esa-sceva/satcom-chunk-collection huggingface dataset:
# QoSComm: A Data Flow Allocation Strategy among SDN-Based Data Centers for IoT Big Data Analytics
## 5 Performance Evaluation
### Phase Two: Application Performance  
We configured an MPI Cluster with MPICH [37] in a Mininet virtual machine. We use an MPI application for bandwidth tests transferring messages between compute nodes. The MPI application provides point-to-point communication for a given number of MPI tasks and uses TCP as a transport protocol. The test goal is to measure completion time running tasks between H1 and H6 nodes using Controller default and QoSComm data flow forwarding methods.  
The MPI application works as follows: We execute the MPI application creating two tasks, one for each host. Each task sends and receives 1,000,000 bytes between H1 and H6 hosts 40 times and then finishes the data transfer process. We executed the MPI application 20 times for each forwarding method and measured job completion time.  
The results showed that the MPI application completion time got benefits using QoSComm data flow forwarding strategy rather than Controller default, as is observed in Figure 8. As mentioned above, QoSComm used Path (S0, S2, S1, S3, S10, S11, S13) and has 11 ms end-to-end delay and 90 mbps of max bandwidth capacity; it took an average of 27 s to complete application jobs. Controller default used Path (S0, S1, S3, S10, S11, S13), which has a 20 ms delay and 100 mbps of maximum bandwidth capacity, and took an average of 32 s to complete. We can see a difference of 5 s using QoSComm instead of Controller default data flow forwarding method.  
For this MPI application test, the results showed that our proposal QoSComm improved the completion time by an average of 18%. We classified the Big Data Analytics as a _time-sensitive application_ that needs to be in constant communication with compute nodes or, at some time, will need to transfer large data sets among compute nodes. For these types of applications, the end-to-end delay becomes a critical parameter instead of just considering the network bandwidth parameter, as is observed in the test results.  
The MPI application is often performed in a single domain; however, we executed an MPI application transferring messages in an inter-domain approach to test its performance when using different data flow forwarding methods versus QoSComm. In addition, there is an implementation such as MPICH-G targeting multi-domains [38], allowing for BDA to run jobs among distributed compute hosts.  
Considering the application test results, QoSComm can contribute to improving connectivity aspects in IoT architecture for big data analytics [39], where authors describe BDA to process a large amount of data that may be stored in a distributed cloud environment.  
Figure 8: Data Flow forwarding method comparison for MPI Application. Average completion time results using simulation topology. STDEV: Controller: 1.15, QoSComm: 1.33.
###### Abstract  
Intravascular Optical Coherence Tomography (IVOCT) images provide important insight into every aspect of atherosclerosis. Specifically, the extent of plaque and its type, which are indicative of the patient's condition, are better assessed by OCT images in comparison to other in vivo modalities. A large amount of imaging data per patient require automatic methods for rapid results. An effective step towards automatic plaque detection and plaque characterization is axial lines (A-lines) based classification into normal and various plaque types. In this work, a novel automatic method for A-line classification is proposed. The method employed convolutional neural networks (CNNs) for classification in its core and comprised the following pre-processing steps: arterial wall segmentation and an OCT-specific (depth-resolved) transformation and a post-processing step based on the majority of classifications. The important step was the OCT-specific transformation, which was based on the estimation of the attenuation coefficient in every pixel of the OCT image. The dataset used for training and testing consisted of 183 images from 33 patients. In these images, four different plaque types were delineated. The method was evaluated by cross-validation. The mean values of accuracy, sensitivity and specificity were 74.73%, 87.78%, and 61.45%, respectively, when classifying into plaque and normal A-lines. When plaque A-lines were classified into fibrobl and fibrobl, the overall accuracy was 83.47% for A-lines of OCT-specific transformed images and 74.94% for A-lines of original images. This large improvement in accuracy indicates the advantage of using attenuation coefficients when characterizing plaque types. The proposed automatic deep-learning pipeline constitutes a positive contribution to the accurate classification of A-lines in intravascular OCT images.  
