esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Optical Error Analysis
### Optical Errors of FPI
#### 3.1.4 Reflectivity Changes in FPI
Figure 8: (**a**) The comparison of the FPI transmittance spectrum under ideal conditions (TFPI) and under reflectance variation errors (\(T_{FPI-R}\)); TFPI and \(T_{FPI-R}\) decomposed into (**b**) the DC component, (**c**) the fundamental frequency component, (**d**) the second harmonic component, and (**e**) the third harmonic component for comparison. Figure 7: The curves of \(a^{\prime}_{0}\) to \(a^{\prime}_{3}\) as functions of \(R_{0}\). The zero points of \(a^{\prime}_{m}\) with respect to \(R_{0}\) are indicated by arrows, representing that at these points, \(a_{m}\) is less affected by changes in \(R_{0}\). Equation (18) is substituted into Equation (4) as follows:  
\[ \begin{array}{ll}I_{2-R}(\Delta)&=\int B(\
u)\cos(2\pi\
u\Delta)\bigg{[}a_{0} (\
u)+\sum\limits_{m=1}^{\infty}2a_{m}(\
u)\cos(2\pi\
u m\Delta_{FPI})\bigg{]}dv \\ &=I_{2}(\Delta)+\int B(\
u)\cos(2\pi\
u\Delta)\bigg{[}a^{\prime}{}_{0}+\sum \limits_{m=1}^{\infty}2a^{\prime}{}_{m}\cos(2\pi\
u m\Delta_{FPI})\bigg{]} \delta R(\
u)dv\\ &=I_{2}(\Delta)+\sum\limits_{m=0}^{\infty}a^{\prime}{}_{m}\delta\widetilde{R}( \Delta)*I_{0}(\Delta\pm m\Delta_{FPI})\end{array}  \tag{19}\]  
where \(\delta\widetilde{R}(\Delta)=\text{FT}[\delta R(\
u)]\) denotes the Fourier transform of the reflectivity deviation function. Consequently, when accounting for wavelength-dependent reflectivity variations, the measured interferogram \(I_{2}(\Delta)\) exhibits additional convolutional coupling between each frequency-shifted component and \(\delta\widetilde{R}(\Delta)\), deviating from the theoretically predicted interferogram. To mitigate these reflectivity-induced perturbations, a dual-pronged strategy is required, i.e., (1) optimizing \(R_{0}\) to drive the sensitivity coefficients \(a^{\prime}{}_{m}\) as close to zero as possible, thereby minimizing their multiplicative interaction with \(\delta\widetilde{R}(\Delta)\), and (2) reducing the magnitude of the reflectivity deviation \(\delta R(\
u)\) through enhanced thin-film coating design and fabrication control, which suppresses the spectral distortion term \(\delta\widetilde{R}(\Delta)\).
# Multi-Device Security Application for Unmanned Surface and Aerial Systems
###### Abstract  
The use of autonomous and unmanned systems continues to increase, with uses spanning from package delivery to simple automation of tasks and from factory usage to defense industries and agricultural applications. With the proliferation of unmanned systems comes the question of how to secure the command-and-control communication links among such devices and their operators. In this work, we look at the use of the Messaging Layer Security (MLS) protocol, designed to support long-lived continuous sessions and group communication with a high degree of security. We build out MAUI--an MLS API for UxS Integration that provides an interface for the secure exchange of data between a ScanEagle unmanned aerial vehicle (UAV) and an unmanned surface vehicle (USV) in a multi-domain ad-hoc network configuration, and experiment on system limits such as the ciphersuite set-up time and message handling rates. The experiments in this work were conducted in virtual and physical environments between the UAV, USV, and a controller device (all of different platforms). Our results demonstrate the viability of capitalizing on MLS's capabilities to securely and efficiently transmit data for distributed communication among various unmanned system platforms.  
