esa-sceva/satcom-chunk-collection huggingface dataset:
# IPCB: Intelligent Pseudolite Constellation Based on High-Altitude Balloons
## 1 Introduction  
Pseudolites are transmitters that can emit navigation signals to improve GNSS performance or to provide navigation service independently [1; 2; 3; 4; 5; 6; 7]. An intelligent pseudolite based on high-altitude balloons (IPBs) is a type of novel air-based pseudolite that utilizes a high-altitude balloon as a platform. An intelligent pseudolite constellation based on high-altitude balloons (IPCBs) is composed of multiple IPBs, which can provide emergency positioning service and regional positioning service independently.  
As an excellent solution for regional positioning, ICPBs have many advantages. Compared with ground-based pseudolites and traditional air-based pseudolites, IPCBs have a wider coverage and can serve more users because their flight altitude can reach tens of kilometers [8; 9]. Furthermore, IPCBs have a low energy requirement since they can accomplish its flight primarily relying on buoyancy and wind rather than oil or electricity [10; 11]. Compared with LEO satellite constellations, IPCBs have a stronger signal, a lower cost, and a shorter deployment period [12; 13]. In addition, the continuous residence duration of an IPB over a service area is longer than that of an LEO satellite, which can reduce navigation signal lock-lose and cycle slip caused by satellite switching [14; 15; 16]. Also, the IPCB operation and maintenance burden is less than that of an LEO satellite constellation because an LEO satellite constellation usually comprises a large number of satellites [17; 18]. These merits give promising potential to IPCB applications.  
In the application of IPCBs, geometry configuration plays a critical role since it affects IPCB service performance significantly [19; 20; 21; 22]. However, the problem becomes verycomplicated because of the unique dynamic features of IPBs. Most traditional research studies about pseudolite geometry configuration are designed for static pseudowires, which are not suitable for IPCBs [23; 24; 25; 26; 27].  
This paper centers on the problem of IPCB geometry configuration and proposes a planning algorithm that emphasizes utilizing different winds smartly. The proposed algorithm can fit the dynamic flight of IPCBs adaptively and is easy to implement. Moreover, it can achieve performance improvements by controlling IPB valves and fans only, without extra hardware cost.  
The rest of this paper is organized as follows. An overview of IPCBs is described in Section 2, a performance indicator of IPCB geometry configuration is defined in Section 3, an IPCB geometry configuration adjustment approach is designed in Section 4, a series of constraints are discussed in Section 5, a planning algorithm based on whale optimization algorithm (WOA) is proposed in Section 6, simulations and discussions are presented in Section 7, and conclusions are stated in Section 8.
# Secure Video Communication Using Multi-Equation Multi-Key Hybrid Cryptography
## 4 Experimentation
### Time to Encrypt the Video Chunks  
In this section, we discussed the length of time required to encrypt each chunk. The implementation assumes that each chunk's size is 1 MB, despite the fact that the video processing modules divide the video file into chunks for each full frame. Each chunk is encrypted using the algorithm detailed in Algorithm 1. As mentioned above, the key size of 128 bits is used by the AES to encrypt the video chunks.  
The amount of delay that is involved in the chunk encryption process is illustrated in Figure 6. The video chunks are encrypted using the AES module before being sent out into the network for streaming. The chunks are virtually identical in that their size ranges from 1 megabyte (MB) to 1.2 megabytes (MB), and the amount of time required to encrypt each chunk similarly ranges from 0.9 s to 1.2 s in the multi-key [13] method. In the proposed method, the encryption time ranges from 1.5 s to 2 s. The increase in the delay is observed due to the equation set and random selection of the equation for generating the key for each chunk.  
Figure 5: Multiple key generation delay (Compared with Multi-Key [13]).  
Figure 6: Time consumed to encrypt the video chunks (Compared with Multi-Key [13]).
