esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Insight into the Kelvin-Helmholtz Instability Appearance in Cavitating Flow
## 2 Experimental Observations  
The experimental method is seen here only briefly, a more detailed study can be found in [8]. The set-up is illustrated in Figure 1. By sandwiching them between two acrylic glass plates, microchannels are constructed of 450 \(\upmu\)m thick stainless-steel sheets that have a laser-cut convergent-divergent narrowing. The Venturi channel's convergent angle measures 18\({}^{\circ}\) and the divergent angle is 10\({}^{\circ}\), with a throat height of 675 \(\upmu\)m. Both the channel entrance and exit are perpendicular to the cross-section plane, with the channel inlet on the left and the outlet on the right side of the channel.  
High-speed cameras (Photron SA-Z and Photron Mini AX200, Photron, Tokio, Japan) have captured cavitation images at a framerate of typically 200,000 fps in both the visible and X-ray light spectrum. Typical flow features that imply the formation of the Kelvin-Helmholtz instability in the channel are shown in Figure 2. The flow is from the left side to the right.  
Figure 2 illustrates common microchannel representations of the Kelvin-Helmholtz instability. In particular, these images are from an experiment with a mass flow (m) 9.15 g/s,  
Figure 1: Microchannel geometry with the 675 \(\upmu\)m throat gap and the 450 \(\upmu\)m channel width.  
Figure 2: Development of the Kelvin-Helmholtz instability in the microchannel (m = 9.15 g/s, \(\Delta\)p = 4.00 bar, \(\sigma\) = 1.24).  
a pressure difference between inlet and outlet (\(\Delta\)p) 4 bar, and a cavitation number (\(\sigma\)) of 1.24. A closer look at the flow conditions shows that the attached cavity first forms in the shape of a large single cavitation bubble extending downstream from the starting point until the pressure rises well above the saturation pressure--the presence of cavitation is like the supercavitation state. Kelvin-Helmholtz instability forms when the bubble approaches its maximum size. The ripple of the interface increases and the instability encircles the entire cavitation cloud while rolling up and induces its very rapid dissolution in the flow. The pattern then continues periodically.  
The numerical simulation that provides further information in the creation of the Kelvin-Helmholtz instability and the cavitation dynamics in the microchannel is presented in the next section.
## 3 Survey of Techniques for Updating
### The Execution Environment
#### 3.3.15 Sensor Operating System (SOS)  
SOS [50] is a microkernel-style operating system, supporting 'C' programs, with support for dynamic module loading and unloading. Services (such as routing, applications _etc._) are provided as dynamic modules. Synchronous communication between modules is provided through function pointers. A function control block (FCB) stores the function prototype and version number. New module advertisements contain the version number, and if this is an updated version, and there is space, the module is downloaded. For an update, a _final_ message is sent to the old module, which is then unlinked from the FCBs, before the new version is loaded and init called to repopulate the FCB. Other modules keep running--if an unavailable function is called, then kernel provides a stub to return failure. SOS does not support update of the core kernel image.  
The dynamic modules, and versioning support, provided by SOS provide flexibility which might prove to be a useful enabling technology for autonomous updates.
## 2 Methodology  
### Electromagnetic Field Governing Equation  
The three-dimensional forward modeling employs the staggered sampling finite difference method. Under the international system of units, the harmonic factor is expressed as \(e^{iwt}\). Combining this with the constitutive equation, [PERSON]'s equations are derived, leading to the Helmholtz equation corresponding to the electric field (Equation (1)):  
\[\
abla\times\
abla\times\mathrm{E}+\mathrm{i}\omega\mu_{0}\sigma\mathrm{E}=- \mathrm{i}\omega\mu_{0}J_{\mathrm{s}} \tag{1}\]  
In the above formula, \(\
abla\) is the Hamiltonian operator, \(\mathrm{E}\) is the electric field strength, and \(\mathrm{i}\), \(\omega\), \(\mu_{0}\), and \(\sigma\) represent imaginary unit, angular frequency, permeability, and conductivity, respectively. For the electric dipole source, the ground source is denoted as \(J_{\mathrm{s}}=\mathrm{Id}\delta(\mathrm{r}-\mathrm{r}_{0})\), where \(\mathrm{I}\) is the amplitude of transmission current, \(\mathrm{d}\mathrm{I}\) is the length of ground source, and \(\delta(\mathrm{r})\) is the unit impulse function. Singular points are formed at the field source \(\mathrm{r}=\mathrm{r}_{0}\) because of the properties of the impulse function. The resistivity model \(\sigma\) is decomposed into the background model \(\sigma^{\mathrm{P}}\) and anomaly model \(\sigma^{\mathrm{s}}\). The total field \(\mathrm{E}=\mathrm{E}^{\mathrm{P}}+\mathrm{E}^{\mathrm{S}}\) is obtained by the strategy of obtaining the primary field (background field) \(\mathrm{E}^{\mathrm{P}}\), and the secondary field \(\mathrm{E}^{\mathrm{S}}\) separately addresses the singularity problem at these points [23]. Finally, the secondary field control equation of the frequency-domain controlled source electromagnetic method is derived (Equation (2)):  
