esa-sceva/satcom-chunk-collection huggingface dataset:
# Aerodynamic Analysis of the Opening Hood Structures at Exits of High-Speed Railway Tunnels
## 4 Numerical Validation
### Validation of Numerical Method  
To further validate the accuracy of the numerical simulation method in simulating MPWs, the extended radiation solid angle prediction formula proposed in Ref. [15] (multipole source model) was employed to verify the micro-pressure wave results obtained from the numerical simulations. The geometric model dimensions and the setups for the simulation are consistent with Sections 3.1 and 3.2 of this paper. Different lengths of hoods without openings were chosen, and the variation in the amplitude of MPWs at measurement points ahead of the tunnel exit centerline (\(r=20\) m, \(\alpha,\beta=0\)) was analyzed concerning the length of the hood. Figure 6b shows the comparison between the predictive formula and the simulation results.  
The trend of MPW amplitude variation with the length of the hood, as predicted by the Multipole Source Model formula, is consistent with the conclusion in Ref. [30]. The MPWs obtained from the simulations exhibit good agreement with the predicted results from the Multipole Source Model formula for all cases except the one without the hood. When there is no hood at the tunnel exit, the dominant effect on the MPWs is the phenomenon of reflection near the mountain [30]. The Multipole Source Model formula did not consider this reflection phenomenon; thus, errors were caused in the calculated results for the case  
Figure 6: Comparison of calculation results: (**a**) field measurement—numerical simulation comparison (compression waves); and (**b**) formula prediction—numerical simulation comparison (micro-pressure waves).  
without a hood. When a hood without an opening is placed at the tunnel exit, there is a certain reduction in the pressure of the MPW. However, the hood has a critical length (\(L_{\sigma}\)) of approximately 20 m [30]. When the length of the hood without an opening exceeds 20 m, the variation in the amplitude of the MPW is minimally influenced by the change in length. When the length of the hood increases from 20 m to 30 m, the reduction ratio of the MPW amplitude is approximately 4.8%. The numerical simulation results in this study are relatively close to the MPW amplitude without a hood, as reported in the existing paper [28]. When the hood length is 10 m, compared to the results from the predictive formula, the maximum error is approximately 1.9%. Therefore, the numerical simulation method employed in this study can effectively predict the aerodynamic effects of MPWs and can be utilized for subsequent analysis.
# Remote Sensing of Environmental Changes in Cold Regions: Methods, Achievements and Challenges
## 2 Principles and Methods
### Remote Sensing of Water Bodies  
Surface water (SW) over inland areas is a key component of the water and energy cycles, covering about three percent (4.46 million km\({}^{2}\)) of Earth's land (2017). The dynamics of SW in cold land regions are closely linked to terrestrial water storage changes (2017), flood and drought events (2017), seasonal thawing and the spring flood pulse (2018), microtopography, underlying geology and permafrost conditions (2018). SW changes are also occurring in Arctic- boreal wetlands, lakes, rivers, and streams as permafrost degrades with regional climate warming (2018, 2018; 2019); surface subsidence during the initial stages of permafrost degradation leads to increased inundation, while later stages of permafrost thaw lead to surface drying and reduced wetland extent as drainage pathways increase (2017). The emerging glacier and thermokarst lakes formed as ice melts have a strong climate feedback and may increase regional hazards from outburst flooding (2018, 2019).  
Clear and calm water appears dark and is readily distinguished from surrounding land features using optical-IR, microwave radiometer, and mono-static radar sensors. Optical-IR satellite sensors such as PlanetScope multispectral cameras, MODIS, AVHRR, and Landsat provide potential daily to 16-day repeat global observations of SW cover at moderate to high-resolution (3-1000 m), while screening and temporal compositing of the data to reduce the influence of cloud and atmosphere contamination may degrade temporal fidelity (2017, 2018; 2019).  
Potential drawbacks from optical-IR sensors can be partially overcome by active [186] and passive [178; 187; 188; 189] microwave remote sensing. Daily satellite passive microwave observations were used to monitor spatial variability and multi-year trends in surface inundation in permafrost affected regions [29; 188]. Lower frequency (e.g., L-band) microwave retrievals have shown greater sensitivity and detection of surface water even under low to moderate vegetation cover [190]. The synergistic use of optical-IR and microwave observations, and ancillary hydrologic information has shown promise in producing optimum SW mapping results in terms of accuracy, and spatial and temporal resolution [190; 191; 192; 193; 194; 195].
