esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Failure Prediction Using Survival Analysis
## 3 Background
### Survival Analysis
#### 3.1.2 COX Proportional-Hazards Model  
The COX proportional hazards model is a regression model commonly used by researchers to determine the relationship between a subject's survival time and one or more predictor variables. It provides insights into how various parameters affect the duration of a subject's survival. Unlike the metrics mentioned previously, the COX model allows for calculations to be made considering more than one variable. Furthermore, the model can be used for both categorical and non-categorical variables, in contrast to Kaplan-Meier and Nelson-Aalen, which can only be used for categorical variables. The COX model can be used to identify how different factors in the dataset affect the event of interest. The hazard function used to calculate COX is:  
\[h(t)=h_{0}\cdot e^{(b_{1}\cdot x_{1}+b_{2}\cdot x_{2}+\dots+b_{n}\cdot x_{n})} \tag{3}\]  
where \(t\) is the survival time, \(h(t)\) is the hazard function, \((x_{1},x_{2},\dots,x_{n})\) are the variables, and \((b_{1},b_{2},\ \dots,b_{n})\) are the regression coefficients of the variables. The values \(e^{b_{i}}\) are known as hazard ratios (HR), and they are used to measure the influence of the variables. HR = 1 \(\Rightarrow\) no effect, HR\(\preceq\)1 \(\Rightarrow\) decrease in risk, HR \(\succ\)1 \(\Rightarrow\) increase in risk.  
The COX proportional hazards model has several advantages over other survival analysis methods. One advantage is that it can handle both continuous and categorical variables simultaneously, allowing for the analysis of multiple variables. Furthermore, the COX model does not make any assumptions about the distribution of survival times, making it more robust to violations of assumptions. Additionally, the model provides information on the direction and magnitude of the effect of each predictor variable on the hazard of the event.
## 4 Application
### Clinical Application  
Because of its contactless sensing mode, iPPG can offer a comfortable method to monitor elderly people, infants, and patients with chronic pain, burnt skin, under dialysis, during and after surgery.
## 1 Introduction
### Approaches to Domain Uncertainty  
Besides material degradation, manufacturing imperfections also lead to problems where the shape of the object may not be perfectly known. Modelling domain uncertainty has been studied by many authors, mostly within model problems. If the domain perturbations are small, application of the perturbation technique as in, e.g., [8; 9; 10], is straightforward. In the perturbation approach, which is based on Eulerian coordinates, the shape derivative is used to linearise the problem under consideration around a nominal reference geometry. Statistical moments of the problem can be derived using simple first-order shape expansions.  
Another approach is the domain-mapping approach. The shape uncertainty is transferred onto a fixed reference domain, and hence reflects the Lagrangian setting. In particular, it allows us to deal with large deformations; see [11; 12], for example. Yet another approach, the one adopted in this study, is to use direct mapping, that is, every realisation is dis  
Figure 1: Examples of 3D-printed capsules. These are not biodegradable. (**a**) Full capsule between gloved fingers. (**b,c**) Details of imperfections due to manufacturing process. Images courtesy of [PERSON], University of Helsinki.  
cretised separately. This is feasible in the context of high-order finite element methods or the \(p\)-version of FEM [13]. For a detailed analysis of this approach in the context of quasi-Monte Carlo methods (QMC), see [14]. In its basic variant, the deformed domain is pulled back to the nominal domain simply by mesh remapping. In complicated domains, conformal mappings in 2D provide the best alternative [15].
