esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Although many studies have been carried out to investigate the support pressure at the limit state of the excavation face, the study of surface displacement induced by an excess/insufficient support pressure especially the quantitative analysis is relatively rare and most of the studies are analytical solutions based on the upper bound theorem and numerical simulation [37]. Furthermore, the extent of ground movement associated with a reduced/increased support pressure is not yet well known. Additionally, the mentioned above studies were focused on clay, cohesive soil or dry sand. Different from those soils, the sand especially in water-rich are the soils whose self-weight forces may themselves cause collapse. Tunneling in such soil can easily induce excessive displacement, even blow-out or collapse of the ground. As there have been limited studies (especially the experimental study) on the relationship between the ground displacement and the support pressure in water-rich sand soil, a satisfactory understanding of the ground deformation mechanism, together with a reliable way for calculating maximum displacement induced by the support pressure, is still limited. In this paper, to investigate the ground deformation induced by the variation of excess/insufficient support pressure in water-rich sandy soil, six groups of centrifuge tests in tunnels located at cover over diameter (C/D) ratios equal to 0.5, 1.0 and 1.5 were carried out to investigate the ground deformation induced by the variation of excess/insufficient support pressure. The soil and pore pressure, maximum surface displacement and the ground movement induced by reduced/increased support pressure were investigated. Based on the centrifuge test, the ground deformation mechanism was analyzed compared with previous studies and a supporting pressure control coefficient was proposed to conduct quantitative analysis of surface deformation caused by support pressure. Finally, the determination of support pressure based on the surface deformation control was discussed to provide some advice for practical engineering.
## 3 Results and Discussion
### pH and Alkalinity  
The changes in solution pH and alkalinity after coagulation with various PMC-based coagulant doses are shown in Figure 5. The initial pH of the raw water was approximately 7.8. The jar test was conducted with varying doses of the PMC-based coagulant, and the effect of PMC dose on the pH of the test water was evaluated. It was observed that the test water pH decreased after addition of the PMC-based coagulant. Increasing the PMC dose decreased the pH and alkalinity of the test water. The pH decreased to approximately 7.0 at the optimum PMC-based coagulant dose, which indicates that the alkalinity consumption of the PMC coagulant is less than that of aluminum-based chemical coagulants [30; 31].
# Joint Communication-Motion Planning in Networked Robotic Systems
## 4 Conclusions and Open Problems  
In this paper, we first reviewed the contributions on NRSs, with a focus on the CAMP scheme and proposed that JCMP may outperform CAMP by exploiting more degrees-of-freedom. Then, we proposed the OODA-based NRS architecture that incorporates JCMP. After reviewing the SoAs in connectivity quality metric for the performance assessment module, the JCMP was discussed in more detail, and two examples were provided to show the benefits of JCMP.  
Figure 5: Optimal trajectories of the relay robot for non-JCMP and JCMP with different PU deployments.  
The two examples in Sections 2 and 3 may be naturally extended to more sophisticated scenarios. An NRS of multiple sensing robots, multiple relay robots along with multiple PUs allowing underlay, overlay and interleave CR approaches will be incorporated in our future works. Additionally, each SU robot may be equipped with multiple antennas for beamforming a null point toward the PUs, which may also greatly improve the communication quality [18].  
Meanwhile, we also point out several problems that JCMP may face in practical application:  
1. **The system complexity of JCMP is high.** This is because JCMP needs to optimize the position and power of multiple robots simultaneously. In practice, it is necessary to reasonably design the hardware architecture of JCMP, so that it can still complete the timely adjustment of various parameters in a flexible external environment.
2. **Stable communication between robots.** This paper is devoted to the development of a networked robot system that completely depends on wireless communication. Since the command and control of robots and the data transmission between robots all depend on wireless communication, a stable communication system is the premise for robots to perform tasks. In the actual environment (especially in the urban environment), multi-path fading, shadow fading, and path loss will affect the stability of wireless communication. Therefore, the wireless channel should be accurately evaluated and modeled before the JCMP is applied in the actual scene.  