# ERS-1/2 and Sentinel-1 SAR Data Mining for Flood Hazard and Risk Assessment in Lima, Peru
## 2 Materials and Methods
### Hazard and Risk Analysis
For example, permanent and seasonal water bodies (either natural or anthropogenic, e.g., water treatment plants The Atarjea and Planta de tratamiento de agua residual-PTAR Carapongo, as well as the reservoirs in Refineria Cajmarquilla), that could have been mistakenly interpreted as flooded areas in the cross-event pairs, were masked out and removed during the interpretation workflow, based on the externally validated ancillary data listed in Table 4 (e.g., Global Surface Water). Further verification of the correct identification of water bodies via this approach was achieved by comparison with the signals and SAR backscatter patterns found in the pre-event pairs, wherein the extent of water bodies was well visible and could not be confused with signals due to flooding. The geospatial comparison in QGIS between the RGB and ratio maps from the different SAR image pairs covering all the three periods--i.e., pre-, cross- and post-event--was also exploited to ascertain where change patterns observed in the cross-event were due to increased soil moisture or wetting, and not to flooding. Furthermore, verification with coeval very high-resolution optical satellite imagery was made when available, such as for the March 2017 event (see Section 3.1.1). In this regard, it is worth noting that very few were the situations for which this check was necessary. As outlined in the description of the geomorphology of the study area (see Section 2.1), floodplains are very localized. Where present, they are either densely urbanized (therefore, no signal from soil moisture can be retrieved) or, if bare, are located at a distance from the river course and at an elevation that makes flooding quite unlikely, and (near-)surface wetting more plausible. In other situations (see Section 3.1.2), the precise delineation and geolocation of the observed SAR change patterns confirmed that the bare ground near the river course was not wet, or the soil moisture increase did not reach levels that could be seen at C-band.
## 4 Results and Discussion
### Detection Result Analysis  
To further study the detection results of the proposed model, we analyze the examples of detected and undetected arching in the testing result. As shown in Figure 13a, the proposed model can detect arching images with different shapes and degrees and avoid the influence of data noise. However, DNN cannot detect these samples due to its poor learning ability of graphical features.  
Some examples of undetected arching are shown in Figure 13b. Among 5948 arching images of the testing set, 125 arching images are not detected, accounting for 2.1% of the total. The error is within an acceptable range and can be further corrected through on-site rechecking. Through analyzing the cause of the error, we can find that it is difficult to detect the arching in the early stage accurately. In the early stage, the arching features reflected on the LLI data are tiny because of the small deformation of the track slabs. Therefore, these samples have high similarity with normal samples, which increases the detection difficulty.
# A Unified Vendor-Agnostic Solution for Big Data Stream Processing in a Multi-Cloud Environment
## 1 Introduction
### Novel Contribution  
The novelty of this study lies in its comprehensive and innovative approach to big data stream processing for SMEs, organizations, and departments characterised by devolved management, tight budgetary constraints, and lack of in-house technical expertise (SMEODs). Our proposed reference architecture, MC-BDP, specifically addresses these challenges by:  
1. Promoting an infrastructure hosted on commercial clouds leveraging the cost-effective pay-as-you-go model. This model is particularly beneficial for SMEODs as it allows for financial flexibility and scalability.
2. Shifting from a traditional SaaS model towards a standardised form of PaaS. This transition is crucial for SMEODs as it offers a more controlled and customisable computing environment suitable for specific big data needs.
3. Reducing the risk of vendor lock-in by recommending the use of portable, interoperable, and vendor-agnostic components. This strategy is vital for ensuring flexibility and independence in a multi-cloud environment.
4. Providing a domain-specific reference architecture that offers guidance and simplifies the implementation process. This aspect is particularly valuable for SMEODs that may lack the technical expertise or resources to navigate the complexities of big data systems.  
