esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Ship Maneuvering Safety with Augmented Virtuality Navigation Information Displays
## 2 Methodology
### Detailed Assumptions of Simulation Study (Simulation Scenarios)
#### 2.3.3 Scenario 3.  
The simulation test scenario assumed that experts would perform a series of simulation passages consisting of safe bow-to-berthing of the sea ferry Skania to the Ferry Terminal in Swinoujscie (Figure 5). The mooring should have been such that the bow door of the ship could be opened, and the loading ramp could be connected.  
It was assumed that the reference point lies in the line of the Ferry Terminal bert edge, on the symmetry plane of the discharge ramp, 10 m from the ramp. The moment of measuring the significant parameters took place at the moment when the navigator decided that he had reached the shortest distance from the reference point assumed in the study.  
The detailed assumptions of Scenario 3 were as follows:  
* Objective to be achieved: The lowest possible value of the ship distance from the theoretical point determining the ship's bow position in which the bow ramp can be effectively connected to the shore.
* Main ship parameters: \(\circ\) LOA = 173.7 m, breadth = 24 m, draught = 6.4 m; \(\circ\) Displacement = 15,800 t; \(\circ\) Block coefficient = 0.684; \(\circ\) Main engine: 4 \(\times\) diesel, total power = 31,680 kW; \(\circ\) Max speed = 27 km; \(\circ\) Type of propulsion: 2 \(\times\)cpp, outwards; \(\circ\) Number of rudders: 2; \(\circ\) Max rudder angle = 45.0 deg; \(\circ\) Number of thrusters: 3; \(\circ\) Bow thruster power = 3800 kW.
* Navigational-maneuvering conditions: \(\circ\) the initial position of the ship: parallel, distance half the ship's length from the ramp and two ship's breadths from the Ferry Terminal Quay; \(\circ\) the ship's final position: Ferry Terminal Quay; \(\circ\) initial SOG: longitudinal ahead v\({}_{\rm w}\) = 0.5 km, transverse to port side v\({}_{\rm p}\) = 0.1 km; \(\circ\) heading: HDG = 227\({}^{\circ}\).
* The criteria used for assessment: \(\circ\) Primary: the value of the minimum distance of ship's hull from the navigational obstruction (assumed reference point); \(\circ\) Secondary: efficiency of maneuvering--time length of maneuvers.
* The moment of measuring significant parameters: on the explicit command of the navigator, when the navigator considers that the ship has reached the lowest distance to the assumed reference point.  
The types of displays used, the name of the series and the number of trials in a given series of the Scenario 3 are presented in the Table 4.
## 3 Experiment  
The software environment for this experiment includes the Win10 operating system and PyTorch program ([[https://pytorch.org/](https://pytorch.org/)]([https://pytorch.org/](https://pytorch.org/))), while the hardware environment consists of an AMD R7-5900X CPU and an NVIDIA GeForce RTX 3090 Ti GPU. To verify the effectiveness of our method, we conducted extensive comparative experiments on the Infrared-ship thermal dataset and performed visual analysis of the experimental results. Additionally, to validate the effectiveness of the proposed modules, we presented relevant ablation experiments and provided detailed analysis for each of them. When evaluating the performance of semantic segmentation-related algorithms, we used the most common metrics, mean Intersection over Union (_mIoI_) and mean Pixel Accuracy (_mPA_), as evaluation metrics.  
_mIoI_ is calculated using a confusion matrix, as shown in Table 1, where _TP_ represents the intersection of predicted and ground truth values, _FN_ is the ground truth without overlap, _FP_ is the predicted values without overlap, and _TN_ is the region with no overlap in either.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Prediction**} \\ \cline{3-4}  & & **True** & **False** \\ \hline \multirow{2}{*}{**Ground truth**} & True & _TP_ (true positive) & _FN_ (false negative) \\  & False & _FP_ (false positive) & _TN_ (true negative) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Confusion matrix.  
