esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis and Experimental Test of Electrical Characteristics on Bonding Wire
## 3 Frequency Domain Simulation
### Eye Diagram Analysis  
The eye diagram is a very successful and effective way of showing the quality and parametric information in digital transmission. Careful analysis of this visual display can give the user a first-order approximation of signal-to-noise, clock timing jitter and skew.  
SP12 bonding wire (H1 = 250 \(\upmu\)m, H2 = 254 \(\upmu\)m, d = 25 \(\upmu\)m) were selected as an illustration. In order to control variables, H2 is constant in this part. The span scanning ranged from 2000 \(\upmu\)m to 4500 \(\upmu\)m. In addition, the interval was 500 \(\upmu\)m: 2000 \(\upmu\)m, 2500 \(\upmu\)m, 3000 \(\upmu\)m, 3500 \(\upmu\)m, 4000 \(\upmu\)m and 4500 \(\upmu\)m. Meanwhile, L1 varied from different span According to the schematic in Figure 15, the excitation signal was added at the input port as follows, the high level was 1 V, the low level was 0 V, the rising and falling time was set to 50 ps, and the bit rate of the signal was 10 Gbps. In this part, it recognizes a high level to be a logic 1 and a low level as a logic 0. The voltage values of logic 0 and logic 1, eye height and eye width in the eye diagram are shown in Table 8.  
Figure 14: Comparison between SPICE and HFSS (**a**) S\({}_{11}\); (**b**) S\({}_{21}\).  
Figure 15: Eye diagram analysis schematic.  
As illustrated in Table 8, with the increase of span, logic 1 voltage decreases, logic 0 voltage increases, and this will cause signal inconsistency between the input port and the output port and the increase of the bit error rate. Furthermore, as shown in Figure 16, the problem of unclear trace was aggravated with the increase of span, which will led to inter-symbol interference.
## 5 Behavior of Composite Beams Under Cyclic Loads
### Behavior of Composite Beams Under Cyclic Loads: Key Findings  
Some of the key findings regarding the behavior of composite beams under cyclic loads are as follows:  
* The beam-column connection performance is essential in composite structures under seismic loads, as these are often critical points for energy dissipation and load transfer.
* Composite beams generally possess excellent energy dissipation capacity under cyclic loading, a crucial factor for seismic performance. This behavior is attributed to the interaction between steel and concrete components.
* Composite beams typically exhibit high ductility when properly designed, allowing for significant deformation without sudden failure. This characteristic is essential for maintaining structural integrity during earthquakes.
* Composite beams may experience stiffness degradation under repeated cyclic loading, often due to concrete cracking, steel yielding, and slip at the steel-concrete interface.
* The strength of composite beams can deteriorate with increasing load cycles, primarily due to local buckling of steel components and concrete crushing.
* The performance of shear connectors, such as headed studs, is critical in composite action. Inadequate shear connection may result in heightened slippage and diminished composite behavior when subjected to cyclic loading.
* Steel components in composite beams, particularly in the beam flanges and web, may experience local buckling under high cyclic loads, affecting the overall performance of the composite beam.
* The concrete slab significantly influences the behavior of composite beams, providing additional stiffness and strength. However, concrete damage and cracking can occur under severe cyclic loading.
* Composite beams may accumulate residual deformations after cyclic loading, which can affect the post-earthquake serviceability of the structure.
* Maintaining composite action throughout cyclic loading is essential for optimal seismic performance. The proper detailing and design of shear connectors are crucial to achieve this.
## 2 Architecture of the Tethered Hexacopter System
### Tether Unwinder  
The design of the cord unwinder was a challenge because of the desired altitude of the flight mission and the hexacopter's power consumption. To achieve a reasonable range of view for the onboard vision system, the flight altitude could be no less than 50 m. Therefore, the total cord length had to also be at least 50 m. On the other hand, the six motors of the hexacopter consumed about 3 kW of power. Hence, the weight of the power cord to be lifted by the hexacopter as payload was about 2.2 kg. It should be noted that the power cord was specially selected for this application. To provide a constant unwinding and winding speed, and to ensure power cord tension regardless of the current length of its unwound part, the cord had to be wound evenly on the main roll. Thus, the structure of the power cord unwinder had to meet these specifications. It is shown in Figure 4.  
