esa-sceva/satcom-chunk-collection huggingface dataset:
# One-Dimensional Mathematical Model for Kayak Propulsion
## 2 Mathematical Model
### Remarks and Quadrature Estimations  
It is important to note that both the work and average power thus calculated approximate well to that which would be obtained numerically up to the instant \(t_{accel}\). However, they somewhat underestimate the total time to complete the acceleration; \(t_{accel}\) equals approximately the time to reach 85% of the asymptotic velocity. A simple alternative is to use the expression (7) to obtain a more approximate estimate of time, e.g., for \(a=0.99\), we obtain \(t_{99}=\ln 199\frac{m}{2f}\sqrt{\frac{f}{d}}\). In this way, we define an acceleration interval (\(t_{accel}\)) that allows us fast calculations of the acceleration and average power, and a stabilization interval up to (\(t_{99}\)) that will allow us to estimate the time or number of strokes to reach maximum speed, followed by a uniform speed interval (Figure 3). This description allows us approximate expressions with which to make quick calculations of the average time, distance, work and power. The area under the velocity-time curve gives us the distance (Figure 3) and the area under the curve \(m\cdot a\) vs. \(v\cdot t\) the work. Entering the parameters from Figure 1, we obtain that the paddler needs \(t_{accel}=8.59\) s to reach 85% of the asymptotic velocity and \(t_{99}=18.61\) s to complete the acceleration. The distance obtained is 20.19 m for 85% of the asymptotic velocity and 44.23 m for 99%. The average work and power obtained are the following:  
\[W_{mean}=992.65J,\qquad\qquad P_{mean}=115.56W. \tag{16}\]  
In the velocity versus time curve, the average acceleration is the slope of the secant straight by the point considered the end of the acceleration interval (see Figure 1). The area under the curve \((m\cdot a)\) against distance \((v\cdot t)\) represents the work we have seen, as the completed work is independent of the interval and only of the problem parameters and is identified with the kinetic energy of the kayak that has reached the asymptotic velocity.  
Figure 3: Velocity versus time curve, where the described intervals are drawn. (I) Constant acceleration interval, (II) stabilization interval and (III) constant velocity interval. (A) Point where 68% of velocity is reached, corresponding to time \(t_{68}\), expression (7); (B) cut-off point between the (I) and (II) intervals, corresponding to time \(t_{accel}\), expression (9); (C) cut-off points between intervals (II) and (III), corresponding to time \(t_{99}\), expression (7). See text.
## 3 Parameter Analyses
### Parameter Definitions  
The radius of the reflector surface is defined as \(R\). The longitudinal length of the FCRA is \(L\). The back frame shell thickness is \(t\). The total track width is \(L_{1}\) and the track thickness is \(t_{1}\). The density of water and the density of the back frame are \(\rho_{w}\) and \(\rho\), respectively. The FCRA mass \(m\) is divided into several parts, which are side plate masses \(m_{m}\), semi-cylindrical shell mass \(m_{l}\), track masses mt, and trolley mass \(m_{s}\). The first three parts constitute the mass of the back frame, which is defined as \(m_{c}=m_{n}+m_{l}+m_{l}\). These masses of each part in the back frame can be calculated as \(m_{n}=\pi R^{2}t\rho\), \(m_{l}\approx\pi RtL\rho\), and \(m_{l}\approx\pi Rt_{1}L_{1}\rho\), respectively.  
Assuming that the aperture \(D\) of the FCRA is 500 mm, the initial values of each design parameter are listed in Table 2 based on the model in Section 2, while reflector radius \(R\) and water density \(\rho_{w}\) are kept constant as their initial values. To easily study the effects of these parameters, the control variables are defined as the non-dimensional variables in Table 3.
