esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of Edge-IoMT Network Architecture with Weight-Based Scheduling
## 5 Conclusions  
This paper simultaneously considers signal prioritization and transmission scheduling through the hierarchical clustering method to organize sensors by setting weights and applying data compression techniques at the network edge, which reduces the amount of data transmission. The proposed scheduling algorithm assigns weights according to the physiological signal types and schedules the data to allocate the corresponding transmission bandwidth for ensuring the priority service of critical data. Through the experimental results and evaluation of the existing scheduling mechanisms, the proposed network topology management scheme is shown to achieve the goal of self-organized population health monitoring for IoMT applications. In future work, we are planning to apply the proposed IoMT architecture to possible applications for group health monitoring (e.g., disaster rescue task, fall detection system, or crowd supervision for a group of hikers), determine an efficient tradeoff between compression and data collection to improve the effectiveness of the system design, tackle data security issues in data processing, and further  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Acquisition Time** & **Clustering Scheme** & **Direct Path** & **Reduction** \\ \hline
15 s & 0.728 ] & 0.741 ] & 1.75\% \\
30 s & 1.270 ] & 1.285 ] & 1.16\% \\
90 s & 3.472 ] & 3.503 ] & 0.88\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Energy consumption.  
integrate the data of key physiological signals with the relationship between the lesion site and severity for establishing a more comprehensive prioritization scheme.  
Conceptualization, L.-M.T., P.-F.C. and C.-Y.W.; Formal analysis, L.-M.T. and P.-F.C.; Funding acquisition, C.-Y.W.; Methodology, L.-M.T., P.-F.C. and C.-Y.W.; Supervision, C.-Y.W.; Visualization, L.-M.T. and P.-F.C.; Writing--original draft, L.-M.T. and P.-F.C.; Writing--review and editing, C.-Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Smart Sustainable New Agriculture Research Center (SMARTer) from the NSTC of Taiwan under grant number 111-2634-F-005-001, and by the \"Innovation and Development Center of Sustainable Agriculture\" from the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan.  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Failure Assessment of Embankment Dam Elements: Case Study of the Pirot Reservoir System
## 2 Theoretical Basis
### Governing Equation of Strength Analysis  
Analysis of strength determines the displacement, stress, strain, and other relevant variables introduced as internal variables [15; 29]. In static conditions, the following equilibrium must be fulfilled at each material point:  
\[\
abla\cdot\boldsymbol{\sigma}+\mathbf{F}^{V}=0, \tag{5}\]  
where \(\boldsymbol{\sigma}\) represents the stress tensor, whereas \(\mathbf{F}^{V}\) is the vector of body forces. Boundary conditions can be specified as displacements or specified load (force or pressure).  
Using equilibrium Equation (5) and corresponding boundary conditions [15], a virtual work principle representing a basic equilibrium equation in mechanic of deformable body can be represented as:  
\[\int\limits_{V}\sigma\delta\epsilon dV=\int\limits_{V}\mathbf{F}^{V}\delta \mathbf{u}dV+\int\limits_{S^{\epsilon}}\mathbf{F}^{S}\delta\mathbf{u}dS. \tag{6}\]  
where \(\delta\epsilon\) represents virtual strain corresponding to virtual displacement \(\delta\mathbf{u}\), and \(\mathbf{F}^{S}\) is the vector of surface forces.  
In the stress-strain analysis of the dam, the Mohr-Coulomb constitutive model was used for simulation of mechanical behavior of the dam and surrounding rock mass [30; 31]. Yield surface of the used constitutive model is the function of stress state and can be formulated using stress invariants as:  
\[f=\frac{I_{1}}{3}\sin\phi+\sqrt{J_{2D}}\bigg{(}\cos\theta-\frac{1}{\sqrt{3}} \sin\theta\sin\phi\bigg{)}-c\cos\phi, \tag{7}\]  
where \(I_{1}\) and \(J_{2D}\) are the first stress invariant and second deviatoric stress invariant, respectively, \(\theta\) represents [PERSON]'s angle, \(c\) is cohesion of the material, and \(\phi\) is the internal friction angle of the material. In the case when the stress point reaches the yield surface (materialfailure), the value of the yield function (7) is equal to zero. This condition can be used to determine the maximum value (failure) of the second deviatoric stress invariant \(J_{2D}\).