Methodology, Z.Z.; Supervision, B.Z. and Y.W.; Writing--original draft, Z.Z.; Writing--review and editing, B.Z. and Y.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under Grant No. 91948303.  
The authors declare no conflict of interest.
## 5 Numerical Experiments
### Impact of IoT-SBL Strategy on Delay  
Delay is a critical metric that significantly impacts the overall assessment of the performance of the signal intersection. It provides essential insights into the efficiency and effectiveness of the transportation system, serving as a key indicator of its functionality and operational success. This subsection focuses on a comprehensive analysis of how the IoT-SBL strategy affects vehicle delays. By comparing the performance of two different strategies across various saturation levels, this analysis aims to highlight the effectiveness of the IoT-SBL strategy in mitigating vehicle delays, ultimately contributing to a better understanding of its potential benefits for real-world transportation systems.  
Figure 4: Queue length evolving over time at different saturation levels: (**a**) Saturation V/C = 0.1; (**b**) Saturation V/C = 0.5; (**c**) Saturation V/C = 0.9; (**d**) Saturation V/C = 0.97.  
Figure 5: Queue reduction.  
Figure 6a presents a comparison of different IoT-SBL strategies against the bus lane strategy at varying saturation levels and capacities. At saturation levels below 0.5, both strategies demonstrate similar vehicle delays, as the signal intersection operates without congestion due to low saturation. However, as saturation levels exceed 0.5, particularly reaching 0.97, the advantages of the IoT-SBL strategy become increasingly evident. During this higher saturation, the traditional bus lane strategy experiences a significant rise in vehicle delays, while the IoT-SBL strategy manages to maintain delays within an acceptable range.  
Furthermore, the IoT-SBL strategy's capacity to reduce vehicle delays is less than 10% at lower saturation levels but escalates to an impressive 69.48% when saturation reaches 0.97. This underscores that the effectiveness of the IoT-SBL strategy is closely tied to its capacity, emphasizing the importance of establishing optimal capacity to maximize its performance across varying saturation conditions.  
Figure 6b examines how varying bus departure frequencies affect the IoT-SBL strategy. Interestingly, the trends in vehicle delays across different saturation levels reflect those observed in Figure 7. It is important to note that bus departure frequency plays a crucial role in influencing vehicle delays, particularly at elevated saturation levels. When departure frequencies are excessively high, they can diminish the effectiveness of the IoT-SBL strategy by interfering with the prioritization of public transportation. As shown in Figure 7, there is a significant variation of 26.59% in vehicle delays caused by different bus departure frequencies at the same saturation level. Despite these fluctuations, the IoT-SBL strategy maintains a consistent level of performance across all scenarios, regardless of the departure frequency employed.  
Figure 6: Comparing average delays at various saturation levels: (**a**) Different IoT-SBL volumes; (**b**) Different bus departure frequencies.  
Figure 7: Reduction degree in average delay: (**a**) Different IoT-SBL volumes; (**b**) Different bus departure frequencies.
