esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Rain Rate Evaluation via Satellite Downlink Signal Attenuation Measurement
## Architecture of the Real-Time Wide-Area Rain-Rate Measurement System  
### Rationale and Underlying Idea  
NEFOCAST (for the sake of simplicity, the more concise name NEFOCAST, originating from the contraction of \"Nefele\", which is the Italian spelling for the mythological cloud nymph Nephele, and \"forecast\" will be used hereafter in place of the, more involved, official name of the project (SVI.I.C.T.PRECIP.) is a research project funded by the government of the Tuscany Region (Italy). Started in September 2016 for a duration of 24 months, NEFOCAST is based on an innovative solution: It uses a dense population of interactive satellite terminals (ISTs) that serve as both weather sensors and modems to send the measurements to a dedicated now-casting platform to receive back rainfall information and potential alerts. The IST used for the project is named SmartLNB (smart low-noise block converter) and is an innovative product, introduced by the satellite operator EUTELSAT with the ambition to replace the traditional direct-to-home (DTH) receivers, providing at the same time additional interactive services through a narrowband return channel. The SmartLNB is a new-generation electronic feed connected to an antenna with an embedded transmitter for interactive applications. Such terminals serve as both weather sensors and modems to send the measurements to a dedicated center (the NEFOCAST service center) that generates precipitation products and eventually related weather warnings. Since ISTs are low-cost and low-power RF transceivers that are also equipped with a traditional satellite television (TV) decoder, they can replace the current population of DTHreceivers. The NEFOCAST precipitation maps will be therefore based on capillary measurements, constantly and rapidly updated. Such system presents interesting aspects when compared to other consolidated precipitation measurement instruments.  
Due to its promising results, the NEFOCAST project received the endorsement from EUTELSAT, a leading satellite operator, and Meteo France, a big player in the weather forecast market, which are both interested in setting up a collaboration based on the project results to develop new services. The business related to weather data has undergone an exponential growth in the last few years and, accordingly, provider companies of weather data grew very fast in terms of value and proposition.
## 1 Introduction
Although it may have false positives like a typical bloom filter, this is ignorable because the worst case is just to adjust a wrong timeout. Secondly, we introduced a new metric, Average Flow Duration (AFD). AFD is an average time duration in which all flow rules remain in a flow table. A newly incoming normal flow as well as an attack flow are also given a short timeout because they have no record. Even a normal flow is erased too early from the flow rule table, which causes control overhead by frequent control messages. Therefore, each flow is installed with the initial timeout based on AFD. The AFD can avoid excessive control messages caused by the early flow rule removal. AFD also works well under attack. As attack flows dominate, AFD decreases because the attack flow's duration is short. The shortened AFD, in turn, makes the timeouts of attack flows shorter and shorter, and finally, attack flows can be removed quickly. Figure 1 shows the overall control flow of the proposed scheme. In the proposed scheme, at first, every flow is installed with the initial hard timeout based on AFD. Once installed, the flow remains until the timeout. A controller receives a _Flow_removed_ message for each expired flow after the timeout, which includes the duration and packet count for the flow. Then the controller updates the packet count for the flow in the 2D counting bloom filter and calculates the AFD for all the flows using the exponential moving average. The calculated AFD is the baseline of the hard and idle timeouts for new incoming flows, and the historical statistics are used for fine adjustments of the timeouts. When a new flow comes, the controller looks up the historical statistics for the flow in the 2D bloom filter and calculates the hard and idle timeouts based on the statistics and AFD and installs the corresponding rule. The proposed scheme can mitigate the flow overflow attack without performance degradation caused by excessive control messages for flow rule installation. The paper makes the following contributions:  
* We developed an algorithm that determines a history-based dynamic timeout using a 2D counting bloom filter. * We implemented the defense system that alleviates the flow table overflow attack using the algorithm. * We proved how effectively our scheme reduces the number of flows through comprehensive experiments in a real testbed.
