esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Feature Aggregation Wrapper Module
#### 2.1.1 Feature Propagation  
For previous T frames of the imagery \(\mathrm{I}^{(t-\tau)}\), where \(\tau=1,2,\ldots,\mathrm{T}\), we utilized their features, \(\mathrm{f}^{(t-\tau)}\in\mathrm{R}^{\mathrm{H}\times\mathrm{W}\times 6}\), from the backbone and the corresponding center heatmaps, \(\mathrm{H}^{(t-\tau)}\in\mathrm{R}^{\mathrm{H}\times\mathrm{W}\times\mathrm{C}}\), as the input. Similar to the characteristics of anchor-free models like CenterNet, we first added Gaussian-shaped peaks to all the center points of all the cate  
Figure 5: Overview of the FAW module. The FAW module may adaptively propagate contextual cues from previous T features for object feature enhancement (i.e., T > 1) through a DCN.  
gories, generating the aggregated center heatmap \(\mathrm{H}_{\mathrm{ctr}}^{\mathrm{(t-r)}}\in\mathrm{R}^{\mathrm{H}\times\mathrm{W} \times\mathrm{C}}\). We then generated central attention features, \(\mathrm{f}^{\mathrm{(t-T)}}\), based on \(\mathrm{H}_{\mathrm{ctr}}^{\mathrm{(t-r)}}\) using the following equation:  
\[\mathrm{f}_{\mathrm{ctr}}^{\mathrm{(t-r)}}=\mathrm{H}_{\mathrm{ctr}}^{\mathrm{ (t-r)}}\odot\mathrm{f}^{\mathrm{(t-T)}},\ \tau=1,2,\ldots,\mathrm{T} \tag{1}\]  
where \(\odot\) denotes the Hadamard product, \(\mathrm{f}_{\mathrm{ctr}}^{\mathrm{(t-r)}}\) contains the feature information of the center points in \(\mathrm{H}_{\mathrm{ctr}}^{\mathrm{(t-r)}}\), which can be utilized for the inheritance of object-central features. Simultaneously, to ensure the effectiveness of feature propagation, we employed adaptive fusion weights, \(\mathrm{w}^{\mathrm{(t-r)}}\in\mathrm{R}^{\mathrm{H}\times\mathrm{W}\times 1}\), to measure the significance of the features in each frame, resulting in the weighted central attention feature \(\overline{\mathrm{f}_{\mathrm{ctr}}}\) in the following equation:  
\[\overline{\mathrm{f}_{\mathrm{ctr}}}=\mathrm{concat}\Big{(}\Big{(}\mathrm{w}^ {\mathrm{(t-r)}}\odot\mathrm{f}_{\mathrm{ctr}}^{\mathrm{(t-r)}}\Big{)}\Big{)} \in\mathrm{R}^{\mathrm{H}\times\mathrm{W}\mathrm{f}\times\mathrm{C}},\ \tau=1,2,\ldots,\mathrm{T} \tag{2}\]  
where \(\mathrm{w}^{\mathrm{(t-r)}}\) is a normalized attention map predicted using a set of convolutions in the FAW module.
## 3 Results
### Associations among Sleep Quality, GMV, and WMH in CSVD Patients  
Results from the linear regression analyses revealed that WMH volume was positively associated with bilateral caudate, right sub-gyral of the temporal lobe, and left cingulate gyrus GMV and negatively correlated with mean GMV of the bilateral calcarine cortex in CSVD patients (Supplementary Table S1). Additionally, there was a positive correlation between PSQI score and WMH volume (\(\beta\) = 0.383, 95% CI 0.110 to 0.656, \(p\) = 0.007). Adjusting for depressive symptoms did not attenuate these associations.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Brain Region**} & \multirow{2}{*}{**Hemisphere**} & \multirow{2}{*}{**Cluster Size**} & \multicolumn{4}{c}{**MNI Coordinates**} & \multirow{2}{*}{**Peak F Values**} \\ \cline{4-6}  & & & **X** & **Y** & **Z** & \\ \hline Caudate & R & 387 & 21 & 22.5 & 10.5 & 28.14 \\ Thalamus & R & 282 & 9 & \(-\)16.5 & 15 & 12.24 \\ Calcarine cortex & Bilateral & 198 & 1.5 & \(-\)64.5 & 10.5 & 15.34 \\ Precentral gyrus & L & 177 & \(-\)46.5 & 4.5 & 42 & 12.84 \\ Caudate & L & 148 & \(-\)18 & 24 & 4.5 & 21.13 \\ Orbitofrontal cortex & R & 61 & 37.5 & 45 & \(-\)18 & 11.16 \\ Cingulate gyrus & L & 58 & \(-\)12 & \(-\)24 & 33 & 12.57 \\ Sub-gyral of the & & & & & & \\ temporal lobe & R & 45 & 27 & \(-\)55.5 & 18 & 16.86 \\ \hline \hline \end{tabular}
\end{table}
Table 2: GMV differences among groups.  
