esa-sceva/satcom-chunk-collection huggingface dataset:
# Status Recognition of Marine Centrifugal Pumps Based on a Stacked Sparse Auto-Encoder
###### Abstract
[24] extracted a multi-scale high-dimensional mapping entropy (MHDME) feature extraction algorithm, which can identify different states of reciprocating compressor bearings. However, the process of these methods is more complicated, and some methods rely on analyses of the mechanisms of the equipment, and the parameter setting depends on human experience. Thus, when the amount of data is too large, the processing time is too long. With the rise of artificial intelligence, deep learning methods have deep structures and strong non-linear feature extraction abilities, thus can realize feature extraction from non-stationary vibration signals, and are increasingly applied in signal feature extraction. [PERSON] et al. [25] proposed a universal fault feature extraction and diagnosis method based on a deep confidence network. [PERSON] et al. [26] input the normalized amplitude spectrum of vibration signals into a deep belief network (DBN) for feature extraction, and evaluated the performance degradation of rolling bearings through the features. Their results show that this method can extract all stages of bearing degradation. [PERSON] et al. [27] used convolutional neural network (CNN) adaptive feature extraction to further explore the hidden micro features in the signals by deepening the network. [PERSON] et al. [28] used an SSAE to extract the fault features of spiral bevel gear with strong non-linear and non-stationary characteristics, and input the fault features into multiple classifiers for classification training. Through their experiments, the recognition accuracy of this method was verified. Through the above research, it can be seen that feature extraction methods for vibration signals based on deep learning have a good effect and can extract weak features reflecting the state of the device in the signal, and their state recognition effect is good. To summarize, in view of the non-stationary and non-linear characteristics of MCP vibration signals, this paper built an MCP test bench to collect vibration signals under different statuses of an MCP, divided the vibration signal dataset into a training set and a test set, trained the SSAE model with the training set, input the extracted features into the classifier training, and verified the model according to the test set. At the same time, two other methods were selected for comparative verification: One involves extracting the time domain characteristics [29] of MCP vibration signals for status recognition. The other involves decomposing the MCP vibration signal via VMD, and calculating the fuzzy entropy characteristics [30] of each modal component after decomposition to identify the state. Finally, the validity of the proposed method was judged according to the accuracy of its status recognition.
# SLR, GRACE and Swarm Gravity Field Determination and Combination
## 2 Materials and Methods
### Slr
In case of SLR-only monthly solutions coefficients of degree 6 (all but order 1), and degrees 7-10 are fixed to zero. In case of combined solutions, all coefficients are estimated. The parameter-space of the SLR solutions is complemented by monthly estimates of the SLR station coordinates, by daily piecewise-linear estimates of the geocenter coordinates and by monthly estimates of the Earth rotation parameters [68]. As indicated above, the number of pseudo-stochastic parameters is very limited due to the sparse observation coverage. Only in along-track pulses are estimated once per orbital revolution of the satellites. The stochastic orbit parameterization is completed by periodic terms on orbit revolution frequency. The complete SLR orbit parameterization and the time intervals for which the individual parameters are set up are detailed in Table 3. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Force** & **Model** & **SLR** & **GRACE** & **Swarm** \\ \hline Earth gravity & AIUB-GRACE03S (static) & degree/order 90 & 160 & 160 \\ Ocean tides & EOT11A [62] & degree/order 30 & 100 & 100 \\ Solid Earth tides & IERS2000 (elastic) [63] & yes & yes & yes \\ Ocean pole tide & Desai [63] & degree/order 100 & 100 & 100 \\ Atmosphere and ocean de-aliasing & RL05, RL06 (since 11/2017) & degree/order 100 & 100 & 100 \\ Air drag & NRLMSIS-00 [64] & yes & ACC & no \\ Solar pressure & uniform sphere approximation & yes & ACC & no \\ Albedo & Knocke [65; 66] & yes & ACC & no \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Background force model details for processing of low Earth orbiters. Where the force model constituent is expanded in spherical harmonics, the max. degree/order is given. Where this is not the case, it is only indicated if the listed model is applied or not. ACC indicates that surface forces are observed by accelerometers.
