esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Privacy and Cultural Differences in the TAM
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Predictor** & **Outcome** & \(\beta\) & \(p\) & **Significance** \\ \hline Privacy Concerns & Perceived Ease of Use & 0.05 & 0.094 & Not Significant \\ Privacy Concerns & Perceived Usefulness & 0.05 & 0.108 & Not Significant \\ Privacy Concerns & Attitude Toward Using & \(-0.05\) & 0.041 & Significant \\ Privacy Concerns & Behavioral Intention to Use & \(-0.01\) & 0.806 & Not Significant \\ Perceived Ease of Use & Perceived Usefulness & 0.47 & 0.000 & Significant \\ Perceived Ease of Use & Attitude Toward Using & \(-0.01\) & 0.720 & Not Significant \\ Perceived Usefulness & Attitude Toward Using & 0.58 & 0.000 & Significant \\ Perceived Usefulness & Behavioral Intention to Use & 0.17 & 0.001 & Significant \\ Attitude Toward Using & Behavioral Intention to Use & 0.43 & 0.001 & Significant \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The results of path analysis for the S. Korean group. Figure 2: The results of path analysis for the S. Korean group. \({}^{*}\)\(p\) < 0.05, \({}^{***}\)\(p\) < 0.001.  
ease of use, or behavioral intention in either country, indicating that privacy concerns primarily act as an indirect barrier, influencing adoption via attitudes. Finally, attitudes toward using ARSGs significantly predicted behavioral intention in both groups (\(\beta\) = 0.47, \(p\) < 0.001 for the U.S.; \(\beta\) = 0.43, \(p\) = 0.001 for South Korea), underscoring the importance of fostering positive attitudes to promote adoption across cultural contexts. These results demonstrate that while the TAM holds broadly across both cultures, privacy concerns exert stronger suppressive effects on American users' acceptance compared with Koreans.
# A Proposal for Rapid Assessment of Long-Distance Oil and Gas Pipelines After Earthquakes
## 2 Analysis of Pipeline Safety Assessment Elements Under Seismic Impacts
### Pipeline Monitoring Device System Sorting
\begin{table}
\begin{tabular}{c c c} \hline \multicolumn{2}{c}{**The Interrelationship of the**} & \multicolumn{2}{c}{**Monitoring Content**} \\ \multicolumn{2}{c}{**Monitoring Data with the Pipeline**} & \multicolumn{2}{c}{**Monitoring Content**} \\ \hline Internal monitoring & \multicolumn{2}{c}{Pipeline corrosion monitoring} \\  & \multicolumn{2}{c}{Transport pressure monitoring} \\ \hline  & Pipe deformation monitoring & \multicolumn{2}{c}{Pipe stress/strain} \\  & Pipeline collateral monitoring & \multicolumn{2}{c}{Accompanying fibre optic cable temperature} \\  & Accompanying fibre optic cable strains & \multicolumn{2}{c}{Accompanying fibre optic cable strains} \\  & \multicolumn{2}{c}{Surface displacement} \\ External monitoring & Natural disaster monitoring & \multicolumn{2}{c}{Deep displacement} \\  & \multicolumn{2}{c}{Surface crack monitoring} \\  & \multicolumn{2}{c}{Others} \\  & Structural monitoring of & \multicolumn{2}{c}{Deformation of buildings and structures} \\  & prevention engineering & \multicolumn{2}{c}{Structural cracks.} \\  & \multicolumn{2}{c}{Others} \\  & Pipe–soil interaction monitoring & \\ \hline \end{tabular}
\end{table} 
Table 1: Monitoring elements along the pipeline. (Modified from [20]). Figure 2: Schematic diagram of the division of the monitoring system for the rapid assessment of pipeline safety. In addition, when the pipeline crossing project is located in the area where the peak ground acceleration (PGA) of basic earth tremor is 0.3 g and 0.4 g, it is appropriate to implement the stress/strain monitoring of the pipe body or the structural monitoring of the crossing project [21].
