esa-sceva/satcom-chunk-collection huggingface dataset:
# PlaIMoS: A Remote Mobile Healthcare Platform to Monitor Cardiovascular and Respiratory Variables
## Data Bus Blood Oxygen Sensor
### Software Application  
The mobile application for the PlaIMoS platforms allows the healthcare provider to view patient body temperature, heart rate and electrocardiogram values, as well as review the latest measurements pertaining to respiration rate, blood oxygen and real-time galvanic resistance.  
This application was developed for iOS (iPhone), Android and Windows Phone in order to cover most of the market users. For safety reasons, persons authorized to use the PlaIMoS platform must register a username and password to access PlaIMoS App via smartphone.  
When healthcare providers begin their sessions in PlaIMoS, they first view the list of assigned patients and select the specific patient to consult details. Once the healthcare provider selects the patient that he/she wants to query, he/she can scroll down a list that contains the patient's most recent measurements. The caregiver can easily scroll a list that efficiently provides the following information:  
* Name of the measurement performed
* Date and time of the measurement performed
* Value of the measurement performed
* Minimum measurement value
* Maximum measurement value  
Healthcare providers can change the minimum and maximum values of each patient measurement to personalize the acceptable range for each person. If any measurement exceeds the range limits, a push notification is executed in real time to notify the person responsible for monitoring the patient [28]. The push notification runs as follows:  
1. The Web service detects if a measurement is outside the normal preset range.
2. The Web service solicits the notification server (Apple, Google and Microsoft Server) to send a push notification to a particular device.
3. The notification server receives the request and forwards the notification push to the indicated device.
4. The device (smartphone) emits a visual and audible alert with the incidence reported by the Web service regardless of whether the device is locked or the application is running.  
Figure 16: PlaIMoS-Routing.  
The PlaIMoS App allows the doctor to view the electrocardiogram of patients in real time. Once entering the electrocardiogram view, the healthcare provider can fully analyze the graphs generated because he/she can detail the real-time data stream to zoom in and analyze a specific waveform.
## 4 SMC Parameters Tuning Based on PACSA  
During the process of designing the SMC, parameters of SMC should be chosen suitably through some optimization algorithms to obtain optimal performance. For the RCSMC, there are six parameters that can be tuned, i.e., \(n_{1}\), \(n_{2}\), \(n_{3}\), \(\eta\), \(\mu\), and \(\zeta\). Likewise, five related parameters of the HDSMC need to be optimized, which are \(\rho\), \(\mu\), \(\eta\), \(\kappa_{1}\), and \(\kappa_{2}\). In this section, two objective functions of parameters optimization are designed through AHP, and then the PACSA is applied to optimize the above parameters of the two controllers based on the objective functions.
## 3 Results
### Phase I: Defining PC On-site Assembly Optimization Principles via BIM-GA Hybrid System
#### 3.1.3 Complexity of Works  
In construction projects with repetitive tasks, streamlining the work duration involves performing identical tasks consecutively and expediting time-consuming activities [9]. PC construction sites tend to install identical components consecutively to ensure workspace clearance and to prevent collisions. However, installing different component types results in longer waiting times because of the additional tasks associated with each component type. Therefore, in such situations, streamlining the work pattern to enhance the overall work speed can be achieved by prioritizing the installation of column components, which involves time-consuming tasks owing to the abundance of additional tasks before and after installation. Thus, in this study, the installation of elements of the same type was considered to determine whether the same type of component was installed repeatedly. Additionally, the study evaluates which component precedes another, assigns a complexity score of 0 if the same task is consecutive, and scores of 1, 2, and 3 for column, beam, and slab precedences, respectively, based on the time each component type takes. The sum of these Cps values is further formalized as A, representing the total Complexity Penalty Score.  
