esa-sceva/satcom-chunk-collection huggingface dataset:
# Technology Identification, Evaluation, Selection, and Optimization of a HALE Solar Aircraft
## 4 General Methodology
### Performance Evaluation
#### 4.6.2 Performance of the Optimized Aircraft  
The photovoltaic power profile for 24 h for the optimized aircraft is also shown in Figure 23. Same as the reference aircraft, the solar power that can be obtained is the largest in the summer season (6/21), and the smallest in the winter solstice (12/22). The maximum solar power of the optimized aircraft is about 700 W higher than that of the reference aircraft on the summer solstice (6/21).  
The total solar power that can be obtained during each season is shown in Figure 24a. The total solar power was also the largest in the summer and the smallest in the winter solstice. As shown in Figure 24b, the energy margin in the summer solstice was 50.73%, showing that it is the only flight possible season.  
The duration of long endurance was estimated during the summer and is shown in Figure 25. The energy margin over 10% for safety was assumed and on 4/18 and 8/24, the energy margin was 9.1%. Therefore, it was shown that the optimized aircraft could fly for a total of 126 days, from 4/19 to 8/23.  
Figure 23: Solar power profile for the optimized aircraft.  
Figure 24: (a) Total solar power for the optimized aircraft; (b) energy margin for the optimized aircraft.
# An Efficient SC-FDM Modulation Technique for a UAV Communication Link
## 4 The Proposed SC-FDM and OFDM
### Ofdm  
In Figure 5, shown above, the data can be transferred by parallel subcarriers of 15 kHz in OFDM. On the time axis, the further divided subcarriers represent blocks of one symbol duration. This basic unit is known as a resource element, and one symbol is carried by one resource element. In addition, a number of resource elements are used to make a resource block that is the basic unit of scheduling. At the beginning of the modulation process, the data is modulated by a particular modulation scheme in order to transfer the data over these resource elements. This modulation scheme depends on the physical channels mapped on the resource grid. Then, M-point inverse discrete Fourier transform (IDFT) transforms the signals of the parallel frequency domain into samples of a composite time domain signal, which are much easier to generate at the transmitter side. All we need to do is to send these time domain samples at radio frequencies. In wireless channels, due to multipath propagation, there can be delay spread and intersymbol interference (ISI) [32]. This interference may cause a given transmitted symbol to be distorted by other transmitted symbols. Since OFDM uses composite IDFT samples, a cyclic prefix is added by taking some samples from the end of a symbol period and placing them at the beginning. It provides orthogonality between the subcarriers by keeping the OFDM symbol periodic in the duration of the extended symbol and for that reason, avoiding intercarrier and interchannel interference simultaneously. In the next step, a sampled signal is converted into an analog wave by a digital to analog converter (DAC). A further composite waveform is modulated at the desired RF for transmission. The noticeable advantage of OFDM over SC-FDM is that the frequency domain representation of signals simplifies the signal error correction at the receiver [33].
# Improving Real-Time Position Estimation Using Correlated Noise Models +
## 3 Position and Uncertainty Estimation Methods  
Before introducing Bayesian inference methods, we create a basic model that uses the raw position as the centre of a prediction interval, while the OU process determines the interval width. Specifically, our prediction for latitude, longitude and altitude are Gaussian distributions with variances \(\sigma^{2}/\theta\), where \(\sigma\) and \(\theta^{2}\) are the means of the fitted OU parameters for all three coordinates observed in Dunedin (see Section 2.2). We consider this model basic, because previous data do not inform later position estimates, hence cannot take into account the noise correlations. By using the parameters derived from the Dunedin time-series for the uncertainty quantification on the Australian time-series, we tested that the method works for datasets independent of those on which it was developed.  
Receivers also often report DOP figures, horizontal and vertical, for the latitude, longitude and altitude coordinates. These numbers, being related to the diagonal elements of the covariance matrix, carry some limited information about time correlation. We incorporated this complementary data-stream into the statistical modelling. In Section 5, we present the effect of the HDOPand VDOPnumbers of the parameter estimates. If these estimates improve, DOP parameters are worthwhile to include in the statistical modelling of the noise.