## 1 Introduction
The relay selection should consider both the efficiency of the transmission path and the load balance between the transmitter and the relay. 3. Taking advantage of HCR: HCR provides an effective solution with low overhead to generate a connected and efficient topology at every round. Therefore, the new algorithm should balance the energy consumption with the advantages of HCR. To overcome these challenges, we propose the HCR-1 protocol, which focuses on balancing the energy consumption in gradient \(k=1\). In HCR-1, an adaptive energy threshold is proposed to decide whether a node requires a relay, and tunable cost functions are designed to optimize relay selection and cluster formation. Analytical and numerical results show that, with minor modifications of the network topology, HCR-1 can prolong the network lifetime by over \(30\%\) compared to that of HCR. The rest of this paper is organized as follows. Section 2 provides a brief survey of related works. Section 3 introduces the HCR protocol and evaluates the imbalanced energy consumption of HCR. Section 4 describes the details of the HCR-1 protocol. Simulation results are provided in Section 5 to evaluate the performance of HCR-1. Finally, Section 6 provides the conclusion of this paper.
# Three-Phase Unbalance Analysis Method Based on Three-Phase Motor Current Instantaneous Information
### General Idea of the Algorithm  
To quickly analyze the motor current three-phase unbalance, this paper analyzes the three-phase unbalance from the perspective of instantaneous information (instantaneous amplitude and instantaneous frequency). The flow of the proposed algorithm in this paper is shown in Figure 1.  
Figure 1: Algorithm flow of the proposed method.  
After obtaining the three-phase motor current signal, the instantaneous amplitude and instantaneous phase are calculated based on the park vector demodulation method, and the instantaneous frequency is obtained from the instantaneous phase. The standard deviation and mean value of the instantaneous amplitude and instantaneous frequency are calculated, where the mean value of the instantaneous frequency can be used for the reference of sliding window size selection. The coefficient of variation of the instantaneous amplitude and instantaneous frequency from the standard deviation and mean value are calculated, and the change in the three-phase unbalance is analyzed.  
The following subsections introduce the relevant methods and explain them in detail.
# A Virtual Reality Soldier Simulator with Body Area Networks for Team Training
###### Abstract
_Cost-Effective Design_: We designed and implemented our own sensor nodes, such that the fundamental operations of the inertial sensors could be adaptively adjusted for acquisition and integration. Therefore, it has a competitive advantage in terms of system cost. 2. _System Capacity_: Based on the proposed simulator, a six-man squad is able to conduct military exercises that are very similar to real missions. Soldiers hold mission rehearsals in the virtual environment such that leaders can conduct tactical operations, communicate with their team members, and coordinate with the chain of command. 3. _Error Analysis_: This work provides an analysis of the quaternion error and further explores the sensing measurement errors. Based on the quaternion-driven rotation, the measurement relation with the quaternion error between the earth frame and the sensor frame can be fully described. 4. _System Delay Time_: The update rate of inertial sensors is about 160 Hz (i.e., the refresh time of inertial sensors has a time delay of about 6 ms). The simulator is capable of training six men at a 60 Hz system update rate (i.e., the refresh time of the entire system needs about 16 ms), which is acceptable for human awareness with delay (\(\leq\)40 ms). 5. _System Feedback_: Instructors can provide feedback after mission rehearsals using the visual after action review (AAR) function in the simulator, which provides different views of portions of the action and a complete digital playback of the scenario, allowing a squad to review details of the action. Furthermore, instructors can analyze data from digital records and make improvements to overcome the shortcomings in the action (Figure 1). Accordingly, in the immersive virtual environment, soldiers and leaders improve themselves with respect to situational awareness, decision-making, and coordination skills.