# Delay-Sensitive Service Provisioning in Software-Defined Low-Earth-Orbit Satellite Networks
## 6 Experimental Result
### Delay and Packet Loss Comparison  
The delay and packet loss probability of the three algorithms are compared. Tables 4-6 show the maximum delay, average delay, and packet loss probability for each flow in a single time slot. In Tables 4 and 5, the delay requirement (upper delay) of each TS flow is also shown. It can be observed that all the algorithms have cases where the maximum delay exceeds the delay threshold, while both the maximum and average delay of TGM for all the flows are higher than the upper delay. This is because the maximum delay is influenced by system jitter, which is uncontrollable. For the average delay shown in Table 5, DT-DVTR demonstrates a significantly lower delay compared to LCP. The reason is that TGM always selects the shortest path, which may result in congestion on certain links and cause high delay for some flows. On the other hand, LCP considers selecting the path with the low link utilization when the delay requirement is satisfied, resulting in a relatively high delay but still meeting the delay requirements of TS flows and exhibiting a better load balancing performance. Table 6 displays the number of flows with packet loss in each algorithm, where only TGM, with its preference for the shortest path, experiences congestion and packet loss.  
Figure 10: ATS function evaluation.  
Figures 11-16 shows the maximum delay, average delay, and packet loss probability for a high-priority and a low-priority TS flow. It can be observed that TGM fails to meet the delay requirement and has high packet loss of the low-priority TS flow. On the other hand, both LCP and DT-DVTR guarantee the delay requirement and low packet loss for the low-priority TS flow. Figure 11 indicates that due to jitters in the experiment testbed, all three algorithms still have maximum delays exceeding the delay thresholds. However, Figure 12 demonstrates that all three algorithms ensure that the high-priority TS flow experiences delays below the delay threshold, and Figure 13 shows a very low packet loss probability. Overall, all three algorithms guarantee the delay and packet loss of the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **LCP** & **DT-DVTR** & **TGM** & **Lshade** \\ \hline The number of flows that have & & & & \\ experienced packet loss & 0 & 0 & 3 & 0 \\ \hline Average & 64.640 & 62.018 & 493.689 & 63.261 & 76.685 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Packet loss in one time slot.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Flow**} & \multicolumn{5}{c}{**Maximum Delay**} \\ \cline{2-6}  & **LCP** & **DT-DVTR** & **TGM** & **Lshade** & **Upper Delay** \\ \hline
1 & 68.068 & 68.595 & 65.451 & 63.800 & 60.040 \\ \hline
2 & 52.394 & 45.882 & 47.577 & 45.998 & 46.790 \\ \hline
3 & 99.825 &
# Bacteria-Mediated Modulatory Strategies for Colorectal Cancer Treatment
## 2 Bacterotherapy in CRC Treatment
### Mechanisms Used in Bacterotherapy in CRC
#### 2.1.4 Bacteria-Mediated Anti-Angiogenesis Therapy  
Another treatment strategy using bacteria is demonstrated by anti-angiogenesis therapy, going hand in hand with tumor growth suppression. The formation of new blood vessels, so called angiogenesis, is crucial for solid tumors to grow and metastasize. Therefore, blocking angiogenesis represents another promising target in cancer treatment [40; 109]. In previous research, a genetically attenuated bacteria strain of _Salmonella_ sp. (VNP20009) was used to administer as a combination therapy together with angiogenesis inhibitor rhEnGostatin in tumor mice models with the aim of targeting angiogenesis, thus tumor growth and proliferation [110]. Since separate application of only bacteria or only rhEndostatin have shown little impact on tumor proliferation, the combination of both exhibited great effects in targeting the tumors and suppressing their growth, indicating unique metabolic properties of bacteria that help to complement or even enhance anti-angiogenesis effects of systematically administered agents in cancer therapy [110]. In other studies, bacterial strains, namely _Bifdobacterium adolescents_[111] and _Bifdobacterium longum_[112], were used as Endostatin vectors for targeting tumor angiogenesis, showing significant results in tumor growth inhibition [111; 112]. Moreover, [PERSON] et al. have found evidence that an oral anti-angiogenic bacterial DNA vaccine, carried by attenuated bacterial strain _Salmonella typhimurium_ and directed against VEGFR-2, displayed remarkable anti-cancer effects in different tumor types, including CRC [113].
