esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data Analysis and Problem Statement  
### Li-Ion Battery Data Analysis  
In this paper, the NASA dataset [25] is exploited. It contains four different Li-ion batteries (#5, #6, #7, and #18), and each Li-ion battery repeats three operations (charge, discharge, and impedance measurements) at room temperature (24 \({}^{\circ}\)C). The test conditions of the NASA battery are listed in Table 1.  
Figure 1 shows the #5 Li-ion charge and discharge process in one cycle. Obviously, the charge process consists of constant current (CC) mode and constant voltage (CV) mode. In the charge in CC mode, the current is kept at 1.5 A until the Li-ion battery voltage is increased to 4.2 V. In the charge CV mode, the voltage holds 4.2 V until the Li-ion battery current drops to 20 mA from 1.5 A. In the whole charge process, the battery terminal voltage, battery output current, battery temperature, measured current, and measured voltage are recorded. The discharge process belongs to the CC mode, and the current is 2 A until the Li-ion battery voltage drops to 2.7 V from 4.2 V. In the discharge process, the recorded variables (except battery capacity) are the same as those of the charge process. Meanwhile, NASA Ames utilizes the electrochemical impedance spectroscopy (EIS) method (frequency sweep from 0.1 Hz to 5 kHz) to measure impedance. In the impedance process, the sensor current, battery current,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Battery No.** & \begin{tabular}{c} **Constant Charge** \\ **Current/A** \\ \end{tabular} & \begin{tabular}{c} **Charge Cut-off** \\ **Voltage/V** \\ \end{tabular} & \begin{tabular}{c} **Discharge** \\ **Current/A** \\ \end{tabular} & \
\begin{tabular}{c} **Discharge Cut-off** \\ **Voltage/V** \\ \end{tabular} &
\begin{tabular}{c} **Nominal** \\ **Capacity/Ah** \\ \end{tabular} \\ \hline
**5** & **1.5** & **4.2** & **2.0** & **2.7** & **2.0** \\
6 & 1.5 & 4.2 & 2.0 & 2.5 & 2.0 \\
7 & 1.5 & 4.2 & 2.0 & 2.2 & 2.0 \\
18 & 1.5 & 4.2 & 2.0 & 2.5 & 2.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test condition of NASA battery data set.  
and ratio of the above currents are recorded to calculate battery impedance, electrolyte resistance (Re), and charge transfer resistance (Rct). As time goes on, the repeated charge and discharge process results in accelerated degradation, and eventually, the end of the battery's service life (EOL).
# Transmitters and Receivers for High Capacity Indoor Optical Wireless Communication
## 2 Beam-Steering for Optical Wireless Communications Transmitter
### Experimental Results
Even at the critical 50\({}^{\circ}\) steering angle, while exhibiting reduced amplitude compared to normal incidence, the eye diagram maintained a sufficient opening for reliable communication. Figure 6: Power and SNR performance versus beam angles. Figure 7: Eye diagram of 10 Gbps NRZ OOK signals at different angles (**a**) 0\({}^{\circ}\) and (**b**) 50\({}^{\circ}\). In this work, a direct modulated laser source was used but the method can be used for external modulation, especially when ultra-high throughput is aimed.
