esa-sceva/satcom-chunk-collection huggingface dataset:
# Laser Technology in Photonic Applications for Space
## 2 Laser Devices  
The first successful use of a laser for a space experiment was registered on 9 May 1962, as part of the Laser Lunar Ranging experiment [6]. Since the laser device was located on the Earth's surface, it did not need to fulfill additional specifications required for space flights, such as mechanical stability, thermal shocks, and radiation resistance.  
The story of laser operation in space started in 1971, when the Apollo 15 carried what would be the first laser outside Earth, a flash-lamp-pumped Q-switched Ruby laser built by the RCA Corporation and used for the Laser Altimeter experiment [7]. The first diode-pumped solid-state laser (DPSSL) to be sent into space was delivered in 1992 and launched as part of the Mars Orbiter Laser Altimeter (MOLA) in 1996; the laser used an Nd:YAG crystal as the active medium [8]. In November 2001, the use of semiconductor laser technology in space was reported for a direct laser application rather than pumping, when the world's first laser-based optical data link connected the Artemis satellite from the ESA with the Centre National d'Etudes Spatiales (CNES) Earth observation satellite SPOT 4 [9], using GaAlAs laser diodes emitting at 0.8 \(\upmu\)m. It is also worth highlighting the first laser pulse emitted on a planet surface other than the Earth on 19 August 2012, by the laser integrated in the ChemCam device installed on the Curiosity Mars rover, which used an Nd:KGW crystal DPSSL [10].  
The constraints associated with laser operation in space are different from those in terrestrial applications. Thus, the lasers suitable for space applications need to present specifications, such as long lifetime, high efficiency, low susceptibility to optical misalignment and contamination, and unattended operation, among others. The best laser has to be selected depending on the requirements of the application and environment. Wavelength, repetition rate, peak power, pulse length, flexibility, maintainability, manufacturing cost, and operating cost are important parameters to take into account, but the environment is also essential, depending on whether the laser will be in the low atmosphere, in deep space, near the sun, or on another planet.  
The following subsections will address the most commonly used types of lasers suitable for space deployment, together with some of their specifications, applications, and examples of real-life deployments.
# Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System
## 5 Results and Discussion
### Comparison with Existing State-of-the-Art Approaches  
The proposed health monitoring technique is compared with the state-of-the-art methods. The study [73] designed an IoT-based cardiovascular risk prediction using ensemble techniques. The ensemble techniques increase complexity and require higher computation resources, which is not appropriate for health-related systems. Moreover, these techniques generate over-fitting problems if not properly implemented. Table 16 shows a performance comparison of the proposed approach with [73]. The results show that the proposed approach has better performance.
# Design of Infinite Impulse Response Filters Based on Multi-Objective Particle Swarm Optimization
## 2 Methodologies and System Description
### Low-Pass Filter  
A low-pass filter is designed to only allow signals with frequencies below a specific cutoff frequency to pass through. When designing an IIR low-pass filter, key design parameters include cutoff frequency, filter order, and transfer function. The transfer function and difference equation [15] are Equations (1) and (2), respectively, essential mathematical formulas for designing IIR low-pass filters.  
\[H(s)=\frac{Y(s)}{X(s)}=\frac{\sum_{k=0}^{M}b_{k}s^{k}}{1+\sum_{k=1}^{N}a_{k}s ^{k}} \tag{1}\]  
\[y_{[n]}=\Sigma_{k=0}^{M}b_{k}x[n-k]-\Sigma_{k=1}^{N}a_{k}y[n-k] \tag{2}\]
# An Efficient Probe-Based Routing for Content-Centric Networking
## 2 Related Work
### Extensions of CCN  
Recent CCN-related studies have focused on improving forwarding efficiency [25; 27; 28; 29], [PERSON] and [PERSON] [27] describes an implicit coordination approach in which a Bread-crumb is used to record the direction and time of the request forwarded in the past. SCAN, as proposed in [28], is a scalable content routing. During the forwarding process, it sequentially queries the surrounding neighbors to obtain a copy of the requested content. [PERSON] et al. [25] designed a routing extension to Open Shortest Path First (OSPF) for NDN, called OSPFN. They defined a link-state announcement (LSA) to carry the content name to build FIB. OSPFN is confined to a single path because it still uses the IP address as the router ID. Therefore, they developed a Named-data Link State Routing protocol (NLSR) to solve the OSPFN problem [25]. [PERSON] et al. [29] introduced the Potential Based Routing (PBR) to tackle namely routing problem. Each router actively broadcasts its cached content to maintain a table of potentially reachable copy information. [PERSON] et al. [30] proposed a novel Energy Efficient Greedy Routing Protocol (EEGRP). It minimizes flooding by using uniquely designed routing table (RT) entries in the network. The provider node selects the shortest path to forward data packets within the minimum delay to achieve high energy efficiency. [PERSON] et al. [31] proposed an Intelligent CCN routing strategy based on Bacterial Quorum pattern (ICBQ). It simulates the quorum sensing behavior of bacteria to obtain parameter information such as bandwidth, delay, error rate, and adaptive chemotaxis behavior to select the optimal interface to forward packets. [PERSON] et al. [32] presented a Software Defined Status Aware routing (SD-STAR) method that combined CCN and SDN. The controller is used by this mechanism to collect network statuses, then update the interest packet, choose the best forwarding interface, and choose the best routing path.
