esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Mobile Application for ECG Interpretation
### Design Architecture of ECG Mobile Applications  
[PERSON], [PERSON] [26] presented a generic design specification of a mobile ECG application. As presented in Figure 8, the general architecture of the mobile application is composed of two actors (patient and doctor) and four ICT processing segments (acquisition, preprocessing/visualization, transmission and interpretation/reporting).  
The patient usually wears an ECG biosensor that collects real-time ECG data (acquisition). Mobile device is the second processing compartment for analyzing the collected ECG data. ECG data acquired by the biosensor is transferred to the patient's mobile device by employing personal area network communication, usually Bluetooth, Zigbee, or Wi-Fi. The mobile device is responsible for reading the data and initial analysis of the ECG signal. At this stage, visualization and monitoring of the patient's ECG are carried out. After the initial preprocessing, the mobile device transmits the preprocessed ECG signal to the cloud for further sophisticated processing representing another segment. The transmission is usually done via a wireless broadband communication network. Further interpretation and processing such as presentation of detected QRS peaks, BPM rate, anomaly detection and the identification of extracted features and diagnosis are done in the cloud processing center by medical experts using sophisticated algorithms and techniques. Reports from this interpretation are sent back to the patient via the same channel.  
Regarding the user interface of the ECG mobile applications, simplistic designs are employed due to public usage. The interface presents ECG visualization and a summary of apparent parameters for the visualization. Some applications carry out basic interpretation processes such as QRS detection, R-R interval detection and heart rate measures on the mobile interface [20; 48; 121] so that users can have a quick grasp of their condition before transmission to cloud processing center for in-depth analysis or in the event of unavailability of cloud services. Most ECG mobile applications employ red-oriented colors for designing mock-ups or ECG traces to portray heart feelings [3; 24; 26]. Visualization is also implemented on the web application to be used by the cardiac patient and medical staff. A simple look of an ECG mobile application can be found in Domazet, [PERSON] [26].  
Apart from presenting the ECG pattern of the patient, a mobile application interface is also used to present the report from in-depth analysis carried out at the cloud processing center to the user [43]. It is usually a summary of the analysis results that is sent as a report. Users receive the reports as an alert on the mobile application. An example of an analysis report alert on an ECG mobile application is presented in [24].
## 3 Results and Discussion
### Question 11 Distribution of Respondents  
In most cases, the quantity of data that a spacecraft is able to generate is greater than the amount of data that it is able to transmit. Given this, the results of any study could, potentially, be impacted by the data that the spacecraft decides to transmit. The prioritization of data is, thus, important. Question 11 asks respondents whether they would trust computer prioritization of data for transmission (as opposed to an approach, for example, where thumbnails or other summary data was sent back and a human determined what data to bring back at higher resolution and in what order).  
Figure 10: Graph of responses to the statement “I would trust computer analysis of data if data sets used for analysis were occasionally (at random time intervals) sent back with the analysis results for verification”.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Strongly** & \multirow{2}{*}{**Agree**} & \multicolumn{3}{c}{**No Preference**} & \multirow{2}{*}{**Disagree**} & \multicolumn{2}{c}{**Strongly Disagree**} & **No Response** & **Invalid** \\
**Agree** & & & & **Preference** & & & **Disagree** & **Response** & **Response** \\ \hline
18 & 14 & 76 & 55 & 41 & 7 & 10 & 2 & 1 & 5 & 1 \\ \hline \end{tabular}
\end{table}
Table 9: Responses to the statement “I would trust computer analysis of data if data sets used for analysis were occasionally (at random time intervals) sent back with the analysis results for verification”.  
In response to this question, 50% of respondents indicated some form of agreement (between agree-no preference and strongly agree), 20% indicated no preference and 27% indicated some form of disagreement (between disagree-no preference and strongly disagree). Note that the percentages do not add up to 100% due to rounding, five surveys where this question was not responded to and one survey where an invalid (an indication of a response between two categories) response was provided. This data is presented in Table 10 and Figure 11.