# Consolidating ICESat-2 Ocean Wave Characteristics with CryoSat-2 during the CRYO2 ICE Campaign
## 2 Data
### In Situ CryoSat-2 Correction  
As CryoSat-2 has been in operation for a longer amount of time than ICESat-2, we are able to compare ICESat-2 SWH observations across the oceans with validated CryoSat-2 observations at in situ stations for a longer time series. We have compared CryoSat-2 with in situ station observations from 31 July 2010 (corresponding to CryoSat-2 cycle 005) to 21 May 2021 (corresponding to CryoSat-2 cycle 144). Validation with in situ stations has been carried out at five locations, which are depicted as white stars in Figure 1, with data from the North-West Shelf Data Portal [20]. The stations are located at ocean depths greater than 1 km, except one station in the North Sea that is located at a depth of 96 m. Information on the stations used is listed in Table 1 in the Appendix A. Observations from CryoSat-2 are used if they are closer than 50 km to the station location, and the average SWH is computed for each pass, which gives 2124 observations below 12 m. The in situ stations record wave heights continuously, and from this time series, the SWH is determined for every hour. The maximum time difference between the in situ stations and CryoSat-2 is therefore one hour.  
The resulting validation of CryoSat-2 using the in situ stations can be seen in Figure 2, which shows fairly good agreement between CryoSat-2 and the in situ stations, with a correlation of 0.983 and a root mean squared error (RMSE) of 0.308 m. The red points plotted in Figure 2 are the 0.1%-quantiles of the in situ station SWH observations. They are plotted against the same quantiles determined using CryoSat-2, and the plot shows good agreement between the two data sources. In the following comparison with ICESat-2, the CryoSat-2 data is corrected with reference to the linear fit between the observations. The linear fit used is \(SWH_{\text{CS2}-corrected}=1.026\cdot SWH_{\text{CS2}}-0.068\) because the in situ data are used as a baseline for the fit. This correction is done to allow for a better validation of the altimeter measurements of ICESat-2.  
Figure 2: Comparison of CryoSat-2 significant wave height (SWH) observations and in situ stations to show any bias or change in the altimeter measurements compared to the true measured data. Using 2124 observations, a mean difference of \(-0.009\) m and an root mean squared error (RMSE) of 0.308 m were found.
# Performance Analysis of a New Non-Orthogonal Multiple Access Design for Mitigating Information Loss
## 3 Proposed Scheme
### Proposed Receiver Design  
Figure 3 shows the block diagram of the proposed receiver design. User 2 can obtain \(s_{2}\) by decoding the received signal, and \(b_{2}\) is given by demodulating \(s_{2}\). In contrast, user 1 can obtain both \(s_{2}\) and \(s_{\mathrm{c},2}\) by decoding the received signal. Similar to user 2, user 1 can obtain \(b_{2}\) by demodulating \(s_{2}\). User 1 can then perform SIC to remove \(s_{\mathrm{c},2}\) from the received signal and obtain \(s_{\mathrm{c},1}\). \(s_{\mathrm{c},1}\) and \(s_{\mathrm{c},2}\) are demodulated to \(b_{\mathrm{XOR},1}\) and \(b_{\mathrm{XOR},2}\), respectively, which are then serially combined to \(b_{\mathrm{XOR}}\). Finally, user 1 can obtain \(b_{1}\), which is the XOR logical operation value of \(b_{2}\) and \(b_{\mathrm{XOR}}(b_{2}\oplus b_{\mathrm{XOR}})\). Since user 1 and user 2 both carry information in \(s_{2}\), the data rate of user \(k\) in the proposed system is given as follows:  
\[\bar{R}_{k}=\bar{\lambda}_{k}\log_{2}\Bigl{(}1+\rho|h_{k}|^{2}\Bigr{)}. \tag{10}\]  
As user 1 and user 2 share common information, the amount of information in a single resource block is the same for both. Furthermore, as \(N\) increases, the proportion of redundancy decreases. Therefore, \(\bar{\lambda}\) is given as follows:  
\[\bar{\lambda}_{1}=\bar{\lambda}_{2}=\frac{N}{1+N}. \tag{11}\]  
Figure 3: Functional block diagram of the proposed receivers for two users.  
