esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data  
### Numerical Weather Prediction Product  
Numerical Weather Prediction (NWP) product is a powerful tool, applying hydrodynamics, thermodynamics, and other equations to describe the weather evolution process initialized from certain boundary conditions of the atmosphere, and predicting the atmospheric motion state in a certain period in the future.  
The NWP product used in this work is the global deterministic (0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\), single member) and ensemble (1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\), 51 members including one control) numerical mesoscale-weather prediction product from European Centre for Medium-Range Weather Forecasts (ECMWF) with a time step of 6 h and lead time of up to 240 h [17]. It is running under TL639 spectral truncation (horizontal resolution \(\sim\)32 km) with 62 vertical levels for up to ten days long. The product contains a variety weather variables from the surface to upper atmosphere with a total of 33 variables, of which 2 are directly related to surface wind: 10 m zonal wind (10 U) and meridional wind (10 V). The core weather variables are listed in Table 1 below. Some of the variables changed dramatically when the \"Oriental Star\" capsized. The fifth column of Table 1 lists the variables' behaviors during the shipw time. The detail discussion is in Section 3.2.
# Cascade Speech Translation for the Kazakh Language
## 4 Materials and Methods
### Speech Recognition
#### 4.1.1 Additional Dataset  
ISSAI KSC. The ISSAI KSC is the largest publicly accessible database created to support Kazakh speech and language processing applications [30]. It consists of over 332 h of data collected through a web-based speech recording platform, which invited volunteers to read sentences from various sources, including books, laws, Wikipedia, news portals and blogs.  
M2 ASRKazakh-78. The speech corpus [31] created by Xinjiang University and made available through the M2 ASR Free Data Program consists of 78 h of recordings obtained from 96 students using a variety of recording equipment. The recordings were made in quiet environments, and the speakers read the sentences in a scripted, reading style. The transcriptions are written using Latin characters and follow the rules established by the authors.  
Kazcorpus. The kazcorpus acoustic corpus [32] consists of two independent subcorpuses-kazspeechdb and kazmedia. The kazspeechdb corpus was used as a starting point in creating the corpus for broadcast news. The size of the subcorpus is 22 h of speech.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Structure** & **Sets** & \begin{tabular}{c} **Amount of Wave-** \\ **Files (Sentences)** \\ \end{tabular} & \begin{tabular}{c} **Overall Duration of** \\ **Wav-Files in Hour** \\ \end{tabular} & \
\begin{tabular}{c} **SRC** \\ **Words** \\ \end{tabular} &
\begin{tabular}{c} **TRG** \\ **Words** \\ \end{tabular} \\ \hline
1 & train & 140,599 & 304.711 & 1,537,535 & 1,587,409 \\ \hline
2 & dev & 3155 & 7.084 & 34,667 & 35,799 \\ \hline
3 & test & 3187 & 6.935 & 34,916 & 35,780 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of ST-kk-ru dataset.  
The body of kazmedia is audio and text data collected from the official websites of the television news agencies, namely \"Khabar\", \"Astana TV\" and \"Channel 31\". The size of this subcorpus is 21 h of speech.  
KazLibriSpeech. We collected audio recordings with relevant texts from open sources. The amount of data collected is 992 h. Each audio file corresponds to one common file with the text of the audiobook, i.e., audio and texts are not aligned either by sentences or by words. Therefore, we employed a segmentation approach based on connectionist temporal classification (CTC) algorithm to extract accurate audiotext alignments even when the audio recording includes unknown speech sections at the beginning or end. In our case, this model defines speech segments in audio files in sentence level. The speech recognition model required for segmentation was trained using the ISSAI KSC dataset in Espnet tool [33].  
More details of the available corpora for the speech recognition in the Kazakh language are shown in Table 2.
