esa-sceva/satcom-chunk-collection huggingface dataset:
# On-Demand Scheduling of Command and Responses for Low-Power Multihop Wireless Networks +
## 4 SCoRe Design
### Concurrent Transmissions for Spatial Reuse  
It is intuitive that assigning a dedicated, exclusive timeslot for each transmission within the whole multihop network may result in a very inefficient use of the wireless channel if the network is large enough for spatial reuse. For example, suppose we have a linear routing topology having only 1 child per each node for \(h\)-hops. The total number of \(N_{\text{dslot}}\) for this network can be expressed as  
\[\sum N_{\text{dslot}}=\frac{h(h+1)}{\sum N_{\text{dslot}}}+\underbrace{(h-1)M}_ {\sum N_{\text{dslot}}} \tag{5}\]  
resulting in the worst case for command-response latency (The \((h-1)\) in the last term assumes that the leaf nodes do not re-disminate the command messages.).  
Nodes that are far away from each other should be allowed to transmit data concurrently for lower latency as long as they do not interfere with each other. For this purpose, SCoRe's design allows concurrent transmissions assuming that 3-hop-away nodes do not interfere each other stochastically in hop-count-based RPL. This is based on the widely accepted intuition that maximum interference range of a wireless transmission is within twice the maximum communication distance [47]. For example, suppose a routing tree is built as shown in Figure 4. Node G requires 4 slots for its response, but it knows that its packet will reach 1-hop node A after 3 timeslots, and node A's transmission is unlikely to interfere with node G or H's transmission. Therefore, node H's packet can coexist with those from node A. Thus, node G requires 3 response timeslots rather than 4, and node H may transmit a slot earlier with node A's transmission. Using this idea, the total \(N_{\text{dslot}}\) for the worst-case linear topology reduces to  
\[\begin{split}\sum slot_{\text{res}}&=1+2+3+3+...+3\\ &=\begin{cases}4h-6&(h>2)\\ \frac{h(h+1)}{2}&(h\leq 2)\end{cases}\end{split} \tag{6}\]  
Figure 3: Slot violation scenario.  
On the other hand, node H's transmission can disrupt a B's packet if node H reserves 3 timeslots as shown in Figure 4. This timeslot compression scheme must guarantee invalidateness, but each node has no way of knowing which node sends after its transmission. However, the problem occurs only when a leaf node reduces its \(N_{\text{dslot}}\), and each node knows whether it has a child. In addition, each node can identify its relative timeslot order from command messages among neighbors having the same parent. Thus an SCoRe node reduces \(N_{\text{dslot}}\) if (1) it has a child, or (2) its timeslot is not located last in the list.
## 1 Introduction
This work aims to develop and characterize the properties of paper coated with compostable bioplastics: in particular, poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) and polycaprolactone (PCL) have been assessed. PHBV belongs to the polyhydroxyalkanotes (PHA) category, an aliphatic biogenic polyesters family, produced by bacterial fermentation and it is an isotactic copolymer obtained by copolymerization of hydroxybutyrate and hydroxyvalerate [9]. PCL is a semicrystalline biodegradable synthetic polyester obtained by a ring-opening polymerization (in presence of catalysts) of \(\epsilon\)-caprolactone, which represents the monomeric unit [10; 11]. Their biodegradability has been extensively studied and many different degradation times can be found; however, biodegradability is a material property that is influenced by many factors. A recent review [12] provides an overview of the main environmental conditions in which biodegradation occurs and then presents the degradability of numerous polymers. For example, strips of PHBV with a thickness of 1 mm showed a total mass loss of 100% after 70 days at 20-24 \({}^{\circ}\)C in industrial composting facilities; a complete degradation via composting can be observed for PCL within a few weeks. Figures on biodegradation of PHA and PHB can be found in another review [13] and the biodegradability in marine environment of PHA and PCL has been extensively discussed [14]. Many studies concerning the processing of these materials and the comparison with traditional plastic materials are available, but they are mainly focused on the development and evaluation of films, while the paper coating application still remains a little-explored research field. First, structural, diffusional and surface (water and oil affinity) properties of paper samples coated with PHBV at different thicknesses (in single and double layer) were evaluated; then, the optimization of PHBV and PCL coating solutions was attempted by addition of polyethylene glycol (PEG) (at 5%, 10% and 20% on biopolymer dry weight) to improve coating uniformity and spreadability. PEG may be defined as a natural-based plasticizer, whose ability to improve the mechanical properties of different biopolymers is well known [9; 15]. PEG was used in the production of film via chloroform casting of 20% PEG and 80% PHB mixtures [16], reducing stiffness and increasing the elongation at break; PHB added with 10% of PEG (different MW) was used to produce film by compression molding [17; 18; 19]; similar results have been obtained for PCL films prepared by solvent casting and added with 10% PEG 400 and PEG 1500 [20]. In this work, different concentrations of PEG have been used, considering a range already investigated by other authors for different application: production of standalone film via solvent casting [21], via extrusion [22] or for the extrusion of PHBV on paper [23].