\[A=\sum_{i=1}^{n-1}Cps_{i,i+1} \tag{1}\]  
\[Cps_{i,i+1}=\left\{\begin{array}{l}0,\textit{ when }ith\textit{ and }i+1 th\textit{ are same element type}\\ 1,\textit{ when }ith\textit{ is column and }i+1 th\textit{ is beam or slab}\\ 2,\textit{ when }ith\textit{ is beam and }i+1 th\textit{ is column or slab}\\ 3,\textit{ when }ith\textit{ is slab and }i+1 th\textit{ is column or beam}\\ \end{array}\right.\]  
\(A\) = sum of penalty score of complexity  
\(Cps_{i,i+1}\) = complexity penalty score between \(i\)th and \(i\) + 1 th components of the assembly sequence  
\(n\) = the number of components
# An Eccentricity Based Data Routing Protocol with Uniform Node Distribution in 3D WSN
## 5 Conclusions  
In this paper, we propose the EDR protocol in a 3D WSN with uniform node distribution. An eccentricity region based RC nodes election algorithm is adopted that balances the energy consumption among member nodes by distributing equal numbers of nodes in the subspaces. The quasi-static feature of RC nodes election reduces a significant number of control messages during the election periods, so that a node can save the extra energy dissipation in this regard. The number of the messages is more reduced with a longer average quasi-static period of a node due to equal member nodes in the subspaces. A routing algorithm is also adopted for multi-hop communication. The imbalanced energy consumption of nodes caused by data forwarding tasks in particular is solved by selecting an RC node determined by the distance, residual energy and number of member nodes that balances the energy consumption among the forward RC nodes and reduces the number of hops towards the sink. With the benefit of the uniform node distribution, the EDR protocol is more robust to dominate such of the energy consumption factors of the constituted nodes that prolongs the steady state and network lifetime significantly.  
We have performed extensive simulation to evaluate the performance of the protocol with uniform node distribution. The selected performance metrics for this analysis is mainly the steady state and network lifetime. Besides, the number of control packets, number of hops and throughput are considered. The simulation results show that the EDR protocol performs better for the selected metrics as compared to the existing protocols.  
This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (No. 2016R1A2B4013002), and by the MSIT (Ministry of Science and ICT), Korea, under the IITRC (Information Technology Research Center) support program (IITP-2017-2015-0-00378) supervised by the IITP (Institute of Information & Communications Technology Promotion).  
[PERSON] conducted the research, performed the simulations and wrote the paper; [PERSON] supported writing and revising the article; [PERSON] provided valuable feedback and revised the paper.  
The authors declare no conflict of interest.
## 3 Results and Discussion  
### Spatial Distribution of Parameters Obtained by Geophysical Sensors  
Data recorded by the ARP and the DUALEM surveys before the rearrangement works of the site (October 2015) were mapped, while zones occupied by heaps and trees corresponded to the parts of the maps that lacked measurements (Figure 2). Descriptive statistics of data were reported in Table 1. In the ARP map corresponding to the surveyed depth from 0 to 0.5 m (Figure 2A), two homogeneous zones (HZs) were  
Figure 2: Maps of apparent electrical resistivity (ERa) obtained by ARP (**A–C**) and DUALEM (**D–F**) surveys for the investigation of different soil depths. (**A**) V1 = 0–0.5 m; (**B**) V2 = 0–1 m; (**C**) V3 = 0–1.7 m; (**D**) PRP2 = 0–1 m; (**E**) PRP4 = 0–2 m; (**F**) PRP6 = 0–3 m.  
identified in site 1, with HZ1 in the western part (an elongated N-S band with blue color) showing the lowest ERa values of the site (0-70 \(\Omega\cdot\)m), and the smaller HZ2 in the eastern part (an elongated N-S strip with light blue-green color) having values ranging from approximately 90-160 \(\Omega\cdot\)m. Moreover, two HZs were identified in site 2, including the HZ3 narrow band with low ERa in the western side (blue color) and the larger HZ4 in the mid-eastern part (green, yellow and red colors) with higher ERa values (varying from 180 and 340 \(\Omega\cdot\)m). These zones were clearly evident in the maps obtained by deeper surveys (0-1 m and 0-1.7 m), even though the highest ERa values of the wider HZ4 in site 2 decreased with depth (Figure 2B,C). By comparing ERa maps (Figure 2A,B) with archived pictures of the year 1998 (Figure 3A), the red narrow zone overlapped with an anthropic white colored form (Figure 3B), most likely a narrow street, which was no longer visible in the successive period, dated 2011 (Figure 3C). Looking at the DUALEM survey, the data showed a shorter range of value variability compared with ARP (0-180 and 0-330 \(\Omega\cdot\)m for DUALEM and ARP, respectively). Even though a high consistency was found between DUALEM and ARP maps, the DUALEM survey confirmed the HZs identified by the ARP (Figure 2). However, in the DUALEM maps, no sign was found of the anthropic artifact identified with the ARP's survey in site 2. Part of the rearrangement works for site 1 comprised the displacement of the materials stored as waste heaps in the middle of the site toward the western part, with the building of a long step approximately 40 cm in height, N-S oriented. Site 2 consisted in a random distribution of the waste materials on the topsoil. A new geophysical survey was carried out with the PROFILER EMP and the measured ECa values at 15 and 7 KHz were spatialized (Figure 4), for an approximately investigated depth of 1.8 m (Figure 4A,B).