# Full-Duplex MAC Protocol for CSMA/CA-Based Single-Hop Wireless Networks
###### Abstract  
Full-duplex (FD) communication provides new opportunities for improving the throughputs of networks. However, this condition means that the number of senders increases from one to two within a certain range. We have to arrange the two nodes to send frames simultaneously in the media access control (MAC) layer. For the single-hop network model, using the FD features of the nodes and the cut-through mechanism, we propose an FD MAC protocol. The protocol improves the throughput of the network from the following two aspects. On the one hand, during the transmission of each node, based on the information of the received frame's header, the protocol can detect collisions in the network, preventing the channel from being ineffectively occupied for a long time. On the other hand, the protocol can provide the FD with as many opportunities as possible for the nodes. According to the working process of the protocol, we modeled the states (\"active\" and \"passive\" transmission, back-off) of each node and their transitions to a Markov chain. We solved the \"active\" transmission probability of the node and further modeled the analytical performance of the protocol. The simulation results showed that the system throughput produced by our protocol was at least twice that of the conventional CSMA/CA protocol used in the half-duplex networks.  
wireless networks; single-hop network model; full-duplex communication; MAC protocol; Markov chain +
Footnote †: journal:  
_Article_  
Full-Duplex MAC Protocol for CSMA/CA-Based Single-Hop Wireless Networks
## 2 Materials and Methods
### Experimental Procedures  
Ground reaction force data were collected for 30 s until five successful trials were completed per quiet standing condition (Table 1). Unsuccessful trials were defined by a major loss of balance, i.e., having to make significant changes in foot position, and/or if participants demonstrated large movements of their torso and changed arm positions. The standing postural condition of eyes open feet together (EOFT) was defined as the most stable and hence was used as a baseline for all entropy comparisons. Participants were asked to hold a quiet standing position for thirty seconds without moving their bodies or stepping out of position. Balance tasks were performed barefoot with the arms positioned with the shoulders and elbows flexed, forearms supinated, and the fingers touching the anterior aspect of the shoulder; the hips and knees were extended with the ankles maintained in neutral (i.e., neither dorsiflexed nor plantarflexed). Participants progressed through increasingly unstable balance conditions, with 2 min breaks between trials. Conditions included eyes open or closed and changing foot position, i.e., feet together on force plate 5 or tandem stance using force plates 3 and 5, as shown in Figure 1. We chose to test tandem standing because although it may be a commonly used clinical test of balance, there was a paucity of research examining the non-linear dynamics of quiet tandem standing. In the feet-together stance, the feet were as close as possible but constrained by contact between the medial malleoli of the right and left tibiae. The order of testing conditions was not randomized. The 2 min breaks between trials allowed participants to rest and change their postural stance. Consequently, each trial was assumed to be independent and could be analyzed without needing an alternative entropy computation, i.e., multiscale entropy.  
Figure 1: Force plate foot placement for feet together and feet tandem standing balance conditions, where the x-axis (anterior–posterior (AP) direction) and y-axis (medial–lateral (ML) direction define the center-of-pressure orientation. Note: D = dominant foot; ND = nondominant foot; DF = dominant foot forward; and DB = dominant foot back [21; 42].
## 3 The Proposed Downloading Scheme
### Calculating the Source and Sink of Interstellar Transmission  
To utilize the ISLs to aid the feeder link in downloading data, it is important to first define the traffic transmission requirements, mainly including the source satellite, destination satellite, and the rate of data transmission. Then, we can calculate the route between visible satellites according to these requirements.  
The set of real numbers is denoted by \(\mathbb{R}\). Let \(D\in\mathbb{R}^{N\times N}\) represent the traffic demand matrix, where each element represents the required data transmission rate of two visible satellites. At the beginning of each time slice \(n\tau\), each satellite updates the capacity and data transmission rate of each link, and the topology of the network changes along with it.  
When \(b_{i}-B_{i}<0\), the satellite \(v_{i}\) can receive additional download data stream. Let \(l_{i}\) denote the rate of this stream and let \(V_{i}\) denote the set of such satellites. On the basis of the capacity of this satellite's ISLs and feeder link, \(l_{i}\) can be calculated as follows:  
\[l_{I}=\min\big{\{}B_{i}-b_{i},\Sigma(L_{ji}-l_{ji})\big{\}}. \tag{6}\]where \(L_{ji}\) denotes the input ISLs capacity of node \(v_{i}\) and \(l_{ji}\) denotes the data transmission rate of the link \(l_{j,i}\).  
