrogen (CGH\({}_{2}\)) & 8.5 & 13–15 & 42 & Type C \\ \hline Biodiesel & 35.7 & 1 & 880 & Integral tank \\ \hline Methanol & 15.7 & 2.5 & 791 & Integral tank \\ \hline LNG & 21.2 & 2–3 & 450 & Type C \\ \hline Electricity (battery) & 2.1 & - & - & Battery \\ \hline \hline \end{tabular} Scoring justification: 5: energy density (MJ/L) 31–50; 4: energy density (MJ/L) 15–30; 3: energy density (MJ/L) 10–14; 2: energy density (MJ/L) 5–9; 1: energy density (MJ/L) 0–4.  
\end{table} 
Table 6: Characteristics regarding fuel storage capacity and rating number details. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Fuel** & **Propulsion System** & **Maturity** \\ \hline Ammonia & ICE & 4 \\ \cline{2-3}  & Fuel cell & 2 \\ \hline Hydrogen & Fuel cell & 4 \\ \hline Biodiesel & ICE & 4 \\ \hline Methanol & ICE & 4 \\ \cline{2-3}  & Fuel cell & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Technical maturity per fuel and rating number details for maturity. \begin{table}
\begin{tabular}{c c} \hline \hline
**Design Scenario** & **Technology** & **Fuel Types** \\ \hline
9 & & LNG + electricity \\ \hline
10 & & \
\begin{tabular}{c} Biodiesel + electricity \\ \end{tabular} \\ \cline{2-3}  & Generator with electric propulsion and battery &
\begin{tabular}{c} Methanol + electricity \\ \end{tabular} \\ \cline{2-3}
11 & &
\begin{tabular}{c} Methanol + electricity \\ \end{tabular} \\ \cline{2-3}
12 & &
\begin{tabular}{c} Ammonia + electricity \\ \end{tabular} \\ \hline
13 & Full battery & Electricity \\ \hline
14 & Fuel cell with battery & Hydrogen \\ \hline
15 & Fuel cell with cracking system and battery & Ammonia \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._Fuel storage capacity and technological maturity can be summarised as shown in the following table, Table 8, based on the energy density, applied fuel, and power source types of the fuel.
# Enhancing the 3 GPP V2X Architecture with Information-Centric Networking
## 2 V2X Communications in 3 GPP
### V2X Communications over the LTE-Uu Interface  
The LTE-Uu interface supports unicast and multicast connections.  
**Unicast communications.** Unicast connections allow data exchange between one UE and the eNodeB, in both uplink (UL) and downlink (DL) directions. If a UE does not have pre-allocated radio resources, it has to request them to the eNodeB via _dynamic scheduling_. The UE sends its request for a transmission grant once a packet arrives at its buffer (UEs are in connected mode to avoid the initial access delay due to the random access procedure). Specifically, it sends a buffer status report (BSR) to notify the eNodeB about the amount of buffered data and its priority. Then, the UE waits for a Scheduling Request (SR) period to send an SR message to the eNodeB. On the basis of the received request, the eNodeB will schedule the resources to be allocated and send this configuration information back to the requesting UE. Eventually, the UE will transmit the packet over the resources allocated by the eNodeB.  
**Multicast communications.** V2X messages can be efficiently disseminated to multiple UEs in DL through the evolved Multimedia Broadcast Multicast Service (eMBMS) specified by 3 GPP [9]. The V2X AS is responsible for the reception of UL unicast data from the UEs and for the identification of the target reception area (a cell or multiple cells) to be covered by the eMBMS transmission.
# A Trajectory Generation Method for Multi-Rotor UAV Based on Adaptive Adjustment Strategy
## 4 Experiment and Result
### Real-World Tasks
#### 4.2.1 Flight Platform and Software Environment  
This section implements the quadrotor shown in Figure 10 as the flight platform, which uses PX4 as the quadrotor control system and carries Dji Manifold 2 as the on-board computer. The processor model of Dji Manifold 2 is Intel I7-8850U, and the RAM size is 8 GB. The Intel RealSense tracking camera T265 provides reliable pose estimation for quadrotor, and the Intel RealSense depth camera D455 provides the depth image of the environment.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Planning Time (s)** & **Trajectory Running Time (s)** & **Trajectory Length (m)** & **Trajectory Smoothness** \\  & & & **(m/s\({}^{2}\))** \\ \hline Fast-Planner & 0.067 & 6.81 & 10.46 & 1.87 \\ Proposed & 0.070 & 5.68 & 9.46 & 1.45 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental results of scenario 2.  
Figure 10: Flight platform used in this section.  
Figure 9: Trajectory acceleration curve comparison. (**a**) Proposed Method. (**b**) Fast-Planner.  
