esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion  
This section looks at the results obtained from the simulation of the scenario described in Section 3.2 using the FDS and FLUENT codes with the simplified fire model and the default EDC combustion model implemented in FDS. The results obtained are the temperature and CO\({}_{2}\) concentration contours in horizontal and vertical sections of the domain, heat flow rates and CO\({}_{2}\) mass flow rates extracted from the ventilation systems, and mass flow rates leaving and entering the compartment through the ceiling vents.  
First, we discuss the sensitivity of the results obtained with FDS to the grid size and different settings of the LES turbulence model. Then, we analyze the predictions of FDS with the simplified fire model, considering four combinations of the inlet temperature, \(T_{\text{in}}\), and mass flow rate, \(\dot{m}_{\text{in}}\), of the mixture of gases introduced in the computational domain (cases G-1 to G-4 of Table 1). The results obtained for the oscillatory flow through vents are compared with experimental results available in the literature. Then, we compare the results for the fire evolution obtained with the simplified fire model (Case G-1 of Table 1) and the EDC combustion model (with RADIATIVE FRACTION\(=0\)) using the FDS code. Finally, the results obtained with the simplified fire model using the FDS and FLUENT codes are compared to each other.  
Except in Section 4.1, the respective default settings for the LES model were used in each code, and a computational grid with \(L/\delta\approx 10\) was used in all the simulations described below.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Grid** & **Total \# of Cells** & \(\delta\) **[mm]** & \(L/\delta\) & \(\Delta z_{\text{BL}}\) **[mm]** \\ \hline I & 1,017,000 & 220 & 6 & 37 \\ II & 3,358,000 & 138 & 10 & 33 \\ III & 7,074,000 & 100 & 14 & 30 \\ IV & 19,864,000 & 74 & 18.5 & 25 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Grid characteristics: total number of cells, approximate cell size (\(\delta\)), approximate number of cells (\(L/\delta\)) spanning the characteristic length \(L\), and grid size (\(\Delta z_{\text{BL}}\)) in \(z\) direction at the ceiling (\(z=3\) m).
# Development and Performance Measurement of an Affordable Unmanned Surface Vehicle (USV)
## 3 Materials and Design Methods
### Mechanical Design  
In this research, the USV is designed using two hulls, which are desired to make USV stable and have good buoyancy, while the USV Nose is designed to be elliptical, and its Frame is a flat shape to reduce the drag force. USV is equipped with two boxes that serve as a storage box for the Controller and Battery.  
T-100 thruster from Blue Robotics is selected as the main actuator for the USV. It is placed on the back of each USV hull. T-100 thruster was chosen because it was built with waterproof material. The thruster is suitable for bathymetry mapping missions. In addition, the weight of the thruster is also relatively light 16.3 g. The proposed USV CAD design is shown in Figure 2.  
The materials chosen to manufacture the USV are easily found on e-commerce or the market. The manufacturing of the USV is undertaken by a simple machining process (grinding, sawing, and boring) and a simple joining process (fastening).  
The selected materials of the USV design are presented in Figure 3. Sturdy and lightweight materials are used for the Frame. Lightweight, st  
Figure 2: USV CAD design.  
buoyancy are appropriate to be applied for the hull whilst non-corrosive materials and lightweight material are obligatory for the thruster.  
According to the design analysis obtained in SolidWorks, the mass of designed USV is 4217 g. The USV is equipped with a battery (440.5 g); Pixhawk controller (33.1 g); other components, such as compass, GPS, Telemetry, Buzzer (25 g), Go Pro action camera; and its holder (250 g). Therefore, the total mass of the USV is 4965.6 g, 4.97 in kg, or 48.76 N, while the total volume of which is 5316 cm\({}^{3}\), or 0.005316 m\({}^{3}\).