unmanned system security; UAS; messaging layer security (MLS); command and control; robotic operating system (ROS) +
Footnote †: journal:  
cred
## 1 Introduction
Skipping under simulated lunar gravity consumes less energy than walking and may be the reason why astronauts favored skipping in the Apollo missions [12]. Further, previous motion studies with spacesuits focused on the energy consumption of astronauts and had been conducted several decades ago during the Apollo missions [13;14]. During Apollo 15 and 16, preliminary studies investigating balance (more so falls) were conducted, but measures were crude compared to today's standards. The main focus was on fall and fall recovery as opposed to balance and gait [13; 14; 15]. Since then, the spacesuit has been redesigned and improved, leading to the current spacesuit (the xEMU), and new investigations are needed to fill the existing knowledge gaps tied to balance and gait while donning the xEMU spacesuit. Currently, there is inadequate data to predict acceptable mass and CoG combinations for the xEMU spacesuit [1], with a current goal being to develop an adjustable mass and CoG simulation [4]. NASA had developed virtual computer-based, 3D CoG spacesuit computer models that integrated the associated mass of spacesuit components [1]. Virtual manikins from individual body segments were aligned inside the CoG spacesuit model to provide an overall CoG location; this was projected onto a virtual forceplate which showed the shift of the CoG is superior on the torso. Model refinement and additional CoG validation testing are ongoing [1]. With the upcoming manned Artemis missions to the Moon and future missions to Mars, there is a need to study how these new xEMU spacesuits impact balance and functional performance [16]. Our study aimed to provide new knowledge on how altering one's natural CoG location due to the added weight of a simulated xEMU spacesuit affects their ability to balance. To investigate our aim, we observed healthy adults with no added weight, with a weighted vest (or \"xEMU\" Vest) and a xEMU hard upper body torso (or \"HUT\"); the latter two were meant to simulate the xEMU spacesuit shift in CoG. Our main focus was to establish a baseline of characteristics of unsuited xEMU Vest and the HUT in earth's gravity and then potentially compare them with simulated microgravity conditions. We hypothesized that the superior shift in the CoG caused by the xEMU Vest and/or the HUT would alter the balance observed by forceplate-derived center of pressure (CoP) parameters. This will assist NASA in developing new xEMU spacesuits for future use in upcoming missions.
## 4 Conclusions  
The presented study demonstrated that fresh-cut apples treated with AEW and stored under cold storage provided effective reduction of TAMB and yeasts and moulds. Overall, both EW treatments maintained good quality and visual acceptability. Treatments with AIEW best maintained flavonoid content of the fresh-cut apples. Based on the findings reported, comparing the effectiveness AEW to that of AIEW: (i) AEW had a better visual representation, disinfection efficacy, and maintained the phenolic concentration as well as antioxidant activity of the fresh-cut apples; and (ii) AIEW was more effective as an anti-browning agent and extending shelf life through pH alteration with mild antimicrobial action. The advantages of AEW are primarily as a disinfectant with strong antimicrobial properties, and the ability to remove residues. Electrolyzed water treatment offers a  
Figure 6: Correlation analysis of antioxidant capacity (FRAP), total flavonoids, and poly-phenol content of fresh-cut ‘Granny Smith’ applies. The red line indicates the trend line which shows the correlation of the variables. The blue dots represent the coefficient estimates from the trend line. Histogram highlights the variance and mean squared error.  
practical application potential for the fresh-cut apple industry. Therefore, future study that will compare a broader spectrum of acidic, neutral, and alkaline EW to commercial and chemical-based disinfectants and anti-browning agents used for fresh-cut apples is needed. Furthermore, based on available studies, EW is generally regarded as safe with no harmful substances. In this work no harmful products were reported; however, full evaluation of the effects of EW in a fresh-cut fruit matrix should be undertaken to provide more information.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14198696/s1](https://www.mdpi.com/article/10.3390/app14198696/s1)]([https://www.mdpi.com/article/10.3390/app14198696/s1](https://www.mdpi.com/article/10.3390/app14198696/s1)).  