## 4 Discussion
### How Does Circumvent the Problem of Soil Drought Limitation?
Development of global evapotranspiration algorithms based on both remotely-sensed MODIS and global meteorology data may provide method to better express ecosystem water limitation without using precipitation amounts or soil moisture. Previous algorithms [54, 90] have been further improved and yielded satisfying estimates of actual evapotranspiration [91]. [PERSON], [PERSON] _et al_. [92] proposed a water stress factor or evaporation fraction as the ratio of latent heat flux to the sum of latent and sensible heat fluxes. Existing coarse resolution soil water content data from active and passive microwave sensors were found beneficial for climatology and hydrology at global to regional large scales (see for instance [93, 94, 95]). It can be anticipated that further applications would become feasible with medium (\(<\)1 km) scales. This downscaling procedure may be critical. These include forest and shrubland ecosystems and soil moisture measurements over the complex landscape mosaic we observed in MT areas. The benefit of a C-band Advanced Synthetic Aperture Radar has already been demonstrated for mapping top soil moisture generally over few centimeter depths [96]. For instance, Sentinel-1 will carry onboard a C-band radar instrument [97] with a configuration and high temporal sampling rate providing great interest for the operational soil water storage estimation we need to calculate a drought scalar for the GPP algorithm. However, land surface features such as dense forest cover, surface roughness and rock outcrops significantly limit its sensitivity [97]. In parallel, considerable efforts are required in order to have a reasonable access to the whole water storage covering the rooting depth for deep-rooted tree or shrub species [98, 99]. Finally, in both cases, evapotranspiration or soil moisture estimates, we advocate the use of a multi-sensor approach to address the practical problem of introducing a scalar describing the soil drought limitations in the MODIS GPP algorithm [100].
# Toward QoS Monitoring in IoT Edge Devices Driven Healthcare--A Systematic Literature Review
## 4 Preliminary Concepts
### Cloud Computing  
In cloud computing (CC) the provision of virtualized, on-demand computing, data storage, and networking capabilities via internet access via cloud services is completely customizable. The complexity and cost of CC resources vary depending on the desired capabilities and degree of application complexity for which they are used [26]. Compared to local computing resources, the cost, manageability, and flexibility of cloud computing resources have recently improved. Each data center in which cloud services are normally housed uses thousands of computers regularly. These systems must be able to scale up to extraordinarily high service demand levels while preserving reasonable processing times and low hardware and energy costs. This can enable interface simplicity that helps in processing widely accessible cloud data and offers a variety of services by linking multiple platforms. Infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service are the three main services that make up CC services (SaaS). Companies may either buy or use CC services from cloud service providers, like Amazon, or they can build their own private clouds for their employees to use. Participatory community clouds are frequently used by research organizations and other pertinent organizations. In hybrid clouds, both private and public strategies are employed [27]. To assist better decision-making across a variety of application domains, including healthcare, applications enabled by CC solutions can extract highly important data. CC is one of the key enabling technologies for smart healthcare, which is made up of a variety of sensors, actuators, apps, and communication technologies, as shown in Figure 9.  
Clinicians can provide services to clients wherever they may be thanks to cloud-based health services. Patients can use applications that operate on a variety of platforms, and their requests can be fulfilled automatically without the need for someone to handle them. The two cloud-based healthcare applications that are most frequently utilized are telemonitoring and electronic health records (EHR). EHR offer a standardized method for gathering electronic patient health data. Clinic settings may more easily share various sets of data, such as medical histories, vital signs, and medication, thanks to this standard platform. To provide healthcare remotely, medical telemonitoring employs information analysis and transmission methods. As indicated in [28], there have been various discussions regarding how cloud resources could be used more efficiently to provide high-quality healthcare services.
# Enhanced Path Planning by Repositioning the Starting Point +
## 4 Experimental Evaluation  
### Experimental Setup  
A simulation model was developed in order to test various scenarios using the mTSP Algorithm 1 as described by Equations (1) and (2).  
The simulation model was implemented in the.NET framework using C#. The machine specifications used for conducting the tests include an Intel Quad Core i7-6820 HK CPU @ 2.70 GHz with 16 GB of RAM (Intel, Santa Clara, CA, USA).
# Comparative Analysis of Viscous Damping Model and Hysteretic Damping Model
## 2 Comparative Analysis of Transient Responses Based on Different Damping Models
### The Transient Response of Viscous Damped System  
The transient response is equivalent to the free vibration response of systems. By analyzing the characteristics of the free vibration response, the law of transient response can be obtained.  