## 7 Conclusions and Future Work  
In this paper, we analyzed vehicle detection using roadside ultrasonic sensors in WSNs and suggested methodologies for power saving and lightweight vehicle detection. From the detailed analysis, we confirmed the importance of determining an appropriate detection interval in minimizing power consumption. We introduced design methodologies to reduce computational complexity and memory usage while maintaining high detection accuracy. However, our experiments showed that vehicle detection across multiple lanes with a single roadside ultrasonic sensor suffers a reduction in detection accuracy under dense traffic flow, because the number of \"overlap\" errors increases. Thus, to guarantee high detection accuracy, ultrasonic sensors should be employed in situations with few lanes and low traffic flows.  
However, the two limitations can be overcome with some effort. For instance, installing sensors on both sides of the road reduces the detection complexity of a single ultrasonic sensor, which can increase vehicle detection accuracy with multiple lanes. Moreover, applying a narrower dissemination angle of an ultrasonic sensor can increase detection accuracy owing to easier separation of two vehicles.  
Our detailed analysis and evaluation will provide a guide for the future development of traffic information acquisition systems using ultrasonic sensors.  
We considered a single sensor for multiple lanes in this paper. In the future, we will apply multiple sensors across multiple lanes. This will require accurate time synchronization among the sensors. Thus, we will study the efficient time synchronization of multiple sensors in WSNs. Moreover, we will examine the use of other sensor types for WSN-based traffic information acquisition systems.  
\"This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF-2013R1A1A2008811)\".
# Research on Seamless Fabric Defect Detection Based on Improved YOLOv8n
## 4 Experiment and Result Analysis
### Comparative Experiments  
To further validate the effectiveness of the algorithm proposed in this paper, comparative tests are conducted under the same experimental conditions. Specifically, the experiments use the same dataset, the same device configuration, and consistent training parameters to ensure that the performance differences between different models are only due to differences in network architecture and optimization strategies. In the experiment, the improved YOLOv8 model proposed in this paper is compared with multiple mainstream lightweight object detection models, including YOLOv3-tiny, YOLOv5n, YOLOv6n, and YOLOv7-tiny. By comparing the performance of these models, we can more comprehensively evaluate the performance of our algorithm in seamless fabric defect detection tasks. The comparison experiment data of different models are shown in Table 5.  
From the above table data, it can be seen that the model proposed in this paper exhibits significant advantages over existing mainstream object detection models in multiple key indicators, especially in precision, recall, and [EMAIL_ADDRESS]. Compared with existing models such as SSD, YOLOv3-tiny, YOLOv5n, YOLOv6n, YOLOv7-tiny, and YOLOv8, our model has significantly improved in these three core performance indicators. Compared to the SSD model, the model proposed in this paper significantly reduces the number of parameters and FLOPs, demonstrating higher efficiency and optimization levels. At the same time, significant improvements have been made in various performance indicators, especially in terms of recall rate, where the SSD model performs relatively poorly. Compared to  
Figure 11: Comparison of P-R curves before and after improvement.  
Figure 10: Comparison of [EMAIL_ADDRESS] curves before and after improvement of the model.  
YOLOv5n, although the improved model in this paper has increased in terms of parameter and computational complexity, the precision, recall, and [EMAIL_ADDRESS] have improved by 5.7%, 9.1%, and 6.1%, respectively. At the same time, compared with YOLOv7-tiny, the improved model in this paper reduces the number of parameters by 2M under the same computational load and shows significant improvements in precision, recall, and [EMAIL_ADDRESS], further verifying its ability to significantly improve detection performance while maintaining a small model size.  
In addition, it can be intuitively seen from Figure 12 that compared to other mainstream object detection models, the model proposed in this paper exhibits a more rapid convergence trend during the training process. This means that the proposed model not only outperforms other models in detection precision but also significantly reduces the consumption of computing resources during the training process, lowers the burden on computing devices, and improves training efficiency. This feature makes the model more adaptable and practical in practical applications, especially in environments with limited computing resources.
