esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction and Related Work  
Developing and deploying health monitoring systems that older adults can use by themselves to self-manage their health while also increasing their health literacy remains a challenge relevant to many societies across the globe [1]. In this regard, it is vital to develop systems that this target group can and wants to use to ensure that they actually provide a benefit. Such systems present an opportunity to combat the challenges arising due to an increase in the number of older adults, with the simultaneously increasing shortage of healthcare workers [2]. Additionally, many older adults want to age in their own homes rather than move to long-term care facilities [3]. Assistive technologies can support and enable this, and many technologies, such as sensing devices or mobile applications, exist to this end [4]. However, they are often difficult to use for older adults, especially those in need of care, as they may have impairments that make it harder to interact with such systems, such as hearing, visual, motor, and cognitive impairments [5]. Therefore, it is important to design, develop and rigorously test assistive technologies for and with this intended target group.  
In previous work [6], we introduced a system architecture for a health monitoring system comprised of a social robot, several connected health devices, and a mobile app for data visualization. An overview of the system is shown in Figure 1. By using a wearable for continuous measurement of health data and certified connected health devices for interval measurements the objective health data, e.g., pulse, activity, blood pressure, or body temperature, can be measured. Through interactions with a social robot, in the form of small talk and validated health assessments, such as PROMs, subjective health data can be acquired. This system was developed for everyday long-term autonomous use by both older adults in care facilities and community-dwelling older adults. In this work, we present a first exploratory UX evaluation of our health monitoring system to investigate factors influencing the reported UX and usability, and their implications for ensuring the system's successful long-term use.
## 2 Related Works
### Existing Routing  
In [PERSON] et al. [7], developers have related LOADng with RPL in applications with diverse traffic patterns. Based on the attained results, it is clear that LOADng outperformed RPL in point-to-multipoint (P2 MP) as well as P2P traffic scenes. However, in MP2P, RPL has projected considerable outcomes. Moreover, the proactive properties of RPL create a better environment for data gathering applications. Unlike, LOADng is capable to perform better with comprehensive traffic; however, it has projected a maximum delay due to the requirement of examining route discovery. The working function of LOADng in IoT domains with P2P as well as MP2P traffic pattern as defined in [PERSON] et al. [8]. The influence of route selection in LOADng has been studied extensively in [PERSON] et al. [9]. An enhancement of LOADng is presented in [PERSON] et al. [10]. Therefore, Neighbor-Disjoint Multipath for LOADng (LOADng + NDM) shows a new multipath mechanism applied for LOADng and decides to enhance network consistency and QoS. A small path has been initialized from the transmitter and receiver node. Followed by, it also manages to develop collective backup routes by eliminating the nodes.  
[PERSON] et al. [11] projected the enhancement routine measure for RPL in IoT platforms with heterogeneous tools. Fuzzy systems apply the routing measures in the network process using the application demands. Hence, the performance results have shown that the newly developed models are capable to resolve the basic RPL measures by means of power utilization as well as Packet Delivery Ratio (PDR). The routing protocol for IoT systems depends upon the routing measures as depicted in [PERSON] et al. [12]. Moreover, Routing by Energy and Link quality (REL) projects the application of Ad-hoc On-demand Distance Vector (AODV) that decides to enhance the scalability and energy efficiency. Then, a protocol applies weakLinks approach to find the connections with limited quality over a path where the route development with maximum weakLinks is eliminated. Afterward, REL surpassed the AODV with respect to delay, PDR, and network survival rate.  
[PERSON] et al. [13] projected a solution based smartphone to enable the interoperability of IoT tools. Developers have presented a structure where a smartphone collects huge communications interfaces to be operated as a gateway for diverse techniques. Hence, the software-based model applies the default execution of various standards. A special testbed is developed to estimate the solution by means of storage space, CPU, and power consumption. In [PERSON] et al. [14], developers have introduced solutions for gateway identification in Mobile Ad Hoc Networks (MANETs). This approach depends upon the application of periodical control messages and assumes the networks with mobile nodes which is ineffective for low-power IoT devices. Hence, the working function of solutions is determined by means of PDR as well as end-to-end delay.
