esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Healthcare Network Management: A Comprehensive Review
###### Abstract  
Recent developments in sensors, wireless communications, and data processing technologies are the main drivers for adopting the Internet of Things (IoT) in healthcare systems. IoT-based healthcare systems can enhance the quality of life significantly and help prevent the occurrence of health problems and epidemics. Deploying IoT-based healthcare on a massive scale raises several issues and challenges. One of the main challenges is the management of the end-to-end network connections of the IoT-based healthcare system. This paper presents a comprehensive survey of smart network management protocols that improve IoT-based healthcare efficiency, ensuring real-time monitoring, secure data transmission, and effective device management. Moreover, a reference architecture has been proposed for the network management of IoT-based smart healthcare systems to ensure the sustainability of service delivery to patients and caregivers. The architecture avoids health-related risks and anomalies by incorporating proper network management techniques and operational requirements pertaining to smart healthcare systems. This paper also discusses architectural implementation insights supported by new technologies such as software-defined networking (SDN) and deep learning (DL). Finally, this paper explores emerging paradigms to advance next-generation network management protocols for future smart healthcare systems.  
wireless body area network; Internet of Things; patient monitoring; smart healthcare; location contexts; network management; wearable devices
## 3 Circuit Design
### Power Amplifier  
The transmitter is completed by a class-AB power amplifier (PA) that drives a \(50\)-\(\Omega\) antenna via an off-chip resonant filter. The PA consists of an open-drain PMOS transistor directly connected to the oscillator output, sized in order to draw about \(3.5\) mA. Thus, the power delivered to the antenna is about \(0\) dBm considering a reasonable efficiency of \(10\%\). The output power, and thus the power consumption of the PA, were set after estimating the sensitivity of the receiver, which depends on the modulation type, the signal bandwidth and the noise factor of the receiver itself. The receiver input noise in the signal bandwidth of \(3\) MHz is:  
\[N_{in}=kTF\cdot BW=-99\text{ dBm} \tag{26}\]  
where \(F\) is the noise factor of the receiver, which was assumed to be equal to \(10\) dB in the above equation. For a low modulation index Manchester-encoded modulation, the \(SNR\) required at the receiver input to ensure a bit error rate (\(BER\)) lower than \(10^{-5}\) is \(SNR_{min}\cong 20\) dB [33]. Thus the input power needed at the receiver is:  
\[P_{min}=N_{in}+SNR_{min}=-79\text{ dBm} \tag{27}\]Once the receiver sensitivity has been computed, it is possible to set the PA power to achieve a desired transmission range. Taking into account the Friis transmission equation, the ratio of power available at the input of the receiving antenna, \(P_{R}\), to the output power of the transmitting antenna, \(P_{T}\), is:  
\[\frac{P_{R}}{P_{T}}=G_{T}G_{R}\left(\frac{\lambda}{4\pi d}\right)^{2} \tag{28}\]  
where \(G_{T}\) and \(G_{R}\) are the gains with respect to an isotropic radiator of the transmitting and receiving antennas, respectively, \(\lambda\) is the wavelength, and \(d\) is the distance between the antennas. While the receiver antenna is a dipole antenna and its gain can be estimated to be \(0\) dB, the transmitter antenna is a whip antenna with a gain that can be as low as \(-20\) dB. Note that Equation (28) represents the power at the receiver antenna that is supposed to be matched with the receiver. Thus, the receiver input power is \(3\) dB lower. Thus, considering Equation (28) and setting the maximum transmission distance to \(10\) m, the power needed at the transmitter antenna is \(11\) dBm, where a \(3\) dB factor accounting for the matching between the receiver antenna and the receiver itself is considered. Finally, to have a safety margin, a PA featuring a \(0\) dBm output power was designed.