This research further extends its novelty through a critical evaluation of big data architectures from leading companies such as Facebook, Twitter, and Netflix. This analysis aims to extract practical insights and non-functional requirements applicable to real-world big data systems. Additionally, the study introduces an innovative formula for adjusting CPU and data transfer requirements in a distributed computing cluster for big data streaming. This formula, based on the windowing function used for data processing, is a novel contribution that aids in optimizing resource utilization and cost-efficiency in big data stream processing.  
This study's unique contribution, in essence, is the development, implementation, and evaluation of the MC-BDP architecture, coupled with insightful analyses and practical tools for enhancing big data stream processing in SMEODs, making it a pioneering study in the field.  
The remainder of this paper is organised as follows; Section 2 reviews the related literature, situating the current work in the wider context of research in the fields of big data, cloud computing, and virtualisation, and discussing related work; Section 3 describes the MC-BDP reference architecture; Section 4 discusses the results of MC-BDP's evaluation; and Section 5 considers the impact of our research within its field and presents recommendations for future work.
## 5 Conclusions  
A wideband measurement campaign at two millimeter-wave frequency bands, 40 GHz and 60 GHz, has been conducted in two indoor scenarios: An auditorium and a narrow corridor. Measurements included both LOS and OLOS situations.  
The results have been processed and two wideband channel parameters have been obtained: The RMS delay spread and the coherence bandwidth. Based on these two parameters, the performance of both frequency bands is compared.  
We conclude that there are more multipath contributions at 40 GHz than at 60 GHz, resulting in a larger delay spread and larger frequency selectivity at 40 GHz than at 60 GHz. The radio channel at 60 GHz seems to be more sensitive to any change in the size of the environment or in the visibility conditions (LOS/OLOS).  
We have also concluded that multipath contributions at 60 GHz can be explained by single reflections on the environment scatterers, while higher order reflections should be considered to model all multipath contributions at 40 GHz.  
The results in this paper should be taken into account if a decision has to be made on the frequency band to be used in an indoor link for a wireless communication system. Results also provide a deep insight into the physical propagation mechanisms that take place and help to understand how these mechanisms determine the differences in the wideband parameters of the radio channel. Finally, results will be valuable also for those developing ray-tracing models for the indoor radio channel at millimeter-wave frequencies.  
**Author Contributions:** Design of the measurement campaign M.G.S. and I.C.; measurement system setup, measurements and data processing: R.H. and M.R.; analysis of results: M.G.S., I.C. and J.V.; writing: all.  
**Funding:** This research was funded by the Spanish Government, Ministerio de Ciencia, Innovacion y Universidades, Secretaria de Estado de Investigacion, Desarrollo e Innovacion, grant number TEC2017-85529-C3-3R); Xunta de Galicia, grant number ED431C 2019/26; atlanTlic Research Center; and the European Regional Development Fund (ERDF).  
**Acknowledgments:** The authors would like to thank [PERSON] for her technical support in the management of the radioelectric equipment.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Challenges and Actionable Pathways for AI Adoption in SMEs
### Designing Scalable AI Solutions for Sustainable Growth  
This subsection addresses the scalability challenge within the technological dimension of the TOE framework. Many SMEs struggle to implement AI solutions that can grow with their business due to resource constraints, infrastructure limitations, and the perceived complexity of large-scale AI deployment.  
Scalability is critical to ensure that early AI investments deliver long-term value. Without scalable design, SMEs may face high sunk costs, low adaptability, and limited return on investment. Therefore, SMEs must adopt strategies that allow for controlled experimentation while maintaining flexibility to expand as their capabilities evolve.  
As noted in [112], small-scale pilots often initiate the adoption of AI, but lack clear frameworks to scale these projects. Ref. [119] emphasize that cloud-based Gen-AI platforms and modular tools enable scalable low-cost experimentation that aligns well with SME capabilities. Ref. [115] highlight that modular AI architectures, combined with agile methodologies, are crucial to balance innovation speed with risk management in resource-constrained environments. Regionally, ref. [27] show that access to flexible infrastructure, such as edge computing and AI computation centers, significantly affects scalability potential in European SMEs. These findings underscore the importance of scalable design not only from a technical perspective but also as a strategic imperative for sustainable digital growth.  
