esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The investigation of the possible influence of the different magnitude thresholds above which the samples of \(\Delta T\) can be considered as statistically independent, or in other words the possibility of a crossover regime in magnitudes in which the respective \(\Delta T\) change their temporal behavior from correlated to uncorrelated, is important information with many potential uses. For example, it is useful for the selection of adequate earthquake data sets for stochastic point processes modeling. For such applications, four characteristics must be qualified: (1) The numbers of events occurring in two different time intervals are mutually independent (thus the aforementioned threshold \(M_{thr}\) has to be detected); (2) The probability distribution of the number of events within a specific time interval depends only on the length of the interval; (3) Two or more events occur simultaneously; and (4) especially for renewal models, the occurrence of the next event depends only on the time elapsed from the last event and not on the full history [20]. The main objectives of the current study are two. Firstly, the identification of the possible correlations between \(\Delta T\) in earthquake data sets with different magnitude thresholds (\(M_{thr}\)). This concerns moderate to large earthquake (\(4.1\leq M_{w}<5.2\)) time intervals in 10 distinctive sub-areas of the Greek territory, and the detection of possible \(M_{thr}\) which designates the temporal crossover between correlated and statistically independent earthquakes. Both procedures are implemented via the application of time series analysis tools, which are widely used in seismicity studies (e.g., [21]). Specifically, in the current study, the Autocorrelation and Partial Autocorrelation Functions along with the Ljung-Box \(Q\)-test are applied. For further investigation of the memory of the \(\Delta T\) data sets, Detrended Fluctuations Analysis is also applied. The second goal is seeking the statistical distribution that optimally fits the \(\Delta T\) data sets for each sub-area for earthquakes with \(M\geq M_{thr}\) which emerge from the application of the previous methodology. For this purpose, we tested four statistical distributions (Weibull, Lognormal, Gamma and Exponential) that are the most commonly used in investigations of earthquake \(\Delta T\) data sets for the identification of the best-performing distribution.
## 2 Related Work
### Attention Mechanism  
In the task of remote sensing image change detection, the attention mechanism achieves the adaptive enhancement of features in changed areas and dynamic suppression of features in unchanged areas by modeling the intrinsic semantic relationships of images. According to existing studies, the attention mechanism is mainly divided into four categories--spatial attention [23; 24], channel attention [25; 26], temporal attention [27; 28], and self-attention [29; 30; 31; 32; 33; 34]--which exhibit better feature representation capabilities compared to traditional convolutional methods.  
In the spatial dimension, [PERSON] et al. [23] proposed using a spatial attention mechanism to decouple the semantic confusion between changed pixels and building backgrounds, while fusing the channel attention mechanism with the Atrous Spatial Pyramid Pooling (ASPP) module to effectively enhance the contextual correlation of multi-scale features. In the channel dimension, [PERSON] et al. [25] constructed a parallel channel-spatial attention mechanism (ARM) which significantly improved the model's perception of subtle differences in remote sensing images and achieved the precise extraction of edge details of changed targets through the collaborative optimization of the cross-scale feature fusion module (CSFM). In terms of temporal modeling, [PERSON] et al. [27] improved lightweight temporal memory module (TTM) design based on the LSTM architecture, which effectively breaks through the weight-sharing limitations of traditional recurrent neural networks while maintaining the efficient transmission of spatio-temporal features. In the research field of self-attention mechanisms, existing technical routes primarily present two paradigms: feature extraction architectures based on self-attention mechanisms [29; 30; 31] and differential feature enhancement architectures based on CNN feature extraction [32; 33; 34]. Although the former enables global semantic modeling, its high computational complexity has prompted current research to favor hybrid designs integrating convolution and self-attention to balance performance and efficiency.
# Joint Optimization of Service Migration and Resource Allocation in Mobile Edge-Cloud Computing
## 3 System Model and Problem Definition  
As illustrated in Figure 1, we examine an edge-cloud collaboration system comprising ESs and a single cloud server. In this system, users continuously move and offload tasks generated by their mobile devices to the edge servers for execution. The ESs then send the task information and users' location data to a centralized scheduling and resource allocation system, which is responsible for making decisions regarding migration and resource allocation. By transmitting only the necessary information to the centralized system for decision making, this approach effectively reduces network load. At the same time, it allows for easier access to global network information, enabling more efficient and reasonable resource allocation strategies [30].