\(mIoU\) calculates the accuracy of segmented images by measuring the overlap between predicted values and ground truth values. Its calculation method is as follows:  
\[mIoU=\frac{1}{m}\sum\limits_{i=1}^{M}{(\frac{TP}{FN+TP+FP})} \tag{11}\]  
\(mPA\) represents the percentage of correctly classified pixels in the image. Its calculation method is as follows:  
\[mPA=\frac{1}{M}\sum\limits_{i=1}^{M}{(\frac{TP}{FN+TP+FP+TN})} \tag{12}\]  
In evaluating regression algorithms, we use Mean Squared Error (\(MSE\)) as the evaluation metric. Its calculation method is as follows:  
\[MSE=\frac{1}{m}\sum\limits_{i=1}^{m}{(t_{i}-p_{i})^{2}} \tag{13}\]  
Among them, \(m\) is the number of longitudinal sampling temperatures of the ship, \(t_{i}\) is the \(i\)-th segment cabin temperature in the sampling temperature, and \(p_{i}\) is the predicted temperature of the \(i\)-th segment cabin.
# Efficient CORDIC Iteration Design of LiDAR Sensors' Point-Cloud Map Reconstruction Technology +
## 5 Experimental Results  
To prove the performance of the LiDAR sensing circuit designed in this paper, we have completed a series of experiments. We used LiDAR to sense different scenes and further decode the measured packet data. Then we performed point-cloud map conversion to verify the decoding results.
# Higher-Order-Mode Triple Band Circularly Polarized Rectangular Dielectric Resonator Antenna
## 2 Materials and Methods
### Single Stub Feed DR Antenna (DRA1)  
The rectangular DRA has been excited using a monopole with a stub feed placed on the rectangular DR side, as shown in Figure 2. The rectangular DR profile dimensions are \(H\) = 26.1 mm, \(B\) = 25.4 mm, and \(C\) = 14.3 mm, with the optimized feeding strip dimensions are \(H_{1}\) = 11 mm with a width of 1 mm, and \(b_{1}\) = 7.1 mm.  
As presented in Figure 3a, the antenna resonates at multiple frequencies, which are 2.78 GHz (5.7%), 3.55 GHz (6.2%), 3.87 GHz (3.87%), and 5.47 GHz (3.1%). A circular polarization is achieved; however, the axial ratio (AR) did not match the resonance frequencies as depicted in Figure 3b. The degenerate mode pair of the fundamental mode TE\({}_{101}^{y}\) has been excited at 3 GHz while TE\({}_{203}^{y}\) and TE\({}_{105}^{y}\) are excited at 4.9 GHz and 5.4 GHz, as depicted in Figure 4. The high order mode promotes the circularly polarized excitation in the antenna [8; 9]. However, due to the small bandwidth and mismatched between AR and the S\({}_{11}\), further improvement on the antenna is discussed in the next section.  
Figure 1: The proposed DRA antenna configuration.  
Figure 2: The initial proposed DR antenna with a single stub feed (DRA1).
## 4 Applications of Multimodal Learning for Fault Detection Diagnosis
### Enhancing Data Preprocessing in Multimodal Systems  
* **Improving Data Preprocessing with Multiscale Representation** Raw data are often noisy and autocorrelated. Multiscale representation using high-pass and low-pass filters can isolate noise and highlight relevant features, improving fault detection accuracy.
* **Addressing Uncertainty with Interval-Valued Representation and Dimensionality Reduction** Interval-valued techniques account for environmental uncertainty. Combined with dimensionality reduction (e.g., Euclidean distance-based filtering), they enhance robustness and reduce computational load.
* **Simplifying Multimodel Design** Multimodel systems benefit from domain knowledge and data-driven optimization. Methods like PSO and GA can optimize parameters such as hidden layers and activation functions to reduce complexity and improve adaptability.
* **Improving Decision-Making in Multimodel Systems** Combining techniques like PCA, KPCA, and Fourier analysis in a unified framework enhances interpretability and decision-making in noisy or rapidly changing environments.
* **Building Enhanced Multimodel Systems with Multiple Learners** Integrated learning systems that combine multiple algorithms can handle temporal dependencies more effectively. Dynamic kernel PCA and ensemble learners improve classification and prediction speed while ensuring robustness.