## 2 Review of Cryogenic-Cooling Benefits
### Spectroscopic, Lasing, Linear and Nonlinear Optical Parameters
#### 2.7.1 Spectroscopic and Lasing Parameters
For CW and pulsed operation, the saturation fluence then decreases by a factor of 5 as well, enhancing the power or energy extraction efficiencies in damage-limited operation. The saturation intensity, found by dividing Equation (76) by the upper level fluorescence lifetime, decreases by the same factor of 5 as well. Clearly cryogenically cooling Yb based laser materials at temperatures \(<\)150 K leads to laser performance benefits that cannot be obtained for room temperature operation. Operating at 300 K and above, a significant fraction of the pump power is needed to just maintain unity gain, and the smaller stimulated-emission coefficients make damage-limited efficient laser extraction more difficult. CW cryogenic lasers have the potential to provide diffraction-limited output with distortion-free lasing  
Figure 33: Energy level configuration for Yb:Lu\({}_{2}\)O\({}_{3}\). elements and with a high wallplug efficiency (laser output average power divided by electrical input power to the diode arrays.greater than 30% using a typical demonstrated optical-optical efficiency of 55% and a typical 55% efficiency for laser diode sources. As diode laser efficiencies continue to increase with time, perhaps to greater than 80%, and CW optical-optical efficiencies are improved to as high as 85%, Yb laser wallplug efficiencies may eventually exceed 70%. These efficiency estimates do not of course include any ancillary equipment need to operate the laser. There are other benefits that accrue from cryogenic cooling. The absorption cross-section for Yb:YAG at the primary pump wavelength of 941 nm increases by over a factor of two. At 77 K, the doping density can be reduced by about the same factor to achieve the same absorption. Another important feature of the absorption spectrum at 77 K is that the absorption bandwidth around 941 nm narrows but still remains very broad. Similar absorption improvements are obtained in other Yb laser materials as well. This is important because the same diode arrays used to pump at room temperature will work well at 77 K. Lower density, less expensive diode pump sources can also be used at 77 K for some pumping configurations because a large pump power density is not needed to reach transparency. Another benefit is that the use of end-caps to suppress the strain-distortion at the ends of rod amplifiers can be in most cases be eliminated.
# Advanced AI and Machine Learning Techniques for Time Series Analysis and Pattern Recognition
## 3 Applications Across Domains
### Data-Driven Finance  
In finance, ML techniques are used for predicting significant market movements, risk assessment, portfolio optimization, and fraud detection. The Extra Trees Classifier has been particularly effective in forecasting stock price changes by capturing complex feature interactions [4]. Fourier transform-based feature engineering has further improved the accuracy of financial forecasting models by uncovering hidden periodicities in stock market data [23, 29, 30].
## 3 SCH Nonlinear Mathematical Model (NMM)
### Modeling According to First Physical Principles
#### Full Dynamics Model  
After establishing the mathematical models of all components, an 11-state nonlinear SCH simulation model is assembled. The model has seven inputs: four command signals of general helicopter \(\left(\delta_{col},\delta_{lat},\delta_{lon},\delta_{ped}\right)\) and three command signals of compound components \(\left(\delta_{duct},\theta_{a},\theta_{e}\right)\).  
To simulate the helicopter responses under the control signals, the rigid-body motion of equations is written in the Newton-Euler form:  
\[\dot{v}=\frac{1}{m}\widehat{F}-\omega\times v \tag{31}\]  
\[\dot{\omega}=I^{-1}\widehat{M}-I^{-1}(\omega\times I\omega) \tag{32}\]  
\[\dot{\theta}=\Phi(\theta)\omega \tag{33}\]  
The states in this motion of equations are: linear velocity \(\mathrm{v}=\left[\begin{array}{cccc}u&v&w\end{array}\right]^{T}\), angular velocity \(\omega=\left[\begin{array}{cccc}p&q&r\end{array}\right]^{T}\) and Euler angles \(\theta=\left[\begin{array}{cccc}\phi&\theta&\psi\end{array}\right]^{T}\). Consider the flap angles \(a\) and \(b\), the full nonlinear model is 11-state. \(\widehat{F}=\left[X,Y,Z\right]\) and \(\widehat{M}=\left[L,M,N\right]\) are the external force and moment vectors. The moment of inertia is defined by \(I=diag_{3\times 3}(\begin{array}{cccc}I_{xx},&I_{yy},&I_{zz}\end{array})\). \(\Phi\) is the velocity transformation matrix from body coordinate system [23] to ground coordinate system.