# Sentence Representation Method Based on Multi-Layer Semantic Network
## 1 Introduction
It uses cosine distance to directly query the representation space, simply adds the same signals, and realizes the improvement of convergence speed by simplifying the skip-thought model. With neural networks and deep learning development, sentence representation technology has gradually developed from combining the simple word embedding model and more complex neural network architecture. For example, convolution networks [15; 16], cyclic neural networks [17], and their variants [18; 19] have been applied to improve the performance of sentence representation. [PERSON] [20] proposed an adjacent convolutional reasoning model by taking advantage of the convolutional neural network's advantages in feature extraction. [PERSON] [20] spliced all the extracted semantic features to form the input text's embedded semantic representation by extracting semantic features of different abstract levels of text. Although the neural network is good at capturing the training model's deviation, it is easy to forget the input statement's complete information. It is too focused on reducing the deviation, which leads to the poor effect of the final model on the test set. [PERSON] [21] combined the monolayer self-attention mechanism based on graves. [PERSON] [22] combined the depth threshold attention mechanism to improve characterization performance. The existing attention mechanism only focuses on a certain level of sentence information. It cannot fully express the meaning of the sentence. Therefore, [PERSON] [23] proposed a multi-head attention mechanism that uses eight layers of 600-dimensional attentions. Each layer has 75 hidden neurons, which are used to encode sentence semantics. Based on these studies, [PERSON] [24] designed a bidirectional self-attention network Disan. It uses a self-attention mechanism to encode sentence semantic scalar and uses multi-layer attention to expand the obtained semantic scalar to vector expression. This method's advantage is that it does not need to use a neural network to code sentence semantics to avoid falling into the local optimum. The disadvantage is that it pays too much attention to semantic information. Too little attention is paid to the sentence's word order information, which easily leads to inaccurate sentence semantic expression. This paper designed a sentence representation method based on multi-layer semantics, analyzed the importance of sentence semantics for sentence representation, and learned the key technologies of sentence representation. The existing sentence representation methods cannot correctly and comprehensively express the meaning of a sentence. This paper uses the combination of bidirectional long-term and short-term memory networks and a multi-attention mechanism to obtain the semantic information of different levels of the sentence. The relative position information between words is added into the sentence word order information. Finally, all the information is fused to form a complete sentence embedded representation.
# Sizing Methodology of a Fast Charger for Public Service Electric Vehicles Based on Supercapacitors
## 1 Introduction
If more than one vehicle is to be charged simultaneously, it would be sufficient to replicate the circuit of Figure 1. Even with simultaneous charging in multiple recharge points of the station, no disturbances would appear in the grid. Since the main complexity of this type of charger lies in establishing the characteristics of the banks of SCs and the inductor, this study will present a method for sizing all of their components. First of all, the mathematical expressions of all the electrical variables involved in vehicle charging, namely the current, internal voltages, maximum charging time, energy dissipated, and the efficiency of the charging process, will be obtained. Then, each of its components will be sized. The calculation of the smoothing inductor is complex; therefore, a graphical method will be presented that guarantees that the assembly functions  
Figure 1: Simplified schematic of a bus connected to a fast-charging station built with SCs. correctly for any initial voltage (remaining energy) of the vehicle's accumulator considering, in addition, the degree of aging that all the SC cells may undergo. The paper has been organized in the following sections: Section 2.1 will show the mathematical analysis of the main electrical variables: current, i(t); internal voltages of the charger, u\({}_{1}\)(t), and of the vehicle accumulator, u\({}_{2}\)(t); maximum time set for charging, t\({}_{\text{charge}}\); limit current, I\({}_{\text{max}}\); energy dissipated during the transient period, w\({}_{\text{d}}\)(t); and efficiency of the charging process, \(\epsilon\)(%). Section 2.2 will thoroughly describe the process of selecting the capacitance of the charger, which consists of several identical modules connected in parallel. A procedure to calculate their initial voltage in order to maximize efficiency, a graphical method for the proper selection of the smoothing inductor, and the values of its inductance and internal resistance will also be presented. In Section 3, a practical case study for a Tecnobus Gulliver U500\({}^{\text{m}}\) prototype electric bus, whose storage system consists of lead-acid batteries and an array of SCs, will be analyzed. Section 4 will finally show the conclusions of the study.
# Probabilistic Shaping for Finite Blocklengths: Distribution Matching and Sphere Shaping
## 6 Approximate Complexity Discussion  
In Section 5, we followed the conventional approach of comparing different shaping architectures by studying the blocklength that is required to obtain a certain shaping gain. While this is certainly a natural choice for analysing and comparing shaped systems, this approach inherently assumes that shorter blocks are always better, for instance because they have advantages regarding implementation. In the following, we comment on the implementation aspect by considering computational complexity, latency, and storage requirements.  