Figure 2: Functional block diagram of the proposed transmitter.
# Wind Turbine Enhancement via Active Flow Control Implementation
## 4 Results
### Momentum Coefficient \(C_{\mu}\) Optimization
As the momentum coefficient increases, the jet becomes more effective in energetically injecting air; a delay in the boundary layer separation is to be expected under these conditions. However, at excessively high momentum coefficients, where the jet velocity is very high, boundary layer tripping is likely to occur, and the boundary layer separation is promoted. The drag coefficient graph, Figure 10b, reveals a minimum value at \(C_{\mu}=0.005\), but it is followed by a slightly larger \(C_{d}\) value for \(C_{\mu}=0.007\). In terms of aerodynamic efficiency, Figure 10c demonstrates that the maximum efficiency is achieved at \(C_{\mu}=0.005\). This reinforces the selection of the momentum coefficient that yields maximum lift (\(C_{\mu}=0.007\)), as it also exhibits a high efficiency, almost the same as the one obtained with \(C_{\mu}=0.005\). Note that the respective peak-to-peak values for \(C_{l}\) and \(C_{d}\) are also implemented in Figure 10. Note as well that all peak-to-peak amplitudes obtained for the lift and drag coefficients, regardless of the momentum coefficient considered, are smaller than the respective peak-to-peak amplitudes obtained in the baseline case simulations; see Table 6, clearly indicating a decrease of the dimension of the downstream vortical structures, which is linked with a delay of the boundary layer separation. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(C_{\mu}\) & \(C_{l}\) & \(C_{d}\) & \(\eta\) & \(PTP_{C_{l}}\) & \(PTP_{C_{d}}\) \\ \hline
0.0001 & 1.947 & 0.073 & 26.678 & 0.122 & 0.047 \\
0.0003 & 1.926 & 0.087 & 22.169 & 0.154 & 0.063 \\
0.0005 & 2.000 & 0.089 & 22.550 & 0.244 & 0.095 \\
0.0007 & 2.006 & 0.086 & 23.279 & 0.237 & 0.091 \\
0.001 & 2.203 & 0.068 & 32.299 & 0.222 & 0.082 \\
0.003 & 2.305 & 0.050 & 46.342 & 0.188 & 0.075 \\
0.005 & 2.352 & 0.048 & 48.982 & 0.160 & 0.064 \\
0.007 & 2.450 & 0.050 & 48.712 & 0.144 & 0.046 \\
0.01 & 2.269 & 0.069 & 33.084 & 0.086 & 0.039 \\ \hline
**BCS S54** & 1.562 & 0.106 & 14.71 & 0.281 & 0.104 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Time-averaged \(C_{l}\), \(C_{d}\), \(\eta\) and peak-to-peak \(PTP_{C_{d}}\), \(PTP_{C_{d}}\) aerodynamic values obtained for the different \(C_{\mu}\) considered. Jet inclination angle (\(\theta_{\rm jet}=10\) deg. ), forcing frequency (\(F^{+}=1\)), groove width \(h=0.005C\), and groove position \(x/C=0.4\). For comparison, the baseline case values are presented in the last row. (Red color indicated the optimum values).
# CF-mMIMO-Based Computational Offloading for UAV Swarms: System Design and Experimental Results
## 3 UAV Formation Control Technologies
### Solution Based on Flocking Control Algorithm  
The algorithm uses three rules, separation, aggregation, and speed alignment, to ensure that the drone group maintains a fixed distance and constant speed during flight, avoids collisions, and achieves the separation and reorganization of any individual. Compared with traditional centralized or distributed formation control methods, the Flocking algorithm shown in Algorithm 1 has better flexibility and robustness, can adapt to complex environmental changes, and improves the coordination ability and task execution efficiency of the drone swarm.  