## 3 Results and Discussions
### Order Picking Performance
#### 3.2.5 The Effect of Order Quantity on the Cost Efficiency Level  
We evaluated how order quantity affected the performance of cost efficiency (see Table 8). In warehouse 1, the midpoint heuristic is the most efficient for all the order quantity considered, followed by the traversal heuristic. This is in line with our previous results in Table 6 where almost half of the subsets for warehouse 1 contain the midpoint heuristic. This supports the literature Ref. [12] that the midpoint outperforms the traversal and return policies for orders containing a small number of picks. The midpoint-voice picking combination results in the best order picking performance for warehouse 1. On the other hand, traversal routing yields the best performance in terms of efficiency levels in warehouse 2 to 4. These are considered to be respectively medium- and big-sized configurations in our study. As previously found, warehouses 2 to 4 have more subsets containing traversal routing. Therefore, the results are in line with the selected literature Ref. [12] that for a large number of picks traversal routing performs the best. In warehouses 2 to 4, the traversal-voice picking combination performs the best. In terms of solution technologies, voice picking still dominates the results. It should, however, be noted that handheld barcode outperforms voice picking in cases of a large amount of orders relative to the warehouse considered. For example, for 500 orders, the barcode handheld leads to the highest level of efficiency when either return or traversal routing is implemented. On the other hand, for 800 and 900 orders, the barcode handheld outperforms voice picking when either midpoint or return routing is used. Although voice picking yields relatively higher picking rate, handheld picking is more cost efficient when warehouse activities reach a certain level due to a greater increase in the voice picking cost compared to the handheld picking cost. Further analysis shows that large order sizes cause order pickers to do overtime shift and thus further exacerbate the operating cost. To reach the optimal service level, in that case, warehouse managers have to trade off cost efficiency. This implies that warehouses are able to provide a service level to a certain level at a maximum possible efficiency.
# Threat Analysis and Risk Assessment (TARA) Analysis of an Autonomous Emergency Braking (AEB) System
## 2 Related Work
### Background  
#### 2.1.1 AEB Systems  
AEB is an advanced driver-assistance system (ADAS) designed to enhance vehicle safety by automatically applying brakes in order to prevent or mitigate collisions. AEB systems employ a variety of sensors, including radar, cameras, and LIDAR, to monitor the vehicle's surroundings and detect potential collision threats. Upon the detection of a potential collision, the system calculates the requisite braking force and automatically engages the brakes in the event that the driver does not respond in a timely manner. The architecture of AEB systems comprises several critical components, including sensor input, data fusion, decision-making algorithms, and actuator control. It is essential that these components function in a seamless manner to guarantee the system's effectiveness and reliability.
# An Efficient Routing Scheme Based on Node Attributes for Opportunistic Networks in Oceans
## 4 Performance Evaluation
### RSNA Parameters  
As shown in Equations (4) and (5), the parameters of \(\alpha\), \(\beta\), and \(\mu\) and \(\lambda\) have a direct influence on routing decisions. We define 10 combinations with different values, as shown in Table 2.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Transmission speed & 250 kbps \\ Message size & 500 KB-1 MB \\ Message interval & 25 s-35 s \\ TTL & 300 min \\ Node speed & 0–30 Km/h \\ Number of groups & 6 \\ Initial energy & 5000 J \\ Simulation time & 43,200 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
From Figure 3, we can see that performance of packet delivery ratio and overhead ratio of combination \(\sharp 10\) is the worst. This is because it gives all weights to a metric without considering any other information, which is not enough to make decisions on relay node selection. We can observe that the performance improves significantly when the multi-metrics are considered (\(\beta\
eq 0\) and \(\lambda\
eq 0\)). Furthermore, the best performance of delivery ratio and overhead ratio is reached when the value of \(\alpha\) (\(\mu\)) and \(\beta\) (\(\lambda\)) is combination \(\sharp 6\). In addition, the values of \(\zeta\) and \(\eta\) have no significant impact on performance, so we set them to 0.5 and 0.5, respectively. They are also used in the experiment by default unless specifically mentioned.