Conceptualization, Z.A.B.; Methodology, G.S., Z.A.B., B.M. and O.J.C.; Formal analysis, N.E.N. and B.M.; Investigation, N.E.N.; Resources, O.J.C.; Data curation, N.E.N. and B.M.; Writing--original draft, N.E.N.; Writing--review & editing, G.S., Z.A.B. and O.J.C.; Supervision, G.S., Z.A.B. and O.J.C.; Project administration, O.J.C.; Funding acquisition, Z.A.B. All authors have read and agreed to the published version of the manuscript.  
This work is based upon research supported by the National Research Foundation (NRF) of South Africa (Grant Nos. 137990 and 146360) awarded to [PERSON]. The PhD Fellowship awarded to [PERSON] by the DSI/IPBS PDP funded by the Agricultural Research Council of South Africa, Infruitec-Nietvoorbij is gratefully acknowledged.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Large Constellations of Small Satellites: A Survey of Near Future Challenges and Missions
## 4 Discussion
\begin{tabular}{c c c c c c} \hline \hline
**Company** & **No. Sats** & **Sats Size** & **Orbit** &
\begin{tabular}{c} **Year** \\ **(Operative)** \\ \end{tabular} & **Reference** \\ \hline Globalstar Inc. & 48 & Medium & LEO &? & [49] \\ Iridium Inc. - Aieron & 75 & Medium & LEO & 2019 & [50] \\ OneWeb & 648 & Mini & LEO &? & [51] \\ O3b (SES mPower) & 27 & Medium & MEO & 2021 & [52] \\ Orbcomm & 11 & Mini & LEO & 2015 & [53] \\ Gonets SS (Roscosmos) & 11 & Mini & LEO & 2014 & [54] \\ SpaceX & 4425 & Mini & LEO & 2024 & [55] \\ Telesat & 117 & & LEO & 2021 & [56] \\ BlackSky Global & 60 & Micro & LEO & 2021 & [57] \\ SPIRE Global & 175 & Nano & LEO & 2020 & [58] \\ Planet Labs & 5 & & LEO & 2008 & [59] \\ Planet Labs & 12 & Nano & LEO & 2015 & [59] \\ Planet Labs & 20 & Nano & LEO & 2016 & [59] \\ Planet Labs & 12 & Nano & LEO & 2016 & [59] \\ Planet Labs & 48 & Nano & LEO & 2017 & [60] \\ Planet Labs (Terra Bella) & 15 & Micro & LEO & 2017 & [61] \\ Kepler Communications, Inc. & 140 & Nano & LEO & 2022 & [62] \\ Kinesi & 25 & Micro & LEO & 2022 & [63] \\ ExactEarth & 67 & Nano & LEO & 2018 & [64] \\ Planet Labs & 88 & Nano & LEO & 2017 & [65] \\ Planet Labs & 20 & Nano & LEO & 2019 & [66] \\ Astro Digital &? & Micro & LEO &? & [67] \\ BRITE partners & 5 & Nano & & 2014 & [68] \\ GHGSat, Inc. & 3 & Micro & & 2020 & [69] \\ Satellite & 60 & Micro & LEO & 2020 & [70] \\ Space View & 16 & Medium & LEO & 2022 & [71] \\ CASIC & 156 & & LEO & 2025 & [72] \\ Leosat (Thales Alenia) & 108 & Large & LEO & * & [73] \\ \hline \hline \end{tabular}
## 4 Design of TLQoS
### Parameter Estimation
#### 4.9.3 Reliability Estimation  
TLQoS estimates the reliability of a node in terms of its packet loss rate. Thus, the less is the packet loss rate, the more is the reliability. The reliability estimator module measures the average packet loss ratio at a node \(n_{i}\). Let \(f\) be the sum of packet losses over a time window, \(\delta t\) sent by node, \(n_{i}\), and \(r\) be the number of successfully acknowledged packets over that time window. Hence, the mean \(\overline{\mu}=\frac{f}{r+f}\) represents the packet loss ratio of node \(n_{i}\) for that time window. This per window packet loss rate is then averaged with the previous measurements using Window Mean with EWMA (WMEWMA) formula [23] as follows:  
\[R(n_{i})=R(n_{i})\times\alpha+(1-\alpha)\times\overline{\mu} \tag{26}\]  
Here, \(\alpha\) is a smoothing factor which controls the history of the estimator and \(0<\alpha<1\). The value of \(\alpha\) is also empirically chosen and we use the value 0.4 in our simulation.
