esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling and Test Results of an Innovative Gyroscope Wave Energy Converter
## 4 Physical Model and Test Environment
### Test Platform and Experimental Environment  
A six degrees of freedom (6-DOF) hydraulic platform is utilized to simulate the wave to test the prototype in this paper before the actual maritime experiment. This can move along three coordinate axes and rotate around three axes, and the external dimensions of the GWEC and the relative equipment of the experiment platform are shown in Figure 4.  
First, the motor drive board is powered by the power supply to drive the flywheel to rotate. Simultaneously, the 6-DOF platform is powered by the hydraul  
Figure 4: 6-DOF platform structure. Figure 3: GWEC mechanical appearance. wave motion and stimulate the GWEC to produce a precession effect to generate electricity. Finally, the load resistance is used to test the power output. To facilitate installation, disassembly, and structural adjustment, a rectangular frame is used to replace the cylindrical frame in the test phase. The working space of the hydraulic 6-DOF platform is 0.4796 m\({}^{3}\). The motor power is 7.5 kW, the system flow rate is 48 L/min, the maximum stroke of the ATOS cylinder is 280 mm with the rated pressure of 10 MPa, and each cylinder adopts the DLHZO proportional servo valve and displacement sensor. The maximum pitch range of the platform movement is \(\pm\) 30\({}^{\circ}\). The platform provides external excitation for the GWEC by simulating wave motion. The view of the GWEC with a wave simulation system and power acquisition device is illustrated in Figure 5. Among them, the 6-DOF platform controller issues instructions to make the platform simulate the movement of the regular wave during the experimental phase. The power supply is utilized to switch on the GWEC, including the operation of the gyroscope flywheel and the motor drive board. The monitor is composed of an industrial computer with A/D conversion and acquisition. The data acquisition board adopts Advantech's PCI-1716 with a 250 KS/s 16-bit A/D converter with a maximum input of 2.4 volts, which collects the voltage value of the PTO, and calculates the average power of the output through Equation (14) after filtering. \[P=\frac{U^{2}}{R} \tag{14}\]  
where \(U\) is the voltage value generated by the PTO after converting the gyroscope precession torque, while the load resistance \(R\) in this experiment is 100 ohms. The drive board controls the motor to drive the flywheel to maintain a certain velocity. The speed-increasing device is used to drive the generator to generate electricity by accelerating the precession angular velocity of the flywheel. The operation of the GWEC system and power monitoring is illustrated in Figure 6. The core specification of the prototype system and rated experimental conditions are presented in Table 1.  
Figure 5: GWEC test system site. Since a 6-DOF platform is used to simulate wave motion in this experiment, the input power can be given by the total power of the platform motion:  
\[P_{in}=P+\left|\frac{1}{2}J_{\mathrm{Pz}}\delta\delta\right| \tag{15}\]
# Optimization Design of the Lower Rocker Arm of a Vertical Roller Mill Based on ANSYS Workbench
###### Abstract  
The lower rocker arm is an important part of the vertical roller mill and its lightweight design is of great significance for reducing the mass and production cost of the roller mill. Firstly, the strength and deformation distribution of the lower rocker arm under working load were analyzed by ANSYS Workbench to determine the maximum stress and maximum deformation. The parts with large strength margin were used as the basis for the optimal design. During the analysis, firstly, the arm body of the lower rocker arm was determined part of the lightweight design. Secondly, the mass of the lower rocker arm was taken as the optimization target, the stress and displacement generated by the load of the lower rocker arm were taken as the constraint conditions, the structural size of the internal cavity of the lower rocker arm was taken as the optimization design parameter, and the mathematical model of the optimization design was established. Finally, the structural size of the internal cavity of the lower rocker arm was optimized by using the response surface optimization module and multi-objective genetic algorithm in ANSYS Workbench. The optimum results show that, compared with the original design model, the lower rocker mass was reduced by 346.3 kg and the decrease was about 5.29%, while the strength and deformation were nearly unchanged. Therefore, by optimizing the design, the material is saved and the cost is reduced, which can provide a reference basis for the design and light weight of the lower rocker arm.  
