esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Model Verification
#### 4.1.3 Comparison with Other Existing Models  
Following [PERSON] et al. (2018), a comparison with several existing algorithms for estimating TC intensities was made. Their RMSE and MAE are shown in Table 5. It is worth pointing out that we did not apply these existing methods to the same dataset used in this study, to calculate their errors. The statistical numbers listed in this table are all calculated from different data in different regions. It is worth mentioning that the smaller RMSE of the proposed method shown in Table 5 does not mean that this method is superior to others. It is only compared with the best track data, but not with an independent observation, like most of the other methods listed in the table. Figure 7: Scatter plot of the comparison between the estimated maximum wind speed and the CMA best-track data. The solid line is y = x. (**a**) all data for training dataset, (**b**) TD, TS and STS for the training dataset, (**c**) TY, STY and the super TY for training dataset, (**d**) all data for test dataset, (**e**) TD, TS, and STS for the test dataset, (**f**) TY, STY, and super TY for test dataset. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Equation** & **a** & **P1** & **b** & **P1** & **c** & **P2** & **d** & **R2** & **RMSE** \\ \hline
1 & 0.24 & TBIH19, RAPT250, C125 & 0.35 & TBIH37, MIN\_C100 & 0.070 & TBV22, RAPT270, C125 & \(-\)50.16 & 0.79 & 6.51 \\
2 & 1.40 & TBIH19, M\_MIN\_C100 & 0.23 & TBIH19, RAPT250, C125 & \(-\)1.78 & TBV19, MIN\_C100 & 167.71 & 0.79 & 6.55 \\
3 & 0.39 & TBIH19, M\_MIN\_C100 & 0.21 & TBIH19, RAPT250, C125 & 0.059 & TBV22, RAPT270, C125 & \(-\)51.12 & 0.79 & 6.55 \\
4 & 0.44 & TBIH19, MIN\_C100 & 0.22 & TBIH19, RAPT250, C125 & \(-\)007 & TBV219, RAPT250, C075 & \(-\)52.95 & 0.79 & 6.55 \\
5 & 0.33 & TBIH19, MIN\_C100 & \(-\)0.15 & TBVCT91, RAPT250, C075 & 0.15 & TBV22, RAPT270, C125 & \(-\)27.28 & 0.79 & 6.55 \\
6 & 0.13 & TBIH19, MIN\_C100 & 0.27 & TBIH19, RAPT250, C125 & 0.30 & TBIH27, MIN\_C100 & \(-\)64.20 & 0.79 & 6.56 \\
7 & 0.43 & TBIH19, M\_MIN\_C100 & 0.24 & TBIH19, RAPT250, C125 & 0.36 & TBV22, MAX\_C150 & \(-\)154.64 & 0.69 & 6.59 \\
8 & 0.28 & TBIH19, RAPT250, C125 & 0.38 & TBIH37, MIN\_C100 & 0.47 & TBV22, MAX\_C150 & \(-\)182.53 & 0.79 & 6.60 \\
9 & 1.41 & TBIH19, M\_MIN\_C100 & \(-\)1.87 & TBV19, MIN\_C100 & 0.13 & TBV22, RAPT270, C125 & 184.14 & 0.79 & 6.60 \\
10 & \(-\)0.16 & TBVCT91, RAPT220, C075 & 0.29 & TBIH37, MIN\_C100 & 0.17 & TBV22, RAPT270, C125 & \(-\)24.55 & 0.79 & 6.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The parameters, coefficients, R2, and RMSE of the candidates for the regression equation.
# An Efficient Data Retrieval Parallel Reeb Graph Algorithm
## 8 Applications
### Choosing Morse Scalar Functions
#### 8.1.1 Harmonic Functions  
Condition 1 is desirable, because it leads, in general, to a simpler Reeb graph. For a surface mesh \(M\) of genus \(g\) one can always construct a scalar function \(f\) on \(M\) with \(2g\) critical points. This can be done by the so-called Harmonic functions. Recall that a harmonic map on a triangulated surface \(M\) is a scalar function \(f:M\to\mathbb{R}\) that satisfies the Laplace equation \(\Delta f=0\) subject to the Dirichlet boundary  
Figure 23: Examples of segmentation of higher genus surfaces into a pair of pants using our Reeb graph algorithm.  