## 3 Target Operational Requirements  
The XRP shall output the TSAT and EIBT, consistently with the previous equations. In order to compute such variables, the XRP is in charge of computing and optimizing the TXET. Moreover, the  
Figure 1: Reference Estimated Taxi in Time (EXIT) breakdown. ELDT, Estimated Landing Time; AMAN, Arrival Manager; ERET, Estimated Runway Exit Time; TXET, Target Taxiways Engagement Time; EIBT, Estimated In-Block Time.  
Figure 2: Reference Estimated Taxi-Out Time (EXOT) breakdown. TWR; TSAT, Target Start-up Approval Time; TOBT, Target Off-Block Time; CDM, Collaborative Decision-Making; EAOT, Estimated Apron-Out Time; TTOT, Target Take-Off Time.  
XRP shall also suggest the taxi route each aircraft shall follow. The XRP thus outputs a timed taxi route, which is a sequence of \"vertices\", _i.e._, points of the airport's taxiway system, which the aircraft shall follow in the specified order. Each vertex of the sequence has a time-tag, which specifies at which time epoch the aircraft is expected to reach, leave or cross the point. The TXET is obtained by the difference of the relevant vertices' time tags. The constraints to which the timed taxi route shall comply are:  
* The first and last vertices shall be consistent with the apron exit point/runway exit point and holding position/apron entry point for outbound/inbound traffic, respectively.
* The route shall be consistent with the airport topology, with the current airport configuration, e.g., in terms of runways used for landings, runways used for departures, taxiways closed or restricted due to weather or maintenance operations, with airport taxi regulations, depending on weather and aircraft parameters, such as wingspan, gross weight, _etc._
* All routes shall be consistent with the expected airport surface traffic, e.g., no vertices can receive two aircraft concurrently.
* Timing shall be consistent with ELDT for arrivals and with TOBT and TTOT for departures, via Equations (1) and (2).  
Feasible timed taxi routes shall thus comply with all the previous constraints. Among feasible timed routes, the algorithm shall determine the optimal one. In order for a timed route to be optimal, TXET shall be minimized. This allows gaining benefits in terms of the safety of airport surface operations (less concurrently moving aircraft on the surface implies reduced potentials for conflicts, reduced controller workload, _etc._) and gaining cost/environmental benefits (minimizing taxi times allows for reduced fuel consumption). Furthermore, conflicts of taxi routes shall be avoided if feasible or else minimized to the minimum possible extent. The resulting XRP input-output interface is summarized in Figure 3.  
Figure 3: Taxi route planner input/output interface. XRP, Taxi Route Planner.
# Assessing Drivers' Physiological Responses Using Consumer Grade Devices
## 4 Discussion and Conclusions
### HRV Analysis  
The results of HRV analysis based on mean and median tests did not show any statistically significant differences among the tested phases. However, the SDNN and SDANN signals showed that it is possible to differentiate between driving and non-driving situations, and to differentiate between different levels of driving demand.  
When comparing the standard deviation (SDNN) of HRV, the analysis showed that E4 can differentiate only between non-driving and driving, but it cannot differentiate between different levels of driving demand, where drivers had to move their hands in order to steer the wheel. On the other hand, the analysis showed that with the signals recorded with Faros 360 it was possible to differentiate between the two different levels of driving difficulty. We speculate that the different results could be a result of the PPG limitations discussed earlier. Another possibility is that the driving demand did not differ significantly, and the sensors could not detect any differences because of the small differences in the driving demand.  
The analysis of the HRV variables for shorter time frames (SDANN, SDNN index) showed statistically significant differences only in the values of SDANN, which suggests that changes in activities with periods longer than 10 s can be detected. However, there were no statistically significant differences in the SDNN index results, suggesting that neither E4 nor Faros can detect changes in different driving difficulties, based on recordings shorter than 10 s.  
The analysis revealed statistically significant differences in RMSSD and pNN50 between the baseline and driving phases only in the data acquired with E4 but not in the data acquired with Faros although the HRV signal was measured with both devices simultaneously. We believe that the reason lies in E4's limitations. As described in the third paragraph of the \"PPG limitations\" subsection, when there are missing samples in a sequence, successive differences cannot be determined. Therefore, wecould perform analysis only on the signals measured with Faros, which however did not provide statistically significant results.  
