esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology of Research
### Word Embedding  
Word2 Vec [49] is a neural word-to-vector model that uses surrounding words to predict the vector of a required word. The skip-gram and Continuous Bag of Words (CBOW) learning techniques are the two most common learning strategies used in the Word2 Vec embedding model. In comparison to distant words, the skip-gram technique of prediction gives more weight to adjacent or close words, whereas the CBOW approach does not affect the sequence of neighboring words because it predicts on the basis of the existing word to close the gap between context words. Both CBOW and skip-gram learn combined vector representations for each word, using only local context.  
Unlike Word2 Vec, GloVe [50] neural word embedding considers the entire context of words. A neural network is used in GloVe word embedding to break down a co-occurring matrix into a word vector. GloVe embedding [50] outperforms Word2 Vec [50] in word similarity and analogy tests because the GloVe embedding model considers the association between word pairs and adds supplementary meanings to the neural network. GloVe embedding also reduces the weights of frequent word pairs such as those including \"the\", \"a\", and so on. The GloVe model, however, is built on a co-occurrence matrix, which necessitates a large amount of memory for storage.  
FastText [51], like Word2 Vec, learns the vector representation of each word as well as the n-grams placed within each word, in order to properly learn the representation of out-of-vocabulary (OOV) words, which is a common challenge encountered by both Word2 Vec and GloVe. Subsequently, at each training step, the representation values are averaged to generate a single vector. Although these embedding models are more computationally expensive than Word2 Vec and GloVe, they allow neural word embedding to encode significant sub-word information. Compared to Word2 Vec, FastText neural word embedding models are significantly more accurate.  
BERT [52] is an acronym for bidirectional encoder representations from transformers. BERT aims to condition both right and left context across all levels to pre-train deep bidirectional representations from unlabeled data. As a result, the pre-trained BERT model  
Figure 4: LSTM architecture with memory cell.  
may be fine-tuned with only one new output layer, to provide state-of-the-art models for a variety of tasks such as sentiment analysis, question answering, and language translations, without requiring significant task-specific design changes. BERT is empirically powerful and abstractly simple. On eleven various NLP-related tasks, it delivered new state-of-the-art results.
# Efficient LDPC Encoder Design for IoT-Type Devices
## 5 LDPC Encoder Design for a Microcontroller Implementation
### Parity-Check Matrix Representations in Memory  
Basically, three different representation methods can be considered. The first method is the direct representation with a Boolean variable table storing the binary parity-check matrix, as shown on the left side of Figure 3 (basic representation). Direct parity-check matrix storage would require an unnecessarily large amount of memory, e.g., 524,288 B for a 512 \(\times\) 1024 matrix.  
The second method is an index representation, in which indexes of nonzero elements in every row are memorized, which is shown in the middle of Figure 3, where \(-1\) indicates a lack of nonzero indexes in the macro-column. The third method is the representation of circular shift values, as shown on the right side of Figure 3 (efficient QC-LDPC), where all-zero matrices are indicated by \(-1\) and non-zero CPMs are represented by their cyclic shift values, which are stored in an integer table.  
We claim that the third (shift values) representation is the most suitable for our implementation, not only because it utilizes memory efficiently, but also because it explicitly defines the circular shift values introduced in the previous section as the components of elementary encoding operations in the described algorithm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Vector** & **Length** & **Vector** & **Length** \\ \hline
**a** & \((I-g)P\) & \(\mathbf{u}\) & \((J-I)P\) \\ \hline
**b** & \(g^{P}\) & \(\mathbf{g}\) & \((I-g)P\) \\ \hline
**c** & \(g^{P}\) & \(\mathbf{h}\) & \((I-g)P\) \\ \hline
**d** & \(g^{P}\) & \(\mathbf{p}_{1}\) & \(gP\) \\ \hline
**e** & \(g^{P}\) & \(\mathbf{p}_{2}\) & \((I-g)P\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of intermediate computation vector sizes.
