esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Real-Time Experiments Using a Load on the AC Motor
\begin{table}
\begin{tabular}{l l l l} \hline
**Operation Region** & **Controller** & \(M_{p}/U_{s}\) **(\%)** & \(T_{s}\) **(s)** \\ \hline
400 RPM & Suboptimal & \(-/-\) & 3.9 \\  & Optimal PI & \(47.25/-\) & 4.1 \\ \hline
200 RPM & Suboptimal & \(-/-\) & 1.1 \\  & Optimal PI & \(11.5/13\) & 2.7 \\ \hline
400 RPM & Suboptimal & \(-/-\) & 0.6 \\  & Optimal PI & \(8.92/-\) & 1.8 \\ \hline
200 RPM & Suboptimal & \(-/-\) & 0.5 \\  & Optimal PI & \(17.9/14.3\) & 3.3 \\ \hline \end{tabular}
\end{table} 
Table 7: Overshoot, undershoot, and settling time for each of the setpoints: 400 RPM and 200 RPM. (Sampling time: \(50\) μs). Figure 19: Angular velocity for both controllers considering a load of 5.25 kg (sampling time: \(100\) μs). Figure 20: Error for the angular velocity for both controllers with loads (sampling time: \(100\) μs). Again, an overshoot of the current in the beginning of the experiment can be observed. Figure 22 shows the estimate of the flux rotor for both controllers when a load is considered, with a sampling time of 100 \(\upmu\)s.  
The calculated control signals are shown in Figure 23 when a load in the motor shaft is considered. The control signal corresponding to the SNDC is softer when it is compared with the control signal of the OPI. Table 9 shows the performance comparison of both controllers when a sampling time of 100 \(\upmu\)s is considered. Table 10 shows the integral absolute errors (IAE) of the angular velocity of the motor shaft (\(\omega\)), the stator current (\(I_{sa}\)), and the rotor flux (\(\
abla_{ra}\)). The energy consumption is calculated based on the current measurements \(I_{sa}\) and the voltage \(V_{sa}\) during all the experiments. Figure 23: Control signals for both controllers with loads on the motor shafts (sampling time: 100 \(\upmu\)s). Figure 21: Current for both controllers with loads on the motor shafts (sampling time: 100 \(\upmu\)s). Figure 22: Estimated fluxes for both controllers with loads on the motor shafts (sampling time: 100 \(\upmu\)s). The SNDC performs better across all state variables and with respect to energy consumption than the OPI control when a load is considered in the motor shaft. **Remark 4**: _Some possible error sources on the implementation of proposed scheme are as follows: On the one hand, the choice of the \(H\), \(Q\), and \(R\) matrices and the correct estimation of the plant parameters are crucial issues to obtain a good performance of the closed-loop system. On the other hand, mechanical vibration sources should be avoided. It is advisable that the load has a uniform mass distribution and a correct coupling to the motor shaft to obtain good measurements of the encoder dispositive. Mechanical vibration produces errors in the measurements and estimation of state variables._
###### Abstract  
Large scientific experimental facilities currently are generating a tremendous amount of data. In recent years, the significant growth of scientific data analysis has been observed across scientific research centers. Scientific experimental facilities are producing an unprecedented amount of data and facing new challenges to transfer the large data sets across multi continents. In particular, these days the data transfer is playing an important role in new scientific discoveries. The performance of distributed scientific environment is highly dependent on high-performance, adaptive, and robust network service infrastructures. To support large scale data transfer for extreme-scale distributed science, there is the need of high performance, scalable, end-to-end, and programmable networks that enable scientific applications to use the networks efficiently. We worked on the AmoebaNet solution to address the problems of a dynamic programmable network for bulk data transfer in extreme-scale distributed science environments. A major goal of the AmoebaNet project is to apply software-defined networking (SDN) technology to provide \"Application-aware\" network to facilitate bulk data transfer. We have prototyped AmoebaNet's SDN-enabled network service that allows application to dynamically program the networks at run-time for bulk data transfers. In this paper, we evaluated AmoebaNet solution with real world test cases and shown that how it efficiently and dynamically can use the networks for bulk data transfer in large-scale scientific environments.  