An example where slightly longer blocklengths can be beneficial also from an implementation perspective is the parallel-amplitude (PA) architecture proposed in (Section III in [58]). By allowing a small additional rate loss, the throughput is increased significantly by using \(|\mathcal{A}|-1\) DMs in parallel. Furthermore, the serialism (and thus, the latency) of the subset ranking (SR) method of (Section IV in [58]) is smaller than AC-CCDM. It can thus be beneficial to make the blocks slightly larger than for conventional CCDM in order to facilitate implementation.  
An interesting example where the selection of the shaping blocklength does not depend only on the complexity vs. shaping gain tradeoff is the nonlinear regime of the optical fibres. The authors of [61] recently found that shaping over shorter blocklengths increases the nonlinear tolerance, and thus, the effective SNR. Their claim is that when the complexity considerations are ignored, there is an optimum \(n\) that optimizes the balance between linear shaping gain and increased nonlinearity-tolerance.  
Finally, we note that in this section, we restrict our attention to algorithmic implementation, while aspects related with hardware implementation such as throughput, clock frequency, and number of instances are outside the scope of this paper. For a detailed discussion on these concepts, shaping algorithms that are discussed in this work should actually be implemented as in [102; 103; 104; 105].  
Figure 11: FER vs. SNR for 64-QAM at transmission rates of 4 and 4.5 bit/2-D. Rate-5/6 IEEE 802.11 LDPC codes of length \(n_{\text{c}}=648\) bits are used for shaped signaling. All shaping schemes use a blocklength of \(n=216\). At this shaping blocklength, each LDPC codeword consists of 1 shaped block.
# An Improved Dynamic Joint Resource Allocation Algorithm Based on SFR
## 2 System Model and Problem Formulation
### Problem Formulation  
The major contribution of the adaptive soft frequency reuse scheme is to allocate RBs and transmit powers dynamically, which can not only maximize the system capacity but also guarantee the minimum data rate requirement for each cell zone. Equation (6) is the objective function.  
\[\max_{N_{i}^{major},N_{i}^{minor},p_{i}^{in},p_{i}^{e}}\sum_{i\in C}R_{i}^{in}+R _{i}^{o} \tag{6}\]  
where  
\[R_{i}^{o}\geq R_{i}^{o\,\min} \tag{7}\]  
\[R_{i}^{in}\geq R_{i}^{in\,\min} \tag{8}\]  
\[P_{i}\leq P_{max} \tag{9}\]  
\[N_{i}^{major}+N_{i}^{min\,or}=N \tag{10}\]  
\[\sum_{i\in C_{adj}^{3}}N_{i}^{major}\leq N \tag{11}\]  
In the above formulas, Constraints (7) and (8) denote the cell-interior and cell-edge users need to satisfy their lowest data rate requirements; Constraint (9) ensures that the total power used in each cell can not exceed the maximum power provided by each base station. Constraint (10) states that all RBs can be used in each cell. Constraint (11) indicates that the RBs in any three neighboring cells are non-overlapped, and \(C_{adj}^{3}\) is the set of three adjacent cells. The total power for cell \(i\) is calculated as  
\[P_{i}=N_{i}^{\min\,or}p_{i}^{in}+N_{i}^{major}p_{i}^{o} \tag{12}\]
## 3 Results  
Case studies--based on the previous results, this section quantifies the throughput for different assumptions. Firstly, we define the baseline case as nominal satellite operations, i.e., the maximum channel rate transmitted in the X band with fixed QPSK modulation without channel coding, as implemented on the CBERS-4 satellite.
# Design and Modelling of a Novel Integrated Photonic Device for Nano-Scale Magnetic Memory Reading
## 2 Materials and Methods
### Mathematical Modelling and Fourier Transformation
\begin{split}\
u_{\text{TE}}&=\frac{n_{\text{TEg}} (L_{1}-L_{2})}{\lambda^{2}},\\ \
u_{\text{TM}}&=\frac{n_{\text{TMg}}(L_{1}-L_{2}+x_{ \text{clad.}}-x_{\text{PC}})}{\lambda^{2}}.\end{split}  \tag{2}\]  
\(n_{\text{TEg}}\) and \(n_{\text{TMg}}\) in Equation (2) indicate group indices of the respective modes. A Fourier transformation can be applied to the resulting output light intensity vs. wavelength data to separate the \(TM\) mode contribution. Thanks to this technique, the amplitude and phase of the \(TM\) mode component can be found. In order to separate the \(TE\) and \(TM\) mode contributions, non-overlapping peaks in the Fourier transform is required. Therefore, at the design stage, it is vital to choose \(x_{\text{clad. }}\) and \(x_{\text{PC}}\) parameters (see Table 1) accordingly.