## 2 Materials and Methods
### Feature Extraction Techniques
#### 2.1.2 Continuous Wavelet Transform  
Similar to the Short-Time Fourier Transform (STFT), the wavelet transform (WT) in both its continuous (CWT) and discrete (DWT) forms is a spectral decomposition. The easiest way to understand the WT is by comparing it with the STFT: for its part, the FT consists of decomposing the signal \(f(t)\) into its harmonic components in the form of sines and  
Figure 3: Results after applying MFCCs: The graph on the left shows, in a colorimetric scale, the value of the MFCCs averaged for each of the class sets. The graph on the right shows a normalized PCG record, randomly selected from each of the classes.  
cosines, while the WT decomposes the signal in the form of mother wavelets with different amplitudes and displacements. The mother wavelets are an effectively limited waveform in duration, with an average equal to zero. The mother wavelet used in the CWT was a Morlet, described by  
\[\psi(t)=e^{-\pi t^{2}}e^{i\pi t} \tag{5}\]  
The CWT of a signal \(f(t)\) is given by [26]:  
\[CWT(a,b)=\langle f,\psi_{a,b}\rangle\sum_{0}^{+\infty}f(t)\psi(\frac{t-b}{a})dt \tag{6}\]  
Therefore, the integral is solved for \(a,b\) (scaling and shifting parameters), which performs a transformation of the signal \(f(t)\) from the time domain to a function in the time domain and scale.  
By applying the CWT, we obtain a matrix representation of the coefficients of size \(NxM\), as shown in Figure 4, where \(N\) is the number of temporary points, while \(M\) is the number of scales used. Given the high dimensionality of these matrices, it was necessary to apply an averaging \(3\times 3\) filter to reduce the computational demand.  
Hilbert Transform: It is known that the multiresolution framework of the CWT can be improved through the Hilbert transform [27] if this is applied to the signal _a priori_. The fundamental reason that this transformation can be integrated with the wavelet transform is due to its scale and translation invariance, and its energy-conserving (unitary) nature [28].  
Formally, the Hilbert transform \(\hat{s}(t)\) of a function \(s(t)\) is obtained by calculating the convolution of \((s(t)\otimes 1/(\pi t))\) defined as [29]:  
\[\hat{s}(t)=\frac{1}{\pi}\int_{-\infty}^{\infty}\frac{s(\tau)}{t-\tau}d\tau \tag{7}\]  
This transformation has been shown to improve the performance of the neural network used, so it was implemented as part of the algorithm, as shown in the yellow box in Figure 6.  
Figure 4: CWT results: The graph on the left shows, in a colorimetric scale, the value of the coefficients obtained for each scale at each time point, averaged for each of the class sets, with 150 scales. The graph on the right shows a normalized PCG record, randomly selected from each of the classes.
## 4 Discussion
### The Role of Blood Flow Restriction (BFR)  
The use of blood flow restriction (BFR) and ischemic preconditioning (IPC) has garnered significant interest among researchers aiming to enhance barbell velocity. Although this mechanism is often considered in the context of post-activation potentiation (PAP) or muscular hypoxia during volume-based training, current findings also explore its potential as a PAPE-inducing tool. Several studies have reported a clear increase in peak velocity (PV) (+5.99-17%) during the initial sets performed with ischemia applied either during rest intervals or immediately before barbell attempts [31; 33]. However, this effect tended to disappear once loads exceeded 60% of 1 RM. It is likely that, within this intensity range,rapid muscular fatigue becomes dominant, overshadowing the potential benefits of hypoxia. When using BFR at higher loads (\(\geq\)70% 1 RM), only a transient increase in velocity or power was observed--typically in the second set--followed by a decline in subsequent sets [32]. This suggests that BFR does not consistently provide long-lasting performance enhancement and may contribute to fatigue accumulation when heavier loads are used. IPC, on the other hand, demonstrated positive effects when applied in short rest intervals (5 min of occlusion followed by 5 min of reperfusion), particularly in the first bench press sets (+6.5-9% MV compared to control) [30]. Interestingly, combining IPC with a PAPE protocol (high intensity at 90% 1 RM) did not prove to be effective over multiple sets--the positive effect was mainly observed in the first set and diminished thereafter.  
Both BFR and IPC may serve as useful tools for eliciting immediate improvements in velocity during resistance exercises performed at low to moderate intensities. However, at higher loads or when exercises are repeated over multiple sets, these benefits are quickly neutralized by fatigue.