AmoebaNet; SDN; network as a service; bulk data transfer; QoS +
Footnote †: journal: Accepted: 22 October 2019; Published: 25 October 2019
# Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges
## 4 Energy Market
### Distributed Energy Resource Integration in the Energy Market
#### 4.3.3 Impact of DER Integration on Energy Markets  
This section delves into how the integration of DERs impacts power markets, with Ref. [69] focusing on the transformation in consumer roles and market dynamics. It examines the adoption of solar panels, micro-cogeneration units, EVs, and distributed storage, illustrating how consumers are becoming active energy providers that affect both the distribution network and wholesale market. The paper also touches on the cybersecurity and resilience issues related to DER integration, advocating for policy reforms, comprehensive pricing strategies, and reduced market participation barriers.  
Additionally, Ref. [202] highlights the necessity for market-based solutions to foster DER participation, emphasizing the significance of digital tools in adapting market frameworks. It addresses the growing inclusion of renewables, consumer empowerment, and the emergence of new entities like EVs within the market. The discussion extends to the potential of DERs in delivering flexibility services, challenging the traditional reliance on large power plants, and the anticipated demand for such services, suggesting the establishment of local energy markets to facilitate DER integration into the broader electricity markets.  
Finally, addressing the challenges of integrating DERs, such as voltage optimization and overcoming regulatory obstacles, digital solutions emerge as key facilitators. Study [128] focuses on employing PV inverters for voltage control and leveraging optimal power flow tools for enhancing network operations, showcasing effective methods for integrating PV systems into distribution networks.  
Ref. [70] introduces consumer digital twins (CDTs) for refining energy pricing within local energy markets (LEMs), utilizing the Internet of Things (IoT) to enable flexible DR and advocating for consumer-centric market models. This strategy enhances market efficiency by aligning with renewable variability and consumer preferences. Additionally, Ref. [70] delves into the development of DR, which is propelled by DERs and SG innovations, examining how digital technologies, including the Internet of Energy and IoT, are shaping DR strategies and their prospective roles in energy markets.
# Leveraging Large Language Models for Enhancing Safety in Maritime Operations
## 3 Understanding Large Language Models
### Technical Mechanisms of LLMs--Advanced Analysis
This hybrid integration enables predictive maintenance by cross-referencing sensor anomalies with historical maintenance records, supports real-time risk analysis by synthesizing weather data and navigational warnings, and facilitates dynamic compliance monitoring by aligning operational data with evolving regulatory standards. Recent advancements in transformer models, such as sparse attention mechanisms and model pruning, have significantly reduced computational overhead. These innovations make it feasible to deploy lightweight LLMs in resource-constrained maritime environments, such as onboard systems, ensuring operational continuity even during connectivity disruptions. Additionally, ongoing research is focused on embedding maritime ontologies within LLMs to improve their domain-specific reasoning, developing Explainable AI techniques tailored to safety-critical operations, and creating energy-efficient models that align with the sustainability objectives of the maritime industry. By leveraging these advanced mechanisms, LLMs offer unprecedented capabilities to analyze complex datasets, improve decision-making processes, and support compliance and safety initiatives within maritime operations. Their ability to adapt to the unique challenges of the sector underscores their transformative potential, promising enhanced efficiency, safety, and operational reliability.
## 2 The Proposed Methodology
### The Construction of Slope Stability Evaluation Model
#### 2.2.1 Graph Representation Learning-Node Vectorization
Each entity contained several descriptive elements, including its type, the corresponding ontology instance, attribute values, and the ObjectPartition classification. The edges represent the relationships between entities, and they also carry specific attributes. The core idea is to propagate and aggregate information from lower-level influencing entities through the network structure to upper-level nodes. In this way, a learned embedding is obtained for each slope unit, which effectively encodes the characteristics of its contributing factors in a structured vector representation.
## 2 Materials and Methods
### Experiment Design  
The stimulation material was organized into four 3 min long image sequences (2 emotional and 2 neutral), and each separate stimulus was demonstrated for 5 s. With the view to perform emotional priming, the image series were displayed as follows: each emotional array preceded a neutral one; the series with positive facial expressions was shown first to avoid emotional trace overlap. The experimental paradigm implied the following:  
1. EEG recording while the subjects rest with closed and open eyes;
2. EEG recording during emotional stimuli perception (positive and negative facial expressions);
3. EEG recording during neutral stimuli perception, modulated by positive images (n1) and negative images (n2).  
Stimuli were taken from the International Affective Pictures System (IAPS) [18] according to the average values of their emotional valence. Thus, the parameters of the stimuli were as follows:  
1. neutral faces (M = 4.22, SD = 1.64 to M = 5.84, SD = 1.62);
2. positive faces (M = 6.94, SD = 1.42 to M = 8.03, SD = 1.13);
3. negative faces (M = 1.82, SD = 1.64 to M = 3.91, SD = 1.62).  