# Portable Sensor System for Registration, Processing and Mathematical Analysis of PPG Signals
###### Abstract  
This article introduces an integrated photoplethysmographic (PPG) based cardiovascular monitoring system that consists of an individually portable PPG device for recording photoplethysmographic signals and a software system with a serverless architecture for processing, storing, and analyzing the obtained signals. The portable device uses the optical plethysmography technique for measuring blood volume in blood vessels. The device was tested and validated by a comparative analysis of three photoplethysmographic signals and one Electrocardiographic signal registered simultaneously in the target subject. The comparative analysis of these signals shows insignificant deviations in the obtained results, with the mean squared error between the studied signals being less than 21 ms. This deviation cannot affect the results that were obtained from the analysis of the interval series tested. Based on this result, we assume that the detected signals with the proposed device are realistic. The designed software system processes the registered data, performs preprocessing, determines the pulse rate variability, and performs mathematical analysis of PP intervals. Two groups of subjects were studied: 42 patients with arrhythmia and 40 healthy controls. Mathematical methods for data analysis in time and frequency domain and nonlinear methods (Poincare plots, Rescaled Range Plot, Detrended Fluctuation Analysis, and MultiFractal Detrended Fluctuation Analysis) are applied. The obtained results are presented in tabular form and some of them in graphical form. The parameters studied in the time and frequency domain, as well as with the nonlinear methods, have statistical significance (\(p<0.05\)) and they can distinguish between the two studied groups. Visual analysis of PP intervals, based on [PERSON]'s nonlinear method, provides important information on the physiological status of patients, allowing for one to see at a glance the entire PP interval series and quickly detect cardiovascular disorders, if any. The photoplethysmographic data of healthy individuals and patients diagnosed with arrhythmia were recorded, processed, and examined through the system under the guidance of a cardiologist. The results were analyzed and it was concluded that this system could serve to monitor patients with cardiovascular diseases and, when the condition worsens, a signal could be generated and sent to the hospital for undertaking immediate measures to stabilize patient's health.  
Keywords:photoplethysmography; health monitoring system; cardiovascular diseases; PPG device; serverless architecture; pulse rate variability +
Footnote †: journal: Applied Sciences
# Accurate Collaborative Globally-Referenced Digital Mapping with Standard GNSS
###### Abstract  
Exchange of location and sensor data among connected and automated vehicles will demand accurate global referencing of the digital maps currently being developed to aid positioning for automated driving. This paper explores the limit of such maps' globally-referenced position accuracy when the mapping agents are equipped with low-cost Global Navigation Satellite System (GNSS) receivers performing standard code-phase-based navigation, and presents a globally-referenced electro-optical simultaneous localization and mapping pipeline, called GEOSLAM, designed to achieve this limit. The key accuracy-limiting factor is shown to be the asymptotic average of the error sources that impair standard GNSS positioning. Asymptotic statistics of each GNSS error source are analyzed through both simulation and empirical data to show that sub-50-cm accurate digital mapping is feasible in the horizontal plane after multiple mapping sessions with standard GNSS, but larger biases persist in the vertical direction. GEOSLAM achieves this accuracy by (i) incorporating standard GNSS position estimates in the visual SLAM framework, (ii) merging digital maps from multiple mapping sessions, and (iii) jointly optimizing structure and motion with respect to time-separated GNSS measurements.  
vehicle localization; SLAM; sensor fusion +
Footnote †: journal: Sensors
# MultiAspect Graphs: Algebraic Representation and Algorithms
## 4 MAG Algorithms
### Degree
#### 4.3.2 Degree of Sub-Determined Vertices
To obtain the separate self-loop degrees, first note that  
\[\mathbf{M}_{\zeta}(H)\;\mathbf{J}(H)\;\mathbf{1}_{n}=\mathbf{M}_{\zeta}(H)\; \mathbf{J}(H)\;\mathbf{M}_{\zeta}(H)^{T}\;\mathbf{1}_{m}. \tag{40}\]  
This follows from the fact that  