# Existing and Emerging Breast Cancer Detection Technologies and Its Challenges: A Review
## 2 Existing Breast Cancer Detection Techniques & Technologies
### Microwave Imaging-Based Technology
#### 2.2.2 Radar-Based Technique  
UWB microwave radar-based imaging technique (known as beamformers) rebuilds the image using the reflected wave from objects [82]. The radar system uses a UWB pulse from a transmit (TX) antenna to illuminate the breast, and the resulting reflections that are created at dielectrically differing tissue boundaries are collected by the receive (RX) components [83,84]. This technique, unlike microwave tomography, reconstructs the scattering power distribution when microwaves are emitted on the breast, and their reflected waves are analyzed. Compared to tomography, UWB microwave imaging leads to faster detection due to the lower computational power needed, relatively simple and robust signal processing [85,86]. The setup is shown in Figure 10. It works very much like a ground-penetrating radar (GPR). The origin dates to 2001 by [PERSON] and [PERSON] in Wisconsin University, USA. Therefore, it becomes imperative to understand the behavior of human tissues as a channel to propagate the UWB-emitted waves in this context.  
The MARIA(r) system is a novel technology that utilizes dielectric value to distinguish between tissue types within the female breast. Radiowave radar-based imaging is well-researched, and the unique physiological characteristics of the breast have made it an important research area for radio-wave technology [87]. As seen in Figure 11, this research  
Figure 10: Radar imaging experimental setup.  
Figure 9: A conceptual view of MWT experimental setup [81].  
area divided into two categories: image processing and signal processing. Most researchers using an available dataset that can get through Wisconsin Breast Cancer Dataset (WBCD) will focus on image processing. In contrast, for signal processing, the researcher will start from data collection until processing the result.
## 3 Performance Evaluations
### Fiber Channel  
Figure 9 illustrates our system, which operates at a carrier frequency of 28 GHz. The system involves several components and stages. A laser diode (LD) generates an optical carrier, which is then split into two paths by an optical coupler--one path for transmission and the other for reception. An arbitrary waveform generator (AWG) produces the RF signal, which is used to modulate the optical carrier via a Mach-Zehnder modulator (MZM). The modulated optical signal is transmitted through a single-mode fiber (SMF) and received by a photodetector (PD), which converts the optical signal into an electrical signal at 28 GHz. This electrical signal is then amplified and wirelessly transmitted. On the communication system side, the received signal is down-converted and processed to extract the information bits. The radar receiver antenna collects the reflected signal. A low-noise amplifier (LNA) amplifies the collected signal and drives another MZM. This MZM modulates the other optical carrier, and the modulated optical signal is sent back through an SMF and converted into an electrical signal using another PD. The electrical signal is subsequently down-converted and processed by an oscilloscope (OSC) for further digital signal processing, enabling target detection and ranging.
## 2 Materials and Methods
### Volatile Compound Analysis  
Volatile compounds in Harbin red sausages were extracted by HS-SPME using 50/30 \(\upmu\)m thickness of divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) fibre in a manual SPME needle and holder (ANPEL Laboratory Technologies, Inc., Shanghai, China). Minced sausages (4.00 g) were weighed into a 20-mL headspace vial (CNW Technologies, Duesseldorf, Germany) with internal standard 4.00 \(\upmu\)L 1,2-dichlorobenzene (100 mg/L in methanol). Following equilibration at 50 \({}^{\circ}\)C for 20 min, the SPME fibre was conditioned prior to heating in a gas chromatograph injection port at 240 \({}^{\circ}\)C for 60 min and subsequently exposed to the headspace above the sample for 30 min. The absorbed volatile compounds were identified and quantified by GC \(\times\) GC-qMS.  