## 2 Materials and Methods
### Field Site and Measurements
#### 2.1.2 Non-Destructive Measurements  
Non-destructive LAI measurements were carried out at all experimental trial plots at key winter wheat growth stages during both growing seasons (Table 1). For each plot, five replicates of LAI were taken using a SunScan Canopy Analyser (Delta-T Devices, Cambridge, UK). These replicates were then combined to give a plot average and standard deviation. The growth stages were also determined on a per plot basis, whereby for a given plot the dominant growth stage was recorded. For each observation date, the average growth stage is then reported across all the experimental treatments.
## 7 Security Analysis and Cryptographic Guarantees
### Mutual Authentication Guarantees  
Authentication is achieved through the exchange of hashed commitments encrypted under long-term private keys. The values \(\Gamma_{3}\) and \(\Psi_{3}\) bind ephemeral data to respective identities, enabling mutual verification without disclosing sensitive material.  
* The initiator \(\mathcal{A}\) proves possession of \(\mathsf{sk}_{\mathcal{A}}\) by transmitting \[\Gamma_{3}=\mathsf{Enc}_{\mathsf{sk}_{\mathcal{A}}}(H(\mathsf{pk}_{\mathcal{ A}}\parallel\Gamma_{1}))\]
* The responder \(\mathcal{B}\) authenticates using \[\Psi_{3}=\mathsf{Enc}_{\mathsf{sk}_{B}}(H(\mathsf{pk}_{B}\parallel\Psi_{1}))\]  
Correct decryption and hash verification at both ends guarantee that no impersonation is possible without access to the private key of the peer entity.
# Novel Real-Time Compensation Method for Machine Tool's Ball Screw Thermal Error
###### Abstract  
The real-time compensation of thermal error in ball screws is an effective means to improve the accuracy of machining tools. However, the trade-off between robustness and computational efficiency of existing ball screw thermal error models is complicated and not conducive to practical, high-precision, real-time error compensation. Focusing on this problem, we propose an iterative prediction model of screw thermal error based on a finite difference equation. By assuming an approximately linear relationship between heat generation and the ball screw's convection power and feed speed, a simplified and more efficient identification of physical parameters needed for the iterative model is achieved. The proposed method is integrated with a three-axis drilling and tapping machine powered by an HNC-848D controller. A test piece machine using the proposed real-time thermal error compensation method exhibited a maximum machining error of 13 \(\upmu\)m, compared to the 71 \(\upmu\)m of an uncompensated specimen. The proposed method is demonstrated to improve machining accuracy, especially in the X- and Y- axes, and overcome the limitations of traditional thermal error prediction models.  
machine tool; thermal error; compensation; ball screw feed system +
Footnote †: journal: Applied
## 3 Target Data Processing and Computing
### Three Data Processing Paradigms
#### 3.1.3 Function Processing  
The serverless computing model has emerged as a subset of the cloud computing model in which the cloud service provider takes responsibility for managing underlying servers and the scaling of applications, while the developers can build and run functions on demand. The most common use cases for serverless computing include event-driven IoT applications, data analytics, and machine learning inferences. The main cloud service providers have services for function processing, such as Amazon Lambda, Google Cloud Functions, IBM Cloud Functions, and Azure Functions. Moreover, some open-source serverless platforms, such as Apache OpenWhisk [29], OpenFaaS [57], and KNative [58], are also available for deployment in private environments. Apache OpenWhisk, with its event-driven programming model, allows for the execution of functions in response to events, offering a scalable and modular approach to serverless computing. OpenFaaS, known for its simplicity and ease of deployment, extends the capabilities of serverless platforms to private environments, enabling users to efficiently build, deploy, and scale functions. KNative extends Kubernetes to provide a set of building blocks for serverless applications. In serverless computing, developers write their business logic code as a set of functions. Each function performs a specific task when it is called by a function invocation event, such as image uploading, an HTTP request, an incoming message, or database state changes. These functions are deployed in serverless computing platforms along with their triggers and function invocation methods. When a function is invoked, it is executed on the running server. The execution process is abstracted from the user/developer and only returns the invocation results to the user.  