## 3 Background
### General CubeSat Specifications
#### 3.1.3 Thermal/Material Selection Specifications  
No direct consideration is paid directly to the design of the thermal system, as this will differ depending on each individual CubeSat's requirement i.e., orbit type, altitude, electrical system, and material selection. Restrictions are however placed upon material selection. CubeSat material outgassing should conform to NASA outgassing standards [24]. CubeSat materials will have a total mass loss (TML) less than or equal to 1% and CubeSat materials will have a collected volatile condensable material (CVCM) equal to or less than 0.1% [1]. All parts will remain attached to the CubeSats during launch, ejection, and operation. No space debris will be created [1]. Typically, Insulation (MLI) and surface coatings to reflect/absorb incoming solar radiation are used as simplistic thermal management systems for craft. However, systems such as passive louvers, non-metallic thermal straps, cryo-coolers, and sunshades are under development for CubeSat platforms [23].
# Unsupervised Anomaly Detection and Segmentation on Dirty Datasets
## 3 Proposed Method
### Iterative Filtering
#### 3.3.2 Different Strategy for Sliding Window  
In the first and other iterations, we used slightly different strategies to determine the slope threshold of the target window.  
As mentioned before, we define the window with a slope greater than a multiplier of the historical mean of the slope as the target window. The value of this multiplier directly affects the determination of the target window. In order to determine this multiplier reasonably, we need to know the trend of the slope in the scatter plot. As shown in Figure 6, the scatter plot at the first iteration is not the same as the scatter plot at a certain iteration. For the first iteration, the starting point is the point with the index at \(N\times 1/3\). For a certain iteration, the starting point is the point with a small offset from the jump point. The points between the start point and the endpoint are divided into three segments. We define Equation (14) to measure the trend in the slope of the scatter plot. In Section 3.2, the parameter \(v\) concerning the slope threshold \(v\times Slope_{mean}\) is defined as Equation (15).  
\[R=\frac{Slope_{lim3}}{(Slope_{line1}+Slope_{line2})\times\frac{1}{2}} \tag{14}\]  
\[v=\frac{R-\tau}{\eta}+\tau \tag{15}\]  
In Equation (14), we use the ratio of the slope of line3 to the mean of the slopes of line1 and line2 to judge the slope trend. If the slope ratio \(R\) is close to 1, the line between the starting point and endpoint can be considered a straight line, and we can directly choose the endpoint as the Mahalanobis distance threshold. If the slope ratio \(R\) is much greater than 1, it means that the slope of the line between the starting point and the endpoint grows large. It indicates the existence of some patches far away from the Gaussian distribution. In Equation (15), \(\tau\) is the hyperparameter. If the slope ratio \(R\) is less than \(\tau\), the slope ratio \(R\) can be considered close to 1. Otherwise, a value between \(\tau\) and \(R\) is taken as the slope threshold \(v\) to search for the target window. The hyperparameter \(\eta\) is greater than 1, ensuring that \(v\) is between \(\tau\) and \(R\). We tested the effect of different values of \(\eta\) on the performance in Section 5.2.  
Figure 5: After estimating Gaussian distribution for each position on the dirty dataset, the iterative process is started.
# Provably Secure ECC-Based Anonymous Authentication and Key Agreement for IoT
## 1 Introduction  
Thanks to advances in chipset production and embedding technologies, sensors and actuators (referred to as end devices) are pervasive in the Internet of Things (IoT), being integrated into intelligent agriculture, smart grid (SG), telemedicine, smart home, intelligent manufacturing, and many other fields to collect and disseminate the data [1]. According to the latest estimates, there will be 83 billion IoT connections by 2024 [2]. In IoT applications, the collected and transmitted data are sensitive. Privacy is another crucial issue, especially regarding user data such as consumption habits, location, and communication activities [3; 4]. To ensure security, authentication key agreement (AKA) schemes for IoT applications have been widely used, which offer mutual authentication and privacy protection and ensure confidentiality, integrity, and non-repudiation of data transmissions based on the negotiated session keys [5]. End devices are often linked to open networks and deployed in unattended environments with limited computation, communication, and storage capabilities. As a result, mutual authentication and key agreement between end devices and servers to sustain efficiency is a critical challenge.
# An Improved MbICP Algorithm for Mobile Robot Pose Estimation
## 3 Results  
The experimental results are outlined in this section. We tested the improved MbICP algorithm with data from a mobile wheelchair platform that was equipped with a Hokuyo UTM-30 LX laser range finder (Hokuyo Co. Ltd., Osaka, Japan). The Hokuyo UTM-30 LX is a 2-D laser range finder with a 30 m effective measurement range, 270\({}^{\circ}\) field of view (angular resolution of 0.25\({}^{\circ}\)), and 40 Hz scanning rate. We designed two types of experiments to compare our algorithm with the original algorithm. The first experiment evaluated the properties of the algorithms by matching a pair of scans that partially overlapped. The second experiment evaluated the global algorithm performance with a run within our academic building.
