esa-sceva/satcom-chunk-collection huggingface dataset:
# A Wideband Digital Pre-Distortion Algorithm Based on Edge Signal Correction
## 2 Wideband DPD Model Based on Edge Signal Correction
### Wideband DPD Algorithm Based on Edge Signal Correction
The corrected power amplifier output signal after modification can be expressed as follows:  
\[y(n)=\sum\limits_{k=0}^{K-1}\sum\limits_{q=0}^{Q}c_{k,q}u(n-q)|u(n-q)|^{k}+ \sum\limits_{k=1}^{K-1}b_{k}u(n)\left[\sum\limits_{q=0}^{Q}d_{q}|u(n-q)|\right] ^{k} \tag{17}\]  
In Equation (17), \(b_{k}\) represents the non-linear polynomial parameters, and \(d_{q}\) represents the envelope parameters, both of which are related to the cross-terms. Cross-terms are incorporated to improve the model's fitting accuracy by utilizing edge signal data and calculating the cross-term parameters through the construction of a cost function. \[\frac{\partial J}{\partial\left(b_{k}^{i}\right)^{*}}=-\sum\limits_{L=1}^{N} e_{L}^{i}t_{k}^{*}(L)=0 \tag{18}\]  
In Equation (18), \(t_{k}(L)\) represents the association expression for \(b_{k}\). The \(b_{k}\) corresponding to the minimum value of \(t_{k}(L)\) is designated as \(b_{k}^{i+1}\). Here, \(b_{k}^{i}\) denotes the \(i\)-th iteration of \(b_{k}\), and \(e_{L}^{i}\) represents the signal difference at index \(L\). \(\left(\right)^{*}\) indicates the complex conjugate. Taking the partial derivative of \(J\) with respect to \(d_{q}\) results in the following:  
\[d_{q}^{i+1}=d_{q}^{i}+\frac{\mu}{h_{0}}(F_{L}^{H}F_{L})^{-1}F_{L}^{H}e_{L}^{i} \tag{19}\]  
In Equation (19), step size factor \(\mu\leq 1\) and \(h_{0}\) represents the gain parameter. In summary, the goal of training the pre-distortion model is to iteratively optimize the pre-distortion processing parameters and cross-term parameters. This algorithm focuses on the signal within the central frequency band, using only the filtered frequency domain information for DPD model parameter estimation. Additionally, by incorporating cross-terms into the model structure and continuously calibrating the entire model using edge signals, the fitting accuracy of the model is significantly enhanced. The design of this algorithm reduces computational resource consumption while maintaining pre-distortion effectiveness, thereby providing a more efficient signal processing solution for wideband wireless communication systems. This method is expected to achieve a better balance between performance and resource usage in practical applications. Simulations and experiments will provide empirical evidence to validate the feasibility and benefits of this theory in actual wireless communication environments.
## 2 Materials and Methods
### Field Data Collection and Laboratory Measurements  
In all remote sensing studies, synchronous with satellite images, field data are needed. These in situ measurements provide the possibility to calibrate and validate the local or regional retrieval algorithms [40]. From July 2016 to November 2020, a total of 40 field campaigns were completed, and 121 georeferenced samples were collected. Following sampling protocols [41], at the reservoirs we located, by echo-sounding, a point in the area of maximum depth, which has to be situated 300-500 m from the dam to prevent disturbances by the possible outflow. These protocols were not applied to the samplings in the marshes due to the fact that they are shallow areas (0.5 m), therefore the sampling was carried out at a point 10-15 m from the shore. For each reservoir and marsh, between 1 and 4 in situ spectrometric measuring points were taken at a suitable distance from shoreline to avoid mixed pixels (land-water mixed reflectance). Tables A1 and A2 in Appendix A shows the number of samples for each test site, their location and additional information.  