## 5 Discussion
### Sustainability  
The performed study is highly significant in terms of sustainability. The use of alum sludge as a soil stabilizer can provide an alternative to traditional stabilizers, which can be expensive and have negative environmental impacts. By utilizing alum sludge, this study offers a sustainable solution for improving soil stability in various engineering applications. Additionally, the application of artificial intelligence (AI) methods to predict the California bearing ratio (CBR) of soils stabilized with alum sludge, provides a more efficient and accurate way to determine the effectiveness of this stabilizer. This can reduce the need for extensive and costly field testing, leading to a more sustainable and cost-effective approach to engineering projects. The results of this study also provide valuable insights into the sensitivity and importance of input parameters, which can guide future research and development in the field of soil stabilization.
# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 3 Sensors and Actuators in the IoMT
### Role of Body Sensors in the loMT
#### 3.1.9 Electromyography (EMG)  
EMG sensors [22] detect electrical activity in skeletal muscles, aiding in diagnosing and monitoring neuromuscular disorders such as muscular dystrophy and carpal tunnel syndrome. They are vital in rehabilitation, where they are used to assess muscle function and guide therapy. Commercial devices such as the MyoWare Muscle Sensor and Delsys Trigno Wireless EMG System are commonly used in these applications.
## 3 Results and Discussion
### MA-SDE Was Compared with the Traditional Extraction Methods  
Three extraction methods, including the HRE, HDE, and MA-SDE methods were compared in terms of their ability to affect the extraction of the polysaccharides and essential oil from the powdered leaves of _T. chinensis_ var. _mairei_ (see Figure 5). The results in Figure 5 clearly show that the yield of the polysaccharides obtained by the MA-SDE method was 6.39% \(\pm\) 0.12%, which was 1.14-fold higher than that of the yield obtained by the conventional HRE method. Furthermore, the yield of essential oil obtained by the MA-SDE method was 0.27% \(\pm\) 0.03%, which was 1.23-fold higher than the yield obtained using the conventional HDE method. The use of an extraction time of 17.5 min with the  
Figure 4: Response surface plots for the extraction of the polysaccharides and essential oil using the MA-SDE method: impact of the extraction time and microwave power on the yields of the polysaccharide (**A**) and essential oil (**D**); impact of the liquid/solid ratio and microwave power on the yields of the polysaccharides (**B**) and essential oil (**E**); and the effects of the liquid/solid ratio and extraction time on the yields of the polysaccharides (**C**); and essential oil (**F**).  
MA-SDE method provided yields that could only be achieved after 6.0 and 3.0 h extraction times for the HDE and HRE methods.
## 3 Results  
### Materials Characterization  
FTIR spectra obtained for the four SPs used are presented in Figure 2. [PERSON] [29] cite that specific functional groups from superplasticizers can be verified through their absorption bands in FTIR spectra as OH- groups (3600-3200 cm\({}^{-1}\)); aliphatic groups (3200-2500 cm\({}^{-1}\)); carbonyl groups (1900-1500 cm\({}^{-1}\)); and ether groups (1250-950 cm\({}^{-1}\)). It can be observed that the chosen SP presented absorption bands in the regions cited, with some differences among them.  
FTIR spectra from SP A and C are analogous, differing only in one absorption band at 1560 cm\({}^{-1}\) (carbonyl groups). It should be noticed that the absorption bands present at 2920 cm\({}^{-1}\) (aliphatic groups) for SP A and C did not appear in SP B and D. Superplasticizer D presented the most differences among all SPs, such as not showing any band at 950 cm\({}^{-1}\) (ether groups), and two additional bands at 1035 and 1190 cm\({}^{-1}\). This difference is associated with its naphthalene basis.  
Raman spectrum and TEM imaging of CNTs prior to sonication are presented in Figure 3. Two peaks can be identified in the Raman spectrum, one at 1344 cm\({}^{-1}\), attributed to amorphous carbon structures and some defects (namely D band), and one at 1579 cm\({}^{-1}\), attributed to tangential C-C bond stretching vibrations (namely G band) [22; 30]. CNTs were found to have a \(\text{I}_{\text{D}}/\text{I}_{\text{G}}\) of 1.29, characteristic of highly graphitized structures. TEM imaging confirmed that the CNTs presented an outer diameter in the scale of nanometers and length in the scale of micrometers. Additional images of the used CNTs can be found in [31; 32].  
Figure 1: (**a**) Electrical circuit adopted for the electrical resistivity measurements and (**b**) cement paste specimen.