```
Input: Number of agents: \(N\) Initial position and velocity: \((\mathbf{p}_{i},\mathbf{v}_{i})\) for each agent \(i\) Perception radius: \(R\) Behavior weights: \(k_{\text{sep}},k_{\text{coh}},k_{\text{align}}\) Motion constraints: \(v_{\text{max}}\), time step \(\Delta t\) Output: Updated position and velocity of all agents over time
1 foreachitem setdo
2 foreachagent \(i=1\) to \(N\)do
3 Step 1: Find Neighboring Agents; \(\mathcal{N}_{i}\leftarrow\{j\
eq i\mid\|\mathbf{p}_{i}-\mathbf{p}_{j}\|\leq R\}\);
4 Step 2: Compute Acceleration Terms; \(\mathbf{a}_{\text{sep}}\leftarrow\mathbf{0}\), \(\mathbf{a}_{\text{coh}}\leftarrow\mathbf{0}\), \(\mathbf{a}_{\text{align}}\leftarrow\mathbf{0}\);
5 if\(\mathcal{N}_{i}\
eq\emptyset\)then
6\(\mathbf{f}_{\text{sep}}\leftarrow\mathbf{0}\);
7 foreach\(j\in\mathcal{N}_{i}\)do
8\(\Delta\mathbf{p}\leftarrow\mathbf{p}_{i}-\mathbf{p}_{j}\);
9\(d\leftarrow\|\Delta\mathbf{p}\|\);
10 if\(d>0\)then
11\(\mathbf{f}_{\text{sep}}\leftarrow\mathbf{f}_{\text{sep}}+\Delta\mathbf{p}/d\);
12
13\(\mathbf{a}_{\text{sep}}\gets k_{\text{sep}}\cdot\mathbf{f}_{\text{sep}}\);
14\(\mathbf{p}\leftarrow\text{average}\{\mathbf{p}_{j}\mid j\in\mathcal{N}_{i}\}\);
15\(\mathbf{a}_{\text{coh}}\gets k_{\text{coh}}\cdot(\tilde{\mathbf{p}}- \mathbf{p}_{i})\);
16\(\mathbf{\tilde{v}}\leftarrow\text{average}\{\mathbf{v}_{j}\mid j\in\mathcal{N} _{i}\}\);
17\(\mathbf{a}_{\text{align}}\leftarrow k_{\text{align}}\cdot(\tilde{\mathbf{v}}- \mathbf{v}_{i})\);
18
19 Step 3: Update Motion State; \(\mathbf{a}_{\text{total}}\leftarrow\mathbf{a}_{\text{sep}}+\mathbf{a}_{\text{ coh}}+\mathbf{a}_{\text{align}}\);
20\(\mathbf{v}_{i}\leftarrow\mathbf{v}_{i}+\mathbf{a}_{\text{total}}\cdot\Delta t\);
21 if\(\|\mathbf{v}_{i}\|>v_{\text{max}}\)then
22\(\mathbf{v}_{i}\leftarrow(\mathbf{v}_{i}/\|\mathbf{v}_{i}\|)\cdot v_{\text{ max}}\);
23\(\mathbf{p}_{i}\leftarrow\mathbf{p}_{i}+\mathbf{v}_{i}\cdot\Delta t\);