conditions \(f(v_{i})=c_{i}\) for all \(v_{i}\in V_{C}\). Here, the set \(V_{C}\subset V\) is a list of constrained vertices and \(c_{i}\) have known scalar values providing the boundary conditions. The reason for our interest in Harmonic functions is that they satisfy the so-called maximum principle property [91], which asserts that the solution for the above system has no local extrema other than the constrained vertices. To achieve this setting in practice, one has to be careful about the choice of the weights utilized to define the Laplacian. More specifically, for a triangulated mesh \(M\), the standard discretization for the Laplacian operator at a vertex \(v_{i}\) is given by:  
\[\Delta f(v_{i})=\sum_{[v_{i},v_{j}]\in M}w_{ij}(f(v_{j})-f(v_{i})),\]  
where \(w_{ij}\) is a scalar weight assigned to the edge \([v_{i},v_{j}]\), such that \(\sum_{[v_{i},v_{j}]\in M}w_{j}=1\). Choosing the weights \(w_{ij}\), such that \(w_{ij}>0\) for all edges \([v_{i},v_{j}]\) guarantees the solution of the Laplace equation has no local extrema other than at constrained vertices \(V_{C}\)[92]. These conditions are satisfied by the mean value weights:  
\[w_{ij}=\frac{tan(\theta_{ij}/2)+tan(\phi_{ij}/2)}{||v_{j}-v_{i}||},\]  
where the angles \(\theta_{ij}\) and \(\phi_{ij}\) are the angles on either sides of the edge \([v_{i},v_{j}]\) at the vertex \(v_{i}\). Mean value weights are used to approximate harmonic map and they have the advantage that they are always non-negative, which prevents any introduction of extrema on non-constrained vertices in the solution of the Laplace equation that is specified above. Such a function can be obtained as a solution for Laplace equation with mean value weights and with only two constrained vertices \(V_{C}=\{v_{min},v_{max}\}\) such that \(f(v_{min})<f(v_{max})\). For instance, all functions shown in Figure 19 are obtained by solving the Laplace equation with exactly two constrained vertices.
# Mechanics and Stability of Force Chain Arch in Excavated Granular Material
## 6 Conclusions  
This work presents visualized force chain arches via photoelastic experiments and their mechanical research, as well as stability. The main results are as follows.  
1. Under biaxial compression conditions, the force chain arch formed within the granular medium via particle flow becomes the dividing line for solid-fluid transition. The particle assemblies above the force chain arch are in a stable solid state, still capable of bearing the overlying load, while those below the force chain arch are in a solid-fluid transitional state, experiencing less force. The force chain arch acts as an interface during the transition from solid to fluid in the granular system, separating the quasi-solid stable region from the solid-liquid transitional area. During this process, the force chain arch alters the original direction of load transfer, bearing most of the load transmitted from above and laterally.
2. The experimental model area is subdivided into grids by combining the results from indoor photoelastic experiments and employing autocorrelation algorithms to identify the main direction of local force chains. The trace line of the solid-fluid transition interface is obtained and fitted, resulting in an equation, which describes the parabolic shape of this interface.
3. A force and stability analysis is conducted on the fitted solid-fluid transition interface, i.e., the arch-shaped parabola. Under constant boundary load conditions on the solid-fluid transition interface caused by vertical particle flow, if the particle system is disturbed and the particles at the arch foot move, the horizontal force \(F_{H}\) and vertical force \(F_{V}\) at the arch foot change, leading to the disruption and rearrangement of the force chain arch. Under varying boundary load conditions, the arch apex and the arch foot are identified as critical sections.
4. When the trapdoor is located at the bottom of the model, changes in the overlying load or lateral load can lead to normal buckling and tensile stretching failure at the arch feet, as well as tensile stretching failure at the arch apex.
5. When the trapdoor is located on the right side of the model, changes in the overlying load adversely affect the normal force at the arch foot and the tangential force at the arch apex. Under different lateral loads, adverse effects on the tangential force at the arch apex and both the normal and tangential forces at the arch foot are observed.
# Indoor Autonomous Vehicle Navigation--A Feasibility Study Based on Infrared Technology
## 3 Results and Discussion
### The Accuracy of Positioning  
Figure 12 shows an illustration of the positioning experiment. There were three _semi-circular_ type invisible routes, named A, B, and C, respectively, concatenated into a straight line for the test. The infrared signals sent to the invisible zones A and B were not encoded, however, and the infrared signal sent to invisible zone C was encoded as an _identification_ of the station. On the other hand, to increase the accuracy of the positioning, the third infrared receiver was equipped in the rear of test vehicle (see Figure 12).  