To respond to this challenge, the following actionable strategies are recommended:
## 3 Mathematical Modeling
### The Basic Problem
#### 3.1.2 MILP Model for FJSP-LS  
Following the problem description and using the notations given above, the MILP mathematical model for the FJSP-LS is presented below.  
Minimize:  
\[Objective=c_{max} \tag{1}\]  
Subject to:  
\[c_{max}\geq c_{o,s,j}\,;\,\,\,\forall(o,s,j) \tag{2}\]  
\[\widehat{c}_{r,m}\geq c_{o,s,j}+\Omega\cdot x_{r,m,o,s,j}-\Omega\,;\,\,\, \forall(r,m,o,s,j) \tag{3}\]  
\[\widehat{c}_{r,m}\leq c_{o,s,j}-\Omega\cdot x_{r,m,o,s,j}+\Omega\,;\,\,\, \forall(r,m,o,s,j) \tag{4}\]\[\widehat{c}_{1,m}-b_{s,j}\cdot T_{o,j,m}-S^{*}_{o,j,m}-\Omega\cdot x_{1,m,o,s,j}+ \Omega\geq D_{m}\;;\;\;\forall(m,o,s,j) \tag{5}\]  
\[\widehat{c}_{r,m}-b_{s,j}\cdot T_{o,j,m}-S_{o,j,m,o^{\prime},j^{ \prime}}-\Omega\cdot(y_{r-1,m,o^{\prime},j^{\prime}}+x_{r,m,o,s,j})+2\Omega\geq \widehat{c}_{r-1,m}\;;\] \[\forall(r,m,o,s,j,o^{\prime},j^{\prime})|(r>1) \tag{6}\]  
\[\widehat{c}_{1,m}-b_{s,j}\cdot T_{o,j,m}-S^{*}_{o,j,m}\cdot A_{o,j}- \Omega\cdot(x_{1,m,o,s,j}+x_{r^{\prime},m^{\prime},o-1,s,j})+2\Omega\geq \widehat{c}_{r^{\prime},m^{\prime}}+L_{o,j};\] \[\forall(m,r^{\prime},m^{\prime},o,s,j)|\{\big{(}(1,m)\
eq(r^{ \prime},m^{\prime})\big{)}\wedge(o>1)\} \tag{7}\]  
\[\widehat{c}_{r,m}-b_{s,j}\cdot T_{o,j,m}-S_{o,j,m,o^{\prime},j^{ \prime}}\cdot A_{o,j}-\Omega\cdot(y_{r-1,m,o^{\prime},j^{\prime}}+x_{r,m,o,s, j}+x_{r^{\prime},m^{\prime},o-1,s,j})+3\Omega\] \[\geq\widehat{c}_{r^{\prime},m^{\prime}}+L_{o,j}\;;\;\;\forall(r, m,r^{\prime},m^{\prime},o,s,j,o^{\prime},j^{\prime})|\] \[\{(r>1)\wedge(o>1)\wedge(r,m)\
eq(r^{\prime},m^{\prime})\wedge(o,j )\
eq(o^{\prime},j^{\prime})\} \tag{8}\]  
\[y_{r,m,o,j}\leq P_{o,j,m}\;;\;\;\forall(r,m,o,j) \tag{9}\]  
\[y_{r,m,o,j}=\sum_{s=1}^{S_{j}}x_{r,m,o,s,j}\;;\;\;\forall(r,m,o,j) \tag{10}\]  
\[\sum_{m=1}^{M}\sum_{r=1}^{R_{m}}x_{r,m,o,s,j}=\gamma_{s,j}\;;\;\;\forall(o,s,j) \tag{11}\]  
\[b_{s,j}\leq B_{j}\cdot\gamma_{s,j}\;;\;\;\forall(s,j) \tag{12}\]  
\[\gamma_{s,j}\leq b_{s,j}\;;\;\;\forall(s,j) \tag{13}\]  
\[\sum_{s=1}^{S_{j}}b_{s,j}=B_{j}\;;\;\;\forall(j) \tag{14}\]  
