esa-sceva/satcom-chunk-collection huggingface dataset:
# Phonocardiography Signals Compression with Deep Convolutional Autoencoder for Telecare Applications
## 1 Introduction  
According to the American heart association report, cardiovascular disease (CVD) is the leading global cause of death and it is expected to have more than 22.2 million deaths by 2030 [1]. Auscultation is one of the significant methods for CVD's monitoring [2]. Besides, heart sound can be used to diagnose heart diseases or to evaluate a human's physiological condition [3, 4]. The electronic stethoscopes exploit the vibrations that are caused by the heartbeats to graphically record the heart sounds called phonocardiography (PCG) signals [5]. The PCG signals provide a non-invasive method for detecting heart valve abnormalities and assisting in diagnosing heart disease. An efficient signal compression method is necessary due to the vast amounts of data that are generated by long-term PCG monitoring. Moreover, telecare, which uses telematics to transmit medical information, has attracted much attention recently, especially for telemedical problems in rural areas [6]. For example, people can put on singlets, which are embedded wearable electrocardiography (ECG) sensors, in order to collect ECG data for telecare purposes [7]. However, for peoples who live in rural areas and have insufficient medical resources, we may use medical electronic stethoscopes to collect the PCG signals and transmit these signals to the doctors at the remote hospital. For the smart healthcare ecosystem, healthcare data privacy and security and data storage issues are some of the most challenging issues and opportunities [8, 9]. Most of the current medical tests are done in medical institutions. To check the patients' health conditions, many of them need to go back and forth between their residences and hospitals. This wastes the patient's time for medical consultation. Therefore, the development of telecare is a critical issue today. It has created a new way of medicalcommunication, which enables synchronized or asynchronous interaction between physicians and patients, overcoming spatiotemporal barriers, improving medical quality, and increasing convenience. In this work, we focus on the compression of PCG signals by using the deep convolutional autoencoder. Furthermore, we consider the impact of communication link quality [10] and float-point to fixed-point conversion issues. Our proposed method achieves a compression ratio of 32 with the percent root-mean-square difference less than 4.8% under the word-length of 10. Our deep autoencoder can be implemented on the existing lightweight deep learning framework on a smartphone by inspecting the model complexity. The remainder of this paper is organized, as follows. Section 2 briefly describes related work regarding PCG compression problems and background about the deep CNN network. Section 3 details our proposed deep convolutional autoencoder. Section 4 reports the experiments to validate the effectiveness of our work on the PCG compression problems. Section 5 discusses the model complexity issues. Conclusions are drawn in Section 6.
# Error Performance of Amplitude Shift Keying-Type Asymmetric Quantum Communication Systems
## 3 Model of an ASK-Type AQC System
### Description of Received Quantum State
#### 3.1.1 Quasi-Bell State with Stinespring Representation  
When a Stinespring representation is used, the loss incurred by an attenuated channel can be expressed using the interaction with the vacuum field as an environment mode E. In other words, the unitary evolution of the composite system SE can be described by applying the unitary operator \(\mathbb{U}_{\text{SE}}^{(\eta)}\) to the SE, where the results represent the interaction between modes S and E:  
\[\mathbb{U}_{\text{SE}}^{(\eta)}|\alpha\rangle_{\text{S}}|0\rangle _{\text{E}} = |\sqrt{\eta}\alpha\rangle_{\text{S}}|\sqrt{1-\eta}\alpha\rangle _{\text{E}}, \tag{16}\] \[\mathbb{U}_{\text{SE}}^{(\eta)}|-\alpha\rangle_{\text{S}}|0 \rangle_{\text{E}} = |-\sqrt{\eta}\alpha\rangle_{\text{S}}|-\sqrt{1-\eta}\alpha \rangle_{\text{E}}, \tag{17}\]  
where \(\eta\) is the energy transmissivity. The reduced state of the composite system SAE on SA, i.e., the received quantum state, can be acquired by performing a partial trace over mode E, because only the composite system SA is actually measured by the detector.  
Figure 1: Protocol of an amplitude shift keying-type asymmetric quantum communication system.  