## 7 Antenna Qualification and Acceptance Test
### Vibration Test  
The detailed test flow for the vibration test is shown in Figure 20. The QM S-Band antenna is installed on a mass dummy for the purpose of conducting shock, thermal vacuum, and vibration tests (see Figure 21a). Subsequent to the installation of the antenna on the mass dummy, new measurements of S-parameters are taken prior to and following each mechanical or environmental test. This procedure is undertaken in order to ensure the functionality of the antenna. Vibration tests are conducted within the Environmental Test Laboratory at INTA, utilizing pneumatic pyroshock systems (Shaker LDS 824 LSC, serial number 527) and Shaker LDS V9-MKII 440, N/S 9033-001), both situated in a cleanroom class ISO 8. The INTA's Testing laboratories have a quality system that meets the requirements of UNE-EN ISO/IEC 17025:2005 [37] for testing laboratories. In this scenario, the mass dummy of the satellite (with QM antenna) is mounted inside the ISIS TEST POD following the procedure issued by the supplier. The TEST POD is then fixed to the vibration I/F plate (Figure 21b). This plate is mechanically characterized (low-level sine/random) in each axis test. The vibration test was performed in the three axes: X, Y, and Z.  
Figure 21: Environmental tests set—up: (**a**) antenna placed on mass dummy; (**b**) vibration I/F plate; (**c**) Thermal Chamber TVC-2 (INTA); (**d**) pneumatic pyroshock system (NTA); (**e**) mass dummy inside test pod.  
Figure 20: Vibration test flow for each axis.
# A Digital Twin-Based Approach for Detecting Cyber-Physical Attacks in ICS Using Knowledge Discovery
## 6 Experimental Evaluation
### Results
#### 6.3.3 Discussion  
The results demonstrate that our method maintains relatively low fitness scores even as the amount of benign data increases, indicating that the model effectively detects anomalies associated with cyber-physical attacks.  
Figure 10: Attack 2 on coil QX00.  
Figure 9: Attack 1 on coil QX01.  
For Attack 1, the gradual increase in fitness scores with added benign events suggests that the method can identify anomalies despite attempts to mask them. Specifically, the fitness score for the initial attack log with no benign events is 0.549, as shown in the third column of Table 6. This score indicates a moderate deviation from normal behavior, reflecting the presence of anomalies due to the attack. When 50% benign events are added, the log size increases to 609 and the fitness score slightly improves to 0.563. The score remains relatively low, suggesting that anomalies are still detectable despite the added benign data. At 200% additional benign events, the log size is 1218 and the fitness score is 0.614. This score suggests that while the model predominantly fits the normal behavior, the anomalies from the attack still impact the overall fitness.  
For Attack 2, the fitness score for the initial attack log is 0.0, highlighting the significant deviation caused by the attack, as seen in the last column of Table 6. With 50% benign events added, the log size increases to 562 and the fitness score jumps to 0.369. This significant increase indicates that while the model captures some normal behavior, the anomalies remain prominent. At 200% additional benign events, the log size is 1125 and the fitness score is 0.396. This score indicates that although the model increasingly fits the normal data, the anomalies remain highly detectable.
# A Dynamic Service Reconfiguration Method for Satellite-Terrestrial Integrated Networks
## 1 Introduction
The proposed FQGA-SR can speed up the convergence and can better outperform the other three methods used in our scenarios in terms of various important service migration metrics. The rest of this paper is organized as follows. In Section 2, we summarize previous studies that are related to the present topic, and in Section 3 we then present descriptions of service migration and reconfiguration problems as well as the network modeling and optimal objective formulation used in STINs. In Section 4, the FQGA-SR migration and reconfiguration algorithm is proposed with detailed basic steps and descriptions. Finally, simulations are presented in Section 5, which is followed by the conclusion and plans for future steps in Section 6.
Impact of Germination and Fermentation on Rheological and Thermo-Mechanical Properties of Wheat and Triticale Flours  
[PERSON] 1,addr1,addr1, [PERSON] 2,addr1,addr1, [PERSON] 1,addr1, [PERSON] addr1 and [PERSON] 1,addr1  
bioactives [3; 4; 7; 8; 9]. In particular, the mineral bioavailability is improved through the activity of phytate degrading enzymes, which exist in yeast and lactic acid bacteria isolated from sourdough, whereas the digestibility of starch is reduced in the presence of lactic acid produced during sourdough fermentation, resulting in low glycemic index products [1; 7].  