## 1 Introduction  
The western Arabian margin (Figure 1A) has experienced a rich history of volcanism during the last 20 Ma [1]. However, these volcanic events are not the only potential geologic hazard that affects this region. Surface fractures and sinkholes within the Arabian margin are also rapidly becoming a challenging hazard. These hazards are frequently linked to surface subsidence. Thus, identifying such regions of subsidence can provide an indication of potential future hazards [2; 3; 4; 5; 6]. Recent studies of surface deformation within Arabia investigate surface fissures and fractures, explore their underlying mechanism, and address mitigation of their potential risks [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20]. These studies used multiple methods, including structural geology, soil analysis, and interferometric synthetic aperture radar (InSAR). Among these approaches, InSAR is becoming one of the most powerful tools that can be used to monitor ground movement over time. This approach relies on determining phase differences in repeat synthetic aperture radar (SAR) images to quantify spatial variations in surface displacement (e.g., [21]). The InSAR phase is a superposition of various signals, including all geophysical contributions to ground displacements and propagation delaysaccrued in the atmosphere. Thus, we must pay particular attention to separating the various contributions to the measured phase change in order to isolate the geophysical signal of interest.  
In this work, we apply an InSAR time series analysis of Sentinel-1 data to cover wide areas (250 \(\times\) 1000 km) of western Arabia at a 90 m spatial resolution and with short revisit times (6-12 days on average). This regional survey allows us to detect the short wavelength anomalies that correspond to local ongoing deformation within the Arabian margin. Then, we reprocess the Sentinel-1 data at a much higher (15 m) spatial resolution for a limited region where we have identified short wavelength deformation anomalies. We use this local analysis to optimize spatial and temporal resolution for the Al-Yutamah Valley in comparison with our regional survey, to investigate domains of earth fissuring and fracturing, and to monitor their activities over time. We develop time series, velocity maps, and fracture event spanning maps. Here, we present both the regional and local analyses, correlate the observed deformation fields with surface fractures and fissures, discuss the possible underlying mechanisms, and forecast future hazards.
# Transient Response of Dynamic Stress Concentration around a Circular Opening: Incident SH Wave
## 2 Governing Equations  
Consider the anti-plane governing equation [13] in the cylindrical coordinate system (\(r\), \(\theta\), \(z\)), where the \(z\)-axis is the polarization direction, as the result is  
\[\frac{\tau_{rz}}{r}+\frac{\partial\tau_{rz}}{\partial r}+\frac{1}{r}\frac{ \partial\tau_{\theta z}}{\partial\theta}=\rho\frac{\partial^{2}w^{*}}{\partial t ^{2}}, \tag{1}\]  
where \(\tau_{rz}\) and \(\tau_{\theta z}\) are the shear stress, \(w^{*}\) is the field function of the elastic wave, and \(\rho\) is the mass density.  
The constitutive relation of the material can be expressed as [11]:  
\[\begin{array}{l}\tau_{rz}=u^{\frac{\partial w^{*}}{\partial r}},\\ \tau_{\theta z}=\frac{r}{r}\frac{\partial w^{*}}{\partial\theta},\end{array} \tag{2}\]  
where \(\mu\) is the shear modulus of elasticity of the material.  
Substituting Equation (2) into Equation (1) yields the wave equation represented by the displacement as:  
\[\
abla^{2}w^{*}-\frac{1}{c^{2}}\frac{\partial^{2}w^{*}}{\partial t^{2}}=0, \tag{3}\]  
where \(\
abla^{2}=\frac{\partial^{2}}{\partial t^{2}}+\frac{1}{r}\frac{\partial}{ \partial r}+\frac{1}{r^{2}}\frac{\partial^{2}}{\partial\theta^{2}}\) is the Laplacian operator. \(c=\sqrt{\mu/\rho}\) is the wave velocity.  
Taking the Fourier transform of Equation (3), we achieve:  
\[\
abla^{2}\int\limits_{-\infty}^{+\infty}w^{*}\mathrm{e}^{-\mathrm{i}\omega t}dt- \frac{1}{c^{2}}\int\limits_{-\infty}^{+\infty}\frac{\partial^{2}w^{*}}{ \partial t^{2}}\mathrm{e}^{-\mathrm{i}\omega t}dt=0. \tag{4}\]  
Letting \(\varphi(r,\theta,\omega)=\int\limits_{-\infty}^{+\infty}w^{*}\mathrm{e}^{- \mathrm{i}\omega t}dt\) and \(w^{*}(r,\theta,t)=\frac{1}{2\pi}\int\limits_{-\infty}^{+\infty}\varphi(r, \theta,\omega)\mathrm{e}^{\mathrm{i}\omega t}d\omega\), we obtain the Helmholtz equation as:  
\[\
abla^{2}\varphi+k^{2}\varphi=0, \tag{5}\]  
where \(k=\omega/c\) is the wave number, \(\varphi(r,\,\theta,\,\omega)\) is the spectral function of \(w^{*}(r,\,\theta,\,t)\), and Equation (5) is the Helmholtz equation satisfied by \(\varphi(r,\,\theta,\,\omega)\).
