esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Limitations of the Study  
Our study has some limitations that should be considered when interpreting the results. On the one hand, there is its duration. The 12-week intervention period may be considered too short to fully assess the long-term effects of beer consumption on cardiovascular health. Changes in blood pressure and RAS enzyme activity may be more pronounced or different with longer exposure to beer. Another limitation is the relatively small sample size, which may limit the statistical power of the study and the ability to detect significant differences between groups. There are also limitations in the measurement of blood pressure. The measurement of systolic blood pressure using the 'tail-cutf' method in non-anaesthetized animals may be prone to variability and stress. This may have introduced biases in the measurements and made it difficult to detect subtle changes in blood pressure. Finally, the results obtained in rats cannot be directly extrapolated to humans. Differences in the metabolism, physiology and patterns of alcohol consumption between species may affect how beer influences cardiovascular health.  
It must be also considered that beer contains a variety of bioactive components, such as B vitamins, including folic acid, riboflavin, niacin, thiamine, pantothenic acid, biotin, and vitamin B12; minerals, including magnesium and calcium; and soluble fiber, natural antioxidants and other substances derived from malted barley, hops, water and yeast. Although our study does not specifically analyze the impact of these bioactive components, some possible effects are antioxidant effects, anti-inflammatory effects, effects on lipid metabolism or interactions with the endocrine system. Antioxidants may neutralize free radicals, which are known to be involved in the development of hypertension; some components of beer, such as polyphenols, have anti-inflammatory properties. Inflammation also plays a role in the development of hypertension. The B vitamins and soluble fiber present in beer may have contributed to the observed changes in lipid profile. Finally, some components of beer may interact with the endocrine system, including the RAS, through mechanisms not yet fully understood.
## 5 Performance Evaluation
### Experiment Setup
#### 5.1.2 Transactions Per Second  
_TPS_ is the most commonly used indicator for network performance evaluation in blockchain architectures. Table 2 presents the _TPS_ results for the _GetBalance_ and _SendCoin_ functions from the same experiment in Figure 7. It shows that in general, TPS is higher with larger number of TNs thanks to parallelism and randomization of SN selection, and the number of SNs do not have  
Figure 7: Average execution time over all topologies for four core functions: _FindHost_, _GetNonce_, _GetBalance_, and _SendCoin_. X-axis denote the number of Terminal Nodes (TNs), _Y-_axis is time in units of milliseconds, and the four colored bars represent the number of Service Nodes (SNs).  
direct correlation with TPS due to the same reasons as the execution time; that is, verification overhead from the increase in the number of SNs is cancelled out thanks to the improvement from parallelism.
# Semantic-Aware Low-Light Image Enhancement by Learning from Multiple Color Spaces
## 2 Related Work  
### Low-Light Image Enhancement  
#### 2.1.1 Traditional Methods  
In early stages, histogram equalization [8; 9; 10] and gamma correction [11; 12] emerged as two classical approaches for low-light image enhancement. The Retinex model [13; 14] enhances the brightness and contrast of the image by decomposing and processing the reflectance and illumination components. Although this model effectively improves low-light image quality with better visual perception, it suffers from high computational complexity, prolonged processing time, and noise susceptibility when enhancing extreme low-light images characterized by severe noise, color distortion, detail loss, and non-uniform illumination. These limitations render traditional approaches inadequate for handling extreme low-light conditions beyond their operational thresholds.
# Multidimensional Group Recommendations in the Health Domain
## 3 Single User Recommendations
### User Similarities
#### 3.2.2 Similarity Based on Health Information
Then,_  
\[\mathit{simN}(A,B)=1-\frac{\mathit{dist}(A,C)+\mathit{dist}(B,C)}{\mathit{ maxPath}*2} \tag{4}\]  
Note that we divide the sum of the two distances with \(\mathit{maxPath}*2\), to normalize the overall similarity, so that the function \(\mathit{simN}\), returns a value in the range of [0,1]. We define \(\mathit{maxPath}\) as follows. **Definition 5** (maxPath). : _Let \(T\) be a tree, and \(A\) and \(B\) two nodes in \(T\), with \(A\) being a node in the highest level and \(B\) the root. Then,_  
\[\mathit{maxPath}=\mathit{dist}(A,B) \tag{5}\]  
Figure 1 presents a snippet of the ICD10 ontology tree, where each node is associated with a weight (in this example, \(w=0.1\)). The root has not been assigned a weight, because when calculating the path that connects a node with its ancestor, we do not include the actual ancestor in the path. Table 2 presents various similarities between nodes from Figure 1.  