## 1 Introduction
Up to now, studies on the aerodynamic characteristics of airfoils moving over wavy surfaces are still limited, and the understanding of this unique problem is not thorough. Previous studies have focused on the aerodynamics of an airfoil moving over wavy ground at a specific angle of attack. The aerodynamics of an airfoil moving over wavy ground for a wide range of angles of attack has not been studied. Therefore, unsteady RANS equations are used to simulate an airfoil moving over wavy ground for a wide range of angles of attack ranging from \(0^{\circ}\) to \(18^{\circ}\), which covers the angle of attack that a WIG craft may encounter in actual flight. The structure of this paper is as follows. Firstly, the numerical calculation model is defined, and the numerical calculation method is verified. Subsequently, the numerical simulation of the airfoil moving over the wavy ground is conducted. The aerodynamic coefficients under a series of angles of attack are analyzed, including the fluctuation amplitude and time-averaged value. The time-averaged value is compared with that of free stream, flat ground, and wavy ground. Finally, the structure of the flow field is analyzed. In this study, it is found that when the airfoil moves over wavy ground, the variation in aerodynamic coefficients at a small angle of attack is different from that of other angles of attack, and the fluctuation amplitude of aerodynamic coefficients is significantly greater than that of other angles of attack. The mechanism for the difference is interpreted through an analysis of the structure of the flow field. The range of angles of attack suitable for the flight of WIG craft is given. By comparing the time-averaged aerodynamics under flat-ground and wavy-ground conditions, it is found that the wavy ground only causes fluctuation in the aerodynamic coefficient, but does not change the time-averaged value. All this is of practical significance for the design and safe flight of WIG rafts.
# Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product
## 2 Data  
### Satellite Data Sets  
The GLASS LAI product was used to derive FCover product, and GEOV1 FCover product was compared with the FCover product proposed in this study. The main characteristics of the GLASS LAI product and the GEOV1 FCover product are described below.
# Privacy Protection Based on Special Identifiers of Intersection Base Computing Technology
## 4 Discussion
### Theoretical Evaluation  
Table 2 presents a qualitative performance comparison, where m and n represent the sizes of the two sets, respectively. However, according to our research, in fact, n in the [23] protocol depends on the item with the highest number of elements in the two sets.  
In literature [23], [PERSON] sends [PERSON] the encryption polynomial \(E_{pk}(f)\), which is one round; [PERSON] sends [PERSON] the encryption polynomial \(E_{pk}(g)\), which is one round; [PERSON] sends [PERSON] the cryptographic \(E_{pk}(\varphi_{1})\), in which \(\varphi_{1}=f\times r_{1}+g\times r_{2}\); and [PERSON] sends [PERSON] the encryption polynomial \(E_{pk}(\varphi)\), where \(\varphi=\varphi_{1}+\varphi_{2}\), \(\varphi_{2}=f\times s_{1}+g\times s_{2}\), which is one round. Because threshold decryption is used and each participant receives \(f\times r+g\times s\), at the end of the protocol, it can be assumed that [PERSON] and [PERSON] send part of their private keys to each other for two rounds. So, the total communication/discussion involves six rounds. More importantly, all participants in the above model finally obtain \(f\times r+g\times s\), and the degree of this polynomial is \(\max\{|X|,|Y|\}\). Moreover, according to the most advanced complexity-solving polynomial methods, its complexity is difficult to decrease rapidly.  
[PERSON]'s iterative method: usually has linear convergence and a complexity of about \(O(n^{2})\sim O(n^{3})\), where n is the order of the polynomial.  
Dichotomy: has convergence and its complexity is about \(O(n\log(M))\), where \(n\) is the order of the polynomial and \(M\) is the range of values of the polynomial roots.  
The Durand-Kerner method is convergent and has a complexity of about \(O(kn)\), where \(k\) is the number of iterations and \(n\) is the order of the polynomial.  
Baistow method: usually has quadratic convergence and the complexity is about \(O(n^{2})\sim O(n^{3})\), where n is the order of the polynomial.  
And the above method will increase the complexity as the degree of the polynomial increases.  