# 4 th Order LC-Based Sigma Delta Modulators
## 5 Conclusions  
In this work, a design methodology for \(\Sigma\Delta\)M using a high-order realistic LC filter without local feedback was presented. This design methodology proposed the use of  
Figure 12: Output spectrum: (**a**) Of the all designed \(\Sigma\Delta\)Ms. (**b**) Enlarged image around the center frequency.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Ref.** & **[This Work]** & **[[PERSON]]**[24] & **[[PERSON]]**[25] & **[[PERSON]]**[2] & **[[PERSON]]**[26] \\  & & **2013** & **2016** & **2017** & **2020** \\ \hline Architecture & Gm-LC \(|\) & Gm-LC & Active RC & Capacitive & LC based \\  & Magnetic Coupling \(|\) & & & Coupling & \\  & Capacitive Coupling & & & Coupling & \\ Order & 4 & 4 & 6 & 4 & 2 \\ Tuning range (GHz) & – & – & 0.180–0.20 & 0.40–0.44 & 1.50–3.00 \\ Sampling Frequency, \(f_{a}\) (GHz) & 1.72 & 0.36 & 0.80 & 0.58 & 6.00–12.00 \\ Center Frequency, \(f_{o}\) (GHz) & 0.432 & 0.90 & 0.40–0.44 & 0.40–0.44 & 1.50–3.00 \\ \(f_{s}/f_{o}\) & 4 & 4 & 4–4/3 & 4/3 & 1/4 \\ Bandwidth, BW (MHz) & 4.00 & 28.13 & 25.55 & 4.50 & 47.00–93.00 \\ SNR (dB) & 51.391 48.48 146.50 & 50.00 & 69.00 & 50.00 & 37 \\ \hline \hline \end{tabular}
\end{table}
Table 5: State of the art on RF SD ADC.  
FIRDACs to increase the degrees of freedom, making possible the transformation of DT-to-CT. The desired loop-filter transfer function was designed taking into account, the LC resonators, the coupling coefficient, and the loop delay.  
System-level simulation shows that the 4 th order \(\Sigma\Delta\)Ms using different coupling techniques achieves similar performances as their 4 th order DT counterpart. For a real implementation, the difficulties of these architectures are related to the filter design, more specifically to the type of coupling used, and the capacitive coupling appears to be a good candidate for implementation. The capacitor is a well-known device, in addition, have good linearity, lower power consumption, and its design does not require other tools than a SPICE simulator.  
Conceptualization, E.C.d.O.L. and D.R.B.; methodology, E.C.d.O.L. and D.R.B.; formal analysis, E.C.d.O.L.; writing--original draft preparation, E.C.d.O.L.; writing--review and editing, E.C.d.O.L., D.R.B. and A.W.A.S.; supervision, D.R.B. All authors have read and agreed to the published version of the manuscript.  
This study was financed in part by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior - Brasil (CAPES)--Finance Code 001.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Results
### Experiment 4 Results: One Device, Distance between 255-1245 m  
In this experiment, the same 44 settings from Experiment 3 were used. In most of the settings, packets at distances greater than 650 m were not received at the gateway, mainly because of obstacles in the line of sight (LoS). Figure 12 shows RSSI levels with respect to distance for some of the best settings, which were obtained with a low BW (125 kHz), a CR with low denominator (4/5) and a SF of 7, 8 or 9.  
