esa-sceva/satcom-chunk-collection huggingface dataset:
# Object-Based Time-Constrained Dynamic Time Warping Classification of Crops Using Sentinel-2
## 4 Discussion
### The Importance of Time Constraints in DTW for Crop Mapping  
In both test areas, the best classification accuracies were obtained with maximum time delays of 30 and 45 days in aligning the temporal sequences using DTW. These delays proved to be adequate, not being too rigid and not allowing too much freedom in computing the DTW matrix, thus avoiding inconsistent matching of crops. Moreover, achieving best results using time-constraints of 30 and 45 days showed that within the two test areas the temporal variations of cropping cycles are around this timeframe. Longer time-constraints (e.g., 100 days) were found best by [PERSON] et al. [18] when analyzing multi-year time series of MODIS Enhanced Vegetation Index (EVI). Using time delays in DTW outperformed both classifications using Euclidean distance and DTW without time constraint. The time constraints in a DTW classification should be knowledge-based and set in accordance with the temporal sampling of the images from SITS (see also Figure 7) [11]. We achieved better results when using a time constraint and this is in line with the results obtained by [PERSON] et al. [11] and [PERSON] et al. [18], who applied DTW at the pixel-level for classifying FORMOSAT-2 SITS and MODIS SITS, respectively.
# A Crypto-Steganography Approach for Hiding Ransomware within HEVC Streams in Android IoT Devices
## 5 Conclusions
Moreover, different multimedia files (e.g., audio and image) may be utilized as cover media. Furthermore, we intend to develop deep learning algorithms to check if they could detect the concealed ransomware samples inside the video streams. Additionally, more steganalysis techniques can be examined to investigate the robustness performance of the proposed crypto-steganography approach. These steganalysis techniques assume that the original video used to hide the ransomware is known to the detectors. Additionally, the detector needs to break the key/algorithm used to encrypt the ransomware. Then the ordinary scanning approaches used by different antivirus systems can be applied. Conceptualization, I.A. and W.E.-S.; methodology, I.A. and W.E.-S.; software, W.E.-S. and A.A.; validation, I.A., A.A. and W.E.-S.; formal analysis, I.A. and W.E.-S.; investigation, I.A., W.E.-S. and A.A.; resources, I.A., W.E.-S. and A.A.; data curation, I.A., W.E.-S. and A.A.; writing--original draft preparation, W.E.-S. and A.A.; writing--I.A. ; visualization, W.E.-S., I.A. and A.A.; supervision, I.A. and W.E.-S.; project administration, I.A. and W.E.-S.; funding acquisition, I.A. All authors have read and agreed to the published version of the manuscript. This work was supported by Prince Sultan University, Saudi Arabia, [Grant number: SEED-CCIS-2021-84]. Not applicable. Not new data were created or analyzed in this study. Data sharing is not applicable to this article. **Acknowledgments:** The authors would like to acknowledge the support of Prince Sultan University for paying the Article Processing Charges (APC) of this publication. Moreover, this research was done during the author [PERSON]'s sabbatical year 2021/2022 from the University of Jordan, Amman-Jordan. **Conflicts of Interest:** The authors declare no conflict of interest.