Based on the viscous damping model, the time-domain equation for an SDOF system is expressed as  
\[m\ddot{x}(t)+f_{\rm v}+kx(t)=0 \tag{1}\]  
where \(m\) is the mass; \(k\) is the stiffness; \(f_{\rm v}\) is the viscous damping force; \(\ddot{x}(t)\) is the acceleration; \(x(t)\) is the displacement.  
The damping force of the viscous damping model is proportional to the velocity, and the viscous damping force is expressed as  
\[f_{\rm v}=\frac{2\xi k}{\omega}\dot{x}(t) \tag{2}\]  
where \(\xi\) is the damping ratio; \(\dot{x}(t)\) is the velocity.  
\(\omega\) is the natural frequency of the system, which is expressed as  
\[\omega=\sqrt{\frac{k}{m}} \tag{3}\]  
The corresponding characteristic equation of Equation (1) is  
\[\lambda^{2}+2\xi\omega\lambda+\omega^{2}=0 \tag{4}\]  
where \(\lambda\) is the complex eigenvalue.  
By solving Equation (4), it is obtained as  
\[\left\{\begin{array}{l}\lambda_{1}=-\xi\omega+\mathrm{i}\omega\sqrt{1-\xi^{2}}\\ \lambda_{2}=-\xi\omega-\mathrm{i}\omega\sqrt{1-\xi^{2}}\end{array}\right. \tag{5}\]  
where \(\mathrm{i}\) is an imaginary unit, \(\mathrm{i}=\sqrt{-1}\).  
The damped natural frequency is  
\[\omega_{\mathrm{v}}=\omega\sqrt{1-\xi^{2}} \tag{6}\]  
Equation (6) shows that when the viscous damped systems are underdamping state, the damping ratio is less than 1.0. The damped natural frequency is less than the natural frequency.  
The attenuation coefficient is  
\[\alpha_{\mathrm{v}}=-\xi\omega \tag{7}\]  
Equation (7) shows that the attenuation coefficient is negative and the stability of free vibration response is guaranteed based on the viscous damping model.  
The transient response is expressed as  
\[x(t)=\mathrm{e}^{\alpha_{\mathrm{v}}}(A\sin\omega_{\mathrm{v}}t+B\cos\omega_{ \mathrm{v}}t) \tag{8}\]  
where \(A\) and \(B\) are undetermined coefficients.
## 5 Discussion
### Lessons Learned
#### 5.1.5. QualiChain Platform as a Whole  
Overall, it can be said that the implementation of the QualiChain platform in the context of this pilot was successful. QualiChain presented to the stakeholders of higher education a combination of technologies that they have not seen before in the context of education. It can be said that leveraging innovative technologies can lead to new and original platforms and tools that meet real needs and provide out-of-the-box solutions. From a technical standpoint, combining those innovative technologies does not always lead to the most robust solutions. However, it is imperative that such approaches are explored to develop the services of the future. On another note, research projects like QualiChain are critical in bringing people in contact with some of those technologies, which can lead to increased trust. For example, while blockchain is still considered by most as synonymous with cryptocurrencies, QualiChain proved that it is largely underutilized in other domains as a solution for dealing with private and sensitive data. Especially in the post-COVID-19 era, with a new reality being shaped when it comes to learning and working, functionalities like certificate verification, smart badge accreditation, and decision support could and should be at the forefront of future solutions.
# Quantitative Study on the Impact of Energy Consumption Based Dynamic Selfishness in MANETs +
## 2 Related Work
### Impact Analysis of Selfish Nodes  
Although a wide range of investigations have been done on node selfishness in various networks, very little research has been devoted to analyze the impact of node selfishness on network performance in MANETs.  