Figure 11: Contour graphs for settings evaluated in Experiment 3.  
Figure 10: RSSI values for the best setting (spreading factor (SF) = 7, BW = 125 kHz, coding rate (CR) = 4/6) in Experiment 3.  
Although there is some variability in the RSSI levels obtained for the three settings (see Figure 12), the range of values in which most of the values vary (before 650 m) is less than 15 dBm. In settings with an SF of 10 packets were received above 750 m, however the RSSI decreased by a few dBm. It was possible to validate in some other cases that with a high SF, higher distances can be achieved with a lower RSSI. Figure 13, shows that with SF = 9 (BW = 125 kHz and CR = 4/6) a greater distance was reached.  
There were some other settings (with BW = 125 kHz), where the maximum distance was not reached with a high SF. Figure 14 shows that maximum distance for BW = 125 kHz was reached with a SF = 7.  
The variation of SF in the maximum distance reached was also observed with the values of BW equal to 250 kHz and 500 kHz, where in some cases the maximum distance was reached with SF = 9 or SF = 10, but in other cases it was reached with SF = 7.  
With respect to percentage of received packets, no settings yielded 100% reception. A total of 27% of the settings featured a reception rate between 99% and 75%; 10% of the settings featured a reception rate below 50%.  
The baseline setting was the best for this experiment. It obtained an average RSSI value of \(-103\) dBm, 94% packets received, and a maximum distance of 848 m.  
Figure 12: RSSI versus distance with bandwidth (BW) = 125 kHz and CR = 4/5, in Experiment 4.  
Figure 13: RSSI versus distance with BW = 125 kHz and CR = 4/6, in Experiment 4.
# Studies on Influence of Chromium Layer on Inner Surface of Steel Tube on Heat Transfer
###### Abstract  
This paper presents the results of numerical calculations of heat transfer in the steel wall of a tube with different chromium layer thicknesses on the inner surface for a single and a sequence of heat impulses. In modeling the thermal phenomenon, the tube material was assumed to be homogeneous, and the inner surface of the tube had a protective chromium layer. The calculations were performed for temperature-dependent parameters of the 30 HN2 MFA steel and chromium. The tube was divided into 30 zones. The time varying the heat transfer coefficient and the heat flux density on the inner surface of the tube in each zone were calculated. The numerical simulations of heat transfer in the tube wall were performed using the FEM implemented in COMSOL Multiphysics software. On account of the high thermal conductivity of chromium, after the first and subsequent heat impulses, as the thickness of the chrome layer increases, the highest peak temperature drops, while the temperature inside the steel wall of the tube increases. After the first heat impulse, the temperature of the wall at a depth 0.5 mm below the inner surface is twice as high for a tube with a 200 \(\upmu\)m thick chrome coating compared to a tube without this coating. This paper showed that, after seven heat impulses, the temperature of the steel tube substrate does not exceed 750 \({}^{\circ}\)C, at which the shrinkage of the material occurs, causing cracks in the chrome coating.  
heat transfer; temperature field; protective coating; modeling Article
# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 3 Research Gaps Addressed by This Study
### Limited QE Research in Badminton  
Research specifically focused on QE in badminton remains limited. Badminton, a sport requiring rapid decision-making, precise hand-eye coordination, and quick adaptation to opponent actions, presents unique challenges. The fast-paced and dynamic nature of this sport presents unique challenges for QE studies, especially in understanding how players focus on critical cues such as the shuttlecock or opponent movements [6; 18]. Our study addresses this gap by applying QE research to badminton, providing insights into the role of visual attention in performance under high-speed, dynamic conditions.
# A Machine Learning Approach for the Prediction of Thermostable \(\beta\)-Glucosidases
## 3 Materials and Methods
### Case Studies  
Firstly, to compare the method proposed here with other methodologies, the 583 sequences of the test dataset were submitted to the TemStaPro software v0.2.6 [42] using default parameters. TemStaPro (Temperatures of Stability for Proteins) is a machine learning-based method for predicting protein thermostability directly from its sequence. The software receives, as input, a set of sequences and returns a binary value indicating whether the protein will be stable at temperatures of 40, 45, 50, 55, 60, and 65 \({}^{\circ}\)C.  