## 3 Results and Discussion
### Chemical Analysis  
According to FTIR spectra shown in Figures 3 and 4. It is clear that there is no chemical reaction between the active compound and particular polymer, which indicates that they are just physically mixed. It can be also concluded that the total amount of active compound added was encapsulated into the fibers or that the surface loading with active compounds is under the detection limit.  
Figure 2: SEM images of composite nanofibers. (**a**) PLA + 10 wt% pomeranate seed oil, (**b**) PLA + 5 wt% sea-buckthorn oil, (**c**) PVP + 10 wt% sea-buckthorn, (**d**) PVP + 5 wt% pomeranate seed oil and (**e**) PVP + 10 wt% of mixture of pomeranate seed oil and extract of fermented pomeranate juice (1:1).  
In Figure 3. where spectra for PVP-based series are shown, typical peaks for PVP are present in all curves: wide peak at 3434 cm\({}^{-1}\) originates from O-H stretching vibration, small peak at 2955 cm\({}^{-1}\) is derived from C-H asymmetric stretching vibration, sharp peak at 1661 cm\({}^{-1}\) corresponds to C = O stretching vibrations. Peak at 1424 cm\({}^{-1}\) is from CH\({}_{2}\) bending vibration and peak at 1290 cm\({}^{-1}\) corresponds to C-N vibrations.  
When comparing spectra of pure and composite PLA fibers, it can be seen that there is no difference between them (Figure 4). Typical peaks for PLA are present, at 2997 and 2945 cm\({}^{-1}\) is peak from C-H stretching, C=O stretching peak is a sharp peak at 1746 cm\({}^{-1}\), C-H bending vibrations are presented with peaks at 1452 and 1365 cm\({}^{-1}\), while C-O stretching derived peaks at 1180 and 1080 cm\({}^{-1}\).
# Physical Layer Security for Military IoT Links Using MIMO-Beamforming at 60 GHz
## 2 MIMO-Beamforming Techniques for PHY Security  
### Sub-Band Tunable Beamforming  
Sub-band adaptive beamforming techniques can be exploited for PHY security by employing rapidly reconfigurable beams over sub-bands in secure mmWave frequencies, such as 60 GHz. In Figure 1, we display the atmospheric attenuation at the mmWave radio spectrum in the presence of water vapor density of 7.5 g/m\({}^{3}\), in comparison with atmospheric attenuation in dry air (zero water vapor density). The figure also displays the peak attenuation values for the case of lossy air with water vapor. These bands are referred to in this paper as secure mmWave bands. The curves in this figure were computed based on the ITU-R document P.676-12 [15].  
As shown in Figure 1, the 60 GHz band is highly lossy and radio transmission in the band is limited to a few meters between the transmitter and receiver. For added security enhancement in cellular IoT links, radio transmissions can be tuned in a pseudo-random or deterministic manner over several sub-bands centered at 60 GHz. Since these sub-bands are very lossy, coding techniques that can assure link reliability in lossy channels must be deployed alongside MIMO-beamforming.  
Figure 1: Specific atmospheric attenuation for 1 to 500 GHz for an atmospheric pressure of 101.300 kPa and a temperature of 15 \({}^{\circ}\)C.
# Reducing Detrimental Communication Failure Impacts in Microgrids by Using Deep Learning Techniques
## 2 Communication System for Residential MG Management  
Figure 1 is a conceptual illustration of a combination of different communication systems operating in coordination to manage an MG cluster-based power-system. It consists of several modern farmhouses that can communicate through a satellite type backhaul link due to the wide separation and possible terrain complications between them. They are also electrically inter-connected which is not shown in the figure. By zooming on to an individual farmhouse, which represents a residential MG, it can be understood that distributed-generators (DGs) and loads are required to be managed by central controllers that can be located relatively far away. That long-range requirement can justify the utilization of LoRa, which is a low-power and long-range wireless protocol [13].  
Zooming in further, the core of the residential MG can be considered as a building that should be equipped with device-to-device (D2D) communication capabilities to supervise indoor and outdoor smart-devices. In this case, wide coverage is no longer required and ease of implementation and scalability are the important required characteristics that define the selected communication protocol. ZigBee is a very good candidate for smart-homes [14; 15]. In line with the development of MG, their low level control structures became less communication dependent and more reliant on local measurements. However, communication is still required for the higher level of the MG control hierarchy and only the primary level operates as communication free [16; 17; 18].  