# Analysis of Subharmonic Oscillation and Slope Compensation for a Differential Boost Inverter
## 5 Accurate Stability Analysis Using Floquet Theory
\] (19) \[\mathbf{A}_{a0} = \begin{pmatrix}\mathbf{A}_{0}&\mathbf{0}  
the third component of \(\mathbf{x}_{d}(DT)\), can be obtained from (17) in steady-state which gives the following expression for \(v_{i}(DT)\)  
\[v_{i}(DT)=\frac{1}{W_{i}}(m_{\text{ramp}}DT-\mathbf{K}^{\intercal}\mathbf{x}( DT)-k_{p}v_{\text{ref}}) \tag{24}\]  
Now that the expression of the monodromy matrix was derived, hereinafter, we will pay special attention to the movement of the Floquet multipliers as the voltage reference \(v_{\text{ref}}\) varies quasi-statically. This is equivalent to changing the phase angle \(\varphi\) or the quasi-steady-state duty cycle \(D\). We will also study the movement of the Floquet multipliers when the proportional gain \(k_{p}\) of the controller or the amplitude of the ramp compensator \(V_{M}\) are varied. Any crossing from the interior of the unit circle to its exterior indicates a lost of stability of the desired orbit. The system becomes unstable, if at least one root of the Floquet multiplier leaves the unit circle, which is equivalent to an eigenvalue \(\mathbf{M}\) leaving the unit circle. Thus, for the stability boundary \(|\lambda|=1\) for at least one eigenvalue of \(\mathbf{M}\) holds. In particular, if a real characteristic multiplier goes through \(-1\) as it moves out of the unit circle, SO at the fast switching scale takes place. To locate the boundary of SO, the Floquet multipliers are obtained. By varying the quasi-steady-state duty cycle \(D\), the operating point \(\mathbf{x}(DT)\) was first calculated and the monodromy matrix was obtained for two different values of the proportional gain \(k_{p}\). At a point where a subharmonic regime emerges, one of the eigenvalues is equal to \(-1\). Figure 6 shows the Floquet multipliers loci in the complex plane when the quasi-steady-state duty cycle varies. The duty cycle \(D\) was varied by varying the voltage reference between \(0\) and its maximum values giving rise to \(D\in(0.5,0.68)\). As it can be observed from Figure 6, for \(k_{p}=0.2\) all the eigenvalues remain inside the unit circle for the full considered range of the duty cycle. Then, the gain \(k_{p}\) was fixed at \(k_{p}=0.24\) then the reference voltage was varied in the same range as before and the results are depicted in Figure 6. It can be observed that one of the eigenvalues of the monodromy matrix crosses the unit circle from the point \((-1,0)\) in the complex plane indicating SO at a certain value of \(v_{\text{ref}}\) very close to its maximum value. The critical value of \(k_{p}\) at which this starts taking place is \(k_{p}\approx 0.22\) which is in a remarkable agreement with the time-domain numerical simulations presented in Section 3. Figure 6: \(Cont.\)
## 2 Materials and Methods
### Description of the Measurement Methods  
A satellite-borne lidar (CALIOP) on the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) satellite was active from 2006 to 2023. CALIPSO was part of the A-train and had an orbit inclination of 98.2\({}^{\circ}\). It provided extensive daily measurement coverage over the polar regions of both hemispheres up to 82\({}^{\circ}\) in latitude, with 14 or 15 orbits per day. Most measurements at Concordia Station were made in coincidence with CALIPSO overpasses, having footprints at distances smaller than 300 km from Concordia Station. This results in a few quasi-coincidences per day (see also [7; 8]). These coincident measurements allowed the comparison of ground-based and satellite-borne instruments for a large data set of PSC observations. Usually, measurements were performed starting 16 min before the overpass and terminating 16 min after the overpass in intervals of 2 min. These 16 measurements with a duration of two minutes were averaged to improve the signal-to-noise ratio, but they might also be used separately at the cost of a worse signal-to-noise ratio, e.g., to observe variations in the PSC observations on the time scale of minutes, which is useful to obtain information about the extension of PSCs considering the wind speed at different altitudes. Raw data were recorded with photon counting with a dwell time of 400 ns, corresponding with a vertical resolution of 60 m.
## 5 Design of Shutters Based on Side Wing Efficiency
### Optimization of Shutters' Geometry
#### 5.1.2 Optimization of Chord Length  
The fin chord length is optimized on the base of the shape of flat airfoil fin. Flat airfoils with chord length \(L=30\), \(60\), \(90\), and \(150\) mm are selected for simulation. The CAD models of shutters are shown in Figure 13. Other parameters remain the same as those listed in the row of flat airfoil in Table 2. The chord length, maximum thickness \(\delta\), and blocking ratio \(p\) are changed correspondingly. The latter two increase with the increase in chord length. The simulation results are shown in Table 4, and the growth analysis is shown in Figure 14.  