## 4 Proposed Suppression System Model  
This section introduces the structures of the proposed adaptation algorithm for the suppression of CWI and MCWI. The proposed algorithm employs a low-complexity technique for detecting and mitigating CW/MCW interference in the TD. TD is used in order to avoid the drawbacks of the FD approach (i.e., complexity, costs). As previously stated, a deep notch is required for perfect jamming removal in the case of high-power jammers. However, ANFs introduce self-noise, significantly reducing the SNR. Thus, in order to reduce the self-noise, the depth of the notch should be controlled according to the power of the interference. In other words, the depth of the notch should be adjusted with respect to the power of the interference for the trade-off between the information signal distortion and interference reduction. In this paper, a suppression system model is proposed to adjust the depth of the NF based on estimated JSR. The suppression proposed consists of two ANFs based on a second IIR NF direct-form structure--ANF \(H_{d1}(z)\) and ANF \(H_{d2}(z)\)--as shown in Figure 4. The ANF \(H_{d1}(z)\) is used in this model to detect and estimate the frequency of the interference (\(\omega_{o}\)) and \(JSR\), respectively, through a novel adaptive method as shown in Equation (11) and Equation (13), where the notch's depth is then adjusted based on the JSR estimation. The ANF \(H_{d2}(z)\) is used to mitigate the CW interference.  
To mitigate MCWI, Figure 5 describes the Multiple Adaptive Notch Filter (MANF) model based on the direct-form structure. The MANF model is multistage, with two ANFs in each stage. The structure of each stage can be implemented using the same structure described in Section 4.2.1, modifying the transfer functions \(H_{d1}(z)\) by indicating the adaptive coefficient of the \(i\)th ANF and \(H_{d2}(z)\). The proposed objective is to remove/reduce and maximize the output SNR of the IIR NF, thus improving the system performance.
## 3 Preliminaries  
scLTL is one fragment of LTL. An scLTL formula commonly consists of a set of atomic propositions, Boolean operators, and temporal operators. Let \(PrP\) be the set of atomic propositions. An scLTL formula over the atomic propositions \(PrP\) is recursively defined as  
\[\varphi::\top\mid prp\mid-prp\mid\varphi_{1}\land\varphi_{2}\mid\varphi_{1}\lor \varphi_{2}\mid\mathbf{F}\varphi\mid\mathbf{X}\varphi\mid\varphi_{1}\mathbf{U} \varphi_{2}. \tag{1}\]  
Note that:  
* \(\bot=\
eg\top\) (False) is an scLTL formula,
* \(\mathbf{F}\varphi=\top\mathbf{U}\varphi\) is an scLTL formula, and
* given an scLTL formula \(\varphi\), it is written in a positive normal form where \(\
eg\) is only written in front of an atomic proposition \(prp\). In addition, \(\
eg\) (negation), \(\land\) (conjunction), and \(\lor\) (disconjuction) are Boolean operators, while \(\mathbf{X}\) (next), \(\mathbf{F}\) (in the future), and \(\mathbf{U}\) (until) are temporal operators. By combining the Boolean operators and temporal operators, complex and rich mission specifications can be described. An scLTL formula is interpreted by an infinite word \(w\in(2^{PrP})^{\omega}\), where \(w=w[0]w[1]w[2]\ldots\). Let \(w(i)=w[i]w[i+1]\ldots\); then, the semantics formula \(\varphi\) over word \(w\) is defined recursively in Equation (2) as follows: \[w(i) \models\top\] \[w(i) \models c\Leftrightarrow c\in w\] \[w(i) \models\varphi_{1}\land\varphi_{2}\Leftrightarrow w(i)\models \varphi_{1}\wedge w(i)\models\varphi_{2}\] (2) \[w(i) \models\varphi_{1}\lor\varphi_{2}\Leftrightarrow w(i)\models \varphi_{1}\lor w(i)\models\varphi_{2}\] \[w(i) \models\mathbf{X}\varphi\Leftrightarrow w(i+1)\models\varphi\] \[w(i) \models\varphi_{1}\mathbf{U}\varphi_{2}\Leftrightarrow\exists k\geq i,\forall j(i\leq j<k),w(k)\models\varphi_{2}\land w(j)\models\varphi_{1}.\]  
For any scLTL formula \(\varphi\), there exists an FSA \(\mathcal{A}_{\varphi}=<Q,PrP,\delta,q_{0},F>\) that accepts all infinite words satisfying the formula \(\varphi\), where \(Q\) is the finite set of states, \(PrP\) is the set of atomic propositions, \(\delta\): \(Q\times 2^{PrP}\to Q\) is the state transition function, \(q_{0}\in Q\) is the initial state, and \(F\subseteq Q\) is the set of the accepting states. Several off-the-shelf tools for the computation of \(\mathcal{A}_{\varphi}\) have been developed [50; 51]. (The tool used in this paper, please see [[https://spot.lre.epita.fr/app/](https://spot.lre.epita.fr/app/)]([https://spot.lre.epita.fr/app/](https://spot.lre.epita.fr/app/)); accessed on 7 July 2025)
# Random Forest-Based Prediction of the Optimal Solid Ink Density in Offset Lithography
## 3 Materials and Methods  
### Experimental Equipment and Materials  
The experimental data used in this model were printed by Heidelberg offset press for sample printing, X-Rite 528 spectrophotometer (Granville, MI, USA) for colorimetric and density measurements, and Hanghua sheet-fed offset ink for ink; the ink viscosity is 19 Pa s, bearing coated paper is of 128 g/m\({}^{2}\), and the paper thickness is 0.096 mm.  