# Single-Carrier Rotation-Interleaved Space-Time Code for Frequency-Selective Fading Channels +
## 3 Rotation-Interleaved Multi-Stream Space-Time Code
### Symbol-Wise Deterministic Phase Rotation
(b) QPSK/QAM codes  
The development above for BPSK codes can be readily extended to QPSK/QAM codes by considering the complex algebraic space \(\mathbb{Q}(i)\) as opposed to \(\mathbb{Q}\). The following proposition is the main result concerning the QPSK/QAM codes, which is similar to that of the BPSK codes:  
**Proposition 3**. : _Assume that the phase-rotation angles \(\theta_{m},m=1,\cdots,M\) are chosen to be \(\theta_{m}=\theta\cdot(m-1)/M\). If \(\{1,e^{i\theta},e^{i2\theta},\cdots,e^{i(M-1)M_{T}\theta}\}\) are algebraically independent over \(\mathbb{Q}(i)\), then the time-interleaved multi-stream space-time code achieves the maximum diversity gain order (\(M_{T}M_{R}L\)) provided that \(M\geq L\) and the constituent QPSK/QAM space-time code used in the construction achieves the maximum diversity gain order in the corresponding flat fading MIMO (\(M_{T}M_{R}\)) channel. In order to achieve maximum diversity, there are two ways to choose \(\theta\):_  
1. \(\theta\) _is chosen such that_ \(\cos(\theta)\) _is an algebraic number with degree of_ \(2\cdot(M-1)M_{T}+1\) _or greater._
2. \(\theta\
eq 0\) _is an algebraic number (i.e.,_ \(e^{i\theta}\) _is transcendental)._  
Proof of Proposition 3.: The first part of the proof is identical to that of Proposition 2 except that the code symbols and the coefficients are now complex rational numbers. Hence, only the difference is highlighted here. The determinant of \(\mathbf{B}\) can be shown as  
\[\det(\mathbf{B})=c_{0}+c_{1}e^{i\theta}+c_{2}e^{j2\theta}+\cdots c_{(M-1)M_{T} \theta}e^{j(M-1)M_{T}\theta} \tag{17}\]  
where \(c_{n}\in\mathbb{Q}(i)\). Using the same arguments as in Proposition 2, one of the coefficients in (17) must be non-zero, and therefore, if \(\{1,e^{i\theta},e^{i2\theta},\cdots,e^{i(M-1)M_{T}\theta}\}\) are algebraically independent over \(\mathbb{Q}(i)\), the matrix \(\mathbf{B}\) is full-rank. For the first construction method of \(\theta\), the argument from [28] can be used to show that in order for the \(\{1,e^{i\theta},e^{j2\theta},\cdots,e^{i(M-1)M_{T}\theta}\}\) to be independent over \(\mathbb{Q}(i)\), the real part of the complex exponential, \(\cos(\theta)\), must have a degree greater than twice the maximum degree \((M-1)M_{T}\) in (17). For the second construction method, the Link proposition guarantees that using a transcendental number is valid.
# Real-time Conflict Resolution Algorithm for Multi-UAV Based on Model Predict Control
## 4 Conflict Resolution Algorithm Based on DNMPC  
Model predictive control is a model-based finite time domain open-loop optimal algorithm. The current optimal control is executed in the next sampling period, and the process is repeated. In this study, a distributed nonlinear MPC algorithm (DNMPC) is introduced to coordinate the conflicting UAVs' trajectories.
## 5 Conclusions  
A histogram-based IFP approach is presented and validated using three use cases with data collected in an operational power transmission system. Actionable information--generated using sixty-minute interval residual histograms--predicts a CVT failure event one hour before triggering the DFR. Additionally, the RMS histogram--calculated via the absolute difference in the RMS variance between transmission lines--predicts arcing seven hours before the arcing is sufficient to trigger the DFR. Lastly, the frequency histogram permits the detection of improper DFR sampling. One of the benefits of generating the cyclic, residual, frequency, and RMS histograms is that they allow for the creation of long-term power grid behavior libraries. This is because the histograms, generated every sixty minutes over twenty-four hours, require only 0.42% (a.k.a., a 99.58% reduction) of the memory needed to store the same period's DFR-recorded OSG files. These histogram libraries could be used to develop more analytical methods to predict more event types with a sufficient lead time to enable preventative or corrective actions that ultimately improve power grid operations. By making the software available via GitHub, the authors hope that other researchers may implement the power signal histogram compression algorithm on other software platforms, such as C, or other hardware platforms, such as an Advanced RISC Machine (ARM) or FPGA processor; such research could lead to low-cost, low-power hardware integration with future power utility infrastructures. In this work, histogram-based IFP is assessed using three use cases, so additional research is needed to identify the complete set of events for which the presented approach is viable.  