In the beginning, the test vehicle moved at normal speed (greater than 20 cm/s) through invisible routes A and B. However, it began to slow down when the front infrared receivers detected the encoded _identification_ signal. Then, the test vehicle continued to move at a relatively slower speed (less than 10 cm/s) until the _third_ receiver detected the encoded _identification_ signal, and then stopped  
Figure 11: The continuous frame of pictures captured during a demonstration of navigation.  
Figure 12: The illustration of positioning.  
immediately at the positioning point. Figure 13 shows the pictures captured around the positioning point. The red mark \"10\" on the ruler was set as the _preset_ position point.  
Table 1 shows the data of the positioning experiment. The data in the \"offset\" row(s) indicate the distance from the preset point. According to the data in Table 1, the _maximum positioning deviation_ is around 30 mm.
## 2 Literature Review
### Energy Consumption
The suggested protocol is compared to existing techniques, such as fuzzy c-means (FCM) clustering and FLS-based CH selection, LEACH-fuzzy clustering protocol, and LEACH based on energy consumption equilibrium, to increase the sustainability of WSNs for environmental monitoring applications. The suggested protocol properly balances energy usage to improve WSN performance and throughput. The simulated results concluded that the network's lifespan and packet transmission were increased. In [22], the authors introduce a model that uses a two-step approach. In the first step, a model employs a trust model to choose the CHs that manage the data communication between the BS and nodes in the cluster area. Moreover, a new hybrid algorithm that combines the PSO algorithm and GA is proposed to define the routes for data transmission. PSOGA simulation results confirm the efficacy of the proposed hybrid algorithm and the results are compared with the existing LEACH method and PSO, with a random route selection for five different cases. The results establish the efficiency of the proposed approach, as it outperforms existing methods with increased energy efficiency and network throughput, high packet delivery rate, and high residual energy throughout the whole iteration. A novel marine observation buoy with built-in wave-excited energy to achieve real-time measuring of the marine environment was suggested in [23]. A small-scale prototype is designed, fabricated, and tested. A mathematical model of the system was presented, and simulation results were confirmed by experimental data, considering the influence of the mooring system and electric load. Numerical simulations and physical experiments have solidly demonstrated the feasibility of this novel wave-powered marine observation buoy.
## 1 Introduction
According to its different working conditions, the driving mode is generally divided into three types: direct drive actuators, inertial drive type actuators, and worm drive type actuators. In contrast with the conventional motors, piezoelectric actuators have lots of superiorities, such as no electromagnetic radiation, compact structure, self-lock at power-off state, short response time, and high precision [39; 40; 41]. Herein, the direct-drive piezoelectric actuator [42] has been chosen to obtain high resolutions and stability, while it is likely to limit the stage stroke [43]. The stage includes wire cutting processing and open-loop control to achieve motion performance. Based on the aforementioned analysis, to improve the performance in terms of the accuracy and motion of the positioning stage, a novel differential drove two-dimensional positioning stage was designed and analyzed in this paper. The organization of the paper is as follows. Section 2 briefly describes the structure of the positioning stage, where the core part and the form of the drives are introduced. Section 3 describes an analytical model of the proposed stage based on the compliance matrix method. Finite element analysis was used to validate the performance of the proposed 2-DOF flexure-based XY micropositioning stage, where the results are presented in Section 4. The experiments are discussed in Section 5 before the conclusions are given in Section 6.
## 4 Vehicular Ad Hoc Networks
### Routing
#### 4.2.7 Bandwidth  
In VANET, there is no central coordinator that manages bandwidth consumption and messages sent in the network. Therefore, designing a routing protocol in this network is very challenging, especially in areas with high density because network congestion is high.