## 3 Low-Power Electronic Converters
### Voltage Multipliers (Doublers and Quadruplers)  
For thin-film piezoelectric micro-power harvesters, rectifiers with voltage multiplication are often used instead of the electronic circuits described above. At these rectifiers, in addition to rectification, a voltage increase is obtained by appropriately connecting groups of diodes and capacitors [48; 58; 59]. Moreover, for components with ideal electrical parameters, the amplitude of the input voltage is increased by an integer number of times; therefore, they are called rectifiers with voltage multiplication. Depending on the topological structure of the circuit, there are various types of multipliers. For the implementation of interface circuits connected to piezoelectric harvesters, single-phase asymmetric multipliers are most suitable, where the signal source and the load have a common connection node, which allows their simultaneous connection to the ground.
# A Review of Deep Learning Based Speech Synthesis
## 4 Deep Learning Based Speech Synthesis
### Deep Bidirectional LSTM-Based Speech Synthesis
#### 4.4.1 Blast  
BLSTM-RNN is an extended architecture of bidirectional recurrent neural network (BRNN) [49]. It replaces units in the hidden layers of BRNN with LSTM memory blocks. With these memory blocks, BLSTM can store information for long and short time lags, and leverage relevant contextual dependencies from both forward and backward directions for machine learning tasks. With a forward and a backward layer, BLSTM can utilize both the past and future information for modeling.  
Given an input sequence \(x=(x_{1},x_{2},...,x_{T})\), BLSTM computes the forward hidden sequence \(\overset{\rightarrow}{h}\) and the backward hidden sequence \(\overset{\leftarrow}{h}\) by iterating the forward layer from \(t=1\) to \(T\) and the backward layer from \(t=T\) to \(1\):  
\[\overset{\rightarrow}{h}_{t} =\phi\big{(}W_{\overset{\rightarrow}{x_{h}}}x_{t}+W_{\overset{ \rightarrow}{h}\overset{\rightarrow}{h}_{t-1}}\overset{\rightarrow}{h}_{t-1 }+b_{\overset{\rightarrow}{h}}\big{)}, \tag{7}\] \[\overset{\leftarrow}{h}_{t} =\phi\big{(}W_{\overset{\leftarrow}{x_{h}}}x_{t}+W_{\overset{ \leftarrow}{h}\overset{\leftarrow}{h}_{t+1}}\overset{\leftarrow}{h}_{t+1}+b_{ \overset{\leftarrow}{h}}\big{)}. \tag{8}\]  
The output layer is connected to both forward and backward layers, thus the output sequence can be written as:  
\[y_{t}=W_{\overset{\rightarrow}{h}_{y}}\overset{\rightarrow}{h}_{t}+W_{ \overset{\leftarrow}{h}_{y}}\overset{\leftarrow}{h}_{t}+b_{y}. \tag{9}\]  
The notations of these equations are explained in [49] and \(\phi(\cdot)\) is the activation function which can be implemented by the LSTM block with equations in [49].
# Application of Machine Learning for Fault Classification and Location in a Radial Distribution Grid
## 3 Proposed Method
### Fault Detection and Classification with ANN  
885 samples comprised of faulty and 526 non-faulty conditions were used for fault detection and classification. To build the best possible network, different number of neurons were tested and the best performance was obtained from ANN with 11 hidden neurons for the balanced load scenario and 12 hidden neurons for the unbalanced scenario (as shown in Figures 2 and 3) with regard to minimum mean square error (MSE) and general precision. A Levenberg-Marquardt (trainlm) algorithm was used to train the network in both cases. In the supervised learning process, 70%, 15%, and 15% of the samples were used for training, validation, and testing purposes, respectively.  
Figure 1: Fault detection, classification, and location flowchart employing the discrete wavelet transform (DWT) approach.