lower rocker arm; ANSYS Workbench; finite element analysis; response surface analysis; optimization design +
Footnote †: journal: Applied Sciences
# Advancements and Research Trends in Microgrids Cybersecurity
## 5 Intrusion Detection Systems
In general, regarding embedded industrial devices, operational requirements for industrial environments, such as real-time capabilities, and availability must be ensured, even in the context of a cybersecurity action. Domain specific standards, guidelines, and recommendations that can be applied for specific industrial sectors must be considered to address this issue. In a microgrid environment, the most time-critical devices are PLC, Remote Terminal Units (RTU), and, in particular, electrical protections. To give a few examples about electrical protections:  
* IEC 60834 requires that the latency of the transmission and reception of a control signal related to a protective action has to be lower than 10 ms, while IEC 61850 imposes a latency lower than 3 ms;
* IEEE 1646-2004 requires information on protective actions to be exchanged by the devices inside the same substation in a time lower than a quarter of a period (i.e., 5 or 4 ms depending on the 50 or 60 Hz frequency); and
* less stringent limits (between 8 and 12 ms) are required for the exchange of information on protective actions with devices outside the substation. Most PLC and RTU are based on Real-Time Operating Systems (RTOS) [36]. The main characteristic of RTOS is the way they handle operations and resources, completing and executing tasks within a defined time frame due to their optimized architecture and features. Multi-tasking is still possible, thanks to task scheduling. RTOS handle priority: each task has a priority, and the task with the highest priority has a preference of execution, even if it is necessary to prevent a lower-priority task from being executed. Real-Time HIDS are sometimes implemented as a kernel module in Linux-based operating systems. This type of implementation can affect the device performance. For this reason, even if some papers already propose HIDS specifically designed for ICD devices [35], further effort has to be put forth to verify the applicability of these solutions to electrical devices within a smart microgrid.
# Mutual Authentication Protocol for D2D Communications in a Cloud-Based E-Health System
## 5 Security Analysis
### Impersonation Attack  
The impersonation attack is avoided because neither the cloud server's real identity nor the entities' real identities are disclosed. Therefore, an attacker cannot impersonate them and generate a valid MAC, because its calculation depends on the entities' real identities.
# Structural Damage Detection through Dual-Channel Pseudo-Supervised Learning
## 2 Methodology
### Conformer Module  
One of the key factors contributing to the successful application of the CNN-LSTM architecture in structural damage detection methods is its capability to capture both the local and global features of signals. Recent studies [42] have shown that Transformers based on self-attention mechanisms have a better ability to capture global features than LSTMs. Based on this, we investigated and studied a Transformer model that includes CNNs and self-attention mechanisms and introduced it into structural damage detection methods to extract more effective local and global features. As shown in Figure 1, the Conformer contains multiple Conformer blocks, each of which is mainly composed of a feedforward module, a self-attention module, and a convolutional module. By alternately extracting features through convolution modules and self-attention modules, local and global features in the signal are effectively extracted.  
Unlike the absolute position encoding of classical Transformer networks, in order to enable the model to effectively capture long-distance dependencies, relative position encoding [43] is incorporated into the self-attention module. Also, the self-attention module employs a multi-head self-attention mechanism, and \(H\) attention heads perform self-attention on the sequence and then concatenate the results of the \(H\) heads. The calculation for each attention head is as follows:  
\[Att(A,V)=softmax(A)V \tag{13}\]  
\[A_{ij}=Q_{i}^{T}K_{j}+Q_{i}^{T}W_{R}R_{ij}+u^{T}K_{j}+v^{T}W_{R}R_{ij} \tag{14}\]  
\[[Q,K,V]=E_{x}\big{[}W_{Q},W_{K},W_{V}\big{]} \tag{15}\]  
where \(softmax(\cdot)\) represents the softmax function; \(E_{x}\) denotes the token-embedding lookup or the output of the upper level Conformer, and \(W_{Q}\), \(W_{K}\), \(W_{V}\), \(W_{R}\in\mathbb{R}^{d_{model}\times d_{model}}\), \(u\), and \(v\in\mathbb{R}^{d_{model}}\) are learnable parameters. \(d_{model}=H\times d_{head}\), \(d_{head}\) denotes the dimension of each attention head, and \(R_{ij}\) denotes a sinusoidal encoding matrix [42].  