```  
**Algorithm 1**Flocking-based swarm motion control.
## 4 Discussion
In peach aroma biosynthesis, _PpERF5_ and _PpERF7_ bind together to form a protein complex that enhances the transcription of LOX4, which plays a key role in volatile biosynthesis during peach fruit ripening [35]. In the petals of sweet osmanthus, _O/ERF61_ can positively regulate the expression of _O/CCD4_ and increase the production of \(\beta\)-ionone [36]. In our study, two ERFs were discovered: _VvERF053_ and _VvERF4_. Based on the Pearson correlation coefficient analysis, they were positively related to the expression of the structural genes _VvOMR1_ and _VvLPD2_, while _VvERF053_ was found to be negatively related to most of the aromatic substances detected in this study. _MYB_-related transcription factors also play crucial roles in plant development and the response to environmental stress through promoting their target gene expression [37; 38; 39]. Some reports have focused on the regulation effect of MYB transfactors in aromatic substance biosynthesis. In petunia, _PhMYB4_ can suppress the production of benzenoid/phenylpropanol biosynthesis by regulating the expression of their downstream genes [40]. In apples, _MdMYB85_ directly interacts with the promoter region of _MAAT1_ and enhances ester aroma synthesis [23], while in sweet osmanthus, many MYB transducers have been discovered, among which _O/MYB1R201_ shows transcriptional activity in regulating the expression of genes related to floral volatile organic compounds [41]. _HcMYB_ genes in _H. coronarium_ can promote the key structural genes related to terpenoid and benzenoid biosynthesis [42]. In our study, two _MYBs_, _VvMYB46_ and _VvMYB340_, were discovered. Based on the Pearson correlation coefficient analysis, _VvMYB46_ was positively related to the expression of the structural genes _VvOMR1_ and _VvAK2_ and the production of \(\alpha\)-Pinene (KMW0148) and 2-methyl-1-methylethyl-Bicyclo-hexan-2-ol(KMW0257). _VvMYB340_ was positively related to the expression of the structural genes _VvSHM7_ and _VvAPS1_ and negatively related to the expression of the structural gene _VvAK2_.
## 4 Discussion
From the dimensioning point of view, the used dimensioning cooling power of space cooling (45 W/m\({}^{2}\)) seems to be enough in average and extreme climate conditions, and there is no need to increase the peak power of space cooling due to climate change. In this study, average ground temperature and therefore domestic cold water temperature is assumed to be the same in the future regardless of global warming. The reason is to ease the analysis of the parameters affected more strongly by climate change. The system losses, efficiencies, and COP of the cooling systems are assumed to be the same now and in the future. This is done to facilitate the comparison and to show clearly the effect of climate change. The performance of HVAC systems may improve in the future, as according to studies performed during recent decades, COP of heat pumps increased by 2% annually [51]. Moreover, in the 2000s, COP of commercial air/water heat pumps increased almost linearly from 3.5 to 5.1 [52]. Any improvement in the energy efficiency of heating and cooling systems affects the purchased energy (district heating and electricity) consumption, and the amount of increase in cooling electricity and decrease in district heating may vary in the future. Thus, even if the cooling demand increase, the technical development of systems may partly compensate the increase. Concerning the increase in the cooling electricity consumption in the future climate, a change from the common practice of split cooling units in apartment buildings to district cooling may be considered. In that case, fan coil units or radiant cooling (ceiling or floor) may be in use. Fan coil units are not limited by power and capacity. For the usage of radiant cooling systems, these limitations must be noted. However, different cooling systems should be considered to carry the increase of cooling demand in the future.
## 4 Deployment and Model Training of the PLA Scheme
### Phase 2: Continuous Authentication and Anomaly Detection  
Phase 2 introduces continuous authentication. In this stage, pre-trained machine learning (ML) and deep learning (DL) models analyze incoming sensor data in real-time. Each record is evaluated for deviations in RSSI, BL, or altitude. If any attribute falls outside acceptable thresholds, the sensor is flagged as spoofed, and its data transmission is blocked. Legitimate data are processed, ensuring uninterrupted operation. Continuous authentication also enables model retraining as new data are collected. This adaptability allows the system to respond effectively to evolving threats and dynamic environments. By continually refining the model based on real-time data, the scheme enhances its ability to differentiate between legitimate and non-legitimate sensors. Phase 2 focuses on real-time detection and prevention of spoofing attacks, as shown in Figure 4.