**Overall Semantic Similarity Between Two Users**  
Using the measures described above, we can compute the similarity between two health problems. However, a patient typically has more than one health problem in his/her profile. Let \(\mathit{Problems}(u)\) be the set of health problems of a patient \(u\in U\). Given two patients, \(u\) and \(u^{\prime}\), their overall similarity is calculated by considering all possible pairs of health problems between \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Node A** & **Node B** & **LCA(A,B)** & **simN(A,B)** \\ \hline S27.43 & S27.49 & S27.4 & \(1-(0.2+0.2/3)=0.87\) \\ S27 & S29 & root & \(1-(0.8+0.8/3)=0.47\) \\ S27.492 & S27.49 & S27.49 & \(1-(0+0.1/3)=0.97\) \\ S27.3 & S27.49 & S27 & \(1-(0.4+0.6/3)=0.67\) \\ S27.492 & S29.001 & root & \(1-(1.5+1.5/3)=0\) \\ S27.491 & S27.492 & S27.49 & \(1-(0.1+0.1/3)=0.93\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Examples of similarities between nodes using Figure 1.  
Figure 1: A snippet of the ontology tree along with the assigned weights in parenthesis. them. Then, for each single problem from \(u\), we consider only the health problem of \(u^{\prime}\) with the maximum similarity. **Definition 6** (SemS). : _Let \(u\) and \(u^{\prime}\) be two patients in \(U\). The similarity based on semantic information between \(u\) and \(u^{\prime}\) is defined as_  
\[SemS(u,u^{\prime})=\frac{\sum_{ie\,Problems(u)}ps(i,u^{\prime})}{|Problems(u)|} \tag{6}\]  
_where_  
\[ps(i,u^{\prime})=max(\forall_{jeProblems(u^{\prime})}\{sim N(i,j)\}) \tag{7}\]  
Instead of the maximum function used in the above process, one can employ the average function. However, according to our experiments, such an approach leads to a large number of unrelated pairs of health problems.
# First Results from Sentinel-1A InSAR over Australia: Application to the Perth Basin
## 4 InSAR Datasets and Methods
### Stacking and Time Series Analysis  
Ground displacements from InSAR are measured relative to a region that is either assumed to be stable (zero displacement) or is deforming in a way that is well characterised by other data. We take the former approach and identify a suitable reference region by calculating the standard deviation of each pixel in both datasets, selecting a point that has a low standard deviation and is therefore unlikely to be undergoing large displacements. However, surface motion in this region cannot be completely ruled out, and any displacements will propagate through the entire scene. An alternative approach would be to select a reference region on the stable Yilgarn Craton, but the extent of the TerraSAR-X swath restricts this to an area proximal to the Darling Scarp (Figure 2), where there may be residual artefacts due to atmospheric stratification.  
After all interferograms are adjusted to the reference region, we perform stacking and time series analysis. Firstly, interferogram displacements at each pixel are summed together and divided by the total duration of the observations to create deformation rate-maps for each dataset that are scaled to give annual rates of vertical displacement. Profiles are then extracted across areas of interest from west to east along lines of latitude by selecting a band that is 10 pixels (\(\sim\)1 km) wide. The errors on the profiles represent 2 \(\sigma\) of these values.  
Up to 90% of the annual rainfall in Perth occurs between April and October [72]. Ground displacements occurring due to near-surface hydrological changes are therefore likely to exhibit a seasonal dependence, which is observed in previous time series at the two publicly-operating continuous GPS receivers PERT and HIL1 [1] (Figure 2). To produce time series from the sets of interferograms, we solve for the cumulative displacement at each epoch using a least squares approach [73; 74]. Errors on the displacement measurements are estimated using Monte Carlo analysis [75]. Randomly-generated atmospheric noise was added to the data prior to the inversion, and the error bars represent 2 \(\sigma\) of the resulting displacement values.  