The operating system of the airborne computer is Ubuntu 18.04. UAV's perception, path planning, and trajectory generation algorithms are developed using C++11 based on ROS (Robot Operating System). ROS is a widely used open-source robot system. It provides robot data communication interfaces, visualization tools, etc. Additionally, cameras and other hardware have also developed corresponding SDKs for ROS. The system software architecture diagram is shown in Figure 11. The system takes the posture and depth image provided by the binocular camera as input and builds the occupied grid map of the environment in real-time. When the target message is obtained, the path-planning algorithm searches for a feasible path in the map. Then, the trajectory generation algorithm generates a smooth continuous trajectory according to the path. Finally, the multi-rotor UAV control algorithm converts the trajectory to the control quantity, realizes the control of the quadrotor UAV based on MAVROS, and finally controls the multi-rotor UAV to reach the target position along the planned trajectory.
## 2 Optimized EEMD Based 2D-MUSIC Method  
### 2D-MUSIC Algorithm for Impact Localization  
In this section, the 2D-MUSIC algorithm in [10,11] is briefly introduced. As seen in Figure 1, a uniform linear array (ULA) consists of M piezoelectric (PZT) sensors on the structure, which are arranged uniformly along the \(x\) axis and asymmetric with the \(y\) axis. The distance between two sensors is \(d\).  
\(\mathbf{A}(r,\theta)\) is defined as the array steering vector  
\[\mathbf{A}(r,\theta)=[a_{1}(r,\theta),a_{2}(r,\theta),\cdots,a_{M}(r,\theta)] \tag{1}\]  
where  
\[a_{i}(r,\theta)=\frac{r}{r_{i}}\exp(-j\omega_{0}\tau_{i})\]  
\[\tau_{i}=\frac{(-d\sin\theta)}{c_{AV}}(i-1)+(-\frac{d^{2}}{c_{AV}}\cos^{2} \theta)(i-1)^{2}\]  
\(r\) is defined as the distance between the impact source and the ULSA, which is the distance from the source to the reference sensor labeled as 1 in the sensor array. \(\theta\) denotes the wave propagating direction caused by the impact with respect to the coordinate \(y\) axis. \(c_{AV}\) is the average velocity of the Lamb wave.  
The basic idea of the MUSIC algorithm is to obtain the signal subspace and noise subspace through eigenvalue decomposition of the signal covariance matrix, and then to estimate the signal parameter using the orthogonality of two spaces. To describe the orthogonality between the signal subspace and noise subspace, the spatial spectrum is used, which can be calculated by  
\[\mathbf{P}_{\text{MUSIC}}\ (r,\theta)=\frac{1}{\mathbf{A}^{\text{H}}(r,\theta) \mathbf{U}_{\text{N}}\mathbf{U}_{\text{N}}^{\text{H}}\mathbf{A}(r,\theta)} \tag{2}\]  
where \(\mathbf{U}_{\text{N}}\) denotes the noise subspace spanned by the eigenvector matrix corresponding to those small eigenvalues. Based on Equation (2), by varying \(r\) and \(\theta\) to realize a scanning process, \(\mathbf{A}(r,\theta)\) is steered to scan the whole structure area. The peak point on the spatial spectrum corresponds to the impact source point. Both the distance and direction of the impact source can be obtained.
# Towards Improved Inertial Navigation by Reducing Errors Using Deep Learning Methodology
## 3 Results  
To obtain CNN-training and -testing datasets, a linear motion stage and a rotary motor capable of generating fixed, precise accelerations and angular velocities, are incorporated in this work. These IMU units are mounted on the stage (Newmark Systems Inc. Model CS-500-1, Rancho Santa Margarita, CA, USA) or motor (Newmark Systems Inc. Model RM-3-110) and data are collected from the accelerometers and gyroscopes. Two IMU units are used in the project, a high-grade MEMS IMU (Epson Inc. Model M-G364 PD, Suwa, Nagano, Japanese) and a low-grade MEMS IMU (TDK Invesense Inc. Model ICM-20648, San Jose, CA, USA). Table 1 shows the specifications of Epson and TDK IMUs. The breakout boards have USB connectors, and the raw data is collected using software on the computer attached by a USB cable.  
Figure 4: Body frame and local frame of strapdown INS mechanization.