Secondly, to better exemplify the results of the model, four structures were selected for further analysis: a thermophilic structure that the model correctly identified (UniProt ID: A0A0B3 BP14) and one that the model incorrectly identified (UniProt ID: A0A08 FE2J5), as well as a non-thermophilic structure that the model correctly identified (UniProt ID: A0A0B2A6F6) and another that the model incorrectly identified (UniProt ID: A0A0G0 PX59). Sequences were collected from the UniProt database [37]. The 3D model structures were collected from the AlphaFold Protein Structure Database [38].  
Sequence alignments were performed using the Clustal Omega web tool 2025 [43]. Structure alignments were performed using the PyMOL tool v3.1.0 [44].  
Analysis of the electrostatic surface charge distribution was performed using the APBS web tool v3.4.1 [45,46]. Initially, the PDB file was converted to PQR format using the PDB2 PQR web tool v3.6.1 [47,48]. PROPKA was used to assign protonation states at a pH of 7.0 and the AMBER force field. The results were visualized through the electrostatic surface with a red-white-blue color scheme ranging from \(-\)5 kT/e to 5 kT/e.  
Fast protein structure flexibility simulations were performed for A0A0B3 BP14 (thermophilic) and A0A0B2A6F6 (non-thermophilic) using the CABSflex web tool v2.0 [49,50]. I used the following parameters: restraint mode ss2 (secondary structure only), minimum length restraint of 3.8 A and maximum of 8 A, gap of 3, and number of cycles of 50. For each protein, an experiment was performed using the default simulation temperature (1.4) and a higher temperature (3.0).
## 2 Methods
### Epidemiological Models  
#### 2.3.1 Compartmental Model  
The SIR model, as shown in Figure 1, is considered the simplest compartmental model. It was first introduced by [PERSON] and [PERSON] [21], and has been used to investigate the spread dynamics of a variety of diseases, such as MERS coronavirus [22] and novel coronavirus [23], and has also been used to forecast seasonal influenza [24]. To model an epidemic, the population is divided into three groups. The first group is defined as the number of individuals who are susceptible to the disease and is represented by \(S(t)\). This group includes those individuals who have not yet been infected by time t. The second group is represented by those individuals who have been infected by time t and is represented by \(I(t)\). The last group is denoted by \(R(t)\) and contains those individuals who have recovered from the disease by time t. This model consists of three differential equations:  
\[\begin{array}{l}\frac{dS}{dt}=-\beta\frac{SI}{N},\\ \frac{dt}{dt}=\beta\frac{SI}{N}-\gamma I,\\ \frac{dR}{dt}=\gamma I\end{array} \tag{2}\]  
where \(S\), \(I\), and \(R\) are the number of susceptible, infected, and recovered cases, respectively. The parameters \(\beta\) and \(\gamma\) represent the contact rate of the disease and the average recovery rate, respectively. From Equation (2), the total size of the population (\(N\)) can be obtained as the sum of the susceptible, infected, and recovered cases (\(N=S+I+R\)).
# Student Behavior Recognition in Classroom Based on Deep Learning
## 3 Framework of Student Behavior Detection
### Problem Scenario  
In a teaching environment, accurately detecting and analyzing student behavior in the classroom is crucial for enhancing real-time responsiveness and optimizing the teaching process. As illustrated in Figure 1, the video stream recorded by classroom cameras provides a high-fidelity data input for the detection model. This video stream is essential for enabling the model to accurately identify student behavior characteristics and rapidly detect and recognize target behaviors. Specifically, to enhance the predictive accuracy of the model, we focus on the following two detection challenges in practical applications:  
1. [label=()]
2. The detection of multiple student behaviors. In real classroom settings, cameras are typically positioned at the front or on the ceiling to obtain a comprehensive view of student activities and interactions. This configuration results in smaller target sizes for students within the images, necessitating the detection of various behaviors at lower resolutions, such as paying attention, daydreaming, raising hands, reading, and resting. This scenario presents a typical challenge of small-object, multi-class behavior detection.