# Fast Fourier Asymmetric Context Aggregation Network:
## 1 Introduction
* We design a new content inference module, the Fast Fourier Channel Dual Residual Block (FF-CDRB), which utilizes the global receptive field characteristics of Fast Fourier Convolution (FFC). This module improves the network's ability to capture contextual information and perform accurate content inference while also improving the efficiency of parameter computation. By integrating the FF-CDRB, the model demonstrates its capability to generate high-quality images with optimized parameter utilization, ensuring both accuracy and computational efficiency. * We propose a novel Asymmetric Context Aggregation Mechanism (ACAM), which constructs global context by effectively leveraging heterogeneous features with distinct information representation characteristics from different scales and stages. This approach addresses the limitations of the existing methods in context modeling. The ACAM significantly improves both the perceptual and pixel-level quality of generated images while also enhancing the model's robustness to variations in texture patch size. To the best of our knowledge, the ACAM is an innovative method for context information interaction, providing potentially valuable references for the research community. * Extensive experiments were conducted on our self-constructed dataset, SKFashion, as well as two public datasets, VITON-HD and Fashion-Gen, to validate the effectiveness of the FCAN. The experimental results show that those images generated by the FCAN outperform advanced baseline methods in terms of both perceptual and pixel-level quality. Moreover, the FCAN demonstrates greater robustness to variations in texture patch conditions compared to the other methods, making it more suitable for meeting practical application requirements.
## 3 Results
### Classification Results for Patients with Early PD and HCs  
When training and test datasets were not separated, backward elimination results based on F-score are shown in Figure 3. The best performance for SVM was observed when all seven ROIs were used. For LR, the best performance was observed when four features were used (GP, DN, SNr, and SNc).  
When we compared the performance of the best SVM model (using GP, RN, DN, SNr, SNc, CN, and PUT) with LR models with the same features, the calculated \(p\)-value was less than 0.01 inaccuracy, AUC, and sensitivity, demonstrating a significant difference between the performance of the SVM model using all seven ROIs and the performance of LR models.  
The optimal SVM model demonstrated accuracy, sensitivity, specificity, and AUC values of 0.79 \(\pm\) 0.07, 0.81 \(\pm\) 0.07, 0.76 \(\pm\) 0.09, and 0.73 \(\pm\) 0.06, respectively (Figure 3). The optimal LR model demonstrated accuracy, sensitivity, specificity, and AUC values of 0.73 \(\pm\) 0.03, 0.74 \(\pm\) 0.04, 0.72 \(\pm\) 0.04, and 0.64 \(\pm\) 0.03, respectively. SVM had approximately 10% higher accuracy than LR overall. The performance of the best SVM model was significantly different from that of the best LR model (\(p\) = 0.027 in accuracy, \(p\) = 0.001 in AUC, and \(p\) = 0.017 in sensitivity) except for specificity (\(p\) = 0.199).  
Table 3 shows classification results from training data when separating datasets into training and test. The SVM model with all features shows the best performance (accuracy = 0.76, AUC = 0.70, sensitivity = 0.77, and specificity = 0.84), while LR has accuracy, AUC, sensitivity, and specificity of  
Figure 3: Performance of SVM and LR in classifying patients with early Parkinson’s disease (PD) and healthy controls (HC) using backward elimination of features. The results for SVM and LR are shown in regard to (**a**) accuracy, (**b**) area under the receiver operating curve (AUC), (**c**) sensitivity, and (**d**) specificity. The features were included as the training set from the region of interest with the highest F-score to the region of interest with the lowest F-score. Asterisk (*) shows significance (\(p\) < 0.05). LR = logistic regression, SVM = support vector machine.  
0.72, 0.64, 0.72, and 0.72, respectively. When using all features for the SVM and LR models, in the test set, SVM revealed better accuracy (=0.8), AUC (=0.90), and sensitivity (=0.75) than those of LR (accuracy = 0.75, AUC = 0.64, and sensivity = 0.5) except for specificity (SVM = 0.88 and LR = 0.92). An LR model with GP, DN, SNr, and SNc selected in Figure 3 also had the same performance as the LR model with all features in the test set.
# Multi-Layer QoE Learning System Implemented by Fiberhost
## 3 Multi-Layer QoE Metric Nature
### Infrastructure Layer  
The Infrastructure layer is the base layer to all the other layers, since it defines the Network Availability that the following services rely on.  