# K-Means Clustering Algorithm Based on Chaotic Adaptive Artificial Bee Colony
## 3 Chaotic Adaptive Artificial Bee Colony (CAABC) for Clustering
### MAX-MIN Distance Product Algorithm  
Initialization affects the global convergence and the performance of the algorithm, so it is particularly important in the evolutionary algorithm. K-means clustering algorithm has high sensitivity to the initial stage. Based on [31; 32], we propose a max-min distance product algorithm for initialization. The initialization process not only reduces the randomness of colony initialization but also reduces the sensitivity of Kmeans clustering to initial points. In [33], the max-min distance means is used to search the optimal initial clustering center, in which case convergence speed and accuracy of the algorithm have been significantly improved. However, it may lead to clustering conflict when the initial clustering center is excessive dense. The maximum distance method proposed in [34] reduces the number of iterations effectively, but there would be the problem of initial point deviation. It is possible that the product of two distances is the same, but the density of the points is quite different.  
To improve the efficiency, we propose a max-min distance product algorithm. We can get \(T_{m}\) from \(T\) according to Equation (6). \(T_{m}\) represents the product of the maximum and minimum values in \(od\).  
\[T_{m}=mix(od)*min(od) \tag{6}\]  
where \(k\) points are randomly selected as the initial cluster centers from the original data set. Meanwhile, is a data set used to store the distance between other points in the data set among cluster centers. \(T\) is an array that stores the product of elements in \(od\). \(T_{m}\) is the product of the maximum center distance and the minimum center distance. The points that corresponding to the \(T_{m}\) are selected as cluster centers instead of initial points. The distribution of current initial points can be dispersed by the max-min distance product. Moreover, it can avoid the situation that two distance products are equal, but the point density of their regions is quite varied, and magnify the difference between points. The enhanced selection method is better.
# Power Autonomy Estimation of Low-Power Sensor for Long-Term ECG Monitoring
## 4 Power Consumption Models
### Idle Mode Power Consumption Model  
The proposed analytical power consumption model for the idle mode \(\overline{P}_{adv}\) is presented in a mathematical form with Equation (3). \(T_{adv}\) is the advertising period for a selected advertising mode: \(T_{adv\_fast}\) for the fast advertising and \(T_{adv\_slow}\) for the slow advertising. \(E_{adv}\) is the total energy consumed during advertising events, \(E_{tick}\) is the total amount of energy consumed during system tick events, and \(P_{sleep}\) is the power consumption during sleep mode. \(T_{sleep}\) is the total sleep time of the ECG sensor during a \(T_{adv}\) period. \(e_{adv}\) and \(e_{tick}\) represent the energy consumption of the individual advertising and system tick events, respectively, while \(tp_{adv}\) and \(tp_{tick}\) are the periods of the advertising and system tick events, respectively. \(t_{adv}\) and \(t_{tick}\) are the duration of the advertising event and the system tick event, respectively.  
\[\begin{split}\overline{P}_{adv}&=\frac{1}{T_{adv}} (E_{adv}+E_{tick})+\frac{1}{T_{adv}}P_{sleep\_idle}T_{sleep}\\ &=\frac{1}{T_{adv}}\bigg{(}\frac{T_{adv}}{tp_{adv}}e_{adv}+ \frac{T_{adv}}{tp_{tick}}e_{tick}\bigg{)}+\frac{1}{T_{adv}}P_{sleep\_idle}T_{ adv}\bigg{(}1-\frac{t_{adv}}{tp_{adv}}-\frac{t_{tick}}{tp_{tick}}\bigg{)}\\ &=\frac{e_{adv}}{tp_{adv}}+\frac{e_{tick}}{tp_{tick}}+P_{sleep\_idle }\bigg{(}1-\frac{t_{adv}}{tp_{adv}}-\frac{t_{tick}}{tp_{tick}}\bigg{)}\end{split} \tag{3}\]  
This model gives us the average power consumption during a selected idle mode setting: fast advertising or slow advertising. If we want to calculate the overall average power consumption \(\overline{P}_{idle}\) in the idle mode (combining fast and slow advertising), Equation (4) should be used. \(T_{adv\_slow}\) and \(T_{adv\_fast}\) are slow advertising and fast advertising periods, respectively, where the average power consumption for slow advertising \(\overline{P}_{adv\_slow}\) and the average power consumption for fast advertising \(\overline{P}_{adv\_fast}\) can be calculated using Equation (3).  