\[\mathbf{M}_{\zeta}(H)^{T}\;\mathbf{1}_{m}=\mathbf{1}_{n}, \tag{41}\]  
since \(\mathbf{M}_{\zeta}(H)^{T}\) is a \(n\times m\) rectangular matrix and has the property that each row has exactly one non-zero entry of value 1. Furthermore, note that the matrix \(\mathbf{M}_{\zeta}(H)\;\mathbf{J}(H)\;\mathbf{M}_{\zeta}(H)^{T}\) is the adjacency matrix of the sub-determined MAG \(H_{\zeta}\). Since the composite vertices representation of a sub-determined MAG is a multigraph, each non-zero entry shows the number of superposed edges in the sub-determination. Therefore, the main diagonal of \(\mathbf{M}_{\zeta}(H)\;\mathbf{J}(H)\;\mathbf{M}_{\zeta}(H)^{T}\) has the self-loop degree of each vertex. Hence,  
\[selfdegree=Diag(\mathbf{M}_{\zeta}(H)\;\mathbf{J}(H)\;\mathbf{M}_{\zeta}(H)^ {T}). \tag{42}\]  
For example, consider the example MAG \(T\) (Figure 3) and the sub-determination \(\zeta_{t}=011_{2}\) defined in Section 4.1. We have that \[\mathit{indegree}=\mathbf{M}_{\zeta t}(T)\;\mathbf{J}(T)^{T}\;\mathbf{1}=\left[ \begin{array}{c}0\\ 7\\ 4\\ 4\\ 7\\ 0\end{array} \right], \tag{43}\]  
\[\mathit{outdegree}=\mathbf{M}_{\zeta t}(T)\;\mathbf{J}(T)\;\mathbf{1}=\left[ \begin{array}{c}0\\ 7\\ 4\\ 4\\ 7\\ 0\end{array} \right], \tag{44}\]  
\[\mathbf{M}_{\zeta t}(T)\;\mathbf{J}(T)\;\mathbf{M}_{\zeta t}(T)^{T}=\left[ \begin{array}{cccccc}0&0&0&0&0&0\\ 0&2&2&0&3&0\\ 0&2&2&0&0&0\\ 0&0&0&2&2&0\\ 0&3&0&2&2&0\\ 0&0&0&0&0&0\end{array} \right], \tag{45}\]  
and  
\[\mathit{selfdegree}=\mathit{Diag}(\mathbf{M}_{\zeta t}(T)\;\mathbf{J}(T)\; \mathbf{M}_{\zeta t}(T)^{T})=\left[ \begin{array}{c}0\\ 2\\ 2\\ 2\\ 2\\ 0\end{array} \right]. \tag{46}\]  
This means that, for instance, the sub-determined composite vertex \((2,\mathit{Subway})\) has outdegree 7, indegree 7, and 2 self-loops. This sub-determination corresponds to the aggregation of all 3 time instants, which means that the edges in which only the time instant changes become self-loops. These edges are shown in red (dotted) in Figure 3. Note that \(\tau_{\zeta_{t}}=(2,3)\), so that \(D((2,\mathit{Subway}),(2,3))=2\), making it correspond to the second element of the degree column vector.
# Hierarchical Resource Management for Mega-LEO Satellite Constellation
## 6 Approaches and Algorithms
### Global Spectrum Planning
#### 6.1.4 Temporal-Spatial Law of Spectrum Usage  
This module stores the patterns of spectrum usage discovered by the data mining module. It records and stores these patterns in terms of time, space, frequency, and power dimensions for a specified time period, area, and frequency band. The level of detail or 'granularity' in each dimension is determined by the system planning parameters.
## 2 Materials and Methods  
### Samples  
This study was carried out at the facilities of the 'Las Vegas del Guadiana' Aquaculture Center of the Regional Government of Extremadura, located in Villafranco del Guadiana (Badajoz, Spain) in July 2024. Tench had been reared for two years in 1500 m\({}^{2}\) enthen bottom ponds at the aquaculture center. At the end of this period, a total of 300 tench were selected at random from the breeding pond. These fish had a mean weight of 72.0 \(\pm\) 13.9 g. They were divided into six fish cages with a 725 L capacity, with 50 tench in each cage. All cages were placed in a single concrete depuration pond of 35 m\({}^{3}\) with a renewal rate of 27 L/min of clean water. The water temperature of the rearing pond was 26.1 \(\pm\) 1.0 \({}^{\circ}\)C, while that of the depuration pond was 24.2 \(\pm\) 1.0 \({}^{\circ}\)C. The quality of the water was measured, ensuring it maintained a constant level of dissolved oxygen of 99.05%, a salinity content of 583.8 mg/L, a pH of 7.6, and conductivity of 1164.8 \(\mu\)S/cm. Fish were selected randomly in batches of 10 for the different experimental samples at 0 h, 12 h, 24 h, 48 h, 72 h, and 96 h in triplicate. They were gathered in a bucket with 80 L of water taken from the depuration pond. Next, the fish were killed by a blunt blow to the head in compliance with the animal euthanasia method defined in Annex III, Section 1.b of the Spanish Royal Decree 53/2013. Furthermore, this study was carried out under the 148b/2020 authorization of the project titled \"Comparative study of the growth and other biometric aspects and susceptibility to parasites in mixed and monosex culture of tench\". Finally, the fish were weighed and frozen at \(-\)20 \({}^{\circ}\)C until the analysis was conducted. The cage and unused fish were returned to the purification pond until the end of the experiment. No supplemental feeding was given during this period. The diagram of the experimental design is shown in Figure 1.