intravascular optical coherence tomography; atheromatic plaque; deep learning; CNN; classification +
Footnote †: journal: Medical Informatics and Biomedical Imaging Technologies, Aristotle University, 54124 Thessaloniki, Greece; [EMAIL_ADDRESS] (G.-A.C.); [EMAIL_ADDRESS] (K.H.) 1
# Biosurfactants' Potential Role in Combating COVID-19 and Similar Future Microbial Threats
## 2 Approaches That Can Use Biosurfactants in Outbreak Prevention and Management
### Applications in Diagnostics  
#### 2.2.1 Nanomaterials and Nanotechnology  
Biosensors (typically simple, sensitive, robust, and cost-effective) combined with nanomaterials, also known as nanobiosensors, can serve as a bridge between advanced diagnostics/detection and routine testing. It is essential that the production processes for nanoparticles become cleaner, less toxic, and more environmentally safe so that the negative impact on the environment from waste can be minimized. Microorganisms are capable of synthesizing inorganic molecules that can be deposited either intracellularly or excreted extracellularly. Sophorolipid-capped cobalt nanoparticles can be used to generate biocompatible particle surfaces by the attachment of bioactive molecules such as glycosases or lectins for diagnostic and medicinal applications. An important issue of biocompatibility properties is the accessibility of the sophorose group at the surface of the nanoparticles [66]. Silver nanoparticles synthesized with purified rhamnids from _P. aeruginosa_ BS-161R strain demonstrated broad-spectrum antimicrobial activity against Gram-negative and Gram-positive bacteria, and against _Candida albicans_[35].  
Nanotechnology is a relatively new field which is growing very rapidly, and nanoparticles are being used in sophisticated devices and in treatment. As the need for nanoparticles grows, it will be necessary to make production environmentally friendly and sustainable. This can be achieved using the biosurfactant-mediated production of nanoparticles. Diagnostics using nanotechnology are the basis for highly sensitive and specific diagnostic devices that require very small sample amounts. During outbreaks, the availability of highly accurate and specific diagnostics is important in the proper identification of the causative agent, and subsequently building a response.
# Using a Random Forest Model to Predict the Location of Potential Damage on Asphalt Pavement
## 6 Results and Discussions
### Number of Trees and Number of Variables Tried at Each Split  
The two key hyperparameters, _ntree_ and _mtry_, are determined by the exhaustive method. At first, an RF forest was constructed with the following default settings: _ntree_ = 500 and _mtry_ = 5. The OOB estimate of the error rate of the RF model is 20.24% and the confusion matrix is shown in Table 2.  
Through the exhaustive method, _mtry_ is assigned for 1 to 35 in the default RF model with the other parameters fixed to gain the minimal error rate. According to the same method, _ntree_ is taken to traversal algorithm again by fixing _mtry_ value. The results and the processes are presented in Figures 5 and 6.  
Figure 4: Data characteristics and correlations matrix: the * represents the significance level.  
With the increase in _mtry_ from 1 to 35, the error rate keeps decreasing. Generally, the number of variables tried at each split in an RF model, namely the maximal deep of a tree the model grows, is random in the range between one to the number of variables. It always needs a balance for lower single-tree correlations and a certain prediction strength. Therefore, it is not a general law for an RF model as the error rate can be reduced by introducing more variables unless the variables are all effective for the model with little correlations.  
In our given RF model, there are some correlated factors. This is not the main reason which affects the model accuracy until _mtry_ equals 23. Before that, the model does not consider enough variables, which strongly helps with the increase in variables. After that or even after 19, the model is improved little when there are less independent residual factors. However, the optimal _mtry_ value is 35 with the lowest error rate. That means that all factors have their own weights in the model even though some parts are subject to dependency.  
With the increase in _ntree_, i.e., the number of trees generated by the model, the noise can be reduced in the model. When _ntree_ arrives at a certain number, the error rate of the model will maintain stability. However, for the calculating speed of a computer, the best _ntree_ value is determined. When _ntree_ passed over 400, the prediction error rates for Y, N, and the average of the model achieved the lowest value and kept the trend. Therefore, the _ntree_ is selected as 400 for the RF model.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Ture N** & **Ture Y** & **Class. Error** \\ \hline Pred. N & 39 & 7 & 0.1521739 \\ Pred. Y & 10 & 28 & 0.2631579 \\ \hline \end{tabular}  
Y presents the points marked as distress; N presents the points marked as in good condition.  