When \(b_{i}-B_{i}>0\), satellite \(v_{i}\) needs to transfer data stream \(l_{O}\) to other visible satellites. Let \(l_{O}\) denote the rate of this stream and let \(V_{O}\) denote the set of such satellites. On the basis of the capacity of this satellite's ISLs and feeder link, \(l_{O}\) can be calculated as follows:  
\[l_{O}=\min\big{\{}b_{i}-B_{i},\Sigma(L_{ij}-l_{ij})\big{\}}. \tag{7}\]  
where \(L_{ij}\) denotes the output ISLs capacity of node \(v_{i}\) and \(l_{ij}\) denotes the data transmission rate of the link \(l_{i,j}\).  
We arrange the satellites in descending order based on the \(l_{O}\) and \(l_{I}\) of each satellite in the \(V_{O}\) and \(V_{I}\) set. The \(\min(l_{O},l_{I})\) between the two satellites is utilized to calculate the element of the source-to-destination demand flow rate \(D(i,j)\). Consequently, matrices \(D\) of the traffic demand are obtained.
## 4 Results
### Correlation Analysis, Feature Elimination, and Heteroscedasticity  
An initial correlation analysis was performed on 186 features derived from the facial and audio analysis software. Given the limited sample size (\(n=84\)), a correlation matrix with a threshold of 0.7 was used to identify and remove highly correlated features, reducing the feature set to 50. To address the potential issue of multi-collinearity among the remaining features, the Variance Inflation Factor (VIF) analysis was employed, setting a threshold of 10. Outliers, identified with z-scores of six or more, were replaced with the median, further trimming the features to 31 [96]. This mitigated multi-collinearity and refined the feature set for subsequent analyses. The Breusch-Pagan test revealed no evidence of heteroscedasticity (Lagrange Multiplier \(p=0.240\), F-statistic \(p=0.171\)).
## Materials and Methods  
### Materials  
The three hydrophilic CSNP suspensions used were Ludox SM (30 wt% suspension in H\({}_{2}\)O, particle diameter 7 nm), Ludox HS-40 (40 wt% suspension in H\({}_{2}\)O, particle diameter 12 nm), and Ludox CL-X (45 wt% suspension in H\({}_{2}\)O, particle diameter 22 nm) (all from Sigma-Aldrich, St. Louis, MO, USA). They were delivered in a colloidal state dispersed in an aqueous phase, of purity > 99.8%. Additionally, hydrophilic FSNPs (dry powder; Aerosil 200, specific surface area approximately 200 m\({}^{2}\) g\({}^{-1}\) and particle diameter 12 nm) were purchased from Evonik Industries, Germany. An anionic polymer, poly (4-styrenesulfonic acid-co-maleic acid) sodium salt (PSS-co-MA) of 99% purity was purchased from Sigma-Aldrich, as was a zwitteration surfactant, 3-(N,\(N\)-dimethylmyristylammoniumio) propanesulfonate (TPS) of 99% purity. The oil used was KF-96-10 (Shinetsu), with a viscosity of approximately 10 cp. Brine (3 wt% NaCl) served as the aqueous phase. The flow experiments employed Berea sandstone with a diameter of 3.8 cm, length of 5-6 cm, porosity of 19-22%, and permeability of 138-154 md.
# Concatenated Coding for GNSS Signals in Urban Environments
## 5 Conclusions  
In this work, a concatenated code consisting of an RS outer code and an LDPC inner code for the application in GNSS data message coding for urban environments was presented. Performance comparisons were conducted between the RS-CC concatenation and two interleaved 64-ary LDPC codes in BDS-3 B-CNAV1 data messages over the LMS and AWGN channels. The simulation results showed that the proposed RS-LDPC concatenation can provide similar decoding performance to the interleaved 64-ary LDPC codes in both the AWGN and the LMS channels, but with a lower decoding complexity. The benefit of the inner LDPC code was shown by comparing it to the RS-CC concatenation, indicating gains of 12 dB and 1.8 dB in the LMS and AWGN channels, respectively.  
[PERSON] conceived and wrote the paper; [PERSON] and [PERSON] supervised the paper and suggested modifications; [PERSON] and [PERSON] analyzed the data. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (No. 11403018).  
The authors declare no conflict of interest.