We validate the measurements from InSAR with ground-truth data by comparing InSAR time series calculated at the five pixels closest to each GPS (HIL1 and PERT) to time series of the vertical GPS displacement components obtained from the Nevada Geodetic Laboratory ([[http://geodesy.unr.edu/NGLStationPages/gpsnetmap/GPSNetMap.html](http://geodesy.unr.edu/NGLStationPages/gpsnetmap/GPSNetMap.html)]([http://geodesy.unr.edu/NGLStationPages/gpsnetmap/GPSNetMap.html](http://geodesy.unr.edu/NGLStationPages/gpsnetmap/GPSNetMap.html))). Unlike the GPS, InSAR measurements are not in a geocentric reference frame, and so, to compare the two types of data, we compute time series of displacements at HIL1 relative to PERT.
## 4 Systematic Performances Analysis of UWSNs
### Systematic Quar-Performance Calculation Model of UWSN
#### 4.2.2 Measurement Formula of Connectivity  
Connectivity is denoted as \(\Phi\), which is determined by the quality of a single link and degree of connection of the network.  
Network connectivity is not only impacted by the degree of connection between sensor nodes, but also can be enhanced when an UUV is used as the mobile communication node. In addition, these mobile nodes can be seen as working sensor nodes inside the network. Therefore, the degree of connection is measured by Equation (5):  
\[k_{b}+\mu\cdot v_{u}\cdot n_{u} \tag{5}\]  
In this equation, \(\mu>0\) represents the contribution that the UUV makes to the network, and the value of this parameter is determined based on the cruising path of the UUV.  
Connection links including underwater and overwater links. The underwater links, whose quality is affected mainly by sensor node underwater channel transmission capacity, are mainly established through acoustic channels, and there is inverse proportional relationship between bandwidth and distance. Moreover, in a short distance, an UUV can transmit data to the master node by cable with high bandwidth and reliability. Overwater communication is achieved mainly through radio, and its bandwidth and distance depend on the sending and receiving power. Therefore, the link quality can be measured by Equation (6):  
\[\omega\cdot\left[\left(1-\eta\right)\cdot b_{b}+b_{u}\right]+\left(1-\omega \right)\cdot b_{b}^{\prime} \tag{6}\]  
In this equation, \(\omega\), \(\eta\) are coefficients, \(0<\eta<1\) represents the error rate of the underwater acoustic channel; \(0<\omega<1\) represents a ratio of the underwater communication to the overwater communication.  
Finally, the data must be sent to the master node, so the data transmission capacity of this master node may affect the connectivity performance of the network. When the transmission bandwidth of the master node is large enough, the transmission bandwidth of the network can be fully used. When the velocity of the master node is fast enough, even partial data cannot be transmitted through the sensor nodes, then the master node can move over and collect the data. Therefore, Equation (7) is adopted to show the effect of the master node on the network data transmission:  
\[I=\frac{v_{s}\cdot n_{s}}{\max(B-b_{s},1)} \tag{7}\]  
In the equation, \(B\) is the largest underwater bandwidth required for data transmission.  
In summary, connectivity can be measured by Equation (8):  
\[\Phi=\alpha\cdot k_{b}\cdot\left[I\cdot\omega\cdot\left(1-\eta\right)\cdot b _{b}+\left(1-\omega\right)\cdot b_{b}^{\prime}\right]+\beta\cdot I\cdot v_{u} \cdot n_{u}\cdot b_{u} \tag{8}\]In this equation, \(\alpha\), \(\beta\) are adjustment indexes.
# TwinNet: A Double Sub-Network Framework for Detecting Universal Adversarial Perturbations
## 4 Method
### Identifying Adversarial Perturbations  
In most multiple network detection systems, a detector's property of passive access to information makes it possible that the target classifier and the detector will be attacked simultaneously. An adversary can fine-tune the perturbation referring to the detector's objective to make it undetectable again. In TwinNet, however, there is no data exchange between two oppositely working sub-networks. Additionally, the final decision will be made by the independent detecting unit.  
We use cosine as the detection criterion in the detecting unit. Thus, the difference between the outputs of the target classifier and the shadow classifier is  
\[\cos(\theta)=\frac{f^{tagt}(x)\cdot f^{shdw}(x)}{|f^{tagt}(x)||f^{shdw}(x)|}= \frac{\sum_{i=1}^{n}f_{i}^{tagt}(x)f_{i}^{shdw}(x)}{\sqrt{\sum_{i=1}^{n}f_{i}^ {tagt}(x)^{2}}\sqrt{\sum_{i=1}^{n}f_{i}^{shdw}(x)^{2}}} \tag{5}\]where \(f_{i}^{cls}(x)\) denotes the \(i\)th component of the output vector of classifier \(cls\in\{tagt\), \(shdw\}\) for a given example \(x\). The range of the cosine value is \([-1,1]\). The larger the cosine value, the smaller the angle between output vectors, and vice versa. The criteria for evaluating an example as normal or adversarial by cosine values is  
\[\left\{\begin{array}{ll}normal,&if\ \cos(\theta)\geq T\\ adversarial,&if\ \cos(\theta)<T\end{array}\right.,\ T\in(-1,\ 1) \tag{6}\]  
where \(T\) is the threshold for detecting whose value is empirically chosen.  