\[\
abla\times\
abla\times\mathrm{E}^{\mathrm{s}}+\mathrm{i}\omega\mu_{0}\sigma \mathrm{E}^{\mathrm{s}}=-\mathrm{i}\omega\mu_{0}(\sigma-\sigma^{\mathrm{P}}) \mathrm{E}^{\mathrm{P}} \tag{2}\]
###### Abstract  
This article presents a three-dimensional numerical investigation of heat and mass transfers and fluid flow in a cavity filled with an Al\({}_{2}\)O\({}_{3}\)/water micropolar fluid under uniform magnetic field. To solve the governing non-dimensional equations, Finite Volume Method (FVM) based on 3-D vorticity-vector potential formulation has been employed. The effects of various parameters such as buoyancy ratio (\(-2\leq\mathrm{N}\leq 0\)), Rayleigh number (\(10^{3}\leq\mathrm{Ra}\leq 10^{5}\)), Hartmann number (\(0\leq\mathrm{Ha}\leq 60\)), nanoparticles volume fraction (\(0\leq\varphi\leq 0.06\)) and micropolar material parameter (\(0\leq\mathrm{K}\leq 5\)) on flow structure and on heat and mass transfers are presented. The results illustrate that for the micropolar nanofluid model, both heat and mass transfer rates and three-dimensional character of the flow are smaller when compared with the pure nanofluid model. It is also observed that increase and decrease in heat and mass transfer rates is experienced due to increase in Rayleigh number and Hartmann number, respectively. It is also noted that increase in vortex viscosity parameter reduces the average heat and mass transfer rates and is more evident when the magnetic field is imposed. Combined effects of magnetic field and nanoparticles volume fraction on heat and mass transfers are also explored.  
magnetic field; double-diffusion; natural convection; three-dimensional cavity; micropolar nanofluid +
Footnote †: journal: Applied Soft 2018, 8, 2342; doi:10.3390/app8122342
# High Performance Computing in Satellite SAR Interferometry: A Critical Perspective
## 2 SAR Interferometric Processing
### SAR Raw Data Focusing  
SAR raw data focusing is a fundamental SAR processing block, which depends on the SAR sensor operating mode. The most common imaging mode is the stripmap mode [28; 29], which is obtained by pointing the antenna along a fixed direction and making the antenna footprint cover a strip on the illuminated surface as the platform moves (see Figure 3a). SAR focusing of stripmap images essentially consists of applying a 2-D matched filter along the azimuth and range directions, which accounts for the transmitted pulsed chirp signals and the SAR image acquisition geometry. For efficient SAR focusing processing, the 2-D filtering operation is performed in the 2-D azimuth/range spectral domain using FFT operations.  
The range-Doppler algorithm (RDA) [30] is one of the first digital processing algorithms for satellite SAR operating in stripmap mode, and it is still widely used. Other methods implemented in the 2-D frequency domain, such as the chirp-scaling algorithm (CSA) [31], the extended CSA algorithm [32], the Omega-K algorithm [33], and the chirp-Z transform algorithm [34], are also commonly used.  
Although the conventional operation mode remains the stripmap one, more recently the growing demand for broader swath coverage has encouraged the development of new imaging modes with the capability to cover large swaths with single flights. This is the case of the _Terrain Observation by Progressive Scans_ (TOPS) [35] mode, which is the principal acquisition mode of interferometric wide (IW) swath SAR images collected by EU Copernicus Sentinel-1 satellites [36]. With TOPS mode, the sensor antenna beam rotates along the azimuth direction throughout the acquisition from backwards to forward, so that all targets are illuminated during the acquisition data duration (burst) within a large portion of the azimuth antenna pattern, see Figure 3b. As a consequence, the targets located at different azimuth positions are imaged with varying squint angles and, even though the point target azimuth bandwidth is worsened with respect to the stripmap case (and the azimuth resolution as well), the total image Doppler spectra result much larger than the stripmap azimuth bandwidth and, therefore, could be aliased. Accordingly, the conventional SAR focusing methods, developed for stripmap images, and implemented in the frequency domain, cannot be directly applied for focusing TOPS raw data efficiently. Several imaging methods have been developed to overcome these problems and efficiently process TOPS raw datasets [37; 38; 39; 40; 41; 42; 43; 44; 45].  