## 2 Results and Discussion
### Chromatographic Performance
Compared with Figure S3, the separation performance of the P(S-DVB)-DR column is better than a commercial C18 column (Agela Technologies, S185006) at the same 75 mm column length. Table 3 shows that the run-to-run (\(n=5\)) RSD of retention time for the organic acids was less than 1%, day-to-day (\(n=7\)) RSD was less than 2%, and column-to-column (\(n=5\)) RSD was less than 3.5%. After a continuous 100 times running in one column, the RSDs of retention time for the organic acids were all less than 2.5%, and the separation performance of the P(S-DVB)-DR column was not deteriorated. Thereby, the DR-modified P(S-DVB)-4 particles were robust and had excellent performance in mix-mode HPLC separations. According to the excellent performance of the P(S-DVB)-DR column in the separation of benzene analogues, C\({}_{60}\) and C\({}_{70}\)--a class of special nanomaterials--were also separated in the P(S-DVB)-DR column using hexane and isopropanol (\(v/v=1\):3.5) as eluent. Considering that both C\({}_{60}\) and C\({}_{70}\) have carbon ring structures, the \(\pi\)-\(\pi\) affinity interactions between benzene rings of P(S-DVB)-DR \begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{4}{c}{**Retention Time RSD (\%)**} \\ \hline
**Benzene** & **Run to Run** & **Day to Day** & **Column to Column** & **Continuous 100** \\
**Analogues a** & _(\(n=5\))_ & _(\(n=7\))_ & _(\(n=5\))_ & _Times Running_ \\ \hline Uracil & 0.56 & 1.54 & 2.21 & 1.65 \\ Toluene & 0.79 & 1.65 & 2.89 & 1.86 \\ Naphthalene & 0.81 & 1.87 & 3.32 & 2.16 \\ Fluorene & 0.86 & 2.23 & 3.47 & 2.32 \\ \hline \end{tabular} \({}^{\text{a}}\) Separation conditions: the same as Figure 11.  
\end{table} 
Table 2: Separation performance of the P(S-DVB)-DR column for benzene analogues. Figure 12: Chromatograms for the separation of four organic acids on the P(S-DVB)-4 column (**a**) and P(S-DVB)-DR (**b**) column: column, 75 mm \(\times\) 4.6 mm I.D. ; flow rate, 0.5 mL/min; injection size, 1 mL; mobile phase, phosphate buffer (pH = 4) and methanol (\(v/v=85\):15); elution order: (1) formic acid, (2) acetic acid, (3) lactic acid, (4) benzoic acid. particles and the carbon rings of fullerenes make the separation practical. As shown in Figure 13, C\({}_{60}\) and C\({}_{70}\) were successfully separated on the P(S-DVB)-DR column (Figure 13b), while they could not be separated on the bare P(S-DVB)-4 column (Figure 13a). Therefore, the DR surface modification contributed to the high efficiency of the P(S-DVB)-4 stationary phase for mix-mode separation. In the columns, C\({}_{60}\)--having less \(\pi\)-\(\pi\) bondings than C\({}_{70}\)--was first eluted due to the weaker interaction with the benzene rings of DR. Compared with Figure S4, the separation performance of the P(S-DVB)-DR column is better than a commercial C18 column (Agela Technologies, S185006) at the same 75 mm column length.
###### Abstract  
Phase measuring deflectometry (PMD) is a competitive method for specular surface measurement that offers the advantages of a high dynamic range, non-contact process, and full field measurement; furthermore, it can also achieve high accuracy. Camera calibration is a crucial step for PMD. As a result, a method based on the calibration of the entrance pupil center is introduced in this paper. Then, our proposed approach is compared with the most popular photogrammetric method based on [PERSON]'s technique (PM) and [PERSON]'s modal phase measuring deflectometry (MPMD). The calibration procedures of these three methods are described, and the measurement errors introduced by the perturbations of degrees of freedom in the PMD system are analyzed using a ray tracing technique. In the experiment, a planar window glass and an optical planar element are separately measured, and the measurement results of the use of the three methods are compared. The experimental results for the optical planar element (removing the first 6 terms of the Zernike polynomial) show that our method's measurement accuracy reached 13.71 nm RMS and 80.50 nm PV, which is comparable to accuracy values for the interferometer.  
phase measuring deflectometry; entrance pupil center; camera calibration; surface shape +
Footnote †: journal: Physics Letters A
# Domain Adaptation Speech-to-Text for Low-Resource European Portuguese Using Deep Learning
## 1 Introduction  
Automatic speech recognition (ASR), commonly known as speech-to-text, is the process of converting sounds into text, i.e., transforming speech into the respective sequence of words. In today's world, most of the writing process is done on computers, tablets, or smartphones. Although typing is predominant, dictation has shown to be faster [1], which opens the opportunity for ASR to become the primary way for writing text. Despite this scenario, some challenges still persist in the ASR field, especially regarding its efficiency in less favorable conditions, e.g., noisy environments, or its application in low-resource languages.  