For the protocol in this article, [PERSON] sends [PERSON] encrypted data for one round, and [PERSON] sent [PERSON] encrypted data for one round. So, the total number of communication rounds is two. Moreover, since we determine and solve based on the number of bits of information the data from both parties, this greatly improves the efficiency of encryption and decryption. Using this protocol, the complexity that may be obtained based on different data may not be the same. Although our protocol's efficiency is not currently the highest known, it is optimal for achieving accurate cardinality testing.
## 3 Model of Underwater Wireless Sensor Network
### Energy Consumption for Sending Data  
WSNs adopt energy consumption models based on multipath transmission and free space. Energy consumed by a node when sending and receiving data packets is related to the transmission distance, data packet size, and communication environment. A UWSN uses underwater acoustic signals to communicate, which produces a qualitative change compared to radio signals. Therefore, the energy consumption of underwater transmission should be reconsidered.  
Due to the uniqueness of the underwater environment, the problem of data transmission has been transformed into a problem of signal attenuation in underwater acoustic channels in many underwater acoustic communication studies. Energy is consumed when sending data is determined by the packet size, minimum transmission power, and power attenuation function of the data.  
Assuming that the sensor node can normally receive 1 bit data, the minimum power is \(P_{min}\), and the power attenuation function with changing in transmission distance \(d\) is \(A(d)\).  
Of these, the power attenuation function \(A(d)\) relates to attenuation coefficient \(\alpha\), transmission distance \(d\), and the underwater acoustic channel transmission model is as follows:  
\[A(d)=\alpha^{d}\times d^{k} \tag{2}\]  
Figure 3: Energy consumption of sensor components.  
where \(k\) represents the energy diffusion factor of the underwater acoustic channel. When \(k=1\), the hydroacoustic channel adopts the cylindrical diffusion model; while \(k=1.5\), the underwater acoustic channel adopts the spherical diffusion model; when \(k=2\), the actual situation is indicated.  
Usually, the attenuation coefficient \(\alpha\) is directly related to the absorption coefficient \(\alpha(f)\) as in the following relationship:  
\[\alpha=10^{\frac{\alpha(f)}{10}} \tag{3}\]  
wherein, the unit of absorption coefficient \(\alpha(f)\) is \(dB/m\), which can be expressed as:  
\[\alpha(f)=0.11\times\frac{10^{-3}\times f^{2}}{1+f^{2}}+44\times\frac{10^{-3} \times f^{2}}{4100+f^{2}}+2.75\times 10^{-7}\times f^{2}+3\times 10^{-6} \tag{4}\]  
where \(f\) is the unit \(k\) Hz, which represents the carrier frequency of the underwater acoustic signal.  
Therefore, the energy required to send \(n\) bit data to another node at a distance of \(d\) meters in a shallow water area is:  
\[E_{s}=n\times P_{min}\times A(d) \tag{5}\]  
\[E_{s}=n\times P_{min}\times 10^{d\times(0.011\times\frac{f^{2}}{1+f^{2}}+4.4 \times\frac{f^{2}}{4100+f^{2}}+2.75\times 10^{-5}\times f^{2}+0.0003)}\times d \tag{6}\]
# Enhancing Sensitivity of Double-Walled Carbon Nanotubes with Longitudinal Magnetic Field
## 1 Introduction
In this research, the effect of changing the configuration of the resonant structure is studied by considering the mass sensor inside a longitudinal magnetic field. In nanoscale mechanics modeling, small-scale effects are crucial in accurately describing the mechanical behavior of nanostructures. At the nanoscale, material properties and structural dimensions deviate from those observed in the macroscopic world [37]. Several theories have been proposed to address these nonclassical phenomena, including [PERSON]'s non-local theory, the modified couple stress theory, and nonclassical fractional-based theories [38; 39; 40; 41]. These theories consider the influences of size and surface effects on the mechanical responses of nanostructures. Among these approaches, [PERSON]'s non-local theory has gained significant popularity. It introduces a non-local parameter for the influence of neighboring atoms or molecules on a material's mechanical behavior. The non-local theory has been successfully applied to various problems [42; 43; 44; 45; 46]. Its ability to accurately capture the size-dependent behavior of nanostructures makes it a valuable tool in the analysis and design of nanoscale systems. This