## 2 Methods
### Polar Code  
Polar Codes are known for their low-complexity coding and decoding algorithms that achieve channel capacity for Binary-Input Discrete Memoryless Channel (B-DMC). Polar Codes are based on the concept of channel polar, which ensures that as the code length increases, the channel is polarized into two different sets of channels: one set of fully noisy channels and another set of noise-free channels [22].
## 2 An ODE-Based Model for OORS
### Use Case: A 1-D Vehicular Network  
The model parameters depend on the network scenario and the mobility pattern. To show the ability of our approximated ODE-based framework to capture the complexity of an offloading process, we use as an illustrative example a synthetic vehicular mobility model derived from a one-dimensional vehicular scenario, illustrated in Figure 4. In particular, we consider \(N+1\) vehicles, each with a fixed transmission range \(l_{tx}\), which move at constant speed in a two-lane road segment of length \(L\). We assume that when a car arrives at the end of the road lane, it returns via the opposite lane. It is intuitive to derive that \(\omega_{0}=4\rho l_{tx}\), where \(\rho=N/2L\) is the node density as observed by the content requester.  
Let \(V_{i}\) be a random variable modelling the constant speed of vehicle \(n_{i}\) (\(i=1,\ldots,N+1\)). We assume that \(V_{i}\) is an independent and identically distributed random variable with a continuous uniform distribution \(\mathcal{U}(v_{a},v_{b})\). To estimate \(\theta\), we need to derive the average number of encounters, \(ne^{+}(\tau)\) and \(ne^{-}(\tau)\), of a content requester with other nodes moving in the same or the opposite direction, respectively, during a generic time interval \([t,t+\tau]\). The mathematical expressions for these two variables are derived in Appendix A. Then, it holds that  
\[\theta=\frac{\tau}{ne^{+}(\tau)+ne^{-}(\tau)}=\frac{1}{2\rho\Big{(}v_{m}+ \frac{\Delta v}{6}\Big{)}}. \tag{18}\]  
The above formula shows that, in the considered scenario, the inter-contact rate is a function only of the node density and speed. It is interesting to highlight that the seminal work [35] demonstrated that many well-known mobility models, such as the random waypoint or the random direction model, are characterised by pairwise meeting times between nodes, which are linearly dependent on the average relative speed between two nodes and the node density.
# Retrieval of High Spatial Resolution Aerosol Optical Depth from HJ-1 A/B CCD Data
## 2 Data and Methods
### Retrieval of AOD with Constraints
The ACI is defined as the normalized difference of atmospheric-corrected reflectance of near-infrared and blue bands:  
\[ACI=\frac{\hat{\rho}_{NIR}-\left|\hat{\rho}_{blue}\right|}{\hat{\rho}_{NIR}+ \left|\hat{\rho}_{blue}\right|}, \tag{14}\]  
where \(\hat{\rho}_{NIR}\) and \(\hat{\rho}_{blue}\) are the atmospheric-corrected reflectance of near-infrared (NIR) and blue bands with different offset steps \(i\), respectively. In this study, the ACI was used to narrow the search range of \(i\) and avoid the occurrence of excessive atmospheric correction. As the offset step \(i\) increases, the intercept \(b\) and the retrieved AOD also increase, and excessive atmospheric correction occurs. When excessive atmospheric correction occurs, the blue band decreases to a negative value, whereas the AOD exerts a small influence on  
Figure 4: The effects of the variation of offset step \(i\) for the interband regression. Figure 3: Sensitivity analysis of the AOD inversion to the variations of offset step \(i\) (where \(a\) is 1.12 and \(b\) is 0.01). the near-infrared band. Therefore, it can be approximately deemed that the near-infrared band does not change with atmospheric correction, and the ACI curve shows a trend of initially increasing and then decreasing (Figure 5). The values of the offset steps that indicated that excessive atmospheric correction occurred were screened out. The seasonal variation of the NDVI was used as a constraint to further determine AOD estimation. The NDVI was first calculated with atmospheric-corrected reflectance over a dense vegetation-covered area. Then, the temporal variation of the NDVI was fitted with a quadratic function (Figure 6). The optimal offset step \(i\) can be obtained when the objection function \(\delta_{NDVI}\) reaches the minimum value  
\[\delta_{NDVI}=\left|NDVI(i,t)-N\widetilde{D}VI(t)\right| \tag{15}\]  
where \(NDVI(i,t)\) is the zonal mean NDVI with an offset step \(i\) at time \(t\); and \(N\widetilde{D}VI(t)\) is the fitting result of the NDVI at time \(t\). It is noted that this constraint is only useful during the growing [25] season of vegetation and is not applicable in winter. Figure 5: The relationship between the aerosol correction index (ACI) and AOD with different offset steps \(i\) (\(a=1.18\) and \(b=0.012\)). Figure 6: Seasonal variation of the normalized differential vegetation index (NDVI) over the vegetation-covered area.