In recent years, the field of artificial intelligence (AI) and related fields, such as machine learning (ML), have seen major growth in popularity and in technological advancements. Deep learning (DL), a subset of ML, makes use of deep neural networks (DNNs), a special type of artificial neural network (ANN), with a large number of layers. This large amount of layers allows features to be extracted from the raw input data without the need for any pre-processing [2]. Alongside a very rapid growth of available data, DNNs have started to be used in a variety of fields, such as computer vision, natural language processing, and speech recognition.  
The Portuguese language is one of the most spoken languages in the world and it is divided into different variants, such as Brazilian and European Portuguese [3]. Among other variants, European Portuguese represents a small fraction of Portuguese speakers, and hence has fewer available resources and research. Developing deep neural models requires large datasets, which are scarce or not available for the European variant of thePortuguese language. Thus, transfer learning can be used as a means of creating ASR systems for languages with limited amounts of data. Such can be achieved by transferring the knowledge from models developed for languages with a larger amount of accessible data, such as the English language. The pre-trained models are then tuned for languages with less available data, such as the Portuguese language [4]. The aforementioned state-of-the-art methodology and the scarcity of resources for European Portuguese are the main motivation for the current work. This work aims to develop an ASR model using deep learning techniques for the Portuguese language. Using NVIDIA NeMo [5], we optimized the QuartzNet15\(\times\)5 [6] architecture to develop a model using a data-centric approach which includes data pre-processing; transfer learning from English language models; and a domain adaptation evaluation, considering a strictly European Portuguese target and mixed (mostly Brazilian) Portuguese sources, which yielded a state-of-the-art word error rate (WER) performance.
## 2 Background for IoT Architecture
### The Perception Layer  
The Perception Layer initiates IoT system functionality by enabling the collection and monitoring of data from the physical environment. Analogous to the physical model of the OSI model, the main components of this layer are sensors and actuators. Sensors detect and measure various environmental parameters--such as temperature, humidity, motion, and light--and transmit this data to the Network Layer for further processing [5]. Actuators, in turn, enable devices to perform specific actions based on data received, such as adjusting temperature settings or activating alarms [3].  
IoT devices in the Perception Layer typically generate two types of data: measurement data from sensors, reflecting real-time environmental conditions, and context data, which provide information on device status, including battery life and connectivity strength [6; 7; 8]. This layer heavily relies on standards such as IEEE 802.15.4, which supports low-power, low-data-rate wireless personal area networks, optimising communication in scenarios where energy conservation is a priority [9].  
Due to the constant need of the layer to gather data, energy consumption is a critical concern. Energy efficiency techniques in this layer often involve managing sensor duty cycles, optimising data transmission, and implementing protocols that allow sensors to enter low-power states during inactivity. These approaches aim to extend device battery life, especially for remote or inaccessible deployments.
## 9 Conclusions and Future Work
#### Guidance for Future Research  
Our study points to crucial areas for future research, emphasizing the need to investigate further machine learning and deep learning algorithms specific to 5G's dynamic settings. Future work should aim to develop comprehensive security protocols that integrate robust architecture with advanced computation. Additionally, exploring user behavior after security breaches could provide valuable insights for creating more user-focused, resilient security solutions in future 5G networks. More exploration into distributed ML, federated learning, and privacy-preserving continual learning is expected to lead the domain.  
H.N.F., S.A. and M.A. were instrumental in the conceptualization of this systematic review; the search strategy and criteria for selection of the literature were designed and implemented by F.M.A. and I.B.H.; data extraction from the chosen literature was handled by H.N.F. and S.A., while M.A. and F.H. took on the responsibility of assessing the quality of the included studies; the synthesis and analysis of the extracted data were jointly overseen by F.M.A., I.B.H., and H.N.F.; the initial draft of the manuscript was prepared by S.A., I.B.H. and M.A.; subsequent reviews and editing were managed by F.H., H.N.F. and F.M.A.; S.A. and M.A. coordinated the project's administration, with H.N.F., S.A. and F.H. providing overarching supervision. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
No data have been used in this study.  
The authors declare no conflict of interest.