Both cereal grains germination and sourdough fermentation were reported to significantly influence the technological performance of the flours and doughs. Many studies were focused on the effect of addition of germinated legumes or pseudocereals, such as pea [10], soy [11], broadbean [12], amaranth [6] etc., on the rheological properties of the wheat flour-based dough. Anyway, there are fewer studies regarding the effect of wheat germination and/or fermentation on dough rheology. [PERSON] et al. [13] reported that wheat germination resulted in a softened dough structure, characterized by lower values of the elastic modulus and viscous modulus. A similar effect on the rheological properties caused by sourdough addition to the wheat flour was reported by [PERSON] et al. [14]. They showed that sourdough addition allowed obtaining more easily extensible doughs. On the other hand, [PERSON] et al. [15] reported less elastic and firmer doughs, most probably as a result of physicochemical changes occurring in the protein network induced by the addition of sourdough. The addition of sourdough lowers the pH of the dough, affecting the structure and properties of different flour components, such as gluten and starch, which influences to a high extent the rheological behaviour of the dough [15].  
The aim of the present study is to investigate the impact of the changes induced by wheat and triticale germination and sourdough addition on the rheological properties and thermomechanical behaviour of the dough. In this respect, sourdough samples were obtained through fermentation of the wholemeal flour suspensions using a mixture of lactic acid bacteria and yeast.
# Towards Low-Cost Pavement Condition Health Monitoring and Analysis Using Deep Learning
## 3 Materials and Methods
### Experimental Setup  
For the training and evaluation, a Windows 10 PC was utilized with an NVIDIA Quadro P4000 GPU (8 GB ram) and total CPU memory of 32 GB. Within the evaluation state, the Intersection Over Union (IOU) evaluation metric was used. This metric is defined by dividing the area of overlap between the bounding boxes by the area of union between them and for this exercise the threshold was set to 0.5. The IOU essentially provides an estimation of the accuracy of the bounding box as compared to the ground truth defined by labels that are kept separate from the training data used.
# Near Real-Time Extracting Wildfire Spread Rate from Himawari-8 Satellite Data
## 2 Study Area and Data  
### Study Area  
The Esperance bushfire happened in Esperance (121\({}^{\circ}\)E-122\({}^{\circ}\)E, 33\({}^{\circ}\)S-34\({}^{\circ}\)S) on 17 November 2015. Esperance is one of the harbor cities located near the South Indian Ocean Coast of Western Australia (Figure 1). The study area is situated in a typical Mediterranean climate consisting of high temperatures from October to March. The average monthly precipitation is approximately between 18.9-97.0 mm, with an average afternoon relative humidity of around 58%. Esperance is flat with an average elevation of 10.8 m. The Esperance fire burned more than 128,000 hectares of crops, shrubs, and grasslands and resulted in the deaths of four people as well as serious damage to facilities.  
Figure 1: Location of the study area. The background is a pseudo-color composited Himawari-8 image (band 6, 4 and 3 corresponding to RGB) acquired at 8:30 a.m. Australia Western Standard Time (AWST) on 17 November 2015.
## 1 Introduction
It is currently used in different domains such as online price comparison, website change detection, weather data monitoring or web research [8]. On the other hand, a very important area in which we generally use the big data collected through various methods to add value for our process is the field of artificial intelligence, which is constantly growing and solves different real-world problems. One of the artificial intelligence research areas is natural language processing, which attempts to process and classify human language data. Moreover, one of the most important uses of natural language processing is sentiment analysis, which is a technique that can help us build systems that try to identify and extract options or real feelings from oral speaking or even written texts. This type of analysis is very important for every organization, as it can take into consideration employee or customer opinions, and accordingly the organization can improve the products or their business [9]. This paper aims to present a complete solution in order to analyze current and former employees' reviews, which are provided on the Indeed website for a particular company, and use the details received for future reviews classification and also for empowering internal data-based decisions and process optimization for one organization. Section 2 presents the background and related work for the algorithms and methods that are used--starting with web scraping for data extraction and sentiment analysis techniques for data analysis. For each algorithm, there is a brief introduction and a description of the used methodology. Furthermore, Section 3 describes the methods and results obtained by using a dataset with Amazon reviews (previously collected using web scraping techniques) and presents all the details starting with the dataset presentation, details regarding the extraction process using web scraping techniques and the text mining methods used for sentiment analysis. Section 4 offers the conclusions of this research, possible improvements that can be taken into consideration for future work in the same area, as well as opportunities for extending the current research.