A guiding trolley 3 mounted on a screw moved proportionally to the length of the unwound/wound power cord 7, in a way that guaranteed that the cord would be wound coil by coil on a single layer. This was crucial because the 1 mm\({}^{2}\) AWG wires of the power cord could be temporarily overloaded and would have to be cooled by the air (the supply voltage was 230 VAC). The openwork structure of the roll with the power cord helped with this. On the other hand, a power cord with 1.5 mm\({}^{2}\) wires would be too heavy. The weight of the entire power cord of 50 m was about 2.2 kg. The roll 2 with the power cord was driven by a belt transmission 5 powered by a triple-phase AC asynchronous motor 4 controlled by a single-phase inverter. A dancer roll on the guiding trolley's end rotated a potentiometer, which was connected to the inverter's speed input. The position of the potentiometer decided both the speed and direction of the roll's rotation. A pulling spring rotated the potentiometer to the winding position of the cord with maximal speed. Applying the required tension to the dancer's roll moved the potentiometer's slider to  
Figure 3: Tethered hexarotor with other UAS components on the ferry’s top deck.  
the neutral position, stopping the unwinding or winding. Excessive tension moved the potentiometer's slider to the unwinding position. In this way, tension was applied to the power cord by the ascending or descending UAV, switching the unwinder between winding and unwinding to provide the desired tension value. To reduce tension oscillations and secure the unwinder against sudden jerks of the power cord, a damper 18 was installed on the unwinder's frame 1.
# Oil Spill Monitoring of Shipborne Radar Image Features Using SVM and Local Adaptive Threshold
## 3 Results and Discussion
### Advantages of Shipborne Radar Oil Spill Monitoring  
Shipborne radar oil spill monitoring is based on the characteristics of sea clutter. Figure 16 shows that shipborne radar transmits electromagnetic waves onto the sea surface with Bragg scattering. However, the oil film covers the surface and reduces the gap between peaks and wave troughs. A smooth oil film surface can reduce backscattering wave beams (Figure 17), weakening the signal in the oil film region more than in other regions.  
Figure 16: Scattering mechanism of shipborne radar electromagnetic waves.  
Figure 14: Visible light images with oil films were acquired during the day. (**a,b**) are airborne and shipborne visible light images, respectively.  
Figure 15: Thermal infrared images with oil films were acquired during the night.  
Spaceborne radar data will always be affected by cloud, and there is a certain period of data acquisition. Shipborne radar can overcome these limitations and monitor in real-time, all day. Airborne radar often fails to work at night or in snowy and rainy weather. The shipborne radar can overcome bad weather for on-site monitoring. Compared with other sensors, a shipborne visible sensor can provide the most intuitive synthetic image, but it cannot detect an oil film at night. Shipborne infrared and laser fluorescence devices can work at night, but the monitoring range is too small. The shipborne radar can not only work at night, but also conduct omni-directional large-area monitoring. In summary, shipborne radar can play not only a role in ship navigation and collision avoidance, but also have significant application and promotion prospects in monitoring marine oil spill.
# Cybersecurity Requirements for Industrial Machine Control Systems
## 5 Case Study--Examples of Determining SLs of Safety Functions Implemented in Machine Safety Systems
### Example 3--Machine Control System: Category 1, PLc; Cybersecurity SL-T 0  
Let us consider an SF implementing an emergency stop function, such as a turning machine (conventional lathe). According to ISO 13850 [27], the emergency stop function  
Figure 8: Example of a mixer control system to achieve Pla (own elaboration).  