## 3 Experiment and Analysis
### Cwtcnn-Hi Construction  
The proposed model comprised a training step and a testing step. During the training step, six bearings were used as a training set to construct the model for CWTCNN-HI estimation. The training bearings are the same as those used in the IEEE PHM 2012 Data Challenge. The training set is represented by \(D_{train}=\left\{x_{t},\ y_{t}\right\}_{t=1}^{T}\), where \(x_{t}\in R^{N\times N}\) is \(N\times N\) image features extracted at time \(t\), and \(y_{t}\in[0,1]\) is a label indicating degradation percentage of bearings at time \(t\). In this study, the size of the input image feature is \(128\times 128\). The degradation percentage is calculated as \(y_{t}=t/T\), where \(t\) is the operation time, and \(T\) is the failure time (i.e., lifetime). For example, if the failure time of a bearing is 28,000 s, the degradation percentage, \(y_{t}\), is 0.5 at 14,000 s. The testing set is denoted by \(D_{test}=\left\{x_{t}\right\}_{t=1}^{T}\), where \(x_{t}\) is also \(N\times N\) image features extracted from the testing dataset. In the testing step, the image features of the testing dataset were entered into the trained model to obtain the CWTCNN-HI.  
In the training step, we used approximately 7500 image features extracted from the six training bearings to construct the CWTCNN-HI model. The training results are shown in Figure 8. The horizontal axis represents the operating time while vertical axis represents the CWTCNN-HI value. The estimated HI is marked as the colored points in the figure and the black line represents the actual HI of the training bearings. The operating time to failure of the training bearings varies from 1 h to 7 h. The estimated HI of bearing 1_1 is slightly different from the actual HI. On the other hand, the estimated HI of the other five bearings generally have a similar pattern to the actual HI. Some training errors are acceptable because most estimations and actual values tend to match each other. This implies that the CWT-based image features can extract high-quality information about the state of the bearing. Thus, CNN is an effective method to estimate the degradation of bearings by analyzing images.
# Early Fire Detection on Video Using LBP and Spread Ascending of Smoke
## 2 Proposed System
### Texture Analysis and Classification
#### 2.3.5 Movement Direction  
The motion vector is estimated as the difference of the \(k\)th centroid estimated at time \(t\) and \(t-\tau\), that is \(\hat{A}_{t}^{k}\) and \(A_{t-\tau}^{\hat{k}}\), respectively. If the angle \(\theta_{t}\) of motion vector is between \(0^{\circ}\) and \(180^{\circ}\), then it is considered the region moves upward, that is  
\[E_{t}^{k}(\hat{A}_{t}^{k})=\left\{\begin{array}{ll}1&\text{if}\quad 0\leq \theta_{t}\leq 180\\ 0&\text{otherwise}\end{array}\right. \tag{28}\]  
where \(E_{t}^{k}\) indicates that region \(\hat{A}_{t}^{k}\) is moving in the accepted angles, as shown in Figure 5.  
Additionally, the direction of displacement of centroids is accumulated in a histogram \(h^{k}(E^{k},\varepsilon)\) during a period \(\varepsilon=r*\tau\), whose dominant direction considers the final moving direction of the region.  
\[E^{\prime k}=\arg\max\{h^{k}(E^{k},\varepsilon)\} \tag{29}\]  
where \(\arg\max(h)\) is the most frequently depicted direction in the histogram, where 1 is used for upward directions and 0 for the remaining ones.
# Artificial Intelligence-Based Discontinuous Reception for Energy Saving in 5G Networks
## 3 AI-DRX for Multiple Beam Communications  
### System Model  
In 5G directional air interface, beam information is a very crucial operation as a UE wakes up to receive data or control packet [3]. Hence, our work considers DRX for multiple beam communications [13], having dynamic long and short sleep cycles instead of fixed length sleep cycles. Dynamic sleep cycles have different values of sleep time in each cycle rather than one static time value in each sleep cycle. The random values of dynamic sleep time can be predicted based on previous values of packet arrival time.  