Assuming \(x_{i}\) is defined as the input and output of the \(i\)-th Conformer module, and the output \(y_{i}\) is calculated as follows:  
\[x_{i}^{ffn}=x_{i}+\frac{1}{2}f_{FFN}(x_{i}) \tag{16}\]  
\[x_{i}^{sa}=x_{i}^{ffn}+f_{SA}\Big{(}x_{i}^{ffn}\Big{)} \tag{17}\]  
\[x_{i}^{conv}=x_{i}^{sa}+f_{Conv}(x_{i}^{sa}) \tag{18}\]  
Figure 1: Conformer network.  
\[y_{i}=f_{Layernorm}\bigg{(}x_{i}^{conv}+\frac{1}{2}f_{FFN}(x_{i}^{conv})\bigg{)} \tag{19}\]  
where \(f_{FFN}(\cdot)\), \(f_{SA}(\cdot)\), and \(f_{Conv}(\cdot)\) represent the feedforward module, the self-attention module, and the convolution module, respectively, and \(f_{Layernorm}(\cdot)\) denotes the layer normalization. This sandwich structure was proposed by reference [44].
# Comparative Analysis of LiDAR and Photogrammetry for 3D Crime Scene Reconstruction
## 3 Methodology
### Model 1: LiDAR with an iPhone and Recon-3D App  
The LiDAR scanner on the iPhone is used together with the Recon-3D app to evaluate how accessible it is and what limitations it might have. The scan processing was performed on the cloud as well as on the device. The first and second scans were taken methodically, moving from top to bottom while circling the room in overlapping paths, all completed within ten minutes for the first model. Next, the scanning time was shortened to just five minutes for the second model. The five-minute scan was processed on the device, whereas trying to process the ten-minute scan caused the application to crash. Hence, the processing was performed on the cloud. These scans took place at noon under the light to ensure sufficient lighting conditions. Scanning from top to bottom in an overlapping manner ensured comprehensive coverage. Avoiding using flash during scanning while relying on sunlight was intentional in testing LiDAR capabilities. During the third and fourth rounds of scanning the crime scene, a significant change was made. The scanning was done at night using iPhone's built-in flash, completed in ten minutes for the fourth scan, and then the time was shortened to five minutes for the final scanning. Likewise, the five-minute scan was taken on the device, while the ten-minute scan had to be processed on the cloud. The adjustment in the nighttime scans aimed to assess how effective the scanning process is under these conditions and determine whether using flash lighting could improve the quality of data collection or if it was unnecessary, particularly considering LiDAR technology.
## 4 Discussion
### Long-Term Durability and Field Application Prospects  
The hyper-arid desert environment imposes multifactorial challenges to the long-term stability of stabilized aeolian sand, including diurnal thermal cycling, UV radiation, and salt-laden winds particularly regarding potential degradation mechanisms induced by salt crystallization (e.g., sulfate-chloride interactions), acid attacks from pH fluctuations in contaminated soils, and ion-exchange reactions with heavy metal pollutants that may alter the pozzolanic reaction kinetics. The chemical compatibility between ISW-derived alkaline components (e.g., free CaO) and acidic contaminants (H\({}^{+}\)/SO\({}_{4}\)\({}^{2-}\)) could critically influence the stability of hydration products like C-(A)-S-H gels, while chloride infiltration might accelerate matrix deterioration through [PERSON]'s salt formation-disintegration cycles under thermal stresses. Such physicochemical interactions necessitate the systematic evaluation of long-term phase evolution using accelerated aging protocols simulating multi-factor desert environmental conditions. While the current study focuses on 28-day mechanical properties, the intrinsic characteristics of ISW-CM hydration products suggest potential durability advantages over conventional cement-based systems. The dense C-(A)-S-H gel matrix observed in SEM micrographs (Figure 9a,b) and the absence of large ettringite clusters (compared to OPC systems) may mitigate crack propagation under thermal stresses. Additionally, the high calcite content detected in XRD patterns (Figure 7) could enhance carbonation resistance, though excessive crystallization risks interfacial debonding as evidenced in low-strength specimens (Figure 10c).  
