esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Possible Use Cases
### SCF Relaying in Multicell Topologies with Homogenous Access Networks  
In the previous section, the single cell operation of SCF relaying was discussed. In this section, by extending the operation of SCF relaying in multicell topologies considerably more opportunities arise. The fundamental difference between previous relaying techniques and the proposed scheme is that non of the past works made use of the inherent mobility of communicating nodes. However, via store-carry and forward relaying, the potential to migrate data traffic from target cells to adjacent cells could be realised by allowing nodes to buffer messages while in transit. In this way, traffic could be distributed across cells to achieve a load balancing effect or shifted to target cells in order for under-utilized cell sites to suspend their operation and save energy. Load balancing in multicell topologies via fixed relaying has been considered in the past [24, 25]. However, the performance of the load balancing operation with fixed relaying heavily depends on users' location. For mobile relays on the other hand, spatial dependencies exist between relay nodes and user populations. Consider for example the number of taxis around large crowds. Therefore SCF relaying could be an effective mechanism for load balancing improving both  
Figure 2: Multihop relaying via store-carry and forwarding within a single cell.  
the call blocking and call dropping probabilities while increasing at the same time the energy efficiency gains by operating the network at stable loads.  
Switching off under-utilized BSs to save energy has been consider in several occasions in the past. The rational behind this work is that only 19-25% of the BSs carry more than 70-85% of the total aggregated traffic in the network [26]. Hence, switching off BSs could improve considerably the energy efficiency of the system. In this case, the SCF scheme can provide the necessary connectivity to cell sites where BSs have been switched off without the need for adjacent cells to increase their transmit power in the coverage holes formed by switching off BSs. What's more, instead of UTs transmitting over longer distances when the serving BSs are switching off, the SCF relaying scheme can be employed to shift traffic closer to the cell center, in effect maintaining and/or improving the radio link performance; obviously at the expense of increases message delivery delays.
# A Thermal Discomfort Index for Demand Response Control in Residential Water Heaters
## 5 Experimental Validation
### Evaluation of the Water Temperature Model  
The linear model described in Section 4.3 was evaluated for a real case study corresponding to an electric water heater with a thermometer to measure the temperature of the water in the tank. The defined setting of the thermostat allowed knowing in advance the values of parameters \(T_{min}\) and \(T_{max}\). The corresponding values are \(T_{min}=55\,\mathrm{\SIUnitSymbolCelsiusC}\) and \(T_{max}=65\,\mathrm{\SIUnitSymbolCelsiusC}\). Then, the other parameters of the model were calculated as described in Section 5.3. Finally, data of twelve hours on blocks of the electric water heater were usedto estimate the temperature and compared with the real temperature measured. Table 1 reports the comparison of the real and the estimated temperature, and the largest difference in the three long utilization periods in the twelve hours analyzed.  
The second utilization had the largest temperature difference (2.7 \({}^{\circ}\)C, marked in light blue in Table 1), which represents a percentage error of 4.5% in the worst case. The other utilizations had a significantly lower error. The accuracy of the temperature model is adequate for the purpose of estimating TDI.
## 4 Results of Oscillation Mode under Different Parameters
### Different Masses  
In addition to the damping and stiffness, the system mass \(m\) also plays an important role in the FIM performance of the triangular prism. In order to reveal the impact of \(m\) on the oscillation mode, three complete responses of the regular triangular prism under different \(m\) are compared in Figure 13. In this figure, the damping coefficients are \(c\) = 92.52 N\(\cdot\)m\(\cdot\)s\({}^{-1}\), and the system stiffness \(K\) = 1400 N/m. The descriptions of \(A^{*}\),\(f^{*}\), \(U_{r}\), LL, UL, and CV are listed in the Abbreviations.  
According to Figure 13, the following characteristics can be found.  
For \(m\) = 36.71 kg, the prism undergoes the SG responses which consist of the incomplete VIV branches, VIV-galloping transition branch and galloping branch. In the responses, the self-excited region is observed accompanied with LL = 7.875 and UL = 10.375.  