The EEG data were subdivided into a range of sub-bands, allowing for evaluating the induced responses generated by the human cortex. Therefore, the decisive list of the EEG spectral ranges included the following constituents:  
1. 3.5-5.8 Hz (\(\theta_{1}\));
2. 5.9-7.4 Hz (\(\theta_{2}\));
3. 7.5-9.4 Hz (\(\alpha_{1}\));
4. 9.5-10.7 Hz (\(\alpha_{2}\));
5. 10.8-13.5 Hz (\(\alpha_{3}\));
6. 13.6-25 Hz (\(\theta_{1}\));
7. 25.1-40 Hz (\(\beta_{2}\)).  
All the analytical techniques mentioned in this study were individually applied to each EEG sub-band.
## 3 Experimental Results and Discussion
### Modulation Signal Characterizations  
The modulation signal measurement was carried out using specific test equipment, including a Mini-Circuits ZVE-3W-83+ pre-amplifier, Keysight AWG M8190A, PSG E8267D, and UXA N9041B devices. The modulation signal used was a 5G New Radio (NR) Frequency Range 1 (FR1) signal with a bandwidth of 100 MHz, utilizing 256-QAM modulation with a coding rate of 3/4.  
Figure 15a,b displays plots representing the measured constellation and error vector magnitude (EVM) for both the split and stack design power amplifiers (PAs) against various average output power values. The split CoB wire-bond assembly PA achieved an average output power of 29.6 dBm while meeting the EVM requirement of 3.5%. Similarly, the stack CoB wire-bond assembly PA yielded an average output power of 28.3 dBm under the same EVM requirement of 3.5%. These results indicate that both the split and stack design PAs are suitable for 5G NR operation as they meet the EVM and output power specifications for reliable and efficient communication in the 5G NR FR1 n77 and n78 bands.  
The results of the DUT power spectrum density (PSD) and adjacent channel leakage power ratio (ACLR) measurements with a 5G NR FR1 100 MHz 256-QAM signal are displayed in Figure 16. With the implementation of digital predistortion (DPD), both the split design PA and the stack design PA were able to meet the stringent \(-\)30 dBc (decibels relative to the carrier) user equipment ACLR requirement for the n77 and n78 frequency bands. The split PA achieved this performance with an average output power (\(P_{out}\)) of  
Figure 14: Measured and simulated power performance versus frequency: (**a**) split CoB wire-bond assembly design; (**b**) stack CoB wire-bond assembly design.  
Figure 15: Plot of the measured constellation and EVM for the split design PA: (**a**) constellation; (**b**) EVM versus output power.  
31.8 dBm, while the stack PA achieved it with a slightly lower average \(P_{out}\) of 31.5 dBm. The implementation of DPD also improved the EVM results. Both designs fulfilled the EVM requirement of 3.5% under the power levels.  
By using DPD, these power amplifiers were able to effectively mitigate distortions and non-linearities, ensuring that the transmitted signals meet the required standards for adjacent channel emissions, which is critical for maintaining a high-quality and interference-free communication in the 5G NR n77 and n78 bands.
## 4 Research Methodology
### Search and Rescue Simulation Environment
IEEE 802.11b was used as a MAC protocol because it is supported by the radio board of the UAV and can be applied in ad hoc communication. It supports theoretical data rates up to 11 Mbps at 2.4 GHz radio frequency. The type of antenna is set to an omni-directional antenna, and free-space path loss is chosen as the propagation model. Each UAV is configured with the simulation parameters stated in Table 3.  
Figure 4: UAV ad hoc network in search and rescue scenario.
## 6 Geological Significance of Yan'an Formation Oil and Gas Reservoirs
### Characteristics of Sandbody Superposition  
The more developed the sand bodies within a formation, the higher the potential to become a high-quality reservoir. The Yan'an Formation reservoir sand bodies are primarily classified into several types: continuous superposition, intermittent superposition, interbedded sand-mud, and single sandbody type (Figure 9). Among these types, the vertical continuity of the stacking types decreases sequentially, the sand-to-mud ratio progressively decreases, and the reservoir quality gradually deteriorates.  