\end{table}
Table 2: Confusion matrix of the default RF model.  
Figure 5: The relationship between _mtry_ and the error rate.
## 3 Demonstration of Tunable Shielding Effectiveness in GHz Bands
### Design and Experiments of Different Shielding Film Structures
#### 3.1.3 Experiments on Shielding Effectiveness of MIM and BIB Structure  
For graphene grown by the CVD method, it is easy to be doped by various ions or impurities, when it is exposed to air. Without bias, the Fermi level is in the p-doped state and will not be located in the center of the energy band [10; 26]. When the voltage is applied, the top and bottom graphene are doped with electrons and holes, respectively, as shown in Figure 5a. Therefore, as the voltage increases from the initial level, the shielding effectiveness will show a nonlinear and nonmonotonic change, as depicted in Figure 5b.  
Figure 4: (**a**) The schematic and (**b**) experimental setup of waveguide testing method.  
The applying of bias voltage will change the p-doped energy level state of the graphene. With the dynamic changing of the voltage from 0 to 6 V, the Fermi level of graphene achieved a dynamic balance from the low doped state in the band gap center to the high doped state. The SE of the samples accordingly showed a monotonous trend with the change of bias voltage. The tested SE curves of MIM and BIB structure are shown in Figure 6a,b.  
The results show that both of the GIG samples could realize the tuning of shielding effectiveness for above 10 dB in the 6-8 GHz band, between which the MIM structure exhibited a slightly larger tuning range. For the BIB structure, with the voltage changing from 0 to 6 V, its SE was tuned from less than 5 dB to close to 15 dB, showing a stronger shielding effectiveness than the MIM sample.
## 4 Numerical Model  
In this paper, a three-dimensional finite element method (FEM) model was established with the same geometry as the experimental specimen. The model was meshed using Hypermesh pre-processing software, and the element type was adopted as an eight-node hexahedral element. The element size of the model for each specimen was approximately 2 mm. The boundary conditions of the FEM model were set up with the clamping side completely fixed and the loading side loaded. In order to be as consistent as possible with the experimental environment, the five degrees of freedom at the loading side were constrained except for the moving degrees of freedom in the loading direction, and the same velocity conditions as in the experiment were applied in the loading direction. Then, the numerical simulations in this paper were all calculated using the LS-DYNA solver. The computational results were read by HyperView and HyperGraph post-processing software for the analysis of the simulation data. Figure 3 shows the finite element models for the different experimental specimens.  
The experimental results were used to analyze the equivalent plastic strain at failure, whereas the FEM simulation results were used to obtain the variation of the stress state during the loading of different specimens. By combining the experimental and FEM simulation results, the parameters of the MMC model were fitted using a Python script. The GISSMO model was applied to simulate instability and eventual material fracture.
# A Study of LoRa: Long Range & Low Power Networks for the Internet of Things
## 1 Introduction
### Related Work
#### 1.1.2 Bluetooth/LE  
Released in 1999 by a consortium led by Ericsson, Nokia and Intel, Bluetooth v1.0 was initially designed to, wirelessly, replace cables to connect devices typically used together, such as cell phones, laptops, headsets, keyboards, etc., offering a lower data rate (1-Mbps raw data rate, max) and a relatively short range (in theory, officially up to 100 m, at maximum transmission power, realistically, 5-10 m) while also a low power consumption.  
Several revisions of Bluetooth later, Bluetooth 4.0 was completed in 2010. Fully compatible with Bluetooth 1.0, this revision supports a higher data rate (24-Mbps raw data rate, based on WiFi) and includes a \"low energy\" extension (called Bluetooth/LE or \"Smart\"). As compared with the \"non-LE version\", Bluetooth/LE provides rapid link establishment functions (simpler pairing) and further trades off the data rate (approximately 200 kbps) for lower energy consumption, with the target to run a wireless sensor for at least one year on a single coin cell (approximately 200 \(mAHr\)) [6].