Figure 3: Sketch of the SAR acquisition mode geometry: **(a)** Stripmap: the antenna beam is fixed and describe a “strip” on the terrain that corresponds to the imaged area; **(b)** Terrain Observation by Progressive Scans (TOPS): the acquisition is achieved in bursts by switching the antenna beam among sub-swaths; during each burst the antenna beam also rotates along the azimuth.
# A Microservice-Based Smart Agriculture System to Detect Animal Intrusion at the Edge
## 6 Evaluation
#### 5.1.4 Animal Detection  
To identify the intrusive animals, we connected a camera to a Raspberry Pi to take pictures of the animals (area where the predicted location is) and identified them using computer vision algorithms. The specifications of the Raspberry Pi are shown in Table 1. We experimented with several popular convolutional neural network (CNN) pre-trained models to test their speed of processing images. We first trained these models on top of a computer and then imported the trained models into the Raspberry Pi. These event-driven models were continuously running on the Raspberry Pi, waiting for images to be taken.  
The average detection time (based on 20 runs for each image) is summarized in Table 3. As we can see, these models take 2 to 5 s to identify an animal, with MobileNet having the best performance with an average time of 1.64 s.
## 2 Materials and Methods  
### Research Problem  
Previous studies mainly concentrated on the identification and analysis of CC and its associated definitions and classifications, types, and deployment models in SMEs [12; 13; 14]. The employment of CC in SMEs was examined in a specific application area, such as accounting and finance [25; 63; 64], customer relationship management (CRM) [65; 66], and project management [67]. In this context, studies were examining factors influencing the adoption of CC [24; 66], as well as benefits and perceived risks of adopting CC for SMEs, from cost savings [25; 27; 33; 34; 35] to doubts regarding data security and privacy [42].  
In view of the above research regarding CC adoption by SMEs, and taking into account the country-specific context in the period of digitalization initiatives at the national level (prior to the COVID-19 pandemic) to the post-pandemic period, the primary focus of this research is to determine whether there have been any changes in adoption practices, usage patterns, and attitudes of Montenegrin SMEs towards the concept of cloud computing. To this end, the following research questions are defined for the study.  
RQ1: Were there any changes in Montenegrin SMEs in 2023 compared to 2017 regarding the following variables: the level of information security, reasons for adopting CC, perceived benefits of adopting CC, and perceived risks of adopting CC?  
RQ2: Were there any changes in Montenegrin SMEs in 2023 compared to 2017 in the perceived suitability of five cloud computing deployment models (public cloud, private cloud, partner cloud, set of different cloud sources (partnership, private, etc.), other)?  
RQ3: Were there any changes in Montenegrin SMEs in 2023 compared to 2017 in the perceived suitability of five cloud layers (SaaS, PaaS, IaaS, security cloud services, other)?  
RQ4: Were there any changes in willingness to use multiple cloud service providers in Montenegrin SMEs in 2023 compared to 2017?  
RQ5: Were there any changes in the intention to employ nine types of cloud computing adoption in Montenegrin SMEs in 2023 compared to 2017?  
The methods applied to the analysis of the research questions were derived from two EU-funded projects: \"Market oriented research on SME perspective about cloud computing solutions in ME--Cloud4 SME@MNE\" (ref.no: EuropeAid/136938/ID/ACT/ME) (implementation period: 2016-2017) [68] and \"Overcoming Digital Divide in Europe and Southeast Asia--ODDEA\" (ref.no: 101086381) (implementation period: 2022-2026) [69], as presented in the following sections.
# Modelling, Analysis and Performance of a Low Inertia AC-DC Microgrid
## 3 Stability Analysis
### Impact of AC Loading Variation  
The eigenvalues of the system are shown while varying the AC loading ratio between the motor dynamic loading and the static loading, while keeping the total AC system loading constant. The eigenvalues of the AC microgrid are shown in Figure 9. It is clear  
Figure 8: Eigenvalues with varying generator inertia.  
Figure 7: Eigenvalues with varying DC loading.  
that the system depicts a more stable behavior as the dynamic loads or motors increase as it adds more inertia to the system.
# A Method for Underwater Wireless Data Transmission in a Hydroacoustic Channel under NLOS Conditions
###### Abstract  
Wireless data transmission in the hydroacoustic channel under non-line-of-sight (NLOS) propagation conditions, for example, during a wreck penetration, is difficult to implement reliably. This is mostly due to the multipath propagation, which causes a reduction in the quality of data reception. Therefore, in this work an attempt has been made to develop a reliable method of wireless underwater communication test it under the NLOS conditions. In our method, we used multiple frequency-shift keying (MPSK) modulation, sending a single bit on two carriers, and diversity combining. The method was tested in laboratory conditions which simulated underwater signal propagation during the penetration of the wreck. The propagation conditions were investigated by determining the impulse responses at selected measurement points using the correlation method. Additionally, for comparison, the data transmission quality was determined by the bit error rate (BER) under the same conditions using direct sequence spread spectrum (DSSS) and binary phase shift keying (BPSK) modulation. The obtained results confirmed the usefulness of the application of the developed method for wireless data transmission in a hydroacoustic channel under NLOS conditions.  