## 1 Introduction
Our team's ongoing efforts focus on systematically reviewing OT protocol specifications to identify vulnerabilities and safety risks, especially those stemming from dependencies on lower-layer IT protocols. Building on our prior capabilities to analyze such vulnerabilities through simulation, emulation, and real-world testbeds, this paper provides a detailed comparison of two widely deployed OT protocols: Modbus TCP and DNP3. We examine how their design choices--particularly their use of TCP/IP--affect communication reliability, especially under conditions such as device faults or malicious disruptions. The core contributions of this study are twofold:* We identify critical stages in the Modbus TCP and DNP3 specifications where communication recovery mechanisms are defined and analyze how these impact time-sensitive OT operations. * We implement a multi-tiered OT testbed to empirically evaluate the real-world implications of these behaviors. We then also evaluate the effectiveness of individual defense strategies for these vulnerabilities. Our results demonstrate that such dependencies can lead to unintended and potentially hazardous consequences in industrial environments. We also evaluate various mitigation strategies within the testbed to provide practical guidance for improving OT network resilience. The remainder of the paper is organized as follows. Section 2 covers related works, Section 3 describes our methodology--including possible communication errors and their network implications. Section 4 presents the results of this study and Section 5 contains a discussion of our obtained results.
## 3 System Design
### System Framework of DGRL
The Actor model contains four inputs, topological adjacency matrix \(A\) of the network, current features \(X\) of all nodes, one-hot code \(l\) of the node where the data packet is currently located, and one-hot code \(d\) of the destination node. The state of the current node and the state of neighbors must be considered when transmitting data packets. Actor uses graph convolutional neural network to extract and aggregate the state features of the current node and all its neighbors. Since it is highly correlated with data forwarding, the destination node is necessary to be considered after extracting the network state. Therefore, we flatten the output of the GCN layers and concatenate the result with \(l,d\). The vector \(state\) represents the state features of the network environment, which is given by  
\[state=[Flatten(GCN(A,GCN(A,X))\parallel d\parallel l] \tag{1}\]  
where \(GCN\) represents the graph convolution operation, \(\parallel\) means concatenate. The detailed calculation of GCN will be given in the next subsection. The output \(\mu\) of the Actor model is generated by Hadamard product operation which consists of the result of fully connected layers and an inner product operation. The activa  
Figure 2: System block diagram of SDN controller using reinforcement learning. tion function of this layer is Softmax, and the output is the next hop policy \(\mu\) of the current data packet. The Critic model contains five inputs. In addition to the four input parameters for the Actor model, it also includes the next hop action \(a\) obtained by the Actor model. The Critic model also contains the GCN layers for extracting the characteristics of the controller and its neighbor nodes, but the weights of the GCN layers are not trainable. Its weights are completely copied from the corresponding GCN layers of the Actor model. The purpose is to ensure that the state features of all nodes extracted by the two model are completely consistent. The Critic model contains a connected layer. The outermost layer uses a single neuron to fit the feedback value \(Q\) of the action policy adopted by the model.
## 2 Materials and Methods
### Data Preparation and Selection  
The original raw data, presented in Figure 2, underwent a decoding and filtering process to eliminate unnecessary information. Following this process, only data from Class A and Class B mobile units were retained. An excerpt of the data used for further analysis is shown in Table 1.  
The data prepared in this manner served as the basis for selecting units for multi-criteria analysis.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Date** & **Time** & **MMSI** & **Lat** & **Lon** & **Status** & **SOG** [n]** & **COG** [\({}^{\circ}\)] & **Type** & **Length** & **Drawight** & **Class** \\ \hline
10.11.2024 & 13:39:13 & 636017901 & 55.20144 & 13.09986 & 0 & 13 & 108.4 & 70 & 224 & 7.3 &
## 6 Discussion
### Theoretical and Practical Implications  
Practical Implication. The model built, trained, and experimented with in this study can be extended with a variety of application strategies in future journal evaluation and analysis tasks and can serve as a guide for resource allocation and the subsequent development planning of journals. For example, when trying to make decisions on arranging the al-location of journal resources, the model built in this study can be used to outline the possible future development status of journals, which can help guide the reasonable allocation of journal resources to a certain extent. For instance, when there are certain planning expectations for the development of journals in a given year, they can be passed into the models as input to obtain dynamic evaluation results, and different dynamic evaluation results can be obtained through the continuous adjustment of the planning, which can, in turn, assist in adjusting journals' development.  
Theoretical Implication. The comprehensive conclusions of the experiments conducted in this study can provide methodological references for other multivariate short-time multi-series time series analysis studies, and the dataset proposed in this paper can also provide data support for similar tasks.