study investigates the sensitivity of biosensors based on DWCNTs subjected to a longitudinal magnetic field. In contrast to SWCNTs, DWCNTs demonstrate superior thermal and chemical stability. They have been applied in various fields, such as those using gas sensors, dielectric devices, nanoelectronic devices, nanocomposites, emitters, and others. The synthetic combination of single-walled, multi-walled, and double-walled carbon nanotubes showcases the electrical and thermal stability of the latter and the flexibility of the former. Meanwhile, using a longitudinal magnetic field is intended to enhance the sensitivity and performance of DWCNT-based biosensors [47; 48; 49; 50]. Additionally, in the case of sensitivity, non-SWCNTs are much easier to manipulate [10; 51]. Therefore, in the current research, the vibrating behavior of DWCNTs resting on an elastic foundation and subjected to a longitudinal magnetic field is investigated through the derivation of the governing equations using [PERSON]'s non-local theory. The Lorentz magnetic force is considered and is obtained from [PERSON]'s relation. The effects of the longitudinal magnetic field are analyzed for three different sensor configurations: simply supported, cantilevered, and bridged. Through studying these effects, this research contributes to the advancement and design of mass sensors utilizing DWCNTs in a longitudinal magnetic field. This study might provide deeper insights into further developments in mass sensing and the application of DWCNT-based sensors.
# Hospital Readmission and Length-of-Stay Prediction Using an Optimized Hybrid Deep Model
## 3 Methodology
### Dataset
#### 3.1.1 Diabetes  
For diabetic patients, we use a dataset of 130 hospitals in the United States from 1999 to 2008 [33]. The dataset consists of 101,766 records with 50 attributes, such as ethnicity, gender, age, weight, and hospital visits. The data also contains features such as patient identification number, admission type, hospital LOS, the speciality of the admitting physician, the number of performed lab tests, glycated haemoglobin (HbA1c) test results, diagnoses, the number of medications, diabetic medications, the number of inpatients and outpatients, and the number of emergency visits in the year before the hospitalization. Weight and age are recorded in and 25-pound and 10-year intervals, respectively. Gender reported as male, female, or unknown. The percentage of patients with male, female, and unknown gender is 53.77%, 46.22%, and 0.01%, respectively.  
The hospital inpatient and outpatient visits within the year before the hospitalization have been recorded in the dataset. The speciality of the admitting physician had been recorded as 84 distinct values such as cardiology, internal medicine, family or general practice, and surgeon. In the dataset, the range of the glucose serum test result had been recorded as \"normal\", \"more than 200\", \"more than 300\", or \"not measured\". The primary, secondary, and additional secondary diagnoses have been recorded in the international-statistical classification of diseases (ICD) codes [34]. The primary, secondary, and additional secondary diagnosis attributes encoded as the first three digits of the ICD-9 [34], having 848, 923, and 954 distinct values, respectively. More than 44% of the primary diagnoses in the dataset are related to circulatory and respiratory system diseases.
# Detecting and Solving Tube Entanglement in Bin Picking Operations
## 3 Bin Picking Algorithm
### Trajectory Planning
#### 3.3.2 Trajectory Evaluation  
The synthesized trajectories alongside the tubes' geometric models are then sent to Gazebo. Each trajectory contains an identifier of the tube to be moved and an ordered list of waypoints.  
In the simulated environment, the tubes are also modeled as a set of cylinders with the same lengths, radii, and poses as those computed during the tube modeling phase. The only differences are that cylinders are created for each joint to connect each pair of endpoints and a sphere with a radius equal to the tube's radius is added to each endpoint involved in a joint. These two changes prevent the tubes from having gaps. To reduce the number of objects in the simulated environment, and thus decrease the computational effort, none of the models for the robot's parts are added to the simulated environment. Figure 4 presents some examples of simulated tubes in Gazebo.  
Figure 3: Visual representation of the escape line and distances for an occluded tube.  