## 6 Results and Analysis
### Results and Analysis
Moreover, Figure 8(e,f) indicate that the messaging overhead in RIM significantly grows for a high actor density and long communication range because the number of recovery participants increases in both cases. On the other hand, the performance gap between PCR and DCR is minor relative to DARA and RIM. Figure 8: The total movement distance as a function of \(N\) in (**a**) and \(r\) in (**b**). The effect of changing \(N\) (**c**) and \(r\) (**d**) on number of nodes moved. Number of coordination messages, as a function of \(N\) in (**e**) and \(r\) in (**f**). _Percentage of coverage reduction_: Figure 9(a,b) report on the impact of recovery on coverage, measured in terms of percentage of coverage reduction relative to the pre-failure level, while changing the \(N\) and \(r\). The action range is set to 50 m throughout these experiments. Overall, DCR confines the coverage loss and consistently outperforms other schemes. This result is attributed to the fact that moving highest degree nodes reduces overlapped coverage with neighbors and occupies the vacant spot. Figure 9(a) demonstrates that increasing the node density helps, other schemes in mitigating the coverage loss. The limited scale relocation slightly helps PCR to minimize loss of coverage. Figure 9: The coverage reduction after recovery, as a function of \(N\) in (**a**) and \(r\) in (**b**). Degree of connectivity after recovery, while varying the network size (**c**) and radio range (**d**). \begin{table}
\begin{tabular}{c c}  & **(c)** \\ \end{tabular}
\end{table} 
Table 8: _Cont._Figure 9(b) indicates that the performance of RIM significantly worsens when growing the communication range. This is because RIM collectively engages neighbors in recovery and with the increased value of \(r\), the network becomes more connected and the number of neighbors of \(F\) grows. RIM moves nodes inwards making the area around \(F\) to be more crowded (as we shall see later) while leaving uncovered parts at the network periphery and thus causes a significant loss of coverage. On the other hand, the schemes that employ a node in recovery among neighbors is not much affected while increasing the communication range. _Degree of connectivity_: Figure 9(c,d) demonstrate the degree of connectivity preserved by all approaches after recovery. Both figures clearly indicate that PCR maintains the same degree of connectivity as the other approaches despite several other performance advantages. This is mainly because PCR prefers to move non-critical and/or least degree nodes that have nominal repercussions on the connectivity.
## 2 The 3-PG Model
### Major Elements of the 3-PG Model for Parameterization  
In this study, we focused on parameterization of deciduous broadleaf forests for GPP. The species-specific parameters involved in estimating GPP are part of biomass partitioning and biomass production. Biomass partitioning is related to biomass allocation to the stem, foliage, and roots, each of which is influenced by the GPP. Biomass partitioning is controlled by the ratio of foliage to stem biomass allocation. Root biomass allocation is influenced by soil nutrition and available soil water. However, root data were not available in this study. The partitioning ratio between foliage biomass and stem biomass (\(pFS\)) is a function of stem diameter, as shown in the allometric relationship in Equation (1):  
\[pFS=a_{p}B^{np} \tag{1}\]  
where \(B\) is diameter at breast height (_DBH_), \(a_{p}=a_{F}\)\(n_{F}\)\(a_{s}\)\(n_{s}\) and \(n_{p}=n_{F}-n_{s}\). Both \(a_{p}\) and \(n_{p}\) are expressed in terms of \(pFS_{2}\) and \(pFS_{20}\). Generally, \(pFS_{2}\) and \(pFS_{20}\) are extracted for \(B\) values of 2 and 20 cm, respectively [10].  
To obtain \(a_{s}\) and \(n_{s}\) (Equation (2)), we determine the relationship between \(Ws\) and \(B\) by fitting the allometric relationship from the observed data. \(a_{F}\) and \(n_{F}\) (Equation (3)) are from the allometric relationship between \(W_{F}\) and \(B\).  
\[Ws=a_{s}B^{ns} \tag{2}\]  
\[W_{F}=a_{F}B^{nF} \tag{3}\]  
In biomass production, growth modifiers directly influence GPP. This modification accounts for environmental conditions affecting forest growth. The six growth modifiers are temperature (\(f_{T}\)), frost day (\(f_{F}\)), site nutrition (\(f_{N}\)), available soil water (\(f_{\theta}\)), vapor pressure deficit (\(f_{D}\)), and stand age (\(f_{AGE}\)). The value of each modifier ranges from 0 to 1. As a modifier approaches 0, it has a stronger influence on forest growth [8,10]. Temperature and frost effects control biomass production in this study area. The equations describing each modifier are as follows. The other modifiers have been reported by [PERSON] and [PERSON] [8].