## 2 Overview of AC Analysis Methods
### Discrete Fourier-Transform-Based Methods
#### 2.1.1 Fast Fourier Transform (FFT)  
Fast-Fourier Transform algorithms speed up the computation of DFT values represented in (1), by removing computational redundancy and splitting the DFT calculation into smaller blocks that can be executed simultaneously. By default, it returns the results of FFT analysis for all frequencies within the available frequency bins of the input signal. One of the most known FFT methods is the Cooley-Tukey algorithm [16], which uses a divide-and-conquer strategy to decompose the calculation of a signal with smaller blocks, provided the number of samples is not a prime number. If the number of the samples is a power of two or four, Radix-2 [10; 17], respectively, Radix-4 could be used [10; 18], which are among the most popular implementations of Cooley-Tukey algorithms. Other algorithms for the FFT are the Prime-Factor algorithm (Good-Thomas) [19; 20], the Rader algorithm [21], and the Bluestein algorithm, also called Chirp-Z Transform [22]. Nevertheless, Cooley-Tukey performs better in general-purpose computing, DSPs, and vector-based microprocessors, with Radix-4 and Radix-2 showing an advantage over the other methods [10], due to the binary nature of digital computing, but also lower complexity, parallelism ability, and efficient memory access (i.e., storing and loading) frequency and addressing of the algorithm [10; 23].  
As our main interest is the computation of DFT components using embedded and/or general computing and given a number of samples of 2048, Radix-2 is considered in this paper, unless otherwise stated. The analysis using Matlab was performed using the FFTW3 library [24]. The determination of amplitude and phases is done according to (2) and (3).
# Penetration Overload Prediction Method Based on a Deep Neural Network with Multiple Inputs
## 4 Experiments
### Model Training
#### 4.2.2 Local Prediction Effect of the Thick Target Model  
The local prediction result obtained by the thick target model through training is shown in Figure 9, where the overload value is normalized. Figure 9a,b refers to the prediction data and real data that only use time as the input, respectively, and Figure 9c,d refers to the prediction data and real data that use multivariate elements as inputs. It can be clearly determined that the effect of prediction with multivariate time inputs is far better than that of prediction using only time as the input, but the overall amplitude exhibits a certain deviation.  
Through the above verification on the verification set, the feasibility of the model is preliminarily verified.
# All about Delay-Tolerant Networking (DTN) Contributions to Future Internet
## 3 Related Work
### Underwater
#### 3.4.1 Underwater Protocols  
Some of the protocols commonly used in underwater applications include the following:  
* _Spray and Wait (SNW)_[146].
* _Resource Allocation Protocol for Intentional DTN_ (RAPID) [147]: Optimizes resource allocation and scheduling for data transmission in DTNs, thereby enhancing efficiency and reliability.
* _Underwater DTN with Probabilistic Spraying (UDTN-Prob)_[146]: Broadcasts underwater messages using probabilistic copy transmission, thereby optimizing data delivery in difficult underwater communication environments.
* _Q Learning-Based DTN Routing Protocol (QDTR)_[138]: Uses reinforcement learning techniques to adjust routing decisions dynamically and optimize message delivery.
* _Redundancy-Based Adaptive Routing (RBAR)_[148]: Optimizes message delivery in delay-tolerant networks by dynamically adjusting routing decisions based on redundancy levels to enhance reliability.
* _Prediction-Based Delay-Tolerant Protocol (PBDTR)_[149] and _Prediction-Assisted Single-copy Routing (PASR)_: Employ prediction information to improve message routing and enhance delivery efficiency.
* _Delay-Tolerant Data Dolphin (DDD)_[144]: Utilizes dolphin-inspired communication strategies to optimize data transmission and improve efficiency.
## 2 Literature Review on the Personalized Recommendation in the MOOC Context
### Comments on Current Research  
Collaborative filtering algorithms are still widely popular owing to their simplicity and efficiency. However, the problems of data sparsity and cold-start must be solved. Currently, hybrid recommendations are the mainstream recommendation algorithms. For example, for the movie dataset scenario, clustering users based on their personal attributes or their rating matrix [23; 24], followed by applying collaborative filtering recommendations, help improve the accuracy of collaborative filtering algorithms.  
In the MOOC context, learners often choose courses according to the information provided by the platform. Accordingly, course information reflects learners' interests in and preferences for a course to a certain extent. Although current recommendation algorithms for MOOCs focus on course ratings, they do not sufficiently consider learners' knowledge domains and learning levels. Thus, this study expanded course rating data to learners' course rating preferences, course attribute preferences, and multidimensional capabilities matched with course traits.  