As a result of the construction of a hybrid training set, the shadow classifier has a bias towards adversarial-related features, which redraws decision boundaries different from that of the target classifier. From the geometric point of view, adversarial examples go deeper smoothly into the the adjacent classification region while normal examples leap over a decision boundary directly into the shadow classifier (Figure 5).  
Thus, the rationale behind using an angle as the key metric to measure the difference between output vectors is that, in terms of using a target classifier and a shadow classifier, the direction of the output vector changes smoothly in adversarial examples. By contrast, it changes dramatically in normal examples. Therefore, the angle of the output vector between two sub-networks in adversarial examples should be smaller than that in normal examples, no matter how adversarial perturbation is fine-tuned. It is going to be harder for an adversary to attack all classifiers in TwinNet at the same time.
# Secure Transmission in mmWave Wiretap Channels: On Sector Guard Zone and Blockages
## 2 System Description and Performance Metrics
### System Description
In light of the pass-loss model and small-scale fading presented in [35], the channel gain received by the legitimate receiver can be expressed as \(M_{S}|h_{D}|^{2}L\left(r_{D}\right)\) and the eavesdroppers can be expressed as \(M_{S}|h_{E}|^{2}L\left(r_{E}\right)\) or \(m_{S}|h_{E}|^{2}L\left(r_{E}\right)\), where both \(|h_{D}|^{2}\) and \(|h_{E}|^{2}\) are normalized Gamma random variable with following \(\Gamma\left(N_{L},1/N_{L}\right)\) or \(\Gamma\left(N_{N},1/N_{N}\right)\), \(r_{D}\) and \(r_{E}\) denote the distance from the transmitter to the legitimate receiver and the distance from the transmitter to the eavesdropper, \(N_{L},N_{N}\) are the Nakagami fading parameter of LOS and NLOS, respectively. \(L\left(r_{D}\right)\) and \(L\left(r_{E}\right)\) denote the path loss function which are modeled as \(L\left(r_{j}\right)=C_{L}r_{j}^{-\kappa_{L}}\) or \(C_{N}r_{j}^{-\kappa_{N}}\), \(j\in\left\{D,E\right\}\), \(r_{j}\) is the distance in meters, \(C_{L}\) and \(\kappa_{L}\) are the constant and path loss exponent depending on the LOS, \(C_{N}\) and \(\kappa_{N}\) depend on the NLOS. Figure 1: Network topology for the considered mmWave wiretap network. A sector secrecy guard zone is employed to approximate the beamforming pattern. \(\Phi_{1}\), \(\Phi_{2}\), \(\Phi_{3}\) indicate the different areas where the eavesdroppers are located. Based on the aforementioned adaptive transmission scheme, there are serious security threats when there exist one or more eavesdroppers in the sector secrecy guard zone. In order to enhance the secure transmission performance, using the superposition coding [18], the transmitter transmits information-bearing signals and sends AN, namely AN assisted transmission. If there exists no eavesdroppers in the sector secrecy guard zone, the transmitter still transmits useful information signals, namely direct transmission. As previously assumed, the locations of eavesdroppers are randomly distributed, the probability of no eavesdropper existed within the sector secrecy guard zone is given by \(p_{e1}=\exp\left(-\theta_{S}\lambda_{E}r^{2}/2\right)\), and the probability of eavesdropper existed within the sector secrecy guard zone is given by \(p_{e2}=1-\exp\left(-\theta_{S}\lambda_{E}r^{2}/2\right)\)[36].