Conceptualization, J.H.T. and T.C.; methodology, J.H.T.; software, J.H.T.; validation, A.M. and T.C.; formal analysis, J.H.T.; investigation, J.H.T.; resources, A.M. and D.R.R.; data curation, A.M.; writing--original draft preparation, J.H.T. and D.R.R.; writing--review and editing, J.H.T. and D.R.R.; visualization, J.H.T.; supervision, D.R.R. and A.M.; project administration, A.M. and D.R.R.; funding acquisition, D.R.R. and A.M. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{4}{c}{**Interval (Minutes)**} \\ \hline
**Process** & **60** & **30** & **10** & **5** \\ \hline Time Synchronization & 61.10 & 28.76 & 9.54 & 4.63 \\ Residual & 16.99 & 5.58 & 1.37 & 0.91 \\ Frequency \& RMS & 879.60 & 426.69 & 147.15 & 72.69 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Task runtime in milliseconds when calculating the cyclic, residual, frequency, and energy histograms at sixty-, thirty-, ten-, and five-minute intervals.
# Comparison of L1 and L5 Bands GNSS Signals Acquisition
## 1 Introduction
Two cases will be considered, one where there is no assistance at all (equivalent to a cold start), and one there is enough assistance to determine the secondary code delay, such that the receiver does not need to search it (kind of warm start). Indeed, it is relatively easy to have an estimate of the current secondary code chip. One chip of the L5 or E5 secondary codes lasts 1 ms, which is equivalent to 300 km. Therefore, if the receiver has an estimate of the current time better than 1 ms and an estimate of its position and of the satellites position better than 300 km (achievable with almanac), it is possible to estimate the current secondary code chip, and thus there is no need to search it via a correlation. If an estimate is available but not so accurate, the number of possibilities can still be reduced, e.g., to three or four, instead of 20 or 100. The remainder of the paper is organized as follows. Section 2 describes briefly the GNSS signals considered, summarizes their characteristics, and introduces the acquisition search method chosen (parallel code search) and the elements of interest. Section 3 provides mathematical expressions of the processing time and the memory requirements for all the possible implementations: Data channel verses pilot channel, assistance verses no assistance, coherent integration only verses use of non-coherent integration, parallel verses semi-parallel verses serial implementation. Then, Section 4 describes the methodology used for the comparison, details all the parameters selected, and compares the acquisition of the GPS L1 C/A and L5 signals, and the acquisition of the Galileo E1 and E5 signals by evaluating the expressions given in Section 3. Finally, Section 5 summarizes the outcomes.
## 2 Related Work
### Protocol Stack and Optimization Algorithms for Traffic Scheduling and Topology Adjustment  
As the demand for efficient traffic scheduling and topology adjustment in satellite networks continues to grow, combining protocol stack optimization with optimization algorithms has proven to be an effective approach for enhancing network performance. Protocol stack optimization improves network latency and throughput by refining transport layer protocols and enabling multi-protocol cooperation, while optimization algorithms address resource allocation and network topology problems through mathematical modeling and problem-solving techniques. For instance, studies have demonstrated that modified transport layer control protocols, such as the Transmission Control Protocol (TCP), integrated with congestion control and traffic scheduling strategies, can significantly reduce data transmission latency and enhance throughput in high-latency environments [7; 8; 9]. In particular, [PERSON] and [PERSON] [8] highlighted how optimizing the TCP protocol effectively reduced latency and improved overall network performance. Similarly, network topology optimization methods based on graph theory and optimization algorithms, including integer and linear programming, have been widely applied in satellite networks to enhance resource allocation efficiency and minimize latency [10; 11; 12]. For example, [PERSON] et al. [10] demonstrated how linear programming techniques were employed to develop optimized resource allocation strategies. In addition, heuristic and metaheuristic algorithms, such as genetic algorithms and particle swarm optimization, have been incorporated to dynamically adjust resource allocation strategies, further reducing latency and improving network reliability [13; 14; 15]. For example, [PERSON] et al. [15]described the design of novel congestion control algorithms to tackle challenges arising from link failures.  
Despite these advancements, these methods often overlook the complex interactions between protocol layers. In dynamic environments, the performance of protocol stacks remains suboptimal, particularly under conditions of frequent topology changes and fluctuating traffic demands.