Figure 9: Example of a mixer control system to achieve PLb level (own elaboration).  
(E-Stop) is required to meet at least PLC/Cat. 1. According to ISO 13849-1 [18], category 1 implies the use of well-tried components and well-tried safety principles. According to ISO 13849-2 [25], electromechanical components with a positive opening are considered as well-tried components (see the E-Stop button in Figure 10--the arrow symbol in a circle indicates positive opening). The standard in question also gives a list of well-tried safety principles, including oversizing the contactor's switching current, separating functional circuits from safety circuits. Figure 10 shows separate circuits for the starting and operational stopping of a machine and a separate circuit for the emergency stop. The drawing was simplified by omitting basic safety principles (e.g., the use of surge suppressors on inductive components). The circuit implementing the considered SF does not contain programmable circuits and coupling to the communication network.  
For the circuit implementing the emergency stop function shown in Figure 10, with the assumptions described above, no protection against cyber-attack is required. Thus, SL-T 0 (no protection) is assumed.
## 2 Materials and Methods
### Membranes Characterization
#### 2.2.4 Mixed Gas Permeation Measurement  
Mixed gas permeation tests were carried out using a custom made constant pressure/variable volume instrument, described in detail elsewhere [50], equipped with a quadrupole mass filter (HPR-20 QIC Benchtop residual gas analysis system, Hiden Analytical, Warrington, UK). Measurements were performed from 1 to 6 bar(a) with two binary gas mixtures of CO2/N2 (15:85 vol %) and CO2/CH4 (35:65 vol %), simulating flue gas and biogas, respectively. Argon was used as a sweeping gas and as the internal standard for the calculation of the permeate gas composition.
# The Impact of the Human Factor on Communication During a Collision Situation in Maritime Navigation
## 1 Introduction
### Communication Process  
A communication model prototype for the transformation of natural-language messages issued by a ship's navigator into an ontological form understood by an autonomous system was presented in [1]. It consists of the following nine steps: input in the form of natural-language messages, tokenization, removal of redundant information, transformation of the words to a basic form, lemmatization, part-of-speech recognition, message type classification [12; 13; 14; 15], rule set generation using artificial intelligence, and, finally, output in the form of an ontology [16; 17; 18; 19; 20; 21; 22]. The algorithm of the communication process is shown in Figure 1.
# Research on Fault Recognition of Roadheader Based on Multi-Sensor and Multi-Layer Local Projection
## 2 The Proposed Framework  
Through the study, it was observed that the low-dimensional features of various faults in the key components of the roadheader differ from those of the healthy state. We postulate that a vector \"Health-Fault\" (HF) can be constructed in a low-dimensional space, extending from the centroid of low-dimensional healthy features to the centroid of fault features. The feature vectors representing different fault categories and normal operation will display distinct differences in orientation and magnitude. Furthermore, the direction and magnitude of the vectors for the same fault type should fall within a specific range without surpassing it. As depicted in Figure 1, varying presentations of HF for different faults emerge from the healthy signal as the reference point, with unique manifestations for different severity levels of the same fault type.  
Based on the analysis, this paper proposes an improved learning method utilizing dual-layer low-dimensional features extracted from multi-sensor vibration signals to effectively identify various faults in key components of roadheaders. Initially, a comparative sample set comprising fault and healthy signals from different sensor measuring points is constructed using time-frequency analysis. Subsequently, manifold learning is employed for initial low-dimensional feature extraction from the sample set, enabling  
Figure 1: HF vector model.  
differentiation between various fault and healthy signals at each measuring point and achieving preliminary fault discrimination. Moreover, the low-dimensional feature parameters from multiple sensors within the same timeframe are integrated to establish a pseudo-manifold network architecture that encompasses both temporal and spatial dimensions, facilitating further dimensionality reduction of the feature samples. The enhanced low-dimensional feature parameters are then extracted to achieve fault identification using multiple sensors. This multi-sensor, multi-layer enhanced manifold learning approach aims to enhance the differentiation of equipment vibration signals under different conditions through low-dimensional mapping, thereby improving the accuracy of fault classification and identification.  