# Robust IoT Activity Recognition via Stochastic and Deep Learning
## 4 Statistical Analysis and Feature Extraction
### Temporal Dynamics: Autocorrelation and Power Spectral Density Analysis  
Beyond basic statistics, we analyze the temporal dynamics of sensor data through autocorrelation and power spectral density (PSD) plots, such as those shown in Figure 6. For the accelerometer data along the x axis during walking, we uncovered the following:  
* Autocorrelation Analysis: The autocorrelation function, which equals 1 at a lag of 0, decreases as the time lag increases. Periodic increases in the autocorrelation at specific lags suggest recurring motion patterns, such as the periodic impact of footsteps.  
Figure 4: Probability density functions (PDFs) of accelerometer and gyroscope data during running.  
Figure 5: Probability density functions (PDFs) of accelerometer and gyroscope data during squatting.  
Figure 3: Probability density functions (PDFs) of accelerometer and gyroscope data during walking.  
* PSD Analysis: The PSD plot reveals that low-frequency components dominate the signal, and this is consistent with the relatively slow and periodic movements of walking. The rapid decline in the PSD at higher frequencies indicates a lack of high-frequency energy, confirming the absence of abrupt motion changes.  
For gyroscope data, similar analyses show that rotational dynamics are concentrated in the low-frequency range, with rapidly diminishing correlation over time. These analyses not only validate the consistency of movement patterns but also provide a robust basis for feature extraction in subsequent classification tasks.
# Axial-Symmetric Diffraction Radiation Antenna with a Very Narrow Funnel-Shaped Directional Diagram
## 1 Introduction
In planar antennas allowing electromechanical beam scanning in two planes, the effective width of the grating and the effective width of the open (dielectric) waveguide, as well as the length of the emitting aperture, are measured in tens and hundreds of wavelengths. Axially symmetric emitters based on circular dielectric waveguides and Goubau lines have been studied well enough theoretically [17; 22; 25], but they were implemented only in laboratory models and in prototypes with short apertures--they were used to solve the problems of synthesizing antennas with full-size apertures. The axially symmetric structure considered in this work implements a wave analogue of Smith-Purcell radiation in the 'radial dielectric waveguide--concentric periodic grating' system and is capable, due to the specific geometry of such a system, to form a radiation field with a very narrow funnel-shaped directivity pattern near the throat. Antennas with such patterns are in demand when solving several specific radar problems. We construct a numerical solution of model problems, providing simulation and analysis of all physical features of the processes of electromagnetic waves transformation in the structures under consideration, applying the method of exact absorbing conditions [25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36]. The results of works [31; 36; 37] convincingly testify to its reliability and efficiency. We use SI, the International System of Units, for all physical parameters except the 'time' \(t\), which is the product of the natural time and the velocity of light in vacuum; thus, \(t\) is measured in meters. In this paper, all dimensions are omitted. According to SI, all geometrical parameters (\(a,\ b,\ c,\) etc.) are given in meters. However, this is obviously not a serious obstacle to extend the results to any other geometrically similar structure.
# Model-Based Assessment of Energy Efficiency in Industrial Pump Systems: A Case Study Approach
###### Abstract  
Outdated, oversized variable speed pump drives (VSDPs) in industry lead to sub-optimal energy efficiency and considerable energy losses. This paper proposes methods to develop 2D efficiency maps for motors, converters, and pumps using polynomial surface fitting, which enables efficiency evaluation in a wide operating range. The method was applied to an oversized VSDP in an industrial chilled water supply system, comparing the original system with five alternative VSDP combinations with high-efficiency motors and pumps. The five VSDP variants demonstrated average energy savings of around 30%, with the synchronous reluctance motor (SRM) configurations outperforming the induction motor (IM) configurations by up to 7 percentage points, particularly at low loads. The high-efficiency SRM-based 252-IE5 variant delivered the best overall energy performance, highlighting the benefits of optimised system sizing and motor selection for energy savings. The proposed method can be used in both industrial and residential applications and offers great advantages in process systems that require variable flow and pressure of water or other fluids during operation, such as HVAC, water supply and wastewater treatment, district heating, etc. The development of a VSDP drive with efficient energy optimisation is an interdisciplinary problem of mechanical and electrical engineering, and without the interaction of engineers from both fields the result will not be optimal. We try to present our method so that it can be a reliable tool for mechanical, electrical, and other engineers or researchers to assist them in finding possible energy savings, performing energy audits, and selecting the most suitable components when modernising existing or developing new systems.  
variable-speed drive; induction motor; synchronous reluctance motor; centrifugal pump; energy efficiency; energy auditing +
Footnote †: journal: applied sciences