In field applications, the workability (e.g., fluidity, setting time), volume stability of the cementitious materials, and their applicability to other specialty soils represent pivotal determinants for large-scale implementation, warranting systematic exploration to validate technological scalability. Additionally, both current and previous studies reveal that cement and ISW-based cementitious materials stabilized aeolian sand typically exhibit limited ductility, posing reliability risks in engineering applications through sudden load-bearing capacity loss upon reaching critical stress thresholds--a phenomenon detrimental to life and property protection under extreme conditions. While geosynthetics (geomembranes, geotextiles) or reinforcement techniques may partially mitigate this issue, enhancing intrinsic ductility through fiber incorporation into stabilized sand matrices presents a promising research direction.
## 2 Materials and Methods
### Methods
#### 2.2.3 Water Levels  
A preliminary multi-look processing [72] in the European Space Agency's Sentinel Application Platform (SNAP) software allowed images to be processed to a 14 m ground resolution after calibrating the images by applying a terrain correction using the Shuttle Radar Topography Mission (SRTM) digital elevation model at 1 arc-second (\(\approx\) 30 m) spatial resolution (HGT format). Then, TOPSAR images were resampled to 28 m ground resolution, using the Pixel Aggregation method and classified. Then, the water surface extent was identified using a K-means unsupervised classification [73], using two classes and a threshold of 1% and the average distance of shoreline nodes extracted from vectorisedclassification images with the contour levels calculated in a GIS environment (QGIS Geographic Information System) [74]. According to [PERSON] et al. [54], the contour level exhibiting the minimum distance from the classification boundary (best matching contour) allows quantification of the water level at the acquisition time.
# Cooperative SWIPT MIMO-NOMA for Reliable THz 6G Communications
## 6 Simulation Results
### System Simulation  
First, the performance of the system's served users was individually implemented, and then we examined the entire system's sum rate. The performance was compared with our previous similar SISO-based system model (as a baseline) to explain how the presented system provides a remarkable improvement of the current wireless communication systems, by employing the edge of technologies mentioned in this paper.
# Point-Line-Aware Heterogeneous Graph Attention Network for Visual SLAM System
## 3 Methodology
### Heterogeneous Graph Attention Network
#### 3.2.1 Edge-Clustering Graph Attention Module  
We propose an edge-aggregated graph attention network (EAGAT) based on GAT [33], which uses edge information for feature enhancement during the aggregation process. In order to make full use of the information of edge features, these different types of links used different attention mechanisms. For features of the same nature (points and points, lines and lines), the self-attention mechanism was used for the aggregation. For features of different nature (points and lines), the cascade method was used for the aggregation. Let the feature of the vertex \(v_{i}^{\prime}\) in the graph be \(f_{i}\), defined as:  
\[\begin{array}{l}f^{\prime}_{i}=\sigma(\sum\limits_{j\in\mathcal{N}_{s}} softmax(\frac{W^{a}f_{i}W^{\beta}f_{j}}{\sqrt{\lambda_{k}}}W^{\gamma}a_{ji})W^{ \varepsilon}f_{j}\\ +\sum\limits_{j\in\mathcal{N}_{d}}softmax([W^{a}f_{i}||W^{\beta}f_{j}||W^{ \gamma}a_{ji}])W^{\varepsilon})\,||f_{i}\end{array} \tag{22}\]  
where \(W^{a}\), \(W^{\beta}\), \(W^{\gamma}\), and \(W^{\varepsilon}\in\mathbb{R}^{f^{\prime}_{\eta}\times f_{\eta}}\) represent the weight parameters, \(\mathcal{N}_{s}\) is the feature set of the same nature, \(\mathcal{N}_{d}\) represents the feature set of different properties, and \(\sigma\) is the ReLU activation function.