In earlier work ([PERSON] et al., 2019), [PERSON] et al. introduced that some hosts may misbehave by failing to adhere to the network protocols at the MAC layer. The authors also presented detection and penalty schemes to handle the misbehavior successfully. Later, [PERSON] et al. (2019) measured the performance of two popular routing protocols (AODV and DSR) under MAC layer selfish attacks in MANETs. The authors suggest that cross-layer cooperation between MAC layer and network layer could be one feasible solution to mitigate the MAC attack effects. [PERSON] et al. [32] analyzed the effect of node selfishness in MANETs. The authors mainly considered two kinds of selfish nodes, namely type-1 and type-2. The type-1 model is the model in which the selfish nodes do not forward packets, while in the type-2 model, the selfish nodes do not take part in the routing operations. According to this work, it is obvious that the node selfishness is more harmful to network performance in the type-2 model than in the type-1 model. [PERSON] et al. [33] investigated the functions of DSR protocol, and they defined several kinds of selfish nodes to examine their impacts on the network performance. Recently, [PERSON] et al. [34] studied the effects of selfishness on the energy consumption in opportunistic networks (OppNet). This work mainly investigated different impacts of selfishness on energy consumption under OppNet routing protocols. The authors acknowledged that the remaining energy in the node can play a crucial role in its willingness to cooperate in the network. However, their work lacked quantitative analysis of node selfishness in terms of presence, mobility, and density. Quantitative analysis of node selfishness based on energy consumption in MANETs is still an open issue, and more work is still required.
# Deep Learning Framework for Advanced De-Identification of Protected Health Information
## 5 Conclusions and Potential Future Works  
The privacy protection mechanism in healthcare has become critical due to the rapid global adoption of EHRs, which contain vast amounts of unstructured textual data. The sensitive nature of Protected Health Information (PHI) within these records presents significant privacy challenges, necessitating robust de-identification techniques. This paper introduces a novel Bi-LSTM-CRF model approach, utilizing the i2b2-2014 dataset from Harvard University to achieve accurate and reliable PHI de-identification. Unlike prior studies that often unify Bi-LSTM and CRF layers, our approach emphasizes the individual design, optimization, and hyperparameter tuning of both components, resulting in precise model performance improvements. This rigorous approach to architecture and tuning, typically underexplored in the existing literature, enhances the model's capacity to effectively detect PHI tags while retaining the essential clinical context.  
Comprehensive evaluations were conducted across the 23 PHI categories defined by HIPAA, ensuring robust security across vital domains. The optimized model demonstrates exceptional performance, achieving a precision of 99%, recall of 98%, and an F1-score of 98%, which underscores its effectiveness in balancing recall and precision. By enabling the de-identification of medical records, this research strengthens patient confidentiality, promotes compliance with privacy regulations, and facilitates safe data sharing for research and analysis. Interestingly, certain PHI categories displayed outstanding accuracy, confirming the model's efficacy in identifying and safeguarding PHI within medical records.  
The performance differences observed between the CRF and Bi-LSTM-CRF models suggest a range of influences, including dataset characteristics, specific tasks, and model architecture.
# Online Identification of Photovoltaic Source Parameters by Using a Genetic Algorithm
## Validation of the GA-Based SDM Parameter Identification Method
Then, the reconstructed curves have been compared with the ones obtained using the guess solution and with the experimental data. The plots are shown in Figure 6, which refers to the case Test #1. As expected, the guess solution does not fit the experimental data in the regions far from the MPP since approximated values of \(I_{sc}\) and \(V_{oc}\) have been used. Instead, the best solution returned by the GA allows reproducing the correct I-V curvature since the information coming from the M experimental test points has been exploited. Finally, in Table 6, the best GA solution is compared with the guess solution: \(R_{h}\) and \(I_{s}\) are the parameters that have been affected by the main variations after the refinement of the guess solution performed by the GA. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & \(I_{ph}\) **(A)** & \(I_{s}\) **(A)** & \(\eta\) & \(R_{s}\) **(\Omega)** & \(R_{h}\) **(\Omega)** & **Fitness** \\ \hline Guess solution (A) & 5.61 & \(5.58\times 10^{-8}\) & 1.05 & 0.833 & 51.5 & 1.56 \\ Best GA solution (B) & 5.19 & \(4.45\times 10^{-6}\) & 1.40 & 0.922 & 3953 & 80.59 \\ \hline Variation (B/A) & 0.92 & 79.7 & 1.33 & 1.11 & 76.7 & 51.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: SDM parameters’ comparison for Test #1. Figure 5: Best fitness value vs. the number of generations for different runs of the genetic algorithm. (**a**) With physical bounds; (**b**) with real boundary conditions and the guess solution. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Population size & \(N=150\) & Elite individuals & \(N_{E}=1\) \\ Number of generations & \(N_{g}=2500\) & Crossover percentage & \(P_{\text{C}}=80\%\) \\ Number of testing points & \(M=8\) & Mutation percentage & \(P_{M}=40\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Genetic algorithm parameters.