The flowchart of the proposed method is depicted in Figure 2, illustrating the three main steps in the feature extraction process. The first step involves dimensionality reduction of a multi-state sample set from a single sensor using locality-preserving projections (LPP). In the second step, mappings from multiple sensors are integrated to achieve fusion mapping. These separate mappings are derived individually from two-class models using LPP, with the goal of capturing and preserving local distinctions between each fault state and the normal state.
# A Self-Powered UHF Passive Tag for Biomedical Temperature Monitoring
## 4 Measurements and Results
### Overall Discussions
This special consideration must be taken into account in the transistors \(m_{1}-m_{5}\) of current/voltage reference (Figures 6 and 7), in the transistor \(m_{pass}\) of the LDO (Figure 8), and in the transistors \(m_{1}-m_{2}\) of the HF section (Figure 9). A carefully planned layout on the transistors \(m_{1}-m_{5}\) of current/voltage reference will help to reduce any drift on the nominal values of \(I_{P}\), \(I_{C}\) and \(V_{REF}\). Likewise, the use of top-metal layers to layout the transistor \(m_{pass}\) of the LDO will reduce the noise that is injected into the \(V_{DD_{2}}\) supply voltage of the temperature sensor. Although it is advisable to apply the same special consideration to transistors \(m_{1}-m_{2}\) in the HF section, the noise generated by the RFI is filtered by the reservoir capacitor \(C_{L}\).
# Damage Detection in Beam Bridges Using Quasi-static Displacement Influence Lines
## 2 Damage Detection Using the Quasi-static Displacement Influence Lines of Bridges
### Damage Detection Using the Null Space Method Based on Quasi-static Displacement Influence Lines
#### 2.2.2 Metric for Evaluating the Damage Feature
For the method described in Reference [25], the same calculation has to be repeated \(n\) times (i.e., the total number of measured points of one quasi-static strain IL) to obtain this metric. Therefore, with the proposed method, the efficiency of damage detection using quasi-static ILs is improved dramatically. Note that the environmental conditions during each quasi-static load test should be controlled to reduce the effects of environmental factors as much as possible. This condition is relatively feasible because the time period of each quasi-static load test is not excessively long, for example, a half-hour for each test according to our experience.
## 3 Materials and Methods
### Data Pre-Processing
#### 3.3.1 Raw Data Modality  
We collected a total of 646,500 samples of right-ankle raw acceleration data, with 426,500 samples falling on good walking surfaces and 220,000 samples on irregular walking surfaces, as shown in Table 1. Since taking large values directly to train a LSTM network could lead to large gradient updates that would result in a slower convergence or even divergence of the training process, we normalized our input data to have a standard deviation of 1 and a mean of 0. The input to be fed into the network had to be fixed-size sequential data, which was a short time series segmented from the raw acceleration signal. Therefore, the acceleration data were sampled into each segment with a 1.10 s fixed-width sliding window, which is equivalent to 110 data points, with a 90% overlap between them. Since the acceleration data had X, Y and Z dimensions, the sliding window would sample data points from all three dimensions into each segment, resulting in a three-dimensional input segment vector.  
This window size was chosen because each window should have included at least one complete period [41], which was one stride. The average stride time for all subjects was 1.10 s. The 90% overlap rate was chosen because it would result in more segmented data samples due to the limitation of our sample size. Additionally, an overlap between windows would carry information from the previous window.