When \(m\) increases to 40.61 kg, the prism exhibits the CG response which consists of the complete VIV branches and complete galloping branch. In the responses, UL and LL coincide at \(U_{r}\) = 8.5, which is equal to CV. Although the prism can be self-excited from VIV to galloping at \(U_{r}\) = 8.5, the self-excited region has already disappeared.  
Figure 13: Oscillation modes under different mass \(m\): (**a**) Amplitude ratio \(A^{*}\) versus reduced velocity \(U_{r}\); (**b**) Frequency ratio \(f^{*}\) versus reduced velocity \(U_{r}\).  
When \(m\) increases to 44.44 kg, the oscillation mode of prism transitions from CG to HG. In the responses, the complete VIV branches and the incomplete galloping branch are observed. Correspondingly, CV increases from 8.5 to 11.625.  
According to the above analysis, the impacts of system mass \(m\) on the oscillation modes of triangular prisms can be summarized as follows. When the mass is low, the oscillation mode of is SG; as the mass increases, the oscillation mode gradually transitions from SG to CG, followed by HG. Therefore, the ability of a triangular prism to be excited (including the self-excited and externally excited) from VIV to galloping is gradually weakened as the stiffness increases.
## 2 Related Work
### Multicast Architectures
#### 2.1.3 LISP-Based Multicast  
Locator/ID Separation Protocol (LISP) [43] is a network layer-based protocol which separates the identification and location of IP address by introducing the Endpoint Identifiers (EIDs) space and Routing Locators (RLOCs) space, respectively. The syntactical format of EIDs and RLOCs is identical to IP address, but their semantics are different. Mapping database is introduced to stores the mapping relationship between EIDs and RLOCs. LISP inherits the traditional host protocol stacks and Internet infrastructure, which is easy to deploy.  
Based on LISP framework, LISP multicast [44] is proposed to support the inter-domain multicast routing, which maps the source EID into RLOC without changing the group address, and is compliant with PIM in RLOC name space. Other work such as signal-free LISP multicast [45], is designed to support the multicast packet delivery when multicast is not available to connect the multicast sites together, which adopts unicast replication and encapsulation based on LISP mapping mechanism.  
Compared with SDN and HIP-based multicast, LISP-based multicast is easy to deploy due to compliance with IP multicast protocols; however, at the same time it also inherits the shortcomings of IP multicast.
# Energy Consumption Prediction for Drilling Pumps Based on a Long Short-Term Memory Attention Method
## 3 Results and Discussion
### Construction of LSTM-Attention Model
The structural design of the LSTM-Attention model specifically addresses the business needs of drilling pump energy consumption prediction, focusing on resolving several key challenges:  
Capturing Complex Temporal Dependencies: The LSTM layer effectively captures the temporal dependencies in energy consumption variations under different operational conditions, such as the dynamic changes in pump pressure, flow rate, and drilling depth. Focus on Key Features: The attention mechanism identifies the most impactful time steps and parameters for energy consumption prediction, allowing the model to focus on features that are critical to the prediction outcome, thus enhancing prediction accuracy. Improvement in Prediction Accuracy: The dense layer integrates all critical contextual information, providing more accurate prediction outputs. This is crucial for real-time decision support and energy optimization in drilling operations.
## 2 Designed 1D CNN-BiLSTM-AdaBoost Model
### Detailed Model Description
#### 2.2.2 Data Fusion  
Sensor fusion refers to the integration and optimization of data collected from multiple sensors of the same or different types, positioned at different spatial locations. It is generally categorized into the following three types: data-level fusion, feature-level fusion, and decision-level fusion [21].  
Data-level fusion involves directly concatenating raw sensor data [22]. Feature-level fusion is an intermediate approach where features are first extracted from the raw data and then integrated [23]. Decision-level fusion first derives decision-related information from the data before integrating the individual decisions [24]. A comparative analysis of the advantages and disadvantages of these three fusion strategies is presented in Table 1.  
Compared to data-level and decision-level fusion, feature-level fusion is a more mature approach. It reduces the complexity of input data, avoids information redundancy, and preserves both local and global information, thereby enhancing accuracy and robustness. Additionally, it is more flexible as it does not require additional model training. Feature-level fusion effectively leverages deep learning models' feature learning capabilities, further improving model performance. Therefore, this study adopts feature-level fusion for data processing.  