The chromaticity of the paper surface reflects the condition of the paper, and paper that meets the standard is better able to achieve accurate color reproduction. In this experiment, 128 g/m\({}^{2}\) coated paper was used, with the same type of paper serving as the white backing. The positions of ten points are evenly determined on the paper, and the colorimetric values at each point are measured using a spectrophotometer to obtain the average value. The obtained data were compared with the colorimetric values specified in the ISO 12647-2:2013 standard. Table 1 presents the calculated colorimetric values of the paper. The results indicate that the surface colorimetric values of the paper selected for this experiment fall within the standard tolerance range specified by ISO 12647-2:2013 [2].  
The choice of ink directly affects the tonal gradation of color reproduction. In this study, Hanghua ink was selected for solid color printing on 128 g/m\({}^{2}\) coated paper. Multiple measurements were taken, and the average colorimetric values were calculated. These values were then compared against the reference standard chromaticity of oil-based ink in ISO 12647-2:2013. To achieve more accurate comparison results, we calculated the color difference between the measured results and the standard values. The color difference (\(\Delta\)E) was calculated using the CIE 1976 (L*, a*, b*) color space formula. The formula calculates the Euclidean distance between two colors in the CIELAB color space, defined as follows:  
\[\Delta E=\sqrt{\left(L_{1}-L_{2}\right)^{2}+\left(a_{1}-a_{2}\right)^{2}+\left( b_{1}-b_{2}\right)^{2}} \tag{10}\]  
where L, a, and b represent the lightness and chromatic coordinates of the two samples, respectively. In this study, these values were obtained from the ink chromaticity measurements using a spectrophotometer and the reference colorimetric values specified by the ISO 12647-2:2013 standard. The specific measurements and calculation results are presented in Table 2. The results indicate that the color difference between the ink colorimetric values and the ISO standard colorimetric values falls within the acceptable tolerance range.  
\begin{table}
\begin{tabular}{l l l l l l l l l l l l l l} \hline \hline  & \multicolumn{6}{c}{**128 g/m\({}^{2}\) Coated Paper**} & \multicolumn{6}{c}{**ISO 12647-2:2013**} \\ \cline{2-13}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **Average** & **Standard** & **Allowance** \\ \hline L & 93.27 & 92.28 & 93.18 & 93.24 & 93.
# A Reconfigurable Framework for Hybrid Quantum-Classical Computing
## 2 Background and Related Work  
### Quantum Computing Fundamentals  
Qubits, or quantum bits, are the basic units of quantum information [18]. Unlike classical bits, which can exist in one of two states (0 or 1), qubits can exist simultaneously in multiple states due to their quantum nature. Superposition [18] refers to the ability of a quantum system, like a qubit, to exist in multiple states at the same time. In the case of a single qubit, this means it can be in a state \(|0\rangle\) and \(|1\rangle\) or any linear combination of the two, represented as \(\alpha|0\rangle+\beta|1\rangle\), where \(\alpha\) and \(\beta\) are complex probability amplitudes. The probabilities of measuring the qubit in the \(|0\rangle\) or \(|1\rangle\) state are determined by the square magnitudes of the probability amplitudes: \(|\alpha|^{2}\) and \(|\beta|^{2}\). These probabilities add up to 1, which means that the qubit will collapse into one of the two states upon measurement. Entanglement [18] is a unique quantum phenomenon where two or more qubits become interconnected in such a way that the state of one qubit cannot be described independently of the state of the other, even when separated by large distances. This property is key for various applications in quantum communication, cryptography, and quantum computing, allowing for parallel processing and enhanced security protocols [19].