In Figure 13d, aerodynamic kits shall not intrude the exclusion area due to formula restrictions of the contest [15]. If the leading edge fin is retained when \(L=150\) mm, then it will interfere with the front wheel. Thus, the leading edge fin is merely cancelled in this group. The endplate shape is changed correspondingly with different chord lengths. However, the shape of endplate is not set as a distinct parameter due to the complexity of describing it by using regularized methods and the limitation of our computational resources.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Fin Chord Length \(L\)/mm** & \(I_{perfm}\) & **Side Wing Lift/N** & **Overall Lift/N** & **Side Wing Drag/N** & **Overall Drag/N** \\ \hline \(150\) & \(8.41\) & \(-103.247\) & \(-92.1368\) & \(29.7194\) & \(30.2976\) \\ \(90\) & \(9.03\) & \(-103.89\) & \(-96.6426\) & \(31.0142\) & \(31.5454\) \\ \(60\) & \(8.07\) & \(-102.814\) & \(-95.9668\) & \(31.0132\) & \(31.548\) \\ \(30\) & \(5.78\) & \(-100.294\) & \(-94.7732\) & \(31.1734\) & \(31.7232\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation results of fin chord length optimization (\(15\) m/s).  
Figure 13: CAD models of different shutters with distinct fin chord length. (**a**) Shutters with chord length L = 30 mm. (**b**) Shutters with chord length L = 60 mm. (**c**) Shutters with chord length L = 90 mm. (**d**) Shutters with chord length L = 150 mm.  
As shown in Figure 14, the blue lines denote the relative increase rate of the aerodynamic lift of each configuration, the red lines indicate the relative increase in drag, the dotted lines represent the side wing itself, and the solid lines represent the combination of side wing and shutter data. With the increase in chord length, the lift of side wing and the overall lift increase first and then decrease, and the drag decreases. With the increase in chord length, the effect of turbulence alleviation is better, but the lift of shutters increases more significantly, thereby reducing the efficiency of side wing behind it. The optimal chord length is selected as 90 mm, and the maximum thickness of the corresponding airfoil is 6.5 mm.
## 4 Discussion
### Study Limitations  
Our study has proven the feasibility of the proposed concept in 2D under simulation conditions. However, it has potential limitations. Firstly, our study is limited to 2D. The 3D case will be more complicated because of the spatial motion as well as the arrangement of CT volume. It may be difficult to determine whether the motion blur is caused by the spatial motion or planar motion, and the arrangement of CT volume increased the complexity of the imaging process. Secondly, our study is conducted under simulation conditions. There is no real data involved. In our simulation, only motion blur generated by a specific reconstruction algorithm was considered. However, real data comprise several noise types, such as beam hardening and hardware-based noises. These noises may add useful information or affect the recognition of motion blur. Furthermore, there are many CT reconstruction algorithms, such as iterative and DNN reconstruction algorithms, and different reconstruction algorithms will make the motion blur pattern a little different. In the simulation, it is difficult to perfectly represent real physical processes. Simulation comes with simplified simulation models, such as the direction of CT imaging, linear elastic  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **SSIM** & **PSNR** \\ \hline Fold 1 & 0.988 \(\pm\) 0.005 & 36.534 \(\pm\) 1.464 \\ Fold 2 & 0.990 \(\pm\) 0.004 & 37.267 \(\pm\) 1.396 \\ Fold 3 & 0.989 \(\pm\) 0.003 & 36.921 \(\pm\) 1.286 \\ Fold 4 & 0.990 \(\pm\) 0.003 & 37.486 \(\pm\) 1.219 \\ Average & 0.989 \(\pm\) 0.004 & 37.052 \(\pm\) 1.341 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Results of four-fold cross-validation.  
Figure 8: Four-fold cross-validation.  
material properties of aortic valves, and incompressible Newtonian fluid of blood flow. In addition, the DNN model and the loss function we used are not optimized, so further improvements are required in future study.  
Despite these limitations, we thought the simple simulation condition was sufficient to prove the proposed concept. Although it cannot perfectly reproduce the complex real-world situation, our study included the necessary variables and complexity of actual problems. Even in this simple in silico experimental setting, the motion simulator included the projection and reconstruction process of CT imaging, and the motion simulator included two-way fluid structure interaction. To verify how the proposed method can deal with varieties of valve motion, the material and geometric properties of aortic valves were varied in the motion simulator, and the CT imaging start times were varied in the CT simulator. Therefore, we considered the generated dataset sufficient to prove the proposed concept.