## 3 Impact of Cold Plasma on Packaging Properties
### Molecular Properties of the Film
It was further investigated at a constant 50 V power, and the \(\alpha\)-helix and random coil content decreased with the increase in treatment time up to 90 s and then again increased, although the overall content was less than the control film. Similarly, \(\beta\)-sheet increased with the increase in treatment time up to 90 s and then decreased, whereas the increase in \(\beta\)-turn content was quadratic. Such variation in the secondary structure of the protein can be ascribed to the destruction of orderly structure at short treatment times and the reproduction of orderly structure at higher treatment times [105]. Also, as shown in Figure 5 III, an increase in the peak intensity at 1543 cm\({}^{-1}\) (zein film) and 1533 cm\({}^{-1}\) (zein/PLA), corresponding to amid II, indicates the impact of plasma on hydrogen bonding resulting in the modification of secondary structures [46, 80]. Such enhancement in \(\beta\)-sheet structure was also reported for Ar and air (10 min) treated protein films (whey protein and gluten) and was evident from the decrease in peak intensity at 1234-1239 cm\({}^{-1}\)[100]. Overall, it was observed that the application of plasma influences the structure of the biopolymer-based film by various mechanisms like creating a reactive site, crosslinking, reordering, and enhancing the structural arrangement of the polymeric network. These structural modifications affect the various properties like mechanical, thermal, and barrier properties of the biopolymer films.
# A Review of Machine Learning-Based Routing Protocols for Wireless Sensor Network Lifetime +
## 4 Estimation of the Enhanced Clustering Methods  
Many clustering algorithms that take machine learning into account in terms of key parameters like data delivery speediness, nonstop power consumption over the time of a network, and algorithm scalability are compared here. The uniformity of nodes, whether or not data collection techniques are integrated, and whether or not the system is centralized all play a role. The rate at which information is transferred from sending nodes to a base station (BS) or other receiving nodes is a critical metric, as it provides an overarching view of the transmission's reliability and the amount of data lost. When evaluating clustering algorithms, one crucial metric is the energy-consumption parameter. Reduction in node energy impacts the lifetime of the sensor network because more communication or transactions at a node can use up energy and decrease the lifespan of the network. As an outcome, we have focused our research on WSNs on a grander scale, debating the scalability of existing algorithms and suggesting that networks with more than 500 nodes receive special attention.  
There are two main methods for implementing clustering algorithms: a centralized method and an extensive method. However, in the centralized method, the BS is an integral part of the decision-making process. In this context, homogeneity in a distributed system refers to a set of nodes in the network that all share the same characteristics, and they require that a BS broadcast this information to its plotting nodes and compute it as part of the ML adaptation to the network. In such a network, the CH is chosen as a clustering algorithm for nodes that have distinctive properties. The sensor network's radio model provides a rough estimate of the node's power consumption in the field. As such, the clustering method takes into account this radio model as part of the network and determines the energy output of each node during transmission. To measure how well the provided methods cope with unexpected situations, we define multipath metrics. Errors are tolerated better in multipath implementation techniques, unless the direct route to the BS is cut off and only an alternate route leads to the BS. This configuration allows the algorithm to continue operating normally in the event that a single node in the network fails.
## 2 Background and Related Work
### The 802.11 Protocol for VANET  
The 802.11-based Wi-Fi protocol is a part of the 802 family, including the 802.11p amendment for Wireless Access in Vehicular Environments (WAVE) [26]. The 802.11 standard primarily focuses on physical and MAC layers in the OSI model. Figure 3 shows the relationship between the 802.11 and OSI layers. The combination of the logical link control (LLC) and MAC sub-layer are known as the data link layer in the OSI model. Various versions of the 802.11 standards, such as 802.11a, 802.11b, 802.11g, and 802.11n share a common MAC and LLC layer while adopting various physical layers. Table 3 summarizes review of literature on Wi-Fi-based VANETs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Technique**} & \multicolumn{3}{c}{**Localization Accuracy**} \\ \cline{2-4}  & **Low** & **Medium** & **High** \\ \hline Routing & x & - & - \\ Data Dissemination & x & - & - \\ Map Localization & x & - & - \\ Coop. Adapt. Cruise Control Coop. Intersection Safety & - & x & - \\ Blind Crossing & - & x & - \\ Platooning & - & x & - \\ Vehicle Col. Warn. System Vision Enhancement & - & x & - \\ Automatic Parking & - & - & x \\ Vision Enhancement & - & - & x \\ \hline \hline \end{tabular}
\end{table}
Table 2: VANET application areas [11].  