A comprehensive two-dimensional gas chromatograph and a QP 2020 NX plus mass spectrometer (GC \(\times\) GC-MS-QP 2020 NX, Shimadzu Co., Kyoto, Japan) were used to identify and quantify the volatile compounds in Harbin red sausages. Two columns of different types are connected in series via the ZX1-GC \(\times\) GC Modulator, which is a unit that enables data sampling for GC \(\times\) GC. The first-dimension column was a nonpolar analytical column (SH-Rtx-1 ms 30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, Shimadzu Co., Ltd., Kyoto, Japan), and the second-dimension column was a medium polar analytical column (BPX-50 2.5 m \(\times\) 0.1 mm \(\times\) 0.1 \(\upmu\)m, SGE Analytical Science, Ringwood, Victoria, Australia). The modulation period was 6 s, and the hot jet width was 350 ms. The initial oven temperature of the GC was maintained at 40 \({}^{\circ}\)C for 2 min. Then, the temperature was heated to 230 \({}^{\circ}\)C at 3 \({}^{\circ}\)C/min and held for 35 min. The split injection inlet had a split ratio of 5:1 at 240 \({}^{\circ}\)C. Helium was used as carrier gas at a constant flow rate of 1.2 mL/min. The transfer line into the MS source was heated at 260 \({}^{\circ}\)C, and the electron impact ionisation source operated at 230 \({}^{\circ}\)C with an ionisation energy of 70 eV. The data acquisition rate was 50 Hz over a mass range of 45-339 amu. Data processing software was GC-image (software for multidimensional chromatography, Zoex Corp., Lincoln, NE, USA). The volatile compounds were identified based on a reverse match factor NIST17 library search (similarity \(>\) 800). The content of a volatile compound was calculated by a semiquantitative method, and it was calculated by dividing the peak volume of the compound by the peak volume of the internal standard (1,2-dichlorobenzene) and multiplying this ratio by the initial concentration of the internal standard, which was expressed as \(\upmu\)g/kg dry matter (DM).
# Automatic Test Data Generation Using the Activity Diagram and Search-Based Technique
## 4 Experiment Description
### Experiment Planning
#### 4.2.6 Experimental Protocol  
Our experiments compared the proposed AutoTDGen with the two baselines as alternative approaches. Traditionally, the effectiveness of a test is measured based on the proportion of statements, lines, or branches covered. However, that coverage measures only which part of the code is executed. In other words, a higher statement or branch coverage might not always reflect the actual effectiveness of a test. Therefore, we performed mutation testing to inject artificial faults into the SUT and ensure that all the statements or branches have truly been tested.  
To answer RQ1 and RQ2, the statement coverage (_SC_), branch coverage (_BC_), and fault detection (_FD_) ratio were measured as follows:  
\[\textit{SC}=\frac{\#\textit{CS}_{\text{covered\_statements}}}{\textit{TS}_{ \text{total\_statements}}}\times 100 \tag{7}\]  
\[\textit{BC}=\frac{\#\textit{CB}_{\text{covered\_braches}}}{\textit{TB}_{ \text{total\_branches}}}\times 100 \tag{8}\]  
\[\textit{FD}=\frac{\#\textit{DF}_{\text{detected\_faults}}}{\textit{TF}_{ \text{total\_faults\_injected}}}\times 100 \tag{9}\]  
A high fault-detection ratio indicates that an approach is effective. All the testing techniques were applied to the three subject systems with the same number of seeded faults. We expected AutoTDGen to achieve a higher mutation coverage than EvoSuite and DFAAD.