###### Abstract  
Energy management plays a crucial role in providing necessary system flexibility to deal with the ongoing integration of volatile and intermittent energy sources. Demand Response (DR) programs enhance demand flexibility by communicating energy market price volatility to the end-consumer. In such environments, home energy management systems assist the use of flexible end-appliances, based upon the individual consumer's personal preferences and beliefs. However, with the latter heterogeneously distributed, not all dynamic pricing schemes are equally adequate for the individual needs of households. We conduct one of the first large scale natural experiments, with multiple dynamic pricing schemes for end consumers, allowing us to analyze different demand behavior in relation with household attributes. We apply a spectral relaxation clustering approach to show distinct groups of households within the two most used dynamic pricing schemes: Time-Of-Use and Real-Time Pricing. The results indicate that a more effective design of smart home energy management systems can lead to a better fit between customer and electricity tariff in order to reduce costs, enhance predictability and stability of load and allow for more optimal use of demand flexibility by such systems.  
dynamic pricing; customer segmentation; recommendation systems; demand response; demand side management; home energy management system; machine learning +
Footnote †: journal: Applied Sciences
# A Deep Learning Method for DOA Estimation with Covariance Matrices in Reverberant Environments
## 3 The Proposed Method
### TFD Matrix  
The received signal of the microphone array in the time-frequency domain is the output vector \(\mathbf{x}(\tau,k_{h})\) of the microphone array. The covariance matrix of the \(\mathbf{x}(\tau,k_{h})\) can be obtained as  
\[\mathbf{M}_{1}=\frac{1}{N_{F}}\frac{1}{N_{T}}\sum_{h=1}^{N_{F}}\sum_{\tau=1}^{ N_{T}}\mathbf{x}(\tau,k_{h})\mathbf{x}^{H}(\tau,k_{h}), \tag{13}\]  
where \(N_{F}\) is the number of the frequency bins, and \(N_{T}\) time frames are considered. In practical applications, the covariance matrix can also be calculated in this way. Under each frequency bin, the covariance matrix of the \(\mathbf{x}(\tau,k_{h})\) can be obtained along the time index. The final step is to average all the matrices with the total frequency bins.
## 5 The Molecular Mechanisms Behind the Resistance of Biofilms to Antimicrobial Agents
### Physical Barrier of the Biofilm Matrix  
The biofilm matrix acts as an essential structural and protective framework, playing a significant role in antibiotic resistance. Research indicates that the matrix limits the diffusion and penetration of antibiotics, thereby protecting bacterial cells within the biofilm from therapeutic agents [119; 120]. The restricted permeability serves as a key contributor to the resistance seen in infections linked to biofilms. Antibiotics frequently struggle to penetrate bacterial cells situated beyond the outermost layer of the biofilm, which diminishes their effectiveness and enables the safeguarded inner populations to endure and continue existing [121].  
The biofilm matrix serves as a physical barrier and triggers physiological changes in bacteria that contribute to increased resistance. For example, the matrix can react to sublethal levels of antibiotics by adjusting its synthesis. This adaptive response enhances the biofilm structure and promotes the rapid evolution of resistance mechanisms within the bacterial community [122].  
Recent studies have highlighted the significance of mechanical signals in the three-dimensional microenvironment of biofilms. For instance, the rigidity of EPS matrix has been shown to affect the biofilm's capacity to endure antibiotics. The mechanical properties could improve the biofilm's resilience, allowing bacterial populations to withstand external stresses, such as antimicrobial treatments, more effectively [64].
## 2 Materials and Methods  
### Commercial 5G NR Network  
This study was conducted in the Swisscom commercial 5G NR network in Bern, Switzerland, in July 2020. Four sites with Ericsson AIR 6488 NR MaMIMO base stations (BSs), representative of the network, were investigated. For each site, the BS antenna specifications were provided by the network operator.  
The NR radios operated in the n78 band (3300-3800 MHz), which is part of Frequency Range 1 (FR1). The peak input powers of the BS antennas ranged from 1.6 W to 8.1 W (32.1-39.1 dBm). These powers are considerably lower than the BS radio product's maximum rated power of 200 W, due to the restrictive EMF limits applicable in Switzerland. The BSs were further characterized by codebook-based beamforming configured with eight channel status information reference signal (CSI-RS) ports, with azimuthal beam steering.  