## 1 Introduction
### Microstrip Coils in MRI  
The use of microstrips in MRI has been proposed and utilized by many researchers [35; 36; 37; 38]. The use of stronger magnets requires operation at higher frequencies, which in turn reduces the electrical wavelength, making it possible to use microstrip technology. For microstrips to be used in MRI applications, they must be able to transmit magnetic fields with uniform distributions along the geometry of the coil [39]. The element should also operate at the resonant frequency. The field intensity and field penetration are also important. For this reason, microstrip elements for MRI must be designed with optimized geometry and by taking the wavelength limitation into consideration. The wavelength of the microstrip is proportional to its geometry and effective dielectric constant.  
In this work, we analyzed and designed a single structure capable of resonating at different frequencies based on an interleaved gapped microstrip arrangement that consists of four strip lines. Each of the lines were tuned to a specific frequency corresponding to the Larmor frequency of an X-nuclei and proton, namely \({}^{1}\)H, \({}^{23}\)Na, \({}^{31}\)P, and \({}^{19}\)F. The corresponding frequencies at 7T are 300, 280, 121, and 78 MHz, respectively. The microstrip arrangement consisted of interleaving dielectric materials with low relative permittivity for the lines operating at higher frequencies, and high relative permittivity for the low frequencies lines. The lines and dielectric materials were placed in an interleaving manner to reduce coupling between the frequencies. Each line was tuned with a loading capacitor. The goal was to obtain a uniform longitudinal \(\mid\)B\({}_{1}\mid\) field at the respective frequencies. The coupling between the lines was reduced by leaving a gap between the excitation line and the current line.  
The proposed design has the capability to operate at four different frequencies; consequently, it can be used for proton and X-nuclei imaging with magnetic resonance. The design of coplanar elements is not in the same plane, which is an advantage over traditional multiple frequency RF coils, in which the coils are located at different distances or layers. The proposed design also has the potential to be used in simultaneous multinuclear MRI acquisition.
# An Efficient Scalable Scheduling MAC Protocol for Underwater Sensor Networks +
## 6 Performance Evaluation
### Simulation Results
Figure 16: Energy consumption vs. node density. Figure 15: Energy consumption vs. offered traffic. In the last set of simulations, we study the effect of increasing the offered traffic on the fairness index among those three protocols. Figure 17 shows the result of an experimental setup consisting of ten sensor nodes that are run for an hour to strenuously test the fairness of the three protocols. First, it is observed that the ED-MAC protocol exhibits a high fairness index of almost 98%, which then slightly decreases over all the traffic loads. In comparison, the fairness index of T-Lohi and UWAN-MAC (within 0.1 offered traffic) achieves nearly 94% and 80%, respectively. When the offered traffic further increases, the fairness index of the latter protocols significantly decreases. This is mainly because of an extensive competition between nodes to access the channel. ED-MAC protocol achieves higher fairness of transmission and throughput than other protocols due to the channel reservation, which is based on the depth criteria. We finally evaluate the fairness index of each protocol by increasing the number of sensor nodes while keeping the load constant at (0.25 packets/s). As illustrated in Figure 18, when the number of nodes increases, the fairness index of all protocols declines significantly. This is because the network congestion reduces the fairness in all three protocols. Due to the large delays in the underwater acoustic network, the distance between nodes becomes a key factor in the competitive channel. In contrast, the fairness index of ED-MAC (within 10 nodes) is higher than both T-Lohi and UWAN-MAC by approximately 8% and 35%, respectively. This is because the latter protocols are highly affected by long propagation delays, which cannot avoid the spatial-temporal uncertainty problem, leading to increasing the number of unfairness transmissions. Moreover, UWAN-MAC involves unknown propagation delays, which affects its fairness compared to other two protocols. Figure 17: Fairness vs. offered traffic. Figure 18: Fairness vs. node density.