## 5 Numerical Results and Discussion
### Parameters Design  
The performance of deep space optical uplink communication can be optimized by designing key parameters of the system. First, the FOV angle \(\Omega_{F}\) critically governs the performance through its dual influence on AOA fluctuations and background noise. As illustrated in (7) and (10), small \(\Omega_{F}\) configurations mitigate background noise by spatially filtering extraneous photons but exacerbate signal loss from AOA fluctuations. In contrast, a large \(\Omega_{F}\) improves beam capture robustness at the cost of amplified background noise. Figure 7 illustrates the relationship between the received background photon count and the field-of-view angle \(\Omega_{F}\). The results demonstrate that, when implementing 16-PPMmodulation with a 10 MHz slot rate, the average background photon number \(\bar{n}_{b}\) exceeds 10 photons/slot once \(\Omega_{F}\) exceeds 0.5 mrad. Therefore, it is necessary to consider the trade-off between AOA fluctuations and background noise when designing the angle of FOV.  
From Figure 6, it is concluded that the APD gain is a significant parameter affecting system performance. As the APD gain \(G\) increases, the SER predicted by the Webb model initially decreases before stabilizing at a relatively constant level, while the Gaussian model exhibits a distinct behavior in which the SER first reduces and then subsequently increases. Given that the Webb model provides more accurate characterization of practical APD operation compared to the Gaussian approximation, the optimal APD gain determination should primarily rely on the SER trend observed through Webb modeling. Specifically, this optimal gain corresponds to the transition point where the Webb model's SER curve approaches its stable plateau region. For example, as shown in Figure 6c, the Webb model identifies approximately 150 as the optimal value of \(G\). However, when employing the computationally simpler Gaussian model while requiring enhanced estimation accuracy, the optimal gain selection must satisfy dual criteria: achieving minimal SER according to the Webb model reference while simultaneously meeting the Gaussian model's operational precondition \(G\geq G_{T}\) established in prior analysis. This explains why, under identical conditions in Figure 6c, despite the Gaussian model suggesting \(G=150\) as its minimum SER point, the practically viable value of \(G\) should be elevated to 500 to comply with both performance optimization requirements and model validity constraints.
## 6 Conclusions  
Based on the comprehensive analysis of static wind stability, buffeting response, internal resonance characteristics, and fatigue damage in ultra-long-span CFRP cable-stayed bridges, the following conclusions can be drawn:  
1. The CFRP cable scheme demonstrated superior static wind stability compared with the steel cable scheme, with reduced lateral displacements at equilibrium positions under design wind speeds. The RMS values of vertical, lateral, and torsional buffeting responses in CFRP cables were reduced by 31.6%, 28.5%, and 20.6%, respectively, at mid-span under critical wind angles of attack. This improvement stems from the CFRP's lower self-weight and higher stiffness, which mitigate sag effects and aerodynamic drag.
2. The steel cables exhibited significant internal resonance under turbulent wind excitation due to frequency coupling between cable modes and structural vibration frequencies. In contrast, the CFRP cables avoided such resonance phenomena owing to their higher natural frequencies and reduced sensitivity to anchorage-end excitations. The sparser frequency distribution of CFRP cables effectively decoupled cable-girder interactions, suppressing mode transitions and clustered spectral peaks.
3. Fatigue analysis based on the Palmgren-Miner rule and rainflow counting demonstrated that the CFRP cables exhibited orders-of-magnitude lower cumulative fatigue damage than the steel cables under identical wind conditions. The maximum fatigue damage in CFRP cables remained confined to critical positions but stayed within safe limits, validating the CFRP's superior durability under stochastic wind loading.  
Figure 23: Buffeting-induced fatigue damage of CFRP cables under different AOA within 1000 s: (**a**) U = 20 m/s; (**b**) U = 30 m/s; (**c**) U = 40 m/s; (**d**) U = 52.97 m/s.  
* Replacing steel cables with CFRP cables in ultra-long-span cable-stayed bridges significantly enhances wind resistance, reduces maintenance costs associated with fatigue and corrosion, and extends service life. This study provides theoretical and computational support for CFRP cable applications in mega-span bridge engineering, addressing key challenges in aerodynamic stability and fatigue performance.  