We model DRX as a ten-state model. These states range from \(S_{0}\) to \(S_{9}\) and are shown in Figure 5. Let us elaborate on the states below.  
* \(S_{0}\) is the active time. A UE sends and receives the data during \(S_{0}\) state. UE predicts the dynamic time \(T_{DY}\) during \(S_{0}\) state.
* \(S_{1}\) state is threshold comparison state. \(S_{1}\) compares \(T_{DY}\) with both threshold values (\(Th_{Min}\) and \(Th_{Max}\)) to decide whether to remain in the active time or switch to the dynamic sleep cycle.
* \(S_{2}\) state is dynamic short sleep cycle. It saves the UE power for a short period of time up to \(T_{DY}\). The predicted value of \(T_{DY}\) in the dynamic sleep cycle is always lower than that of value in \(S_{3}\) state.
* \(S_{3}\) state highlights the dynamic long sleep cycle. It is similar to \(S_{2}\) state but has a larger sleep period than \(S_{2}\). The predicted value of \(T_{DY}\) always has larger value of dynamic long sleep cycle than that of the dynamic short sleep cycle.
* \(S_{4}\) state shows beam training during active time. This state allows UE to train and search for best available beam pairs between gNB and UE.
* \(S_{5}\) state represents feedback after beam training operation in active time.
* \(S_{6}\) state shows active time after best beam pairs are aligned. \(S_{6}\) is different from \(S_{0}\) as \(S_{6}\) always occurs after beam training and feedback process. Moreover, \(S_{6}\) enables UE to search beam pairs in active time until best beam pairs are found.
* \(S_{7}\) state delineates beam training process after the execution of dynamic long or short sleep cycle.
* \(S_{8}\) state is feedback operation that occurs after beam training and after the execution of dynamic long or short sleep cycle, respectively.
* \(S_{9}\) state is the ON period, a UE only monitors PDCCH for an incoming packet. UE could not send or receive data packets during ON time.
## 3 Results and Discussion
### Four-Point Bending Fatigue Test
#### 3.3.2 Fatigue Equation of Two-Parameter Weibull Distribution
According to Equation (4), the survival rate \(p\) and \(\ln\left[\ln\left(\frac{1}{p}\right)\right]\) of CNFPUC specimens at various stress levels were calculated. The fatigue test results were tested for the Weibull probability distribution at various ambient temperatures and stress levels, as shown in Figure 8.  
Table 10 shows the regression coefficients and the correlation coefficients of various ambient temperatures and stress levels obtained by regression analysis of fatigue data. It can be seen from the regression analysis that the slope decreased with the increase of temperature at the same stress level, indicating that the dispersion of fatigue life increased with the increase of temperature. CNFPUC had a good statistical linear relationship between the \(\ln[\ln(1/p)]\) and \(\ln N\) at the stress levels and ambient temperatures, and the corresponding determination coefficient \(R^{2}\) was above 0.90. This indicated that the fatigue life of CNFPUC showed better adherence to the two-  
Figure 8: Two-parameter Weibull distribution test for fatigue life. (**a**) Stress level is \(S=0.6\); (**b**) stress level is \(S=0.7\); (**c**) stress level is \(S=0.8\).
## 3 Regulatory Barriers to Implementing an EU-Wide Grid Digital Twin
### Addressing the Barriers  
In summary, the regulatory barriers to a pan-European grid digital twin relate to data issues such as privacy, access, and interoperability, to institutional frameworks including harmonization, roles, and coordination, and to economic regulation concerning incentive alignment [4]. The European Union is aware of these challenges. The mandate of the Joint Task Force and the regulatory workstream within the TwinEU project has been established to develop solutions [5]. Potential approaches include the creation of an EU-wide regulatory sandbox for the digital twin, allowing for trials of data sharing and new market mechanisms, as well as the revision of incentive schemes, which is currently under consultation by ACER and CEER [23]. Another option is to formalize the governance of the digital twin, for example, by establishing an EU entity or introducing a legal obligation for ENTSO-E and the EU DSO Entity to maintain it [10]. As one industry analysis noted, the harmonization of European regulations must be promoted, and policy must evolve in parallel with technology to unlock the full benefits of the digital twin [25]. Bridging the gap between the existing regulatory framework and the requirement of a continental digital twin remains a work in progress and will determine the pace and effectiveness of its implementation.  
To consolidate the regulatory insights discussed above, Table 1 summarizes the main barriers identified in the literature, alongside critical evaluations of current frameworks and proposed regulatory solutions advanced by the authors. This synthesis is intended to highlight both the structural shortcomings and the emerging pathways for reform that underpin the regulatory feasibility of a pan-European grid digital twin.