For the case where \(\mathsf{b}\in\{0,1\}\), we now consider the received quantum states \(\Psi^{(\mathsf{b})}_{\mathrm{SA}}\) when the binary information that was recorded using the reflectivities \(R_{\mathsf{b}}\) is denoted by \"0\" and \"1\". Suppose that the transmitted quantum state at the light source is the maximum quasi-Bell state. Then, the received quantum state \(\Psi^{(\mathsf{b})}_{\mathrm{SA}}\) can be represented by  
\[\Psi^{(\mathsf{b})}_{\mathrm{SA}}=\mathrm{Tr}_{\mathrm{E}}\Big{\{}\big{(}( \mathbb{U}^{(R_{\mathsf{b}})}_{\mathrm{SE}}\otimes\mathbb{I}_{\mathrm{A}} \big{)}\big{(}|\Psi_{2}\rangle_{\mathrm{SA}}\langle\Psi_{2}|\otimes|0\rangle_{ \mathrm{E}}\langle 0|\big{)}\big{(}\mathbb{U}^{(R_{\mathsf{b}})}_{\mathrm{SE}} \otimes\mathbb{I}_{\mathrm{A}}\big{)}^{\dagger}\Big{\}}. \tag{18}\]  
The same supposition can be applied to the non-maximum quasi-Bell state.
# MPResiSDN: Multipath Resilient Routing Scheme for SDN-Enabled Smart Cities Networks
## 2 Background
### Software Defined Networking  
Software-Defined Networking (SDN) is an emerging technology that employs network architecture that differs from the architecture used in traditional networks. SDN-enabled networks decouple the control plane from the data plane by separating the routing decisions and the forwarding decisions. This separation is the key factor that enables all SDN capabilities. Figure 3 represents a logical view of SDN architecture [22]. The application layer deals with end-user applications that utilize SDN services. The control layer is the main layer in the architecture and comprises a set of software-enabled SDN controllers that centralize the network intelligence and maintain a global view of the network. Network administrators use the controllers to apply custom policies to the devices of the infrastructure layer; the infrastructure layer consists of physical devices, such as switches and routers. In SDN architecture, routers are no longer responsible for routing. Instead, they only forward packets based on flow tables that are obtained from SDN controllers. SDN controllers connect to routers through southbound APIs, and applications connect to SDN controllers through northbound APIs [23].  
Implementing SDN has numerous advantages [24; 25]. First, SDN provides centralized network management because an SDN controller has a centralized view of the entire net  
Figure 3: SDN architecture.  
Figure 2: An example of smart city monitoring.  
work status, which makes network management much easier. In addition, SDN enhances the efficiency of network administration, as it permits network characteristics to be changed remotely. Moreover, SDN offers programmability features such that policies and protocols can be deployed to any network device. SDN also facilitates configuration and reconfiguration processes. Furthermore, implementing SDN is cost-efficient because most SDN products are open source. In addition, SDN supports up to layer-3; therefore, enterprises do not need to purchase expensive hardware [26]. SDN also provides fine-grained security to end-devices [27].
## 4 Analysis and Results
### Dynamic Analysis
#### 4.2.3 Case III--Abduction  
Impact on a constrained finger from the side results in deformation in the abduction/adduction mode. The DIP and PIP revolute joints allow motion in the flexion/extension direction only. Only the MCP joint can act as a revolute joint within the prescribed limit in this mode of loading. These simulations, as shown in Figure 11, therefore, represent a much harsher scenario where tissue damage as well as bone fracture can occur at lower speeds to the constrained motion of the finger.  
Figure 8: Shear Elastic Strain under hyperextension for effective masses (a) 3.6, (b) 7.2, and (c) 14.4 kg.  
Figure 9: Principal stress under hyperextension for effective masses (a) 3.6, (b) 7.2, and (c) 14.4 kg.  
Figure 10: Flexion as a function of end effector velocity and effective mass, with contour results at the points shown: (**a**) Elastic Strain, (**b**) Principal Stress, and (**c**) Contact Force.  