# Deep Learning for Computational Mode Decomposition in Optical Fibers
## 2 Methodology
### Deep-Learning-Based Modal Decomposition
The amplitude values can be obtained directly from the predicted vector, while the \(\Psi\) values should be firstly rescaled from \((0,1)\) to \((-1,1)\), and then the relative phase differences \(\phi\) can be calculated by means of arccos function. At this point however, the ambiguity of the arccos function must be taken into account. For this reason, intensity distributions are first calculated with all possible combinations of the phase values. The final prediction of the DNN is then determined by computing the 2D correlation coefficient \(\Gamma\) between one possible intensity distributions \(I_{p}\) and the target distribution \(I_{o}\)[31]. \(\Gamma\) is defined as  
\[\Gamma=\frac{\sum_{ROI}(I_{p}-\overline{I_{p}})(I_{o}-\overline{I_{o}})}{ \sqrt{\left(\sum_{ROI}(I_{p}-\overline{I_{p}})^{2}\right)\left(\sum_{ROI}(I_{ o}-\overline{I_{o}})^{2}\right)}}, \tag{6}\]  
where \(\overline{I}\) indicates the mean value of the respective intensity distribution. The phase vector from which the intensity distribution with the highest correlation coefficient was determined is chosen as the resulting prediction from the DNN. To evaluate the robustness of a measurement system, the standard deviation \(\sigma\) of multiple measurements is often given. The more robust the measuring system is, the less measured values scatter around a determined mean value. In this work, the respective MD methods are also examined with respect to \(\sigma\) when determining \(\Gamma\). \(\sigma\) is determined as follows:  
\[\sigma=\sqrt{\frac{1}{N-1}\sum_{i=1}^{N}\left(\Gamma_{i}-\overline{\Gamma} \right)^{2}}, \tag{7}\]  
where \(\overline{\Gamma}\) is the mean correlation value. Figure 1: Architecture of the deep neural network (DNN) used to run a mode decomposition (MD).
# Effect of Mineral Additives on the Behavior of an Expansive Soil for Use in Highway Subgrade Soils
## 4 Results and Discussion
### Physical-Mechanical Properties
#### 4.1.3 Unconfined Compressive Strength (UCS)  
The increase in the UCS of the stabilized BCS following the increase of stabilizer dosage and curing time is shown in Figure 5. Lime-stabilized BCS presents a prominent increase in UCS. Lime increases the UCS of BCS from 0.215 MPa to 2.2 MPa and 3.5 MPa with the additions of 6% and 9%, respectively. This behavior is attributed to the reactions between the soil and lime, including cation exchange, flocculation, carbonation, and pozzolanic reaction. The use of VA shows poor results when compared with lime. Even when 25% VA was added, the UCS of VA-stabilized BCS only increased from 0.215 MPa to 0.81 MPa after 28 days of curing. Low pH can be observed in the VA-stabilized clay [38], the few hydroxyl ions (OH\({}^{-}\)) provided by VA-soil mixes lead to slow progress of the pozzolanic reaction, and a low UCS was observed in VA-stabilized BCS.  
The combinations of lime and VA for stabilizing BCS present a better result than do the single stabilizers. The UCS of BCS stabilized with a combination of 3% lime and 15% VA reaches 1.95 MPa after 28 days of curing and is higher than the UCS of BCS stabilized with 3% lime or 15% VA, for which the UCS values are 1.5 MPa and 0.56 MPa, respectively. The reason for this might be that the calcium hydroxide in lime accelerated the pozzolanic reaction between the clay soil and VA. A similar trend was observed by [PERSON] et al. [39] in stabilizing the clay obtained from a site situated near the town of Chlef in west Algeria. The UCS of clays stabilized with the combination of 4% lime + 20% pozzolana was 1.66 MPa after 28 days of curing, which is about 16 and 1.6 times those of clays stabilized with 20% pozzolana and 4% lime, respectively. Natural pozzolana cannot be employed solely for soil stabilization. However, lime-stabilized soils can be intensified by adding between 10% and 20% natural pozzolana.  
Figure 5: \(Cont\).