Figure 2: Basic principle of the 1D CNN.
# Preddiagnosis of Heart Failure (HF) Using Deep Learning and the Korotkoff Sound
## 1 Introduction
A common method of blood pressure monitoring, the KS-based blood pressure test is widely recognized and expertly performed. KS is a group of short and rapid pulse sounds produced by brachial artery blood flow during the process of overcoming cuff pressure, which is closely related to brachial artery vibration and blood turbulence [27]. KS is only a pulse signal in comparison to heart sound, but its transient characteristics are obvious, and signal strength and characteristics are highly related to time and cuff pressure, posing some challenges to KS signal processing and analysis. However, if heart disease prediagnosis can be completed concurrently with blood pressure monitoring, it will improve many problems in current heart disease prevention and increase the likelihood of early detection of heart disease. The primary objective of this study is to test whether we can develop a simple, non-invasive heart disease prediagnosis system for ordinary people to achieve a broader range of community and family promotion. Therefore, we reintroduce this traditional, minor source of concern: KS. It has a wide range of applications and a high penetration rate, and most people are very familiar with the blood pressure detection process, which simplifies KS signal acquisition and increases the popularity of related detection systems. This paper proposes an innovative method for the prediagnosis of patients with HF using a DL network and KS. We extracted the continuous wavelet transform (CWT) and Mel Frequency Cepstrum Coefficient (MFCC) features of KS and sent the two feature maps to four common DL networks for training and classification, including AlexNet, VGG-19, ResNet50, and Xception. To confirm the best analysis model, the performance of each model is evaluated using accuracy (Acc), specificity (Sp), sensitivity (Se), AUC criteria, and time consumption (Tc). The contributions in this paper may be summarized as follows:An innovative method for the prediagnosis of HF based on KS and DL networks has been proposed, analyzed, and validated in four types of DL networks. * The impact of signal segmentation and feature extraction methods on KS-based HF prediagnosis is thoroughly investigated in this paper. * The pre-trained neural network model is transferred to the KS-based HF prediagnosis task, which improved the model's training efficiency and ensured the reliability of this paper's conclusion. The structure of this paper is as follows: Section 2 discusses related work in cardiovascular disease research; Section 3 describes the analysis method and materials used in this article; Section 4 describes the performance of our method in the prediagnosis of HF, Section 5 delves deeply into the experimental process and outcomes; and Section 6 presents the conclusions and future work.
## 4 Discussion
Additionally, we further evaluated the effect on matrix metallopeptidases (MMP) activity, with no difference being observed when compared to controls (unpublished data), and, although a study has previously shown that MMP-2 and MMP-9 activity was inhibited by _S. hortensis_, those assays were only performed with the essential oil [60]. Rather than the more traditional mechanisms for cancer initiation and progression, inflammatory processes seem to be the main regulation of colitis-associated cancer, an imbalance in intestinal microbiota, and a crosstalk between various signaling pathways [61]; therefore, a pleiotropic approach seems to present itself as better pharmacological tool, not only targeting inflammation associated with colitis but also associated with the mechanism controlling cancer development and progression. Several studies have already identified that extracts of plants known to possess high concentrations of rosmarinic acid exhibit anticancer effects in different in vitro and in vivo models of various types of cancer [62; 63; 64]. One study evaluated rosmarinic acid administration in a rat model of 1,2 dimethylhydrazine (DMH)-induced CRC [65]. Rosmarinic acid was administered (5 mg/kg) through the whole period of CRC induction and exhibited tumor reduction, reduction of stress oxidative markers, reduction mucosal bacterial enzymes activity, regulation of xenobiotic metabolizing enzymes, and up-regulation of apoptotic factors. In another study, rosmarinic acid antagonized activator protein-1-(AP-1)dependent activation of COX-2 expression in HT-29 colon cancer cells [66]. Given the well-known relevance of these two crucial inflammatory enzymes, the impairment of COX-2 and iNOS expression in our work confirmed the beneficial effect of Summer Savory not only on IBD pathogenesis but also on disease progression and the possible link to cancer proliferation. Their roles in angiogenesis, apoptosis, and metastatic processes and resistance to chemotherapy/radiation are well understood and of significant importance for IBD and CRC patients [67; 68; 69; 70; 71; 72]. In fact, the role of COX-2 and iNOS in intestinal inflammation has pointed them out as great targets for chemoprevention of colon cancer [72].
# Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System
## 3 How IoMT Works
### Challenges of IoMT
#### 3.2.4 Power Consumption  
Another obstacle to the widespread deployment of IoMT devices is power consumption. When a sensor is attached, the majority of IoMT devices require either a regular backup solution or the usage of a strong battery [53]. Designing sustainable medical equipment that can provide its own electricity and integrating the IoMT system with renewable power sources that can also help to mitigate the global energy problem should be the current emphases.
# Performance Tradeoff Analysis of Hybrid Signaling SWIPT Systems with Nonlinear Power Amplifiers
## 4 SNDR Analysis  
In the hybrid signaling SWIPT scenario, we have a mixture of a high-power energy signal and a low-power information signal. When the hybrid signal passes through nonlinear PAs [62], both signals will generate undesirable distortion signals that act as interference with the information signal. As a commonly used metric of signal distortion in communication systems, SNDR is broadly defined as the ratio of the useful signal to the total distortion power and noise power. The exact definitions of SNDR can take several forms, as proposed by [PERSON] [63], [PERSON] [64], and [PERSON] [65]. This paper adopts the definition in [65]. Considering an information transmission system, the output signal \(y\) of the PA can be written as the sum of two parts:  
\[y=\alpha x+d. \tag{22}\]  
The first item on the right-hand side is a linear amplification of the useful information signal, while the second item is the distortion signal, which is independent of \(x\). The linear amplification factor \(\alpha\) is defined as  
\[\alpha=\frac{E[x^{*}y]}{E\big{[}|x|^{2}\big{]}}. \tag{23}\]  
The power \(\varepsilon_{d}\) of the distortion term is defined as  
\[\varepsilon_{d}=E\Big{[}|d|^{2}\Big{]}=E\Big{[}|y|^{2}\Big{]}-|\alpha|^{2}E \Big{[}|x|^{2}\Big{]}. \tag{24}\]  
Given the above definitions, the SNDR can be defined as  
\[\text{SNDR}=\frac{|\alpha|^{2}E\Big{[}|x|^{2}\Big{]}}{\varepsilon_{d}+\sigma_ {v}^{2}} \tag{25}\]  
where \(\sigma_{v}^{2}\) is the noise power. Because the SWIPT signal consists of a large energy signal and a small information signal, the PA could have different linear amplification effects with respect to these two signals. When the effects of nonlinearity are not severe, the amplification coefficients of the two components can be approximately treated as the same. Otherwise, different amplification coefficients should be applied. For a comprehensive discussion, we further distinguish the following two cases of SNDR definitions.
# Use of Anionic Polysaccharides in the Development of 3D Bioprinting Technology
## 2 Overview of Natural Anionic Polysaccharides from Plant, Bacteria and Algae  
Carbohydrate polymers from natural sources have been studied and utilized in the pharmaceutical and biotechnological industries for many years. Besides its desired gelling properties, these natural polysaccharides have been used as hydrogels for 3D bioprinting [3; 4; 5; 10; 11]. For example, in the presence of divalent cations, such as calcium, anionic polysaccharides are able to form gels because of associations between the calcium ion and the acid residues. This section gives a short emphasis on the most described and principal natural anionic polysaccharides of interest, including: polygalacturonic acid (PGA), polyglucuronic acid (PGU) and alginate reported for their gelling properties and their potential for 3D bioprinting technology.