# Time-Domain Blind ICI Compensation in Coherent Optical FBMC/OQAM System
## 4 Modified Algorithm Based on Overlapped Symbol Structure
\begin{array}{l}\mathbf{\gamma}_{-1}=\frac{1}{b}\mathbf{\tau}^{T}\mu_{-1}\bm {\tau}\\ \mathbf{\gamma}_{+1}=\frac{1}{b}\mathbf{\tau}^{T}\mu_{+1}\mathbf{\tau}\end{array} \right. \tag{31}\]  
Assuming that three neighboring FBMC/OQAM blocks participate in the estimation process, Equation (21) can be expanded as  
\[\left[ \begin{array}{c}\hat{A}_{n-1}\\ \hat{A}_{n}\\ \hat{A}_{n+1}\end{array} \right]=\left[ \begin{array}{c}\mathbf{V}_{n-1}\mathbf{ \gamma}_{-1}\\ \mathbf{V}_{n}\\ \mathbf{V}_{n+1}\mathbf{\gamma}_{+1}\end{array} \right]\mathbf{C}_{n} \tag{32}\]  
The matrices of \(\overline{\mathbf{a}}_{n}\) and \(\overline{\mathbf{P}}_{n}\) are separately defined as \(\overline{\mathbf{a}}_{n}=\left[\hat{\mathbf{a}}_{n-1}^{T},\hat{\mathbf{a}}_{n}^{T},\hat{ \mathbf{a}}_{n+1}^{T}\right]^{T}\) and  
\[\overline{\mathbf{P}}_{n}=\left[ \begin{array}{cc}\mathrm{Re}(\mathbf{V}_{n- 1}\mathbf{\gamma}_{-1})&-\mathrm{Im}(\mathbf{V}_{n-1}\mathbf{\gamma}_{-1})\\ \mathrm{Re}(\mathbf{V}_{n})&-\mathrm{Im}(\mathbf{V}_{n})\\ \mathrm{Re}(\mathbf{V}_{n+1}\mathbf{\gamma}_{+1})&-\mathrm{Im}(\mathbf{V}_{n+1}\bm {\gamma}_{+1})\end{array} \right]\right. \tag{33}\]  
Similar to Equations (22)-(24), the LS solution of the unknown DCT coefficient vector can be obtained from the following expression,  
\[\mathbf{Q}_{n}=\left(\left(\overline{\mathbf{S}}_{n}\overline{\mathbf{P}}_{n} \right)^{T}\overline{\left(\overline{\mathbf{S}}_{n}\overline{\mathbf{P}}_{n} \right)}\right)^{-1}\overline{\left(\overline{\mathbf{S}}_{n}\overline{ \mathbf{P}}_{n}\right)}^{T}\overline{\left(\overline{\mathbf{S}}_{n}\overline {\mathbf{a}}_{n}\right)} \tag{34}\]  
where \(\overline{\mathbf{S}}_{n}=\left[\mathbf{S}_{n-1}^{T},\mathbf{S}_{n}^{T}, \mathbf{S}_{n+1}^{T}\right]^{T}\). In the modified proposed algorithm, we throw away the first \(M\)/2 samples of the \((n-1)\)th PN vector \(\mathbf{\varphi}_{n-1}\) and the last \(M\)/2 samples of the \((n+1)\)th PN vector \(\mathbf{\varphi}_{n+1}\); in spite of this, the relatively small power is on both ends of impulse response of the prototype filter so that the approximation affects rarely its performance, as the vector \(\mathbf{C}_{n}\) is obtained in such a way, and the vectors of \(\mathbf{C}_{n-1}\) and \(\mathbf{C}_{n+1}\) are easily calculated using the recurrence relations in Equations (30)-(31). Therefore, only one inverse operation in Equation (34) is required for every three neighboring FBMC/OQAM blocks, and the complexity can be decreased by the simplified operation. The detailed complexity analysis will be described in the next section. The acronyms used in this article are summarized in the following Table 1.