The lowest level of communication, consists of power-electronics-based converters that interface the DGs and storage systems to the buses. These devices require access to critical readings (namely voltage and current measurements from sensors) in order to regulate their output characteristics based on their embedded control algorithms. This accessibility requires other types of communication links which can be very different from the higher levels in terms of specifications.  
From a communication network point of view, the illustrated setting consists of four levels of networks that are briefly summarized in Table 1. Those levels are device area network (DAN), smart home area network (SHAN), microgrid area network (MAN), and finally multimicrogrid area network (MMAN). As it can be observed, the specifications/requirements and protocols of each of the levels differ considerably from each other.
## 1 Introduction
In [20], a review of recent artificial intelligence-based CAD systems for COVID-19 diagnosis in CT and X-ray images is presented; however, since our work is based on X-ray images, we only focused on the latter. [PERSON] et al. [21] presented a Bayesian Convolutional Neural Network (CNN) to estimate the diagnosis uncertainty in COVID-19 prediction, distinguishing between COVID-19 and non-COVID-19 cases (other types of pneumonia and healthy patients), obtaining an accuracy of 92.9%. [PERSON] et al. [22] performed a binary classification between COVID-19 and normal cases comparing different Deep Learning (DL) models, achieving 98.0% accuracy with the ResNet50 model in the best case. [PERSON] et al. [23] presented a ResNet-based model for classifying COVID-19 (0.952 AUC), highlighting the pneumonia-affected regions by applying the Gradient-weighted Class Activation Mapping (Grad-CAM) method. Finally, [PERSON] et al. [24] proposed a Deep CNN to classify between COVID-19, non-COVID-19 (distinguishing between viral and bacterial), and normal cases, obtaining an accuracy of 83.5%. These studies achieved accurate solutions to fight against the COVID-19 pandemic. However, they have some limitations that should be considered. First of all, they used small datasets with less than 400 COVID-19 X-ray images in total in the best case. To validate the system, some of them only used 10 X-ray images for the COVID-19 class. Moreover, works that proposed not only COVID-19 detection, but also locating affected areas of the lungs, did not include any ground truth comparison or medical supervision with the obtained results. In this work, we present a DL-based CAD system, named COVID-XNet, which classifies between COVID-19 and normal frontal X-ray chest images. The network focuses on specific regions of the lungs in order to perform a prediction and detect whether the patient has COVID-19. The output of the system can be then represented in a heatmap-like plot by performing the Class Activation Map (CAM) algorithm, which locates the affected areas. The high reliability obtained in the results, which were supervised by a lung specialist, indicates that this system could be used to aid expert radiologists as a screening test for COVID-19 diagnosis in patients with clinical manifestations, helping them throughout this stage and to overcome this situation. The rest of the paper is structured, as follows: first, materials and methods are presented in Section 2, focusing on the dataset and the CNN model. Subsequently, results and discussion are presented in Section 3, where a quantitative evaluation is performed, along with the visualization of the pulmonary areas that are affected by COVID-19. Finally, the conclusions over the obtained results are presented in Section 4.
# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## 2 RS Applications in Ocean
### Ocean Oil Spill (OOS)
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**RS System** & **RS System (Type)** & **Advantage** & **Disadvantage** \\ \hline \multirow{6}{*}{Optical Multispectral} & \multirow{6}{*}{Less costly, easy to use, widely available} & \multicolumn{3}{c}{Useful only for documentation purposes, acquire images only during the daytime and non-cloudy regions, difficult to distinguish oil from the background, sun glint issue, wind sheen} \\  & & & \\  & & Can retrieve the oil slick thickness, richest RS data in terms of spectral information, the possibility of distinguishing the type of oil pollution & \multicolumn{3}{c}{Expensive, challenges in the transmission, storage, and real-time processing mainly due to the high dimensionality of hyperspectral images} \\ \multirow{2}{*}{Passive} & \multirow{6}{*}{TIR radiometers} & \multicolumn{3}{c}{Provides information about the relative thickness of oil spills, less costly, easy to use, widely available} & \multicolumn{3}{c}{Cannot detect thin layers of OOS and the emulsions of oil in the water, acquires images only during the daytime and non-cloudy regions, interrupptive presence of OOS lookalikes, such as seaweeds and shorelines} \\  & & & Provides information about oil spill thickness, provides data in both day and night times, works well in bad weather conditions & \multicolumn{3}{c}{Low spatial resolution, existence of false alarms owing to biogenic materials} \\ \multirow{6}{*}{Active} & \multirow{6}{*}{SAR} & \multicolumn{3}{c}{Provides data in both day and night times, works well in adverse weather conditions, a good estimate of OOS extent} & \multicolumn{3}{c}{The success of analysis depends on wind speed, presence of numerous oil spill lookalikes, cannot measure} \\  & & & good estimate of OOS thickness & \multicolumn{3}{c}{OOS thickness} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Different RS systems for OOS detection along with their advantages and disadvantages. expensive and mostly publicly unavailable compared with multispectral and microwave RS systems [233]. Several studies on OOS successfully applied hyperspectral systems. For example, [PERSON] et al. (2013) [234] applied a normalization method to retrieve the oil slick thickness within a specific range using Hyperion data. Moreover, [PERSON] et al. (2021) [235] developed a CNN model to employ both spectral and spatial information of hyperspectral images for classifying OOS into thick and thin oil classes. Moreover, [PERSON] et al. (2019) [236] combined spatial information and the Stacked Autoencoders neural network for OOS classification using Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data. In summary, SAR systems provide data both day and night without being affected by bad weather conditions. Thus, SAR systems attract more attention to OOS studies [224; 226]. In the following subsection, more details of OOS mapping using SAR data are provided.
# Ultra-Low-Power Wide Range Backscatter Communication Using Cellular Generated Carrier +
## 3 Propagation Models
### 3 GPP Indoor Hotspot (InH) Model
#### 3.3.1 Open Indoor  
\[Pr_{LoS}=\begin{cases}1&d_{2D}\leq 5m\\ e^{-\frac{d_{3D}-5}{3D8}}&5m\geq d_{2D}\leq 49m\\ 0.54e^{-\frac{d_{2D}-65}{21D}}&d_{2D}>49m\end{cases} \tag{12}\]
## 2 Biomedical Image Segmentation
### Introduction of Probabilistic Models in Biomedical Image Segmentation  
Probabilistic models play a pivotal role in biomedical image segmentation, offering a principled framework for capturing uncertainty and variability inherent in complex imaging data. Among the diverse set of probabilistic models, Gaussian Mixture Models (GMMs) and Hidden Markov Models (HMMs) have gained prominence for their versatility and ability to model intricate statistical relationships within biomedical images.  
Probabilistic models provide a mathematical foundation to represent and understand uncertainty in data. In the context of biomedical image segmentation, where pixel intensities can vary significantly, probabilistic models offer a robust framework for capturing the inherent stochastic nature of the imaging process. These models allow for the estimation of probability distributions, enabling a more nuanced representation of the complex relationships between image features.  
Gaussian Mixture Models (GMM)  
GMMs are a powerful class of probabilistic models commonly employed in biomedical image segmentation. They assume that the observed data are generated from a mixture of several Gaussian distributions, each corresponding to a different underlying structure or class in the image. The combination of these Gaussians forms a flexible and expressive model for capturing the diverse intensity variations present in biomedical images.  
GMMs find applications in various segmentation tasks, including tissue classification in medical images. By modeling the intensity distribution of different tissues with Gaussian components, GMMs can effectively delineate regions with varying contrasts and textures.  
This flexibility makes them well-suited for scenarios where traditional thresholding methods may fall short [37; 38].