# Satellite Measured Ionospheric Magnetic Field Variations over Natural Hazards Sites
###### Abstract  
Processes and threats related to natural hazards play an important role in the evolution of the Earth and in human history. The purpose of this study is to investigate magnetic field variations measured at low Earth orbit (LEO) altitudes possibly associated with earthquakes, volcanic eruptions, and artificial outbursts. We focus on two missions with well equipped magnetometer packages, the China Seismo-Electromagnetic Satellite (CSES) and ESA's three spacecraft _Swarm_ fleet. After a natural hazards survey in the context of this satellites, and consideration of external magnetospheric and solar influences, together with spacecraft interferences, wavelet analysed spatio-temporal patterns in ionospheric magnetic field variations related to atmospheric waves are examined in detail. We provide assessment of the links between specific lithospheric or near surface sources and ionospheric magnetic field measurements. For some of the diverse events the achieved statistical results show a change in the pattern between pre- and post-event periods, we show there is an increase in the fluctuations for the higher frequency (smaller scales) components. Our results are relevant to studies which establish a link between space based magnetic field measurements and natural hazards.  
S 2021 132360 17 June 20  
the magnetic field in the key role, i.e., a single dominant physical parameter. In recent complementary efforts, a broad spectrum of satellite based observations had already an important contribution to the overall characterisation of these phenomena with the potential of a variety of societal benefits and applications [1].
# Research on Characteristics of Flow Noise and Flow-Induced Noise
## 3 Validation of Calculation Method
### Computational Method  
Reference [21] compared several turbulence models and concluded that better simulation accuracy could be obtained by using the RNG \(k\)-\(\varepsilon\) turbulence model. The authors did not use the LES model due to its high computer hardware requirements. In this paper, the steady flow computation used the RNG \(k\)-\(\varepsilon\) turbulence model; pressure\(-\)velocity coupling used the SIMPLE algorithm; and momentum, turbulent kinetic energy, and turbulent diffusivity used second-order upwind spatial discretization to improve the computational accuracy. After the residuals stabilized, the relevant physical variables could be observed and analyzed. With the stable steady flow results, non-steady flow calculations could begin. The LES turbulence model was chosen for the non-steady flow calculations and the FW-H acoustic model was turned on, while the export corresponded to the acoustic \"CGNS\" file (CGNS stands for CFD General Notation System [22]; it contains mesh data, physical variables, and storage structures in CFD calculations). The solid model material was aluminum and the fluid was water. The time step was 0.00005 s, with 1000 iterations and 50 iterations per time step to ensure convergence. The reference pressure used to calculate the sound pressure level in the water was 1 \(\times\) 10\({}^{-6}\) Pa.
## 3 DTC Modelling  
The principle of DTC control is to generate pulses that will be applied directly to the inverter switches to generate frequency-variable currents depending on the reference speed. To do this, the rotor and stator fluxes and the motor torque must all remain within the defined hysteresis bands in order to avoid significant torque ripples. The use of this strategy ensures that the torque and flux are not coupled. The seven non-zero voltage vectors and one zero vector are represented on a 360\({}^{\circ}\) circle, which results in eight voltage vector sequences at the outputs of the inverters, which are activated as per to the states of the hysteresis comparators and the position of the voltage vectors. The voltage vectors are generated subsequently and applied to the motor armatures [1].  
The working principle of this strategy lies in the regulation of the parameters of flux and torque without having direct measurements of these parameters by estimating the flux and torque and making a comparison with flux and torque references.  
