esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Simulation and Result Analysis  
### Influence of Coolant Flow on Thermal Resistance  
ANSYS finite element analysis software was used to analyze the thermal simulation of the microchannel heat sink models of the above three structures. As shown in Figure 6, first, we compare the chip thermal resistance variation curves of the three microchannel heat sinks at different coolant flow rates.  
It can be seen from Figure 6 that the three thermal resistance curves varying with coolant flow were approximately parallel, and the thermal resistance decreased with the increase of coolant flow, The corresponding chip thermal resistance of honeycomb, pin fin and double-layer reflux heat sink decreased from 0.439 \({}^{\circ}\)C/W, 0.466 \({}^{\circ}\)C/W and 0.376 \({}^{\circ}\)C/W at 10 L/h to 0.340 \({}^{\circ}\)C/W, 0.376 \({}^{\circ}\)C/W, and 0.312 \({}^{\circ}\)C/W at 40 L/h, respectively. However, with the increase of coolant flow to 22 L/h, the decreasing trend of thermal resistance gradually leveled off, and continuously increasing the coolant flow rate will cause the pressure of the pipe in the microchannel to rise, so the coolant flow rate cannot be increased pindefinitely.  
Figure 7 shows the temperature nephogram of the heat source surface of the microchannel heat sink with three structures when the cooling working flow rate was 14 L/h. The maximum chip temperatures were 37.6 \({}^{\circ}\)C, 40.2 \({}^{\circ}\)C, and 47.7 \({}^{\circ}\)C on the dual-layer reflow, honeycomb, and pin-fin microchannel heat sinks, respectively. By simulating the flow velocity cloud in the channel of the pin-finned radiator, the highest temperature at its heat source surface might be analyzed because the pin-fin can change the flow state of the cooling mass and enhance the turbulent flow state of the cooling mass for enhanced heat transfer, but there was no water passing through the dead space behind the needle fin, and the cooling mass is prone to large cavities (shown in Figure 8), and these cavities significantly reduce the heat dissipation capacity. The reason for the best heat dissipation effect of the double-layer return microchannel radiator may be that the two water inlets provided by the adjacent bi-directional flow channels increase the flow of cooling mass while achieving secondary cooling of the lower channel to the upper channel. The combined thermal resistance and temperature results show that the double-layer return microchannel heat sink had the best thermal performance among the three structural designs.  
Figure 6: Curve of thermal resistance versus coolant flow.
_Appl. Sci._**2015**, \(5\), 637-665; doi:10.3390/app5040637  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Review_  
**Status of the High Average Power Diode-Pumped Solid State Laser Development at HiLASE**  
**[PERSON] *, [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]**  
HiLASE Centre, Institute of Physics, Academy of Sciences of the Czech Republic, Za Radnici 828, Dolni Brezany 252 41, Czech Republic; E-Mails: [EMAIL_ADDRESS] (T.M.); [EMAIL_ADDRESS] (M.S.); [PERSON]SS] (M.C.); [PERSON].); [EMAIL_ADDRESS] ([PERSON]); [PERSON]] (J.L.); [PERSON]] (H.T.); [PERSON]; [PERSON]S] (O.S.); [PERSON]M.S.-C.); [PERSON]DRESS] (J.P.); [EMAIL_ADDRESS] (S.B.); [PERSON]S] (M.D.); [PERSON]S] (J.M.); [PERSON] (A.P.); [PERSON](P.S.); [PERSON]] (J.H.); [PERSON]P.S.); [PERSON]S] (P.N.); [PERSON]ESS] (D.V.); [PERSON] (L.H.); [PERSON]L_ADDRESS] (K.M.); [PERSON].L.); [PERSON]RESS] (A.E.); [PERSON]D.R.); [PERSON]ESS] (T.M.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +420-314-007-752; Fax: +420-286-890-527.  