# Novel Fuzzy Logic Scheme for Push-Based Critical Data Broadcast Mitigation in VNDN
## 4 Proposed Push-Based Data Forwarding Scheme with Fuzzy Logic
### Selection of Cluster Head (CH) using Fuzzy Logic
#### 4.2.4 Total Weight  
After calculating the input values of a node/vehicle, we obtain the total weight for each node/vehicle in the cluster by using the following Equation (8):  
\[Total_{vi}=(dist_{vi}+speed_{vi})\times slope_{vi} \tag{8}\]  
\[CH=\min_{i}^{n}(Total_{vi})\,\ \ \ \ \ CH\
eq 0. \tag{9}\]  
Any node/vehicle carrying the minimum value will be selected as the CH of that cluster. It is noteworthy that the value must be greater than zero in order to participate in the selection process.
## 3 Validation
### Result Analysis
Also, the comparison to PSO, executed with a computational budget of 1000 EM simulations (i.e., almost twice as high as the typical cost of the proposed method), provides important indications concerning the performance of nature-inspired procedures for the considered class of size reduction tasks. Based on a typical evolution of the objective function value for population-based methods (rapid initial decrease followed by a plateau), it is expected that reaching the design quality offered by the proposed method would require many thousand EM analyses, if possible at all. Furthermore, one can observe that Antenna IV is by far the most challenging case, primarily due to the number of its geometry parameters (thirteen). All benchmark algorithms, including Algorithm II (adaptive penalty factors), perform poorly for this structure, not only in terms of achievable miniaturization rates and solution repeatability but also in constraint control. At the same time, Algorithm III (PSO) performs considerably better than both Algorithms I and II, which confirms the challenging nature of this case study. Notwithstanding, the proposed procedure produces results that are superior to all benchmark methods. It yields designs with average antenna sizes being about half of that obtained using Algorithms I and II, a standard deviation almost six times smaller than Algorithm III, and excellent enforcement of the design constraint. The collective performance of the introduced globalized size reduction approach is encouraging, both in terms of the average size reduction ratio, the solution repeatability, and computational efficacy. The latter means that although the CPU expenses of our procedure are higher than those of local algorithms (Algorithms I and II), the difference is not overwhelming given the efficacy benefits. The reliability of the developed globalized knowledge-based miniaturization framework results from an accurate identification of the feasible region boundary, where the designs meeting the size-reduction-related design specifications reside. This identification is carried out by extracting knowledge on actual constraint violations from random observables as well as the use of an automated decision-making procedure, which selects observables of sufficient quality. At the same time, the costs are practically acceptable and significantly lower than what is required using nature-inspired methods. This is possible due to the use of variable-fidelity EM simulations as well as dimensionality reduction when defining the domain of the surrogate. Note that both mechanisms compromise accuracy; notwithstanding, this does not affect overall reliability because all simplifications are pertinent to the intermediate stages of the optimization process. The final steps, i.e., the construction of the surrogate model and the final tuning, are both executed at the high-fidelity level.
# Reflective Fiber Surface Plasmon Resonance Sensor for High-Sensitive Mercury Ion Detection
## 3 Performance Test  
### Refractive Index Measurement Experiment  
A fiber SPR sensor can detect the change of the external refractive index, which is the basic measurement principle of an Hg\({}^{2+}\) sensor. Therefore, we first carried out the refractive index measurement experiment of the reflective fiber SPR sensor before coating the CS/PAA sensitive film. As shown in Figure 7, when the external refractive index increases from 1.3333 to 1.3787, the resonant dip of the SPR moves towards the long wavelength direction. From the fitting result shown in Figure 7b, the refractive index sensitivity of the SPR sensor reaches 2110.33 nm/RIU and the coefficient of determination, \(R^{2}\) (also known as goodness of fit), is 0.9798. The maximum value of \(R^{2}\) is 1, and the closer the \(R^{2}\) gets to 1, the better the linearity of the fitting curve. The experiment results are consistent with the simulation results shown in Figure 2c, which demonstrates the feasibility of the  
Figure 5: Coating process of the Hg\({}^{2+}\) sensitive film.  
Figure 6: Reflective fiber SPR Hg\({}^{2+}\) measurement system.  
fabricated sensor in high-sensitive refractive index sensing. In addition, combined with the sensitive properties of the CS/PAA sensitive film, we can conclude that the proposed sensor can be used for the sensing of Hg\({}^{2+}\) concentrations. Therefore, in subsequent experiments, we will investigate and analyze the performances of the proposed sensor in the measurement of Hg\({}^{2+}\) concentrations.