## 2 Overview of Recent Radio Trends
### System-Defined Radio Paradigm (SDR)  
In SDR systems, several selected functions previously implemented at the hardware level have migrated to software blocs. In fact, SDR technologies implement wireless functionalities in programmable devices: the digital signal processor (DSP) and the field programmable gate array (FPGA). The implemented smart logic is further fused with modem software and radio frequency transceivers to manage the use of the available frequency spectrum and provide new innovative services to end users.  
Figure 3 represents the architecture of an SDR transceiver. The SDR architecture is composed of a transmitter (Tx) and a receiver (Rx). The hardest things in the hardware part are the digital/analogue converter (ADC/DAC) relative sampling rate issues. To attain an optimal performance, the ADC/DACs are placed near an intermediate frequency section.  
On the transmitter side, the incoming data are passed through a baseband signal processing block. Afterwards, the signals are modulated with an IF carrier signal; the IF signal is converted to an RF signal, and finally, it is transmitted to an antenna which will inject it into the communication channel.  
At the receiver, the RF signal is filtered and down-converted into an IF signal. Subsequently, the IF signals are digitized, demodulated, and baseband-processed to finally extract the originally transmitted information.  
Figure 3: SDR architecture concept: the scheme clearly demonstrates the hardware and software parts that are fused together.  
Figure 2: Cognitive radio network architecture.
# Variants of the Low-Energy Adaptive Clustering Hierarchy Protocol: Survey, Issues and Challenges
## 2 Background and Overview of WSNs
### LEACH-Based Routing Protocols in WSNs
#### 2.3.4 CH Selection-based Routing Protocols  
In routing protocols, selection of the CH is a significant criterion to maximize network lifetime [74; 75; 76]. The main idea in all CH selection-based routing protocols is to choose the most efficient node to be the head of a cluster [77; 78]. These protocols consider specific metrics to choose the suitable CH. Based on the selection mechanism of the CH, these protocols are classified into three classes, namely, residual energy, energy efficiency, and distance. The following paragraphs discuss these categories in detail as regards CH selection.
# High Reliability Evaluation and Lifetime Prediction of 50 GHz Athermal AWG Module
## 5 Lifetime Estimation
### Accelerated Life Test
#### 5.1.2 Accelerated Life Test Model  
As shown in Table 5, the likelihood function values of the Weibull distribution, lognormal distribution, and exponential distribution were compared using ALTA S/W to find a suitable distribution for the data measured for 4000 h during the accelerated life test. As a result, the lognormal distribution, which had the largest likelihood function value, was the most suitable lifetime distribution.  
Figure 11 shows the failure data observed in the accelerated life test using ALTA S/W. Since the data of each test condition are close to a straight line and the lifetime distribution estimation line is parallel, the lognormal distribution is suitable, and acceleration is established between the test conditions.
# An Attempt to Establish a Mathematical Model for an Unconventional Worm Gear with Bearings
## 4 Conclusions  
A mathematical model consisting of a non-conventional worm gear is presented in this study. The main aim of the paper was to develop a mathematical model for the non-conventional worm gear that is simple and easy to implement in practice but, at the same time, correct. The main problem was given by the globoid shape of the worm and the fact that the profile of the gap between the turns are rectangular, and this profile was found in the median frontal plane of the wheel, a plane that also contains the axis of the worm. Any machine on a CNC machine can be easily programmed taking these aspects into account.  
The contact between the worm coil and the rollers is a linear Hertzian contact, and the relative motion is a rolling one. Due to this fact, the efficiency of such a transmission is obviously much higher than that of classic globoid gear: the friction force caused by the rolling of the bearing on the surface of the worm is lower.  
The contact generatrix of the bearing is a straight line that skews from the worm axis, so they are not coplanar.  
A finite element analysis of the stresses to which the active elements of the gear (the spiral of the worm and the rollers) are subjected can be performed based on its 3D model.  
The presented mathematical model is viable, having been validated both theoretically and experimentally. It can be applied for different transmission ratios and component sizes.  
A dynamic analysis of the studied mechanism can be carried out, including a calculation of the transmission efficiency. For example, the stresses to which the bolts on which the bearings are fixed are subjected can be analyzed.  
Mechanical transmissions with gears of this type have applications in various technical fields, ranging from robotics and precision industrial transmissions to the field of micro air vehicles.  