Secchi disk is a standard white disc 20 cm in diameter that is submerged vertically until it is no longer visible. Secchi disk depth (SDD) consists of measuring the mean value of the point at which the disk completely disappears and the depth at which it reappears [42]. SDD was measured in situ. At each sampling point, water samples were collected and stored in refrigeration at 4 \({}^{\circ}\)C in the dark, for later transport to the laboratory. PVC tubes were used to take samples from the surface down to SDD, because integrated samples are more representatives. [Chl-\(a\)] was measured by fluorometry (Turner C3) and in the laboratory by spectrometry. Samples were filtered through 0.4-0.6 \(\upmu\)m pore size Whatman GF/F glass fiber filters, extracted according to Shoaf & Lium [43] and calculated as in Jeffrey & Humphrey [44]. TSM was determined by gravimetry [45], consecutively measuring the particulate matter retained on individual filters Whatman 934-AH type (1.5 \(\upmu\)m pore). This method involves two steps; first, the filter that contains the particulate matter is dried at 105 \({}^{\circ}\)C to obtain the value of dry TSM and, subsequently, it is heated at 460 \({}^{\circ}\)C for six hours. The value obtained is from the particulate inorganic matter (PIM) and, by difference from the value obtained previously (TSM), particulate organic matter (POM) is calculated. The combustion of particulate organic matter (POM) supposes a loss of weight that we usually call Loss on Ignition (LOI).  
Figure 3: Marshes site sampling location in Albufera of Valencia Natural Park from an RGB composition of the S2 image dated 5 January 2021.
# A Novel Rolling Bearing Fault Diagnosis and Severity Analysis Method
## 5 Proposed Fault Diagnosis and Severity Analysis Method
### Fault Severity Analysis Process  
The fault diameter of the rolling bearing directly affects the running state of the rolling bearing, which is a manifestation of the fault severity. Therefore, the energy contained in the vibration signal of the rolling bearing under different fault severities is different. In this paper, the wavelet packet energy entropy is used to describe the fault severity of the rolling bearing. To further analyze the fault severity on the basis of accurate fault type identification, this paper adopts the mRVM to further identify the fault severity under the same fault type. The fault severity analysis process is described below:  
1. Vibration signals of the rolling bearing with different fault diameters under the same fault are sampled and segmented according to a certain length to form a sample set of vibration signals under different fault severities;
2. WPEE feature extraction is performed on the sample set composed of vibration signals with different fault severities under different fault types and the set of feature vectors representing the fault severity is composed;
3. The mRVM model is modeled by using the sample set obtained in step (2), which can be used to analyze the fault severity of the rolling bearing.  
As long as there are enough samples of vibration signals of the rolling bearing with different fault diameters, the WPEE can be used to describe the fault severity and the mRVM can be used to identify the fault severity.
## 4 Discussion  
The results obtained align with the criteria established for the identification, selection, and analysis of the reviewed literature. While broadening these criteria might have yielded a wider set of findings, this does not constitute a significant limitation or shortcoming; the present scope was adequate for the intended objective: to generate a critical synthesis and identify key directions for future research on life-cycle cost (LCC) assessment.  
The findings highlight several strategic challenges and research opportunities in the current application of LCC methodologies. First, the evolving definition and scope of LCC suggest the need for clearer methodological frameworks, particularly in distinguishing its applications across public procurement and private real estate. This includes not only cost quantification but also how LCC supports long-term investment decision-making under uncertain conditions.  
Second, despite the theoretical robustness of LCC, its methodological development remains constrained by technical barriers such as limited integration with digital platforms (e.g., BIM), fragmented data environments, and the absence of tools adapted to manage qualitative or intangible impacts. These shortcomings hinder the operationalization of LCC in the early design and investment phases, where its strategic value would be most impactful.  
Third, the increasing relevance of decision-making frameworks that incorporate social and environmental externalities necessitates the development of hybrid approaches. These should combine LCC with complementary evaluation tools that address stakeholder values and policy-driven sustainability goals. However, without adequate data quality, standardized taxonomies, and interoperable digital environments, such integrative models are difficult to implement effectively.  
Lastly, the study underlines the growing importance of probabilistic modeling and risk-based assessment in enhancing the reliability of LCC outputs. Despite their potential, these methods remain underutilized, mainly due to the lack of accessible datasets and computational resources.  
This study could be expanded by means of sector-specific surveys of project managers, facility managers, designers, and other stakeholders with open-ended responses, even with the limitations that this type of study presents.