# Enhanced LoRaWAN Adaptive Data Rate for Mobile Internet of Things Devices
## 4 Experimental Results and Analysis
### Average Energy Consumption Analysis  
In this study, we compute the energy consumption as the total energy spent by EDs during the simulation time divided by successfully received packets. This study assumes a state-based (i.e., transmit, receive, standby, and sleep) energy consumer module [23]. The energy consumption in LoRaWAN mainly depends on the amount of time that is spent by a LoRa radio in a particular state. In this study, we have utilized the energy consumer module, as shown in [42] and the Semtech SX1272/73 datasheet with a supply voltage of 3.3 V [26].
## 2 Experiments
### Characterization and Testing  
The cross-sectional morphology of the composite absorber was observed using a JSM-7500F field emission scanning electron microscope (Japan Electronics). The conductivity of the composite absorber was tested with a TH2512B DC low resistance tester (Beijing Crown Test Equipment Co., Ltd. Yongfeng Information Industry Base, Haidian District, Beijing, China). The electromagnetic parameters of the composite absorber in the frequency range of 8.2 to 18.0 GHz were tested by the waveguide method. The test instrument was a test system which was composed of a HP8720 ES vector network analyzer (Zhongshan Huayitong Electronic Instrument Co., Ltd. Yihu Peninsula, Zhongshan, China) and corresponding waveguide. According to the measured electromagnetic parameters, the transmission line theory was used to calculate and analyze their reflection loss. The formulas are as follows [8]\[Z_{0}=\sqrt{\mu_{0}/\varepsilon_{0}} \tag{1}\]  
\[Z_{in}=Z_{0}\sqrt{\mu_{r}/\varepsilon_{r}}tanh\big{(}j2\pi fd\sqrt{\mu_{r} \varepsilon_{r}}/c\big{)} \tag{2}\]  
\[RL=20 log\big{|}(Z_{in}-Z_{0})/(Z_{in}+Z_{0})\big{|} \tag{3}\]  
where \(\mu_{0}\) stands for complex permeability of free space, \(\varepsilon_{0}\) is complex permittivity of free space, \(Z_{0}\) represents input impedance of free space, \(\mu_{r}\) is complex permeability of material, \(\varepsilon_{r}\) is complex permittivity of material, \(Z_{in}\) stands for normalized input impedance of material, \(d\) is material thickness, \(c\) represents speed of light in vacuum, and \(RL\) is material reflection loss.
## 3 Testbed Applications Representing Various Parallel Processing Paradigms
### Geometric Single Program Multiple Data--Iterative Stencil Computations  
Potential applications implementing this paradigm typically include simulations using iterative stencil loops such as simulations of physical phenomena in 1, 2 or 3D spaces, modeled with differential equations and subsequently solved over successive time steps. Examples include computational fluid dynamics (CFD) codes, weather forecasting [24], heat distribution, Multidimensional Positive Definite Advection Transport Algorithm--part of the EULAG geophysical model [25], medical simulations [26].  
Actual implementation tested in this paper corresponds to an iterative stencil loop application in a 3D domain in which a new value for a given cell is computed based on the values of this very cell and neighboring cells from the previous time step. We performed tests with various values of a compute coefficient corresponding to how many computations per cell need to be performed (which may vary depending on the actual application)--one sample is presented in the main results section, more available at the github link presented at the end of the paper. The larger the coefficient, the larger the ratio of computational to communication times and the larger speed-up. Moreover, the implementation optimizes partitioning of the 3D domain into cuboids in such a way that the number of cells assigned to each cuboid, assigned to a different process, is balanced as well as the maximum of the total areas across cuboids is minimized as it corresponds to the number of cells to be exchanged between processes. Additionally, the implementation uses custom MPI data types representing boundary walls: in XY, XZ and YZ dimensions. In each time step, processes update values of the cells of their cuboids and subsequently processes send values of their boundary cells to the processes handling neighboring cuboids. We tested simulation within a 3D domain of size 200 \(\times\) 400\(\times\) 600 (X \(\times\) Y \(\times\) Z) for 300 time steps.