We chose Apache OpenWhisk for function processing, which is an open-source serverless computing platform designed to execute functions in response to events, offering a flexible and scalable approach to distributed computing. In the context of edge and fog computing, where resource constraints are common, OpenWhisk's serverless architecture allows for efficient and dynamic resource utilization. This is particularly beneficial in scenarios where devices at the edge have limited computational capabilities. Furthermore, OpenWhisk's ability to break down applications into small, independent functions aligns with the requirements of edge and fog computing [29]. This modular approach allows for the deployment of lightweight functions to edge devices, ensuring that computational tasks are distributed optimally. The event-driven nature of OpenWhisk makes it well-suited for real-time processing, which is crucial in edge and fog computing applications [59]. By responding to events in near-real-time, OpenWhisk can efficiently handle tasks that require immediate responsiveness.
_Applied_  
_sciences_  
_Article_  
**Enhanced Sensitivity of Microring Resonator-Based Sensors Using the Finite Difference Time Domain Method to Detect Glucose Levels for Diabetes Monitoring**  
**[PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 2, [PERSON] 3,*, [PERSON] 3, [PERSON] 3,**  
**[PERSON] 3,4,*, [PERSON] 5, [PERSON] 6 and [PERSON] 7**  
1 Department of Physics Education, Universitas Pendidikan Indonesia (UPI), Bandung 40154, Indonesia; [EMAIL_ADDRESS] (L.H.); [EMAIL_ADDRESS] (H.S.N.); [EMAIL_ADDRESS] (C.W.)  
2 Department of Electrical Engineering Education, Universitas Pendidikan Indonesia (UPI), Bandung 40154, Indonesia; [EMAIL_ADDRESS]  
3 Institute of Microengineering and Nanoelectronics (IMEN), Universitas Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia; [EMAIL_ADDRESS] (M.H.H.); [EMAIL_ADDRESS] (P.S.M.)  
4213 John McKay Lab, John A. Paulson Building, 31 Oxford St, Harvard University, Cambridge, MA 02138, USA  
5 Department of Mechanical Engineering Education, Universitas Pendidikan Indonesia (UPI), Bandung 40154, Indonesia; [EMAIL_ADDRESS]  
6 Physics of Electronic Materials Research Division, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Bandung 40132, Indonesia; [EMAIL_ADDRESS]  
7 Department of Mechanical Engineering, Universiti Malaysia Pahang, Pekan 26600, Malaysia; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS] (D.D.B.); [EMAIL_ADDRESS] (A.R.M.Z.); Tel.: +60-012-442-2292 (D.D.B.); +60-019-573-8877 (A.R.M.Z.)  
Received: 5 December 2019; Accepted: 6 February 2020; Published: 18 June 2020  
**Abstract:** The properties of light and its interaction with biological analytes have made it possible to design sophisticated and reliable optical-based biomedical sensors. In this paper, we report the simulation, design, and fabrication of microring resonator (MRR)-based sensors for the detection of diabetic glucose levels. Electron Beam Lithography (EBL) with 1:1 hydrogen silsesquioxane (HSQ) negative tone resist were used to fabricate MRR on a Silicon-on-Insulator (SOI) platform. Scanning Electron Microscopy (SEM) was then used to characterize the morphology of the MRR device. The full-width at half-maximum (FWHM) and quality factors of MRR were obtained by using a tunable laser source (TLS) and optical spectrum analyzer (OSA). In this paper, the three-dimensional Finite Difference Time Domain (3D FDTD) approach has been used to simulate the proposed design. The simulation results show an accurate approximation with the experimental results. Next, the sensitivity of MRR-based sensors to detect glucose levels is obtained. The sensitivity value for glucose level detection in the range 0% to 18% is 69.44 nm/RIU. This proved that our MRR design has a great potential as a sensor to detect diabetic glucose levels.  
**Keywords:** microring resonator (MRR); quality factor; sensitivity; three-dimensional Finite Difference Time Domain (3D FDTD); diabetes detection