# A Low-Ambiguity Signal Waveform for Pseudolite Positioning Systems Based on Chirp
## 5 Simulation
The FLP of BOC(5,1) is larger than ChPN(5,1) due to its large MSPR and numerous side-peaks as shown in Figure 3. Owing to the smallest side-peak amplitude, as Figures 3 and 9 show, ChPN(3,3) owns the smallest FLP among the ChPN signals with a MLB of \(12f_{0}\), i.e., ChPN(3,3), ChPN(4,2) and ChPN(5,1). Furthermore, ChPN(10,1) has a slightly larger FLP than ChPN(5,1) due to the similar MSPR and the more side-peaks, as Figures 3 and 9 show. In summary, the side-peaks number and MSPR are the two factors that impact the ACF peaks ambiguity, but MSPR is the major factor. Figure 14: Simulated multipath errors for ChPN(5,1), ChPN(4,2), ChPN(3,3), ChPN(10,1), and BOC(5,1) from top to down. The dash lines indicate the out of phase multipath, the solid lines indicate the in phase multipath. Figure 15: Standard deviation of multipath error. Near-far effect is caused by the cross correlation of the signals that are coming from the transmitters of different distances, which will affect the receiver to track the correct but weaker navigation signal. The Monte Carlo simulation is carried out with a PDB of 24 MHz, a sampling rate of 61 MHz, and a coherent integration time of 1 ms to explore the anti-near-far effect performance of the proposed signals. In the simulation, the signal with the C/A code of GPS satellite 1 is acting as the \"far\" signal; the signal with the C/A code of GPS satellite 2 is acting as the \"near\" signal, which is treated as the \"interference\". As Figure 17 shows, the signal with a larger C/N0 has a smaller FLP; as near-to-far signal amplitude ratio increase, the FLP become larger; the O-ChPN has the best anti-near-far performance, followed by ChPN and BPSK. O-ChPN can be used in a system that do not need much transmitters or the bandwidth is sufficient, such as the ISM band.
# Role of Ion-Exchange Resins in Hydrogen Reactions
## 2 Ion-Exchange Resins as Metal Catalysts Supports
Doping IERs with metal catalysts is a well-known technique that can be roughly summarized as a two-steps procedure consisting of (a) metalation step, that is, dispersion of a metal into the IER, typically conducted by contacting the functionalized IER with a solution of a metal salt, which acts as a precursor, to allow ion-exchange ((MLn)\({}^{2+}\) cations for cationic resins and (MLn)\({}^{2-}\) anions for anionic resins are required); and (b) activation of the catalyst, that is, reduction of the metal in its ionic form to its zero-valence form, to produce catalytically active metal nanoparticles. Scheme 1 illustrates such a technique for cation and anion exchange resins with an ST-DVB polymeric matrix. 2. Several metals have been used, including Pd, Pt, Au, and more. No works have been found reporting on particular difficulties in doping IERs with specific metals. 3. Several resin types have been used, including gel-type, macroreticular and hyper-crosslinked resins, either lab-made, commercially available or modified commercially available resins. Acrylic and ST-DVB resins stand out as the most widely used resins among the covered literature. The effect of a variety of functional groups has also been tested and, apparently, acidic resins would be more favored metal supports than basic ones. 4. Metal distribution within the resin beads depends on the resin type as follows: (a) homogeneous distributions are easily achieved in gel-type resins; (b) in contrast, when macroreticular resins are used, a clear heterogeneous distribution of the metal is observed, with the metal being concentrated at the macroproces surface; and (c) as a strategy to circumvent this phenomenon, hyper-crosslinked resins can be used to obtain homogeneous metal distributions while maintaining a permanent, open mesopore structure of the polymeric backbone. 5. Several methods, solvents, and precursor metal salts have been used for the metalation step of IERs, with no clear evidence of a more favored procedure. 6. Metalation yields are generally high, easily achieving 100% anchoring levels of the metal contained in the precursor salt in the final metal-doped IER. 7. Several methods, solvents, and reducing agents are used in the reduction step of the metal ion to its zero-valence or elemental form. In this case, the specific catalytic application seems to be the determinant feature. Accordingly, metal-doped IERs aimed at catalyzing hydrogenation reactions do not appear to need a specific, separated reduction step, since the metal cation would be reduced in the reaction conditions. If this strategy is followed, notice that an induction period for the catalyst would take place before the reaction which must be taken into account for further results interpretation. 8. A variety of metal nanoparticle size distributions is reported in the literature, with values as low as about 1 nm, and a remarkably good size control can be achieved.