# Blood Pressure Estimation by Photoplethysmogram Decomposition into Hyperbolic Secant Waves
## 2 Methods
### Pulse Wave Model for Blood Pressure Estimation
#### 2.1.1 Mathematical Models for Pulse Decomposition Analysis  
The PDA model assumes that each pulse wave measured by a PPG device is composed of multiple pulse wave reflections, as illustrated in Figure 1. The multiple reflections do not necessarily propagate at the same speed. Here, we use three-wave decomposition, which has been shown to be adequate for blood pressure estimates using Gaussian decomposition [6]. For this study, we used both the Gaussian PDA (Equation (1)) and our proposed sech PDA (Equation (2)):  
\[f_{G}=a_{G}\exp\left[-\frac{(t-\mu_{G})^{2}}{2\sigma_{G}^{2}}\right]. \tag{1}\]  
\[f_{S}=a_{Sh}\text{sech}\left[\frac{t-\mu_{Sh}}{2\sigma_{Sh}}\right]. \tag{2}\]  
where \(a_{G}\) and \(a_{Sh}\) are amplitudes, \(\sigma_{G}\) and \(\sigma_{Sh}\) are widths, and \(\mu_{G}\) and \(\mu_{Sh}\) are the center positions along the time axis of the respective waveforms. The waves in the pulse wave decomposition of Figure 2 can be described by a set of three Gaussian waves, or by three sech waves. These models were used to perform na analysis, described below, to determine between Gaussian waves and sech waves, and which one was better suited to estimate blood pressure by PDA.
# An Improved CASA Model for Estimating Winter Wheat Yield from Remote Sensing Images
## 4 Results and Analysis
### Net Primary Production  
Figure 5 shows the spatial distribution of NPP in the study area from March to May, 2009. The NPP generally increased from March to May. In March, winter wheat gradually enters the greening stage and the erecting stage. The results for NPP in March were all less than 100 g C m\({}^{-2}\), and the average NPP was 25 g C m\({}^{-2}\). In April, most of the winter wheat was in the jointing stage, booting stage, and heading stage and the growth rate continued to accelerate. The NPP was mainly concentrated in the range 50-150 g C m\({}^{-2}\), with an average of 88 g C m\({}^{-2}\). In May, the average NPP reached a maximum of 128 g C m\({}^{-2}\) and mostly remained above 100 g C m\({}^{-2}\). The winter wheat growth increased continuously, and the dry matter quality accumulated rapidly.  
Figure 4: Spatial distribution of light use efficiency of winter wheat from March to May 2009: (**a**) March; (**b**) April; (**c**) May.  
The Tongzhou and Shunyi districts in Beijing have typical continental monsoon climates. The seasonal climate directly affects the formation of winter wheat NPP. Figure 6 shows the mean monthly NPP of winter wheat in the study area as a function of time over the entire growing season. From October to December 2008, the winter wheat NPP in the seedling stage decreased monotonically, primarily because, as the study area gradually entered the winter season, climatic factors such as solar radiation, temperature, and precipitation reached their low point. Thus, winter wheat growth was limited; the growth rate was slow, and the rate of NPP formation was relatively low. From January to March 2009, with the improvement of climatic conditions, the winter wheat gradually passed through the over-wintering stage and entered the stage of rapid growth and development, so the NPP began to increase. From April to June 2009, the winter wheat leaf area continued to increase, photosynthesis, respiration, and other growth activities continued to increase, and dry matter quality accumulated rapidly. Finally, in May, the winter wheat growth and NPP peaked.  
Figure 5: Spatial distribution of winter wheat net primary production (NPP) from March to May, 2009: (a) March; (b) April; (c) May.  
Figure 7 shows the spatial distribution of the total winter wheat NPP for the entire growing season, which was obtained by summing the NPP of each month. The overall distribution of winter wheat NPP in Tongzhou District was similar to that in Shunyi District and was concentrated in the range 300-700 g C m\({}^{-2}\), with an average NPP of 464 g C m\({}^{-2}\).  
Figure 6: Mean winter wheat NPP as a function of time.  
Figure 7: Spatial distribution of winter wheat NPP averaged for the 2008–2009 growing season.