Academic Editor: [PERSON]  
_Received: 31 July 2015 / Accepted: 6 September 2015 / Published: 25 September 2015_  
**Abstract:** An overview of the latest developments of kilowatt-level diode pumped solid state lasers for advanced applications at the HiLASE Centre is presented. An overview of subcontracted and in-house-developed laser beamlines is presented. The aim of development is to build kW-class beamlines delivering picosecond pulses between 1- and 100-kHz repetition rates and high-energy nanosecond pulses at 10 Hz. The picosecond beamlines are based on Yb:YAG thin-disk amplifiers and chirped pulse amplification. The current status of the beamlines' performance is reported. The advantages of zero-phonon line and pulsedpumping are demonstrated with respect to efficiency, thin disk temperature and beam quality. New diagnostics methods supporting the high average power lasers' development, such as the high-resolution spectroscopy of Yb-doped materials, _in situ_ thin disk deformation measurements, single-shot \(M^{2}\) measurement, realization of wavefront correction by a deformable mirror and the laser performance of a new mixed garnet ceramics, are described. The energetic, thermal and fluid-mechanical numerical modeling for the optimization of the multi-slab amplifiers is also described.  
Keywords:diode-pumped solid state lasers (DPSSL); pulsed lasers; high average power laser; Yb:YAG; laser amplifiers; thin disk; chirped pulse amplification +
Footnote †: journal: Scientific Reports
# Methodology for Conceptual Navigational 3D Chart Assessment Based on Eye Tracking Measures
## 2 Materials and Methods
### Indicator Elaboration  
The development of indicators was conducted in stages and was experimental in nature. Attention was first focused on developing a map readability indicator, which was based on eye-tracking areal measures, which were heatmaps. The development of this indicator was related to the decomposition of heatmaps and the analysis of their components on the basis of mutual correlation with various eye-tracking measures. In the next step, measures were selected from the analyses to develop indicators for the correctness of map symbol identification and timing. While the correctness of map symbol identification is an obvious factor in the effectiveness of a map, identification time takes on particular importance in the navigation process. Given the movement of the vessel, the shorter time allows the navigator to make a quicker decision, which should potentially have a positive impact on the efficiency of the entire navigation process.
# Experimental Study on the Strength and Durability for Slag Cement Mortar with Bentonite
###### Abstract  
This study focused on improving the durability of concrete infrastructures located in marine environments. When structures made of concrete are exposed to a marine environment, the combined effects of mechanical and chemical actions, such as attacks from sulfate and chloride, may deteriorate the material, then cause corrosion of the embedded reinforced steel bars, and finally result in shortening of the service life of the structure. Therefore, proposed in this work is a method mainly focused on improving the strength and durability of concrete composed with slag cement as a binder, which due to its pozzolanic reaction is commonly applied in construction projects in marine environments. The durability of slag cement mortar can be improved through the application of bentonite material by utilizing bentonite's very finely divided form and its reactivity to sulfate so that the larger capillary pores in concrete can be filled. It was found that a slight (approximately 0.25%) replacement of slag cement with bentonite material effectively improved the durability of the slag cement mortar in terms of moisture absorption, compressive strength, and weight loss after a sulfate resistance test and the evaluation for chloride permeability.  
b 2021 11 1176 10.3390/app11031176
## 2 Model of NSOFCMNN
### Structure of the NSOFCMNN  
The structure of the proposed NSOFCMNN is shown in Figure 1.  
NSOFCMNN has a five-layer structure.  
The first layer is the input space, where \(\mathbf{P}\) is the input data, \(\mathbf{P}=[p_{1},p_{2},\ldots,p_{n_{i}}]^{T}\).  
The second layer is the numerical space after fuzzification of the input data, also known as \"Association Memory\" space which is a hyperspace. A fuzzy membership function, also known as the basis function, is added to each block of each layer, as shown in Equation (2). Each input data will be obfuscated and stored in blocks in different layers, as shown in Figure 2.  
\[\begin{array}{l}\zeta_{ijk}=\exp\left[\frac{-\left(p_{i}-a_{ijk}\right)^{2}} {\beta_{ijk}\kappa^{2}}\right],\\ fori=1,2,3,\cdots,n_{i}\end{array} \tag{2}\]  
where \(\kappa_{ijk}\) is the central value of the \(i\)th input, \(j\)th layer and the \(k\)th block of the Gaussian function, and \(\beta_{ijk}\) is the variance of the \(i\)th input, the \(j\)th layer and the \(k\)th block of a Gaussian function.  