Furthermore, an Oppo Road Pro 5G mobile phone with 5G NR capability (further denoted as 'UE' (user equipment)) was used. With the iPerf3 tool ([[https://iperf.fr/](https://iperf.fr/)]([https://iperf.fr/](https://iperf.fr/)), accessed on 15 April 2021), a downlink stream was set up using the User Datagram Protocol (UDP) from the BS to the UE at--in theory--100% capacity of Physical Downlink Shared Channel (PDSCH) resource usage.
# One-Shot Distributed Generalized Eigenvalue Problem (DGEP): Concept, Algorithm and Experiments +
## 6 Conclusions  
The paper proposes a general distributed algorithm for a generalized eigenvalue problem (GEP) in one-shot communication. For multi-view analysis in DGEPs such as distributed CCA, the algorithm meets hard convergence when there are no eigengaps in the approximated covariance matrix. The one-shot distributed multiple CCA algorithm produced solves this problem. The theoretical analysis of the approximation error reveals the divergence of data covariance in the distributed system and gives the upper bound concerned with the eigenvalues of the data covariance and the number of local servers. The quantities of numerical experiments demonstrate the effectiveness of proposed algorithms in different applications.  
As a one-shot method, our proposed algorithms amplify the computation efficiency for communication efficiency. Furthermore, we are devoted to developing an advanced distributed algorithm to obtain a more precise solution. It is believed that deep learning has achieved great development in many applications, including in distributed learning fields. How to combine our works and deep learning networks is the goal of our future work.  
Conceptualization and methodology, K.L., F.H. and X.H.; formal analysis, K.L. and F.H.; supervision, X.H. and J.Y.; project administration, Z.S. and J.Y.; writing--original draft preparation, K.L.; writing--review and editing, F.H., Z.S., X.H. and J.Y. All authors have read and agreed to the published version of the manuscript.  
This research is partly supported by the National Key R&D Program of China (No.2019 YFB1311 503); National Science Foundation, China (61876107, U1803261, 61977046); and the Committee of Science and Technology, Shanghai, China (No. 19510711200).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors are grateful to the Editor-in-Chief, Editor, and anonymous Reviewers for their valuable reviews.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Experimental Equipment and Methodology
#### 2.2.2 Differential Scanning Calorimetry  
The specific heat was evaluated by means of calorimetric measurement performed by DSC1 METTLER TOLEDO. The instrument before all measurements was calibrated by pure indium sample.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Component** & **Z0** & **Z20** & **Z40** & **Z60** \\ \hline PDMS–Siloxane & 42.6\% & 37.4\% & 29.8\% & 21.7\% \\ PMHS–Siloxane & 21.3\% & 18.7\% & 14.9\% & 10.9\% \\ Ethanol—Solvent & 14.9\% & 12.3\% & 10.3\% & 7.6\% \\ Water—Solvent & 10.6\% & 8.7\% & 7.4\% & 5.4\% \\ Sn(II)—Catalyst & 10.6\% & 8.7\% & 7.4\% & 5.4\% \\ Zeolite—Filler & 0\% & 14.2\% & 30.2\% & 48.9\% \\ Zeolite/Foam & 0\% & 20\% & 40\% & 60\% \\ \hline \end{tabular}
\end{table}
Table 1: Siloxane solution compositions (wt.%) at varying zeolite content.  
Figure 1: Preparation steps of the silicone-SAPO34 composite foam.  
The test was performed measuring the baseline of the empty crucible, the baseline of the sample, and the baseline of the reference material (sapphire). Subsequently, the difference between sample and empty crucible is defined as \(H\), while the difference between reference and empty crucible is defined as \(h\). Knowing these values, the specific heat of the sample under investigation can be calculated as  
\[c_{p,\ sample}=\frac{H}{h}\ \frac{m_{ref}}{m_{sample}}\ c_{p,ref} \tag{2}\]  
The used protocol for each measurement is as follows, consistent with the American Standard ASTM E1269-11:  
- Drying of the foam sample up to 85 \({}^{\circ}\)C over night.  
- Loading of the dry adsorbent material inside a crucible and sealing it to prevent humidity adsorption.  
- Measurement of the specific heat in temperature interval between 30 \({}^{\circ}\)C and 70 \({}^{\circ}\)C, employing the standard specific heat method against a reference material (sapphire method). The heating rate for specific heat measurement is usually 2 \({}^{\circ}\)C min\({}^{-1}\).