Based on these results, we can conclude that from the observed HRV signals, only SDNN or SDANN can be used for differentiating between the two observed levels of driving demand. However, we cannot rule out the possibility that the differences could be captured also in the other HRV variables, which could be investigated, for example, by using longer trials for each condition. Furthermore, it is better to use Faros than E4 as motion artefacts influence the measurement process and collected data.
## 8 Current Challenges in the Development of 3D-Printed Prostheses
### Regulation and Standardization  
The lack of clear regulations and standardization in the use of 3D printing for prosthetics is another major challenge. Three-dimensional-printed prostheses must meet strict quality and safety standards before being used by patients, but current regulations vary widely between countries. In addition, the personalized nature of these prostheses complicates the regulation process, as each device must be adapted to the specific needs of the user.  
Setting international standards for the manufacture and use of 3D-printed robotic prosthetics is crucial to ensure their quality and safety. Regulatory organizations should collaborate with engineers, manufacturers, and healthcare professionals to develop guidelines that balance technological innovation with patient protection [6].
# Effect of Controlled Expiratory Pressures on Cerebrovascular Changes During Valsalva Maneuver
## 3 Results
### Comparison of Blood Velocity Changes  
The PSV significantly increased after both 30 and 40 mmHg VM (Table 3). The change in PSV significantly increased in POST2 compared to PRE for both 30 and 40 mmHg VM, and no significant change was observed in POST1. For 30 mmHg, there was a significant increase of 5.36 cm/s (T = \(-\)2.82, \(p=0.031\)) in POST2 compared to PRE, and for 40 mmHg, there was a significant increase of 4.77 cm/s (T = \(-\)2.72, \(p=0.04\)) in POST2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Outcome** & **Testing** & **Mean \(\pm\) SD** & \multicolumn{2}{c}{**ADIFF** (Mean \(\pm\) SE)**} & \multirow{2}{*}{**T**} & \multirow{2}{*}{\(p\)**} \\  & **Variable** & & & & & \\ \hline \multirow{3}{*}{30 mmHg} & Stiffness index & PRE & 6.67 \(\pm\) 2.23 & \multirow{3}{*}{1.76 \(\pm\) 0.42} & \multirow{3}{*}{4.24} & \multirow{3}{*}{\(<\)0.001 *} \\  & POST & 4.91 \(\pm\) 1.58 & & & & \\ \cline{3-6}  & PWV (m/s) & PRE & 5.19 \(\pm\) 0.84 & \multirow{3}{*}{0.69 \(\pm\) 0.17} & \multirow{3}{*}{4.05} & \multirow{3}{*}{\(<\)0.001 *} \\  & POST & 4.50 \(\pm\) 0.69 & & & & \\ \hline \multirow{3}{*}{40 mmHg} & Stiffness index & PRE & 6.00 \(\pm\) 2.79 & \multirow{3}{*}{0.79 \(\pm\) 0.40} & \multirow{3}{*}{2.01} & \multirow{3}{*}{0.056} \\  & POST & 5.21 \(\pm\) 3.21 & & & & \\ \cline{3-6}  & PWV (m/s) & PRE & 4.90 \(\pm\) 0.99 & \multirow{3}{*}{0.34 \(\pm\) 0.14} & \multirow{3}{*}{2.43} & \multirow{3}{*}{0.022 *} \\ \cline{3-6}  & POST & 4.56 \(\pm\) 1.14 & & & & \\ \hline \hline \end{tabular} Abbreviations: CCA, common carotid artery; PWV, pulse wave velocity; SD, standard deviation; SE, standard error. *\(p<0.05\).  
\end{table}
Table 2: Changes in CCA function before and after VM.  
In contrast, significant changes in RI occurred only in the 40 mmHg VM. The RI significantly increased by 0.015 in POST1 compared to PRE (T = \(-\)3.11, \(p\) = 0.017) and decreased by 0.004 in POST2 compared to POST1, but the difference was not significant (T = 1.07, \(p\) = 0.573). In contrast, at 30 mmHg VM, there was an average tendency to increase after the VM; however, the difference was not significant (F = 3.09, \(p\) = 0.069).  