The fluxes in the fixed reference (\(\alpha\), \(\beta\)), are calculated using the following equations [5; 6]:  
\[\begin{cases}\frac{\overline{\psi}_{\mathrm{s}\alpha}}{\psi_{\mathrm{s}\beta} }=\int\left(\overline{V}_{\mathrm{s}\beta}-R_{\mathrm{s}}\cdot\overline{I}_{ \mathrm{s}\beta}\right)dt\\ \overline{\psi}_{\mathrm{s}\beta}=\int\left(\overline{V}_{\mathrm{s}\beta}-R_ {\mathrm{s}}\cdot\overline{I}_{\mathrm{s}\beta}\right)dt\end{cases} \tag{5}\]  
\[\begin{cases}\frac{\overline{\psi}_{\mathrm{r}\alpha}}{\psi_{\mathrm{r}\beta} }=\int\left(\overline{V}_{\mathrm{r}\beta}-R_{\mathrm{r}}\cdot\overline{I}_{ \mathrm{r}\alpha}\right)dt\\ \overline{\psi}_{\mathrm{r}\beta}=\int\left(\overline{V}_{\mathrm{r}\beta}-R_ {\mathrm{r}}\cdot\overline{I}_{\mathrm{r}\beta}\right)dt\end{cases} \tag{6}\]  
The machine is powered by two voltage inverters controlled by Sa, Sb, and Sc, which are supplied with continuous voltages U\({}_{\mathrm{dcs}}\) and U\({}_{\mathrm{dcr}}\), allowing them to generate voltages expressed by:  
\[\begin{cases}V_{\alpha}=\frac{U_{DC}}{3}(2\cdot S_{\alpha}-S_{b}-S_{c})\\ V_{\beta}=\frac{\sqrt{3}U_{DC}}{3}(S_{b}-S_{c})\end{cases} \tag{7}\]  
In the DTC control, the stator and rotor fluxes are not measured, but are estimated, which are expressed by their modules and arguments, expressed as follows:  
\[\begin{cases}|\psi|=\sqrt{\psi_{\mathrm{a}}^{2}+\psi_{\mathrm{\beta}}^{2}}\\ \theta=\tan^{-1}\frac{\psi_{\mathrm{s}}}{\psi_{\mathrm{a}}}\end{cases} \tag{8}\]  
You may do it this way: it is possible to obtain the electromagnetic torque from the following expression:  
\[\overline{T}_{em}=p\left(\overline{\psi}_{\mathrm{s}\alpha}\cdot i_{\mathrm{s }\beta}+\overline{\psi}_{\mathrm{s}\beta}\cdot i_{\mathrm{s}\alpha}\right) \tag{9}\]
## 2 System Architecture
### Storage and Analytics Node  
The designated endpoint edge or cloud-based hardware runs the SAN software stack that receives, processes, and exports data transmitted by SynCHubs. The SAN's software stack is compatible with commodity hardware. To date, we have deployed SANs on AWS cloud clusters and, in a simplified form (simplified user interface), on Panasonic Toughbook laptop computers and Xavier NVIDIA Jetson-based single-board computers.  
Figure 4 shows a simplified schematic of the SAN's architecture as it is implemented in an instance on the AWS cloud.  
Users' physiological data are ingested by the system through a specific receiving IP address, where they are validated by an edge service (the exposed service on the SAN that receives network external traffic) and forwarded to a message queue that is implemented using Apache Kafka(r)(Apache Software Foundation, Wakefield, MA, USA). Once in the queue, messages are distributed to internal consumers, such as a database write service and data-processing algorithms. The write service writes data and algorithm outputs to a database to be persistently stored on disk. These data are then available for distribution by the data API, a service that receives and responds to requests for data by internal or external consumers, such as visualization clients or other third-party applications.
## 4 Optimization
### Optimization Formulation  
For a given set of design parameter \(p\) that describes the UAV configuration, the set of actuator failure possibilities \(\xi\), and the defined mission requirement, the optimization problem was formulated as the fitting geometry of the mission profile moment requirement into an AMS convex polytope.  