Conceptualization, S.H. and L.T.; methodology, S.H., R.N. and O.B.; software, S.H., A.H. and I.M.S.-B.; validation, S.H., F.A.C., O.B. and I.M.S.-B.; formal analysis, L.T. and F.A.C.; investigation, R.N. and A.H.; resources, S.H. and I.M.S.-B.; data curation, S.H.; writing--original draft preparation, S.H.; writing--review and editing, S.H., O.B. and I.M.S.-B.; visualization, S.H., R.N., O.B., L.T., A.H., F.A.C. and I.M.S.-B.; supervision, S.H. and I.M.S.-B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflicts of interest.
## 3 Experiment and Results Analysis
### Comparison of Model Experiments
#### 3.3.3 Efficiency of Scene Semantic Segmentation in Different Networks  
In order to better test the processing ability of the method proposed in this paper in the actual urban scene, we use the real image collected by the front camera of the vehicle to test. Three pieces of test data in three different scenarios are mainly used. Finally, the average value of the shortest time taken to obtain reliable test results is counted. The test results are shown in Table 1 in milliseconds.  
From the experimental results, the method in this paper performs well in the actual tests in many scenarios. Although ESNet's average detection speed ranks second, its detection accuracy is more than 15% higher than that of the fastest ESPNet network. In general, this method has apparent efficiency and accuracy advantages in dealing with semantic  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **SegNet** & **ENet** & **ESNet** & **CGNet** & **ERFNet** & **ICNet** & **EDANet** & **ESNet** \\ \hline Daytime scene & 67.5 & 38.6 & 9.6 & 25.1 & 25.7 & 40.0 & 21.2 & 20.8 \\ Night scene & 43.2 & 30.0 & 9.1 & 21.6 & 24.5 & 37.2 & 20.8 & 19.6 \\ In rainy weather & 60.0 & 33.8 & 9.4 & 24.5 & 23.5 & 38.6 & 21.6 & 20.4 \\ Average value & 56.9 & 34.1 & 9.3 & 23.8 & 24.6 & 38.6 & 21.2 & 20.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Efficiency comparison table of different methods (ms).  
Figure 17: Prediction accuracy of ESNet on various categories.  
segmentation in the complex urban environment. In other words, the ESNet network considers these two aspects simultaneously and has excellent practical application value.
# Endoscopic Optical Imaging Technologies and Devices for Medical Purposes: State of the Art
## 2 Medical Imaging Applications
### Nonlinear Microscopy
#### 2.3.3 Raman Scattering Microscopy  
The Raman effect arises when the incident photon on a molecule interacts with its electric dipole, causing a perturbation and moving it to a virtual state. The virtual vibrational state is unstable and is followed by a de-excitation state generating a scattered photon. When the initial state of the molecule is at ground level and the final state is characterized by a slightly higher energy, it is referred to as the  
Figure 7: Second harmonic generation (SHG) and third harmonic generation (TPEF) images of differentiated neuroblastoma cells (taken with the permission of [31]).  
Stokes scatter. The scattered photon in this case has less energy than the exciting photon (also called the red-shifted photon). When the initial state of the molecule is already in an excited virtual state, the incoming photon's interaction with the molecule generates a scattered photon with higher energy than that of the incoming beam. It is called the anti-Stokes or blue-shifted photon [10].  
Coherent anti-Stokes Raman scattering (CARS) microscopy is a nonlinear microscopy where a molecule is first excited from the ground state to an intermediate virtual state with a pump beam at an intermediate wavelength. Using a simultaneous illumination by a second Stokes beam, the molecule is excited into a higher vibrational level. The molecule is unstable at that state and returns to its ground level with higher energy given by the sum of energies of the two beams. In CARS microscopy, this scattered photon is used for imaging the tissue structure [25].  
Stimulated Raman scattering (SRS) is another variation of Raman microscopy. It is similar to the Stokes-shifted Raman process, with the difference being that two lasers are coherently used to stimulate the scattering. The scattered signal is higher than that of the spontaneous Stokes shift [10]. A large number of CARS microscopy probes have been designed to image cancer cells [33; 34], as well as SRS microscopy systems to image the distribution of molecules, intracellular particles, proteins, and tumor cells [35; 36]. A list of CARS and SRS microscopy devices is included in. [PERSON] et al. combined the CARS and TPEF technologies to obtain detailed histo-morphological details of the tumor area from a squamous cell carcinoma metastasis, as shown in Figure 8[33].