# Propagation of Cosh-Airy and Cos-Airy Beams in Parabolic Potential
## 2 Propagation of Cosh-Airy and Cos-Airy Beams in Parabolic Potential
 \tag{16}\]  
When \(z=\left(m+\frac{1}{2}\right)\frac{\pi}{L}\), the intensity distribution of the one-dimensional cos-Airy beam in the parabolic potential is expressed as:  
\[\left|\psi(x,z)\right|^{2}=\frac{L}{4\pi}\exp\left(\frac{2a^{3}}{3}-2a\beta^{ 2}\right)\exp(-2 aL^{2}x^{2})\left[1+\cos\left(2\beta L^{2}x^{2}-2a^{2}\beta+ \frac{2\beta^{3}}{3}\right)\right]. \tag{17}\]  
When \(z=m\pi/L\), Equation (15) is simplified as:  
\[\psi(x,z)=\frac{-1}{\sqrt{s}}Ai(sx-x_{0})\exp[a(sx-x_{0})\cos[\beta(sx-x_{0}]. \tag{18}\]  
The cosh-Airy and cos-Airy beams propagating in the parabolic potential are periodic and have the same period, \(T=2\pi/L\). The optical field expressions of the cosh-Airy beam in the general and the phase transition points are completely different, and so is the cos-Airy beam. The intensity of the cosh-Airy and cos-Airy beams in the phase transition point has a symmetric Gaussian distribution. The center of gravity of the cosh-Airy and cos-Airy beams in the parabolic potential is defined by [52; 53; 54]:  
\[X=\frac{\int_{-\infty}^{\infty}x\left|\psi(x,z)\right|^{2}dx}{\int_{-\infty}^{ \infty}\left|\psi(x,z)\right|^{2}dx}. \tag{19}\]  
The effective beam size of the cosh-Airy and cos-Airy beams in the parabolic potential is given by [52; 53; 54; 55; 56; 57]:  
\[W_{x}=\left[\frac{\int_{-\infty}^{\infty}\left(x-X\right)^{2}\left|\psi(x,z) \right|^{2}dx}{\int_{-\infty}^{\infty}\left|\psi(x,z)\right|^{2}dx}\right]^{1/2 }=\left[\frac{\int_{-\infty}^{\infty}x^{2}\left|\psi(x,z)\right|^{2}dx}{\int_{- \infty}^{\infty}\left|\psi(x,z)\right|^{2}dx}-X^{2}\right]^{1/2}. \tag{20}\]  
Because of the periodic behavior of the intensity distribution, the center of gravity and the effective beam size are also periodic, and the period is still \(T=2\pi/L\). Note that the fractional Fourier transform is indefinite in a general sense at distances multiple to the half-period [58], therefore, the direct application of the fractional Fourier transform to the input field leads to problems calculating the field at these distances. The algorithm used here is more convenient for calculations at the half-period.
# Secure Encapsulation Schemes Using Key Recovery System in IoMT Environments
## 4 Proposed Scheme-I (Key Recovery System Using Signcryption)
### KRF Generation Phase  
In this phase, when the key is lost or corrupted, the necessary KRF is generated to recover the key as shown in Figure 6.  
* **Step 1:**\(Dev_{A}\) requests \(SGN\) to M-KRA.
* **Step 2:** Each of the KRAs requested for \(SGN\) from \(Dev_{A}\) randomly selects \(R_{KRA_{i}}\in\mathbb{Z}_{p}^{*}\). After that, each KRA generates an \(SGN\) by sharing \(R_{KRA_{i}}\) generated through a secure channel with each other. \[SGN=R_{KRA_{1}}\oplus R_{KRA_{2}}\oplus,\ldots,\oplus R_{KRA_{n}}\] (12)
* **Step 3:** M-KRA send \(SGN\) to \(Dev_{A}\).  
Figure 5: Session key exchange and encryption phase of proposed scheme-I.  
Figure 6: KRF generation phase of proposed scheme-I.  