## 4 Foundations of Recommendation Systems and LLM Integration
### Large Language Models: Capabilities and Evolution
#### 4.2.17 GLaM (Gated Language Model)  
GLaM (Gated Language Model) is a new architecture known as a sparse mixture of experts for neural networks. The model activates a subset of the model's parameters on each forward pass. This enhances the efficiency of the computational resources and simultaneously helps to preserve the best models in natural language understanding and  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Aspect** & **CTRL** & **LaMDA** & **GLaM** & **CLIP** & **DALL-E** \\ \hline Training Objective & Conditional generation using control codes & Dialogue-focused language modeling & Sparse mixture of expert language modeling & Contrastive language-image pretraining & Text-to-image generation \\ \hline Pre-training Method & Supervised with control code annotations & Pretraining on dialogue-style corpora with safety filters & Autoregressive with sparse expert activation & Contrastive loss on image-text pairs & Autoregressive generation with discrete VQ-VAE image tokens \\ \hline Bidirectional Context & Yes (encoder-style) & Yes, via optimized attention & No (autoregressive and text encoders) & No (separate image and text encoders) & No (autoregressive image decoder) \\ \hline Multimodal Support & No & No & No & Yes (image + text) & Yes (generates images from text) \\ \hline Fine-tuning Requirement codes/domain & Required for new control codes/domains & Fine-tuned for conversational quality & Not typical (few-shot capable) & Rarely fine-tuned (zero-shot) & Prompt-based; fine-tuning uncommon \\ \hline Key Applications & Domain-specific text control (e.g., news, reviews) & Human-like multi-turn dialogue & Efficient large-scale language modeling & Zero-shot image classification, content understanding & Creative image synthesis from text prompts \\ \hline Strengths & Control over generation content and domain & Sensible, specific, and engaging response & Trillion-scale sparse model with high efficiency & Connects vision and language, zero-shot capable & High-quality, text-driven image generation \\ \hline Limitations & Restricted to known control codes; limited flexibility & Still prone to hallucination; needs strong moderation & Sparse activation introduces architectural complexity & Limited generative capabilities; sensitivity to text encoding & Requires extensive compute; less robust for abstract prompts \\ \hline Evaluation Metrics & Perplexity on WikiText-103: & Primary Eval (SSI): Sensibleness & SuperGLUE Accuracy: 80.4\%, 92.3\%, Specificity: 91.7\%, Interestingness: 86.5\% & SuperGLUE Accuracy: 80.4\%, BERTScore: 0.89 & ImageNet & FID: 8.6, SSIM: Zero-shot Accuracy: 76.2\%, CLIPScore: 0.78 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of Transformer-based models: CTRL, LaMDA, GLaM, CLIP, DALL-E. Source: Author’s own design.  
generation tasks. GLaM has good scalability, making it applicable for the larger language models in practical use [65].
## 4 Finite Element Analysis
### Parametric Studies  
#### 4.3.1 Layer Number of Steel Mesh  
Since the SMRC layer can accommodate limit number of steel mesh, the investigated layer number is limited to five in this study. Figure 17 shows the effect of steel mesh layer number on the load capacity of the column. The load capacity increases with the layer number of steel mesh; however,  
Figure 16: Comparison of lateral deformation curves.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Designation** & **Experiment (kN)** & **Simulation (kN)** & **Discrepancy (\%)** \\ \hline S-0 & 399 & 401 & 0.5 \\ S-1 & 492 & 494 & 0.3 \\ S-2 & 516 & 537 & 4.0 \\ S-3 & 539 & 568 & 5.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of load capacity.  
the increasing rate decreases with the layer number. The improvement percentage of the load capacity changes along a logarithmic function line with the increase of the layer number of steel mesh.  
Table 4 shows the effect of the steel mesh layer number on the improvement proportion of load capacity (compared with non-strengthened specimen) of the column. The improvement of load capacity is within 8% for the specimen that is strengthened by SMRC without steel mesh. The same conclusion was drawn by [PERSON] et al. [39]. Eccentric compressive behavior of RC column strengthened by textile-reinforced mortar was investigated. When the column was only strengthened by mortar, the load-deflection curve was almost the same with non-strengthened column.