Technical Specification ISO/TS 15066 presents calculated transient speed limits as a function of robot effective mass using a simplified elastic collision model. Here, we validated and expanded upon those results using virtual experiments for low- and medium-payload-capacity robots (such as UR3 and UR5, respectively), and the results were further extended to heavy-payload-capacity robots. A summary of estimated speed limits for small-, medium-, and heavy-payload-capacity robots under the three loading modes is presented in Table 3. A high risk of fracture in the cortical bones was used as the failure criteria. The analysis suggests that high-payload-capacity robots (UR10 or equivalent) must be restricted to around 80% of the speed limits of low-payload-capacity robots (UR3 or equivalent) despite having 4 times more effective mass.  
A comparison of the three loading conditions and three robot sizes with an impact velocity of 1000 mm/s is presented in Figure 12. The unsafe region of operation is marked in the figure. The figure highlights the importance of the mode of impact and robot payload capacity.
# Device-To-Device Communication in 5G Environment: Issues, Solutions, and Challenges
## 7 Challenges in D2D Communication
### Challenges in Device Discovery
#### 7.1.3 Multicell Device Discovery  
The device discovery will become a big challenge if a device belonging to a different cell in the cellular network has that device mobility, as a result of how the radio resources are being provided and who will benefit from it. The authors in [83] proposed one multicell DD scheme for the solution. The proposed method made involve core network along with the BS. Thus, it is essential and critical to propose the discovery of signal scheduling.
# A Trust-Based Model for Secure Routing against RPL Attacks in Internet of Things
## 7 Conclusions and Future Work  
The IoT is emerging in several applications, such as, healthcare, grids, industries, traffic, etc. The number of IoT is increasing exponentially and is estimated to reach billions  
Figure 11: Comparison of Power Consumption (mW).  
in coming years. The smart appliances usually are resource constrained; thus, a computationally intensive security mechanism cannot be embedded in the IoT. Several security solutions, including trust-based approaches, deep learning, and IDS, are proposed for networks security in the IoT. A trust-based method is suitable according to the limitations and requirements of the IoT. In existing literature, the mobility of nodes has been insufficiently addressed by trust-based approaches. Hence, it is imperative to explore trust-based security methods with optimum resource utilization for RPL routing attacks. This will help with IoT adoption on a wide scale. This research critically analyzes the parameters involved in trust computation and proposes security, mobility, and trust-based routing protocol, SMTrust. The proposed protocol allows only the trustworthy nodes to be selected as preferred parents and participate in the network. The proposed trust model has successfully outperformed MRHOF, SecTrust, DCTM, and MRTS. SMTrust shows enhanced performance in Scenario I, II and III, comprising static nodes, mobile sender nodes and mobile sink nodes, respectively. Thus, indicating the importance of mobility metrics for trust computation in mitigating RPL attacks in mobile IoT environment.  
In the future, we plan to improve the power consumption and evaluate the proposed protocol for E2E delay and colluding attacks. Furthermore, the testbed implementation and experiments will be carried out accordingly.  
Conceptualization, S.M.M., R.K.M., N.Z.J., M.H., A.O.I. and A.A.; Data curation, S.M.M., R.K.M., N.Z.J., M.H., A.O.I. and A.A.; Formal analysis, S.M.M., R.K.M., N.Z.J., M.H., A.O.I. and A.A.; Funding acquisition, S.M.M., R.K.M., N.Z.J., M.H., A.O.I. and A.A.; Investigation, S.M.M., R.K.M., N.Z.J., M.H., A.O.I. and A.A.; Methodology, S.M.M., R.K.M., N.Z.J., M.H., A.O.I. and A.A.; Resources, S.M.M., R.K.M., N.Z.J., M.H., A.O.I. and A.A.; Supervision, R.K.M. and N.Z.J.; Visualization, S.M.M., R.K.M., N.Z.J., M.H., A.O.I. and A.A.; Writing--original draft, S.M.M., R.K.M., N.Z.J., M.H., A.O.I. and A.A.; Writing--review & editing, S.M.M., R.K.M., N.Z.J., M.H., A.O.I. and A.A. All authors have read and agreed to the published version of the manuscript. The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups. (Project under grant number (RGP.2/111/43)).  
Not applicable.  