Figure 3: IEEE 802 Family of Protocols.
# Deep Learning for Medical Image Cryptography: A Comprehensive Review
## 2 Deep Learning-Based Medical Image Analysis
### Classification or Diagnosis  
Deep learning applications in medical image analysis fall primarily under the category of classification or computer-aided diagnosis (CAD). In CAD, deep learning models are utilized to classify medical images into different classes or categories based on specific diagnostic tasks. Deep learning models are trained on large datasets of medical images. These datasets consist of images with corresponding classification of the presence or absence of specific diseases. Numerous researchers have reported their findings related to deep learning-based classification while analyzing the medial image datasets. [PERSON] et al. [12] have reviewed the application of deep learning in the field of big data analysis and early diagnosis of diseases while analyzing medical images, especially their segmentation and classification. To classify 14 diseases based on an X-ray of the chest, [PERSON] et al. [37] updated the DenseNet 121 model, known as the CheXNet model. It is also found that the CheXNet exceeds average radiologist's performance on the F1 metric and also the model is able to detect all 14 diseases in ChestX-ray14. [PERSON] et al. [38] evaluated two approaches based on ResNet [39] and VGGNet [40] architectures for Alzheimer diagnosis. Single-lesion segmentation and recognition have been successfully accomplished using deep learning-based methods. Because there is less fluctuation between lesions, or a greater variety of lesions is present, multiple-lesion recognition is more challenging than  
Figure 4: A typology of significant medical imaging modalities (adapted from [30]).  
single-lesion recognition. Recent studies have looked into using deep learning-based methods to overcome the difficulty of recognizing multiple lesions. Ref. [41] provides a comprehensive overview and analysis of recent advances in deep learning-based multiple-lesion recognition techniques, including the identification of many diseases throughout the entire body as well as multiple lesions in various body regions.  
In most of the research papers, the base algorithm used for different tasks related to medical image analysis is CNN. As a result of its higher accuracy and lower error rate, the CNN model is frequently utilized for image classification. However, in order to generalize the hidden correlations discovered in the learning data, large datasets are required. To enhance/optimize the performance of these CNN-based techniques there are two commonly used approaches reported in the literature, i.e., transfer learning [42; 43; 44; 45] and general adversarial network (GAN) [46; 47; 48].
# Monitoring of Ionospheric Anomalies Using GNSS Observations to Detect Earthquake Precursors
## 2 Materials and Methods
### Model Implementation: From GNSS Doppler Observations to VTEC and VTEC Rate
#### 2.1.3 Kalman Filter Implementation  
At this point, the real-time knowledge of \(VTEC(t)\) and \(VTEC(t)\) depends on the real-time estimation of the parameters \(g_{k}^{m}\), \(g_{k}^{m}\), \(h_{k}^{m}\), and \(h_{k}^{m}\) and on \(D\hat{C}B_{r}(t)\) and \(D\hat{C}B^{s}(t)\)The first four types of variables are those actually involved in the estimation procedure; to test our model, we considered an expansion of order 10 and degree 8. \(D\hat{C}B^{s}(t)\) can be instead computed using the \(DCB^{s}(t)\) values in IONEX files [56]. To achieve this, we created series of these parameters, then we computed the derivative using a five-point formula for each of them. Finally, \(D\hat{C}B_{r}(t)\) are considered as stochastic parameters; thus, their contributions were taken into account through the covariance matrix [57].