## 3 Review of ANN Models for Prediction in the Renewal Energy Sector
### ANN in Wind Energy
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Author**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Case Study**} & \multirow{2}{*}{**Obj.**} & \multirow{2}{*}{**Met.**} & \multicolumn{6}{c}{**Parameters**} & \multicolumn{6}{c}{**Results**} \\ \cline{5-16}  & & & & & & **T** & **D** & **H** & **SR** & **WH** & **O** & **1** & **2** & **3** & **4** \\ \hline
[77] & 2004 & Canada & B & D & \(\blacksquare\) & \(\square\) & \(\square\) & \(\square\) & \(\square\) & \(\square\) & \(\square\) & \(\square\) & \(\vee\) & & & \\
[78] & 2007 & Lawton & B & D & \(\blacksquare\) & \(\square\) & \(\blacksquare\) & \(\square\) & \(\blacksquare\) & \(\square\) & \(\square\) & \(\square\) & \(\vee\) & & & \\
[79] & 2008 & Muppandal, Tamil Nadu (India) & B & D & \(\blacksquare\) & \(\blacksquare\) & \(\blacksquare\) & \(\square\) & \(\square\) & \(\square\) & \(\square\) & \(\vee\) & & & \\
[80] & 2013 & EEUU & BC & F & \(\square\) & \(\square\) & \(\square\) & \(\square\) & \(\square\) & \(\square\) & \(\vee\) & & & & \(\vee\) & \\
[81] & 2014 & Turkey & B & D & \(\square\) & \(\square\) & \(\square\) & \(\square\) & \(\square\) & \(\square\) & \(\square\) & \(\vee\) & & & \\
[82] & 2014 & China & C & D & \(\square\) & \(\square\) & \(\square\) & \(\square\) & \(\square\) & \(\square\) & \(\blacksquare\) & \(\vee\) & & & \(\vee\) & \\
[83] & 2017 & China & B & D & \(\square\) & \(\square\) & \(\square\) & \(\square\) & \(\square\) & \(\square\) & \(\square\) & \(\vee\) & & & \\
[84] & 2017 & India & B & D & \(\blacksquare\) & \(\blacksquare\) & \(\blacksquare\) & \(\blacksquare\) & \(\blacksquare\) & \(\blacksquare\) & \(\square\) & \(\vee\) & & & \\
[85] & 2018 & India & B & D & \(\blacksquare\) & \(\blacksquare\) & \(\blacksquare\) & \(\blacksquare\) & \(\square\) & \(\square\) & \(\square\) & \(\vee\) & & & \\
[86] & 2018 & China & B & D & \(\blacksquare\) & \(\square\) & \(\square\) & \(\square\) & \(\square\) & \(\blacksquare\) & \(\blacksquare\) & \(\vee\) & & & \(\vee\) \\
[87] & 2018 & India, EEUU & B & D & \(\square\) & \(\square\) & \(\square\) & \(\square\) & \(\square\) & \(\blacksquare\) & \(\blacksquare\) & \(\vee\) & & & \\
[88] & 2018 & United Kingdom & B & EF & \(\square\) & \(\square\) & \(\square\) & \(\square\) & \(\square\) & \(\blacksquare\) & \(\vee\) & & & \\ \hline \hline \end{tabular}  
* Obj. = Objective (A = IMEP; B = MFM; C = ARAM) Met = Methodology (D = Neural Network; E = Comparisons Models; F = Others) T = Temperature; D = Date; H = Humidity; WH = Work Hour; SR = Solar Radiation; O = Others. I. Validate the use ANN 2. The improvement of ANN vs. classic models is included. 3. ANN used a complement for a higher level reliability analysis framework. 4. Authors recommend complementing the analysis with other techniques.  
\end{table} 
Table 3: Reviewed ANN studies on Wind Energy. In both references, appropriate parameters are determined and they can provide technical reference values for online monitoring, early warning, and condition-based maintenance of wind turbines.
## 4 Results
### Dynamic Feature Monitoring of Glacier Movement Based on Sentinel-1  
To verify the effect of the Luding earthquake on the surface velocity of Mt. Gongga glaciers, Sentinel-1A data covering the study area from 12 September 2021 to 5 January 2023 were used for POT processing. 26 August 2022 and 7 September 2022 were used as the cutoff points to obtain the Mt. Gongga glacier surface flow velocities before, during, and after the earthquake. A long-term POT time-series analysis of the characteristics of glacier movement after the earthquake was conducted to further explore the evolution of glacier movement and the impact of earthquakes. To reduce the problem of errors due to inconsistent time intervals, the calculated results were uniformly converted into units of days for intercomparison. Due to the low accuracy of the POT method based on Sentinel-1A multiperiod data in the azimuthal direction, only the flow velocities in the range direction of the glaciers in Mt. Gongga were analyzed.