Figure 3: ROI post hoc tests. X-axis represents three groups of subjects; Y-axis indicates the mean GMV of each ROI. * Significant at \(p<0.05\) (Bonferroni corrected). ROI—region of interest; GMV—gray matter volume; OFC—orbitofrontal cortex; CSVD—cerebral small vessel disease; HC—healthy controls; GS—good sleepers; PS—poor sleepers.  
In CSVD patients, the overall PSQI score was positively associated with the mean GMV of the bilateral caudate and right sub-gyral of the temporal lobe and negatively associated with the mean GMV of the bilateral calcarine cortex after adjusting for age and sex. However, the association between the PSQI score and mean GMV of the right sub-gyral of the temporal lobe was no longer significant after adjusting for depressive symptoms (model 2) (Table 3). In the fully adjusted model (model 3), the PSQI score was positively associated with the mean GMV of the right caudate and negatively associated with the mean GMV of the bilateral calcarine cortex (Table 3).
# Dynamic Performance of a New-Type PSC I-girder for Railway Bridge Application
## 3 Performance Evaluation on Full-Scale PSC I-girder Specim
### Evaluation of Dynamic Characteristics  
The dynamic test was conducted to verify the validity of the analytical model in Section 2. The natural frequencies of the specimen were compared to the values obtained by moving load analysis. Forced vibration was applied using the exciter shown Figure 15a and by impact through manpower as shown in Figure 15b.  
Figure 14: Shape of 35 m-long full-scale PSC I-girder specimen: (**a**) longitudinal view with 3 segments (12 + 10.9 + 12 m); (**b**) sectional view (A-A); and (**c**) sectional view (B-B).  
Figure 13: Moving load analysis results for span length of 45 m: (**a**) vertical acceleration response; (**b**) vertical displacement response; and (**c**) twist response.  
For the vibration caused by the exciter, the frequency of the forcing excitation was increased stepwise and the behavior of the specimen was observed. Figure 16a plots the acceleration measured at the bottom of the girder. The largest amplitude of the acceleration occurred under excitation frequencies between 4.2 Hz and 4.4 Hz. After having roughly verified the range of the natural frequency of the specimen, impact test was conducted by two men jumping on the girder as shown in Figure 15b to generate free vibration and check the natural frequency. FFT (Fast Fourier Transform) analysis was then performed on the vertical acceleration data acquired during the free vibration of the girder as shown in Figure 16b. The so-obtained natural frequency was 4.2 Hz, which agreed fairly with the analytic value of 4.27 Hz for the span length of 35 m. Figure 16c plots the residual vertical displacement from which a damping ratio higher than 1% was estimated by curve fitting. Accordingly, the value of 1% adopted in the dynamic analysis can be assumed to be valid for the conservative design of the PSC I-girder railway bridge.  
Figure 16: \(Cont.\)  
Figure 15: Forced vibration methods: (**a**) exciter; (**b**) manpower.
# Review: A Survey on Objective Evaluation of Image Sharpness
## 2 Evaluation Methods and Analysis
### Spatial Domain-Based Methods
#### 2.1.2 Edge Detection-Based Methods  
The image edge is the most basic feature of an image, which refers to the discontinuity of the local characteristics of the image [37; 38]. Among the edge detection-based methods, the most widely used algorithms are Canny operator [39], Sobel operator [40], Prewitt operator [41] et al.  
The Canny operator is a multi-level edge detection algorithm. The Sobel operator combines Gaussian smoothing and differential derivatives to detect the horizontal and vertical edges. The Prewitt operator uses the difference generated by the grayscale values of pixels in a specific region to achieve edge detection. The process of image sharpness evaluation based on the Prewitt operator is shown in Figure 2.  