As shown in Figure 4, a 2D example of moment data points of various mission requirements demonstrates the formulation visually. The first mission demands equal control authority in all moment directions; in contrast, the second data set requires higher strength in one of its directions, resulting in weighted control authority requests. Both data distribution are represented geometrically as a circle and an ellipse using Equations (11)-(14), respectively, and the concentric geometries portray different levels of their magnification. Similarly, the violet polygon signifies the AMS, whereas the concentric convex polytope (broken line) shows the marginal constraint. Recalling the properties of the AMS and controllability criteria, sufficient magnification, and fitting of these geometries into the AMS by adjusting design parameters ensure the enclosure of the required moment point within the geometries and inside the AMS.  
Figure 3: Optimization framework.  
Our proposed optimization framework uses this algorithm to assess whether performance criteria are met for specified marginal requirements. Furthermore, the number of points residing outside of the margin of the AMS for an arbitrary \(S\) can be quantified using by exclusion ratio \(\gamma\), as expressed:  
\[\gamma=\frac{Z^{*}}{Z} \tag{21}\]  
where \(0\leq\gamma\leq 1\) and it is defined as the ratio of the set of points outside the margin of the AMS \(Z^{*}\) to the set of all points of the required moment \(Z\). \(\gamma=0\) indicates the existence of all points inside the AMS, whereas \(\gamma=1\) implies the existence of all points outside the AMS envelope.
# Sensory Perception Systems and Machine Learning Methods for Pesticide Detection in Fruits
## 3 Results
### Classification of Pesticide Concentrations in Fruits with Multivariate Analysis Methods
#### 3.3.3 Apple  
Figure 11 demonstrates that the E-tongue effectively distinguishes between organic apples and those treated with varying concentrations of Amistar and Funlate pesticides. The PCA plots reveal a generally good separation, though some overlap is observed between closely related concentrations. In contrast, the LDA plots demonstrate clear and distinct separation without overlap. This means that while both PCA and LDA are valuable for classification, LDA could classify between different pesticide concentrations in apple samples.  
For this data analysis, the apple samples obtained by E-tongue were included. It is noticed that for Amistar, a variance of 97.34% was achieved (see Figure 12A). Additionally, there is a slight scatter at the 3 ppm concentration, where the data overlap with the 2 ppm  
Figure 11: Discrimination and classification plots of pesticide concentrations and apple using E-nose: (**A**) PCA of Amistar, (**B**) PCA of Funlate, (**C**) LDA of Amistar, and (**D**) LDA of Funlate.  
Figure 10: Discrimination and classification plots of pesticide concentrations and strawberry using E-tongue: (**A**) PCA of Across, (**B**) PCA of Bricol, (**C**) LDA of Across, and (**D**) LDA of Bricol.  
concentrations. The dispersion observed in some concentration groups represents that the accuracy of the E-tongue may vary depending on the pesticide concentration and sample composition, or due to limitations of the platinum screen-printed sensors used, as these have a limited capacity to adsorb molecules on their surface. At higher concentrations, such as 2 ppm and 3 ppm, the active sites on the electrode may begin to saturate, meaning that a further increase in pesticide concentration does not result in a proportional increase in the electrochemical signal. Consequently, the sensor's electrode may not accurately distinguish between closely related concentrations, leading to overlap in the data.  
Consequently, the same behavior is represented in the LDA analysis (Figure 12C). Additionally, in the PCA analysis of the Funlate pesticide was reached a discrimination of 98.52% in PC1, which denoted a discrimination between the organic sample and the pesticide concentrations, although there was a slight dispersion between the data for the organic apple.