# CrowdPower: A Novel Crowdsensing-as-a-Service Platform for Real-Time Incident Reporting
## 3 System Architecture and Design
### Client/Server Architectural Components
* _Database:_ The main functionalities are similar to that of the client data store, but on the server side, the data model maintains data of multiple clients, communities, data relationships, and respective services. * _Location Tracker:_ This component keeps track of the user's location. It uses Android's built-in Location API to approximate the user's current location. Its main interaction is with the Service Manager, which periodically updates the user location to the server. * _Schedule Manager:_ CrowdPPower has many asynchronous background operations performed at different times, such as collecting data at a scheduled time and sending periodic user status updates to the server. Hence, this component keeps track of these time-delayed events and initiates other components to perform tasks at scheduled times. * _Information Processor:_ The packing and unpacking of messages happen in this component. The main sub-components are Syntax Analyzer, Syntax Validator, and Data Formatter. The syntax of messages received is analyzed, validated, and formatted when messages are sent to a client. Messages that are Web-service requests or responses are moved to the database. * _Service Tracker:_ This component manages the lifecycle of all service requests. The main sub-components are Device Matcher, Mode Manager, and Service Manager. This component keeps track of the producers that are assigned to each service. Moreover, this component identifies producers and related devices most suitable for handling the service. In addition, if the service request requires interaction with an Online Social Media (OSM), it handles the communication with the relevant OSM plug-in. * _Web Manager:_ This component manages the RESTful APIs and OSM requests; therefore, the main sub-components are the Web Service Manager and the OSM Extractor. OSM Extractor gathers data from social media by interacting using the OSM plug-ins. The Web Service Manager manages RESTful resources, which allow direct machine-to-machine communication between the MSaaS platform and the client-side application without direct user interaction. * _Data Analysis:_ This component analyzes the sensor data submitted by producers for a service request. The analyzed data are then passed onto the database. A detailed description of the quality analysis performed by the component is presented later. * _Registration Manager:_ The responsibilities of this component are to handle the registration of users and respective service requests. This is also responsible for updating and removing users and expired services from the server. This component provides a unique identifier to the user concerning the SIM details and maps the registered user to communities and groups.
## 3 Results and Discussion
### Machine Learning-Based Prediction of Thermal Conductivity
This is because they can capture nonlinear relationships and apply regularization. Thermal conductivity is affected by many factors, including density, cement content, and the Cag/Fag ratio. Figures 11-13 provide detailed insights into the prediction behavior of different models for thermal conductivity. The SVR model consistently produces accurate and well-centered predictions across the full range, with minimal overestimation or underestimation in both low- and high-conductivity regions. In contrast, the LMR model shows obvious deviations from the ideal prediction line. It tends to underestimate thermal conductivity at low values and overestimate it at high values. The RF model performs well on the training set but exhibits overfitting, with increased error and dispersion in the testing set. DTR improves noticeably during testing, suggesting better generalization due to its pruning mechanism. KNNR and RR models display scattered predictions in data-sparse regions, reflecting sensitivity to local variations. These patterns underscore the importance of model selection based on thermal conductivity range and data distribution. Figure 12: Comparison of experimental and predicted thermal conductivity. (**a**) SVR; (**b**) LMR; (**c**) RF; (**d**) DTR; (**e**) KNNR; (**f**) RR; (**g**) GPR; (**h**) NNR.
# A Tiered Control Plane Model for Service Function Chaining Isolation
## 5 Protocol and Interfaces
### Control Plane Interfaces
#### 5.2.3. Tiers 2 and 3 Interfaces  
The Tier 2 and the Tier 3 control plane interfaces consist of BGP messages previously explained in Section 4.2.3. Figure 18 exemplifies how the original SFPR-RD messages [10] are changed into two new versions of SFRP-RD (Tier 2) and SFPR-E-RD (Tier 3) messages. The SFRP-RD message contains all the SFC hops in the SFC and describes the inner Encryption Links as SFPR-E-RD inner hops. Each hop consists of a Service Index (SI) that is decremented for every hop. The Route Distinguisher (RD) globally identifies the Compute Node (AS number + IP address) and also contains a Service Function Identifier (SFI) that defines the VNF instance ID (i.e., AS1:82.147.36.200,3). This RD is also the global VNF identifier used for authentication (see Section 4.2.4).  
The setup of the Tier 2 and the Tier 3 peering between network controllers and Compute Nodes are considered domain specific and assumed as manually provisioned. Tiers 2 and 3 control agents on the Compute Node contain a domain specific application interface that enables the attachment and detachment of a VNF to the network, in order to announce the presence of a VNF on the Compute Node. The control agents on the network controller correspondingly hold an interface that listens for incoming rendered SFCs. In summary, the interfaces to the Tiers 2 and 3 services are:  
* BGP speakers on Compute Nodes that announce connected VNFs (SFIR and SFIR-E).
* A BGP speaker on the network controller that announces the SFCs (SFPR-RD and SFPR-E-RD).
* Compute Node agent configuration interfaces for maintaining SFIRs and SFIR-Es.
* A Network controller agent configuration interface to maintain SFPR-RDs and SFPR-E-RDs.
* A Network controller agent interface that can transform YAML into BGP Tiers 2 and 3 routes and vice versa.