Conceptualization, Y.N. and Z.Z.; Methodology, Y.N.; Formal analysis, Y.N.; Investigation, F.H.; Writing--original draft preparation, Y.N.; Writing--review and editing, Y.N.; Visualization, Y.N. and F.H.; Project administration, Z.Z. and J.Z.; Funding acquisition, Z.Z. and J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant numbers: 51938012; 52268073; 52068020) and the Natural Science Fund projects of Hainan Province (Grant number: 525 RC708).  
Not applicable.  
The original contributions presented in this study are included in this article; further inquiries can be directed to the corresponding author.  
We hereby extend our sincere gratitude to [PERSON] for his support and supervision and data curation.  
The authors declare no conflicts of interest.
## 3 Methodology
### Evaluation Datasets
#### 3.1.1 UCI-HAR Dataset  
This research employs the UCI human behavior recognition dataset obtained through handheld electronic devices [23] to observe events in the community. The UCI-HAR dataset comprises action data from thirty individuals, with ages, nationalities, heights, and weights varying between 18 and 48 years. Individuals carried a Samsung Galaxy S-II smartphone (Suwon, Republic of Korea) at waist height while engaging in routine activities. Each participant performed six distinct activities: walking, ascending stairs, descending stairs, sitting, standing, and lying down. Sensor data was captured utilizing integrated tri-axial measurements from the smartphone's accelerometer and gyroscope. Sensor data were recorded at a sampling rate of 50 Hz, capturing linear acceleration and angular velocity throughout the six predefined activities.
# Dynamic Aeroelastic Response of Stall-Controlled Wind Turbine Rotors in Turbulent Wind Conditions
## 3 Reduced-Order Characterization of the Aero-Structural Interaction  
### Objectives and Methodology  
Following a limited set of preliminary experiments reported in [PERSON] et al. [22], we conducted an extensive series of experiments on the aero-elasto-inertial response of wind turbine rotors by using Delft University's stall-control rotor (see jaimes [23]), an alternative to the NREL-5 MW-RWT benchmark turbine. The DU-5 MW stall RWT has a nominal wind speed of 15.3 m/s and a nominal rotational speed of 10.7 rpm. By studying the time evolution of the rotor's oscillatory behavior when stimulated by pulses in the wind of controlled amplitude and time span (see Figure 1), we gained a deeper understanding of the underlying physics. We also obtained a reduced-order characterization of the rotor as an oscillatory system based on the principle of energy transfer between the kinetic energy of the wind-speed pulse and the elasto-inertial energy accumulated in the rotor. These wind pulses are mounted on top of a uniform stream inflow of different constant speeds covering the entire operational regime of the turbine from 5 to 25 m/s. The resultant study provides extremely valuable information to the wind power control community in order to produce Reduced-Order Models (ROMs) that could be solved in real time to develop predictive control strategies.  
Figure 1: An example of a wind-speed pulse stimulus of controlled amplitude and time span.
# A Numerical Model to Estimate the Soil Thermal Conductivity Using Field Experimental Data
## 1 Introduction
In this work, we propose a new numerical model to estimate thermal conductivity from experimental measurements of soil heat flux and soil temperature. The model uses the Fourier Law adding a constant empirical parameter (\(\varepsilon\)) to minimize the uncertainties contained in the data from field experiments. Numerically, the solution model is a linear data fitting of the experimental data using the Least Squares Method (LSM). This methodology avoids numerical indetermination and possible data loss. The soil data collected in a field experiment on native vegetation of the Brazilian Pampa biome will be used to calibrate and validate the proposed model. This work is divided into three sections: Materials and Methods; Results and Discussions; and Conclusions. The Materials and Methods section presents the description of the experimental site, the proposed model, the justification for the proposition of the model and the method used to estimate \(\lambda\), in addition to the analysis sequence. The Results and Discussions section presents the results of the proposed model in comparison to the different models used in here. This section also shows the influence of the different methodologies on evaluating the dependence of the thermal conductivity on the soil water content. Finally, the Conclusions present the pertinent reflections on the results obtained.