As shown in Figure 3, an image is plotted with the results of the Canny operator, the Sobel operator and the Prewitt operator, respectively.  
[PERSON] et al. [42] proposed a method to detect the edges of an image using the Sobel operator and utilized the image edge width as the sharpness evaluation score. [PERSON] et al. [43] proposed an image-filtering evaluation method based on the Sobel operator and image entropy. [PERSON] et al. [44] used the Canny edge detection algorithm based on the activation mechanism to obtain the image edge position and direction, established the histogram of edge width and obtained the sharpness evaluation metric by weighting the average edge width. The method was proven to have good accuracy and predictive monotonicity. [PERSON] et al. [45] used the Prewitt operator and the Gabor filter to calculate the average gradient of the image to predict the local sharpness value of a fabric surface image.  
Figure 2: The process of the image sharpness evaluation based on the Prewitt operator.  
Among the edge detection-based methods, the Canny operator, the Sobel operator and the Prewitt operator are widely used, and each of these operators has its own advantages. The Canny operator is sensitive to weak edges but computationally intensive; the Sobel operator is fast but susceptible to noise interference; the Prewitt operator is better at extracting the edges of images disturbed by neighboring pixels.
# Preliminary Analysis of the Water Quality Status in an Urban Mediterranean River
## 2 Materials and Methods
### Statistical Analysis  
First, the Shapiro-Wilk's test was performed to assess for normality of distribution of the river water quality parameters due to the small sample size. Then, the [PERSON]'s non-parametric correlation test was employed to reveal the relationships among the water quality parameters. We analyze the following groups of pollutants: nutrients (nitrate-nitrogen as NO3-N (mg/L), nitrite-nitrogen as NO2-N (mg/L), ammonium-nitrogen  
Figure 1: Kifisos catchment and location of the five sampling sites (K Ekv, K 5, K 12, K 17, K MD).  
as NH4\({}^{+}\)-N (mg/L), dissolved inorganic nitrogen as DIN (mg/L), total nitrogen as TN (mg/L), orthophosphate as PO\({}_{4}\)-P (mg/L), total phosphorus as TP (mg/L), pathogens (such as _E. coli_), and physicochemical parameters (dissolved oxygen as DO (mg/L), electrical conductivity as EC (\(\mu\)S/cm), pH, salinity (ppt) and temperature as T (\({}^{\circ}\)C) using monthly water surface samples collected from May 2021 to April 2022 from five sampling sites. The data obtained were subjected to statistical analysis using Statistical Package for Social Sciences (SPSS), version 28 software.  
Box and whisker plots per sampling site were developed for nutrients, electrical conductivity (EC) and temperature (T) using an open-source application, (BoxPlotR, [[http://boxplot.tverslab.com/](http://boxplot.tverslab.com/)]([http://boxplot.tverslab.com/](http://boxplot.tverslab.com/)), accessed on 08 March 2023), to examine the distribution of the measured concentrations. Information about sample sizes was represented by the width of each box, where the widths are proportional to the square roots of the number of observations. Box and whiskers plots present the min-max and median in addition to the first and third quartiles of each variable and site for the entire sampling period.  
Due to the large variability in the _E. coli_ concentrations, the visualization of this pathogen was made via beamplots to provide a clear view of the central tendency and variability of the data. In the beanplots, the individual observations are presented as small white lines. Results were compared with EU recommendations (Directive 2006/7/EC; Annex 1 for inland waters). Comparisons were made based on the Hazen method according to the addendum of the World Health Organization (2009).  
Quality classes were assigned to the sampling sites according to the Hellenic Water Quality Index (HWQI). Finally, [PERSON]'s correlation was applied between the study variables to check for covariances. Correlations were used to identify monotonic relationships between two quantitative parameters simultaneously using the 1-tailed Spearman's rank correlation. The criterion used to check the reliability was the correlation coefficient (\(p\)).