###### Abstract  
Voice over IP is quickly becoming the industry standard voice communication service. While using an IP-based method of communication has many advantages, it also comes with a new set of challenges; voice networks are now accessible to a multitude of internet-based attackers from anywhere in the world. One of the most prevalent threats to a VoIP network are Denial-of-Service attacks, which consume network bandwidth to congest or disable the communication service. This paper looks at the current state of research into the mitigation of these attacks against VoIP networks, to see if the mechanisms in place are enough. A new framework is proposed titled the \"Call Me Maybe\" framework, combining elements of latency monitoring with dynamic protocol switching to mitigate DoS attacks against VoIP systems. Research conducted around routing VoIP over TCP rather than UDP is integrated into the proposed design, along with a latency monitoring mechanism to detect when the service is under attack. Data gathered from a Cisco Packet Tracer simulation was used to evaluate the effectiveness of the solution. The gathered results have shown that there is a statistically significant improvement in the response times of voice traffic when using the \"Call Me Maybe\" framework in a network experiencing a DoS attack. The research and findings therefore aim to provide a contribution to the enhancement of the security of VoIP and future IP-based voice communication systems.  
Voice-over-IP (VoIP); Denial-of-Service (DoS); Transmission Control Protocol (TCP)  
This paper proposes a two-stage 'detecting and rerouting' technique for the mitigation of UDP based attacks. The design uses a latency monitoring system to continuously scan VoIP connections within a network for increases in response times, which could indicate the presence of a DoS attack. Upon detection, the VoIP packets are dynamically rerouted through a twin TCP network set up alongside the original UDP network. The TCP network also contains a UDP restricted stateless firewall, which blocks common UDP based DoS attacks such as UDP flooding. By combining these techniques, a method is set out for the reactive mitigation of UDP based DoS attacks. The paper also presents evidence that VoIP over TCP has a place in highly secure networks despite the disadvantages of using TCP for continuous data transfer.  
The latency monitoring system for detecting (D)DoS attacks enables the framework to act as a tool-proof final layer of defense for the network that can be used in conjunction with alternative higher-level techniques. The proposed framework provides as main contributions a scalable, adaptable solution for UDP DoS attacks that incorporates both rules-based and statistical approaches for maximum effectiveness while circumventing the issues commonly associated with these methods.
## 2 Internet of Vehicles and Smart Cities
### Architecture
#### 2.2.4 Seven-Layer Architecture  
The work of [PERSON] et al. [11] refers that the three and four-layered proposed models have weaknesses and do not contemplate important concerns of IoV systems, such as the need for the existence of layers for security and data dissemination/transmission, the communication between the driver and the vehicle (interface), and network congestion.  
The authors then present a complete layered architecture interpretation which divides the IoV into the following seven layers:  
* _Processing layer_ that, as the name suggests, is responsible for big data processing using various cloud computing technologies, which helps develop strategies for business models.
* _The control and management layer_ manages the network service providers in IoV, utilizing policies and functions for that objective.
* _The communication layer_ is responsible for selecting the best network to serve the needs of the user.
* _The data filter and pre-processing layer_ analyze data to avoid network congestion by the transmission of irrelevant information.
* _The data acquisition layer_ collects data from their respective sources (i.e., sensors, infrastructure connection points, other vehicles).
* _The user interface layer_ is responsible for dealing with how the information is passed from the vehicle and sensors to the driver and users inside the car.
* Finally, the _Security layer_, transversal to the six ones noted before, and is one of the major differences between this architecture and the first two presented before, being responsible for all security properties guaranteed, using proposed solutions to mitigate the damage from cyberattacks and malfunctions.
## Result and Discussion
### Particle Size Analysis  
The particle sizes and size distributions of the pure PS and its nanocomposite samples are shown in Figure 8. Table 2 summarizes the average particle size data and PDI values. As shown in Figure 8, for pure PS, a unimodal and relatively broad particle size distribution was observed, and the average particle diameter was approximately 129.5 nm. However, in the case of the PS-OMMT nanocomposite samples, the particle distributions were narrower, and the average particle diameters were in the range of 62.83-64.58 nm. The PDI values of PS, PS-OMMT-1, PS-OMMT-3 and PS-OMMT-5 samples were 0.204, 0.082, 0.079 and 0.102, respectively. The particle sizes of pure PS and the nanocomposite samples were also confirmed by the TEM images, as shown in Figure 4. These results indicated that a certain OMMT load (\(<\)5 wt %) could decrease the particle size diameter and improve the monodispersity of the particles. The OMMT layers might act as nucleating agents to form smaller and more uniform nanoparticles during the polymerization process [45]. However, the minimum average particle size and PDI values were achieved in the samples containing 3 wt % OMMT loading. This was because of the higher degree of exfoliation and better dispersion of the clay layers in PS-OMMT-3 could more effectively impede the movement of the styrene monomers and increase the concentration of SLS to form the finer and more stable emulsion, which lead to reduction in the size of nanocomposite particles. As shown in Figure 8 and Table 2, however, the difference in the particle size of the three clay containing samples is not significant. In other words, the three clay containing samples are equal with respect to particle size, considering the error in the measurement.