Before spawning all of the tubes in the simulation world, a small amount of time is given to allow all objects to reach a _stable state_, where they are no longer moving due to gravity and contact forces between them. Before testing each trajectory, the world is reset back to this same stable state.  
For each trajectory, the associated tube is moved at a constant velocity along each segment between consecutive waypoints until the last endpoint is reached. The velocity in the simulation environment is the same as the gripper's during the motion execution. The poses of the other tubes are computed at each time step using the physics engine. After each trajectory is concluded, the displacement of each tube is measured as the distance between the positions of its center of mass after following the trajectory and on the stable state. The cost for each trajectory corresponds to the sum of the displacements of all tubes, with the exception the one that was intentionally moved. This cost function enables the detection of cases where other tubes were unintentionally lifted and is also sensitive to trajectories that cause too much disruption in the bin.
## 3 Results and Discussion
### Oxide Surface Characterization
#### 3.1.2 Oxide Surface Topographies  
Oxide optical microscopy and SEM images are presented in Figure 2. Low-magnification optical images are displayed in the left column, while the center and right columns show SEM micrographs highlighting micro- and nano-scale surface features, respectively. Micro- and nano-scaled SEM images revealed complex surface topographies for both oxides with somewhat different morphological appearances. The NOJ CP oxides showed characteristic petal-like surface features that have been previously associated with hydroxyapatite [3,6,24]. In contrast, NOJ MP nano-scaled images contained surface features with a more rounded or nodular appearance. This NOJ MP oxide surface feature morphology was noted to be similar to those of the magnesium-doped hydroxyapatite anodized oxides formed on several titanium alloys in a previous citric-acid based electrolyte study [22].
## 3 Data and Empirical Setup
### Policy Adherence Thresholds  
Maintaining operational capacity that satisfies environmental and legal criteria depends on policy adherence. Table 7 lists the least operating capacity fractions needed for every project, guaranteeing policy compliance.  
Solar Farm Alpha must keep at least 80% of its enlarged capacity; Wind Farm must maintain 70%; and Hydro Plant must maintain 60%.
# A DRL-Based Satellite Service Allocation Method in LEO Satellite Networks
## Abbreviations  
The following abbreviations are used in this manuscript:  
LEO - low Earth orbit - SSA - satellite service allocation - DRL - deep reinforcement learning - MDP - Markov decision process - SATCOM - satellite communication - BPP - binomial point process - PPP - Poisson point process - CDF - Cumulative distribution function - BW - bandwidth - EUA - edge user allocation - ISTN - integrated satellite-terrestrial network
## 1 Introduction
As a result of its high speed, automaticity, and rapid acquisition of a large amount of point cloud data, it has become an important means to obtain detailed data of object surfaces and 3D models and is widely used in the deformation monitoring field. As a result of the difficulty of accurately and completely reflecting the three-dimensional deformation of tunnel arch uplifts mentioned above, deformation monitoring results have certain limitations and data accuracy cannot be guaranteed, which affects the accuracy of subsequent deformation analyses. A method for monitoring the uplift deformation of soft rock tunnel arches based on 3D laser scanning is studied. A Leica Scan Station2 3D laser scanner and the Mallat algorithm are used to extract the slice point cloud with a fixed distance from the optimal reference plane to the soft rock tunnel arch point cloud, extract the geometric parameters of the slice point cloud, and improve the accuracy of monitoring the uplift deformation of soft rock tunnel arches.
# A Hierarchical Sparse Discriminant Autoencoder for Bearing Fault Diagnosis
## 3 Proposed Methodology
### Parameter Optimization Strategy
#### 3.3.2 Dynamic Optimization of Learning Rate  
The learning rate mainly affects the convergence speed and direction of the loss function in the deep neural network training process. In order to solve the optimization problem of the learning rate \(l_{r}\), this paper adopts the inverse time learning rate decay method to optimize the learning rate dynamically, and the specific expression is as follows:  
\[l_{r}=0.01/(1+10\times(t/T))^{0.75} \tag{17}\]  
where \(T\) is the total number of iteration steps, and \(t\) is the current number of iteration steps.