Data Availability Statement: Data will be provided on request.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Adsorption and Desorption Experiments
#### 3.2.9 Comparative Study  
The prepared Cd-I-MA-co-AN was compared with the previously reported Cd(II) imprinted polymers for the adsorption capacities of Cd(II) on the base of functional monomers and polymerization techniques. The results are given in Table 5. The comparison table showed that Cd-I-MA-co-AN demonstrates higher adsorption capacity than most of the Cd(II) ion-imprinted polymers.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **Added/\(\mu\)g L\({}^{-1}\)** &
\begin{tabular}{c} **Found/\(\mu\)g L\({}^{-1}\)** \\ **(\(\mu\) = 3, \(p\) = 90\%)** \\ \end{tabular} & **Recovery/\%** \\ \hline \multirow{3}{*}{Tap water} & 0 & - & - \\  & 1.00 & 0.98 \(\pm\) 0.06 & 98.0 \\  & 5.00 & 4.94 \(\pm\) 0.12 & 98.8 \\ \cline{1-1}  & 0 & 0.26 \(\pm\) 0.09 & - \\ \cline{1-1} Spring water & 1.00 & 1.22 \(\pm\) 0.10 & 96.2 \\ \cline{1-1}  & 5.00 & 5.13 \(\pm\) 0.13 & 97.4 \\ \cline{1-1}  & 0 & 0.31 \(\pm\) 0.11 & - \\ \cline{1-1} River water & 1.00 & 1.29 \(\pm\) 0.08 & 97.6 \\ \cline{1-1}  & 5.00 & 5.26 \(\pm\) 0.15 & 98.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Analytical results of trace Cd(II) in natural water samples.  
Figure 10: Reusability of Cd-I-MA-co-AN.
## 2 Materials and Methods
### Results of the Software Operation
#### 2.7.5 Movement Schedule  
The last tab, entitled \"Timetable\", is the proper result of the software operation (Figure 8). It is a table specifying the inspection vehicle's movement (starting and stopping) in such a manner that the quality of GNSS measurements during the campaign is at its highest.  
The software presents four columns. The first column specifies the manoeuvre performance date, the next two specify the coordinates of the manoeuvre performance location, and the last column specifies the action to be performed. The first line specifies the time and location (this is the first point of the linear object) of the beginning of the inspection vehicle's movement. The next two, in pairs, specify the location of the stop and the time of starting again. The last line indicates the moment when the campaign ends.  
The schedule presented can be exported to a file in the CSV format. This file can be displayed in an Excel spreadsheet or imported to a GIS software and converted to files in GPX or KML formats used by navigation equipment. The data included in the file exported in this manner coincide with the data presented in the table. It is also possible to generate a file containing more detailed information. The resulting file will contain point sequences that make up the optimal method for the campaign implementation. The sequence contains all the points analysed by the program. The purpose of this export type is a further, advanced analysis using a GIS software.
## 2 Materials and Methods
### Analytical Techniques
The evaluation of C contained in TEC, HA and FA was obtained by reverse titration with 0.2 N Fe\({}_{2}\)SO\({}_{4}\) after oxidation of the C contained in the sample to CO\({}_{2}\) adding 5 mL of 1 N K\({}_{2}\)Cr\({}_{2}\)O\({}_{7}\) + 20 mL of H\({}_{2}\)SO\({}_{4}\) and heating at 150 \({}^{\circ}\)C for 10 min [33]. The C extraction was carried out on one replicate. Humification Index (HI) and Humification Degree (HD) were calculated as the ratio of C contained into NHS divided by the C contained into Humic Substances (HS) and as the ratio between HS and TEC expressed as percentage respectively (Equations (2) and (3), [33; 34]). \[HI=\frac{[TEC-(HA+FA)]}{HA+FA} \tag{2}\]  
\[HD=\frac{[100\times(HA+FA)]}{TEC} \tag{3}\]  
where HI is the Humification Index, DH is the Humification Degree, TEC is Total Extractable C, HA is Humic Acids and FA are Fulvic Acids. C and N immobilized by microbial biomass (C\({}_{\text{mic}}\) and N\({}_{\text{mic}}\)) were obtained by the chloroform fumigation-extraction method [35]. Following this, 10 g of soil samples in four replicates were humidified until 60% of water holding capacity (WHC) and incubated at room temperature for 1 week at constant WHC. After this period, two replicates were extracted with 0.5 M K\({}_{2}\)SO\({}_{4}\), representing the non-fumigate sample (NF). On the other two samples, CHCl\({}_{3}\) was added in a closed drier equipped with a void pump to allow CHCl\({}_{3}\) volatilization at room temperature. In these fumigate (F) samples the presence of CHCl\({}_{3}\) atmosphere guarantees the death of the microbial biomass and allows release of the immobilized C and N after the same extraction with 0.5 M K\({}_{2}\)SO\({}_{4}\) performed for NF. After the extraction, both NF and F samples were analyzed with a Shimadzu Total Organic Carbon Analyser TOC-V CPN coupled with a TN unit (TNM-1). Sorghum harvest was carried out with a harvester and the total yield of each parcel was weighted separately directly in the farm using the local vehicle scale. It was not possible to harvest separately the three sub parcels of each treatment, as this practice would have required an excessive time and precision not compatible with the host farm agricultural practices.