The HR decreased significantly only at 40 mmHg VM. HR showed a significant decrease of 4 bpm in POST1 compared to PRE (T = 3.84, \(p\) = 0.003), and it tended to significantly increase by approximately 2 bpm in POST2 compared to POST1 (T = \(-\)2.93, \(p\) = 0.025). Even at 30 mmHg VM, the HR tended to decrease after VM. It decreased by approximately 2 bpm in POST1 compared to PRE and tended to rise again by approximately 1 bpm in POST2 compared to POST1; however, the difference was not significant (F = 2.28, \(p\) = 0.129).
## 4 Discussion
The data in Table 3 and Figure 3 show significant increases in both antioxidants after Gallium-68 injection, and they clearly show that additional protective antioxidant supplementation before injection leads to enhanced resistance. Treatment with _A. angelica_ and _G. biloba_, i.e., G2 and G3, increased hemoglobin's oxidative stress resistance, and combined treatment with both antioxidants with the same doses, time, and duration resulted in better outcomes. Autoxidation of hemoglobin is the rate at which hemoglobin is converted to a non-functional derivative due to its direct exposure to reactive species. Hemoglobin resistance to this process is a factor that can describe healthy hemoglobin [25]. Recently, hemoglobin engineering studies have focused on how to improve hemoglobin health by reducing its autoxidation rate [26]. Spectrophotometrically, the methemoglobin derivative has a characteristically higher energy state of its iron atoms, with a peak at 630 nm, which is completely absent in a normal healthy state [27]. In normal incubation circumstances, a dramatic methemoglobin formation rate means that a blood sample contains more oxidized activity [28]. Our findings, presented in Table 4 and Figure 4, show a higher autoxidation rate in rabbits injected with Gallium-68 with no prior treatment, whereas rabbits that received protective antioxidant doses exhibited lower autoxidation rates. It should be highlighted that in Group 4, the rabbits were treated with a combined dose before injection, and the autoxidation rate of this group was approximately normal. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & & & & & & & \\ \hline
**Time (h)** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline Control & 0.534 & 0.897 & 1.345 & 1.789 & 1.998 & 2.456 & 2.985 & 3.345 \\ G1 & 0.678 & 2.567 & 4.678 & 5.678 & 6.875 & 7.456 & 8.567 & 9.456 \\ G2 & 0.678 & 1.087 & 1.945 & 2.789 & 3.998 & 3.456 & 3.985 & 4.345 \\ G3 & 0.576 & 1.234 & 1.876 & 2.998 & 4.998 & 3.956 & 4.185 & 5.345 \\ G4 & 0.511 & 0.697 & 1.605 & 1.78 & 2.345 & 2.156 & 3.985 & 3.945 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Hemoglobin autoxidation rate of rabbits exposed to radiation by Gallium-68 injection, injected with \({}^{68}\)Ga protective treatment with _Angelica archangelica_ (G2) and _Ginkgo biloba_ (G3), as well as those with the combined dose as compared with the control (G4). Figure 4: Hemoglobin autoxidation rate in rabbits injected with Gallium-68 with and without the application of protective antioxidants, single and combined doses, as compared with the control.
## 2 Spectral Functional Equations in an Angular Region Filled by Arbitrary Linear Media
 \tag{53}\]Focusing our attention on the RHS for each term of each equation in (52), using dummy subscripts and going back to the representation in (51), we have the following regularized expression:  
\[ \begin{array}{l}\frac{1}{2\pi\eta}\int\limits_{\gamma_{tt}}\frac{B(t)H_{+}(-m(t ))}{t-\eta}dt=\frac{1}{2\pi\eta}\int\limits_{\gamma_{tt}}\frac{(B(t)-B(\eta))H _{+}(-m(t))}{t-\eta}dt+\frac{B(\eta)}{2\pi\eta}\int\limits_{\gamma_{tt}}\frac {H_{+}(-m(t))}{t-\eta}dt\\ =\frac{1}{(2\pi\eta)^{2}}\int\limits_{-\infty}^{\infty}\int\limits_{-\infty} ^{\infty}\frac{(B(t)-B(\eta))H_{+}(\eta^{\prime})}{(t-\eta)(\eta^{\prime}+m(t) )}dt\;dt\eta^{\prime}+\frac{B(\eta)}{(2\pi\eta)^{2}}\int\limits_{-\infty}^{ \infty}\int\limits_{\gamma_{tt}}\frac{1}{(t-\eta)(\eta^{\prime}+m(t))}dtH_{+} (\eta^{\prime})d\eta^{\prime}+n.s.\,terms.\end{array}  \tag{54}\]  
Given the expressions of \(m_{i}(\eta)\) in (24)-(25) with \(Im[m_{i}(t)]<0\) in lossy media (Figure 4), Equation (54) requires the computation of  
\[M_{e}(\eta,\eta^{\prime})=\int\limits_{\gamma_{tt}}\frac{1}{(t-\eta)(\eta^{ \prime}+m(t))}dt, \tag{55}\]  
which can be performed either numerically or analytically while paying attention to the branch cuts of \(m(t)\). Furthermore, in (54) we also need to consider n.s. singularities related to the field. The validity of the estimation of \(M_{e}(\eta,\eta^{\prime})\) extends to complex values of \(\eta^{\prime}\) as long as \(\eta^{\prime}\) does not cross the singularity line determined by the behavior of \(-m(t)\) for \(t\in\mathbb{R}\), as shown in Figure 4.  