# Exploring the Frontiers of Cybersecurity Behavior: A Systematic Review of Studies and Theories
## 6 Conclusions and Future Research Agendas
The role of moderators, such as age, sex, and technical experience, in shaping the effects of other determinants on cybersecurity behavior should also be investigated at the individual level. This can help to explain inconsistencies in the existing literature. Moreover, research at the individual level should be conducted in collectivistic societies to understand how cultural values and group norms shape cybersecurity behavior. Future research needs to be conducted longitudinally to understand how cybersecurity behavior changes over time and in response to different factors and events. Comparative studies can also be suggested across different cultures and regions to understand how cultural and regional factors influence cybersecurity behavior. In summary, cybersecurity is crucial for both organizations and individuals. It has been observed that an increasing number of non-expert social media users are becoming aware of the significance of various security measures [106]. Furthermore, individuals are less inclined to divulge personal information due to privacy concerns [107]. As stated in [108], security, privacy, and resilience are vital components of healthcare applications. Additionally, the Metaverse is not immune to security and privacy violations linked to human behavior, as noted in [109; 110]. Consequently, [PERSON] and [PERSON] [47] underscored the necessity for implementing policies to regulate employee conduct within organizations. Enhanced education and awareness contribute to improved cybersecurity behavior [111; 112]. Therefore, raising information security awareness can foster positive behavior among employees [113]. Conceptualization, A.A. and M.A.-E.; methodology, A.A. and M.A.-E.; validation, A.A. and M.A.-E.; formal analysis, A.A.; investigation, A.A.; resources, A.A.; writing--original draft preparation, A.A.; writing--review and editing, M.A.-E. and K.S. ; supervision, M.A.-E. and K.S. ; project administration, M.A.-E. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The data presented in this study are available on request from the authors. The authors declare no conflict of interest. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \# & **Source** & **Year** & **Theoretical Model** & **Independent Variables** & **Dependent Variables** & **Technology/Service** & **Methodology** & **Participants** & **County** \\ \hline \multirow{4}{*}{S21} & [119] & \multirow{4}{*}{2017} & \multirow{4}{*}{Protection Motivation Theory} & \multirow{4}{*}{“Computer skills”, “Information seeking skills”, “Eperience with cybersecurity practice”, “Freoted susceptibility”, “Freoted severity”, “Freoted severity”, “Seller-efficacy”, “Freoted barriers”, “Respense efficacy”, “Cues to action”, “and “Freot behavior”} & Not Specified & \ \begin{tabular}{c} Quantitative \\ (Survey) \\ \end{tabular}  & \begin{tabular}{c} Organizational \\ employees \\ \end{tabular}
# On Modeling Eavesdropping Attacks in Underwater Acoustic Sensor Networks +
## 2 Underwater Acoustic Channel Model
### Ambient Noise  
The ambient noise in underwater acoustic channel is more complicated than that in terrestrial wireless channel. We usually model the ambient noise by four kinds of sources: turbulence, shipping activities, waves, and thermal noise. Specifically, the noise caused by shipping activities can be modeled by shipping activity factor \(s\), whose value ranges from 0 to 1 representing the intensity of shipping activities (0 representing low and 1 representing high). The noise of waves, aroused by wind, can be described by wind speed \(w\) (m/s). In UASNs, the ambient noise depends on signal frequency \(f\) and the formula between power spectral density of the four noise sources and frequency in kHz in dB re \(\mu\) Pa per Hz are given by [38].  
\[\begin{split} 10\log N_{t}(f)&=17-30\log(f)\\ 10\log N_{s}(f)&=40+20(s-0.5)+26\log(f)-60\log(f+0.03)\\ 10\log N_{w}(f)&=50+7.5w^{1/2}+20\log(f)-40\log(f+ 0.4)\\ 10\log N_{th}(f)&=-15+20\log(f)\end{split} \tag{5}\]  
where \(N_{t}(f)\), \(N_{s}(f)\), \(N_{w}(f)\) and \(N_{th}(f)\) denote the noise sources caused by turbulence, shipping activity, wind and thermal, respectively.  
Then, the total noise in dB is as follows:  
\[10\log N(f)=10\log(N_{t}(f)+N_{s}(f)+N_{w}(f)+N_{th}(f)) \tag{6}\]  
Figure 1 shows the power spectral density of the ambient noise \(N(f)\)(dB) in various values of shipping activity \(s\) and wind speed \(w\). It can be seen from Figure 1 that shipping activity factor \(s\) mainly affects the noise when \(0.001<f<1\) kHz, while wind speed \(w\) mainly affects the noise when \(f>1\) kHz, which is the frequency band usually used in underwater communications. Therefore, we will consider the impacts of wind speed \(w\) on the eavesdropping activities in this paper.  
Figure 1: Power spectral density of the ambient noise \(N(f)\) (dB re \(\mu\) Pa).