# Link Quality Estimation for Wireless ANDON Towers Based on Deep Learning Models
## 3 Materials and Methods
### Dataset  
From the experimental raw data, we selected 10,000 random data. In order to avoid the problem of class imbalance [35], 2500 random samples were selected for each of the four LQE categories. Then, 80% of the dataset was used for training, 10% for validation, and 10% for testing. A mini-batch size of 2048 data points was chosen with a random shuffling method.  
Figure 6 shows the dataset's structure. The input variables data rate, power, and payload depend on the transceiver's setting options, in contrast to the distance and BER that depend on experimental measurements.
## 6 Existing Multi-UAV Communication Architectures
### Centralized Communication Architecture  
A centralized multi-UAV architecture is a centralized data-oriented architecture that promotes only communication between UAVs and the GCS (air-to-ground) [6]. As shown in Figure 9, each UAV establishes directly two-way communication with the GCS. Moreover, the control and command operations are centralized in the GCS. Generally, this architecture supports mini-UAVs and implements simple routing protocols [73; 74]. Its field of application is limited to straightforward missions with limited coverage.  
However, this type of architecture does not support intra-UAV communications (air-to-air) [75]. Therefore, it is not possible to implement collaborative missions. Moreover, since there is a single point of failure in this architecture, it is vulnerable and can lead to system paralysis in case of failure [73]. In this context, the decentralization of the architecture will allow us to have intra-communication between the UAVs and to eliminate the dependencies on the GCS. The next section will cover the different types of decentralized architectures.  
Figure 8: Types of multi-UAV communication architectures discussed in this paper.  
Figure 9: Multi-UAV communication architecture based on centralized framework.
## 6 Solution Method of Distributed Cooperative Dispatch for DN-DHN-BESS  
### Modification of the Objective Function  
Under the framework of ADMM, the boundary power consistency constraint needs to be relaxed and added as a penalty term to the objective functions of the optimal dispatch models of DN, DHN, and BESS so that the boundary power is as close as possible during the calculation process and finally achieves consistency [33]. The modified objective functions of the optimal dispatch models of DN, DHN, and BESS are shown in (50)-(52):  