For example, cable cuts and power outages are the most common causes of service disruptions. This can be seen in Figure 2, where we classified the major service outages in a typical year at Fiberhost. It is observable that the cable cuts and power outages contribute to more than 94% of all service outages. Therefore, when building telecommunications networks, we should strive for the maximum dispersion and diversification of the resources. Additionally, it is important to increase the existing network capacity and density, so that efficient resources take over the function of corrupted resources when needed.  
Machine learning methods are not required to analyze this type of data, however they can be useful in performing the root cause analysis to identify the source of a problem and reduce the number of alarms the operator needs to analyze.  
The signal level parameters belong to the infrastructure layer as well. These can be used to check if the optical budget of the optical link is met. The distribution of the signal levels received at the 1310 nm and 1490 nm wavelengths in the Fiberhost GPON network is shown in Figure 3. Even though the distribution in Figure 3 shows that some ONTs are below the optical budget, it does not necessarily mean that the service will be interrupted. In addition, we found that the levels of signals received on the 1310 nm and 1490 nm wavelengths in the GPON optical links do not correlate with bit errors, as it is shown in Figure 4. The bit errors include both bit-interleaved parity (BIP) errors, which provide  
Figure 2: Reasons for service disruptions.  
measurement of the bit errors for the GPON frame header (PCBd), and HEC errors, which indicate the bit errors on the GEM header (the header of the payload). However, as shown in the Pearson's correlations diagram in Figure 4, the correlation between bit errors does not exist. This is because dirty connectors, broken fibers, etc, may be present even if there is not a significant signal attenuation.  
A simple analysis of the signal parameters can result in many trips of technicians to address too-low parameter values. However, a more complex multi-layer analysis shows that there is no simple correlation of signal parameters with BER. So, this is an example that demonstrates the benefits of a multi-layer analysis, which allows the operator to avoid unnecessary trips and in this way improves the operational efficiency.
# Domain Adaptation for Satellite-Borne Multispectral Cloud Detection
## 9 Discussion
### Adapting to Constraints  
Real-world scenarios are rife with challenging operational constraints, which EO missions need to adapt to in order to maximise return on investment. These constraints include the following [87]:  
* Energy constraint: Onboard machine learning can be computationally intensive and thus power-hungry, but EO satellites typically rely on photovoltaics and battery storage for energy supply. To avoid the risk of unrecoverable energy depletion, it is crucial to monitor the state of charge (SoC) of the battery and only perform power-hungry computation such as onboard updating of machine learning models when the SoC is at a sustainable level.
* Downlink bandwidth constraint: The time during which a satellite and a ground station are within communication range of each other, allowing the satellite to downlink its data to the ground station, is known as the communication window. The size of the communication window, the capacity of the communication channel (e.g., RF, optical) and environmental effects (e.g., multipath, atmospheric turbulence, space weather) determine the amount of data that can be downlinked; if this amount is less than how much the satellite needs to downlink, then the satellite should selectively downlink the data that improve domain adaptation the most. The detail of the selection strategy is a topic of our ongoing research.
* Uplink bandwidth constraint: The fact that uplink bandwidth is more limited than downlink bandwidth, as mentioned in Section 3.3.1, incentivises further conservation of uplink bandwidth by the bandwidth-efficient SDA method proposed in Section 6.1. A naive application of the proposed SDA method could see the ground station (i) executing the FISH Mask algorithm on the labelled target dataset \(\mathcal{D}^{I}\) to update the model weights and (ii) uploading the updated model weights defined by Equation (6) to the EO satellite during every communication window. However, valuable uplink bandwidth can be conserved if the FISH Mask algorithm is only executed on \(\mathcal{D}^{I}\) whenever the sum of losses \(\sum L(f^{\delta}(\cdot;\theta^{\delta}),\cdot)\) exceeds a certain threshold. How this threshold can be set for a desired trade-off between domain gap and bandwidth usage is another topic of our ongoing research.  
In summary, the proposed domain adaptation methods can be extended to account for the preceding constraints.