\[\sum_{j=1}^{J}\sum_{s=1}^{S_{j}}\sum_{\alpha=1}^{O_{j}}x_{r,m,o,s,j}=z_{r,m}\;; \;\;\forall(r,m) \tag{15}\]  
\[z_{r+1,m}\leq z_{r,m}\;;\;\;\forall(r,m) \tag{16}\]  
\[x_{r^{\prime},m,o^{\prime},s,j}\leq 1-x_{r,m,o,s,j};\;\;\forall(r,r^{\prime},m, o,o^{\prime},s,j)|\{(o^{\prime}>o)\wedge(r^{\prime}<r)\} \tag{17}\]  
\[x_{r^{\prime},m,o^{\prime},s,j}\leq 1-x_{r,m,o,s,j};\;\;\forall(r,r^{\prime},m, o,o^{\prime},s,j)|\{(o^{\prime}<o)\wedge(r^{\prime}>r)\} \tag{18}\]  
\[x_{r,m,o,s,j},\;y_{r,m,o,j},\;\gamma_{s,j}\;\text{and}\;z_{r,m}\;\text{are binary} \tag{19}\]  
The complete description and the meanings of the objective function in Equation (1) and the constraints in Equations (2)-(19) can be found in [2].The expansion of this single objective FJSP lot streaming model into a multi-objective one is presented in the following section.
## 3 Component Design and Modeling
### Interleaved AMMI Multiplexer/Demultiplexer
As can be seen, the insertion loss is about 0.52 dB, and the crosstalk between adjacent channels is below \(-23\) dB. The MZI optical interleaver is formed by an apodized bidirectional grating coupler connecting with taper waveguides, single mode waveguide arms and a 2 \(\times\) 2 MMI coupler. The working principle is very similar to the MZI used for optical modulators. Ideally, if the waveguide loss and power combiner loss can be neglected and the fiber is in perfect coupling position, the optical transmission spectra of the MZI filter can be expressed as follows:  
\[\left\{ \begin{array}{l}T_{1}(\lambda)=\frac{C(\lambda)}{2}.\left[1+\cos \bigl{(}\varphi(\lambda)+\frac{\pi}{2}\bigr{)}\right]\\ T_{2}(\lambda)=\frac{C(\lambda)}{2}.\left[1+\cos\bigl{(}\varphi(\lambda)-\frac {\pi}{2}\bigr{)}\right]\end{array} \right. \tag{17}\]  
\[\varphi(\lambda)=\frac{2\pi N_{effw}}{\lambda}.\Delta L \tag{18}\]  
where the definition of the parameters in the equations are the same as mentioned before. In order to match the AMMIs and the MZI comb filter, the arm length difference (\(\Delta L\)) of the MZI needs to be calculated accurately. By using the S-parameter matrices of the discrete optical components, the circuit-level simulation of the MZI is performed and the waveguide length difference is designed as 60.45 \(\upmu\)m.  