# Regulation of the _Pseudomonas syringae_ Type III Secretion System by Host Environment Signals
## 4 Additional Global Regulators of the T3 SS in _P. syringae_
### Nucleotide Second Messengers in T3 SS Regulation  
Nucleotide second messengers are common intermediates in bacterial signal transduction. These compounds typically exhibit transient spikes in intracellular concentration under select conditions, effecting a temporally delimited response to an inducing stimulus [193]. One such messenger is (p)ppGpp, also referred to as the bacterial alarmone, a signal of bacterial amino acid starvation involved in broadly arresting cellular protein synthesis and redirecting energetic resources towards survival [194]. The synthesis of (p)ppGpp is required for _P. syringae_ pv. _syngae_ B728a to fully express T3 SS genes, including master regulators _hrPL_ and _hrPRS_, and is also required for virulence and epiphytic survival on host plants [195]. Another nucleotide second messenger, cyclic diguanylate (c-di-GMP), additionally influences the T3 SS in _P. syringae_. High intracellular levels of c-di-GMP have been observed to suppress T3 SS expression in _P. syringae_ pv. _syngae_ B728a, while enhancing oxidative stress tolerance [196]. In _P. syringae_ pv. tomato DC3000, the gene encoding a c-di-GMP synthase termed Chp8 is regulated by HrpL and contributes to the virulence and tolerance of host immune defenses by an unknown mechanism [197]. While the role(s) of c-di-GMP in _P. syringae_ virulence is(are) not fully understood, intracellular levels of c-di-GMP may balance T3 SS deployment with environmental stress tolerance, both of which are required for the pathogen to effectively survive in the host environment. High intracellular levels of c-di-GMP additionally suppress flagellar motility in B728a [196]. While other reports suggest that T3 SS and flagellar motility may be inversely regulated in _P. syringae_[161; 163; 173], the inhibitory effects of c-di-GMP on both T3 SS and motility suggest a complex regulatory relationship between these processes.
# Enhanced Label Noise Filtering with Multiple Voting
## 3 Analysis of Decision Point, Error Probability, and Cost for MVFilter  
The multiple-voting based filter (MVFilter) consists of several single-voting based filters (SVFilter). The MVFilter treats data as mislabeled if at least m out of t SVFilters identify these data as mislabeled. Obviously, for different m values, the recognized noises by an MVFilter will be different. The selection of the m value plays an important role in an MVFilter. Because the m value decides the noise identifyingresults, it is called the \"decision point\" in this work. Our goal is to find a way to decide the \"optimal decision point\" to maximize the performance of MVFilter.  
When a filter works on a noisy training dataset, it is usually hard to recognize all the noises perfectly. The errors made by a filter include two types: The first type (E1) occurs when declaring a correctly labeled example as mislabeled and is subsequently discarded. The second type of error (E2) corresponds to declaring a mislabeled example as correctly labeled. For a well designed filter, it is desirable to avoid both E1 and E2 errors. However, conceptually, E1 and E2 are conflicting. To reduce E1 errors, the filter should make a more severe noise detection policy, which tends to increase E2 errors. In MVFilter, the selection of the decision point will influence the probability to make an E1 or E2 error.
# Victim Detection and Localization in Emergencies
## 8 Discussion  
In this section, the results are examined and debated, showing the advantages of using multi-technology fusion for detecting and locating victims when a disaster occurs. While LTE and 5G NR may offer a very gross approximation of the localization, they have serious disadvantages: in a disaster, the fixed infrastructure may be partially or totally damaged; they offer a much lower precision when using RSSI measurements. In this work, the scenario emulated a realistic building collapse to compare the performance of UWB and WiFi in this kind of scenario. We found that WiFi performed better in detection and localization than UWB. However, the use of both technologies is still useful for locating victims. A combination of these high-precision ranging technologies helps to augment the number of ranges, which helps to deal with the interruptions of communication with UWB due to WiFi being stronger. Once the victim is detected, a fleet of drones can approach the place where the victim is located with meter-level precision.  
In this work, we showed how the accuracy decreases when a disaster occurs and the victim is found under rubble, as shown in Figure 5. However, we demonstrated feasible drones that can easily integrate the small chipsets. From the victim's point of view, the latest smartphone trends show that the UWB [7,8] and WiFi [9] chipsets are integrated in several smartphones, and the tendency is to increase this number. Thus, the cost associated with this integration is virtually null.
## 2 Literature Review on the Personalized Recommendation in the MOOC Context
### Recommendation Algorithm Based on Collaborative Filtering  
Collaborative filtering is another widely used method. It is mainly based on the assumption that similar things or people fit well together. Correspondingly, learners who like the same items are more likely to have the same interests and preferences in general [12; 13]. At present, collaborative filtering algorithms are mainly of two types, namely, user- and item-based.  