## 3 Results
### Spatial Distribution of ESVs  
There was little difference in the spatial distribution of EVs in Dongting Lake counties (cities and districts) between 2000, 2005, 2010 and 2018 (Figure 4). From 2000 to 2018, the counties with significantly reduced EVs included Junshan (CNY 0.67 \(\times\) 10\({}^{-3}\) billion), Shashi (CNY 0.43 \(\times\) 10\({}^{-3}\) billion), Wangcheng (CNY 0.36 \(\times\) 10\({}^{-3}\) billion), Huanrong (CNY 0.32 \(\times\) 10\({}^{-3}\) billion) and Ziyang (CNY 0.18 \(\times\) 10\({}^{-3}\) billion). Counties with significant increases in EVs included Honghu (CNY 0.93 \(\times\) 10\({}^{-3}\) billion), Anxiang (CNY 0.62 \(\times\) 10\({}^{-3}\) billion), Jianli (CNY 0.43 \(\times\) 10\({}^{-3}\) billion), Yuanjiang (CNY 0.34 \(\times\) 10\({}^{-3}\) billion), Xiangyin (CNY 0.26 \(\times\) 10\({}^{-3}\) billion), and Miluo (CNY 0.21 \(\times\) 10\({}^{-3}\) billion). The EVs in other counties (cities and districts) did not change significantly. The high value areas of EVs in the Dongting Lake were concentrated in the areas with more water area and forestland land  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**LULC** & **Year** & **GR** & **CR** & **WS** & **SFR** & **WD** & **BD** & **FP** & **RM** & **EC** \\ \hline \multirow{4}{*}{CL} & 2000 & 2.467 & 4.392 & 2.961 & 7.204 & 8.092 & 3.503 & 4.934 & 0.493 & 0.049 \\  & 2005 & 2.431 & 4.327 & 2.917 & 7.098 & 7.973 & 3.452 & 4.861 &  
use types, that is, areas with less interference from human activities, including Yuanjiang, Honghu, Yueyang, Anhua and Hanshou counties. The low value areas of EVs in the Dongting Lake were mainly concentrated in areas with more arable land area, i.e., areas with more anthropogenic disturbance, including Jinzhou, Gongan and Jiangling counties.
## 3 Methodology
### Data Preprocessing  
As noted in Section 3.1, the raw data stored in Moodle is not immediately suitable for predictive modeling due to its high volume, complexity, and lack of a structured format. Consequently, preprocessing is essential to transform the data into a matrix with dimensions \(M\times N\), where \(M\) represents the number of students and \(N\) is the number of features for each student. This preprocessing comprises data cleaning and feature extraction steps, which are detailed below.
on latency and jitter requires careful configuration of the transmission process. Selecting the appropriate shaping option requires a clear understanding of traffic requirements and characteristics, as well as the use of suitable algorithms to derive a coherent network-wide configuration. The literature proposes various algorithms based on different assumptions and optimization objectives and satisfying different constraints. However, the lack of common evaluation criteria for network schedules and scheduling algorithms adds to the complexity of making the right choice. Therefore, we believe that a comprehensive analysis of the different choices related to the scheduling problem, including optimization objectives, constraints, and assumptions, would be valuable to the community. This analysis would provide a solid foundation for making informed decisions regarding the selection and configuration of shaping mechanisms using suitable scheduling algorithms.  
The primary contributions of this survey include addressing the limitations of various shaping mechanisms and establishing a connection between traffic types and potential shaping mechanisms. Specifically focusing on Time-Aware Streams and their associated shaping mechanism, Time-Aware Shaping (TAS), we propose a methodology that enables a fair comparison of different network scheduling approaches. We begin by presenting and analyzing recent research on network-wide scheduling, highlighting advancements, current challenges, and future research directions. We delve into the optimization goals discussed in the literature and examine their impact on schedule quality. Furthermore, we explore the scheduling constraints and assumptions presented in previous works. Emphasizing determinism, we investigate the underlying causes of nondeterminism. Building on this analysis, we develop a methodology for benchmarking schedule quality by defining a set of evaluation criteria that facilitate comparisons among different approaches.  
The paper is organized as follows. Section 2 summarizes the background and provides references to surveys presenting the larger TSN context. In Section 3, we discuss the different shapers, their configuration, and their limitations. In Section 4, we review the state of the art concerning the scheduling approaches, analyze the challenges, and elaborate on future research directions. We deeper examine the TAS-scheduling in Section 5 concerning the objectives, constraints, and assumptions. We elaborate on network schedule quality benchmarking in Section 6 and conclude our paper in Section 7.