# State Estimation of Over-Sensored Systems Applied to a Low-Cost Robotic Manipulator
## 2 Models  
The design process behind the implementation of a state estimator starts with knowing the system, as well as its components. More specifically, mathematical models capable of describing any of the subsystems and sensors of the robotic manipulator must be developed. A study of the used subsystems is performed throughout this section.
## 3 Results
Finally, as indicated in Table 4, most participants agreed with the six possible reasons, identified initially by participants in Study 1, why they should prefer an institutionally developed i-AIDA over a publicly available p-AIDA like ChatGPT. The most important reason according to the participants would be that an i-AIDA would be more _accurate and specific_ as the content would be generated by the OU. Another important reason for \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline  & \multicolumn{2}{c}{**Study 1**} & \multicolumn{2}{c}{**Study 2**} & \multicolumn{2}{c}{**Study 3**} & \multicolumn{2}{c}{**F-Value**} & \multicolumn{1}{c}{**Eta**} \\  & **M** & **SD** & **M** & **SD** & **M** & **SD** & **F-Value** & **Eta** \\ \hline _Academic Integrity_: potential misuse of AI for completing assignments, potential plagiarism, and academic integrity. & 4.38 & 0.74 & 4.16 & 1.08 & 4.13 & 1.00 & 0.222 & 0.001 \\ _Data Privacy and Use_: how student data and inputs to the AI are used, stored, and potentially shared, emphasizing the need for transparency and consent. & 4.13 & 0.84 & 4.11 & 1.05 & 4.08 & 1.02 & 0.976 & 0.000 \\ _Operational Challenges_: how AI might inadvertently affect learning outcomes and student interaction, and the importance of ensuring that AI tools are accurate and reliable. & 4.25 & 0.71 & 4.03 & 0.99 & 4.05 & 0.93 & 0.199 & 0.001 \\ _Ethical and Social Implications_: the potential impact of AI on learning processes and the necessity of balancing technological advancement with human-centric educational practices, keeping the human (student/tutor/academic staff) in the process, and being able to talk to a human. & & & & & & & & & \\ _Future of Education_: how AI integration might change the nature of education and assessments, necessitating a shift in teaching methods and learning expectations. & 4.00 & 0.76 & 4.03 & 1.10 & 3.95 & 1.04 & 0.811 & 0.001 \\ \hline \hline \end{tabular} Study 1, N = 8; Study 2, N = 96; Study 3, N = 212; \(\alpha\) = 0.872. Items from [15].  
\end{table} 
Table 3: Student concerns about the use of AI in teaching and learning. participants was that an i-AIDA would be available 24/7 and would provide immediate feedback in the academic setting of the learner, rather than providing generic content. Another important reason would be that an i-AIDA would be integrated in the students' learning experience, rather than being hosted elsewhere, and it would provide personalised responses based upon the learners' needs and their respective journey into the learning materials. Using ANOVAs, no significant differences were found across the three studies, indicating that participants across studies had similar preferences in terms of the reasons for preferring an i-AIDA over a p-AIDA.
## 5 Proposed Model  
### Constraints and Objectives Analysis  
The main objective of the designed model is minimizing the localization error while maximizing the localization ratio. Similar to most path planning models, a number of constraints are considered in planning the MA movement as follows. First, every visited localization point is unique; this means the MA cannot visit a localization point more than once. Second, in order to avoid the collinearity of localization points, the optimization requires that every three consecutive localization points are not collinear. In addition, and as in [22; 23], another constraint is considered in designing this model that the movement distance is limited, which therefore means MA cannot exceed the maximum traveling distance. These constraints may be broken in only one case where the MA trapped in a corner or a border of the network. Two objective functions are to be optimized, the minimum average localization error and the maximum localization ratio. They are respectively represented in the following formulas  
\[Mininize\ Error_{avg} \tag{1}\]  
\[Maximize\ RN \tag{2}\]
## 5 Conclusions  
In this work, the performance analysis of SLIPT-based DH DF FSO communication systems is presented. Analytical expressions such as OP, ergodic capacity, average BER, throughput, SOP, and SPSC are obtained for the proposed communication system. Obtained expressions are verified with Monte Carlo simulations. Overall, it is shown that the proposed SLIPT-based FSO system outperforms the non-SLIPT FSO systems. This is due to the fact that the e2e SNR is an increasing function of the EH parameter, which in turn improves the performance of the proposed system model. It is also highlighted that there is a trade-off between the EH time and the throughput of the proposed communication system.  