The expressions in (53) and (54) are regularized integral terms, as their kernels are compact. Moreover, they include the n.s. terms of the field components in \(\eta\) and \(-m_{i}\), respectively. The detailed proof of this assertion needs to be performed for specific problems. While numerically implementing the method we observe that one of the main difficulties resides in the correct estimation of kernel functions \(A(\eta),B(\eta)\) for the presence of multivalued functions that need particular attention in their definition and calculation. For simplicity and compactness of discussion, we examine the properties of integral equations in the simple case of a PEC wedge immersed in an isotropic medium in Section 5.2. Equation (51) yields a 4 \(\times\) 4 system of Fredholm integral equations of the second kind by utilizing (52), (53), and (54). This system is expressed in terms of \(H_{oz}(\eta)\), \(H_{op}(\eta)\), \(H_{az}(\eta)\), \(H_{ap}(\eta)\). It is important to highlight that the system only depends on the spectral variable \(\eta\), ensuring that functions do not rely on \(m_{i}\) outside of the integration sign. This property is fundamental to avoid the analysis of unknowns defined in different complex planes (\(\eta\) and various \(m_{i}\)) that are correlated through cumbersome improper sheet properties.
## 7 Conclusions and Prospects  
### Conclusions  
The present study utilizes a steel truss bridge in Fujian Province, China, as its engineering background and conducts a comprehensive technical analysis based on the bridge's actual main beam deflection monitoring data. This analysis includes anomaly detection, anomaly data processing, and data accuracy assessment. The ensuing conclusions are hereby summarized:  
(1) The efficacy of the screening process for anomaly detection was substantiated by the implementation of data selection, correlation heat map, and Gray correlation calculation theories. The anomaly data at the pivotal measurement point 2 (mid-span of the main girder) was localized and investigated, yielding an anomaly rate of 4.06%.  
(2) The ATT-BiLSTM model has been established to address the anomalous monitoring data, with the repair results presented in graphical and correlation coefficient calculation formats. The results indicate that the repaired data at the anomalous location exhibit a trend of amplitude reduction. At the deflection jump point, the repaired data do not fully eliminate the prominence of the jump point, which effectively ensures the characteristics of the data.  
(3) In terms of correlation performance, a comparison of the data restoration results of the ATT-BiLSTM model and the SVR analysis method reveals that the restoration effect of the ATT-BiLSTM model is superior to that of the SVR model.  
(4) The efficacy of the ATT-BiLSTM model was assessed by employing various evaluation metrics, including mean square error, root mean square error, and coefficient of determination. The ATT-BiLSTM model was demonstrated to exhibit superiority in the realm of data monitoring and processing. The efficacy of this model is evidenced by its ability to repair 93.88% of abnormal data, while concurrently exhibiting an RMS error of 0.0226 with actual complete data. This outcome signifies the model's capacity to effectively incorrectly abnormal data, thereby enhancing the reliability of the data.  
The findings of this paper offer a robust scientific foundation for the analysis and evaluation of deflection monitoring data from analogous bridges. Conversely, by modifying the parameters of the ATT-BiLSTM model (e.g., the number of hidden layers, the design of the Attention Mechanism, and the selection of hyperparameters, etc.), the model could be applied to the analysis of other bridges of different types and environmental conditions, and longer monitoring cycles. It is evident that the methodology delineated in this paper exhibits extensive applicability in the domains of data analysis and the evaluation of structural health monitoring.