User-based collaborative filtering has been applied to MOOCs' personalized recommendations to determine target learners' preferences based on their course ratings to group together learners that are similar to some extent (hereafter referred to as similar learner groups). On this basis, personalized recommendations to target learners are provided according to the courses preferred by similar learner groups.  
Item-based collaborative filtering (Item-CF) has been applied to personalized recommendations to find similar courses by modeling courses and calculating course similarities to recommend similar courses for learners. Thus, the recommendation algorithm based on collaborative filtering is easy to understand and implement. Personalized recommendation research in the MOOC context has shown that an adaptive recommendation scheme based on collaborative filtering and time series can improve the accuracy of personalized recommendations [14].  
However, the essence of the collaborative filtering algorithm for MOOCs is to make recommendations based on learners' historical data, leading to certain unavoidable disadvantages. For newly registered MOOC learners and newly started courses, there may be a cold-start problem, resulting in sparse data, thus affecting recommendation outcomes.
# Enhancement of Ultrasonic Guided Wave Signals Using a Split-Spectrum Processing Method
## 3 Signal Analysis
### Signal Synthesis
The result of the synthesised signal showed that SSP application has the potential to reduce the level of coherent noise significantly, due to the presence of DWM, hence enhancing the SNR and the spatial resolution of signals. In addition, a threshold (0.4 m distance between two features) was defined as a distance limitation, below which threshold the temporal resolution will be reduced. However, further work is required for the different scenarios. Furthermore, in order to validate the outcome of these synthesised results, SSP was applied to the experimental data that was gathered in the lab. These validations are described next.
## 4 Comparison of HASP and MCC Models  
The MCC model is a very well-known isotropic elastoplastic soil model based on the critical state concept [39]. The behavior of soil is elastic for a stress state inside the yield surface (bounding surface for HASP model), and elastoplastic for stress paths that reach the yield surface. The MCC model has an associated flow rule. The shapes of the yield surface and plastic potential surface are both circles in the deviatoric plane. The HASP model includes the same model parameters as the MCC model (\(\lambda,\ \kappa,\ M,\ \mu\) and \(e_{0}\)). These material model parameters have clear physical meanings and can be easily obtained from basic laboratory tests. The drained strength parameter \(M\) is a constant, as opposed to being a variable in HASP model, as described in the previous section. The hardening law of the MCC model depends only on the plastic volumetric strains (volumetric hardening rule). On the other hand, the HASP model uses the combined hardening rule (Equation (8)). The difference in predicting an overconsolidated clay's behavior in a consolidated undrained triaxial test for a Cardiff clay between the MCC and HASP soil models is given in Figure 2[27]. A detailed presentation of the test results can be found in [27].
## 5 Results and Discussion
### The Comparative Performance of PRDFS and BRDFS in Detecting Malicious Nodes
#### 5.4.2 Possible Explanations  
We attribute the finding to the robustness of PRDFS against non-stationarity and high dimensionality. To deal with the non-stationarity in the demand time series, the DFS of PRDFS applies wavelet multiresolution analysis on the raw demand time series and decomposes the original input time series into multiple component time series with a simpler structure. Each component time series contains the information content of the original demand time series at a particular level or scale. Our study suggests that the wavelet analysis eliminates the requirement of selecting optimal seasonal lagged predictor variables (i.e., non-consecutive lagged variables) from the component time series for the demand forecasting model, which are highly difficult to identify. In the process, it also reduces the dimensionality of the feature space.
* The curse of dimensionality hits particularly hard on time series forecasting models of high or even moderate dimensional input data, since they use lagged variables as predictor variables and the lagged variables are highly correlated with each other. Hence, to reduce the dimensionality further, the DFS of PRDFS uses a hierarchical ensemble model where each member CNN is trained with only a small number of non-seasonal lagged variables. Ensemble members are trained sequentially. The first ensemble member is trained on the original demand time series. Each of the remaining ensemble members is trained on the residual series of the previous trained ensemble member. Additionally, the DFS of PRDFS uses CNNs as ensemble members. Our study suggests that CNN is more robust to the high dimensionality compared to other existing learning algorithms.
* The DFS of BRDFS does not have any such effective strategy to deal with non-stationarity and high dimensionality. It just uses a single LSTM regression network to forecast the demand.