underwater communication; NLOS; MFSK; data transmission; hydroacoustic channel; diversity combining +
Footnote †: journal: Sensors
# Evaluation of Green Strategies for Prolonging the Lifespan of Linear Wireless Sensor Networks
## 2 Framework of Linear Wireless Sensor Networks
### The Need for Specialized Protocols in LWSNs  
The presentation of LWSNs provided in Section 2.1 shows that the most significant difference between traditional WSNs and LWSNs lies in the network topology and routing.  
Traditional WSNs are typically deployed in multi-dimensional or randomly distributed configurations, allowing nodes to have multiple neighboring nodes and offering more flexibility in routing and data transmission. LWSNs, on the other hand, are constrained by their linear deployment, where each node has only a few immediate neighbors along the path.  
From a routing point-of-view, the overheads required in traditional WSNs for tasks such as route discovery, route maintenance, handling node failures to ensure network reliability, and managing node heterogeneity are significantly reduced in LWSNs due to their well-defined and structured topology [8]. In traditional WSNs, nodes are often deployed randomly across a wide area, requiring complex routing protocols to establish multi-path and multi-hop communications, deal with dynamic network changes, and ensure robust data delivery. In contrast, LWSNs feature a linear topology where nodes are typically placed along a defined path (e.g., pipelines, borders, or roads), meaning the routing paths are more straightforward and predictable. This defined structure simplifies routing, as nodes generally forward data in a unidirectional or bidirectional manner along the line. Consequently, the need for frequent route updates and resource-intensive discovery processes is minimized, leading to lower overheads [42]. Additionally, the concept of shortest path routing, which is commonly used in traditional WSNs, is not well-suited for LWSNs because of the linear configuration that limits the routing options to either forwarding data to the left or to the right. Given this constrained topology, routing decisions are less about finding the shortest path and more about ensuring reliable data transmission along the linear structure while minimizing energy consumption [28]. Lastly, fault tolerance mechanisms can be more straightforwardly implemented, e.g., using the opposite direction of the failed node to reach the sink or increasing the transmission power to jump over the failed sensor node and reach the next node along the line [8]. The focus is on maintaining linear connectivity, reducing the need for energy-intensive recovery protocols, and optimizing energy consumption in LWSNs [43]. Therefore, LWSNs require specialized routing protocols that can account for the limited routing possibilities, focusing on factors such as node energy levels, link quality, and fault tolerance to extend the network's operational lifetime [30].
###### Abstract
Logistic regression, a support vector machine, and k-nearest neighbors were used as components, and the final classification was determined using a majority voting classifier. Random forest, which is an ensemble of decision trees, was also considered. [PERSON] [34] studied ensemble algorithms for personal identification using ECG data. The ensemble approach incorporates convolutional neural networks and long short-term memory networks as components. To diversify the models, multiple time-frequency representations were considered, including the short-time Fourier transform, Fourier synchrosqueezed transform, and wavelet synchrosqueezed transform. The outputs of each model were combined using multiplication, and the resulting combined output was used for the final classification. We conducted research on ensemble deep neural network-based person identification using ECG signals acquired on different days. When acquiring the ECG data in one session, the environmental variables were relatively similar, which resulted in similar ECG signals. However, in different sessions, even for the same individual, changes in environmental variables can alter the shape of the signal, posing challenges for individual identification using ECGs acquired on different days. In simpler terms, performance significantly declines when using data collected on different days for training and validation. Therefore, our research aims to enhance the performance of personal identification using ECG data, even when the data comes from different days. This study proposes an ensemble deep neural network-based model by comparing and analyzing ECG recognition rates under various conditions. The proposed ensemble deep network comprises three streams that incorporate two well-known pretrained models. Each network uses the time-frequency representation of ECG signals as input, and a stream reuses the same network structure under different learning conditions with or without data augmentation. The Physikalisch-Technische Bundesanstalt (PTB) dataset, which consists of data acquired from 15 leads, was used for the experimentation. This includes 12 standard leads and the Frank XYZ leads and encompasses data from 290 individuals, including both healthy subjects and individuals with various heart conditions. The results show that the proposed ensemble deep neural network outperforms existing methods. This study describes an ensemble deep neural network-based person identification method that uses electrocardiogram signals acquired on different days. Section 2 describes the study methods, and Section 3 presents the ensemble deep neural network-based method for person identification using ECG signals acquired on different days. Section 4 describes the experiments and the results of this study, and Section 5 presents our conclusions.