Figure 1: Structure of a novel self-organizing fuzzy cerebellar model neural network. (NSOFCMNN).  
The third layer is the \"Receptive-field\" space, which consists of multiple receptive fields. Each receptive field also has a layer and a block. After the blocks activated by the same layer are accumulated, they correspond to a weight address space, so it is also called a weight value address space. Among them, each receptive field \(\psi_{jk}\) is related to the \(j\)th layer and the \(k\)th block.  
\[\begin{array}{c}\psi_{jk}=\prod\limits_{i=1}^{n_{i}}\zeta_{ijk}=\prod\limits _{i=1}^{n_{i}}\exp\left[\frac{-\left(p_{i}-a_{jk}\right)^{2}}{\beta_{ijk}{}^{2 }}\right],\\ for\;i=1,2,3,\cdots,n_{i},j=1,2,3,\cdots,n_{j},\\ and\;k=1,2,3,\cdots,n_{k}\end{array} \tag{3}\]  
The fourth layer is the \"Weight Memory\" space, where \(\omega_{jko}\) is a hypercube representing the weights required to compute the output of each dimension.  
\[\begin{array}{c}\mathbf{\omega}_{0}=\left[\omega_{11\sigma},\cdots,\omega_{1n_ {k}\sigma},\omega_{21\sigma},\cdots,\omega_{1n_{1}\sigma},\cdots,\omega_{n_{ j}1\sigma},\cdots,\omega_{n_{j}n_{k}}\right]^{T}\in\Re^{n_{j}n_{k}},\\ for\;o=1,\cdots,n_{o}\end{array} \tag{4}\]  
The fifth layer is the output layer, and the output \(\mathcal{O}_{o}\) is obtained by solving the algebraic sum of the activated values of the \"Receptive-field\" space and \"Weight Memory\" space of each block in each layer. This is actually a defuzzification process.  
\[\begin{array}{c}u_{\text{NSOFCMNN}_{o}}=\mathcal{O}_{o}=\mathbf{\omega}_{o}^{T} \mathbf{\psi}=\sum\limits_{j=1}^{n_{j}}\sum\limits_{k=1}^{n_{k}}\omega_{jko}\psi_{ jk},\\ for\;j=1,2,3,\cdots,n_{j},\;k=1,2,3,\cdots,n_{k},\\ and\;o=1,\cdots,n_{o}\end{array} \tag{5}\]
Therefore, if the manual creation of a custom dataset was previously required, it can become a recurring task that is necessary for the deployment of new monitoring stations or the evaluation of footage from disparate areas. This paper focuses on the challenge of automation of the data annotation process [10] required for the training pipeline of an object detection model intended for underwater fish monitoring. Similar to previous work on fish detection [11], the detection stage is separated from the classification phase, with the latter being out of the scope of the present paper. The proposed approach tackles the creation of a new custom object detection dataset in reverse, focusing on the static nature of the target location footage of underwater monitoring stations and BRUVs, allowing the dataset annotations retrieved by automatic pseudo-labeling to refit a previously trained model from a foreign coastal environment to different marine fauna. The OzFish dataset [12] is used for model initialization through the technique of transfer learning [13], and the object detector process is performed using the YOLO architecture [5; 14]. The main contribution of this paper is twofold: (a) The presentation of a transversal method able to automatically generate localized underwater datasets from foreign locations, regardless of the deployment application and the baseline dataset quality, type of species, and environmental conditions. (b) The presentation of a full pipeline, which allows the duplication of the method with different objects and environments (not limited to underwater objects--fish). The structure of this paper is as follows: The present section introduced the agenda and the contributions of the paper; related works and the current state of the art are presented in Section 2, followed by a brief introduction to the KTTSeaDrones project in Section 3, and an analysis of the target data for this project and the pertinent available data in Section 4. Next, the proposed method is presented and detailed in Section 5, succeeded by the experimentation and achieved results, accompanied by the discussion in Section 6, and finally, the conclusions and prospects for future work in Section 7.