# Clustering Method of Large-Scale Battlefield Airspace Based on Multi A * in Airspace Grid System
## 5 Experimental Simulation and Analysis
### Evaluation of Airspace Clustering Effect
#### 5.2.2 Experimental Results and Analysis
Simulation 3: Keep the number of target airspace clusters \(M\) and the safety margin distance \(R\) between airspace unchanged, adjust the total number \(N\) of airspace, and detect airspace conflicts in the whole task airspace to obtain the corresponding result parameters. After clustering, airspace conflict detection is carried out in each cluster according to the same process, and new result parameters are obtained. After comparing the two result parameters, the experimental simulation results are shown in the Figures 25 and 26. Figure 24: Comparison of airspace detection speed ratio (number of target airspace clusters change). From the experimental results of simulation 3, it can be found that in the whole experiment process, the number of mission airspace has been adjusted for 10 times, and the corresponding detection result parameters have been adjusted for each time. In terms of the accuracy rate of detection results, 8 of the 10 detection results are accurate, of which two detection results have errors; the error values are 1 and 1. Although there are differences in the accuracy of detection results between airspace conflict detection based on airspace clustering and traditional airspace conflict detection based on wide area, it can be found that with the increase in the number of airspace, the accuracy of the method proposed in this paper has been gradually improved and the error rate has been continuously reduced. In terms of detection speed ratio, the speed obtained from simulation 3 is always lower than 1000. Figure 26: Comparison of airspace detection speed ratio (total number of airspace change). Figure 25: Comparison of airspace conflict detection accuracy (total number of airspace change). than the same speed reference line (\(y=1\)) set in advance, except that the values in the broken line chart are the same in the second detection. The detection efficiency is increased by 1.76-30.25%. This shows that the efficiency of conflict detection speed is improved by clustering the whole task airspace. It is also observed that with the increase of the number of airspace, airspace conflict detection based on airspace clustering saves more time compared with traditional detection methods, and the speed difference is greater, which also reflects that airspace clustering is a pre preparation work when facing the challenge of large-scale airspace tasks. It has great application potential and great significance.
## 3 Experiment and Results
### Classification Result
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **SpectralEn** & **ApproxEn** & **SampleEn** & **FuzzyEn** & **CombinedEn** \\ \hline
1 & 84.2108 & 90.3983 & 94.8875 & 91.8033 & 98.3625 \\
2 & 72.4208 & 90.4583 & 90.1075 & 94.4533 & 99.2325 \\
3 & 73.9208 & 87.8983 & 91.1175 & 92.4733 & 98.8825 \\
4 & 76.0708 & 85.9283 & 90.3975 & 92.4933 & 99.6925 \\
5 & 67.9408 & 80.9783 & 87.7875 & 94.0633 & 99.3725 \\
6 & 78.2808 & 83.2783 & 89.3975 & 93.2733 & 98.8725 \\
7 & 62.9308 & 82.4783 & 85.4575 & 95.3433 & 97.5425 \\
8 & 79.6808 & 91.6483 & 89.9775 & 95.4333 & 98.0625 \\
9 & 77.2308 & 91.5383 & 90.7775 & 93.4333 & 99.6925 \\
10 & 79.5308 & 90.4383 & 89.8175 & 92.3833 & 98.6825 \\
11 & 76.0208 & 81.4883 & 87.5675 & 91.7333 & 97.7025 \\
12 & 71.7608 & 90.4683 & 89.7075 & 95.1133 & 98.9025 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The classification results.
# An Experimental Study on the Reflection Characteristics of Laser Echo Light Waves
Re-entry transmission technology using single-ended laser transmission and reception can effectively carry out long-distance atmospheric remote sensing detection, turbulence monitoring, and laser communication even under changing geographical conditions. Therefore, in-depth exploration of the influence of the atmosphere on the transmission characteristics of the laser beam in the re-entry path is of great significance to the development of related technologies. Corner reflectors are mature and reliable passive jammers. Due to their special structure, they can generate strong radar echo signals in the direction of radar illumination, simulating the radar scattering characteristics of targets much larger than their size. They are often used to jam and deceive the terminal guidance radars of anti-ship missiles [13]. In addition, traditional single-reflector backhaul solutions are susceptible to multipath interference and cannot easily meet the high signal-to-noise ratio requirements of long-distance links. To address the above gaps, this paper proposes a dual-distance backhaul experimental framework based on a corner reflector array. Through experimental measurements of 0.8 km and 4 km links, the system reveals the influence of transmission distance and weather conditions on turbulence intensity, the light intensity scintillation index, and the spot drift range. Compared with single-link research, this work further verifies the advantages of corner reflector arrays in suppressing signal attenuation and improving backhaul efficiency, and provides key design criteria for infinite optical communication systems in complex environments. This research not only provides a measured basis for the optimization of related links, but also lays a theoretical and technical foundation for future fields such as optical measurement, communication link design, and target detection in more complex environments. It has important academic value and engineering significance.