## 7 Proportional--Integral--Derivative (PID) Controller Enhanced with Deep Learning (DL)
### Enhancing PID Control with Deep Learning  
Deep learning provides a robust solution for enhancing the performance of PID controllers by automating the tuning of the controller's parameters. Through multi-layer neural networks, deep learning models learn to optimize the proportional, integral, and derivative gains in response to system data, ensuring that the PID controller achieves optimal performance without manual intervention (see [22] and the references therein). These models process input data, such as error signals or sensor readings, and adjust the PID parameters to improve control accuracy, minimize steady-state errors, and respond effectively to changing conditions.  
By integrating deep learning into the PID tuning process, the controller gains adaptability, leading to enhanced performance in tasks like vibration management or other complex control scenarios. This adaptability allows the controller to automatically maintain optimal settings, ensuring efficiency and responsiveness even under varying operating conditions [22]. This hybrid approach yields a more precise and efficient control system, significantly reducing the effort involved in parameter tuning while enhancing overall system performance.
# A Pilot Study of Stacked Autoencoders for Ship Mode Classification
## 3 Theoretical Background  
### Autoencoder  
An autoencoder consists of an encoder, a latent layer, and a decoder, as shown in Figure 1. The encoder is called a recognition network and extracts the features of the original data entered as input. The layer that stores the extracted features during this task is called the latent layer. The decoder is known as a generative network, and it converts the features into output. Through this process, the autoencoder can reorganize the core information in the input data.  
The encoder of the autoencoder can be defined as  
\[h=\sigma(W_{e}x+b_{e})\]  
Here, \(x\) is the input data, and \(W_{e}\) and \(b_{e}\) are the \(e\)th weight value matrix and bias vectors, respectively. \(\sigma\) is the activation function. \(h\) is the encoder output. The decoder of the autoencoder can be expressed as  
\[\hat{x}=\sigma(W_{d}h+b_{d})\]  
In the decoder, the encoder output \(h\) is used as an input variable. \(W_{d}\) and \(b_{d}\) are the \(d\)th weight value matrix and bias vectors, respectively. \(\sigma\) is the activation function. \(\hat{x}\) is the decoder output.  
The autoencoder learns in order to make the decoder's output value as similar to the input value as possible. Therefore, minimizing the difference between the input value and the decoder output value by adjusting the parameters during the training of the autoencoder model is important. As such, selecting a loss function that is suitable for the goals of the appropriate model is also critical. If the mean square error is used as the loss function, it can be expressed as follows:  
\[L(x,\hat{x})=\frac{1}{N}\sum_{i=1}^{N}(\hat{x}-x)^{2}\]  
Here, \(x\) is the input value, and \(\hat{x}\) is the decoder output value. \(N\) is the \(N\)th term.  
Figure 1: Autoencoder.
# Thermal-Mechanical Behaviour of Road-Embedded Wireless Charging Pads for EVs
## 3 Material Characterization
### Polyamide Casing (PA 2200)  
Both the coefficient of thermal expansion and the Young's modulus of the casing material were measured using the DMA Q800. Thermal expansion was measured in 'film tension', while the modulus of the 3D printed material was measured in 'dual-cantilever' mode. A sinusoidal strain of 0.1% was applied to the specimen at 0.1 Hz.  
The thermal expansion of the casing material increased non-linearly with temperature. The thermal expansion of three specimens were fitted to 3 rd order polynomials, which were then differentiated to obtain the polymer CTE, Figure 4a. The temperature dependent modulus of PA2200 is given in Figure 4b. A thermal conductivity of 0.144 W/m\(\cdot\)K was taken from readily available datasheets [28].