3. Long-distance human posture recognition. In the teaching environment, students' posture and behavior serve as important dynamic parameters, characterized by high autonomy and variability. Variations in camera angles and distances often hinder the complete capture of students' full-body posture characteristics. Traditional recognition algorithms, which rely on key points of the human skeleton, struggle to accurately reflect the actual physical space dynamics [20].  
In summary, when target pixels are small and feature information is sparse, the convolutional computations of current deep learning algorithms are often applied to areas where target features are not prominent. This can result in significant computational  
Figure 1: Detection model diagram.  
resource waste and reduced detection efficiency. Small targets are particularly susceptible to losing key information during multi-layer convolution processing, making it challenging to achieve accurate detection and regression analysis, especially in complex classroom environments with varying lighting conditions and student occlusion. Therefore, enhancing the robustness of detection algorithms is essential to improve model accuracy in processing student behavior.
# Cybersecurity and Medical Imaging: A Simulation-Based Approach to DICOM Communication
## 2 Background
### Encoding and Compression Techniques in DICOM  
The Transfer Syntax [42] in DICOM refers to the encoding and compression techniques used to represent and transmit DICOM data. It determines how DICOM objects, including images, are serialized into a byte stream that can be transmitted or stored. DICOM supports multiple Transfer Syntaxes to provide flexibility, interoperability, and efficient handling of various data formats. Commonly used Transfer Syntaxes in DICOM include the following [42]:  
* **Implicit VR Little Endian:** a Transfer Syntax that uses a combination of implicit value representation encoding and little-endian byte ordering. It is widely used for uncompressed DICOM images, and supports a variety of data types. This is the default Transfer Syntax for most DICOM files.
* **Explicit VR Little Endian:** a Transfer Syntax that uses explicit VR encoding and little-endian byte ordering. Commonly used for uncompressed DICOM images, it provides explicit VR tags, which make it easier to parse and understand the data.
* **JPEG Lossless:** a Transfer Syntax that employs the Joint Photographic Experts Group (JPEG) Baseline algorithm for lossless compression of DICOM images, JPEG Lossless [12,42] is utilized when a balance between compression and preservation of image quality is required. It achieves significant compression ratios while maintaining the integrity of the image data. JPEG Lossless is particularly useful for storing or transmitting DICOM images where lossy compression is not acceptable.  
* **JPEG Baseline:** a specific Transfer Syntax that utilizes the JPEG Baseline algorithm for compressing DICOM images, JPEG Baseline [42; 43] is a lossy compression technique that achieves significant compression ratios while maintaining acceptable image quality for many medical imaging applications. JPEG Baseline is often used for transmitting DICOM images over networks with limited bandwidth or storage capacity, as it helps reduce the size of image data.  
By supporting various Transfer Syntaxes, DICOM enables interoperability between different systems, ensuring that medical imaging data can be exchanged and interpreted correctly across diverse platforms and applications.
# On the Impact of Wavelength Dependency on Supercontinuum Generation in Photonic Crystal Fibers
## 2 Materials and Methods
### The PCF Model  
The PCFs are characterized by a variety of complex designs built upon a mix of materials that, working as a whole, try to induce interesting behaviors over the light along its propagation, so that, when talking about the PCF design, it would not be misguiding to speak about a tailoring process.  
In this paper, a very specific design has been chosen to study the supercontinuum phenomena [16]. The next figure shows the transversal section of this fiber. In our case, the core is made of Si and is surrounded by four concentric rings of air holes. The main body of the PCF is made of MgF\({}_{2}\). Concrete details appear in the comments for Figure 1.  
As can be seen, a radial pattern of concentric rings surrounds a central core of silicon. Each of the rings contains \(6n\) air holes, where \(n\) is the order of the ring starting from the inside to the outside. The cladding and core materials are MgF\({}_{2}\) and Si, respectively. Using the Sellmeier equation, with the coefficients presented in Table 1, the dependency of the materials' refractive index can be expressed as a function of wavelength (in \(\upmu\)m) as [3]:  
\[n(\lambda)=\sqrt{1+A_{1}\lambda^{2}(\lambda^{2}-B_{1})^{-1}+A_{2}\lambda^{2}( \lambda^{2}-B_{2})^{-1}+A_{3}\lambda^{2}(\lambda^{2}-B_{3})^{-1}} \tag{1}\]The values used for the different coefficients in Equation (1) that appear in Table 1 coming from [18; 19] are given below.