# Predicting the Aggregate Mobility of a Vehicle Fleet within a City Graph
###### Abstract  
Predicting vehicle mobility is crucial in domains such as ride-hailing, where the balance between offer and demand is paramount. Since city road networks can be easily represented as graphs, recent works have exploited graph neural networks (GNNs) to produce more accurate predictions on real traffic data. However, a better understanding of the characteristics and limitations of this approach is needed. In this work, we compare several GNN aggregated mobility prediction schemes to a selection of other approaches in a very restricted and controlled simulation scenario. The city graph employed represents roads as directed edges and road intersections as nodes. Individual vehicle mobility is modeled as transitions between nodes in the graph. A time series of aggregated mobility is computed by counting vehicles in each node at any given time. Three main approaches are employed to construct the aggregated mobility predictors. First, the behavior of the moving individuals is assumed to follow a Markov chain (MC) model whose transition matrix is inferred via a least squares estimation procedure; the recurrent application of this MC provides the aggregated mobility prediction values. Second, a multilayer perceptron (MLP) is trained so that--given the node occupation at a given time--it can recursively provide predictions for the next values of the time series. Third, we train a GNN (according to the city graph) with the time series data via a supervised learning formulation that computes--through an embedding construction for each node in the graph--the aggregated mobility predictions. Some mobility patterns are simulated in the city to generate different time series for testing purposes. The proposed schemes are comparatively assessed compared to different baseline prediction procedures. The comparison illustrates several limitations of the GNN approaches in the selected scenario and uncovers future lines of investigation.  
mobility in graphs; Markov chain; graph neural network (GNN) +
Footnote †: journal:  
0000-0001-4600-0000][PERSON]. [PERSON]  
1 Departamento de Ingenieria de Sistemas Telematicos, ETSI Telecomunicacion, Universidad Politecnica de Madrid, 28006 Madrid, Spain 12 [PERSON], ETSI Telecomunicacion, Universidad Politecnica de Madrid, 28040 Madrid, Spain 24 Departamento Matematica Aplicada a las TIC, Information Processing and Telecommunications Center (IPTC), ETSI Telecomunicacion, Universidad Politecnica de Madrid, 28040 Madrid, Spain 4
## 2 Modeling and the Proposed Method
### Estimation of Spin Rate  
In 2D, for TF signals \(g(t,f)\) with N columns and M rows, the corresponding 2D discrete Fourier transform is given as  
\[G(u,v)=\sum_{t=0}^{M-1}\sum_{f=0}^{N-1}g(t,f)e^{-j2\pi(ut/M+vf/N)} \tag{4}\]  
By computing the magnitude squared of the complex matrix \(G(u,v)\), one has  
\[|G(u,v)|=\left[R(u,v)^{2}+I(u,v)^{2}\right]^{1/2} \tag{5}\]  
Here, \(R(u,v)\) and \(I(u,v)\) are the real and imaginary parts of \(G(u,v)\), respectively. Furthermore, the partial sum yields  
\[|G(u_{i})|=\sum_{j=0}^{N-1}|G\big{(}u_{i},v_{j}\big{)}|,i=0,1,2,\ldots,\text{ M}-1 \tag{6}\]  
Accordingly, one has a sequence  
\[|G(u)|=\left\{|G(u_{0})|,|G(u_{1})|,\ldots,|G(u_{M-1})|\right\} \tag{7}\]  
which consists of the features of the frequency spectrum.  
It should be noticed that for the cube-shaped target, the spectrum is actually a superposition of TF curves corresponding to numerous scattering points. Based on the spectrum \(|G(u)|\) obtained as mentioned above, the cycle of TF data can be easily determined. In particular, the corresponding frequency of the maximum value of \(|G(u)|\) is just the characteristic frequency \(f_{\ell}\). In this work, the cube rotates around the symmetric axis across the upper and lower bottom faces (see Figure 1). Due to the equivalence of the four side faces, the characteristic period of TF data corresponds to a quarter of the spin cycle. Thus, one eventually has  
\[f_{s}=f_{f}/4 \tag{8}\]  
Here, \(f_{s}\) represents the period of spinning.