# Thermodynamic Formalism in Neuronal Dynamics and Spike Train Statistics
## 2 Mathematical Setting of Thermodynamic Formalism
### Potentials of Range One  
A specific case where the variational principle (4) holds, is when the potential has the form (5). Then, equilibrium states are probability distributions \(\mu_{\lambda}\), that maximise the entropy (3), under the constraints of expected values of \(K\) observables \(\mathbb{E}_{\mu_{\lambda}}(f_{k}):=\sum_{x}f_{k}(x)\mu_{\lambda}(x)=C_{k}\) for \(k=1,\ldots,K\). This problem can be solved by introducing a Lagrange multipliers \(\lambda_{k}\) in the potential (5):  
\[F(U_{\lambda}):=\max_{p}\{H(p)+\mathbb{E}_{p}(U_{\lambda})\}=H(\mu_{\lambda})+ \mathbb{E}_{\mu_{\lambda}}(U_{\lambda}). \tag{12}\]There exists a unique maximum entropy distribution \(\mu_{\lambda}\) (equilibrium state) satisfying the constraints. It turns out that the maximising distribution can be explicitly found for range one potentials and the distribution satisfies the Gibbs property (1), which, in this particular case, reduces to,  
\[\mu_{\lambda}(x)=\exp\big{(}-F(U_{\lambda})+U_{\lambda}(x)\big{)}=\frac{\exp \big{(}U_{\lambda}(x)\big{)}}{Z}, \tag{13}\]  
for all \(x\in X\). Equation (13) is obtained by considering \(F(U_{\lambda})=\log Z\), where \(Z\) is the \"partition function\". From Equation (12), the expression for the entropy (3) and the Jensen inequality, one can obtain the formula for the pressure in this case:  
\[F(U_{\lambda})=\log\sum_{x\in X}e^{U_{\lambda}(x)}.\]  
The constrained problem can be uniquely solved, because the map \(\lambda\mapsto\mathbb{E}_{\mu_{\lambda}}(U)\) maps the real line monotonically onto the interval \((\min U,\max U)\)[76].  
For range one potentials, the measure of a block becomes a product distribution, as given by:  
\[\mu_{\lambda}([x_{0,n-1}])=\prod_{i=0}^{n-1}\frac{\exp\big{(}U_{\lambda}(x_{i} )\big{)}}{Z}. \tag{14}\]  
As the index \(n\) corresponds to time, having an interaction depending on one single coordinate implies that configurations at distinct times are independent.
# Product Demand Prediction with Spatial Graph Neural Networks
## 3 Methodology
### Constructing Graph with Spatial Adjacency  
In the realm of e-commerce, understanding the spatial dynamics of product demand is crucial for tailoring marketing strategies and optimizing inventory distribution. The geographic location of retail outlets significantly influences consumer behavior, as customers are more likely to purchase from nearby stores due to convenience and lower shipping costs. Furthermore, sociodemographic factors and regional preferences can lead to variations in demand patterns across different areas. Recognizing these spatial dependencies is essential for accurately predicting demand at each retail location. By constructing a graph with spatial adjacency, we can model the complex interplay between geographic proximity and demand similarity, providing a structured framework to capture these nuanced relationships. This approach allows us to not only predict demand more accurately but also to uncover insights into how spatial factors influence consumer preferences and buying behavior. Transitioning from this rationale, the construction of the adjacency matrix \(\mathbf{A}\in\mathbb{R}^{|\mathcal{V}|\times|\mathcal{V}|}\) becomes a foundational step in our methodology, enabling us to quantitatively analyze and leverage these spatial relationships for enhanced demand prediction.  
The adjacency matrix \(\mathbf{A}\in\mathbb{R}^{|\mathcal{V}|\times|\mathcal{V}|}\) is constructed based on spatial proximity among the retail locations, capturing the notion that nearby locations may exhibit similar demand patterns. An edge weight \(w_{ij}\) is assigned to each edge \((v_{i},v_{j})\in\mathcal{E}\), formulated as  
\[w_{ij}=\exp{(-\delta\cdot d(v_{i},v_{j}))}, \tag{1}\]  
where \(\delta\) is a decay factor and \(d(v_{i},v_{j})\) measures the geographical distance between locations \(v_{i}\) and \(v_{j}\).