# Improved Algorithm for Vehicle Bottom Safety Detection Based on YOLOv8n: PSP-YOLO
## 1 Introduction
In 2018, YOLOv3 added multi-scale prediction and an improved loss function, strengthening its ability to detect small objects. In 2020, YOLOv4 adopted the CSPDarknet53 backbone network and incorporated various optimization strategies, improving performance on large-scale datasets. In 2022, YOLOv6 introduced adaptive convolution kernels and optimized learning strategies, boosting detection accuracy and speed. Shortly thereafter, YOLOv7 was released, further optimizing training strategies and improving small-object detection. In 2023, YOLOv8n, the latest version in this series, was introduced. It offers fast real-time object detection with low model complexity, but its ability to extract features in small object scenarios remains limited. To enhance the performance of YOLOv8n, reduce parameter count, and achieve high-precision real-time detection, several improvements were made. First, a P2 small-object detection head was introduced, significantly enhancing YOLOv8n's ability to detect small targets. Second, the SPD-Conv module was added to the backbone network to enable feature extraction across multiple spatial scales, thereby improving detection performance for low-quality samples in small-target scenarios. Finally, the PConv lightweight module was incorporated into the feature fusion network, effectively reducing the model's parameter count and computational load while maintaining strong performance in detecting occluded dangerous objects. The structure of this paper is arranged as follows:  
* Section 1 introduces the development of vehicle undercarriage detection and the research conducted by peers in this field and presents the improved algorithm based on YOLOv8n. * Section 2 presents the modified YOLOv8n network structure and provides a detailed explanation of the three new modules' structures and functions. * Section 3 describes the construction of the dual dataset, the training process, experimental parameter settings, and evaluation metrics. * Section 4 discusses the impact of each module on the model's performance and demonstrates the performance of the improved model. * Finally, Section 5 summarizes the excellent performance of the improved model and looks forward to future work directions.
## 5 Discussion
### Advantage and Challenge of Glove-Type Sensor  
In contrast to the EMG sensor, the glove-type sensor is believed to offer ease of use, comfort, and wearability [46; 47; 48; 49; 50]. This condition arises due to its proximity to daily life, where everyone is already familiar with it and considers it a part of daily clothing. Therefore, an approach utilizing a glove as the base of constructing a sensing technology is preferable. Several studies have introduced the advantages of the glove system for sensing purposes, especially in home-based rehabilitation. Glove-based devices are commonly equipped with typical sensors such as flex sensors, force-sensitive resistors (FSR), and accelerometers. Thus, it enables a logging function for several finger-related parameters, such as joint angles, finger movements, and hand gestures or postures, making it more suitable for kinematic analysis. Especially in rehabilitation, this approach is ideal for gesture recognition, motor training, and hand monitoring, providing clinicians with essential data.  
Along with its practicality for patients' self-implementation and several benefits in finger-related parameters, this approach is limited in capturing only the peripheral products originating from the muscle activity that controls the finger. The glove-based sensing method currently remains incapable of directly detecting the muscle condition linked to a finger's posture, let alone the force of contraction that is crucial in the monitoring process.  
Figure 17: Average of the estimated RMS of EMG of RF model across eight channels of the first finger condition in perceived easy and difficult cylinders.  
Following the potential of a stretch sensor mentioned in Section 2.4, the proposed method utilized the RMS of the EMG signal as the ground truth, allowing the system to estimate the muscle contraction force based on input from the stretch-sensor glove. Therefore, the output of the proposed method would contribute to improving the monitoring function in home-based rehabilitation. Furthermore, the final product of this study may lead to the broader implementation of the stretch-sensor glove, which can output the correlated muscle contraction force. Accordingly, the system's good estimation performance served as the primary focus of this study.