\[\begin{split}\text{min}F^{\text{DN}}=C^{\text{DN}}+\sum\limits_{ t}\sum\limits_{i}\biggl{\{}\lambda_{i,t}^{\text{D-H}}\Bigl{(}P_{i,t}^{\text{D-H}}- \overline{P}_{i,t}^{\text{D-H}}\Bigr{)}+\Bigl{[}\rho_{i,t}^{\text{D-H}}\Bigl{(} P_{i,t}^{\text{D-H}}-\overline{P}_{i,t}^{\text{D-H}}\Bigr{)}\Bigr{]}^{2} \biggr{\}}+\\ \sum\limits_{t}\sum\limits_{i}\biggl{\{}\lambda_{i,t}^{\text{D-E}} \Bigl{(}P_{i,t}^{\text{D-E}}-\overline{P}_{i,t}^{\text{D-E}}\Bigr{)}+\Bigl{[} \rho_{i,t}^{\text{D-E}}\Bigl{(}P_{i,t}^{\text{D-E}}-\overline{P}_{i,t}^{\text{D -E}}\Bigr{)}\Bigr{]}^{2}\biggr{\}}\biggr{\}}\\ \text{min}F^{\text{HN}}=C^{\text{HN}}+\sum\limits_{t}\sum\limits_{ i}\biggl{\{}\lambda_{i,t}^{\text{D-H}}\Bigl{(}\overline{P}_{i,t}^{\text{D-H}}-P_{i,t}^{ \text{H-D}}\Bigr{)}+\Bigl{[}\rho_{i,t}^{\text{D-H}}\Bigl{(}\overline{P}_{i,t}^ {\text{D-H}}-P_{i,t}^{\text{H-D}}\Bigr{)}\Bigr{]}^{2}\biggr{\}}\biggr{\}}\\ \text{min}F^{\text{ES}}=C^{\text{ES}}+\sum\limits_{t}\sum\limits_{ i}\biggl{\{}\lambda_{i,t}^{\text{D-E}}\Bigl{(}\overline{P}_{i,t}^{\text{D-E}}-P_{i,t}^{ \text{E-D}}\Bigr{)}+\Bigl{[}\rho_{i,t}^{\text{D-E}}\Bigl{(}\overline{P}_{i,t}^ {\text{D-E}}-P_{i,t}^{\text{E-D}}\Bigr{)}\Bigr{]}^{2}\biggr{\}}\biggr{\}}\end{split} \tag{52}\]  
where \(\lambda_{i,t}^{\text{D-H}}\) and \(\lambda_{i,t}^{\text{D-E}}\) are the Lagrange multipliers; \(\rho_{i,t}^{\text{D-H}}\) and \(\rho_{i,t}^{\text{D-E}}\) are the penalty parameters; \(\overline{P}_{i,t}^{\text{D-H}}\) and \(\overline{P}_{i,t}^{\text{D-E}}\) are the average value of boundary power, which can be calculated by (53).  
\[\left\{\begin{array}{l}\overline{P}_{i,t}^{\text{D-H}}=\frac{1}{2}\Bigl{(}P_ {i,t}^{\text{D-H}}+P_{i,t}^{\text{H-D}}\Bigr{)}\\ \overline{P}_{i,t}^{\text{D-E}}=\frac{1}{2}\Bigl{(}P_{i,t}^{\text{D-E}}+P_{i,t}^ {\text{E-D}}\Bigr{)}\end{array}\right. \tag{53}\]
# Potential Synergistic Inhibition of _Enterococcus faecalis_ by Essential Oils and Antibiotics
## 5 Conclusions  
In conclusion, our study highlights the enhanced effectiveness of combining essential oils (EOs) with antibiotics in inhibiting the growth of _E. faecalis_, a pathogen associated with persistent dental infections and treatment failures. Our results show that both cinnamon and clove EOs, when tested alone, exhibited dose-dependent growth-inhibiting effects on _E. faecalis_, with cinnamon EO displaying superior efficacy at lower concentrations. Moreover, enhanced antimicrobial effects were observed when 5% cinnamon EO was combined with 5% aminoglycos (k kanamycin 2.5 mg/mL, streptomycin 2.5 mg/mL, gentamicin 1.5 mg/mL), where their combined effects were significantly stronger than the antimicrobial effects of corresponding 10% antibiotics alone (k kanamycin 5 mg/mL, streptomycin 5 mg/mL, gentamicin 3 mg/mL) against _E. faecalis_. Our results demonstrate that the enhanced antimicrobial effects achieved by combining essential oils with antibiotics may be an effective strategy to maintain high antimicrobial effects while using a lower concentration of antibiotics. The antimicrobial properties of EOs by disruption of bacterial genes and cell membrane permeabilization may offer novel strategies to combat antibiotic-resistant pathogens such as _E. faecalis_. Future research should explore these strategies in in vivo and clinical settings to assess their safety, efficacy, and duration of action.  
Not applicable.  
The authors declare no conflict of interest.