* **Step 4:**\(Dev_{A}\) generates \(Tc_{i},Tr_{i},Ts_{i}\) using \(c_{i},r_{i},s_{i}\) and \(SGN\). Then, \(TT_{i}\) is generated using \(Tc_{i}\), \(Tr_{i}\), and \(Ts_{i}\). \[Tc_{i} =c_{i}\oplus SGN\] (13) \[Tr_{i} =r_{i}\oplus SGN\] (14) \[Ts_{i} =s_{i}\oplus SGN\] (15) \[TT_{i} =(Tc_{i}\parallel\Upsilon_{i}\parallel Ts_{i})\] (16)
* **Step 5:**\(Dev_{A}\) generates \(KRF\) using \(KRF_{i}\). \[KRF_{i}=E_{pk_{KRA_{i}}}(c_{i}\parallel r_{i}\parallel s_{i}\parallel SGN \parallel TT_{i})\] (17) \[KRF =E_{pk_{KRC}}(KRF_{1}\parallel KRF_{2}\parallel\ldots\parallel KRF _{n}\parallel H(SGN))\] (18)
* **Step 6:** Then, the generated \(KRF\) is attached to the ciphertext \(C\). \[(C\parallel KRF)\] (19)
# Advances in High-Speed, High-Power Photodiodes: From Fundamentals to Applications
## 2 Overall Design Considerations for Photodiodes
### Equivalent Circuit Models
The RC-delay time of this new model is shown in Figure 13e, where two RC parallel circuits (R\({}_{2}\)C\({}_{2}\) and R\({}_{3}\)C\({}_{3}\)) are used to model the two spacers (or transition layers) in UTC-PD, while the R\({}_{4}\)C\({}_{4}\) parallel circuit denotes the carrier collector, and the R\({}_{1}\) represents the series resistance with a small value (related to P\({}^{+}\), N\({}^{+}\) layers and ohmic contact resistance of metallic connection/doped materials). In addition, the heavily p-doped neutral thick absorber usually can be ignored due to a negligible resistive effect [99]. It is noted that the parasitic capacitance, C\({}_{\rm P}\), and inductance, L\({}_{4}\) of the CPW pad cannot be ignored if the designed device photoresponse is above 75 GHz. This model provides useful and accurate information to optimize the UTC-PD complex impedance over the frequency range, thus providing a method for power maximization from UTC-PD to an antenna and then radiating to the air, by employing complex conjugate impedance matching between them [99,120]. Based on this new accurate model, some UTC-PD structures are designed and integrated with antennas [22,25,121]. In Figure 14, the matching network is employed to optimize the impedance matching to increase the RF output power, while stray capacitance is introduced in model-2 (without in model-1) to represent the parasitic effect resulting from the flip-chip bonding [25]. Compared with Figure 14a, the accurate equivalent circuit model is used for UTC-PD in Figure 14b, and the extra capacitance is also employed [22]. Moreover, by optimizing the impedance transmission line of the signal pad in CPW, a new transmission line with a characteristic impedance of 96 \(\Omega\) is introduced forming a new low-inductive peaking CPW [36,37,101]. Subsequently, the charge-compensated MUTC-PD (CC-MUTC-PD) [78] is flip-bonded with this new CPW on aluminum nitride (AlN) (or diamond submounts for thermal dissipation [38,122]) die for further increasing the bandwidth and decreasing the output power roll-off at >120 GHz frequency [121]. Similarly, an inductive 115 \(\Omega\) characteristic impedance CPW, as shown in Figure 15, is proposed and fabricated to improve the PD bandwidth from 28 GHz to 37.5 GHz [90],  
Figure 13: (**a**) RF small–signal equivalent circuit model of PD without air–bridge; (**b**–**e**) RC–delay time models designed by [PERSON] [89], [PERSON] [107], [PERSON] [96] and [PERSON] [99,120]. while further optimization is applied for MUTC-PD to obtain higher 3-dB bandwidth (and RF output power) from 156 GHz (\(-0.16\) dBm @ 170 GHz [111]) up to 220 GHz (\(-4.94\) dBm @ 230 GHz [113]). Note that some special examples with detailed parameters have been reported in the above-mentioned literatures cited in this paper, so here they are not given.