To reiterate, learners' preferences and capabilities change dynamically over time in the MOOC context. Although some algorithms recommend based on learners' learning sequences [14], current recommendation algorithms seldom consider dynamic changes in these attributes. Moreover, the runtime efficiency of the collaborative filtering algorithm was not high when directly measuring the learners' similarities in the MOOC context.  
Therefore, this study proposes a hybrid recommendation algorithm for MOOCs by integrating clustering and collaborative filtering based on multidimensional item response theory (MIRT) and the Ebbinghaus forgetting curve. MIRT can be viewed as an extension of IRT for better interpreting the interaction between item characteristics and individual behaviors due to the focus on multidimensional traits [25]. By integrating memory weight into learners' course rating preferences, course attribute preferences, and multidimensional capabilities that match course traits, the proposed recommendation algorithm aims to solve the data sparsity and singularity problems in the collaborative filtering algorithm.
## 1 Introduction
Figure 1: Pulses to be applied on individual elements of a phased array transducer in order to generate four focused transmit beams: (**a**) Transmit direction 1, (**b**) transmit direction 2, (**c**) transmit direction 3, (**d**) transmit direction 4, consecutively, or (**e**) simultaneously; and (**f**) A beam pattern of 4 simultaneously transmit beams. Coded excitation has been shown to improve the penetration, signal-to-noise ratio, as well as the frame rate for ultrasound imaging. In terms of increasing frame rate, it has been proposed to suppress crosstalk in many applications [15; 16; 17; 18; 19; 20; 21] by using simultaneous transmission, of two or more orthogonal transmit pulses whose mutual cross-correlation is very small. When simultaneously transmitting two or more such mutually orthogonal codes, the received signals contain information from all codes. This information can then be separated through decoding process. Common orthogonal codes are the Golay or chirp codes. Golay codes possess a good orthogonal property, but require the transmission of complementary code pairs to constrain the range lobes, which in turn halve the effective frame rate. On the other hand, orthogonal chirp pairs can be obtained by sweeping a certain frequency band in opposite directions or by sweeping separate frequency (sub)bands. For the former case, the cross-correlation of two orthogonal chirps can be relatively high, whereas the imaging performance of the latter is typically limited by the narrow bandwidth of ultrasound transducers. However, for chirps, the transmission of complementary codes is not necessary, and so the frame rate would be compromised. Both orthogonal Golay and chirp codes have been proposed for crosstalk reduction in increasing frame rate for synthetic aperture imaging with multiple transmit positions [15; 17] and in maintaining frame rate for multi-zone focusing by simultaneous transmissions for linear [16; 17; 18; 19; 22] and convex array imaging [20; 21]. However, towards phased array based high frame rate cardiac imaging, it has not been revealed yet that the feasibility of using these orthogonal codes to (further) reduce crosstalk among focused MLT beams. Hence, the aim of this study was to test this feasibility in both simulation and experimental setups.
## 3 Proposed Framework
### Cross-Validation Method  
Generally, the performance of a classifier is evaluated by constructing a model with the given data. However, using a single-fold data split reduces the model's generalization capability. Hence the k-fold cross-validation method is employed to split the data into k-folds. While building the model, k-1 folds are used for training, and one fold is used for testing. Likewise, the different combinations of folds are used for constructing the model, and the average classification accuracy is used to assess the performance. This study used a 10-fold cross-validation method to evaluate the performance of the classifiers.
## 2 Overview of Network Architecture and Networking Technologies
### Networking Technologies for Multiple Network Services  
Figure 2 shows the network elements and networking technologies used for accommodating the above-mentioned network services. SINET3 has twelve core nodes that are located at public data centers and composed of layer-1 switches (next-generation SDH devices) and IP routers; and 63 edge nodes that are located at selected universities; these edge nodes accommodate campus networks of neighboring universities and research institutions and are composed of layer-1 switches and layer-2 multiplexers. The campus networks are connected to SINET3 with Ethernet-family interfaces (the eVLBI project described later currently uses 2.4-Gbps SDH interfaces). These edge and core nodes are controlled and managed through a control and management plane from a remote operation center. The layer-1 services are provided with an operation system (L1-OPS), which controls and manages the layer-1 switches, and a layer-1 BoD server, which was developed by NII and loads the L1-OPS for the BoD services.  