\[\overline{P}_{idle}=\frac{T_{adv\_slow}\overline{P}_{adv\_slow}+T_{adv\_fast} \overline{P}_{adv\_fast}}{T_{adv\_slow}+T_{adv\_fast}} \tag{4}\]
## 2 Significant Wave Height and Wind Velocity Data Used  
The significant wave heights and wind velocities are obtained from a buoy in the Gulf of Mexico operated by the National Data Buoy Center (NDBC, www.ndbc.noaa.gov). It is in the Gulf of Mexico at 25.942\({}^{\circ}\) N 89.657\({}^{\circ}\) W, denoted as buoy 42,001 [27]; its data were used in a previous study [28]. It is selected to illustrate the proposed approach because of its location at open sea and because it has recordedsimultaneous information of metocean variables since 1975, but the method can be readily extended to other buoys at open sea in the Gulf of Mexico or anywhere else. For the scope of the present study, the maximum monthly significant wave heights, _Hs_, recorded at buoy 42,001 are used, together with the wind velocity, \(V_{w}\), recorded and listed in [27] within the same time bin. It is pointed out that \(V_{w}\) is not necessarily the monthly maximum wind speed, but the one which occurred simultaneously with the monthly maximum significant wave height (level of resolution within [27]); thus it is also referred to as companion wind. The significant wave height and wind speeds used are those defined in [27], denoted as WVHT and WSPD [27], respectively. The wind velocity is averaged over an eight-minute period and is recorded at 3.6 m above sea level [27]. For the scope of this study, the data are used as directly given in [27]. Both metocean variables, _Hs_ and \(V_{w}\), are to be probabilistically characterized with a model that can represent seasonality effects, as described in the following sections.
## 3 Results and Discussion
### Model Results and Validation
#### 3.2.2 Model Results--Zone 4
This fragility is especially common in low-bias, high-variance algorithms like KNN when trained on small datasets. Taking into account that our methodology relies on scheduled batch retraining rather than sensitivity analyses (which are limited by small datasets), we expect model sensitivity to decrease with each retraining cycle as new deviations are incorporated and the input space becomes more thoroughly covered. Given these results, a model revision and retraining phase is necessary. This revision should include:  
Figure 17: Actual vs. predicted \(\Delta\)time in the production scenario for Zone 3. Figure 18: Residuals vs. predicted \(\Delta\)time in the production scenario for Zone 3. * An updated exploratory analysis of the new batch to detect emerging trends or shifts in the distribution of key features. * A model retraining phase using an expanded dataset that incorporates the newly labeled data. * A re-evaluation of model complexity, considering whether the original KNN architecture remains appropriate or whether the use of more expressive models (e.g., deeper Decision Trees or ensemble methods like Random Forests) becomes justified as more data becomes available. This adaptive retraining step ensures that the model remains aligned with evolving real-world dynamics and maintains its predictive value in operational settings.
# Marine Internet for Internetworking in Oceans: A Tutorial
## 5 Internetworking Strategies of Marine Internet
### Interconnection of Underwater Networks  
As mentioned above, the high-speed coverage of a UAN is very small, and many UANs should be deployed in different sites for high-speed wide coverage. However, these networks are usually isolated from each other, and interconnecting them is necessary. This can be achieved with the following methods.  
* Underwater internetworking: This can be achieved by using deployed seafloor cables to link underwater networks such as those for cabled underwater observatory systems. This method can provide high-speed reliable network connections and sustainable power supply. However,it is difficult and costly to densely deploy such infrastructure for wide underwater coverage. Alternatively, underwater acoustic links can be used to connect these networks directly, which is simpler and cheaper than using cables but with small capacities for communication and power supply. The performance also degrades quickly as the number of links increases due to slow acoustic propagation speed and low transmission rates.
* Internetworking on/over water surface: This can be realized through jointly using RF, acoustic wave or green/blue lasers. That is, each UANET links a surface gateway, which collects data from the UANET and forward it to the sink, and can broadcast data to the UANET linked to it. The gateways themselves can form a surface WANET or connect a backbone network such as a satellite or a NANET. This method can avoid severe performance degradation of the underwater internetworking mentioned above, and is more flexible, easier and cheaper but without provisioning of power supply to underwater devices.