# Quantum Dilation and Erosion
## 6 Conclusions  
In this work, a quantum algorithm is designed to accomplish the functions of image dilation and erosion. Compared to classical image processing algorithms, the quantum algorithm has several differences. First, rather than encoding the image by bits as in a classical computer, we use entangled qubits to realize this coding process. Second, the operations to the classical bits are straightforward. Because every single classical bit has only two different states. The coefficient of two quantum states can take continuous values rather than only two discrete values. Beside, in a quantum circuit, the qubits are significantly entangled. The change of any qubit will largely influence the states of the other qubits. Both of the features of qubits tremendously contribute to the complexity of quantum algorithms, at the same time, also largely expand the possibilities of the quantum algorithms.  
The quantum algorithm in this paper realizes all of the functions of the image dilation and erosion processes via an improved method. It illustrates an efficient way to transplant classical algorithms into the quantum pattern. In this way, we can make full use of the quantum computer and promote understanding of this new field, which holds great potential.  
Figure 11: The target image and its dilation and erosion according to the structure element in Figure 7. (**a**) A simple example with \(8\times 8=64\) pixels and the central \(4\times 4=16\) pixels being white and the rest black. (**b**) The dilated image after applying the dilation operator to the original image. It’s actually the union set of the original image and two images after applying position shifting operator at opposite directions. (**c**) The eroded image after applying the dilation operator to the original image. It’s the intersection of the original image and two position shifted ones.  
**Author Contributions:** Conceptualization, H.E., H.H., S.-Y.M. and A.K.; methodology, S.-Y.M., H.E., H.H. and A.K.; software, S.-Y.M.; validation, H.E., H.H. and A.K.; formal analysis, S.-Y.M.; Writing--Original draft preparation, S.-Y.M., A.K., and H.E.; Writing--Review and editing, H.E., H.H. and A.K.; project administration, H.E., H.H. and A.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Communication Framework for Image Transmission through LPWAN Technology
## 3 Proposed Framework
### Stage I: Processing and Classification  
In the transmitter node, image processing was used to classify images and reduce the amount of data in an image using compressive sensing. The image data is transformed using a wavelet representation base [74; 82; 83; 84; 85; 86; 87; 88]. As a result, this transformation unites the samples and reduces the variability of the original data. In this work, wavelet transforms prior to evaluation is used in between Kronecker, Discrete Cosine, and wavelet transforms. Furthermore, this process reduces data dispersion and allows for greater efficiency in the compression process. Similarly, the receiver used the inverse wavelet transformation to recover the original data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Transmission Time**} & \multirow{2}{*}{**Packets**} & \multirow{2}{*}{**Image Compression**} \\  & & **(One Image)** & & & **Method** \\ \hline \multirow{2}{*}{[PERSON], 2016 [73]} & Image compression & \multirow{2}{*}{-} & \multirow{2}{*}{8–10} & \multirow{2}{*}{JPEG} \\  & Discrete Cosine Transform & & & \\ \hline \multirow{2}{*}{[PERSON] et al., 2017 [78]} & Fragmentation data & 6 & 700 & JPEG/JPEG2000 \\ \hline \multirow{2}{*}{[PERSON] et al., 2018 [15]} & Data encrypted hexadecimal & 1.1 & 314 & JPEG \\ \hline \multirow{2}{*}{[PERSON], 2018 [18]} & CSMA (avoid collisions) & - & 5 & JPEG \\ \hline \multirow{2}{*}{[PERSON] and [PERSON], 2018 [16]} & Multiple out single in & - & - & - \\ \hline \multirow{2}{*}{[PERSON] et al., 2019 [79]} & MPLR protocol Tx/Rx & 0.3 & - & - \\ \hline \multirow{2}{*}{[PERSON] et al., 2019 [76]} & Image processing & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} \\  & (Only transmits data with change) & & & \\ \hline \multirow{2}{*}{[PERSON] et al., 2020 [80]} & 1 & 85 & JPEG \\ \hline \multirow{2}{*}{[PERSON] et al., 2021 [81]} & - & 858 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Image transmission through LoRaWAN networks.  
Figure 1: Framework to transmit and receive LoRa symbols.