## 3 Results and Discussion
### Removal of Lead from Aqueous Solution
#### 3.4.2 Effect of pH  
Figure 9 depicts the effect of pH on the adsorption of Pb (II) onto different clays in the study over the pH range 2-11. In general, the adsorption amount (q\({}_{\rm e}\)) of Pb (II) onto C1, C3 and C4 clays tends to increase slowly at pH \(<\) 4 and increase sharply at pH range ca. 4-6. It can also be seen that q\({}_{\rm e}\) reaches a plateau at pH range 6-8 and then decreases slightly when increasing the pH \(>\) 9. Such a trend of q\({}_{\rm e}\) versus pH of C1, C3 and C4 clays can be attributed to the existence of palygorskite as the dominant mineral in these clays. Several authors reported a similar trend for adsorption of Pb(II) onto neat palygorskite [40,41]. Moreover, in the case of C1 clay, q\({}_{\rm e}\) reaches its maximum at lower pH (viz., 5), which seems to be linked with the lower point of zero charge (P.Z.C = 4.4), compared to that of the C3 and C4 clays (namely, 9.0 and 7.6, respectively). On the other hand, the surface of the clay is negatively charged at pH \(>\) P.Z.C and the increase in pH leads to increase in the negativity and thus to increase the Pb (II) adsorption until a plateau is reached. On the other hand, the removal of Pb (II) by C2 clay increases dramatically as the pH of the solution increases and even reaches equilibrium at pH 5, attributed mostly to its lower P.Z.C = 3.8 than the other clays. However, the removal of Pb (II) by C5 is negligible at pH 2 while it increases sharply as the initial pH of solution increases from 3 to 6, and then reaches a plateau at pH \(\geq\) 6. The results obtained suggest that the adsorption of Pb (II) on C5 clay follows the precipitation reaction due to the presence of high amounts of calcite in the sample [42].
## 7 Future Directions
### Integration with Quantum Communication  
The integration of THz-FSO communication systems with quantum communication technologies sheds light on a new approach to secure high-bandwidth networks. Putting QKD protocols into THz systems might offer improved security levels to applications that require high-level protection, like financial transactions and governmental uses.
# A Cloud-Based Distributed Architecture to Accelerate Video Encoders
## 1 Introduction
For instance, Netflix uses Amazon Web Services for nearly all its computing and storage needs, including video transcoding [13]. The main contribution of this paper is the design, implementation, and validation of a cloud-based distributed architecture to accelerate video encoders. It also provides the specification of the required nodes, the tune of the network and the storage, the specification of the jobs, the development of a distributed application based on a message broker to dynamically perform encoding jobs, and the validation of the proposal in a private cloud. In the proposed solution, a video is split in chunks of fixed size, for each chunk to be encoded an encoding job is generated, and each job is consumed by a worker virtual machine (VM) node that performs the encoding of the chunk. Relevant features of the proposed solution are: elasticity (horizontal scalability), hyperconvergence (software defined storage, computation, and networks that are deployed on top of off-the-shelf servers), adaptability (can work with different codecs), fault-tolerance (if a node fails, then another node will perform the job), and automation (when a node is started, no intervention is required to deploy, configure, and start the application). Architecture and application have been tested with H.265 and VP9 encoders with different chunks sizes (full video, 25, 50, 100, and 200 frames), and bit rates (0.75 Mb, 1.5 Mb, 3 Mb, and 4.5 Mb). The rest of the paper is structured as follows. Section 2 reviews the state of the art in this field. Section 3 explains both cloud-based architecture and application. Section 4 describes the experimental setup used to deploy the architecture and analyses its scalability and resource consumption, by varying the number of virtual machines to encode videos, and quality metrics for different video chunk sizes, bit rates and codecs. A qualitative comparison of the previous work with our proposal is discussed in Section 5. Finally, some concluding remarks and future work are drawn in Section 6.