Continuous superposition type: This type is characterized by strong river scouring capability, high frequency, adequate hydrodynamic energy, and low migration rates. It forms in deltaic environments with continuous sedimentation and sustained subsidence over long periods. Commonly found in distributary channels and turbidite deposits, this type is often represented by box-shaped curves on well logs (Figure 10a). Sand bodies in this type extend longer in the direction of river flow and shorter in the vertical direction. Rivers are more developed compared to deltaic settings, with the most abundant development occurring in the Yan 10 member.  
Intermittent superposition type: This type is characterized by strong river scouring, relatively high frequency, sufficient hydrodynamics, and lower migration rates. It forms in deltaic environments where continuous subsidence and river meandering create thick sand bodies interbedded with mudstone layers. This produces sedimentary facies characterized by underwater distributary channels interspersed with small-scale distributary bay deposits. On well logs, this type is represented by multiple box-shaped or bell-shaped curves (Figure 10a,b). Sand bodies of this type also extend further in the direction of river flow and shorter in the vertical direction. Rivers are more developed than in deltaic settings, with the greatest development observed in the Yan'an Formation.  
Figure 10: Logging responses of various sandbody types.  
Interbedded sand-mud type: This type is characterized by weaker river scouring capability, reduced hydrodynamics, and higher migration rates, with a sand-to-mud ratio below 50%. It forms from river meandering, leading to thick mudstone layers interbedded with sand bodies. Typically commonly found in underwater distributary channels and distal sand bars, this type is often represented by funnel-shaped or finger-shaped curves on well logs (Figure 10c,d). The interbedded sand-mud type is more developed in deltaic settings and demonstrates high heterogeneity.  
Single sandbody type: This type is characterized by weak river scouring, insufficient hydrodynamics, and finer sediment grain sizes, with a sand-to-mud ratio below 20%. It forms from turbidity current overflow and is commonly found in distributary bay deposits, sheet sands, and estuarine bars. Well logs generally display sawtooth-shaped or finger-shaped curves (Figure 10d,e). The single sandbody type primarily occurs at the delta front and in pre-deltaic settings, and it also exhibits considerable heterogeneity.
## 2 Related Work
### Multi-Objective Optimization Routing in Satellite Networks
We noticed that most of the approaches were simulation based, whereas we performed an experimental-based study with physical links emulated with delay, rate limiting, and packet loss.
## 4 Application of Hyperchaotic Sequence in Secure Communication
### Receive Structure  
When the receiver receives a transmission signal from the sender, the correlator can be used to recover the original bits of information. The RM-DCSK system mainly utilizes chaotic delay characteristics and noncoherent demodulation. The structure diagram of the receiving end is shown in Figure 8. Since signals may be interfered by noises when transmitted in different networks, the received signals are different from the original signals. In this paper, the Additive White Gaussian Noise (AWGN) channel model is used as the main communication transmission medium. The interference in the channel is mainly the noise signal \(\zeta\), so the received signal \(r_{i}\) is as follows:  
\[r_{i}=s_{i}+\zeta_{i}. \tag{17}\]  
The output of the correlator \(Z_{n}\) is the product of the received signal \(r_{i}\) and its delayed version \(r_{i-M}\). Therefore, the mathematical expression of correlator \(Z_{n}\) is calculated as the sum  
\[Z_{n}=\sum_{i=nM+1}^{(n+1)M}r_{i}r_{i-M}. \tag{18}\]  
Then, the mathematical expression of correlator \(Z_{2k}\) for recovering bit \(b_{2k}\) is rewritten as the sum  
\[\begin{split} Z_{2k}&=\sum_{i=2 kM+1}^{(2k+1)M}(s_{ i}+\zeta_{i})(s_{i-M}+\zeta_{x_{i-M}})\\ &=\sum_{i=2 kM+1}^{(2k+1)M}(b_{2k-1}b_{2(k-1)}x_{i-2M}+x_{i-M}+ \zeta_{i-M})\\ &\times(b_{2k}x_{i}+\zeta_{i}).\end{split} \tag{19}\]  
Equation (16) tells us that \(x_{i-M}=x_{i}\), then Equation (19) can be simplified as  
\[\begin{split} Z_{2k}&=\sum_{i=2 kM+1}^{(2k+1)M}(b_{ 2k-1}b_{2(k-1)}x_{i-2M}+x_{i-M}+\zeta_{i-M})\\ &\times(b_{2k}x_{i}+\zeta_{i})\\ &=b_{2k}\sum_{i=2 kM+1}^{(2k+1)M}x_{i}^{2}+\sum_{i=2 kM+1}^{(2k+1)M }x_{i}A_{i}\end{split} \tag{20}\]  
where \(A_{i}\) is calculated as follows:  
\[\begin{split} Z_{2k}&=b_{2k}b_{2k-1}b_{2(k-1)}x_{ i}x_{i-2M}+b_{2k}x_{i}\zeta_{i-M}\\ &+b_{2k-1}b_{2(k-1)}x_{i-2M}\zeta_{i}+x_{i}\zeta_{i}+\zeta_{i} \zeta_{i-M}.\end{split} \tag{21}\]  
Since the noise signal needs much less energy than the useful signal, the square term containing chaotic signal is the signal of subject discrimination. Then the positive and  
Figure 7: Structure of the transmission signal in RM-DCSK.  