## 3 Time-Synchronization Algorithm of Heterogeneous Networks
### Time- Synchronization Algorithm of the CAN-Ethernet Network  
Figure 4 presents the time-synchronization algorithm of the master (Ethernet) and slave (CAN) nodes in the CAN-Ethernet network. In Figure 4a, the master transmits the synchronization message to the slave and stores the timestamp (t\({}_{1}\)) in the memory buffer. Then, after storing t\({}_{1}\) in the follow-up message, it is transmitted to the slave. Next, it checks whether the delay-request message transmitted from the slave node has been received. If the delay-request message has been received, then the timestamp (t\({}_{4}\)) and timestamp included in the message (t\({}_{CAN2 Eth}\)) are stored in the memory buffer. Finally, the calculated value of t4-t\({}_{CAN2 Eth}\) is stored in the delay-response message and transmitted to the slave.  
In Figure 4b, the slave checks whether the synchronization message transmitted from the master has been received. If the message has been received, then the timestamp (t\({}_{2}\)) and timestamp included in the synchronization message (t\({}_{Eth2 CAN}\)) are stored in the memory buffer. Next, it waits until the follow-up message transmitted from the master is received. If the follow_up message is received, then the timestamp (t\({}_{1}\)) included in the message is stored in the memory buffer. A delay-required message is then transmitted to the master, and after storing the timestamp (t\({}_{3}\)), it waits until the delay-response message transmitted from the master is received. If the delay-response message is received, then the timestamp (t\({}_{4}\)-t\({}_{CAN2 Eth}\)) included in the message is stored. Finally, the saved timestamps (t\({}_{1}\), t\({}_{2}\), t\({}_{3}\), t\({}_{4}\)-t\({}_{CAN2 Eth}\), and t\({}_{Eth2 CAN}\)) are used to calculate the delay and offset.  
Figure 4c shows the time-synchronization algorithm of the gateway in the CAN-Ethernet network. The gateway first checks whether a synchronization message transmitted from the master is received. If a synchronization message is received, then it is converted into a CAN message, and t\({}_{Eth2 CAN}\), which is the time from the receipt of the synchronization message until the completion of conversion, is calculated. t\({}_{Eth2 CAN}\) is then stored in the synchronization message converted for the CAN network and transmitted to the slave. If a follow-up message or delay-response message transmitted from the master is received, it is converted into a CAN message and transmitted to the slave. Finally, once the delay-request message transmitted from the slave is received, it is converted into an Ethernet message, and t\({}_{CAN2 Eth}\), which is the time from the receipt of the delay-request message until the completion of conversion, is calculated. t\({}_{CAN2 Eth}\) is then stored in the converted delay-request message and transmitted to the master.  
Figure 3: Comparison of the PTP message format in the CAN and Ethernet network.
## 3 Methodology
### Multi-View Stance Detection
#### 3.3.2 LLM Approach  
Although the GCN-based approach effectively captures explicit stance structures, its reliance on labeled training data limits its ability to generalize in zero-shot scenarios.  
To address this limitation, we introduce an LLM-based stance detection method, which enhances the model's reasoning capability by incorporating external knowledge from the HSN.  
Rather than depending solely on textual content, our method enriches the LLM's input by incorporating structured knowledge extracted from the HSN. Specifically, we identify stance-relevant paths within the HSN, particularly those connecting the post and target through intermediate entities in two hops. These paths are then converted into natural language statements and appended to the LLM prompt as additional context.  
The choice of two-hop paths is intentional and based on the structure of the HSN. Paths between the post and target always have an even length, such as post-entity-target or post(A)-entity(B)-post(C)-entity(D)-target(E). Beyond two hops, the number of potential paths grows exponentially, leading to increased complexity and resource consumption. This could also introduce irrelevant information that might hurt model performance. Thus, restricting the path length to two hops ensures a balance between enriching the input and maintaining efficiency.  
As illustrated in Figure 4, the given post does not explicitly state its stance toward _Atheism_. However, the HSN provides critical clues: (1) the post expresses an against stance toward _religion_, and (2) _Atheism_ also holds an against stance toward _religion_. By analyzing these indirect stance relationships, the LLM deduces that the post expresses a favor stance toward _Atheism_. This method effectively enables zero-shot generalization by allowing the LLM to leverage structured knowledge for stance detection, even when direct post-target interactions are absent.