[PERSON] made substantial contributions to the conception, theoretical derivations, and design of the work; [PERSON] and [PERSON] were major contributors to the interpretation of results and writing of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data leading to the results presented in this study are available on request from the corresponding author.  
A thank you to [PERSON] for fruitful discussions during the preparation of this work.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AC & Alternating current \\ AF & Amplify-and-forward \\ ASC & Average secrecy capacity \\ AWGN & Additive white Gaussian noise \\ BER & Bit error rate \\ CDF & Cumulative distribution function \\ DC & Direct current \\ DF & Decode-and-forward \\ DH & Dual-hop \\ e2e & End-to-end \\ EH & Energy harvesting \\ FSO & Free-space optical \\ LOS & Line-of-sight \\ OP & Outage probability \\ P-D & Photo-detector \\ PDF & Probability distribution function \\ RF & Radio frequency \\ RV & Random variable \\ SH & Single-hop \\ SLIPT & Simultaneous lightwave information and power transfer \\ SNR & Signal-to-noise ratio \\ SOP & Secrecy outage probability \\ SPSC & Strictly positive secrecy capacity \\ \end{tabular}
# A Crypto-Steganography Approach for Hiding Ransomware within HEVC Streams in Android IoT Devices
## 4 Results and Comparisons
### Visual Results Analysis  
Visual analysis is used to check the amount of modification resulting in the stego frames after hiding the ransomware within the cover frames. Table 4 presents the visual outcomes of the examined cover and stego frames in case of utilizing various sizes of secret ransomware data. Furthermore, Table 4 presents the acquired difference between the stego and the cover frames (Entropy). From the obtained outcomes, it is noticed that the proposed crypto-steganography approach successes in hiding more ransomware samples (different sizes) inside the cover media frames (different resolutions) while preserving the perceptual quality (imperceptibility). Besides, the similarity assessment between the stego and cover frames can be evaluated using the estimation of the bit error rate (BER). BER estimates the error rate in the pixel bits between the stego and cover frames, which is represented in Equation (1) [41]:  
\[\text{BER}=\frac{\sum_{m=1}^{M}\sum_{m=1}^{N}\lvert x(m,n)-x^{\prime}(m,n)\rvert} {M\times N} \tag{1}\] \begin{table}
\begin{tabular}{c c} \hline \hline
**HEVC Stream** & **Resolution** \\ \hline GTFly & 3840 \(\times\) 2160 \\ Microworld & 2560 \(\times\) 1600 \\ Shark & 1920 \(\times\) 1088 \\ Kimono & 1280 \(\times\) 720 \\ Balloon & 640 \(\times\) 480 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Resolution of the tested video streams. \begin{table}
\begin{tabular}{c c} \hline \hline
**Test Sample** & **Size** \\ \hline Ransomware 1 & 1.036 MB \\ Ransomware 2 & 734 KB \\ Ransomware 3 & 390 KB \\ Ransomware 4 & 171 KB \\ Ransomware 5 & 60 KB \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Size of the tested ransomware samples. where \(x(m,\,n)\) refers to the cover frame, \(x^{\prime}(m,\,n)\) refers to the stego frame, and \(M\) and \(N\) are the width and height of the video frame, respectively. Thus, the BER metric can be determined as the actual number of bit positions that are modified in the stego video frame compared to the cover video frame. It is recommended to achieve lower BER for accomplishing higher steganography performance. The results reveal that the stego frames were completely similar to the cover frames by obtaining zero BER values. Additionally, the diminutive difference in the entropy values for all examined HEVC streams proves the proposed steganography approach's high concealing performance and efficacy. Additionally, we calculated the computational CPU time of the proposed approach as indicated in Table 4 (Column 2, next to the Entropy value). The results confirm that the proposed stego-cryptography approach has satisfied complexity in embedding the encrypted ransomware file inside a video frame. This also shows the complexity of the extraction process, which is an identical inverse of the embedding process.