negative of the correlator \(Z_{2k}\) is mainly determined by the bit information \(b_{2k}\). In addition, the correlator \(Z_{2k+1}\) can similarly recover information bit \(Z_{2k+1}\). Finally, information bit \(Z_{n}\) will be demodulated according to the decision threshold.  
\[b_{n}=\begin{cases}1,&b_{n}>0\\ -1,&b_{n}\leq 0\end{cases}. \tag{22}\]  
where \(x_{i}\) satisfies the following conditions  
\[x_{(2k+1)M+m}=x_{2(k+1)M+m},0<m\leq M;k\in\mathbb{Z}. \tag{23}\]
# A Conceptual Design of Deployable Antenna Mechanisms
## 2 Materials and Methods
### Hierarchical Composition--Twelve Bays Composed of Three Mechanisms  
#### 2.2.1 Six-Bar Linkage Mechanism  
The 6-bar linkage mechanism consists of six joints and links, acting both as actuators driving the deployment mechanism and as a batten connecting the top and bottom nodes within a single bay. As depicted in Figure 2a, the 6-bar linkage mechanism allows the entire structure to exhibit one degree of freedom in movement by engaging both the single pantograph and the V-folding mechanism. The upper and bottom net fixed joints serve as the longeron that connects the upper and lower rings of the reflector. In the configurations of links, the driving joints and bottom net fixed joints are coupled with the pantograph mechanism. This design allows the linkage configuration to move in a manner similar to a slider-crank mechanism. Due to the difference in the lengths of the linkages, the upper net fixed joint can move to generate an inclined motion. Additionally, the driving joint is constrained to unidirectional motion, resulting in a linear movement along the y-axis. Thus, once all 12 days are connected, creating a ring shape, it is then possible to determine the degree of taper in the conical frustum shape of the deployable antenna structure.
## 1 Introduction
It also overcomes the limitation of RCSP, which assigns equal weights to all source domain subject data, thereby weakening the discriminative power of suboptimal feature extraction. As a result, the target subject's data is better aligned with a more stable and low-condition bias feature space. By jointly optimizing the spatial-frequency patterns in the frequency-domain and spatial-domain, this method further enhances the discriminability of the features and improves the model's generalization ability across different subjects. Compared to existing transfer-learning strategies, the WFBRCSP method directly operates on the EEG signals and spatial filters, rather than on the extracted features. This allows the WFBRCSP to more directly capture the important information in the EEG signals, reducing information loss during the feature extraction process. It has a significant advantage in cross-subject feature space alignment, enabling better adaptation to the EEG signal distributions of different subjects. As a result, the model's performance is significantly improved. The structure of this paper is organized as follows. Section 2 reviews related work, analyzes the current state and advancements of transfer-learning methods in EEG signal analysis, and highlights the existing limitations in current research. Section 3 describes the framework and methodological principles of the weighted filter bank and regularized common spatial pattern (WFBRCSP) approach. Section 4 presents experimental comparisons between the WFBRCSP and other methods, including classification accuracy, computational time, and model generalization ability. Section 5 evaluates the WFBRCSP and the comparedmethods, discussing the results and their implications. Section 6 summarizes the theoretical contributions and practical significance of the research findings.