## 2 Materials and Methods
### Method
#### 2.2.2 Downscaling Process and Data Preprocessing  
This study adopts a two-step downscaling approach to generate high-resolution SIF data using a downscaling model. First, at a coarse spatial resolution of 0.5\({}^{\circ}\), the GOME-2 SIF data are linked with explanatory variables to establish a predictive model. These explanatory variables include reflectance, photosynthetically active radiation (PAR), and other meteorological and vegetation parameters. The study uses data from the entire year of 2007-2008 as the sample, with two-thirds used as the training set and one-third as the validation set. Second, once the model is trained, higher-resolution (0.05\({}^{\circ}\)) explanatory variables are input to predict and generate SIF data at a 0.05\({}^{\circ}\) resolution.  
During the model construction process, all input explanatory variables are first re-sampled to ensure spatial consistency with the coarse 0.5\({}^{\circ}\) resolution of the GOME-2 SIF. In addition, a quality control procedure is applied to filter out low-quality observations and eliminate cloud contamination. A Savitzky-Golay (SG) filter is used to fill spatial gaps left by cloud removal [19]. The objective of this study is to create a high spatiotemporal resolution SIF dataset with a spatial resolution of 0.05\({}^{\circ}\) and a temporal frequency of 8 days. The daily SIF predictions were aggregated into an 8-day resolution, primarily to generate complete spatial maps by mitigating the impact of clouds on the data and to improve the signal-to-noise ratio. To achieve this, an additional temporal aggregation step is performedto summarize daily data into 8-day averages. The full processing workflow is illustrated in Figure 2.
## 2 Theoretical Model of Loss Calculation and Thermal Analysis
### Advanced Thermal Modeling Methods
For radiation in vacuum environments, the emissivity values were measured using infrared thermography calibration, yielding \(\epsilon\) = 0.85 \(\pm\) 0.05 for anodized aluminum surfaces and \(\epsilon\) = 0.92 \(\pm\) 0.03 for painted steel surfaces. The thermal contact resistances between components were experimentally determined using the steady-state comparative cut-bar method, resulting in values ranging from 2.1 \(\times\) 10\({}^{-5}\) to 8.4 \(\times\) 10\({}^{-5}\) m\({}^{2}\)K/W depending on interface pressure and surface finish. These experimentally validated thermal parameters ensure reliable temperature predictions across the entire operating range, with maximum temperature prediction errors below 5.8 \({}^{\circ}\)C as confirmed through experimental validation.
# Modification of Mineral Content in Bread with the Addition of Buckwheat Husk
## 1 Introduction
Over the years, buckwheat hull has been used in non-food industries as a filling for mattresses and pillows, and there have been attempts to use it as a biofuel [10]. Meanwhile, the husk, due to its health-promoting properties resulting from its high bioactive contents, can be successfully used to enrich various food products [11]. In the available literature, only a few articles on enriching bread with buckwheat husk are available, and they are mainly concerned with husk addition's effect on bread structure [11; 12]. Ground buckwheat hulls, available in food markets, are a dusty product and cannot be consumed on their own as they can cause choking and suffocation; they can only be used as an addition to food. Buckwheat husk's high fiber content, which is well known for its ability to improve digestive health, increase satiety and help with weight management, is one of its most notable qualities. The husk is a rich source of various mineral compounds, and their contents primarily depend on the variety of buckwheat and the method of removing the husk during buckwheat groat production. Moreover, it was indicated that when added to wheat bread products, buckwheat components enhanced the microbiological properties of bread and modified its sensory qualities without affecting customer approval [13]. In previous research, we have shown that the addition of buckwheat husk to bread can improve its sensory properties, and, depending on the size and method of addition, it can increase its phenolic content and antioxidant activity [14; 15]. While existing research has shown the nutritional benefits of buckwheat groats and flour, there is a notable gap regarding the mineral fortification potential of buckwheat hulls in bread formulations. However, it can be hypothesized that the addition of buckwheat husk to bread formulations will lead to a significant increase in the overall mineral content of the bread. This can enhance bread nutritional value and potentially contribute to the meeting of daily mineral requirements through its consumption. The aim of the study was to determine the degree of mineral content modification in bread after adding the functional ingredient of buckwheat husk. The test material consisted of two types of bread, wholemeal and toasted, which were baked in laboratory conditions with various additions of commercially available ground buckwheat husk.