Each layer-2 multiplexer attaches an internal VLAN tag to each IP packet for layer-3 services, IPv4/IPv6 dual stack, and L3 VPN, or to each Ethernet packet for layer-2 services, L2 VPN, and VPLS. The layer-2/3 service packets are accommodated into a layer-1 path for layer-2/3 services between edge and core layer-1 switches by using generic framing procedure (GFP) [11] and virtual concatenation (VCAT) [12] technologies (bandwidth is selected in multiples of 150 Mbps). The opposite IP router receives and distributes the layer-2/3 service packets to virtual routers corresponding to each network service by looking at their VLAN tags. The IP router encapsulates each VPN packet with multi-protocol label switching (MPLS) headers and attaches another VLAN tag identifying each virtual service network between IP routers. The layer-2/3 service packets are transferred between neighboring IP routers through a layer-1 path for layer-2/3 services between core layer-1 switches. The bandwidth of the layer-1 path for layer-2/3 services can be varied without any packet losses by using the link capacity adjustment scheme (LCAS) [13].  
Figure 2: Network elements and networking technologies.  
While each VPN in layer-2/3 services is established statically, a layer-1 VPN using layer-1 paths is established on demand because layer-1 service need dedicated network resources. On-demand layer-1 paths are created by users directly making requests (using a simple web screen) to the layer-1 BoD server. The layer-1 BoD server only establishes these paths for the specified durations. The layer-1 switches set up and release these paths by exchanging the generalized MPLS (GMPLS) protocols [14; 15; 16] through the control and management plane. If a layer-1 service needs more network resources, the layer-1 BoD server can change the bandwidths of the layer-1 paths for layer-2/3 services by using LCAS.
## 2 Materials and Methods
### Dynamic Identification Procedures  
Real-time dynamic identification can be used to obtain a variety of dynamic structural properties that describe how a structure oscillates and behaves. The oscillation of a structure is mathematically governed by its mass, stiffness, and capacity to dissipate energy; these parameters are represented generally in dynamic identification through modal parameters. Modal parameters are typically frequencies of vibration, the relative shapes of the modes associated with these frequencies, and damping [8]. The modal parameters can wholistically represent the response of a structure to an input, regardless of any damage or heterogeneity in the structure [4]. They can represent the current state of a structure, together with its material and mechanical properties. Periodically performing dynamic identification on a structure, especially to collect data before and after some damage has occurred, is an effective way to continuously monitor its health non-destructively and without the need for more invasive testing [8].  
For this campaign, 8 piezoelectric accelerometers (Model 393B12 PCB Piezotronics) with a sensitivity of 10,000 mV/g, a measurement range of \(\pm\)0.5 g pk, and a frequency range of 0.15 Hz to 1000 Hz were used with a National Instruments DAQ (Model NI 9174 with two NI 9234) for data collection. A sampling rate of 200 Hz for 30 min was used for  
Figure 4: (**a**) Indirect sonic test location (extent shown by black rectangle); (**b**) test grid with locations for vertical indirect sonic tests; (**c**) test grid with locations for horizontal indirect sonic tests. Hammer impact points are highlighted in yellow, accelerometer points are red, and points with null readings are white.  
Figure 5: (**a**) Direct sonic test location outside the keep (extent shown by black rectangle); (**b**) test grid with locations for direct sonic tests.  
each acquisition; this frequency is commonly applied for masonry heritage structures as it can capture frequency content up to 100 Hz [2,7]. Under ambient conditions for excitation, which is considered an Ambient Vibration Test (AVT), the setups are unlikely to capture any bandwidth above 20 Hz. Using a common reference point and accelerometer, multiple acquisitions of data can be combined. For the analysis of the west wall, two instances of 30 min acquisitions with two distinct setups were used to measure the response of the structure to an unexcited input. This method of output-only dynamic identification measures the ambient vibration of the structure, assumed to follow the input of a Gaussian white noise stochastic process for Operational Modal Analysis (OMA) [8]. Acquisitions were then processed using ARteMIS 6.0 modal identification software to identify modal parameters according to their spectral densities.