Figure 11a shows the calculated optical transmission spectra of the MZI optical interleaver. MZI-1 and MZI-2 stand for the two complementary outputs of the MZI optical interleaver, while R represents the return loss of the device with the optical input from the grating interface. By connecting the MZI and the AMMIs in the circuit-level model, the optical spectra of the interleaved AMMI can be obtained as shown in Figure 11b. The insertion loss of the WDM device is (1.57-2.46) dB, which means the channel nonuniformity is below 1 dB. The average crosstalk between adjacent channels is about \(-23\) dB. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{\(W_{a}\) = 8 \(\upmu\)m, \(W_{m}\) = 23 \(\upmu\)m, \(\theta\) = 17.23\({}^{\circ}\), \(X_{min}\) = 1 \(\upmu\)m} \\ \hline AMMI1 & \(\lambda_{i}\) (nm) & 1540 & 1550 & 1560 & 1570 \\  & \(L_{i}\) (\(\upmu\)m) & 3484 & 3454 & 3424 & 3394 \\  & \(\lambda_{i}\) (nm) & 1545 & 1555 & 1565 & 1575 \\ AMMI2 & \(L_{i}\) (\(\upmu\)m) & 3469 & 3438 & 3409 & 3380 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Optimized design parameters of the two \(1\times 4\) AMMIs. Figure 10: **(a)** The calculated electric field intensity distribution of AMMI at the wavelength of 1550 nm. **(b)** The zoomed-in field profile showing the access waveguide window on the sidewall of the multimode waveguide. **(c)** The calculated transmission spectra of the \(1\times 4\) AMMI1. **(d)** The calculated transmission spectra of the \(1\times 4\) AMMI2.
# [PERSON]'s Law and Perceptual Features for Face Image Quality Assessment
## 5 Conclusions  
In this study, I investigated the effectiveness of Benford's law-inspired FDD and perceptual features for FIQA. To be more specific, FPDs of multiple image domains, such as wavelet, DCT, singular value, and shearlet domains, were analyzed for FIQA. Our analysis revealed that the FDD of an image domain is a rather mediocre predictor for face image quality. However, the fusion of different FPDs is able to provide a high correlation with the ground-truth quality scores. Moreover, the performance of FDD fusion can be further increased by considering several simple perceptual features, such as colorfulness, the global contrast factor, the dark channel feature, entropy, and phase congruency. Detailed experimental results were presented on the recently published GFIQA-20k [56], which is currently the biggest database containing quality-labeled face images. My future work involves the real-time implementation of FDD feature extraction using CUDA C++ for real-world applications.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
Figure 12: Radar graph for the visual comparison of median PLCC, SROCC, and KROCC values obtained on GFIQA-20k [56] after 100 random train–test splits.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Method** & **PLCC** & **SROCC** & **KROCC** \\ \hline CurveleQA [70] & 0.799 & 0.779 & 0.591 \\ GM-LOG-BIQA [71] & 0.740 & 0.732 & 0.543 \\ IL-NIQE [72] & 0.728 & 0.714 & 0.518 \\ NIQE [73] & 0.191 & 0.183 & 0.127 \\ OG-IQA [74] & 0.747 & 0.735 & 0.546 \\ PIQE [75] & 0.207 & 0.095 & 0.066 \\ SSEQ [76] & 0.715 & 0.690 & 0.509 \\ \hline BL-IQA & 0.816 & 0.810 & 0.619 \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._Data Availability Statement: The GFIQA-20k dataset is publically available from http://database. mmsp-kn.de/gfiqa-20k-database.html (accessed on 12 January 2023).  
I thank the anonymous reviewers and the academic editor for their careful reading of my manuscript and their many insightful comments and suggestions.  
The author declares no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} BDT & binary decision tree \\ CF & colorfulness \\ DCF & dark channel feature \\ DWT & discrete wavelet transform \\ E & entropy \\ FDD & first digit distribution \\ FIQA & face image quality assessment \\ GAM & generalized additive model \\ GFIQA-20k & generic face image quality assessment 20k database \\ GPR & Gaussian process regression \\ JPEG & joint photographic experts group \\ IQA & image quality assessment \\ KROCC & Kendall's rank order correlation coefficient \\ NN & neural network \\ PC & phase congruency \\ PLCC & Pearson's linear correlation coefficient \\ RBF & radial basis function \\ SROCC & Spearman's rank order correlation coefficient \\ SVD & singular value decomposition \\ SVR & support vector regressor \\ YHCC100M & Yahoo Flickr creative commons 100 million dataset \\ \end{tabular}