# Moving Healthcare AI Support Systems for Visually Detectable Diseases to Constrained Devices
## 3 Ethical Considerations  
The ethical use of AI assistants in a healthcare setting is discussed in the literature with four main points of focus: collection and storage of sensitive data, impacts of diagnoses on users, medical verification and legal approval of medical devices, and bias and diversity within AI.  
Collecting and storing healthcare data following ethical principles is discussed by [PERSON] et al. [34]. The risk of stigmatization or discrimination against users if their data are wrongfully disclosed makes health data especially sensitive. The work of [PERSON] et al. also raised the point that diagnostic predictions given by chatbots can lack transparency, and this can impact users. This is an example of the \"black box\" problem. This lack of transparency in diagnostic inference could potentially be lessened using explainable AI [35]. Additionally, with the recent rise in chatbots such as ChatGPT, their reliability has been scrutinized. Chatbots frequently produce verifiably false and highly convincing statements [36]. If a user were to undergo an incorrect course of treatment before confirming a predicted diagnosis with a medical professional, the outcome could be fatal. However, the long-term impacts that chatbots could have in healthcare, a vastly complex sociotechnical system, are currently unknown [37].  
As an attempt to regulate and deter healthcare chatbots from producing inaccurate diagnoses, EU and US laws require that software tools used for diagnosis are approved as medical devices before being put on the market [36]. Another important ethical consideration is potential bias and lack of diversity within AI. Determining the source(s) of potential bias internally within a system (training data) and externally (developers of that system) could help enable systems to better reflect those they are designed to benefit [34]. Ensuring that both the developers of a system, and the data it is trained on, are diverse in terms of gender, race, socioeconomic background, and other factors, could positively influence the health of its users [37]. This is especially important as some diseases disproportionally affect one sex or racial group, which could be under-represented or missed in training data. Specifically, VDDs such as skin lesions could look quite different depending on skin colouration.
# Derivation and Verification of Laminated Composite T-Beam Theory
## 2 Derivation of the Formula for Laminated T beam
### 1D Theoretical Analysis of the Relationship between the Displacement and Strain  
As shown in Figure 1, the assumptions made to calculate the displacement of the T-beam are as follows:  
\[U(x,z)\approx u(x)-zu_{1}(x) \tag{1}\]  
\[W(x,z)\approx w(x) \tag{2}\]  
where \(U\) and \(W\) are the displacements of the beam after deformation.  
The plane and shear strains are expressed as:  
\[\varepsilon_{x}=\frac{\partial U}{\partial x}=u^{\prime}(x)-zu_{1}^{\prime}(x) \tag{3}\]  
\[\gamma_{xz}=\frac{\partial U}{\partial z}+\frac{\partial W}{\partial x}=-u_{1}( x)+w^{\prime}(x) \tag{4}\]  
According to the Kirchhoff-Love hypothesis, the T-section perpendicular to the central axis of the beam remains perpendicular to the central axis after bending. Therefore, the shear strain \(\gamma_{xz}\) is considered negligible in Equation (4). Hence,  
\[\gamma_{xz}=-u_{1}(x)+w^{\prime}(x)=0 \tag{5}\]  
\[\text{then }u_{1}(x)=w^{\prime}(x) \tag{6}\]  
The strain is derived from Equations (3) and (6), as shown in Equation (7).  
\[\varepsilon x=u^{\prime}(x)-zw^{\prime\prime}(x) \tag{7}\]