## 1 Introduction
used the spatial correlation of higher-order eigenbeams to estimate the directional characteristics of the reverberant field, and this approach was able to achieve an accurate estimation of direct-to-reverberant energy ratio and dominant reflection directions. The majority of the existing methods of directional characterization of room reflections derive the parameters from the aggregate sound field formed by the direct and reflected waves. Even though the direct path can be removed from the RIRs, the spatial resolution for directional analysis will be limited by the number of microphones. Moreover, a fine-scale separation of the spatial components of the direct path and reflected path is difficult without the knowledge of the source directivity. Additionally, the lack of incorporation of frequency-dependent surface reflectivities with distinct decay times can cause severe errors in the reflected sound field power distribution estimated by the existing methods [18; 24; 32]. Hence, a competent room characterization tool should integrate the frequency, time, and spatial dependencies in the formulation of the reflected sound field. In this paper, we utilize the spatial correlation of higher-order eigenbeams to estimate the directional power response of room reflections by processing the RIR measurements. The proposed technique further facilitates room mode analysis and directional decay rate estimation. In comparison to the previous version of this method in [33; 34], we model the reflection power as a function of time, frequency, and direction for comprehending the influence of frequency-dependent wall absorption properties of the room surfaces. This method allows the estimation of the directional features of reflections with higher spatial resolution independent of the direct sound component. The room mode features, directional decay rates and dominant reflection locations generated from the proposed tool can serve many applications like room response equalization, acoustic treatment design, architectural design simulations, room geometry inference, auralization of historic buildings, archaeoacoustics, and other machine hearing technologies. The remainder of this paper is organized as follows: Section 2 discusses the formulation and implementation procedure of the eigenbeam spatial correlation model for estimating the reflection power response. Section 3 presents the experimental results including the time-frequency spectrum of reflection power, directional decay rates, and dominant reflection directions. Section 4 concludes the paper with a summary of the key findings and mentions the future research plans.
Non-Probabilistic Reliability Bounds Method for Series Structural Systems Considering Redundant Failure Modes  
Xinzhou Qiao  
School of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an 710054, China  
1  
Fan Zhang  
School of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an 710054, China  
1  
Jiangbin Zhao  
School of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an 710054, China  
1  
Xi'an 710054, China  
1  
Xi'an 710054, China  
1  
Xi'an 7  
also been developed. Two system non-probabilistic reliability indices were proposed for the interval model, in which the optimal criterion method and the branch-and-bound method were used to enumerate the main failure modes of the structural system [27; 28]. An efficient method was further developed for calculating a system non-probabilistic reliability index [29]. A Monte Carlo simulation (MCS) method was also developed to calculate system non-probabilistic reliability [26]. The MCS can provide a theoretical reference solution but generally has a lower efficiency. In our recent work [30], the NRBM was proposed to estimate the above system non-probabilistic reliability. This method is expected to provide an efficient means for the assessment of system reliability due to advantages such as convenient use and a good balance between accuracy and efficiency. However, in its current state, the NRBM still has a major drawback: Due to the ordering dependency problem existing in the NRBM, all of the possible ordering alternatives need to be considered to yield the narrowest bounds. The number of these alternatives, however, becomes enormously large when a system involves more failure modes. This size problem requires a higher computational cost and, hence, becomes a hindrance for practical applications of this method.  
In view of the size problem, a novel non-probabilistic reliability bounds method is proposed for series systems by considering redundant failure modes. The proposed method has two advantages over the NRBM [30]: Firstly, the proposed method significantly reduces the number of the possible ordering alternatives by identifying and removing the redundant failure modes, leading to a higher efficiency. Secondly, the proposed method has equal or greater accuracy than the NRBM, making it more suitable for practical applications.  
The remainder of this paper is structured as follows: Section 2 briefly reviews the NRBM; Section 3 presents the proposed method and its implementation; Section 4 provides three examples to demonstrate the effectiveness of the proposed method; and Section 5 presents the conclusions.